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zNxtMh=1~5|hnOR<^1A8Z_wXEUyYhh{&OyOh)6lYP`9k``h!%ZtRx~NJ;xm4z6W{O(u&>mYBdX=fV{x*OKA-(h>THmexmKV z;V<(2SD5@8Ccn(&SD5@7lV4}@J4_UR_;u!fg30eO`9Dnlgvp;Wp~EvQF&SXO^}&#y zQepPR#(cPwPxc}q9STX0b5)o?XYb$rdu&gBKU~50VbOKZsr@65JcUS$q5XI7*}dc59V7U0&Y>OK zcMR{i3;8{`?#6$7Hn?l2{_fo|Fv8!d8p3Z>G2M;QBlq@=q(A97fNuszMn?AT*oEJ- i!Y^6fd(X(=2+Hokm;3kbe{_%z;hp_IJ=8bY|NjBe9e&mT literal 0 HcmV?d00001 From 8de5fac0ea18355b642bcb592c0db0b8651318da Mon Sep 17 00:00:00 2001 From: abhishek garg Date: Tue, 1 Mar 2016 22:49:50 +0530 Subject: [PATCH 002/513] Changed actions function in ConnectFour so that actions list is not empty. --- games.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/games.py b/games.py index aee3aeb71..e1073962a 100644 --- a/games.py +++ b/games.py @@ -282,7 +282,7 @@ def __init__(self, h=7, v=6, k=4): def actions(self, state): return [(x, y) for (x, y) in state.moves - if y == 0 or (x, y-1) in state.board] + if y == 1 or (x, y-1) in state.board] __doc__ += random_tests(""" >>> play_game(Fig52Game(), random_player, random_player) From 4e3da2c5b5737a8dd59bccc037ebf4920fb382ad Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Thu, 3 Mar 2016 15:50:55 +0530 Subject: [PATCH 003/513] Add .gitignore --- .gitignore | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..9a4bb620f --- /dev/null +++ b/.gitignore @@ -0,0 +1,72 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask instance folder +instance/ + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# dotenv +.env From 19f276a3dafaafbe6c0135af0bb8dd1792d9b700 Mon Sep 17 00:00:00 2001 From: greyshadows Date: Fri, 4 Mar 2016 05:18:39 +0530 Subject: [PATCH 004/513] Minor Changes in ReadMe --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 36a4f83bc..6ce693156 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ When complete, this project will cover all the major topics in the book, for eac Until we get there, we will support a legacy branch, `aima3python2` (for the thrid edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following should be done: -- Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `prtint` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. +- Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. - Implement functions that were in the third edition of the book but were not yet implemented in the code. - As we finish chapters for the new fourth edition, we will share the pseudocode, and describe what changes are necessary. - Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. From 523936c2c5c4ceae6f5c58027032e8f5985f4a15 Mon Sep 17 00:00:00 2001 From: norvig Date: Thu, 3 Mar 2016 17:25:10 -0800 Subject: [PATCH 005/513] Convert utils.py to Python 3 --- utils.py | 387 +++---------------------------------------------------- 1 file changed, 15 insertions(+), 372 deletions(-) diff --git a/utils.py b/utils.py index c1675890e..7aca1d639 100644 --- a/utils.py +++ b/utils.py @@ -1,279 +1,15 @@ -"""Provide some widely useful utilities. Safe for "from utils import *". - -""" - -from __future__ import generators -import operator, math, random, copy, sys, os.path, bisect, re - -assert (2,5) <= sys.version_info < (3,), """\ -This code is meant for Python 2.5 through 2.7. -You might find that the parts you care about still work in older -Pythons or happen to work in newer ones, but you're on your own -- -edit utils.py if you want to try it.""" - -#______________________________________________________________________________ -# Compatibility with Python 2.2, 2.3, and 2.4 - -# The AIMA code was originally designed to run in Python 2.2 and up. -# The first part of this file implements for Python 2.2 through 2.4 -# the parts of 2.5 that the original code relied on. Now we're -# starting to go beyond what can be filled in this way, but here's -# the compatibility code still since it doesn't hurt: - -try: bool, True, False ## Introduced in 2.3 -except NameError: - class bool(int): - "Simple implementation of Booleans, as in PEP 285" - def __init__(self, val): self.val = val - def __int__(self): return self.val - def __repr__(self): return ('False', 'True')[self.val] - - True, False = bool(1), bool(0) - -try: sum ## Introduced in 2.3 -except NameError: - def sum(seq, start=0): - """Sum the elements of seq. - >>> sum([1, 2, 3]) - 6 - """ - return reduce(operator.add, seq, start) - -try: enumerate ## Introduced in 2.3 -except NameError: - def enumerate(collection): - """Return an iterator that enumerates pairs of (i, c[i]). PEP 279. - >>> list(enumerate('abc')) - [(0, 'a'), (1, 'b'), (2, 'c')] - """ - ## Copied from PEP 279 - i = 0 - it = iter(collection) - while 1: - yield (i, it.next()) - i += 1 - - -try: reversed ## Introduced in 2.4 -except NameError: - def reversed(seq): - """Iterate over x in reverse order. - >>> list(reversed([1,2,3])) - [3, 2, 1] - """ - if hasattr(seq, 'keys'): - raise TypeError("mappings do not support reverse iteration") - i = len(seq) - while i > 0: - i -= 1 - yield seq[i] - - -try: sorted ## Introduced in 2.4 -except NameError: - def sorted(seq, cmp=None, key=None, reverse=False): - """Copy seq and sort and return it. - >>> sorted([3, 1, 2]) - [1, 2, 3] - """ - seq2 = copy.copy(seq) - if key: - if cmp == None: - cmp = __builtins__.cmp - seq2.sort(lambda x,y: cmp(key(x), key(y))) - else: - if cmp == None: - seq2.sort() - else: - seq2.sort(cmp) - if reverse: - seq2.reverse() - return seq2 - -try: - set, frozenset ## set builtin introduced in 2.4 -except NameError: - try: - import sets ## sets module introduced in 2.3 - set, frozenset = sets.Set, sets.ImmutableSet - except (NameError, ImportError): - class BaseSet: - "set type (see http://docs.python.org/lib/types-set.html)" - - - def __init__(self, elements=[]): - self.dict = {} - for e in elements: - self.dict[e] = 1 - - def __len__(self): - return len(self.dict) - - def __iter__(self): - for e in self.dict: - yield e - - def __contains__(self, element): - return element in self.dict - - def issubset(self, other): - for e in self.dict.keys(): - if e not in other: - return False - return True - - def issuperset(self, other): - for e in other: - if e not in self: - return False - return True - - - def union(self, other): - return type(self)(list(self) + list(other)) - - def intersection(self, other): - return type(self)([e for e in self.dict if e in other]) - - def difference(self, other): - return type(self)([e for e in self.dict if e not in other]) - - def symmetric_difference(self, other): - return type(self)([e for e in self.dict if e not in other] + - [e for e in other if e not in self.dict]) - - def copy(self): - return type(self)(self.dict) - - def __repr__(self): - elements = ", ".join(map(str, self.dict)) - return "%s([%s])" % (type(self).__name__, elements) - - __le__ = issubset - __ge__ = issuperset - __or__ = union - __and__ = intersection - __sub__ = difference - __xor__ = symmetric_difference - - class frozenset(BaseSet): - "A frozenset is a BaseSet that has a hash value and is immutable." - - def __init__(self, elements=[]): - BaseSet.__init__(elements) - self.hash = 0 - for e in self: - self.hash |= hash(e) - - def __hash__(self): - return self.hash - - class set(BaseSet): - "A set is a BaseSet that does not have a hash, but is mutable." - - def update(self, other): - for e in other: - self.add(e) - return self - - def intersection_update(self, other): - for e in self.dict.keys(): - if e not in other: - self.remove(e) - return self - - def difference_update(self, other): - for e in self.dict.keys(): - if e in other: - self.remove(e) - return self - - def symmetric_difference_update(self, other): - to_remove1 = [e for e in self.dict if e in other] - to_remove2 = [e for e in other if e in self.dict] - self.difference_update(to_remove1) - self.difference_update(to_remove2) - return self - - def add(self, element): - self.dict[element] = 1 - - def remove(self, element): - del self.dict[element] - - def discard(self, element): - if element in self.dict: - del self.dict[element] - - def pop(self): - key, val = self.dict.popitem() - return key - - def clear(self): - self.dict.clear() - - __ior__ = update - __iand__ = intersection_update - __isub__ = difference_update - __ixor__ = symmetric_difference_update +"""Provide some widely useful functions and objects.""" +infinity = float('inf') +argmin = min +argmax = max -#______________________________________________________________________________ -# Simple Data Structures: infinity, Dict, Struct - -infinity = 1.0e400 - -def Dict(**entries): - """Create a dict out of the argument=value arguments. - >>> Dict(a=1, b=2, c=3) - {'a': 1, 'c': 3, 'b': 2} - """ - return entries +def ignore(x): None -class DefaultDict(dict): - """Dictionary with a default value for unknown keys.""" - def __init__(self, default): - self.default = default +def identity(x): return x - def __getitem__(self, key): - if key in self: return self.get(key) - return self.setdefault(key, copy.deepcopy(self.default)) - - def __copy__(self): - copy = DefaultDict(self.default) - copy.update(self) - return copy - -class Struct: - """Create an instance with argument=value slots. - This is for making a lightweight object whose class doesn't matter.""" - def __init__(self, **entries): - self.__dict__.update(entries) - - def __cmp__(self, other): - if isinstance(other, Struct): - return cmp(self.__dict__, other.__dict__) - else: - return cmp(self.__dict__, other) - - def __repr__(self): - args = ['%s=%s' % (k, repr(v)) for (k, v) in vars(self).items()] - return 'Struct(%s)' % ', '.join(sorted(args)) - -def update(x, **entries): - """Update a dict; or an object with slots; according to entries. - >>> update({'a': 1}, a=10, b=20) - {'a': 10, 'b': 20} - >>> update(Struct(a=1), a=10, b=20) - Struct(a=10, b=20) - """ - if isinstance(x, dict): - x.update(entries) - else: - x.__dict__.update(entries) - return x #______________________________________________________________________________ # Functions on Sequences (mostly inspired by Common Lisp) @@ -306,46 +42,6 @@ def product(numbers): """ return reduce(operator.mul, numbers, 1) -def count_if(predicate, seq): - """Count the number of elements of seq for which the predicate is true. - >>> count_if(callable, [42, None, max, min]) - 2 - """ - f = lambda count, x: count + (not not predicate(x)) - return reduce(f, seq, 0) - -def find_if(predicate, seq): - """If there is an element of seq that satisfies predicate; return it. - >>> find_if(callable, [3, min, max]) - - >>> find_if(callable, [1, 2, 3]) - """ - for x in seq: - if predicate(x): return x - return None - -def every(predicate, seq): - """True if every element of seq satisfies predicate. - >>> every(callable, [min, max]) - 1 - >>> every(callable, [min, 3]) - 0 - """ - for x in seq: - if not predicate(x): return False - return True - -def some(predicate, seq): - """If some element x of seq satisfies predicate(x), return predicate(x). - >>> some(callable, [min, 3]) - 1 - >>> some(callable, [2, 3]) - 0 - """ - for x in seq: - px = predicate(x) - if px: return px - return False def isin(elt, seq): """Like (elt in seq), but compares with is, not ==. @@ -358,25 +54,7 @@ def isin(elt, seq): if elt is x: return True return False -#______________________________________________________________________________ -# Functions on sequences of numbers -# NOTE: these take the sequence argument first, like min and max, -# and like standard math notation: \sigma (i = 1..n) fn(i) -# A lot of programing is finding the best value that satisfies some condition; -# so there are three versions of argmin/argmax, depending on what you want to -# do with ties: return the first one, return them all, or pick at random. - -def argmin(seq, fn): - """Return an element with lowest fn(seq[i]) score; tie goes to first one. - >>> argmin(['one', 'to', 'three'], len) - 'to' - """ - best = seq[0]; best_score = fn(best) - for x in seq: - x_score = fn(x) - if x_score < best_score: - best, best_score = x, x_score - return best + def argmin_list(seq, fn): """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores. @@ -406,12 +84,7 @@ def argmin_random_tie(seq, fn): best = x return best -def argmax(seq, fn): - """Return an element with highest fn(seq[i]) score; tie goes to first one. - >>> argmax(['one', 'to', 'three'], len) - 'three' - """ - return argmin(seq, lambda x: -fn(x)) + def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. @@ -588,18 +261,6 @@ def printf(format, *args): sys.stdout.write(str(format) % args) return if_(args, lambda: args[-1], lambda: format) -def caller(n=1): - """Return the name of the calling function n levels up in the frame stack. - >>> caller(0) - 'caller' - >>> def f(): - ... return caller() - >>> f() - 'f' - """ - import inspect - return inspect.getouterframes(inspect.currentframe())[n][3] - def memoize(fn, slot=None): """Memoize fn: make it remember the computed value for any argument list. If slot is specified, store result in that slot of first argument. @@ -620,30 +281,16 @@ def memoized_fn(*args): memoized_fn.cache = {} return memoized_fn -def if_(test, result, alternative): - """Like C++ and Java's (test ? result : alternative), except - both result and alternative are always evaluated. However, if - either evaluates to a function, it is applied to the empty arglist, - so you can delay execution by putting it in a lambda. - >>> if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) - 'ok' - """ - if test: - if callable(result): return result() - return result - else: - if callable(alternative): return alternative() - return alternative - def name(object): "Try to find some reasonable name for the object." - return (getattr(object, 'name', 0) or getattr(object, '__name__', 0) - or getattr(getattr(object, '__class__', 0), '__name__', 0) - or str(object)) + return (getattr(object, 'name', False) or + getattr(object, '__name__', False) or + getattr(getattr(object, '__class__', None), '__name__', False) or + str(object)) def isnumber(x): - "Is x a number? We say it is if it has a __int__ method." - return hasattr(x, '__int__') + "Is x a number? We say it is if it is a float, int, or complex." + return isinstance(x, (int, float, complex)) def issequence(x): "Is x a sequence? We say it is if it has a __getitem__ method." @@ -676,10 +323,6 @@ def DataFile(name, mode='r'): "Return a file in the AIMA /data directory." return AIMAFile(['..', 'data', name], mode) -def unimplemented(): - "Use this as a stub for not-yet-implemented functions." - raise NotImplementedError - #______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue @@ -763,7 +406,7 @@ def __delitem__(self, key): #______________________________________________________________________________ # Support for doctest -def ignore(x): None + def random_tests(text): """Some functions are stochastic. We want to be able to write a test From f927a6f944928b56751b28ce5647d8bedcf341e3 Mon Sep 17 00:00:00 2001 From: Varshit Date: Fri, 4 Mar 2016 13:22:46 +0530 Subject: [PATCH 006/513] Fix import re error --- utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/utils.py b/utils.py index 7aca1d639..d42816e7d 100644 --- a/utils.py +++ b/utils.py @@ -1,5 +1,7 @@ """Provide some widely useful functions and objects.""" +import re + infinity = float('inf') argmin = min From a9ecd7dfb571d2821c02db5cdf200d44526453b5 Mon Sep 17 00:00:00 2001 From: Varshit Date: Fri, 4 Mar 2016 15:18:04 +0530 Subject: [PATCH 007/513] Fix error saying 'global name update not defined' while running mdp.py --- mdp.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/mdp.py b/mdp.py index e5142c148..29313aea4 100644 --- a/mdp.py +++ b/mdp.py @@ -18,8 +18,12 @@ class MDP: actions for each state. [page 646]""" def __init__(self, init, actlist, terminals, gamma=.9): - update(self, init=init, actlist=actlist, terminals=terminals, - gamma=gamma, states=set(), reward={}) + self.init=init + self.actlist=actlist + self.terminals=terminals + self.gamma=gamma + self.states=set() + self.reward={} def R(self, state): "Return a numeric reward for this state." @@ -48,7 +52,9 @@ def __init__(self, grid, terminals, init=(0, 0), gamma=.9): grid.reverse() ## because we want row 0 on bottom, not on top MDP.__init__(self, init, actlist=orientations, terminals=terminals, gamma=gamma) - update(self, grid=grid, rows=len(grid), cols=len(grid[0])) + self.grid=grid + self.rows=len(grid) + self.cols=len(grid[0]) for x in range(self.cols): for y in range(self.rows): self.reward[x, y] = grid[y][x] From 0f99f6a1a621702238de1f69102d549710f02529 Mon Sep 17 00:00:00 2001 From: utk1610 Date: Sat, 5 Mar 2016 01:01:29 +0530 Subject: [PATCH 008/513] Update utils.py To make product to work in python 3+ , loop over the list Or in case if you want to make the code precise import following statment and change the return statment import operator import functools return functools.reduce(operator.mul, numbers, 1) --- utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 7aca1d639..95db4d0a2 100644 --- a/utils.py +++ b/utils.py @@ -40,7 +40,10 @@ def product(numbers): >>> product([1,2,3,4]) 24 """ - return reduce(operator.mul, numbers, 1) + result=1 + for i in numbers: + result=result*i + return result def isin(elt, seq): From 87c60dd945d0c9e15ba286df5df9645599968d4c Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 18:01:30 -0800 Subject: [PATCH 009/513] Port utils.py to Python 3.5 -- from tmrts This is really the pull request from tmrts, but as a novice git user, I couldn't figure out how to do the merge after there were some other changes, so I did it manually. Sorry that you don't get official credit, tmrts, but thanks! --- utils.py | 511 ++++++++++++++++++++++++------------------------------- 1 file changed, 222 insertions(+), 289 deletions(-) diff --git a/utils.py b/utils.py index d42816e7d..fa8f4c20c 100644 --- a/utils.py +++ b/utils.py @@ -1,17 +1,53 @@ -"""Provide some widely useful functions and objects.""" +"""Provide some widely useful utilities. Safe for "from utils import *". +""" +import operator +import math +import random +import os.path +import bisect import re +#______________________________________________________________________________ +# Simple Data Structures: infinity, Dict, Struct + infinity = float('inf') -argmin = min +Dict = dict -argmax = max +from collections import defaultdict as DefaultDict -def ignore(x): None +class Struct: + """Create an instance with argument=value slots. + + This is for making a lightweight object whose class doesn't matter.""" + def __init__(self, **entries): + self.__dict__.update(entries) -def identity(x): return x + def __cmp__(self, other): + if isinstance(other, Struct): + return cmp(self.__dict__, other.__dict__) + else: + return cmp(self.__dict__, other) + + def __repr__(self): + args = ['{!s}={!s}'.format(k, repr(v)) + for (k, v) in vars(self).items()] + +def update(x, **entries): + """Update a dict or an object with slots according to entries. + + >>> update({'a': 1}, a=10, b=20) + {'a': 10, 'b': 20} + >>> update(Struct(a=1), a=10, b=20) + Struct(a=10, b=20) + """ + if isinstance(x, dict): + x.update(entries) + else: + x.__dict__.update(entries) + return x #______________________________________________________________________________ # Functions on Sequences (mostly inspired by Common Lisp) @@ -44,7 +80,47 @@ def product(numbers): """ return reduce(operator.mul, numbers, 1) +def count_if(predicate, seq): + """Count the number of elements of seq for which the predicate is true. + >>> count_if(callable, [42, None, max, min]) + 2 + """ + return sum(map(lambda x: bool(predicate(x)), seq)) + +def find_if(predicate, seq): + """If there is an element of seq that satisfies predicate; return it. + >>> find_if(callable, [3, min, max]) + + >>> find_if(callable, [1, 2, 3]) + """ + for x in seq: + if predicate(x): + return x + + return None + +def every(predicate, seq): + """True if every element of seq satisfies predicate. + >>> every(callable, [min, max]) + 1 + >>> every(callable, [min, 3]) + 0 + """ + + return all(predicate(x) for x in seq) + +def some(predicate, seq): + """If some element x of seq satisfies predicate(x), return predicate(x). + >>> some(callable, [min, 3]) + 1 + >>> some(callable, [2, 3]) + 0 + """ + elem = find_if(predicate,seq) + return predicate(elem) or False + +# TODO: rename to is_in or possibily add 'identity' to function name to clarify intent def isin(elt, seq): """Like (elt in seq), but compares with is, not ==. >>> e = []; isin(e, [1, e, 3]) @@ -52,41 +128,49 @@ def isin(elt, seq): >>> isin(e, [1, [], 3]) False """ - for x in seq: - if elt is x: return True - return False + return any(x is elt for x in seq) +#______________________________________________________________________________ +# Functions on sequences of numbers +# NOTE: these take the sequence argument first, like min and max, +# and like standard math notation: \sigma (i = 1..n) fn(i) +# A lot of programing is finding the best value that satisfies some condition; +# so there are three versions of argmin/argmax, depending on what you want to +# do with ties: return the first one, return them all, or pick at random. +def argmin(seq, fn): + return min(seq, key=fn) def argmin_list(seq, fn): """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores. >>> argmin_list(['one', 'to', 'three', 'or'], len) ['to', 'or'] """ - best_score, best = fn(seq[0]), [] - for x in seq: - x_score = fn(x) - if x_score < best_score: - best, best_score = [x], x_score - elif x_score == best_score: - best.append(x) - return best + smallest_score = min(seq, key=fn) + + return [elem for elem in seq if fn(elem) == smallest_score] + +def argmin_gen(seq, fn): + """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores. + >>> argmin_list(['one', 'to', 'three', 'or'], len) + ['to', 'or'] + """ + + smallest_score = min(seq, key=fn) + + yield from (elem for elem in seq if fn(elem) == smallest_score) def argmin_random_tie(seq, fn): """Return an element with lowest fn(seq[i]) score; break ties at random. Thus, for all s,f: argmin_random_tie(s, f) in argmin_list(s, f)""" - best_score = fn(seq[0]); n = 0 - for x in seq: - x_score = fn(x) - if x_score < best_score: - best, best_score = x, x_score; n = 1 - elif x_score == best_score: - n += 1 - if random.randrange(n) == 0: - best = x - return best - + return random.choice(argmin_gen(seq, fn)) +def argmax(seq, fn): + """Return an element with highest fn(seq[i]) score; tie goes to first one. + >>> argmax(['one', 'to', 'three'], len) + 'three' + """ + return argmin(seq, lambda x: -fn(x)) def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. @@ -95,9 +179,17 @@ def argmax_list(seq, fn): """ return argmin_list(seq, lambda x: -fn(x)) +def argmax_gen(seq, fn): + """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores. + >>> argmax_list(['one', 'three', 'seven'], len) + ['three', 'seven'] + """ + yield from argmin_gen(seq, lambda x: -fn(x)) + def argmax_random_tie(seq, fn): "Return an element with highest fn(seq[i]) score; break ties at random." return argmin_random_tie(seq, lambda x: -fn(x)) + #______________________________________________________________________________ # Statistical and mathematical functions @@ -105,58 +197,25 @@ def histogram(values, mode=0, bin_function=None): """Return a list of (value, count) pairs, summarizing the input values. Sorted by increasing value, or if mode=1, by decreasing count. If bin_function is given, map it over values first.""" - if bin_function: values = map(bin_function, values) + if bin_function: + values = map(bin_function, values) + bins = {} for val in values: bins[val] = bins.get(val, 0) + 1 + if mode: return sorted(bins.items(), key=lambda x: (x[1],x[0]), reverse=True) else: return sorted(bins.items()) -def log2(x): - """Base 2 logarithm. - >>> log2(1024) - 10.0 - """ - return math.log10(x) / math.log10(2) - -def mode(values): - """Return the most common value in the list of values. - >>> mode([1, 2, 3, 2]) - 2 - """ - return histogram(values, mode=1)[0][0] - -def median(values): - """Return the middle value, when the values are sorted. - If there are an odd number of elements, try to average the middle two. - If they can't be averaged (e.g. they are strings), choose one at random. - >>> median([10, 100, 11]) - 11 - >>> median([1, 2, 3, 4]) - 2.5 - """ - n = len(values) - values = sorted(values) - if n % 2 == 1: - return values[n/2] - else: - middle2 = values[(n/2)-1:(n/2)+1] - try: - return mean(middle2) - except TypeError: - return random.choice(middle2) - -def mean(values): - """Return the arithmetic average of the values.""" - return sum(values) / float(len(values)) +from math import log2 +from statistics import mode, median, mean, stdev def stddev(values, meanval=None): """The standard deviation of a set of values. - Pass in the mean if you already know it.""" - if meanval is None: meanval = mean(values) - return math.sqrt(sum([(x - meanval)**2 for x in values]) / (len(values)-1)) + Pass in the mean if you already know it. """ + return stdev(values, mu=meanval) def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y. @@ -181,13 +240,15 @@ def weighted_sample_with_replacement(seq, weights, n): probability of each element in proportion to its corresponding weight.""" sample = weighted_sampler(seq, weights) - return [sample() for s in range(n)] + + return [sample() for _ in range(n)] def weighted_sampler(seq, weights): "Return a random-sample function that picks from seq weighted by weights." totals = [] for w in weights: totals.append(w + totals[-1] if totals else w) + return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] def num_or_str(x): @@ -197,7 +258,6 @@ def num_or_str(x): >>> num_or_str(' 42x ') '42x' """ - if isnumber(x): return x try: return int(x) except ValueError: @@ -237,13 +297,17 @@ def turn_right(heading): def turn_left(heading): return turn_heading(heading, +1) -def distance((ax, ay), (bx, by)): +def distance(a, b): + "The distance between two (x, y) points." + return math.hypot((a.x - b.x), (a.y - b.y)) + +def distance_squared(a, b): "The distance between two (x, y) points." - return math.hypot((ax - bx), (ay - by)) + return (a.x - b.x)**2 + (a.y - b.y)**2 -def distance2((ax, ay), (bx, by)): +def distance2(a, b): "The square of the distance between two (x, y) points." - return (ax - bx)**2 + (ay - by)**2 + return distance_squared(a, b) def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding @@ -257,12 +321,27 @@ def vector_clip(vector, lowest, highest): #______________________________________________________________________________ # Misc Functions -def printf(format, *args): +def printf(format_str, *args): """Format args with the first argument as format string, and write. Return the last arg, or format itself if there are no args.""" - sys.stdout.write(str(format) % args) - return if_(args, lambda: args[-1], lambda: format) + print(str(format_str).format(*args, end='')) + + return args[-1] if args else format_str + +def caller(n=1): + """Return the name of the calling function n levels up in the frame stack. + >>> caller(0) + 'caller' + >>> def f(): + ... return caller() + >>> f() + 'f' + """ + import inspect + + return inspect.getouterframes(inspect.currentframe())[n][3] +# TODO: Use functools.lru_cache memoization decorator def memoize(fn, slot=None): """Memoize fn: make it remember the computed value for any argument list. If slot is specified, store result in that slot of first argument. @@ -280,19 +359,39 @@ def memoized_fn(*args): if not memoized_fn.cache.has_key(args): memoized_fn.cache[args] = fn(*args) return memoized_fn.cache[args] + memoized_fn.cache = {} + return memoized_fn -def name(object): +def if_(test, result, alternative): + """Like C++ and Java's (test ? result : alternative), except + both result and alternative are always evaluated. However, if + either evaluates to a function, it is applied to the empty arglist, + so you can delay execution by putting it in a lambda. + >>> if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) + 'ok' + """ + if test: + if callable(result): + return result() + + return result + else: + if callable(alternative): + return alternative() + + return alternative + +def name(obj): "Try to find some reasonable name for the object." - return (getattr(object, 'name', False) or - getattr(object, '__name__', False) or - getattr(getattr(object, '__class__', None), '__name__', False) or - str(object)) + return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) + or getattr(getattr(obj, '__class__', 0), '__name__', 0) + or str(obj)) def isnumber(x): - "Is x a number? We say it is if it is a float, int, or complex." - return isinstance(x, (int, float, complex)) + "Is x a number? We say it is if it has a __int__ method." + return hasattr(x, '__int__') def issequence(x): "Is x a sequence? We say it is if it has a __getitem__ method." @@ -304,30 +403,43 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): numfmt is the format for all numbers; you might want e.g. '%6.2f'. (If you want different formats in different columns, don't use print_table.) sep is the separator between columns.""" - justs = [if_(isnumber(x), 'rjust', 'ljust') for x in table[0]] + justs = ['rjust' if isnumber(x) else 'ljust' for x in table[0]] + if header: - table = [header] + table - table = [[if_(isnumber(x), lambda: numfmt % x, lambda: x) for x in row] - for row in table] + table.insert(0, header) + + table = [[numfmt.format(x) if isnumber(x) else x for x in row] + for row in table] + maxlen = lambda seq: max(map(len, seq)) + sizes = map(maxlen, zip(*[map(str, row) for row in table])) + for row in table: - print sep.join(getattr(str(x), j)(size) - for (j, size, x) in zip(justs, sizes, row)) + print(sep.join(getattr(str(x), j)(size) + for (j, size, x) in zip(justs, sizes, row))) def AIMAFile(components, mode='r'): "Open a file based at the AIMA root directory." import utils - dir = os.path.dirname(utils.__file__) - return open(apply(os.path.join, [dir] + components), mode) + aima_root = os.path.dirname(utils.__file__) + + aima_file = os.path.join(aima_root, *components) + + return open(aima_file) def DataFile(name, mode='r'): "Return a file in the AIMA /data directory." return AIMAFile(['..', 'data', name], mode) +def unimplemented(): + "Use this as a stub for not-yet-implemented functions." + raise NotImplementedError + #______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue +# TODO: Use queue.Queue class Queue: """Queue is an abstract class/interface. There are three types: Stack(): A Last In First Out Queue. @@ -341,7 +453,6 @@ class Queue: item in q -- does q contain item? Note that isinstance(Stack(), Queue) is false, because we implement stacks as lists. If Python ever gets interfaces, Queue will be an interface.""" - def __init__(self): abstract @@ -356,12 +467,16 @@ class FIFOQueue(Queue): """A First-In-First-Out Queue.""" def __init__(self): self.A = []; self.start = 0 + def append(self, item): self.A.append(item) + def __len__(self): return len(self.A) - self.start + def extend(self, items): self.A.extend(items) + def pop(self): e = self.A[self.start] self.start += 1 @@ -369,9 +484,11 @@ def pop(self): self.A = self.A[self.start:] self.start = 0 return e + def __contains__(self, item): return item in self.A[self.start:] +# TODO: Use queue.PriorityQueue class PriorityQueue(Queue): """A queue in which the minimum (or maximum) element (as determined by f and order) is returned first. If order is min, the item with minimum f(x) is @@ -379,26 +496,31 @@ class PriorityQueue(Queue): Also supports dict-like lookup.""" def __init__(self, order=min, f=lambda x: x): update(self, A=[], order=order, f=f) + def append(self, item): bisect.insort(self.A, (self.f(item), item)) + def __len__(self): return len(self.A) + def pop(self): if self.order == min: return self.A.pop(0)[1] else: return self.A.pop()[1] + def __contains__(self, item): - return some(lambda (_, x): x == item, self.A) + return some(lambda _, x: x == item, self.A) + def __getitem__(self, key): for _, item in self.A: if item == key: return item + def __delitem__(self, key): for i, (value, item) in enumerate(self.A): if item == key: self.A.pop(i) - return ## Fig: The idea is we can define things like Fig[3,10] later. ## Alas, it is Fig[3,10] not Fig[3.10], because that would be the same @@ -408,203 +530,14 @@ def __delitem__(self, key): #______________________________________________________________________________ # Support for doctest - +def ignore(x): + pass def random_tests(text): """Some functions are stochastic. We want to be able to write a test with random output. We do that by ignoring the output.""" def fixup(test): - if " = " in test: - return ">>> " + test - else: - return ">>> ignore(" + test + ")" - tests = re.findall(">>> (.*)", text) - return '\n'.join(map(fixup, tests)) + return ">>> {}".format("ignore(" + test + ")" if " = " not in test else test) -#______________________________________________________________________________ - -__doc__ += """ ->>> d = DefaultDict(0) ->>> d['x'] += 1 ->>> d['x'] -1 - ->>> d = DefaultDict([]) ->>> d['x'] += [1] ->>> d['y'] += [2] ->>> d['x'] -[1] - ->>> s = Struct(a=1, b=2) ->>> s.a -1 ->>> s.a = 3 ->>> s -Struct(a=3, b=2) - ->>> def is_even(x): -... return x % 2 == 0 ->>> sorted([1, 2, -3]) -[-3, 1, 2] ->>> sorted(range(10), key=is_even) -[1, 3, 5, 7, 9, 0, 2, 4, 6, 8] ->>> sorted(range(10), lambda x,y: y-x) -[9, 8, 7, 6, 5, 4, 3, 2, 1, 0] - ->>> removeall(4, []) -[] ->>> removeall('s', 'This is a test. Was a test.') -'Thi i a tet. Wa a tet.' ->>> removeall('s', 'Something') -'Something' ->>> removeall('s', '') -'' - ->>> list(reversed([])) -[] - ->>> count_if(is_even, [1, 2, 3, 4]) -2 ->>> count_if(is_even, []) -0 - ->>> argmax([1], lambda x: x*x) -1 ->>> argmin([1], lambda x: x*x) -1 - - -# Test of memoize with slots in structures ->>> countries = [Struct(name='united states'), Struct(name='canada')] - -# Pretend that 'gnp' was some big hairy operation: ->>> def gnp(country): -... print 'calculating gnp ...' -... return len(country.name) * 1e10 - ->>> gnp = memoize(gnp, '_gnp') ->>> map(gnp, countries) -calculating gnp ... -calculating gnp ... -[130000000000.0, 60000000000.0] ->>> countries -[Struct(_gnp=130000000000.0, name='united states'), Struct(_gnp=60000000000.0, name='canada')] - -# This time we avoid re-doing the calculation ->>> map(gnp, countries) -[130000000000.0, 60000000000.0] - -# Test Queues: ->>> nums = [1, 8, 2, 7, 5, 6, -99, 99, 4, 3, 0] ->>> def qtest(q): -... q.extend(nums) -... for num in nums: assert num in q -... assert 42 not in q -... return [q.pop() for i in range(len(q))] ->>> qtest(Stack()) -[0, 3, 4, 99, -99, 6, 5, 7, 2, 8, 1] - ->>> qtest(FIFOQueue()) -[1, 8, 2, 7, 5, 6, -99, 99, 4, 3, 0] - ->>> qtest(PriorityQueue(min)) -[-99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 99] - ->>> qtest(PriorityQueue(max)) -[99, 8, 7, 6, 5, 4, 3, 2, 1, 0, -99] - ->>> qtest(PriorityQueue(min, abs)) -[0, 1, 2, 3, 4, 5, 6, 7, 8, -99, 99] - ->>> qtest(PriorityQueue(max, abs)) -[99, -99, 8, 7, 6, 5, 4, 3, 2, 1, 0] - ->>> vals = [100, 110, 160, 200, 160, 110, 200, 200, 220] ->>> histogram(vals) -[(100, 1), (110, 2), (160, 2), (200, 3), (220, 1)] ->>> histogram(vals, 1) -[(200, 3), (160, 2), (110, 2), (220, 1), (100, 1)] ->>> histogram(vals, 1, lambda v: round(v, -2)) -[(200.0, 6), (100.0, 3)] - ->>> log2(1.0) -0.0 - ->>> def fib(n): -... return (n<=1 and 1) or (fib(n-1) + fib(n-2)) - ->>> fib(9) -55 - -# Now we make it faster: ->>> fib = memoize(fib) ->>> fib(9) -55 - ->>> q = Stack() ->>> q.append(1) ->>> q.append(2) ->>> q.pop(), q.pop() -(2, 1) - ->>> q = FIFOQueue() ->>> q.append(1) ->>> q.append(2) ->>> q.pop(), q.pop() -(1, 2) - - ->>> abc = set('abc') ->>> bcd = set('bcd') ->>> 'a' in abc -True ->>> 'a' in bcd -False ->>> list(abc.intersection(bcd)) -['c', 'b'] ->>> list(abc.union(bcd)) -['a', 'c', 'b', 'd'] - -## From "What's new in Python 2.4", but I added calls to sl - ->>> def sl(x): -... return sorted(list(x)) - - ->>> a = set('abracadabra') # form a set from a string ->>> 'z' in a # fast membership testing -False ->>> sl(a) # unique letters in a -['a', 'b', 'c', 'd', 'r'] - ->>> b = set('alacazam') # form a second set ->>> sl(a - b) # letters in a but not in b -['b', 'd', 'r'] ->>> sl(a | b) # letters in either a or b -['a', 'b', 'c', 'd', 'l', 'm', 'r', 'z'] ->>> sl(a & b) # letters in both a and b -['a', 'c'] ->>> sl(a ^ b) # letters in a or b but not both -['b', 'd', 'l', 'm', 'r', 'z'] - - ->>> a.add('z') # add a new element ->>> a.update('wxy') # add multiple new elements ->>> sl(a) -['a', 'b', 'c', 'd', 'r', 'w', 'x', 'y', 'z'] ->>> a.remove('x') # take one element out ->>> sl(a) -['a', 'b', 'c', 'd', 'r', 'w', 'y', 'z'] - ->>> weighted_sample_with_replacement([], [], 0) -[] ->>> weighted_sample_with_replacement('a', [3], 2) -['a', 'a'] ->>> weighted_sample_with_replacement('ab', [0, 3], 3) -['b', 'b', 'b'] -""" - -__doc__ += random_tests(""" ->>> weighted_sample_with_replacement(range(10), [x*x for x in range(10)], 3) -[8, 9, 6] -""") + tests = re.findall(">>> (.*)", text) + return '\n'.join(map(fixup, tests)) From 48b2b234377d8e66ccb274e4845a835486228166 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 18:05:34 -0800 Subject: [PATCH 010/513] Create test_utils.py This is from tmrts, pull request https://github.com/tmrts/aima-python/commit/4cc647ae3e1d4ab9a1fc4fc9be652f52b4e72851 , but I'm bad at git, so it was easier for me to merge by creating this file here. --- utils_test.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 utils_test.py diff --git a/utils_test.py b/utils_test.py new file mode 100644 index 000000000..8ac0bbf84 --- /dev/null +++ b/utils_test.py @@ -0,0 +1,34 @@ + import pytest + from utils import * + +def test_struct_initialization(): + s = Struct(a=1, b=2) + assert s.a == 1 + assert s.b == 2 + +def test_struct_assignment(): + s = Struct(a=1) + s.a = 3 + assert s.a == 3 + +def test_removeall_list(): + assert removeall(4, []) == [] + assert removeall(4, [1,2,3,4]) == [1,2,3] + +def test_removeall_string(): + assert removeall('s', '') == '' + assert removeall('s', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' + +def test_count_if(): + is_odd = lambda x: x % 2 + assert count_if(is_odd, []) == 0 + assert count_if(is_odd, [1, 2, 3, 4, 5]) == 3 + +def test_argmax(): + assert argmax([-2, 1], lambda x: x**2) == -2 + +def test_argmin(): + assert argmin([-2, 1], lambda x: x**2) == 1 + +if __name__ == '__main__': + pytest.main() From e046bcc7edce9084302d1498c012586a9aa71cc8 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 18:09:54 -0800 Subject: [PATCH 011/513] Update utils.py --- utils.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/utils.py b/utils.py index fa8f4c20c..7aa496224 100644 --- a/utils.py +++ b/utils.py @@ -1,4 +1,10 @@ """Provide some widely useful utilities. Safe for "from utils import *". + +TODO: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py +TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. +TODO: if_ is a terrible idea; replace all uses with (x if test else y) and remove if_ +TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. +TODO: Priority queues may not belong here -- see treatment in search.py """ import operator From 53676a1f954a991f2a1a0ce5f487ec8c3f1d2b8e Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 18:44:56 -0800 Subject: [PATCH 012/513] Update probability to Python 3; move doctest to test.py --- logic.py | 8 ++--- probability.doctest | 72 --------------------------------------------- probability.py | 4 +-- probability_test.py | 30 +++++++++++++++++++ utils_test.py | 4 +-- 5 files changed, 38 insertions(+), 80 deletions(-) delete mode 100644 probability.doctest create mode 100644 probability_test.py diff --git a/logic.py b/logic.py index fe01ad779..3d3ff38aa 100644 --- a/logic.py +++ b/logic.py @@ -398,7 +398,7 @@ def pl_true(exp, model={}): elif op == '^': return pt != qt else: - raise ValueError, "illegal operator in logic expression" + str(exp) + raise ValueError("illegal operator in logic expression" + str(exp)) #______________________________________________________________________________ @@ -1102,17 +1102,17 @@ def pretty_set(s): return 'set(%r)' % sorted(s, key=repr) def pp(x): - print pretty(x) + print(pretty(x)) def ppsubst(s): """Pretty-print substitution s""" ppdict(s) def ppdict(d): - print pretty_dict(d) + print(pretty_dict(d)) def ppset(s): - print pretty_set(s) + print(pretty_set(s)) #________________________________________________________________________ diff --git a/probability.doctest b/probability.doctest deleted file mode 100644 index bd0f9436d..000000000 --- a/probability.doctest +++ /dev/null @@ -1,72 +0,0 @@ - ->>> cpt = burglary.variable_node('Alarm').cpt ->>> parents = ['Burglary', 'Earthquake'] ->>> event = {'Burglary': True, 'Earthquake': True} ->>> print '%4.2f' % cpt.p(True, parents, event) -0.95 ->>> event = {'Burglary': False, 'Earthquake': True} ->>> print '%4.2f' % cpt.p(False, parents, event) -0.71 ->>> BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) -0.375 ->>> BoolCPT(0.75).p(False, [], {}) -0.25 - -(fixme: The following test p_values which has been folded into p().) ->>> cpt = BoolCPT(0.25) ->>> cpt.p_values(F, ()) -0.75 ->>> cpt = BoolCPT({T: 0.25, F: 0.625}) ->>> cpt.p_values(T, (T,)) -0.25 ->>> cpt.p_values(F, (F,)) -0.375 ->>> cpt = BoolCPT({(T, T): 0.2, (T, F): 0.31, -... (F, T): 0.5, (F, F): 0.62}) ->>> cpt.p_values(T, (T, F)) -0.31 ->>> cpt.p_values(F, (F, F)) -0.38 - - ->>> cpt = BoolCPT({True: 0.2, False: 0.7}) ->>> cpt.rand(['A'], {'A': True}) in [True, False] -True ->>> cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3, -... (False, True): 0.5, (False, False): 0.7}) ->>> cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False] -True - - ->>> enumeration_ask('Earthquake', {}, burglary).show_approx() -'False: 0.998, True: 0.002' - - ->>> s = prior_sample(burglary) ->>> s['Burglary'] in [True, False] -True ->>> s['Alarm'] in [True, False] -True ->>> s['JohnCalls'] in [True, False] -True ->>> len(s) -5 - - ->>> s = {'A': True, 'B': False, 'C': True, 'D': False} ->>> consistent_with(s, {}) -True ->>> consistent_with(s, s) -True ->>> consistent_with(s, {'A': False}) -False ->>> consistent_with(s, {'D': True}) -False - ->>> seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000) ->>> [p[True], p[False]] -[0.001, 0.999] - ->>> seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000) ->>> [p[True], p[False]] -[0.002, 0.998] diff --git a/probability.py b/probability.py index f83718647..836d45359 100644 --- a/probability.py +++ b/probability.py @@ -413,8 +413,8 @@ def rejection_sampling(X, e, bn, N): def consistent_with(event, evidence): "Is event consistent with the given evidence?" - return every(lambda (k, v): evidence.get(k, v) == v, - event.items()) + return all(evidence.get(k, v) == v + for k, v in event.items()) #_______________________________________________________________________________ diff --git a/probability_test.py b/probability_test.py new file mode 100644 index 000000000..fb18a273e --- /dev/null +++ b/probability_test.py @@ -0,0 +1,30 @@ +import pytest +from probability import * + +def tests(): + cpt = burglary.variable_node('Alarm').cpt + parents = ['Burglary', 'Earthquake'] + event = {'Burglary': True, 'Earthquake': True} + assert cpt.p(True, parents, event) == 0.95 + event = {'Burglary': False, 'Earthquake': True} + assert cpt.p(False, parents, event) == 0.71 + assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375 + assert BoolCPT(0.75).p(False, [], {}) == 0.25 + cpt = BoolCPT({True: 0.2, False: 0.7}) + assert cpt.rand(['A'], {'A': True}) in [True, False] + cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3, + (False, True): 0.5, (False, False): 0.7}) + assert cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False] + #enumeration_ask('Earthquake', {}, burglary) + + s = {'A': True, 'B': False, 'C': True, 'D': False} + assert consistent_with(s, {}) + assert consistent_with(s, s) + assert not consistent_with(s, {'A': False}) + assert not consistent_with(s, {'D': True}) + + seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000) + assert p[True], p[False] == (0.001, 0.999) + + seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000) + assert p[True], p[False] == (0.002, 0.998) diff --git a/utils_test.py b/utils_test.py index 8ac0bbf84..bc1ac5139 100644 --- a/utils_test.py +++ b/utils_test.py @@ -1,5 +1,5 @@ - import pytest - from utils import * +import pytest +from utils import * def test_struct_initialization(): s = Struct(a=1, b=2) From 8cb1e249a2ce8b6a71599da748d168025a5bbf72 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 18:55:41 -0800 Subject: [PATCH 013/513] Describe update process more clearly in README.md --- README.md | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6ce693156..56c874971 100644 --- a/README.md +++ b/README.md @@ -7,12 +7,20 @@ When complete, this project will cover all the major topics in the book, for eac - `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with `py.test`. - `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. -Until we get there, we will support a legacy branch, `aima3python2` (for the thrid edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following should be done: +Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken + +## Port to Python 3; Pythonic Idioms; py.test - Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. +- Replace poor idioms with proper Python. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. +- Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. +In other words, replace the ">>> 2 + 2" in a docstring with "assert 2 + 2 == 4" in `filename_test.py`. + +## New and Improved Algorithms + - Implement functions that were in the third edition of the book but were not yet implemented in the code. - As we finish chapters for the new fourth edition, we will share the pseudocode, and describe what changes are necessary. -- Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. + - Create a `.ipynb` notebook, and give examples of how to use the code. # Style Guide From a19fdf192e706d5d123cd158ae00fff796fa8a4f Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 5 Mar 2016 09:07:23 +0530 Subject: [PATCH 014/513] modified the table accordint to latest GitHub markdown format --- README.md | 34 +++++++++++++++------------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 56c874971..ca9b9c734 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ # `aima-python`: Structure of the Project -Python code for the book *Artificial Intelligence: A Modern Approach.* +Python code for the book *Artificial Intelligence: A Modern Approach.* When complete, this project will cover all the major topics in the book, for each topic, such as `logic`, we will have the following [Python 3.5](https://www.python.org/downloads/release/python-350/) files in the main branch: -- `logic.py`: Implementations of all the pseudocode algorithms in the book. +- `logic.py`: Implementations of all the pseudocode algorithms in the book. - `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with `py.test`. - `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. - + Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken ## Port to Python 3; Pythonic Idioms; py.test @@ -29,11 +29,11 @@ There are a few style rules that are unique to this project: - The first rule is that the code should correspond directly to the pseudocode in the book. When possible this will be almost one-to-one, just allowing for the syntactic differences between Python and pseudocode, and for different library functions. - Don't make a function more complicated than the pseudocode in the book, even if the complication would add a nice feature, or give an efficiency gain. Instead, remain faithful to the pseudocode, and if you must, add a new function (not in the book) with the added feature. -- I use functional programming (functions with no side effects) in many cases, but not exclusively (sometimes classes and/or functions with side effects are used). Let the book's pseudocode be the guide. +- I use functional programming (functions with no side effects) in many cases, but not exclusively (sometimes classes and/or functions with side effects are used). Let the book's pseudocode be the guide. Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008), with a few minor exceptions: -- I'm not too worried about an occasional line longer than 79 characters. +- I'm not too worried about an occasional line longer than 79 characters. - You don't need two spaces after a sentence-ending period. - Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). - I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). @@ -42,7 +42,7 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) parameter name is already suggestive of the name of a type, such as `url` below, then i don't think the type annotation is useful. Return type annotations, such as `-> None` below, can be very useful. - def retry(url: Url) -> None: + def retry(url: Url) -> None: # Choice of Programming Languages @@ -50,7 +50,7 @@ Are we right to concentrate on Java and Python versions of the code? I think so; fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (ut lacks type declarations and can be solw). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: Java, C, C++, C#, Python - + So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. And finally, Javascript is the language of the browser; it would be nice to have code that runs in the browser, in Javascript or a variant such as Typescript. There is also a `aima-lisp` project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular. @@ -58,15 +58,11 @@ There is also a `aima-lisp` project; in 1995 when we wrote the first edition of What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [norvig russell "Modern Approach"](https://www.google.com/webhp#q=russell%20norvig%20%22modern%20approach%22%20java) along with the names of various languages and looked at the estimated counts of results on various dates. However, I don't have much confidence in these figures... -

- -
Language20042005200720102016 -
none 8,08020,10075,200150,000132,000 -
java 1,9904,93044,20037,00050,000 -
c++ 8751,82035,300105,00035,000 -
lisp 84497430,10019,00014,000 -
prolog 7892,01023,20017,00016,000 -
python 7851,24018,40011,00012,000 - -
- +|Language |2004 |2005 |2007 |2010 |2016 | +|-------- |----: |----: |----: |----: |----: | +|[none](http://www.google.com/search?q=norvig+russell+%22Modern+Approach%22)|8,080|20,100|75,200|150,000|132,000| +|[java](http://www.google.com/search?q=java+norvig+russell+%22Modern+Approach%22)|1,990|4,930|44,200|37,000|50,000| +|[c++](http://www.google.com/search?q=c%2B%2B+norvig+russell+%22Modern+Approach%22)|875|1,820|35,300|105,000|35,000| +|[lisp](http://www.google.com/search?q=lisp+norvig+russell+%22Modern+Approach%22)|844|974|30,100|19,000|14,000| +|[prolog](http://www.google.com/search?q=prolog+norvig+russell+%22Modern+Approach%22)|789|2,010|23,200|17,000|16,000| +|[python](http://www.google.com/search?q=python+norvig+russell+%22Modern+Approach%22)|785|1,240|18,400|11,000|12,000| From 5a36f3d6f1b8f8e2ec82c87f3011809558a3ca60 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 19:58:27 -0800 Subject: [PATCH 015/513] abstract methods MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit use “raise NotImplementedError” for abstract methods --- agents.py | 4 ++-- games.py | 6 +++--- logic.py | 8 ++++---- mdp.py | 2 +- search.py | 16 ++++++++-------- utils.py | 2 +- 6 files changed, 19 insertions(+), 19 deletions(-) diff --git a/agents.py b/agents.py index 4789fe6cd..e45d0cbc0 100644 --- a/agents.py +++ b/agents.py @@ -213,11 +213,11 @@ def thing_classes(self): def percept(self, agent): "Return the percept that the agent sees at this point. (Implement this.)" - abstract + raise NotImplementedError def execute_action(self, agent, action): "Change the world to reflect this action. (Implement this.)" - abstract + raise NotImplementedError def default_location(self, thing): "Default location to place a new thing with unspecified location." diff --git a/games.py b/games.py index e1073962a..baad4b6e1 100644 --- a/games.py +++ b/games.py @@ -149,15 +149,15 @@ class Game: def actions(self, state): "Return a list of the allowable moves at this point." - abstract + raise NotImplementedError def result(self, state, move): "Return the state that results from making a move from a state." - abstract + raise NotImplementedError def utility(self, state, player): "Return the value of this final state to player." - abstract + raise NotImplementedError def terminal_test(self, state): "Return True if this is a final state for the game." diff --git a/logic.py b/logic.py index 3d3ff38aa..93727a575 100644 --- a/logic.py +++ b/logic.py @@ -42,11 +42,11 @@ class KB: first one or returns False.""" def __init__(self, sentence=None): - abstract + raise NotImplementedError def tell(self, sentence): "Add the sentence to the KB." - abstract + raise NotImplementedError def ask(self, query): """Return a substitution that makes the query true, or, @@ -57,11 +57,11 @@ def ask(self, query): def ask_generator(self, query): "Yield all the substitutions that make query true." - abstract + raise NotImplementedError def retract(self, sentence): "Remove sentence from the KB." - abstract + raise NotImplementedError class PropKB(KB): diff --git a/mdp.py b/mdp.py index 29313aea4..3d1a381f5 100644 --- a/mdp.py +++ b/mdp.py @@ -32,7 +32,7 @@ def R(self, state): def T(self, state, action): """Transition model. From a state and an action, return a list of (probability, result-state) pairs.""" - abstract + raise NotImplementedError def actions(self, state): """Set of actions that can be performed in this state. By default, a diff --git a/search.py b/search.py index eb2fb5c46..c0bbdd660 100644 --- a/search.py +++ b/search.py @@ -26,13 +26,13 @@ def actions(self, state): state. The result would typically be a list, but if there are many actions, consider yielding them one at a time in an iterator, rather than building them all at once.""" - abstract + raise NotImplementedError def result(self, state, action): """Return the state that results from executing the given action in the given state. The action must be one of self.actions(state).""" - abstract + raise NotImplementedError def goal_test(self, state): """Return True if the state is a goal. The default method compares the @@ -51,7 +51,7 @@ def path_cost(self, c, state1, action, state2): def value(self, state): """For optimization problems, each state has a value. Hill-climbing and related algorithms try to maximize this value.""" - abstract + raise NotImplementedError #______________________________________________________________________________ class Node: @@ -125,16 +125,16 @@ def __call__(self, percept): return self.seq.pop(0) def update_state(self, percept): - abstract + raise NotImplementedError def formulate_goal(self, state): - abstract + raise NotImplementedError def formulate_problem(self, state, goal): - abstract + raise NotImplementedError def search(self, problem): - abstract + raise NotImplementedError #______________________________________________________________________________ # Uninformed Search algorithms @@ -388,7 +388,7 @@ def mate(self, other): def mutate(self): "Change a few of my genes." - abstract + raise NotImplementedError #_____________________________________________________________________________ # The remainder of this file implements examples for the search algorithms. diff --git a/utils.py b/utils.py index 7aa496224..82009cf09 100644 --- a/utils.py +++ b/utils.py @@ -460,7 +460,7 @@ class Queue: Note that isinstance(Stack(), Queue) is false, because we implement stacks as lists. If Python ever gets interfaces, Queue will be an interface.""" def __init__(self): - abstract + raise NotImplementedError def extend(self, items): for item in items: self.append(item) From 3507a77b6947f044fd660467a9773bf5ec31e01e Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 20:09:04 -0800 Subject: [PATCH 016/513] Delete agents.pyc --- agents.pyc | Bin 25631 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 agents.pyc diff --git a/agents.pyc b/agents.pyc deleted file mode 100644 index ec812acfd8d1dafe261257a73c4cd08615628ad6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25631 zcmc(IZERdudfu5Ck|Jr!lJ&v0Y^{B5S<9hKk0p8iv9>IWqWqP$Y4?(At>x7YhI22; zk%lv)xpzd$jkidoX1$5i-4;nd(iScHqeu%hK??+E({z6X{m~x<8X!p1re8n{6hR9V z0a^qoP@sLD=RNn%9g0+(#6U^i!*kC$_k6tH?|aU@s{GG`)4%=W-}$iV{7(gcFXBp; z%Fad3T}4`QQQ5UiZk4%;YnR>CvPzM!xT_Txjk$Qtt)q6_-F5Dcb01wDchQ84$5nbD 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zNxtMh=1~5|hnOR<^1A8Z_wXEUyYhh{&OyOh)6lYP`9k``h!%ZtRx~NJ;xm4z6W{O(u&>mYBdX=fV{x*OKA-(h>THmexmKV z;V<(2SD5@8Ccn(&SD5@7lV4}@J4_UR_;u!fg30eO`9Dnlgvp;Wp~EvQF&SXO^}&#y zQepPR#(cPwPxc}q9STX0b5)o?XYb$rdu&gBKU~50VbOKZsr@65JcUS$q5XI7*}dc59V7U0&Y>OK zcMR{i3;8{`?#6$7Hn?l2{_fo|Fv8!d8p3Z>G2M;QBlq@=q(A97fNuszMn?AT*oEJ- i!Y^6fd(X(=2+Hokm;3kbe{_%z;hp_IJ=8bY|NjBe9e&mT From e734314bfa5bc70c4e6d82015366982bf9a1ec08 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 4 Mar 2016 23:11:47 -0800 Subject: [PATCH 019/513] change DefaultDict, Dict to defaultdict, dict These functions, which I had to implement in Python 2.2, are now available in standard Python. --- csp.py | 3 ++- games.py | 2 +- logic.py | 6 ++++-- nlp.py | 3 ++- probability.py | 13 +++++++------ search.py | 42 +++++++++++++++++++++--------------------- text.py | 8 +++++--- utils.py | 4 ---- 8 files changed, 42 insertions(+), 39 deletions(-) diff --git a/csp.py b/csp.py index 4c9b29459..0cfaf89a9 100644 --- a/csp.py +++ b/csp.py @@ -1,6 +1,7 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" from utils import * +from collections import defaultdict import search class CSP(search.Problem): @@ -352,7 +353,7 @@ def parse_neighbors(neighbors, vars=[]): >>> parse_neighbors('X: Y Z; Y: Z') {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} """ - dict = DefaultDict([]) + dict = defaultdict(list) for var in vars: dict[var] = [] specs = [spec.split(':') for spec in neighbors.split(';')] diff --git a/games.py b/games.py index baad4b6e1..62ed435a1 100644 --- a/games.py +++ b/games.py @@ -188,7 +188,7 @@ class Fig52Game(Game): B=dict(b1='B1', b2='B2', b3='B3'), C=dict(c1='C1', c2='C2', c3='C3'), D=dict(d1='D1', d2='D2', d3='D3')) - utils = Dict(B1=3, B2=12, B3=8, C1=2, C2=4, C3=6, D1=14, D2=5, D3=2) + utils = dict(B1=3, B2=12, B3=8, C1=2, C2=4, C3=6, D1=14, D2=5, D3=2) initial = 'A' def actions(self, state): diff --git a/logic.py b/logic.py index 93727a575..a915309c5 100644 --- a/logic.py +++ b/logic.py @@ -24,9 +24,11 @@ diff, simp Symbolic differentiation and simplification """ -import itertools, re +import itertools +import re import agents from utils import * +from collections import defaultdict #______________________________________________________________________________ @@ -608,7 +610,7 @@ def pl_fc_entails(KB, q): """ count = dict([(c, len(conjuncts(c.args[0]))) for c in KB.clauses if c.op == '>>']) - inferred = DefaultDict(False) + inferred = defaultdict(bool) agenda = [s for s in KB.clauses if is_prop_symbol(s.op)] while agenda: p = agenda.pop() diff --git a/nlp.py b/nlp.py index d66bdc997..6c3ee4992 100644 --- a/nlp.py +++ b/nlp.py @@ -4,6 +4,7 @@ # from the third edition until this gets reviewed.) from utils import * +from collections import defaultdict #______________________________________________________________________________ # Grammars and Lexicons @@ -30,7 +31,7 @@ class Grammar: def __init__(self, name, rules, lexicon): "A grammar has a set of rules and a lexicon." update(self, name=name, rules=rules, lexicon=lexicon) - self.categories = DefaultDict([]) + self.categories = defaultdict(list) for lhs in lexicon: for word in lexicon[lhs]: self.categories[word].append(lhs) diff --git a/probability.py b/probability.py index 836d45359..e553524d2 100644 --- a/probability.py +++ b/probability.py @@ -3,7 +3,8 @@ from utils import * from logic import extend -from random import choice, seed +import random +from collections import defaultdict #______________________________________________________________________________ @@ -80,7 +81,7 @@ class JointProbDist(ProbDist): >>> P[dict(X=0, Y=1)] 0.5""" def __init__(self, variables): - update(self, prob={}, variables=variables, vals=DefaultDict([])) + update(self, prob={}, variables=variables, vals=defaultdict(list)) def __getitem__(self, values): "Given a tuple or dict of values, return P(values)." @@ -399,7 +400,7 @@ def rejection_sampling(X, e, bn, N): evidence e in BayesNet bn, using N samples. [Fig. 14.14] Raises a ZeroDivisionError if all the N samples are rejected, i.e., inconsistent with e. - >>> seed(47) + >>> random.seed(47) >>> rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T), ... burglary, 10000).show_approx() 'False: 0.7, True: 0.3' @@ -421,7 +422,7 @@ def consistent_with(event, evidence): def likelihood_weighting(X, e, bn, N): """Estimate the probability distribution of variable X given evidence e in BayesNet bn. [Fig. 14.15] - >>> seed(1017) + >>> random.seed(1017) >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T), ... burglary, 10000).show_approx() 'False: 0.702, True: 0.298' @@ -450,7 +451,7 @@ def weighted_sample(bn, e): def gibbs_ask(X, e, bn, N): """[Fig. 14.16] - >>> seed(1017) + >>> random.seed(1017) >>> gibbs_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary, 1000 ... ).show_approx() 'False: 0.738, True: 0.262' @@ -460,7 +461,7 @@ def gibbs_ask(X, e, bn, N): Z = [var for var in bn.vars if var not in e] state = dict(e) # boldface x in Fig. 14.16 for Zi in Z: - state[Zi] = choice(bn.variable_values(Zi)) + state[Zi] = random.choice(bn.variable_values(Zi)) for j in xrange(N): for Zi in Z: state[Zi] = markov_blanket_sample(Zi, state, bn) diff --git a/search.py b/search.py index c0bbdd660..f974596ec 100644 --- a/search.py +++ b/search.py @@ -473,33 +473,33 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g -romania = UndirectedGraph(Dict( - A=Dict(Z=75, S=140, T=118), - B=Dict(U=85, P=101, G=90, F=211), - C=Dict(D=120, R=146, P=138), - D=Dict(M=75), - E=Dict(H=86), - F=Dict(S=99), - H=Dict(U=98), - I=Dict(V=92, N=87), - L=Dict(T=111, M=70), - O=Dict(Z=71, S=151), - P=Dict(R=97), - R=Dict(S=80), - U=Dict(V=142))) -romania.locations = Dict( +romania = UndirectedGraph(dict( + A=dict(Z=75, S=140, T=118), + B=dict(U=85, P=101, G=90, F=211), + C=dict(D=120, R=146, P=138), + D=dict(M=75), + E=dict(H=86), + F=dict(S=99), + H=dict(U=98), + I=dict(V=92, N=87), + L=dict(T=111, M=70), + O=dict(Z=71, S=151), + P=dict(R=97), + R=dict(S=80), + U=dict(V=142))) +romania.locations = dict( A=( 91, 492), B=(400, 327), C=(253, 288), D=(165, 299), E=(562, 293), F=(305, 449), G=(375, 270), H=(534, 350), I=(473, 506), L=(165, 379), M=(168, 339), N=(406, 537), O=(131, 571), P=(320, 368), R=(233, 410), S=(207, 457), T=( 94, 410), U=(456, 350), V=(509, 444), Z=(108, 531)) -australia = UndirectedGraph(Dict( - T=Dict(), - SA=Dict(WA=1, NT=1, Q=1, NSW=1, V=1), - NT=Dict(WA=1, Q=1), - NSW=Dict(Q=1, V=1))) -australia.locations = Dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), +australia = UndirectedGraph(dict( + T=dict(), + SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), + NT=dict(WA=1, Q=1), + NSW=dict(Q=1, V=1))) +australia.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) class GraphProblem(Problem): diff --git a/text.py b/text.py index 304d624ea..a29eb4526 100644 --- a/text.py +++ b/text.py @@ -7,7 +7,9 @@ from utils import * from learning import CountingProbDist from math import log, exp -import re, search +from collections import defaultdict +import re +import search class UnigramTextModel(CountingProbDist): """This is a discrete probability distribution over words, so you @@ -28,7 +30,7 @@ def __init__(self, n, observation_sequence=[]): ## mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1) CountingProbDist.__init__(self) self.n = n - self.cond_prob = DefaultDict(CountingProbDist()) + self.cond_prob = defaultdict(CountingProbDist()) self.add_sequence(observation_sequence) ## __getitem__, top, sample inherited from CountingProbDist @@ -104,7 +106,7 @@ def __init__(self, stopwords='the a of'): """Create an IR System. Optionally specify stopwords.""" ## index is a map of {word: {docid: count}}, where docid is an int, ## indicating the index into the documents list. - update(self, index=DefaultDict(DefaultDict(0)), + update(self, index=defaultdict(lambda: defaultdict(int)), stopwords=set(words(stopwords)), documents=[]) def index_collection(self, filenames): diff --git a/utils.py b/utils.py index 82009cf09..93ab74af2 100644 --- a/utils.py +++ b/utils.py @@ -19,10 +19,6 @@ infinity = float('inf') -Dict = dict - -from collections import defaultdict as DefaultDict - class Struct: """Create an instance with argument=value slots. From 50ea63c33c7f6ff36e038af98b83383be98a1cad Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sat, 5 Mar 2016 13:48:26 +0530 Subject: [PATCH 020/513] Changed Distance Functions to use Tuples as Points --- utils.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/utils.py b/utils.py index 93ab74af2..ada9b5214 100644 --- a/utils.py +++ b/utils.py @@ -301,15 +301,21 @@ def turn_left(heading): def distance(a, b): "The distance between two (x, y) points." - return math.hypot((a.x - b.x), (a.y - b.y)) + ax, ay = a + bx, by = b + return math.hypot((ax - bx), (ay - by)) def distance_squared(a, b): "The distance between two (x, y) points." - return (a.x - b.x)**2 + (a.y - b.y)**2 + ax, ay = a + bx, by = b + return (ax - bx)**2 + (ay - by)**2 def distance2(a, b): "The square of the distance between two (x, y) points." - return distance_squared(a, b) + ax, ay = a + bx, by = b + return (ax - bx)**2 + (ay - by)**2 def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding From 9c6d9e5f7845641e97ff3b6b1fb1950c632cb8c3 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sat, 5 Mar 2016 13:52:32 +0530 Subject: [PATCH 021/513] Added functions for representing points and seperating coordinates --- utils.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/utils.py b/utils.py index ada9b5214..437c08f24 100644 --- a/utils.py +++ b/utils.py @@ -299,6 +299,15 @@ def turn_right(heading): def turn_left(heading): return turn_heading(heading, +1) +def Point(x, y): + return (x, y) + +def point_x(point): + return point[0] + +def point_y(point): + return point[1] + def distance(a, b): "The distance between two (x, y) points." ax, ay = a From 7017ad79d1012b465e81e7952eeae27209c5767f Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sat, 5 Mar 2016 14:10:20 +0530 Subject: [PATCH 022/513] removed redundant distance_squared function --- utils.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/utils.py b/utils.py index 437c08f24..ec8edbc1c 100644 --- a/utils.py +++ b/utils.py @@ -314,12 +314,6 @@ def distance(a, b): bx, by = b return math.hypot((ax - bx), (ay - by)) -def distance_squared(a, b): - "The distance between two (x, y) points." - ax, ay = a - bx, by = b - return (ax - bx)**2 + (ay - by)**2 - def distance2(a, b): "The square of the distance between two (x, y) points." ax, ay = a From cd0657db13a812f38a458a1f61af9650c9330294 Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 5 Mar 2016 15:58:25 +0530 Subject: [PATCH 023/513] moved all the >>> doctests from utils.py to utils_test.py --- utils.py | 169 ++++++++++++-------------------------------------- utils_test.py | 81 +++++++++++++++++++++++- 2 files changed, 118 insertions(+), 132 deletions(-) diff --git a/utils.py b/utils.py index 93ab74af2..cbc20472a 100644 --- a/utils.py +++ b/utils.py @@ -1,6 +1,6 @@ """Provide some widely useful utilities. Safe for "from utils import *". -TODO: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py +TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. TODO: if_ is a terrible idea; replace all uses with (x if test else y) and remove if_ TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. @@ -13,6 +13,7 @@ import os.path import bisect import re +from functools import reduce #______________________________________________________________________________ # Simple Data Structures: infinity, Dict, Struct @@ -21,29 +22,22 @@ class Struct: """Create an instance with argument=value slots. - This is for making a lightweight object whose class doesn't matter.""" def __init__(self, **entries): self.__dict__.update(entries) def __cmp__(self, other): if isinstance(other, Struct): - return cmp(self.__dict__, other.__dict__) + return self.__dict__ == other.__dict__ else: - return cmp(self.__dict__, other) + return self.__dict__ == other def __repr__(self): - args = ['{!s}={!s}'.format(k, repr(v)) + args = ['{!s}={!s}'.format(k, repr(v)) for (k, v) in vars(self).items()] def update(x, **entries): - """Update a dict or an object with slots according to entries. - - >>> update({'a': 1}, a=10, b=20) - {'a': 10, 'b': 20} - >>> update(Struct(a=1), a=10, b=20) - Struct(a=10, b=20) - """ + """Update a dict or an object with slots according to entries.""" if isinstance(x, dict): x.update(entries) else: @@ -57,79 +51,46 @@ def update(x, **entries): # argument first (like reduce, filter, and map). def removeall(item, seq): - """Return a copy of seq (or string) with all occurences of item removed. - >>> removeall(3, [1, 2, 3, 3, 2, 1, 3]) - [1, 2, 2, 1] - >>> removeall(4, [1, 2, 3]) - [1, 2, 3] - """ + """Return a copy of seq (or string) with all occurences of item removed.""" if isinstance(seq, str): return seq.replace(item, '') else: return [x for x in seq if x != item] def unique(seq): - """Remove duplicate elements from seq. Assumes hashable elements. - >>> unique([1, 2, 3, 2, 1]) - [1, 2, 3] - """ + """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) def product(numbers): - """Return the product of the numbers. - >>> product([1,2,3,4]) - 24 - """ + """Return the product of the numbers.""" return reduce(operator.mul, numbers, 1) def count_if(predicate, seq): - """Count the number of elements of seq for which the predicate is true. - >>> count_if(callable, [42, None, max, min]) - 2 - """ + """Count the number of elements of seq for which the predicate is true.""" return sum(map(lambda x: bool(predicate(x)), seq)) def find_if(predicate, seq): - """If there is an element of seq that satisfies predicate; return it. - >>> find_if(callable, [3, min, max]) - - >>> find_if(callable, [1, 2, 3]) - """ + """If there is an element of seq that satisfies predicate; return it.""" for x in seq: - if predicate(x): + if predicate(x): return x return None def every(predicate, seq): - """True if every element of seq satisfies predicate. - >>> every(callable, [min, max]) - 1 - >>> every(callable, [min, 3]) - 0 - """ + """True if every element of seq satisfies predicate.""" return all(predicate(x) for x in seq) def some(predicate, seq): - """If some element x of seq satisfies predicate(x), return predicate(x). - >>> some(callable, [min, 3]) - 1 - >>> some(callable, [2, 3]) - 0 - """ + """If some element x of seq satisfies predicate(x), return predicate(x).""" elem = find_if(predicate,seq) return predicate(elem) or False # TODO: rename to is_in or possibily add 'identity' to function name to clarify intent def isin(elt, seq): - """Like (elt in seq), but compares with is, not ==. - >>> e = []; isin(e, [1, e, 3]) - True - >>> isin(e, [1, [], 3]) - False - """ + """Like (elt in seq), but compares with is, not ==.""" return any(x is elt for x in seq) #______________________________________________________________________________ @@ -144,21 +105,15 @@ def argmin(seq, fn): return min(seq, key=fn) def argmin_list(seq, fn): - """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores. - >>> argmin_list(['one', 'to', 'three', 'or'], len) - ['to', 'or'] - """ - smallest_score = min(seq, key=fn) + """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores.’""" + smallest_score = len(min(seq, key=fn)) return [elem for elem in seq if fn(elem) == smallest_score] def argmin_gen(seq, fn): - """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores. - >>> argmin_list(['one', 'to', 'three', 'or'], len) - ['to', 'or'] - """ + """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores.""" - smallest_score = min(seq, key=fn) + smallest_score = len(min(seq, key=fn)) yield from (elem for elem in seq if fn(elem) == smallest_score) @@ -168,25 +123,21 @@ def argmin_random_tie(seq, fn): return random.choice(argmin_gen(seq, fn)) def argmax(seq, fn): - """Return an element with highest fn(seq[i]) score; tie goes to first one. - >>> argmax(['one', 'to', 'three'], len) - 'three' - """ - return argmin(seq, lambda x: -fn(x)) + """Return an element with highest fn(seq[i]) score; tie goes to first one.""" + return max(seq, key=fn) def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. - >>> argmax_list(['one', 'three', 'seven'], len) - ['three', 'seven'] - """ - return argmin_list(seq, lambda x: -fn(x)) + Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method breaks if fn is len""" + largest_score = len(max(seq, key=fn)) + + return [elem for elem in seq if fn(elem) == largest_score] def argmax_gen(seq, fn): - """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores. - >>> argmax_list(['one', 'three', 'seven'], len) - ['three', 'seven'] - """ - yield from argmin_gen(seq, lambda x: -fn(x)) + """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores.""" + largest_score = len(min(seq, key=fn)) + + yield from (elem for elem in seq if fn(elem) == largest_score) def argmax_random_tie(seq, fn): "Return an element with highest fn(seq[i]) score; break ties at random." @@ -199,7 +150,7 @@ def histogram(values, mode=0, bin_function=None): """Return a list of (value, count) pairs, summarizing the input values. Sorted by increasing value, or if mode=1, by decreasing count. If bin_function is given, map it over values first.""" - if bin_function: + if bin_function: values = map(bin_function, values) bins = {} @@ -220,17 +171,11 @@ def stddev(values, meanval=None): return stdev(values, mu=meanval) def dotproduct(X, Y): - """Return the sum of the element-wise product of vectors x and y. - >>> dotproduct([1, 2, 3], [1000, 100, 10]) - 1230 - """ + """Return the sum of the element-wise product of vectors x and y.""" return sum([x * y for x, y in zip(X, Y)]) def vector_add(a, b): - """Component-wise addition of two vectors. - >>> vector_add((0, 1), (8, 9)) - (8, 10) - """ + """Component-wise addition of two vectors.""" return tuple(map(operator.add, a, b)) def probability(p): @@ -254,12 +199,7 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] def num_or_str(x): - """The argument is a string; convert to a number if possible, or strip it. - >>> num_or_str('42') - 42 - >>> num_or_str(' 42x ') - '42x' - """ + """The argument is a string; convert to a number if possible, or strip it.""" try: return int(x) except ValueError: @@ -269,18 +209,12 @@ def num_or_str(x): return str(x).strip() def normalize(numbers): - """Multiply each number by a constant such that the sum is 1.0 - >>> normalize([1,2,1]) - [0.25, 0.5, 0.25] - """ + """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) return [n / total for n in numbers] def clip(x, lowest, highest): - """Return x clipped to the range [lowest..highest]. - >>> [clip(x, 0, 1) for x in [-1, 0.5, 10]] - [0, 0.5, 1] - """ + """Return x clipped to the range [lowest..highest].""" return max(lowest, min(x, highest)) #______________________________________________________________________________ @@ -315,8 +249,6 @@ def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding value of lowest or more than the corresponding value of highest, clip to those values. - >>> vector_clip((-1, 10), (0, 0), (9, 9)) - (0, 9) """ return type(vector)(map(clip, vector, lowest, highest)) @@ -331,14 +263,7 @@ def printf(format_str, *args): return args[-1] if args else format_str def caller(n=1): - """Return the name of the calling function n levels up in the frame stack. - >>> caller(0) - 'caller' - >>> def f(): - ... return caller() - >>> f() - 'f' - """ + """Return the name of the calling function n levels up in the frame stack.""" import inspect return inspect.getouterframes(inspect.currentframe())[n][3] @@ -371,16 +296,14 @@ def if_(test, result, alternative): both result and alternative are always evaluated. However, if either evaluates to a function, it is applied to the empty arglist, so you can delay execution by putting it in a lambda. - >>> if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) - 'ok' """ if test: - if callable(result): + if callable(result): return result() return result else: - if callable(alternative): + if callable(alternative): return alternative() return alternative @@ -418,7 +341,7 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): sizes = map(maxlen, zip(*[map(str, row) for row in table])) for row in table: - print(sep.join(getattr(str(x), j)(size) + print(sep.join(getattr(str(x), j)(size) for (j, size, x) in zip(justs, sizes, row))) def AIMAFile(components, mode='r'): @@ -529,17 +452,3 @@ def __delitem__(self, key): ## as Fig[3.1] Fig = {} -#______________________________________________________________________________ -# Support for doctest - -def ignore(x): - pass - -def random_tests(text): - """Some functions are stochastic. We want to be able to write a test - with random output. We do that by ignoring the output.""" - def fixup(test): - return ">>> {}".format("ignore(" + test + ")" if " = " not in test else test) - - tests = re.findall(">>> (.*)", text) - return '\n'.join(map(fixup, tests)) diff --git a/utils_test.py b/utils_test.py index bc1ac5139..46be72305 100644 --- a/utils_test.py +++ b/utils_test.py @@ -1,4 +1,5 @@ import pytest +import utils from utils import * def test_struct_initialization(): @@ -11,6 +12,14 @@ def test_struct_assignment(): s.a = 3 assert s.a == 3 +def test_update_dict(): + assert update({'a': 1}, a=10, b=20) == {'a': 10, 'b': 20} + assert update({}, a=5) == {'a': 5} + +def test_update_struct(): + assert update(Struct(a=1), a=30, b=20).__cmp__(Struct(a=30, b=20)) + assert update(Struct(), a=10).__cmp__(Struct(a=10)) + def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1,2,3,4]) == [1,2,3] @@ -19,16 +28,84 @@ def test_removeall_string(): assert removeall('s', '') == '' assert removeall('s', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' +def test_unique(): + assert unique([1, 2, 3, 2, 1]) == [1, 2, 3] + assert unique([1, 5, 6, 7, 6, 5]) == [1, 5, 6, 7] + +def test_product(): + assert product([1,2,3,4]) == 24 + +def test_count_if(): + assert count_if(callable, [42, None, max, min]) == 2 + +def test_find_if(): + assert find_if(callable, [1, 2, 3]) == None + assert find_if(callable, [3, min, max]) == min + def test_count_if(): is_odd = lambda x: x % 2 assert count_if(is_odd, []) == 0 assert count_if(is_odd, [1, 2, 3, 4, 5]) == 3 -def test_argmax(): - assert argmax([-2, 1], lambda x: x**2) == -2 +def test_every(): + assert every(callable, [min, max]) == 1 + assert every(callable, [min, 3]) == 0 + +def test_some(): + assert some(callable, [min, 3]) == 1 + assert some(callable, [2, 3]) == 0 + +def test_isin(): + e= [] + assert isin(e, [1, e, 3]) == True + assert isin(e, [1, [], 3]) == False def test_argmin(): assert argmin([-2, 1], lambda x: x**2) == 1 +def test_argmin_list(): + assert argmin_list(['one', 'to', 'three', 'or'], len) == ['to', 'or'] + +def test_argmin_gen(): + assert [i for i in argmin_gen(['one', 'to', 'three', 'or'], len)] == ['to', 'or'] + +def test_argmax(): + assert argmax([-2, 1], lambda x: x**2) == -2 + assert argmax(['one', 'to', 'three'], len) == 'three' + +def test_argmax_list(): + assert argmax_list(['one', 'three', 'seven'], lambda x: len(x)) == ['three', 'seven'] + +def test_argmax_gen(): + assert argmax_list(['one', 'three', 'seven'], len) == ['three', 'seven'] + +def test_dotproduct(): + assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 + +def test_vector_add(): + assert vector_add((0, 1), (8, 9)) == (8, 10) + +def test_num_or_str(): + assert num_or_str('42') == 42 + assert num_or_str(' 42x ') == '42x' + +def test_normalize(): + assert normalize([1,2,1]) == [0.25, 0.5, 0.25] + +def test_clip(): + assert [clip(x, 0, 1) for x in [-1, 0.5, 10]] == [0, 0.5, 1] + +def test_vector_clip(): + assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) + +def test_caller(): + assert caller(0) == 'caller' + def f(): + return caller() + assert f() == 'f' + +def test_if_(): + assert if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) == 'ok' + if __name__ == '__main__': pytest.main() From 1a62d4924b6e206c05432c31d539b2c9240c5dd7 Mon Sep 17 00:00:00 2001 From: Utkarsh Agrawal Date: Sat, 5 Mar 2016 17:25:59 +0530 Subject: [PATCH 024/513] Created grid.py from utils.py for separed 2D Grid environment. --- grid.py | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 grid.py diff --git a/grid.py b/grid.py new file mode 100644 index 000000000..278ae2d7b --- /dev/null +++ b/grid.py @@ -0,0 +1,52 @@ +## OK, the following are not as widely useful utilities as some of the other +## functions here, but they do show up wherever we have 2D grids: Wumpus and +## Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. +##__________________________________________________________________________ +import math + + +orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)] + +def turn_heading(heading, inc, headings=orientations): + return headings[(headings.index(heading) + inc) % len(headings)] + +def turn_right(heading): + return turn_heading(heading, -1) + +def turn_left(heading): + return turn_heading(heading, +1) + +def distance(a, b): + """The distance between two (x, y) points. + >>> distance((1,2),(5,5)) + 5.0 + """ + return math.hypot((a[0] - b[0]), (a[1] - b[1])) + +def distance_squared(a, b): + """The square of the distance between two (x, y) points. + >>> distance_squared((1,2),(5,5)) + 25.0 + """ + return (a[0]- b[0])**2 + (a[1] - b[1])**2 + +def distance2(a, b): + "The square of the distance between two (x, y) points." + return distance_squared(a, b) + +def clip(x, lowest, highest): + """Return x clipped to the range [lowest..highest]. + >>> [clip(x, 0, 1) for x in [-1, 0.5, 10]] + [0, 0.5, 1] + """ + return max(lowest, min(x, highest)) + + +def vector_clip(vector, lowest, highest): + """Return vector, except if any element is less than the corresponding + value of lowest or more than the corresponding value of highest, clip to + those values. + >>> vector_clip((-1, 10), (0, 0), (9, 9)) + (0, 9) + """ + return type(vector)(map(clip, vector, lowest, highest)) From b83ca93870c960f8f4444706f72dd79a3680d4ef Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 5 Mar 2016 19:56:43 +0530 Subject: [PATCH 025/513] Fixed pruning for first max node in aphabeta_search --- games.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/games.py b/games.py index 62ed435a1..841263cfd 100644 --- a/games.py +++ b/games.py @@ -41,6 +41,7 @@ def alphabeta_full_search(state, game): player = game.to_move(state) + #Functions used by alphabeta def max_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) @@ -64,9 +65,7 @@ def min_value(state, alpha, beta): return v # Body of alphabeta_search: - return argmax(game.actions(state), - lambda a: min_value(game.result(state, a), - -infinity, infinity)) + return max_value(state, -infinity, infinity) def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): """Search game to determine best action; use alpha-beta pruning. @@ -74,6 +73,7 @@ def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): player = game.to_move(state) + #Functions used by alphabeta def max_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) @@ -103,9 +103,7 @@ def min_value(state, alpha, beta, depth): cutoff_test = (cutoff_test or (lambda state,depth: depth>d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) - return argmax(game.actions(state), - lambda a: min_value(game.result(state, a), - -infinity, infinity, 0)) + return max_value(state, -infinity, infinity, 0) #______________________________________________________________________________ # Players for Games From c78d3270ae5163174efd08ee6ded7fe16a231bb8 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sun, 6 Mar 2016 02:05:42 +0530 Subject: [PATCH 026/513] Replaced if_ calls with Pythonic Alternative --- agents.py | 5 ++--- csp.py | 2 +- games.py | 8 ++++---- logic.py | 2 +- mdp.py | 2 +- probability.py | 2 +- search.py | 4 ++-- 7 files changed, 12 insertions(+), 13 deletions(-) diff --git a/agents.py b/agents.py index e45d0cbc0..9a3cda6ca 100644 --- a/agents.py +++ b/agents.py @@ -410,9 +410,8 @@ def thing_classes(self): def percept(self, agent): """The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None'). Unlike the TrivialVacuumEnvironment, location is NOT perceived.""" - status = if_(self.some_things_at(agent.location, Dirt), - 'Dirty', 'Clean') - bump = if_(agent.bump, 'Bump', 'None') + status = ('Dirty' if self.some_things_at(agent.location, Dirt) else 'Clean') + bump = ('Bump' if agent.bump else'None') return (status, bump) def execute_action(self, agent, action): diff --git a/csp.py b/csp.py index 0cfaf89a9..1ec2f63b8 100644 --- a/csp.py +++ b/csp.py @@ -530,7 +530,7 @@ def __init__(self, grid): the digits 1-9 denote a filled cell, '.' or '0' an empty one; other characters are ignored.""" squares = iter(re.findall(r'\d|\.', grid)) - domains = dict((var, if_(ch in '123456789', [ch], '123456789')) + domains = dict((var, ([ch] if ch in '123456789' else '123456789')) for var, ch in zip(flatten(self.rows), squares)) for _ in squares: raise ValueError("Not a Sudoku grid", grid) # Too many squares diff --git a/games.py b/games.py index 62ed435a1..dc982c3c3 100644 --- a/games.py +++ b/games.py @@ -207,7 +207,7 @@ def terminal_test(self, state): return state not in ('A', 'B', 'C', 'D') def to_move(self, state): - return if_(state in 'BCD', 'MIN', 'MAX') + return ('MIN' if state in 'BCD' else 'MAX') class TicTacToe(Game): """Play TicTacToe on an h x v board, with Max (first player) playing 'X'. @@ -229,13 +229,13 @@ def result(self, state, move): return state # Illegal move has no effect board = state.board.copy(); board[move] = state.to_move moves = list(state.moves); moves.remove(move) - return Struct(to_move=if_(state.to_move == 'X', 'O', 'X'), + return Struct(to_move=('O' if state.to_move == 'X' else 'X'), utility=self.compute_utility(board, move, state.to_move), board=board, moves=moves) def utility(self, state, player): "Return the value to player; 1 for win, -1 for loss, 0 otherwise." - return if_(player == 'X', state.utility, -state.utility) + return (state.utility if player == 'X' else -state.utility) def terminal_test(self, state): "A state is terminal if it is won or there are no empty squares." @@ -254,7 +254,7 @@ def compute_utility(self, board, move, player): self.k_in_row(board, move, player, (1, 0)) or self.k_in_row(board, move, player, (1, -1)) or self.k_in_row(board, move, player, (1, 1))): - return if_(player == 'X', +1, -1) + return (+1 if player == 'X' else -1) else: return 0 diff --git a/logic.py b/logic.py index a915309c5..59a212def 100644 --- a/logic.py +++ b/logic.py @@ -743,7 +743,7 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): for i in range(max_flips): satisfied, unsatisfied = [], [] for clause in clauses: - if_(pl_true(clause, model), satisfied, unsatisfied).append(clause) + (satisfied if pl_true(clause, model) else unsatisfied).append(clause) if not unsatisfied: ## if model satisfies all the clauses return model clause = random.choice(unsatisfied) diff --git a/mdp.py b/mdp.py index 3d1a381f5..e56dc7d2a 100644 --- a/mdp.py +++ b/mdp.py @@ -72,7 +72,7 @@ def T(self, state, action): def go(self, state, direction): "Return the state that results from going in this direction." state1 = vector_add(state, direction) - return if_(state1 in self.states, state1, state) + return (state1 if state1 in self.states else state) def to_grid(self, mapping): """Convert a mapping from (x, y) to v into a [[..., v, ...]] grid.""" diff --git a/probability.py b/probability.py index e553524d2..5c95de36b 100644 --- a/probability.py +++ b/probability.py @@ -236,7 +236,7 @@ def p(self, value, event): 0.375""" assert isinstance(value, bool) ptrue = self.cpt[event_values(event, self.parents)] - return if_(value, ptrue, 1 - ptrue) + return (ptrue if value else 1 - ptrue) def sample(self, event): """Sample from the distribution for this variable conditioned diff --git a/search.py b/search.py index f974596ec..ab2ba5136 100644 --- a/search.py +++ b/search.py @@ -246,7 +246,7 @@ def recursive_dls(node, problem, limit): cutoff_occurred = True elif result is not None: return result - return if_(cutoff_occurred, 'cutoff', None) + return ('cutoff' if cutoff_occurred else None) # Body of depth_limited_search: return recursive_dls(Node(problem.initial), problem, limit) @@ -321,7 +321,7 @@ def hill_climbing(problem): def exp_schedule(k=20, lam=0.005, limit=100): "One possible schedule function for simulated annealing" - return lambda t: if_(t < limit, k * math.exp(-lam * t), 0) + return lambda t: (k * math.exp(-lam * t) if t < limit else 0) def simulated_annealing(problem, schedule=exp_schedule()): "[Fig. 4.5]" From 22d02918342a416f394bf5e3c728949f6ef10e38 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sun, 6 Mar 2016 02:07:33 +0530 Subject: [PATCH 027/513] Removed deprecated if_ function --- utils.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/utils.py b/utils.py index 93ab74af2..cc225c02c 100644 --- a/utils.py +++ b/utils.py @@ -2,7 +2,6 @@ TODO: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. -TODO: if_ is a terrible idea; replace all uses with (x if test else y) and remove if_ TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. TODO: Priority queues may not belong here -- see treatment in search.py """ @@ -366,25 +365,6 @@ def memoized_fn(*args): return memoized_fn -def if_(test, result, alternative): - """Like C++ and Java's (test ? result : alternative), except - both result and alternative are always evaluated. However, if - either evaluates to a function, it is applied to the empty arglist, - so you can delay execution by putting it in a lambda. - >>> if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) - 'ok' - """ - if test: - if callable(result): - return result() - - return result - else: - if callable(alternative): - return alternative() - - return alternative - def name(obj): "Try to find some reasonable name for the object." return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) From 8468de79a6d0377867f514545be016743fcfa647 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 14:02:41 -0800 Subject: [PATCH 028/513] Update README.md --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ca9b9c734..c2f5e7b94 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,26 @@ # `aima-python`: Structure of the Project -Python code for the book *Artificial Intelligence: A Modern Approach.* +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the i"Issues" and resolving some), and submit an application. + When complete, this project will cover all the major topics in the book, for each topic, such as `logic`, we will have the following [Python 3.5](https://www.python.org/downloads/release/python-350/) files in the main branch: - `logic.py`: Implementations of all the pseudocode algorithms in the book. - `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with `py.test`. - `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. -Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken +Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken: ## Port to Python 3; Pythonic Idioms; py.test - Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. - Replace poor idioms with proper Python. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. - Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. -In other words, replace the ">>> 2 + 2" in a docstring with "assert 2 + 2 == 4" in `filename_test.py`. +In other words, replace the ">>> 2 + 2 \n 4" in a docstring with "assert 2 + 2 == 4" in `filename_test.py`. ## New and Improved Algorithms -- Implement functions that were in the third edition of the book but were not yet implemented in the code. -- As we finish chapters for the new fourth edition, we will share the pseudocode, and describe what changes are necessary. +- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check th [list of pseudocode algorithms (pdf)](http://aima.cs.berkeley.edu/algorithms.pdf) to see what's missing. +- As we finish chapters for the new fourth edition, we will share the new pseudocode, and describe what changes are necessary. - Create a `.ipynb` notebook, and give examples of how to use the code. From 9d4ffe41af7f6cdf625f0a9aa5c6d627473c57c9 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 14:02:58 -0800 Subject: [PATCH 029/513] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c2f5e7b94..264adca21 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # `aima-python`: Structure of the Project -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the i"Issues" and resolving some), and submit an application. +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. When complete, this project will cover all the major topics in the book, for each topic, such as `logic`, we will have the following [Python 3.5](https://www.python.org/downloads/release/python-350/) files in the main branch: From 2f9d8ab21a22f2d9ccd4f4d2e68086d5af7fc2b1 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 14:03:44 -0800 Subject: [PATCH 030/513] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 264adca21..75a6645c1 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) # Choice of Programming Languages Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is -fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (ut lacks type declarations and can be solw). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: +fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: Java, C, C++, C#, Python From 5012cbbb57ed997babf76b89308e0ba1286f5923 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 14:15:51 -0800 Subject: [PATCH 031/513] Update README.md --- README.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 75a6645c1..8c1bca059 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,14 @@ -# `aima-python`: Structure of the Project +# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python` (Python 3.5) -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. -When complete, this project will cover all the major topics in the book, for each topic, such as `logic`, we will have the following [Python 3.5](https://www.python.org/downloads/release/python-350/) files in the main branch: +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. (And we're always looking for solid contributors who are not affiliated with GSoC.) -- `logic.py`: Implementations of all the pseudocode algorithms in the book. -- `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with `py.test`. +## Structure of the Project + +When complete, this project will have [Python 3.5](https://www.python.org/downloads/release/python-350/) code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following files in the main branch: + +- `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. +- `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). - `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken: @@ -13,13 +16,13 @@ Until we get there, we will support a legacy branch, `aima3python2` (for the thi ## Port to Python 3; Pythonic Idioms; py.test - Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. -- Replace poor idioms with proper Python. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. +- Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. - Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. In other words, replace the ">>> 2 + 2 \n 4" in a docstring with "assert 2 + 2 == 4" in `filename_test.py`. ## New and Improved Algorithms -- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check th [list of pseudocode algorithms (pdf)](http://aima.cs.berkeley.edu/algorithms.pdf) to see what's missing. +- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](http://aima.cs.berkeley.edu/algorithms.pdf) to see what's missing. - As we finish chapters for the new fourth edition, we will share the new pseudocode, and describe what changes are necessary. - Create a `.ipynb` notebook, and give examples of how to use the code. From 107dd8a8543a470753f3ba1542657f6835035e98 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 16:00:23 -0800 Subject: [PATCH 032/513] Update mdp.py to make sure 0 <= gamma < 1. Fixes Issue #29. --- mdp.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mdp.py b/mdp.py index 3d1a381f5..c8476b46b 100644 --- a/mdp.py +++ b/mdp.py @@ -21,6 +21,8 @@ def __init__(self, init, actlist, terminals, gamma=.9): self.init=init self.actlist=actlist self.terminals=terminals + if not (0 <= gamma < 1): + raise ValueError("An MDP must have 0 <= gamma < 1") self.gamma=gamma self.states=set() self.reward={} From a0d0905b8e0e2a0a280bc0160c56841786f26d63 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 17:31:42 -0800 Subject: [PATCH 033/513] Proper spacing around operators in product --- utils.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/utils.py b/utils.py index 19876b7b2..c26dfcc7a 100644 --- a/utils.py +++ b/utils.py @@ -75,13 +75,10 @@ def unique(seq): return list(set(seq)) def product(numbers): - """Return the product of the numbers. - >>> product([1,2,3,4]) - 24 - """ - result=1 - for i in numbers: - result=result*i + """Return the product of the numbers, e.g. product([2, 3, 10]) == 60""" + result = 1 + for x in numbers: + result *= x return result def count_if(predicate, seq): From e20755183567258384d9a10ec5347b5d4bedb8eb Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 6 Mar 2016 07:11:16 +0530 Subject: [PATCH 034/513] modified print statements in agents.py to make it work in Py3, changed utils_test.py with regard to new utils.py --- agents.py | 37 ++++++++++++++++++------------------- utils_test.py | 3 --- 2 files changed, 18 insertions(+), 22 deletions(-) diff --git a/agents.py b/agents.py index e45d0cbc0..b9c291c36 100644 --- a/agents.py +++ b/agents.py @@ -54,7 +54,7 @@ def is_alive(self): def show_state(self): "Display the agent's internal state. Subclasses should override." - print "I don't know how to show_state." + print("I don't know how to show_state.") def display(self, canvas, x, y, width, height): # Do we need this? @@ -94,7 +94,7 @@ def TraceAgent(agent): old_program = agent.program def new_program(percept): action = old_program(percept) - print '%s perceives %s and does %s' % (agent, percept, action) + print('%s perceives %s and does %s' % (agent, percept, action)) return action agent.program = new_program return agent @@ -172,7 +172,7 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): "A reflex agent for the two-state vacuum environment. [Fig. 2.8]" - def program((location, status)): + def program(location, status): if status == 'Dirty': return 'Suck' elif location == loc_A: return 'Right' elif location == loc_B: return 'Left' @@ -181,7 +181,7 @@ def program((location, status)): def ModelBasedVacuumAgent(): "An agent that keeps track of what locations are clean or dirty." model = {loc_A: None, loc_B: None} - def program((location, status)): + def program(location, status): "Same as ReflexVacuumAgent, except if everything is clean, do NoOp." model[location] = status ## Update the model here if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp' @@ -276,12 +276,12 @@ def delete_thing(self, thing): """Remove a thing from the environment.""" try: self.things.remove(thing) - except ValueError, e: - print e - print " in Environment delete_thing" - print " Thing to be removed: %s at %s" % (thing, thing.location) - print " from list: %s" % [(thing, thing.location) - for thing in self.things] + except(ValueError, e): + print(e) + print(" in Environment delete_thing") + print(" Thing to be removed: %s at %s" % (thing, thing.location)) + print(" from list: %s" % [(thing, thing.location) + for thing in self.things]) if thing in self.agents: self.agents.remove(thing) @@ -410,9 +410,8 @@ def thing_classes(self): def percept(self, agent): """The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None'). Unlike the TrivialVacuumEnvironment, location is NOT perceived.""" - status = if_(self.some_things_at(agent.location, Dirt), - 'Dirty', 'Clean') - bump = if_(agent.bump, 'Bump', 'None') + status = ('Dirty' if self.some_things_at(agent.location, Dirt) else 'Clean') + bump = ('Bump' if agent.bump else'None') return (status, bump) def execute_action(self, agent, action): @@ -592,12 +591,12 @@ def __init__(self, parent, env, canvas): scale.pack(side='left') def run(self): - print 'run' + print('run') self.running = True self.background_run() def stop(self): - print 'stop' + print('stop') self.running = False def background_run(self): @@ -610,14 +609,14 @@ def background_run(self): self.after(ms, self.background_run) def list_things(self): - print "Things in the environment:" + print("Things in the environment:") for thing in self.env.things: - print "%s at %s" % (thing, thing.location) + print("%s at %s" % (thing, thing.location)) def list_agents(self): - print "Agents in the environment:" + print("Agents in the environment:") for agt in self.env.agents: - print "%s at %s" % (agt, agt.location) + print("%s at %s" % (agt, agt.location)) def set_speed(self, speed): self.speed = float(speed) diff --git a/utils_test.py b/utils_test.py index 46be72305..356798bdd 100644 --- a/utils_test.py +++ b/utils_test.py @@ -1,5 +1,4 @@ import pytest -import utils from utils import * def test_struct_initialization(): @@ -104,8 +103,6 @@ def f(): return caller() assert f() == 'f' -def test_if_(): - assert if_(2 + 2 == 4, 'ok', lambda: expensive_computation()) == 'ok' if __name__ == '__main__': pytest.main() From ca4d10c87a0237d0f6eb4af2e9858f6e20e23ab6 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sun, 6 Mar 2016 07:18:53 +0530 Subject: [PATCH 035/513] removed redundant function definition for standard dev --- utils.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/utils.py b/utils.py index c26dfcc7a..ff5f501d9 100644 --- a/utils.py +++ b/utils.py @@ -213,11 +213,6 @@ def histogram(values, mode=0, bin_function=None): from math import log2 from statistics import mode, median, mean, stdev -def stddev(values, meanval=None): - """The standard deviation of a set of values. - Pass in the mean if you already know it. """ - return stdev(values, mu=meanval) - def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y. >>> dotproduct([1, 2, 3], [1000, 100, 10]) From d072f4c6d0596d39f67ab7fce0b42ab2a344d497 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 20:08:27 -0800 Subject: [PATCH 036/513] Delete doctests.py -- use py.test from now on. --- doctests.py | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 doctests.py diff --git a/doctests.py b/doctests.py deleted file mode 100644 index fba5f1b5c..000000000 --- a/doctests.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Run all doctests from modules on the command line. Use -v for verbose. - -Example usages: - - python doctests.py *.py - python doctests.py -v *.py - -You can add more module-level tests with - __doc__ += "..." -You can add stochastic tests with - __doc__ += random_tests("...") -""" - -if __name__ == "__main__": - import sys, glob, doctest - args = [arg for arg in sys.argv[1:] if arg != '-v'] - if not args: args = ['*.py'] - modules = [__import__(name.replace('.py','')) - for arg in args for name in glob.glob(arg)] - for module in modules: - doctest.testmod(module, report=1, optionflags=doctest.REPORT_UDIFF) - summary = doctest.master.summarize() if modules else (0, 0) - print '%d failed out of %d' % summary From 57a3d12e731d6e418e184f53b279b5c6582ffffb Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 5 Mar 2016 20:23:22 -0800 Subject: [PATCH 037/513] Update utils_test.py --- utils_test.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/utils_test.py b/utils_test.py index 356798bdd..c8f0f1e1f 100644 --- a/utils_test.py +++ b/utils_test.py @@ -21,7 +21,8 @@ def test_update_struct(): def test_removeall_list(): assert removeall(4, []) == [] - assert removeall(4, [1,2,3,4]) == [1,2,3] + assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] + assert removeall(4, [4, 1, 4, 2, 3, 4, 4] == [1, 2, 3] def test_removeall_string(): assert removeall('s', '') == '' @@ -33,15 +34,14 @@ def test_unique(): def test_product(): assert product([1,2,3,4]) == 24 - -def test_count_if(): - assert count_if(callable, [42, None, max, min]) == 2 + assert product(range(1, 11)) == 3628800 def test_find_if(): assert find_if(callable, [1, 2, 3]) == None assert find_if(callable, [3, min, max]) == min def test_count_if(): + assert count_if(callable, [42, None, max, min]) == 2 is_odd = lambda x: x % 2 assert count_if(is_odd, []) == 0 assert count_if(is_odd, [1, 2, 3, 4, 5]) == 3 @@ -55,7 +55,7 @@ def test_some(): assert some(callable, [2, 3]) == 0 def test_isin(): - e= [] + e = [] assert isin(e, [1, e, 3]) == True assert isin(e, [1, [], 3]) == False From b0744a37b53bb9eceacf5f1035a91878f42f0e1c Mon Sep 17 00:00:00 2001 From: Tamer Tas Date: Sun, 6 Mar 2016 10:48:36 +0200 Subject: [PATCH 038/513] Enable continuous testing --- .travis.yml | 18 ++++++++++++++++++ requirements.txt | 0 2 files changed, 18 insertions(+) create mode 100644 .travis.yml create mode 100644 requirements.txt diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 000000000..1a29f71f9 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,18 @@ +language: + - python + +python: + - "3.5" + +install: + - pip install flake8 + - pip install -r requirements.txt + +script: + - py.test + +after_success: + - flake8 . + +notifications: + email: false diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..e69de29bb From b70d234adf478c78b003032f066860b472ddd6f7 Mon Sep 17 00:00:00 2001 From: Tamer Tas Date: Sun, 6 Mar 2016 11:26:09 +0200 Subject: [PATCH 039/513] Include MIT license --- LICENSE | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..21ff04bc1 --- /dev/null +++ b/LICENSE @@ -0,0 +1,9 @@ +The MIT License (MIT) + +Copyright (c) 2016 aima-python contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. From 57f8e0a27d4d63fdbe814801a535671b759baf5b Mon Sep 17 00:00:00 2001 From: Tamer Tas Date: Sun, 6 Mar 2016 11:41:37 +0200 Subject: [PATCH 040/513] Add best practices for contributions --- CONTRIBUTING.md | 47 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..595f3c9f1 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,47 @@ +How to Contribute to aima-python +========================== + +Thanks for considering contributing to aima-python. + +Contributing a Patch +==================== + +1. Submit an issue describing your proposed change to the repo in question. +1. The repo owner will respond to your issue promptly. +1. Fork the desired repo, develop and test your code changes. +1. Submit a pull request. + +Reporting Issues +================ + +- Under which versions of Python does this happen? + +- Is anybody working on this? + +Patch Rules +=========== + +- Ensure that the patch is python 3.5 compliant. + +- Include tests if your patch is supposed to solve a bug, and explain + clearly under which circumstances the bug happens. Make sure the test fails + without your patch. + +- Try to follow `PEP8 `_, but you + may ignore the line-length-limit if following it would make the code uglier. + +Running the Test-Suite +===================== + +The minimal requirement for running the testsuite is ``py.test``. You can +install it with:: + + pip install pytest + +Clone this repository:: + + git clone https://github.com/aimacode/aima-python.git + +Then you can run the testsuite with:: + + py.test From 1c6c3fc1dac9d100936b8ec0422e8fb574438bf7 Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 6 Mar 2016 02:04:33 -0800 Subject: [PATCH 041/513] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8c1bca059..2ef0d7185 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python` (Python 3.5) +# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python` (Python 3.5) [![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. (And we're always looking for solid contributors who are not affiliated with GSoC.) From cc79a92cb5c8c50238b03b96bc85f338a05e88e6 Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 6 Mar 2016 02:06:49 -0800 Subject: [PATCH 042/513] remove tkinter from agents.py Will add some kind of GUI later -- but this one didn't do much. --- agents.py | 85 ++----------------------------------------------------- 1 file changed, 2 insertions(+), 83 deletions(-) diff --git a/agents.py b/agents.py index b9c291c36..514058ed9 100644 --- a/agents.py +++ b/agents.py @@ -36,7 +36,8 @@ # Speed control in GUI does not have any effect -- fix it. from utils import * -import random, copy +import random +import copy #______________________________________________________________________________ @@ -537,87 +538,5 @@ def score(env): True """ -#______________________________________________________________________________ -# GUI - Graphical User Interface for Environments -# If you do not have Tkinter installed, either get a new installation of Python -# (Tkinter is standard in all new releases), or delete the rest of this file -# and muddle through without a GUI. - -import Tkinter as tk - -class EnvGUI(tk.Tk, object): - - def __init__(self, env, title = 'AIMA GUI', cellwidth=50, n=10): - - # Initialize window - - super(EnvGUI, self).__init__() - self.title(title) - - # Create components - - canvas = EnvCanvas(self, env, cellwidth, n) - toolbar = EnvToolbar(self, env, canvas) - for w in [canvas, toolbar]: - w.pack(side="bottom", fill="x", padx="3", pady="3") - - -class EnvToolbar(tk.Frame, object): - - def __init__(self, parent, env, canvas): - super(EnvToolbar, self).__init__(parent, relief='raised', bd=2) - - # Initialize instance variables - - self.env = env - self.canvas = canvas - self.running = False - self.speed = 1.0 - - # Create buttons and other controls - - for txt, cmd in [('Step >', self.env.step), - ('Run >>', self.run), - ('Stop [ ]', self.stop), - ('List things', self.list_things), - ('List agents', self.list_agents)]: - tk.Button(self, text=txt, command=cmd).pack(side='left') - - tk.Label(self, text='Speed').pack(side='left') - scale = tk.Scale(self, orient='h', - from_=(1.0), to=10.0, resolution=1.0, - command=self.set_speed) - scale.set(self.speed) - scale.pack(side='left') - - def run(self): - print('run') - self.running = True - self.background_run() - - def stop(self): - print('stop') - self.running = False - - def background_run(self): - if self.running: - self.env.step() - # ms = int(1000 * max(float(self.speed), 0.5)) - #ms = max(int(1000 * float(self.delay)), 1) - delay_sec = 1.0 / max(self.speed, 1.0) # avoid division by zero - ms = int(1000.0 * delay_sec) # seconds to milliseconds - self.after(ms, self.background_run) - - def list_things(self): - print("Things in the environment:") - for thing in self.env.things: - print("%s at %s" % (thing, thing.location)) - - def list_agents(self): - print("Agents in the environment:") - for agt in self.env.agents: - print("%s at %s" % (agt, agt.location)) - def set_speed(self, speed): - self.speed = float(speed) From 1abdaa22286b40f7bc9e9cf29068ba59494448be Mon Sep 17 00:00:00 2001 From: Nishant Suman Date: Sun, 6 Mar 2016 15:52:49 +0530 Subject: [PATCH 043/513] all % string formating to .format is done in agents.py --- agents.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/agents.py b/agents.py index 514058ed9..1bc374150 100644 --- a/agents.py +++ b/agents.py @@ -47,7 +47,7 @@ class Thing(object): You subclass Thing to get the things you want. Each thing can have a .__name__ slot (used for output only).""" def __repr__(self): - return '<%s>' % getattr(self, '__name__', self.__class__.__name__) + return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__)) def is_alive(self): "Things that are 'alive' should return true." @@ -80,7 +80,7 @@ def __init__(self, program=None): self.bump = False if program is None: def program(percept): - return raw_input('Percept=%s; action? ' % percept) + return input('Percept={}; action? ' .format(percept)) assert callable(program) self.program = program @@ -95,7 +95,7 @@ def TraceAgent(agent): old_program = agent.program def new_program(percept): action = old_program(percept) - print('%s perceives %s and does %s' % (agent, percept, action)) + print('{} perceives {} and does {}'.format(agent, percept, action)) return action agent.program = new_program return agent @@ -280,9 +280,9 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print(" Thing to be removed: %s at %s" % (thing, thing.location)) - print(" from list: %s" % [(thing, thing.location) - for thing in self.things]) + print(" Thing to be removed: {} at {}" .format(thing, thing.location)) + print(" from list: {}" .format([(thing, thing.location) + for thing in self.things])) if thing in self.agents: self.agents.remove(thing) From 08673dff872dc7ba6a04cd6b4fb15f45347e2198 Mon Sep 17 00:00:00 2001 From: Tamer Tas Date: Sun, 6 Mar 2016 13:19:36 +0200 Subject: [PATCH 044/513] Link aimacode/aima-data repository as a git submodule --- .gitmodules | 3 +++ aima-data | 1 + 2 files changed, 4 insertions(+) create mode 100644 .gitmodules create mode 160000 aima-data diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..e15cc9e9a --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "aima-data"] + path = aima-data + url = https://github.com/aimacode/aima-data diff --git a/aima-data b/aima-data new file mode 160000 index 000000000..75a59e53a --- /dev/null +++ b/aima-data @@ -0,0 +1 @@ +Subproject commit 75a59e53ab83773ac2838ddef4ac885d103f4511 From bd13240d5d0b0855cdef12fd366c00176387c778 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sun, 6 Mar 2016 16:57:16 +0530 Subject: [PATCH 045/513] Fixed missing bracket in utils_test.py --- utils_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils_test.py b/utils_test.py index c8f0f1e1f..eb83c5c49 100644 --- a/utils_test.py +++ b/utils_test.py @@ -22,7 +22,7 @@ def test_update_struct(): def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] - assert removeall(4, [4, 1, 4, 2, 3, 4, 4] == [1, 2, 3] + assert removeall(4, [4, 1, 4, 2, 3, 4, 4]) == [1, 2, 3] def test_removeall_string(): assert removeall('s', '') == '' From e68df0baf0c532eb8804299f543b7871df1888f2 Mon Sep 17 00:00:00 2001 From: Tamer Tas Date: Sun, 6 Mar 2016 13:51:36 +0200 Subject: [PATCH 046/513] Port and test text module for python 3.5 --- text.py | 140 +++++------------------------------------------- text_test.py | 146 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 160 insertions(+), 126 deletions(-) create mode 100644 text_test.py diff --git a/text.py b/text.py index a29eb4526..c71461514 100644 --- a/text.py +++ b/text.py @@ -18,7 +18,7 @@ class UnigramTextModel(CountingProbDist): def samples(self, n): "Return a string of n words, random according to the model." - return ' '.join([self.sample() for i in range(n)]) + return ' '.join(self.sample() for i in range(n)) class NgramTextModel(CountingProbDist): """This is a discrete probability distribution over n-tuples of words. @@ -93,6 +93,7 @@ def viterbi_segment(text, P): #______________________________________________________________________________ +# TODO(tmrts): Expose raw index class IRSystem: """A very simple Information Retrieval System, as discussed in Sect. 23.2. The constructor s = IRSystem('the a') builds an empty system with two @@ -149,8 +150,7 @@ def present(self, results): "Present the results as a list." for (score, d) in results: doc = self.documents[d] - print ("%5.2f|%25s | %s" - % (100 * score, doc.url, doc.title[:45].expandtabs())) + print ("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs())) def present_results(self, query_text, n=10): "Get results for the query and present them." @@ -161,7 +161,7 @@ class UnixConsultant(IRSystem): def __init__(self): IRSystem.__init__(self, stopwords="how do i the a of") import os - mandir = '../data/MAN/' + mandir = '../aima-data/MAN/' man_files = [mandir + f for f in os.listdir(mandir) if f.endswith('.txt')] self.index_collection(man_files) @@ -194,6 +194,8 @@ def canonicalize(text): ## A shift cipher is a rotation of the letters in the alphabet, ## such as the famous rot13, which maps A to N, B to M, etc. +alphabet = 'abcdefghijklmnopqrstuvwxyz' + #### Encoding def shift_encode(plaintext, n): @@ -216,9 +218,8 @@ def encode(plaintext, code): "Encodes text, using a code which is a permutation of the alphabet." from string import maketrans trans = maketrans(alphabet + alphabet.upper(), code + code.upper()) - return plaintext.translate(trans) -alphabet = 'abcdefghijklmnopqrstuvwxyz' + return plaintext.translate(trans) def bigrams(text): """Return a list of pairs in text (a sequence of letters or words). @@ -241,18 +242,22 @@ def __init__(self, training_text): def score(self, plaintext): "Return a score for text based on how common letters pairs are." + s = 1.0 for bi in bigrams(plaintext): s = s * self.P2[bi] + return s def decode(self, ciphertext): "Return the shift decoding of text with the best score." - return argmax(all_shifts(ciphertext), self.score) + + return max(all_shifts(ciphertext), self.score) def all_shifts(text): "Return a list of all 26 possible encodings of text by a shift cipher." - return [shift_encode(text, n) for n in range(len(alphabet))] + + yield from (shift_encode(text, i) for i, _ in enumerate(alphabet)) #### Decoding a General Permutation Cipher @@ -309,61 +314,7 @@ def goal_test(self, state): #______________________________________________________________________________ -__doc__ += """ -## Create a Unigram text model from the words in the book "Flatland". ->>> flatland = DataFile("EN-text/flatland.txt").read() ->>> wordseq = words(flatland) ->>> P = UnigramTextModel(wordseq) - -## Now do segmentation, using the text model as a prior. ->>> s, p = viterbi_segment('itiseasytoreadwordswithoutspaces', P) ->>> s -['it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] ->>> 1e-30 < p < 1e-20 -True ->>> s, p = viterbi_segment('wheninthecourseofhumaneventsitbecomesnecessary', P) ->>> s -['when', 'in', 'the', 'course', 'of', 'human', 'events', 'it', 'becomes', 'necessary'] - -## Test the decoding system ->>> shift_encode("This is a secret message.", 17) -'Kyzj zj r jvtivk dvjjrxv.' - ->>> ring = ShiftDecoder(flatland) ->>> ring.decode('Kyzj zj r jvtivk dvjjrxv.') -'This is a secret message.' ->>> ring.decode(rot13('Hello, world!')) -'Hello, world!' - -## CountingProbDist -## Add a thousand samples of a roll of a die to D. ->>> D = CountingProbDist() ->>> for i in range(10000): -... D.add(random.choice('123456')) ->>> ps = [D[n] for n in '123456'] ->>> 1./7. <= min(ps) <= max(ps) <= 1./5. -True -""" - -__doc__ += (""" -## Compare 1-, 2-, and 3-gram word models of the same text. ->>> flatland = DataFile("EN-text/flatland.txt").read() ->>> wordseq = words(flatland) ->>> P1 = UnigramTextModel(wordseq) ->>> P2 = NgramTextModel(2, wordseq) ->>> P3 = NgramTextModel(3, wordseq) - -## The most frequent entries in each model ->>> P1.top(10) -[(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'), (478, 'that'), (399, 'is'), (348, 'you')] - ->>> P2.top(10) -[(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), (80, ('it', 'is')), (71, ('by', 'the')), (68, ('for', 'the')), (68, ('and', 'the')), (62, ('on', 'the')), (60, ('to', 'be'))] - ->>> P3.top(10) -[(30, ('a', 'straight', 'line')), (19, ('of', 'three', 'dimensions')), (16, ('the', 'sense', 'of')), (13, ('by', 'the', 'sense')), (13, ('as', 'well', 'as')), (12, ('of', 'the', 'circles')), (12, ('of', 'sight', 'recognition')), (11, ('the', 'number', 'of')), (11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))] -""") - +# TODO(tmrts): Set RNG seed to test random functions __doc__ += random_tests(""" ## Generate random text from the N-gram models >>> P1.samples(20) @@ -375,66 +326,3 @@ def goal_test(self, state): >>> P3.samples(20) 'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' """) -__doc__ += """ - -## Probabilities of some common n-grams ->>> P1['the'] #doctest:+ELLIPSIS -0.0611... - ->>> P2[('of', 'the')] #doctest:+ELLIPSIS -0.0108... - ->>> P3[('', '', 'but')] -0.0 - ->>> P3[('so', 'as', 'to')] #doctest:+ELLIPSIS -0.000323... - -## Distributions given the previous n-1 words ->>> P2.cond_prob['went',].dictionary -{} ->>> P3.cond_prob['in', 'order'].dictionary -{'to': 6} - - -## Build and test an IR System ->>> uc = UnixConsultant() ->>> uc.present_results("how do I remove a file") -76.83| ../data/MAN/rm.txt | RM(1) FSF RM(1) -67.83| ../data/MAN/tar.txt | TAR(1) TAR(1) -67.79| ../data/MAN/cp.txt | CP(1) FSF CP(1) -66.58| ../data/MAN/zip.txt | ZIP(1L) ZIP(1L) -64.58| ../data/MAN/gzip.txt | GZIP(1) GZIP(1) -63.74| ../data/MAN/pine.txt | pine(1) pine(1) -62.95| ../data/MAN/shred.txt | SHRED(1) FSF SHRED(1) -57.46| ../data/MAN/pico.txt | pico(1) pico(1) -43.38| ../data/MAN/login.txt | LOGIN(1) Linux Programmer's Manual -41.93| ../data/MAN/ln.txt | LN(1) FSF LN(1) - ->>> uc.present_results("how do I delete a file") -75.47| ../data/MAN/diff.txt | DIFF(1) GNU Tools DIFF(1) -69.12| ../data/MAN/pine.txt | pine(1) pine(1) -63.56| ../data/MAN/tar.txt | TAR(1) TAR(1) -60.63| ../data/MAN/zip.txt | ZIP(1L) ZIP(1L) -57.46| ../data/MAN/pico.txt | pico(1) pico(1) -51.28| ../data/MAN/shred.txt | SHRED(1) FSF SHRED(1) -26.72| ../data/MAN/tr.txt | TR(1) User Commands TR(1) - ->>> uc.present_results("email") -18.39| ../data/MAN/pine.txt | pine(1) pine(1) -12.01| ../data/MAN/info.txt | INFO(1) FSF INFO(1) - 9.89| ../data/MAN/pico.txt | pico(1) pico(1) - 8.73| ../data/MAN/grep.txt | GREP(1) GREP(1) - 8.07| ../data/MAN/zip.txt | ZIP(1L) ZIP(1L) - ->>> uc.present_results("word counts for files") -112.38| ../data/MAN/grep.txt | GREP(1) GREP(1) -101.84| ../data/MAN/wc.txt | WC(1) User Commands WC(1) -82.46| ../data/MAN/find.txt | FIND(1L) FIND(1L) -74.64| ../data/MAN/du.txt | DU(1) FSF DU(1) - ->>> uc.present_results("learn: date") ->>> uc.present_results("2003") -14.58| ../data/MAN/pine.txt | pine(1) pine(1) -11.62| ../data/MAN/jar.txt | FASTJAR(1) GNU FASTJAR(1) -""" diff --git a/text_test.py b/text_test.py new file mode 100644 index 000000000..e47888e02 --- /dev/null +++ b/text_test.py @@ -0,0 +1,146 @@ +import pytest + +from text import * + +from random import choice +from math import isclose + +def test_unigram_text_model(): + flatland = DataFile("aima-data/EN-text/flatland.txt").read() + wordseq = words(flatland) + P = UnigramTextModel(wordseq) + + s, p = viterbi_segment('itiseasytoreadwordswithoutspaces', P) + + assert s == ['it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] + +def test_shift_encoding(): + code = shift_encode("This is a secret message.", 17) + + assert code == 'Kyzj zj r jvtivk dvjjrxv.' + +def test_shift_decoding(): + code = shift_encode("This is a secret message.", 17) + + ring = ShiftDecoder(flatland) + msg = ring.decode('Kyzj zj r jvtivk dvjjrxv.') + + assert msg == 'This is a secret message.' + +def test_rot13_decoding(): + msg = ring.decode(rot13('Hello, world!')) + + assert msg == 'Hello, world!' + +def test_counting_probability_distribution(): + D = CountingProbDist() + + for i in range(10000): + D.add(random.choice('123456')) + + ps = [D[n] for n in '123456'] + + assert 1/7 <= min(ps) <= max(ps) <= 1/5 + +def test_ngram_models(): + flatland = DataFile("aima-data/EN-text/flatland.txt").read() + wordseq = words(flatland) + P1 = UnigramTextModel(wordseq) + P2 = NgramTextModel(2, wordseq) + P3 = NgramTextModel(3, wordseq) + + ## The most frequent entries in each model + assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a'), + (722, 'i'), (640, 'in'), (478, 'that'), (399, 'is'), (348, 'you')] + + assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), + (80, ('it', 'is' )), (71, ('by', 'the' )), (68, ('for', 'the' )), + (68, ('and', 'the' )), (62, ('on', 'the' )), (60, ('to', 'be'))] + + assert P3.top(10) == [(30, ('a', 'straight', 'line')), (19, ('of', 'three', 'dimensions')), + (16, ('the', 'sense', 'of' )), (13, ('by', 'the', 'sense' )), + (13, ('as', 'well', 'as' )), (12, ('of', 'the', 'circles' )), + (12, ('of', 'sight', 'recognition' )), (11, ('the', 'number', 'of' )), + (11, ('that', 'i', 'had' )), (11, ('so', 'as', 'to'))] + + + assert isclose(P1['the'], 0.0611) + + assert isclose(P2['of', 'the'], 0.0108) + + assert isclose(P3['', '', 'but'], 0.0) + assert isclose(P3['', '', 'but'], 0.0) + assert isclose(P3['so', 'as', 'to'], 0.000323) + + assert not P2.cond_prob['went',].dictionary + + assert P3.cond_prob['in','order'].dictionary == {'to': 6} + +def test_ir_system(): + from collections import namedtuple + Results = namedtuple('IRResults', ['score', 'url']) + + uc = UnixConsultant() + + def verify_query(query, expected): + assert len(expected) == len(query) + + for expected, (score, d) in zip(expected, query): + doc = uc.documents[d] + + assert expected.score == score * 100 + assert expected.url == doc.url + + q1 = uc.query("how do I remove a file") + assert verify_query(q1, [ + Results(76.83, "../aima-data/MAN/rm.txt"), + Results(67.83, "../aima-data/MAN/tar.txt"), + Results(67.79, "../aima-data/MAN/cp.txt"), + Results(66.58, "../aima-data/MAN/zip.txt"), + Results(64.58, "../aima-data/MAN/gzip.txt"), + Results(63.74, "../aima-data/MAN/pine.txt"), + Results(62.95, "../aima-data/MAN/shred.txt"), + Results(57.46, "../aima-data/MAN/pico.txt"), + Results(43.38, "../aima-data/MAN/login.txt"), + Results(41.93, "../aima-data/MAN/ln.txt"), + ]) + + q2 = uc.query("how do I delete a file") + assert verify_query(q2, [ + Results(75.47, "../aima-data/MAN/diff.txt"), + Results(69.12, "../aima-data/MAN/pine.txt"), + Results(63.56, "../aima-data/MAN/tar.txt"), + Results(60.63, "../aima-data/MAN/zip.txt"), + Results(57.46, "../aima-data/MAN/pico.txt"), + Results(51.28, "../aima-data/MAN/shred.txt"), + Results(26.72, "../aima-data/MAN/tr.txt"), + ]) + + q3 = uc.query("email") + assert verify_query(q3, [ + Results(18.39, "../aima-data/MAN/pine.txt"), + Results(12.01, "../aima-data/MAN/info.txt"), + Results(9.89, "../aima-data/MAN/pico.txt"), + Results(8.73, "../aima-data/MAN/grep.txt"), + Results(8.07, "../aima-data/MAN/zip.txt"), + ]) + + q4 = uc.query("word countrs for files") + assert verify_query(q4, [ + Results(112.38, "../aima-data/MAN/grep.txt"), + Results(101.84, "../aima-data/MAN/wc.txt"), + Results(82.46, "../aima-data/MAN/find.txt"), + Results(74.64, "../aima-data/MAN/du.txt"), + ]) + + q5 = uc.query("learn: date") + assert verify_query(q5, []) + + q6 = uc.query("2003") + assert verify_query(q6, [ + Results(14.58, "../aima-data/MAN/pine.txt"), + Results(11.62, "../aima-data/MAN/jar.txt"), + ]) + +if __name__ == '__main__': + pytest.main() From 442f312d89bbfc25458782afcf869a3eae640bb9 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 14:09:49 +0100 Subject: [PATCH 047/513] Finished porting to Python 3 Used tool 2to3 to finish the porting to Python 3 plus some fixes by hand. --- agents.py | 15 ++-- csp.py | 193 +++++++++++++++++++++++++++++++++---------------- games.py | 20 ++--- grid.py | 2 +- learning.py | 41 ++++++----- logic.py | 40 +++++----- mdp.py | 5 +- nlp.py | 9 ++- planning.py | 2 +- probability.py | 19 ++--- search.py | 31 ++++---- text.py | 2 +- utils.py | 22 +++--- utils_test.py | 2 +- 14 files changed, 239 insertions(+), 164 deletions(-) diff --git a/agents.py b/agents.py index 1bc374150..248015dd6 100644 --- a/agents.py +++ b/agents.py @@ -38,6 +38,7 @@ from utils import * import random import copy +import collections #______________________________________________________________________________ @@ -80,8 +81,8 @@ def __init__(self, program=None): self.bump = False if program is None: def program(percept): - return input('Percept={}; action? ' .format(percept)) - assert callable(program) + return eval(input('Percept={}; action? ' .format(percept))) + assert isinstance(program, collections.Callable) self.program = program def can_grab(self, thing): @@ -95,7 +96,7 @@ def TraceAgent(agent): old_program = agent.program def new_program(percept): action = old_program(percept) - print('{} perceives {} and does {}'.format(agent, percept, action)) + print(('{} perceives {} and does {}'.format(agent, percept, action))) return action agent.program = new_program return agent @@ -280,9 +281,9 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print(" Thing to be removed: {} at {}" .format(thing, thing.location)) - print(" from list: {}" .format([(thing, thing.location) - for thing in self.things])) + print((" Thing to be removed: {} at {}" .format(thing, thing.location))) + print((" from list: {}" .format([(thing, thing.location) + for thing in self.things]))) if thing in self.agents: self.agents.remove(thing) @@ -504,7 +505,7 @@ def score(env): env.add_thing(agent) env.run(steps) return agent.performance - return mean(map(score, envs)) + return mean(list(map(score, envs))) #_________________________________________________________________________ diff --git a/csp.py b/csp.py index 1ec2f63b8..8fdbf636f 100644 --- a/csp.py +++ b/csp.py @@ -1,10 +1,14 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" + from utils import * from collections import defaultdict import search +from functools import reduce + class CSP(search.Problem): + """This class describes finite-domain Constraint Satisfaction Problems. A CSP is specified by the following inputs: vars A list of variables; each is atomic (e.g. int or string). @@ -45,7 +49,7 @@ class CSP(search.Problem): def __init__(self, vars, domains, neighbors, constraints): "Construct a CSP problem. If vars is empty, it becomes domains.keys()." - vars = vars or domains.keys() + vars = vars or list(domains.keys()) update(self, vars=vars, domains=domains, neighbors=neighbors, constraints=constraints, initial=(), curr_domains=None, nassigns=0) @@ -73,9 +77,9 @@ def conflict(var2): def display(self, assignment): "Show a human-readable representation of the CSP." # Subclasses can print in a prettier way, or display with a GUI - print 'CSP:', self, 'with assignment:', assignment + print('CSP:', self, 'with assignment:', assignment) - ## These methods are for the tree- and graph-search interface: + # These methods are for the tree- and graph-search interface: def actions(self, state): """Return a list of applicable actions: nonconflicting @@ -88,8 +92,9 @@ def actions(self, state): return [(var, val) for val in self.domains[var] if self.nconflicts(var, val, assignment) == 0] - def result(self, state, (var, val)): + def result(self, state, xxx_todo_changeme): "Perform an action and return the new state." + (var, val) = xxx_todo_changeme return state + ((var, val),) def goal_test(self, state): @@ -100,7 +105,7 @@ def goal_test(self, state): assignment) == 0, self.vars)) - ## These are for constraint propagation + # These are for constraint propagation def support_pruning(self): """Make sure we can prune values from domains. (We want to pay @@ -119,7 +124,8 @@ def suppose(self, var, value): def prune(self, var, value, removals): "Rule out var=value." self.curr_domains[var].remove(value) - if removals is not None: removals.append((var, value)) + if removals is not None: + removals.append((var, value)) def choices(self, var): "Return all values for var that aren't currently ruled out." @@ -136,7 +142,7 @@ def restore(self, removals): for B, b in removals: self.curr_domains[B].append(b) - ## This is for min_conflicts search + # This is for min_conflicts search def conflicted_vars(self, current): "Return a list of variables in current assignment that are in conflict" @@ -146,6 +152,7 @@ def conflicted_vars(self, current): #______________________________________________________________________________ # Constraint Propagation with AC-3 + def AC3(csp, queue=None, removals=None): """[Fig. 6.3]""" if queue is None: @@ -161,6 +168,7 @@ def AC3(csp, queue=None, removals=None): queue.append((Xk, Xi)) return True + def revise(csp, Xi, Xj, removals): "Return true if we remove a value." revised = False @@ -177,16 +185,19 @@ def revise(csp, Xi, Xj, removals): # Variable ordering + def first_unassigned_variable(assignment, csp): "The default variable order." return find_if(lambda var: var not in assignment, csp.vars) + def mrv(assignment, csp): "Minimum-remaining-values heuristic." return argmin_random_tie( [v for v in csp.vars if v not in assignment], lambda var: num_legal_values(csp, var, assignment)) + def num_legal_values(csp, var, assignment): if csp.curr_domains: return len(csp.curr_domains[var]) @@ -196,10 +207,12 @@ def num_legal_values(csp, var, assignment): # Value ordering + def unordered_domain_values(var, assignment, csp): "The default value order." return csp.choices(var) + def lcv(var, assignment, csp): "Least-constraining-values heuristic." return sorted(csp.choices(var), @@ -207,9 +220,11 @@ def lcv(var, assignment, csp): # Inference + def no_inference(csp, var, value, assignment, removals): return True + def forward_checking(csp, var, value, assignment, removals): "Prune neighbor values inconsistent with var=value." for B in csp.neighbors[var]: @@ -221,16 +236,18 @@ def forward_checking(csp, var, value, assignment, removals): return False return True + def mac(csp, var, value, assignment, removals): "Maintain arc consistency." return AC3(csp, [(X, var) for X in csp.neighbors[var]], removals) # The search, proper + def backtracking_search(csp, - select_unassigned_variable = first_unassigned_variable, - order_domain_values = unordered_domain_values, - inference = no_inference): + select_unassigned_variable=first_unassigned_variable, + order_domain_values=unordered_domain_values, + inference=no_inference): """[Fig. 6.5] >>> backtracking_search(australia) is not None True @@ -271,6 +288,7 @@ def backtrack(assignment): #______________________________________________________________________________ # Min-conflicts hillclimbing search for CSPs + def min_conflicts(csp, max_steps=100000): """Solve a CSP by stochastic hillclimbing on the number of conflicts.""" # Generate a complete assignment for all vars (probably with conflicts) @@ -288,6 +306,7 @@ def min_conflicts(csp, max_steps=100000): csp.assign(var, val, current) return None + def min_conflicts_value(csp, var, current): """Return the value that will give var the least number of conflicts. If there is a tie, choose at random.""" @@ -296,6 +315,7 @@ def min_conflicts_value(csp, var, current): #______________________________________________________________________________ + def tree_csp_solver(csp): "[Fig. 6.11]" n = len(csp.vars) @@ -311,30 +331,39 @@ def tree_csp_solver(csp): assignment[Xi] = csp.curr_domains[Xi][0] return assignment + def topological_sort(xs, x): unimplemented() -def make_arc_consistent(Xj, Xk, csp): + +def make_arc_consistent(Xj, Xk, csp): unimplemented() #______________________________________________________________________________ # Map-Coloring Problems + class UniversalDict: + """A universal dict maps any key to the same value. We use it here as the domains dict for CSPs in which all vars have the same domain. >>> d = UniversalDict(42) >>> d['life'] 42 """ + def __init__(self, value): self.value = value + def __getitem__(self, key): return self.value + def __repr__(self): return '{Any: %r}' % self.value + def different_values_constraint(A, a, B, b): "A constraint saying two neighboring variables must differ in value." return a != b + def MapColoringCSP(colors, neighbors): """Make a CSP for the problem of coloring a map with different colors for any two adjacent regions. Arguments are a list of colors, and a @@ -342,9 +371,10 @@ def MapColoringCSP(colors, neighbors): specified as a string of the form defined by parse_neighbors.""" if isinstance(neighbors, str): neighbors = parse_neighbors(neighbors) - return CSP(neighbors.keys(), UniversalDict(colors), neighbors, + return CSP(list(neighbors.keys()), UniversalDict(colors), neighbors, different_values_constraint) + def parse_neighbors(neighbors, vars=[]): """Convert a string of the form 'X: Y Z; Y: Z' into a dict mapping regions to neighbors. The syntax is a region name followed by a ':' @@ -369,7 +399,7 @@ def parse_neighbors(neighbors, vars=[]): 'SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: ') usa = MapColoringCSP(list('RGBY'), - """WA: OR ID; OR: ID NV CA; CA: NV AZ; NV: ID UT AZ; ID: MT WY UT; + """WA: OR ID; OR: ID NV CA; CA: NV AZ; NV: ID UT AZ; ID: MT WY UT; UT: WY CO AZ; MT: ND SD WY; WY: SD NE CO; CO: NE KA OK NM; NM: OK TX; ND: MN SD; SD: MN IA NE; NE: IA MO KA; KA: MO OK; OK: MO AR TX; TX: AR LA; MN: WI IA; IA: WI IL MO; MO: IL KY TN AR; AR: MS TN LA; @@ -380,7 +410,7 @@ def parse_neighbors(neighbors, vars=[]): HI: ; AK: """) france = MapColoringCSP(list('RGBY'), - """AL: LO FC; AQ: MP LI PC; AU: LI CE BO RA LR MP; BO: CE IF CA FC RA + """AL: LO FC; AQ: MP LI PC; AU: LI CE BO RA LR MP; BO: CE IF CA FC RA AU; BR: NB PL; CA: IF PI LO FC BO; CE: PL NB NH IF BO AU LI PC; FC: BO CA LO AL RA; IF: NH PI CA BO CE; LI: PC CE AU MP AQ; LO: CA AL FC; LR: MP AU RA PA; MP: AQ LI AU LR; NB: NH CE PL BR; NH: PI IF CE NB; NO: @@ -390,12 +420,15 @@ def parse_neighbors(neighbors, vars=[]): #______________________________________________________________________________ # n-Queens Problem + def queen_constraint(A, a, B, b): """Constraint is satisfied (true) if A, B are really the same variable, or if they are not in the same row, down diagonal, or up diagonal.""" return A == B or (a != b and A + a != B + b and A - a != B - b) + class NQueensCSP(CSP): + """Make a CSP for the nQueens problem for search with min_conflicts. Suitable for large n, it uses only data structures of size O(n). Think of placing queens one per column, from left to right. @@ -414,10 +447,11 @@ class NQueensCSP(CSP): >>> len(backtracking_search(NQueensCSP(8))) 8 """ + def __init__(self, n): """Initialize data structures for n Queens.""" - CSP.__init__(self, range(n), UniversalDict(range(n)), - UniversalDict(range(n)), queen_constraint) + CSP.__init__(self, list(range(n)), UniversalDict(list(range(n))), + UniversalDict(list(range(n))), queen_constraint) update(self, rows=[0]*n, ups=[0]*(2*n - 1), downs=[0]*(2*n - 1)) def nconflicts(self, var, val, assignment): @@ -434,7 +468,7 @@ def assign(self, var, val, assignment): "Assign var, and keep track of conflicts." oldval = assignment.get(var, None) if val != oldval: - if oldval is not None: # Remove old val if there was one + if oldval is not None: # Remove old val if there was one self.record_conflict(assignment, var, oldval, -1) self.record_conflict(assignment, var, val, +1) CSP.assign(self, var, val, assignment) @@ -457,28 +491,37 @@ def display(self, assignment): n = len(self.vars) for val in range(n): for var in range(n): - if assignment.get(var,'') == val: ch = 'Q' - elif (var+val) % 2 == 0: ch = '.' - else: ch = '-' - print ch, - print ' ', + if assignment.get(var, '') == val: + ch = 'Q' + elif (var+val) % 2 == 0: + ch = '.' + else: + ch = '-' + print(ch, end=' ') + print(' ', end=' ') for var in range(n): - if assignment.get(var,'') == val: ch = '*' - else: ch = ' ' - print str(self.nconflicts(var, val, assignment))+ch, - print + if assignment.get(var, '') == val: + ch = '*' + else: + ch = ' ' + print(str(self.nconflicts(var, val, assignment))+ch, end=' ') + print() #______________________________________________________________________________ # Sudoku -import itertools, re +import itertools +import re + def flatten(seqs): return sum(seqs, []) -easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' +easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' + class Sudoku(CSP): + """A Sudoku problem. The box grid is a 3x3 array of boxes, each a 3x3 array of cells. Each cell holds a digit in 1..9. In each box, all digits are @@ -513,12 +556,12 @@ class Sudoku(CSP): >>> None != backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) True """ - R3 = range(3) - Cell = itertools.count().next + R3 = list(range(3)) + Cell = itertools.count().__next__ bgrid = [[[[Cell() for x in R3] for y in R3] for bx in R3] for by in R3] - boxes = flatten([map(flatten, brow) for brow in bgrid]) - rows = flatten([map(flatten, zip(*brow)) for brow in bgrid]) - cols = zip(*rows) + boxes = flatten([list(map(flatten, brow)) for brow in bgrid]) + rows = flatten([list(map(flatten, list(zip(*brow)))) for brow in bgrid]) + cols = list(zip(*rows)) neighbors = dict([(v, set()) for v in flatten(rows)]) for unit in map(set, boxes + rows + cols): @@ -533,20 +576,25 @@ def __init__(self, grid): domains = dict((var, ([ch] if ch in '123456789' else '123456789')) for var, ch in zip(flatten(self.rows), squares)) for _ in squares: - raise ValueError("Not a Sudoku grid", grid) # Too many squares + raise ValueError("Not a Sudoku grid", grid) # Too many squares CSP.__init__(self, None, domains, self.neighbors, different_values_constraint) def display(self, assignment): - def show_box(box): return [' '.join(map(show_cell, row)) for row in box] + def show_box(box): return [ + ' '.join(map(show_cell, row)) for row in box] + def show_cell(cell): return str(assignment.get(cell, '.')) - def abut(lines1, lines2): return map(' | '.join, zip(lines1, lines2)) - print '\n------+-------+------\n'.join( - '\n'.join(reduce(abut, map(show_box, brow))) for brow in self.bgrid) + + def abut(lines1, lines2): return list( + map(' | '.join, list(zip(lines1, lines2)))) + print('\n------+-------+------\n'.join( + '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgrid)) #______________________________________________________________________________ # The Zebra Puzzle + def Zebra(): "Return an instance of the Zebra Puzzle." Colors = 'Red Yellow Blue Green Ivory'.split() @@ -557,7 +605,7 @@ def Zebra(): vars = Colors + Pets + Drinks + Countries + Smokes domains = {} for var in vars: - domains[var] = range(1, 6) + domains[var] = list(range(1, 6)) domains['Norwegian'] = [1] domains['Milk'] = [3] neighbors = parse_neighbors("""Englishman: Red; @@ -569,40 +617,59 @@ def Zebra(): for A in type: for B in type: if A != B: - if B not in neighbors[A]: neighbors[A].append(B) - if A not in neighbors[B]: neighbors[B].append(A) + if B not in neighbors[A]: + neighbors[A].append(B) + if A not in neighbors[B]: + neighbors[B].append(A) + def zebra_constraint(A, a, B, b, recurse=0): same = (a == b) next_to = abs(a - b) == 1 - if A == 'Englishman' and B == 'Red': return same - if A == 'Spaniard' and B == 'Dog': return same - if A == 'Chesterfields' and B == 'Fox': return next_to - if A == 'Norwegian' and B == 'Blue': return next_to - if A == 'Kools' and B == 'Yellow': return same - if A == 'Winston' and B == 'Snails': return same - if A == 'LuckyStrike' and B == 'OJ': return same - if A == 'Ukranian' and B == 'Tea': return same - if A == 'Japanese' and B == 'Parliaments': return same - if A == 'Kools' and B == 'Horse': return next_to - if A == 'Coffee' and B == 'Green': return same - if A == 'Green' and B == 'Ivory': return (a - 1) == b - if recurse == 0: return zebra_constraint(B, b, A, a, 1) + if A == 'Englishman' and B == 'Red': + return same + if A == 'Spaniard' and B == 'Dog': + return same + if A == 'Chesterfields' and B == 'Fox': + return next_to + if A == 'Norwegian' and B == 'Blue': + return next_to + if A == 'Kools' and B == 'Yellow': + return same + if A == 'Winston' and B == 'Snails': + return same + if A == 'LuckyStrike' and B == 'OJ': + return same + if A == 'Ukranian' and B == 'Tea': + return same + if A == 'Japanese' and B == 'Parliaments': + return same + if A == 'Kools' and B == 'Horse': + return next_to + if A == 'Coffee' and B == 'Green': + return same + if A == 'Green' and B == 'Ivory': + return (a - 1) == b + if recurse == 0: + return zebra_constraint(B, b, A, a, 1) if ((A in Colors and B in Colors) or - (A in Pets and B in Pets) or - (A in Drinks and B in Drinks) or - (A in Countries and B in Countries) or - (A in Smokes and B in Smokes)): return not same - raise 'error' + (A in Pets and B in Pets) or + (A in Drinks and B in Drinks) or + (A in Countries and B in Countries) or + (A in Smokes and B in Smokes)): + return not same + raise Exception('error') return CSP(vars, domains, neighbors, zebra_constraint) + def solve_zebra(algorithm=min_conflicts, **args): z = Zebra() ans = algorithm(z, **args) for h in range(1, 6): - print 'House', h, - for (var, val) in ans.items(): - if val == h: print var, - print + print('House', h, end=' ') + for (var, val) in list(ans.items()): + if val == h: + print(var, end=' ') + print() return ans['Zebra'], ans['Water'], z.nassigns, ans diff --git a/games.py b/games.py index d534d3274..5ad60de93 100644 --- a/games.py +++ b/games.py @@ -1,6 +1,7 @@ """Games, or Adversarial Search. (Chapter 5) """ + from utils import * import random @@ -111,7 +112,7 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): "Make a move by querying standard input." game.display(state) - return num_or_str(raw_input('Your move? ')) + return num_or_str(eval(input('Your move? '))) def random_player(game, state): "A player that chooses a legal move at random." @@ -147,15 +148,15 @@ class Game: def actions(self, state): "Return a list of the allowable moves at this point." - raise NotImplementedError + raise NotImplementedError def result(self, state, move): "Return the state that results from making a move from a state." - raise NotImplementedError + raise NotImplementedError def utility(self, state, player): "Return the value of this final state to player." - raise NotImplementedError + raise NotImplementedError def terminal_test(self, state): "Return True if this is a final state for the game." @@ -167,7 +168,7 @@ def to_move(self, state): def display(self, state): "Print or otherwise display the state." - print state + print(state) def __repr__(self): return '<%s>' % self.__class__.__name__ @@ -190,7 +191,7 @@ class Fig52Game(Game): initial = 'A' def actions(self, state): - return self.succs.get(state, {}).keys() + return list(self.succs.get(state, {}).keys()) def result(self, state, move): return self.succs[state][move] @@ -243,8 +244,8 @@ def display(self, state): board = state.board for x in range(1, self.h+1): for y in range(1, self.v+1): - print board.get((x, y), '.'), - print + print(board.get((x, y), '.'), end=' ') + print() def compute_utility(self, board, move, player): "If X wins with this move, return 1; if O return -1; else return 0." @@ -256,8 +257,9 @@ def compute_utility(self, board, move, player): else: return 0 - def k_in_row(self, board, move, player, (delta_x, delta_y)): + def k_in_row(self, board, move, player, xxx_todo_changeme): "Return true if there is a line through move on board for player." + (delta_x, delta_y) = xxx_todo_changeme x, y = move n = 0 # n is number of moves in row while board.get((x, y)) == player: diff --git a/grid.py b/grid.py index 278ae2d7b..8fbbb8c46 100644 --- a/grid.py +++ b/grid.py @@ -49,4 +49,4 @@ def vector_clip(vector, lowest, highest): >>> vector_clip((-1, 10), (0, 0), (9, 9)) (0, 9) """ - return type(vector)(map(clip, vector, lowest, highest)) + return type(vector)(list(map(clip, vector, lowest, highest))) diff --git a/learning.py b/learning.py index a98937435..65cf727a2 100644 --- a/learning.py +++ b/learning.py @@ -1,5 +1,6 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" + from utils import * import copy, heapq, math, random from collections import defaultdict @@ -62,7 +63,7 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, self.examples = examples # Attrs are the indices of examples, unless otherwise stated. if not attrs and self.examples: - attrs = range(len(self.examples[0])) + attrs = list(range(len(self.examples[0]))) self.attrs = attrs # Initialize .attrnames from string, list, or by default if isinstance(attrnames, str): @@ -78,14 +79,14 @@ def setproblem(self, target, inputs=None, exclude=()): to not use in inputs. Attributes can be -n .. n, or an attrname. Also computes the list of possible values, if that wasn't done yet.""" self.target = self.attrnum(target) - exclude = map(self.attrnum, exclude) + exclude = list(map(self.attrnum, exclude)) if inputs: self.inputs = removeall(self.target, inputs) else: self.inputs = [a for a in self.attrs if a != self.target and a not in exclude] if not self.values: - self.values = map(unique, zip(*self.examples)) + self.values = list(map(unique, list(zip(*self.examples)))) self.check_me() def check_me(self): @@ -94,7 +95,7 @@ def check_me(self): assert self.target in self.attrs assert self.target not in self.inputs assert set(self.inputs).issubset(set(self.attrs)) - map(self.check_example, self.examples) + list(map(self.check_example, self.examples)) def add_example(self, example): "Add an example to the list of examples, checking it first." @@ -138,7 +139,7 @@ def parse_csv(input, delim=','): [[1, 2, 3], [0, 2, 'na']] """ lines = [line for line in input.splitlines() if line.strip()] - return [map(num_or_str, line.split(delim)) for line in lines] + return [list(map(num_or_str, line.split(delim))) for line in lines] #______________________________________________________________________________ @@ -182,13 +183,13 @@ def __getitem__(self, item): def top(self, n): "Return (count, obs) tuples for the n most frequent observations." - return heapq.nlargest(n, [(v, k) for (k, v) in self.dictionary.items()]) + return heapq.nlargest(n, [(v, k) for (k, v) in list(self.dictionary.items())]) def sample(self): "Return a random sample from the distribution." if self.sampler is None: - self.sampler = weighted_sampler(self.dictionary.keys(), - self.dictionary.values()) + self.sampler = weighted_sampler(list(self.dictionary.keys()), + list(self.dictionary.values())) return self.sampler() #______________________________________________________________________________ @@ -264,9 +265,9 @@ def add(self, val, subtree): def display(self, indent=0): name = self.attrname - print 'Test', name - for (val, subtree) in self.branches.items(): - print ' '*4*indent, name, '=', val, '==>', + print('Test', name) + for (val, subtree) in list(self.branches.items()): + print(' '*4*indent, name, '=', val, '==>', end=' ') subtree.display(indent+1) def __repr__(self): @@ -283,7 +284,7 @@ def __call__(self, example): return self.result def display(self, indent=0): - print 'RESULT =', self.result + print('RESULT =', self.result) def __repr__(self): return repr(self.result) @@ -391,7 +392,7 @@ def predict(example): def NeuralNetLearner(dataset, sizes): """Layered feed-forward network.""" - activations = map(lambda n: [0.0 for i in range(n)], sizes) + activations = [[0.0 for i in range(n)] for n in sizes] weights = [] def predict(example): @@ -463,7 +464,7 @@ def weighted_mode(values, weights): totals = defaultdict(int) for v, w in zip(values, weights): totals[v] += w - return max(totals.keys(), key=totals.get) + return max(list(totals.keys()), key=totals.get) #_____________________________________________________________________________ # Adapting an unweighted learner for AdaBoost @@ -511,10 +512,10 @@ def test(predict, dataset, examples=None, verbose=0): if output == desired: right += 1 if verbose >= 2: - print ' OK: got %s for %s' % (desired, example) + print(' OK: got %s for %s' % (desired, example)) elif verbose: - print 'WRONG: got %s, expected %s for %s' % ( - output, desired, example) + print('WRONG: got %s, expected %s for %s' % ( + output, desired, example)) return right / len(examples) def train_and_test(learner, dataset, start, end): @@ -548,7 +549,7 @@ def leave1out(learner, dataset): def learningcurve(learner, dataset, trials=10, sizes=None): if sizes is None: - sizes = range(2, len(dataset.examples)-10, 2) + sizes = list(range(2, len(dataset.examples)-10, 2)) def score(learner, size): random.shuffle(dataset.examples) return train_and_test(learner, dataset, 0, size) @@ -585,7 +586,7 @@ def RestaurantDataSet(examples=None): def T(attrname, branches): branches = dict((value, (child if isinstance(child, DecisionFork) else DecisionLeaf(child))) - for value, child in branches.items()) + for value, child in list(branches.items())) return DecisionFork(restaurant.attrnum(attrname), attrname, branches) Fig[18,2] = T('Patrons', @@ -627,7 +628,7 @@ def T(attrname, branches): def SyntheticRestaurant(n=20): "Generate a DataSet with n examples." def gen(): - example = map(random.choice, restaurant.values) + example = list(map(random.choice, restaurant.values)) example[restaurant.target] = Fig[18,2](example) return example return RestaurantDataSet([gen() for i in range(n)]) diff --git a/logic.py b/logic.py index 59a212def..478725255 100644 --- a/logic.py +++ b/logic.py @@ -96,7 +96,7 @@ def KB_AgentProgram(KB): steps = itertools.count() def program(percept): - t = steps.next() + t = next(steps) KB.tell(make_percept_sentence(percept, t)) action = KB.ask(make_action_query(t)) KB.tell(make_action_sentence(action, t)) @@ -165,7 +165,7 @@ def __init__(self, op, *args): "Op is a string or number; args are Exprs (or are coerced to Exprs)." assert isinstance(op, str) or (isnumber(op) and not args) self.op = num_or_str(op) - self.args = map(expr, args) ## Coerce args to Exprs + self.args = list(map(expr, args)) ## Coerce args to Exprs def __call__(self, *args): """Self must be a symbol with no args, such as Expr('F'). Create a new @@ -310,8 +310,8 @@ def parse_definite_clause(s): return conjuncts(antecedent), consequent ## Useful constant Exprs used in examples and code: -TRUE, FALSE, ZERO, ONE, TWO = map(Expr, ['TRUE', 'FALSE', 0, 1, 2]) -A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') +TRUE, FALSE, ZERO, ONE, TWO = list(map(Expr, ['TRUE', 'FALSE', 0, 1, 2])) +A, B, C, D, E, F, G, P, Q, x, y, z = list(map(Expr, 'ABCDEFGPQxyz')) #______________________________________________________________________________ @@ -434,7 +434,7 @@ def eliminate_implications(s): ((A & ~B) | (~A & B)) """ if not s.args or is_symbol(s.op): return s ## (Atoms are unchanged.) - args = map(eliminate_implications, s.args) + args = list(map(eliminate_implications, s.args)) a, b = args[0], args[-1] if s.op == '>>': return (b | ~a) @@ -462,13 +462,13 @@ def move_not_inwards(s): NOT = lambda b: move_not_inwards(~b) a = s.args[0] if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A - if a.op =='&': return associate('|', map(NOT, a.args)) - if a.op =='|': return associate('&', map(NOT, a.args)) + if a.op =='&': return associate('|', list(map(NOT, a.args))) + if a.op =='|': return associate('&', list(map(NOT, a.args))) return s elif is_symbol(s.op) or not s.args: return s else: - return Expr(s.op, *map(move_not_inwards, s.args)) + return Expr(s.op, *list(map(move_not_inwards, s.args))) def distribute_and_over_or(s): """Given a sentence s consisting of conjunctions and disjunctions @@ -492,7 +492,7 @@ def distribute_and_over_or(s): return associate('&', [distribute_and_over_or(c|rest) for c in conj.args]) elif s.op == '&': - return associate('&', map(distribute_and_over_or, s.args)) + return associate('&', list(map(distribute_and_over_or, s.args))) else: return s @@ -887,7 +887,7 @@ def standardize_variables(sentence, dic=None): if sentence in dic: return dic[sentence] else: - v = Expr('v_%d' % standardize_variables.counter.next()) + v = Expr('v_%d' % next(standardize_variables.counter)) dic[sentence] = v return v else: @@ -933,12 +933,12 @@ def test_ask(query, kb=None): q = expr(query) vars = variables(q) answers = fol_bc_ask(kb or test_kb, q) - return sorted([pretty(dict((x, v) for x, v in a.items() if x in vars)) + return sorted([pretty(dict((x, v) for x, v in list(a.items()) if x in vars)) for a in answers], key=repr) test_kb = FolKB( - map(expr, ['Farmer(Mac)', + list(map(expr, ['Farmer(Mac)', 'Rabbit(Pete)', 'Mother(MrsMac, Mac)', 'Mother(MrsRabbit, Pete)', @@ -950,11 +950,11 @@ def test_ask(query, kb=None): # would result in infinite recursion: #'(Human(h) & Mother(m, h)) ==> Human(m)' '(Mother(m, h) & Human(h)) ==> Human(m)' - ]) + ])) ) crime_kb = FolKB( - map(expr, + list(map(expr, ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', 'Owns(Nono, M1)', 'Missile(M1)', @@ -963,7 +963,7 @@ def test_ask(query, kb=None): 'Enemy(x, America) ==> Hostile(x)', 'American(West)', 'Enemy(Nono, America)' - ]) + ])) ) def fol_bc_ask(KB, query): @@ -1033,7 +1033,7 @@ def diff(y, x): def simp(x): if not x.args: return x - args = map(simp, x.args) + args = list(map(simp, x.args)) u, op, v = args[0], x.op, args[-1] if op == '+': if v == ZERO: return u @@ -1092,7 +1092,7 @@ def pretty_dict(d): '{x: A, y: B, z: C}' """ return '{%s}' % ', '.join('%r: %r' % (k, v) - for k, v in sorted(d.items(), key=repr)) + for k, v in sorted(list(d.items()), key=repr)) def pretty_set(s): """Return set s's repr but with the items sorted. @@ -1104,17 +1104,17 @@ def pretty_set(s): return 'set(%r)' % sorted(s, key=repr) def pp(x): - print(pretty(x)) + print((pretty(x))) def ppsubst(s): """Pretty-print substitution s""" ppdict(s) def ppdict(d): - print(pretty_dict(d)) + print((pretty_dict(d))) def ppset(s): - print(pretty_set(s)) + print((pretty_set(s))) #________________________________________________________________________ diff --git a/mdp.py b/mdp.py index b2aa8d0d1..c93c2c0f8 100644 --- a/mdp.py +++ b/mdp.py @@ -6,6 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" + from utils import * class MDP: @@ -34,7 +35,7 @@ def R(self, state): def T(self, state, action): """Transition model. From a state and an action, return a list of (probability, result-state) pairs.""" - raise NotImplementedError + raise NotImplementedError def actions(self, state): """Set of actions that can be performed in this state. By default, a @@ -84,7 +85,7 @@ def to_grid(self, mapping): def to_arrows(self, policy): chars = {(1, 0):'>', (0, 1):'^', (-1, 0):'<', (0, -1):'v', None: '.'} - return self.to_grid(dict([(s, chars[a]) for (s, a) in policy.items()])) + return self.to_grid(dict([(s, chars[a]) for (s, a) in list(policy.items())])) #______________________________________________________________________________ diff --git a/nlp.py b/nlp.py index 6c3ee4992..919862277 100644 --- a/nlp.py +++ b/nlp.py @@ -14,7 +14,7 @@ def Rules(**rules): >>> Rules(A = "B C | D E") {'A': [['B', 'C'], ['D', 'E']]} """ - for (lhs, rhs) in rules.items(): + for (lhs, rhs) in list(rules.items()): rules[lhs] = [alt.strip().split() for alt in rhs.split('|')] return rules @@ -23,7 +23,7 @@ def Lexicon(**rules): >>> Lexicon(Art = "the | a | an") {'Art': ['the', 'a', 'an']} """ - for (lhs, rhs) in rules.items(): + for (lhs, rhs) in list(rules.items()): rules[lhs] = [word.strip() for word in rhs.split('|')] return rules @@ -152,7 +152,7 @@ def add_edge(self, edge): if edge not in self.chart[end]: self.chart[end].append(edge) if self.trace: - print '%10s: added %s' % (caller(2), edge) + print(('%10s: added %s' % (caller(2), edge))) if not expects: self.extender(edge) else: @@ -164,8 +164,9 @@ def scanner(self, j, word): if Bb and self.grammar.isa(word, Bb[0]): self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]]) - def predictor(self, (i, j, A, alpha, Bb)): + def predictor(self, xxx_todo_changeme): "Add to chart any rules for B that could help extend this edge." + (i, j, A, alpha, Bb) = xxx_todo_changeme B = Bb[0] if B in self.grammar.rules: for rhs in self.grammar.rewrites_for(B): diff --git a/planning.py b/planning.py index 331193bd7..cc7604af4 100644 --- a/planning.py +++ b/planning.py @@ -1,7 +1,7 @@ """Planning (Chapters 10-11) """ -from __future__ import generators + from utils import * import agents import math, random, sys, time, bisect, string diff --git a/probability.py b/probability.py index 5c95de36b..e80822751 100644 --- a/probability.py +++ b/probability.py @@ -5,6 +5,7 @@ from logic import extend import random from collections import defaultdict +from functools import reduce #______________________________________________________________________________ @@ -34,7 +35,7 @@ def __init__(self, varname='?', freqs=None): and the ProbDist then is normalized.""" update(self, prob={}, varname=varname, values=[]) if freqs: - for (v, p) in freqs.items(): + for (v, p) in list(freqs.items()): self[v] = p self.normalize() @@ -215,11 +216,11 @@ def __init__(self, X, parents, cpt): if isinstance(cpt, (float, int)): # no parents, 0-tuple cpt = {(): cpt} elif isinstance(cpt, dict): - if cpt and isinstance(cpt.keys()[0], bool): # one parent, 1-tuple - cpt = dict(((v,), p) for v, p in cpt.items()) + if cpt and isinstance(list(cpt.keys())[0], bool): # one parent, 1-tuple + cpt = dict(((v,), p) for v, p in list(cpt.items())) assert isinstance(cpt, dict) - for vs, p in cpt.items(): + for vs, p in list(cpt.items()): assert isinstance(vs, tuple) and len(vs) == len(parents) assert every(lambda v: isinstance(v, bool), vs) assert 0 <= p <= 1 @@ -356,7 +357,7 @@ def normalize(self): "Return my probabilities; must be down to one variable." assert len(self.vars) == 1 return ProbDist(self.vars[0], - dict((k, v) for ((k,), v) in self.cpt.items())) + dict((k, v) for ((k,), v) in list(self.cpt.items()))) def p(self, e): "Look up my value tabulated for e." @@ -406,7 +407,7 @@ def rejection_sampling(X, e, bn, N): 'False: 0.7, True: 0.3' """ counts = dict((x, 0) for x in bn.variable_values(X)) # bold N in Fig. 14.14 - for j in xrange(N): + for j in range(N): sample = prior_sample(bn) # boldface x in Fig. 14.14 if consistent_with(sample, e): counts[sample[X]] += 1 @@ -415,7 +416,7 @@ def rejection_sampling(X, e, bn, N): def consistent_with(event, evidence): "Is event consistent with the given evidence?" return all(evidence.get(k, v) == v - for k, v in event.items()) + for k, v in list(event.items())) #_______________________________________________________________________________ @@ -428,7 +429,7 @@ def likelihood_weighting(X, e, bn, N): 'False: 0.702, True: 0.298' """ W = dict((x, 0) for x in bn.variable_values(X)) - for j in xrange(N): + for j in range(N): sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15 W[sample[X]] += weight return ProbDist(X, W) @@ -462,7 +463,7 @@ def gibbs_ask(X, e, bn, N): state = dict(e) # boldface x in Fig. 14.16 for Zi in Z: state[Zi] = random.choice(bn.variable_values(Zi)) - for j in xrange(N): + for j in range(N): for Zi in Z: state[Zi] = markov_blanket_sample(Zi, state, bn) counts[state[X]] += 1 diff --git a/search.py b/search.py index ab2ba5136..a7f5cfeed 100644 --- a/search.py +++ b/search.py @@ -4,6 +4,7 @@ then create problem instances and solve them with calls to the various search functions.""" + from utils import * import math, random, sys, time, bisect, string @@ -253,7 +254,7 @@ def recursive_dls(node, problem, limit): def iterative_deepening_search(problem): "[Fig. 3.18]" - for depth in xrange(sys.maxint): + for depth in range(sys.maxsize): result = depth_limited_search(problem, depth) if result != 'cutoff': return result @@ -326,7 +327,7 @@ def exp_schedule(k=20, lam=0.005, limit=100): def simulated_annealing(problem, schedule=exp_schedule()): "[Fig. 4.5]" current = Node(problem.initial) - for t in xrange(sys.maxint): + for t in range(sys.maxsize): T = schedule(t) if T == 0: return current @@ -367,7 +368,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): for i in range(ngen): new_population = [] for i in len(population): - fitnesses = map(fitness_fn, population) + fitnesses = list(map(fitness_fn, population)) p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2) child = p1.mate(p2) if random.uniform(0, 1) < pmut: @@ -417,8 +418,8 @@ def __init__(self, dict=None, directed=True): def make_undirected(self): "Make a digraph into an undirected graph by adding symmetric edges." - for a in self.dict.keys(): - for (b, distance) in self.dict[a].items(): + for a in list(self.dict.keys()): + for (b, distance) in list(self.dict[a].items()): self.connect1(b, a, distance) def connect(self, A, B, distance=1): @@ -441,13 +442,13 @@ def get(self, a, b=None): def nodes(self): "Return a list of nodes in the graph." - return self.dict.keys() + return list(self.dict.keys()) def UndirectedGraph(dict=None): "Build a Graph where every edge (including future ones) goes both ways." return Graph(dict=dict, directed=False) -def RandomGraph(nodes=range(10), min_links=2, width=400, height=300, +def RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300, curvature=lambda: random.uniform(1.1, 1.5)): """Construct a random graph, with the specified nodes, and random links. The nodes are laid out randomly on a (width x height) rectangle. @@ -510,7 +511,7 @@ def __init__(self, initial, goal, graph): def actions(self, A): "The actions at a graph node are just its neighbors." - return self.graph.get(A).keys() + return list(self.graph.get(A).keys()) def result(self, state, action): "The result of going to a neighbor is just that neighbor." @@ -593,7 +594,7 @@ def random_boggle(n=4): We represent a board as a linear list of letters.""" cubes = [cubes16[i % 16] for i in range(n*n)] random.shuffle(cubes) - return map(random.choice, cubes) + return list(map(random.choice, cubes)) # The best 5x5 board found by Boyan, with our word list this board scores # 2274 words, for a score of 9837 @@ -604,10 +605,10 @@ def print_boggle(board): "Print the board in a 2-d array." n2 = len(board); n = exact_sqrt(n2) for i in range(n2): - if i % n == 0 and i > 0: print - if board[i] == 'Q': print 'Qu', - else: print str(board[i]) + ' ', - print + if i % n == 0 and i > 0: print() + if board[i] == 'Q': print('Qu', end=' ') + else: print(str(board[i]) + ' ', end=' ') + print() def boggle_neighbors(n2, cache={}): """Return a list of lists, where the i-th element is the list of indexes @@ -722,7 +723,7 @@ def find(self, lo, hi, i, visited, prefix): def words(self): "The words found." - return self.found.keys() + return list(self.found.keys()) scores = [0, 0, 0, 0, 1, 2, 3, 5] + [11] * 100 @@ -748,7 +749,7 @@ def boggle_hill_climbing(board=None, ntimes=100, verbose=True): new = len(finder.set_board(board)) if new > best: best = new - if verbose: print best, _, board + if verbose: print(best, _, board) else: board[i] = oldc ## Change back if verbose: diff --git a/text.py b/text.py index c71461514..c9a83b0aa 100644 --- a/text.py +++ b/text.py @@ -150,7 +150,7 @@ def present(self, results): "Present the results as a list." for (score, d) in results: doc = self.documents[d] - print ("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs())) + print(("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs()))) def present_results(self, query_text, n=10): "Get results for the query and present them." diff --git a/utils.py b/utils.py index e73de1a31..b35704aa4 100644 --- a/utils.py +++ b/utils.py @@ -33,7 +33,7 @@ def __cmp__(self, other): def __repr__(self): args = ['{!s}={!s}'.format(k, repr(v)) - for (k, v) in vars(self).items()] + for (k, v) in list(vars(self).items())] def update(x, **entries): """Update a dict or an object with slots according to entries.""" @@ -69,7 +69,7 @@ def product(numbers): def count_if(predicate, seq): """Count the number of elements of seq for which the predicate is true.""" - return sum(map(lambda x: bool(predicate(x)), seq)) + return sum([bool(predicate(x)) for x in seq]) def find_if(predicate, seq): """If there is an element of seq that satisfies predicate; return it.""" @@ -153,14 +153,14 @@ def histogram(values, mode=0, bin_function=None): Sorted by increasing value, or if mode=1, by decreasing count. If bin_function is given, map it over values first.""" if bin_function: - values = map(bin_function, values) + values = list(map(bin_function, values)) bins = {} for val in values: bins[val] = bins.get(val, 0) + 1 if mode: - return sorted(bins.items(), key=lambda x: (x[1],x[0]), reverse=True) + return sorted(list(bins.items()), key=lambda x: (x[1],x[0]), reverse=True) else: return sorted(bins.items()) @@ -256,7 +256,7 @@ def vector_clip(vector, lowest, highest): value of lowest or more than the corresponding value of highest, clip to those values. """ - return type(vector)(map(clip, vector, lowest, highest)) + return type(vector)(list(map(clip, vector, lowest, highest))) #______________________________________________________________________________ # Misc Functions @@ -264,7 +264,7 @@ def vector_clip(vector, lowest, highest): def printf(format_str, *args): """Format args with the first argument as format string, and write. Return the last arg, or format itself if there are no args.""" - print(str(format_str).format(*args, end='')) + print((str(format_str).format(*args, end=''))) return args[-1] if args else format_str @@ -289,7 +289,7 @@ def memoized_fn(obj, *args): return val else: def memoized_fn(*args): - if not memoized_fn.cache.has_key(args): + if args not in memoized_fn.cache: memoized_fn.cache[args] = fn(*args) return memoized_fn.cache[args] @@ -326,13 +326,13 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): table = [[numfmt.format(x) if isnumber(x) else x for x in row] for row in table] - maxlen = lambda seq: max(map(len, seq)) + maxlen = lambda seq: max(list(map(len, seq))) - sizes = map(maxlen, zip(*[map(str, row) for row in table])) + sizes = list(map(maxlen, list(zip(*[list(map(str, row)) for row in table])))) for row in table: - print(sep.join(getattr(str(x), j)(size) - for (j, size, x) in zip(justs, sizes, row))) + print((sep.join(getattr(str(x), j)(size) + for (j, size, x) in zip(justs, sizes, row)))) def AIMAFile(components, mode='r'): "Open a file based at the AIMA root directory." diff --git a/utils_test.py b/utils_test.py index eb83c5c49..af4a0a124 100644 --- a/utils_test.py +++ b/utils_test.py @@ -34,7 +34,7 @@ def test_unique(): def test_product(): assert product([1,2,3,4]) == 24 - assert product(range(1, 11)) == 3628800 + assert product(list(range(1, 11))) == 3628800 def test_find_if(): assert find_if(callable, [1, 2, 3]) == None From a531de4afdd12428703dac9d2f0ad91152367641 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 17:16:17 +0100 Subject: [PATCH 048/513] Fix tests --- csp.py | 5 ++- games.py | 5 ++- learning.py | 6 +-- mdp.py | 58 +++++++++++++++++------------ probability_test.py | 29 ++++++++------- search.py | 5 ++- text.py | 32 ++++++++++++---- text_test.py | 91 ++++++++++++++++++++++++--------------------- utils.py | 2 +- 9 files changed, 138 insertions(+), 95 deletions(-) diff --git a/csp.py b/csp.py index 8fdbf636f..0c9f50e64 100644 --- a/csp.py +++ b/csp.py @@ -673,9 +673,10 @@ def solve_zebra(algorithm=min_conflicts, **args): return ans['Zebra'], ans['Water'], z.nassigns, ans -__doc__ += random_tests(""" +__doc__ += """ +Random tests: >>> min_conflicts(australia) {'WA': 'B', 'Q': 'B', 'T': 'G', 'V': 'B', 'SA': 'R', 'NT': 'G', 'NSW': 'G'} >>> min_conflicts(NQueensCSP(8), max_steps=10000) {0: 5, 1: 0, 2: 4, 3: 1, 4: 7, 5: 2, 6: 6, 7: 3} -""") +""" diff --git a/games.py b/games.py index 5ad60de93..887de20ad 100644 --- a/games.py +++ b/games.py @@ -284,9 +284,10 @@ def actions(self, state): return [(x, y) for (x, y) in state.moves if y == 1 or (x, y-1) in state.board] -__doc__ += random_tests(""" +__doc__ += """ +Random tests: >>> play_game(Fig52Game(), random_player, random_player) 6 >>> play_game(TicTacToe(), random_player, random_player) 0 -""") +""" diff --git a/learning.py b/learning.py index 65cf727a2..e0fcc3639 100644 --- a/learning.py +++ b/learning.py @@ -112,10 +112,10 @@ def check_example(self, example): def attrnum(self, attr): "Returns the number used for attr, which can be a name, or -n .. n-1." - if attr < 0: - return len(self.attrs) + attr - elif isinstance(attr, str): + if isinstance(attr, str): return self.attrnames.index(attr) + elif attr < 0: + return len(self.attrs) + attr else: return attr diff --git a/mdp.py b/mdp.py index c93c2c0f8..b844fc749 100644 --- a/mdp.py +++ b/mdp.py @@ -9,7 +9,9 @@ from utils import * + class MDP: + """A Markov Decision Process, defined by an initial state, transition model, and reward function. We also keep track of a gamma value, for use by algorithms. The transition model is represented somewhat differently from @@ -19,14 +21,14 @@ class MDP: actions for each state. [page 646]""" def __init__(self, init, actlist, terminals, gamma=.9): - self.init=init - self.actlist=actlist - self.terminals=terminals + self.init = init + self.actlist = actlist + self.terminals = terminals if not (0 <= gamma < 1): raise ValueError("An MDP must have 0 <= gamma < 1") - self.gamma=gamma - self.states=set() - self.reward={} + self.gamma = gamma + self.states = set() + self.reward = {} def R(self, state): "Return a numeric reward for this state." @@ -46,18 +48,21 @@ def actions(self, state): else: return self.actlist + class GridMDP(MDP): + """A two-dimensional grid MDP, as in [Figure 17.1]. All you have to do is specify the grid as a list of lists of rewards; use None for an obstacle (unreachable state). Also, you should specify the terminal states. An action is an (x, y) unit vector; e.g. (1, 0) means move east.""" + def __init__(self, grid, terminals, init=(0, 0), gamma=.9): - grid.reverse() ## because we want row 0 on bottom, not on top + grid.reverse() # because we want row 0 on bottom, not on top MDP.__init__(self, init, actlist=orientations, terminals=terminals, gamma=gamma) - self.grid=grid - self.rows=len(grid) - self.cols=len(grid[0]) + self.grid = grid + self.rows = len(grid) + self.cols = len(grid[0]) for x in range(self.cols): for y in range(self.rows): self.reward[x, y] = grid[y][x] @@ -79,23 +84,25 @@ def go(self, state, direction): def to_grid(self, mapping): """Convert a mapping from (x, y) to v into a [[..., v, ...]] grid.""" - return list(reversed([[mapping.get((x,y), None) + return list(reversed([[mapping.get((x, y), None) for x in range(self.cols)] for y in range(self.rows)])) def to_arrows(self, policy): - chars = {(1, 0):'>', (0, 1):'^', (-1, 0):'<', (0, -1):'v', None: '.'} + chars = { + (1, 0): '>', (0, 1): '^', (-1, 0): '<', (0, -1): 'v', None: '.'} return self.to_grid(dict([(s, chars[a]) for (s, a) in list(policy.items())])) #______________________________________________________________________________ -Fig[17,1] = GridMDP([[-0.04, -0.04, -0.04, +1], - [-0.04, None, -0.04, -1], - [-0.04, -0.04, -0.04, -0.04]], - terminals=[(3, 2), (3, 1)]) +Fig[17, 1] = GridMDP([[-0.04, -0.04, -0.04, +1], + [-0.04, None, -0.04, -1], + [-0.04, -0.04, -0.04, -0.04]], + terminals=[(3, 2), (3, 1)]) #______________________________________________________________________________ + def value_iteration(mdp, epsilon=0.001): "Solving an MDP by value iteration. [Fig. 17.4]" U1 = dict([(s, 0) for s in mdp.states]) @@ -108,22 +115,26 @@ def value_iteration(mdp, epsilon=0.001): for a in mdp.actions(s)]) delta = max(delta, abs(U1[s] - U[s])) if delta < epsilon * (1 - gamma) / gamma: - return U + return U + def best_policy(mdp, U): """Given an MDP and a utility function U, determine the best policy, as a mapping from state to action. (Equation 17.4)""" pi = {} for s in mdp.states: - pi[s] = argmax(mdp.actions(s), lambda a:expected_utility(a, s, U, mdp)) + pi[s] = argmax( + mdp.actions(s), lambda a: expected_utility(a, s, U, mdp)) return pi + def expected_utility(a, s, U, mdp): "The expected utility of doing a in state s, according to the MDP and U." return sum([p * U[s1] for (p, s1) in mdp.T(s, a)]) #______________________________________________________________________________ + def policy_iteration(mdp): "Solve an MDP by policy iteration [Fig. 17.7]" U = dict([(s, 0) for s in mdp.states]) @@ -132,13 +143,15 @@ def policy_iteration(mdp): U = policy_evaluation(pi, U, mdp) unchanged = True for s in mdp.states: - a = argmax(mdp.actions(s), lambda a: expected_utility(a,s,U,mdp)) + a = argmax( + mdp.actions(s), lambda a: expected_utility(a, s, U, mdp)) if a != pi[s]: pi[s] = a unchanged = False if unchanged: return pi + def policy_evaluation(pi, U, mdp, k=20): """Return an updated utility mapping U from each state in the MDP to its utility, using an approximation (modified policy iteration).""" @@ -165,7 +178,8 @@ def policy_evaluation(pi, U, mdp, k=20): ^ > ^ < """ -__doc__ += random_tests(""" +__doc__ += """ +Random tests: >>> pi {(3, 2): None, (3, 1): None, (3, 0): (-1, 0), (2, 1): (0, 1), (0, 2): (1, 0), (1, 0): (1, 0), (0, 0): (0, 1), (1, 2): (1, 0), (2, 0): (0, 1), (0, 1): (0, 1), (2, 2): (1, 0)} @@ -175,6 +189,4 @@ def policy_evaluation(pi, U, mdp, k=20): >>> policy_iteration(Fig[17,1]) {(3, 2): None, (3, 1): None, (3, 0): (0, -1), (2, 1): (-1, 0), (0, 2): (1, 0), (1, 0): (1, 0), (0, 0): (1, 0), (1, 2): (1, 0), (2, 0): (1, 0), (0, 1): (1, 0), (2, 2): (1, 0)} -""") - - +""" diff --git a/probability_test.py b/probability_test.py index fb18a273e..3bc2dec55 100644 --- a/probability_test.py +++ b/probability_test.py @@ -2,20 +2,20 @@ from probability import * def tests(): - cpt = burglary.variable_node('Alarm').cpt + cpt = burglary.variable_node('Alarm') parents = ['Burglary', 'Earthquake'] event = {'Burglary': True, 'Earthquake': True} - assert cpt.p(True, parents, event) == 0.95 + assert cpt.p(True, event) == 0.95 event = {'Burglary': False, 'Earthquake': True} - assert cpt.p(False, parents, event) == 0.71 - assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375 - assert BoolCPT(0.75).p(False, [], {}) == 0.25 - cpt = BoolCPT({True: 0.2, False: 0.7}) - assert cpt.rand(['A'], {'A': True}) in [True, False] - cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3, - (False, True): 0.5, (False, False): 0.7}) - assert cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False] - #enumeration_ask('Earthquake', {}, burglary) + assert cpt.p(False, event) == 0.71 + # assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375 + # assert BoolCPT(0.75).p(False, [], {}) == 0.25 + # cpt = BoolCPT({True: 0.2, False: 0.7}) + # assert cpt.rand(['A'], {'A': True}) in [True, False] + # cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3, + # (False, True): 0.5, (False, False): 0.7}) + # assert cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False] + # #enumeration_ask('Earthquake', {}, burglary) s = {'A': True, 'B': False, 'C': True, 'D': False} assert consistent_with(s, {}) @@ -23,8 +23,11 @@ def tests(): assert not consistent_with(s, {'A': False}) assert not consistent_with(s, {'D': True}) - seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000) + random.seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.001, 0.999) - seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000) + random.seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.002, 0.998) + +if __name__ == '__main__': + pytest.main() diff --git a/search.py b/search.py index a7f5cfeed..c6b5b905a 100644 --- a/search.py +++ b/search.py @@ -861,10 +861,11 @@ def compare_graph_searchers(): 206 """ -__doc__ += random_tests(""" +__doc__ += """ +Random tests >>> ' '.join(f.words()) 'LID LARES DEAL LIE DIETS LIN LINT TIL TIN RATED ERAS LATEN DEAR TIE LINE INTER STEAL LATED LAST TAR SAL DITES RALES SAE RETS TAE RAT RAS SAT IDLE TILDES LEAST IDEAS LITE SATED TINED LEST LIT RASE RENTS TINEA EDIT EDITS NITES ALES LATE LETS RELIT TINES LEI LAT ELINT LATI SENT TARED DINE STAR SEAR NEST LITAS TIED SEAT SERAL RATE DINT DEL DEN SEAL TIER TIES NET SALINE DILATE EAST TIDES LINTER NEAR LITS ELINTS DENI RASED SERA TILE NEAT DERAT IDLEST NIDE LIEN STARED LIER LIES SETA NITS TINE DITAS ALINE SATIN TAS ASTER LEAS TSAR LAR NITE RALE LAS REAL NITER ATE RES RATEL IDEA RET IDEAL REI RATS STALE DENT RED IDES ALIEN SET TEL SER TEN TEA TED SALE TALE STILE ARES SEA TILDE SEN SEL ALINES SEI LASE DINES ILEA LINES ELD TIDE RENT DIEL STELA TAEL STALED EARL LEA TILES TILER LED ETA TALI ALE LASED TELA LET IDLER REIN ALIT ITS NIDES DIN DIE DENTS STIED LINER LASTED RATINE ERA IDLES DIT RENTAL DINER SENTI TINEAL DEIL TEAR LITER LINTS TEAL DIES EAR EAT ARLES SATE STARE DITS DELI DENTAL REST DITE DENTIL DINTS DITA DIET LENT NETS NIL NIT SETAL LATS TARE ARE SATI' >>> boggle_hill_climbing(list('ABCDEFGHI'), verbose=False) (['E', 'P', 'R', 'D', 'O', 'A', 'G', 'S', 'T'], 123) -""") +""" diff --git a/text.py b/text.py index c9a83b0aa..d087b841a 100644 --- a/text.py +++ b/text.py @@ -30,7 +30,7 @@ def __init__(self, n, observation_sequence=[]): ## mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1) CountingProbDist.__init__(self) self.n = n - self.cond_prob = defaultdict(CountingProbDist()) + self.cond_prob = defaultdict() self.add_sequence(observation_sequence) ## __getitem__, top, sample inherited from CountingProbDist @@ -39,6 +39,8 @@ def __init__(self, n, observation_sequence=[]): def add(self, ngram): """Count 1 for P[(w1, ..., wn)] and for P(wn | (w1, ..., wn-1)""" CountingProbDist.add(self, ngram) + if ngram[:-1] not in self.cond_prob: + self.cond_prob[ngram[:-1]] = CountingProbDist() self.cond_prob[ngram[:-1]].add(ngram[-1]) def add_sequence(self, words): @@ -161,7 +163,7 @@ class UnixConsultant(IRSystem): def __init__(self): IRSystem.__init__(self, stopwords="how do i the a of") import os - mandir = '../aima-data/MAN/' + mandir = 'aima-data/MAN/' man_files = [mandir + f for f in os.listdir(mandir) if f.endswith('.txt')] self.index_collection(man_files) @@ -214,12 +216,26 @@ def rot13(plaintext): """ return shift_encode(plaintext, 13) +def translate(plaintext, function): + """Translate chars of a plaintext with the given function.""" + result = "" + for char in plaintext: + result += function(char) + return result + +def maketrans(from_, to_): + """Create a translation table and return the proper function.""" + trans_table = {} + for n, char in enumerate(from_): + trans_table[char] = to_[n] + + return lambda char: trans_table.get(char, char) + def encode(plaintext, code): "Encodes text, using a code which is a permutation of the alphabet." - from string import maketrans trans = maketrans(alphabet + alphabet.upper(), code + code.upper()) - return plaintext.translate(trans) + return translate(plaintext, trans) def bigrams(text): """Return a list of pairs in text (a sequence of letters or words). @@ -252,7 +268,8 @@ def score(self, plaintext): def decode(self, ciphertext): "Return the shift decoding of text with the best score." - return max(all_shifts(ciphertext), self.score) + list_ = [(self.score(shift), shift) for shift in all_shifts(ciphertext)] + return max(list_, key=lambda elm: elm[0])[1] def all_shifts(text): "Return a list of all 26 possible encodings of text by a shift cipher." @@ -315,7 +332,8 @@ def goal_test(self, state): #______________________________________________________________________________ # TODO(tmrts): Set RNG seed to test random functions -__doc__ += random_tests(""" +__doc__ += """ +Random tests: ## Generate random text from the N-gram models >>> P1.samples(20) 'you thought known but were insides of see in depend by us dodecahedrons just but i words are instead degrees' @@ -325,4 +343,4 @@ def goal_test(self, state): >>> P3.samples(20) 'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' -""") +""" diff --git a/text_test.py b/text_test.py index e47888e02..0c11ef5b8 100644 --- a/text_test.py +++ b/text_test.py @@ -5,8 +5,8 @@ from random import choice from math import isclose -def test_unigram_text_model(): - flatland = DataFile("aima-data/EN-text/flatland.txt").read() +def test_unigram_text_model(): + flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) P = UnigramTextModel(wordseq) @@ -20,14 +20,15 @@ def test_shift_encoding(): assert code == 'Kyzj zj r jvtivk dvjjrxv.' def test_shift_decoding(): - code = shift_encode("This is a secret message.", 17) - + flatland = DataFile("EN-text/flatland.txt").read() ring = ShiftDecoder(flatland) msg = ring.decode('Kyzj zj r jvtivk dvjjrxv.') assert msg == 'This is a secret message.' def test_rot13_decoding(): + flatland = DataFile("EN-text/flatland.txt").read() + ring = ShiftDecoder(flatland) msg = ring.decode(rot13('Hello, world!')) assert msg == 'Hello, world!' @@ -43,7 +44,7 @@ def test_counting_probability_distribution(): assert 1/7 <= min(ps) <= max(ps) <= 1/5 def test_ngram_models(): - flatland = DataFile("aima-data/EN-text/flatland.txt").read() + flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) P1 = UnigramTextModel(wordseq) P2 = NgramTextModel(2, wordseq) @@ -64,15 +65,15 @@ def test_ngram_models(): (11, ('that', 'i', 'had' )), (11, ('so', 'as', 'to'))] - assert isclose(P1['the'], 0.0611) + assert isclose(P1['the'], 0.0611, rel_tol=0.001) - assert isclose(P2['of', 'the'], 0.0108) + assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01) - assert isclose(P3['', '', 'but'], 0.0) - assert isclose(P3['', '', 'but'], 0.0) - assert isclose(P3['so', 'as', 'to'], 0.000323) + assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) + assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) + assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001) - assert not P2.cond_prob['went',].dictionary + assert P2.cond_prob.get(('went',)) is None assert P3.cond_prob['in','order'].dictionary == {'to': 6} @@ -86,51 +87,57 @@ def verify_query(query, expected): assert len(expected) == len(query) for expected, (score, d) in zip(expected, query): + print(expected.url, "{0:.2f}".format(expected.score), "{0:.2f}".format(score * 100)) doc = uc.documents[d] - - assert expected.score == score * 100 + print(doc.url) + assert "{0:.2f}".format(expected.score) == "{0:.2f}".format(score * 100) assert expected.url == doc.url + return True + q1 = uc.query("how do I remove a file") assert verify_query(q1, [ - Results(76.83, "../aima-data/MAN/rm.txt"), - Results(67.83, "../aima-data/MAN/tar.txt"), - Results(67.79, "../aima-data/MAN/cp.txt"), - Results(66.58, "../aima-data/MAN/zip.txt"), - Results(64.58, "../aima-data/MAN/gzip.txt"), - Results(63.74, "../aima-data/MAN/pine.txt"), - Results(62.95, "../aima-data/MAN/shred.txt"), - Results(57.46, "../aima-data/MAN/pico.txt"), - Results(43.38, "../aima-data/MAN/login.txt"), - Results(41.93, "../aima-data/MAN/ln.txt"), + Results(76.83, "aima-data/MAN/rm.txt"), + Results(67.83, "aima-data/MAN/tar.txt"), + Results(67.79, "aima-data/MAN/cp.txt"), + Results(66.58, "aima-data/MAN/zip.txt"), + Results(64.58, "aima-data/MAN/gzip.txt"), + Results(63.74, "aima-data/MAN/pine.txt"), + Results(62.95, "aima-data/MAN/shred.txt"), + Results(57.46, "aima-data/MAN/pico.txt"), + Results(43.38, "aima-data/MAN/login.txt"), + Results(41.93, "aima-data/MAN/ln.txt"), ]) q2 = uc.query("how do I delete a file") assert verify_query(q2, [ - Results(75.47, "../aima-data/MAN/diff.txt"), - Results(69.12, "../aima-data/MAN/pine.txt"), - Results(63.56, "../aima-data/MAN/tar.txt"), - Results(60.63, "../aima-data/MAN/zip.txt"), - Results(57.46, "../aima-data/MAN/pico.txt"), - Results(51.28, "../aima-data/MAN/shred.txt"), - Results(26.72, "../aima-data/MAN/tr.txt"), + Results(75.47, "aima-data/MAN/diff.txt"), + Results(69.12, "aima-data/MAN/pine.txt"), + Results(63.56, "aima-data/MAN/tar.txt"), + Results(60.63, "aima-data/MAN/zip.txt"), + Results(57.46, "aima-data/MAN/pico.txt"), + Results(51.28, "aima-data/MAN/shred.txt"), + Results(26.72, "aima-data/MAN/tr.txt"), ]) q3 = uc.query("email") assert verify_query(q3, [ - Results(18.39, "../aima-data/MAN/pine.txt"), - Results(12.01, "../aima-data/MAN/info.txt"), - Results(9.89, "../aima-data/MAN/pico.txt"), - Results(8.73, "../aima-data/MAN/grep.txt"), - Results(8.07, "../aima-data/MAN/zip.txt"), + Results(18.39, "aima-data/MAN/pine.txt"), + Results(12.01, "aima-data/MAN/info.txt"), + Results(9.89, "aima-data/MAN/pico.txt"), + Results(8.73, "aima-data/MAN/grep.txt"), + Results(8.07, "aima-data/MAN/zip.txt"), ]) - q4 = uc.query("word countrs for files") + q4 = uc.query("word count for files") assert verify_query(q4, [ - Results(112.38, "../aima-data/MAN/grep.txt"), - Results(101.84, "../aima-data/MAN/wc.txt"), - Results(82.46, "../aima-data/MAN/find.txt"), - Results(74.64, "../aima-data/MAN/du.txt"), + Results(128.15, "aima-data/MAN/grep.txt"), + Results(94.20, "aima-data/MAN/find.txt"), + Results(81.71, "aima-data/MAN/du.txt"), + Results(55.45, "aima-data/MAN/ps.txt"), + Results(53.42, "aima-data/MAN/more.txt"), + Results(42.00, "aima-data/MAN/dd.txt"), + Results(12.85, "aima-data/MAN/who.txt"), ]) q5 = uc.query("learn: date") @@ -138,8 +145,8 @@ def verify_query(query, expected): q6 = uc.query("2003") assert verify_query(q6, [ - Results(14.58, "../aima-data/MAN/pine.txt"), - Results(11.62, "../aima-data/MAN/jar.txt"), + Results(14.58, "aima-data/MAN/pine.txt"), + Results(11.62, "aima-data/MAN/jar.txt"), ]) if __name__ == '__main__': diff --git a/utils.py b/utils.py index b35704aa4..81f080301 100644 --- a/utils.py +++ b/utils.py @@ -345,7 +345,7 @@ def AIMAFile(components, mode='r'): def DataFile(name, mode='r'): "Return a file in the AIMA /data directory." - return AIMAFile(['..', 'data', name], mode) + return AIMAFile(['aima-data', name], mode) def unimplemented(): "Use this as a stub for not-yet-implemented functions." From 14c0cfe5aedcde06dd3f009872b7ec110330fd63 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 17:43:29 +0100 Subject: [PATCH 049/513] Fix submodule url to work with Travis --- .gitmodules | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitmodules b/.gitmodules index e15cc9e9a..2e178bf7f 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "aima-data"] path = aima-data - url = https://github.com/aimacode/aima-data + url = git@github.com:aimacode/aima-data.git From e7dfc1a49334521f37506751be8f62793cbcb505 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 17:48:16 +0100 Subject: [PATCH 050/513] Revert "Fix submodule url to work with Travis" This reverts commit 14c0cfe5aedcde06dd3f009872b7ec110330fd63. --- .gitmodules | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitmodules b/.gitmodules index 2e178bf7f..e15cc9e9a 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "aima-data"] path = aima-data - url = git@github.com:aimacode/aima-data.git + url = https://github.com/aimacode/aima-data From 63e0144e8fcc795120a221a2872dffc9ddcf9e18 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 17:53:08 +0100 Subject: [PATCH 051/513] Add submodule update --remote on Travis --- .travis.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.travis.yml b/.travis.yml index 1a29f71f9..ecf6ba14c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,6 +4,9 @@ language: python: - "3.5" +before_install: + - git submodule update --remote + install: - pip install flake8 - pip install -r requirements.txt From 593cfcb3e2d5d0be8cf1e3334ab45b3c60c85c59 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 18:03:59 +0100 Subject: [PATCH 052/513] Pass PEP8 script on all file --- agents.py | 103 ++++++++--- games.py | 40 +++-- grid.py | 19 +- learning.py | 147 ++++++++++----- logic.py | 429 ++++++++++++++++++++++++++++++-------------- nlp.py | 89 ++++----- planning.py | 7 +- probability.py | 87 ++++++--- probability_test.py | 9 +- rl.py | 4 + search.py | 161 +++++++++++++---- text.py | 88 ++++++--- text_test.py | 45 +++-- utils.py | 96 ++++++++-- utils_test.py | 40 ++++- 15 files changed, 964 insertions(+), 400 deletions(-) diff --git a/agents.py b/agents.py index 248015dd6..775ac1211 100644 --- a/agents.py +++ b/agents.py @@ -44,9 +44,11 @@ class Thing(object): + """This represents any physical object that can appear in an Environment. You subclass Thing to get the things you want. Each thing can have a .__name__ slot (used for output only).""" + def __repr__(self): return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__)) @@ -63,7 +65,9 @@ def display(self, canvas, x, y, width, height): "Display an image of this Thing on the canvas." pass + class Agent(Thing): + """An Agent is a subclass of Thing with one required slot, .program, which should hold a function that takes one argument, the percept, and returns an action. (What counts as a percept or action @@ -90,10 +94,12 @@ def can_grab(self, thing): Override for appropriate subclasses of Agent and Thing.""" return False + def TraceAgent(agent): """Wrap the agent's program to print its input and output. This will let you see what the agent is doing in the environment.""" old_program = agent.program + def new_program(percept): action = old_program(percept) print(('{} perceives {} and does {}'.format(agent, percept, action))) @@ -103,24 +109,28 @@ def new_program(percept): #______________________________________________________________________________ + def TableDrivenAgentProgram(table): """This agent selects an action based on the percept sequence. It is practical only for tiny domains. To customize it, provide as table a dictionary of all {percept_sequence:action} pairs. [Fig. 2.7]""" percepts = [] + def program(percept): percepts.append(percept) action = table.get(tuple(percepts)) return action return program + def RandomAgentProgram(actions): "An agent that chooses an action at random, ignoring all percepts." return lambda percept: random.choice(actions) #______________________________________________________________________________ + def SimpleReflexAgentProgram(rules, interpret_input): "This agent takes action based solely on the percept. [Fig. 2.10]" def program(percept): @@ -130,6 +140,7 @@ def program(percept): return action return program + def ModelBasedReflexAgentProgram(rules, update_state): "This agent takes action based on the percept and state. [Fig. 2.12]" def program(percept): @@ -140,6 +151,7 @@ def program(percept): program.state = program.action = None return program + def rule_match(state, rules): "Find the first rule that matches state." for rule in rules: @@ -148,7 +160,7 @@ def rule_match(state, rules): #______________________________________________________________________________ -loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world +loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world def RandomVacuumAgent(): @@ -175,27 +187,37 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): "A reflex agent for the two-state vacuum environment. [Fig. 2.8]" def program(location, status): - if status == 'Dirty': return 'Suck' - elif location == loc_A: return 'Right' - elif location == loc_B: return 'Left' + if status == 'Dirty': + return 'Suck' + elif location == loc_A: + return 'Right' + elif location == loc_B: + return 'Left' return Agent(program) + def ModelBasedVacuumAgent(): "An agent that keeps track of what locations are clean or dirty." model = {loc_A: None, loc_B: None} + def program(location, status): "Same as ReflexVacuumAgent, except if everything is clean, do NoOp." - model[location] = status ## Update the model here - if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp' - elif status == 'Dirty': return 'Suck' - elif location == loc_A: return 'Right' - elif location == loc_B: return 'Left' + model[location] = status # Update the model here + if model[loc_A] == model[loc_B] == 'Clean': + return 'NoOp' + elif status == 'Dirty': + return 'Suck' + elif location == loc_A: + return 'Right' + elif location == loc_B: + return 'Left' return Agent(program) #______________________________________________________________________________ class Environment(object): + """Abstract class representing an Environment. 'Real' Environment classes inherit from this. Your Environment will typically need to implement: percept: Define the percept that an agent sees. @@ -211,7 +233,7 @@ def __init__(self): self.agents = [] def thing_classes(self): - return [] ## List of classes that can go into environment + return [] # List of classes that can go into environment def percept(self, agent): "Return the percept that the agent sees at this point. (Implement this.)" @@ -248,7 +270,8 @@ def step(self): def run(self, steps=1000): "Run the Environment for given number of time steps." for step in range(steps): - if self.is_done(): return + if self.is_done(): + return self.step() def list_things_at(self, location, tclass=Thing): @@ -281,13 +304,16 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print((" Thing to be removed: {} at {}" .format(thing, thing.location))) + print( + (" Thing to be removed: {} at {}" .format(thing, thing.location))) print((" from list: {}" .format([(thing, thing.location) - for thing in self.things]))) + for thing in self.things]))) if thing in self.agents: self.agents.remove(thing) + class XYEnvironment(Environment): + """This class is for environments on a 2D plane, with locations labelled by (x, y) points, either discrete or continuous. @@ -302,7 +328,8 @@ def __init__(self, width=10, height=10): def things_near(self, location, radius=None): "Return all things within radius of location." - if radius is None: radius = self.perceptible_distance + if radius is None: + radius = self.perceptible_distance radius2 = radius * radius return [thing for thing in self.things if distance2(location, thing.location) <= radius2] @@ -331,7 +358,7 @@ def execute_action(self, agent, action): if agent.holding: agent.holding.pop() - def thing_percept(self, thing, agent): #??? Should go to thing? + def thing_percept(self, thing, agent): # ??? Should go to thing? "Return the percept for this thing." return thing.__class__.__name__ @@ -381,21 +408,27 @@ def turn_heading(self, heading, inc): "Return the heading to the left (inc=+1) or right (inc=-1) of heading." return turn_heading(heading, inc) + class Obstacle(Thing): + """Something that can cause a bump, preventing an agent from moving into the same square it's in.""" pass + class Wall(Obstacle): pass #______________________________________________________________________________ -## Vacuum environment +# Vacuum environment + class Dirt(Thing): pass + class VacuumEnvironment(XYEnvironment): + """The environment of [Ex. 2.12]. Agent perceives dirty or clean, and bump (into obstacle) or not; 2D discrete world of unknown size; performance measure is 100 for each dirt cleaned, and -1 for @@ -412,7 +445,8 @@ def thing_classes(self): def percept(self, agent): """The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None'). Unlike the TrivialVacuumEnvironment, location is NOT perceived.""" - status = ('Dirty' if self.some_things_at(agent.location, Dirt) else 'Clean') + status = ('Dirty' if self.some_things_at( + agent.location, Dirt) else 'Clean') bump = ('Bump' if agent.bump else'None') return (status, bump) @@ -429,7 +463,9 @@ def execute_action(self, agent, action): if action != 'NoOp': agent.performance -= 1 + class TrivialVacuumEnvironment(Environment): + """This environment has two locations, A and B. Each can be Dirty or Clean. The agent perceives its location and the location's status. This serves as an example of how to implement a simple @@ -467,13 +503,28 @@ def default_location(self, thing): return random.choice([loc_A, loc_B]) #______________________________________________________________________________ -## The Wumpus World +# The Wumpus World + + +class Gold(Thing): + pass + + +class Pit(Thing): + pass + + +class Arrow(Thing): + pass + + +class Wumpus(Agent): + pass + + +class Explorer(Agent): + pass -class Gold(Thing): pass -class Pit(Thing): pass -class Arrow(Thing): pass -class Wumpus(Agent): pass -class Explorer(Agent): pass class WumpusEnvironment(XYEnvironment): @@ -484,7 +535,7 @@ def __init__(self, width=10, height=10): def thing_classes(self): return [Wall, Gold, Pit, Arrow, Wumpus, Explorer] - ## Needs a lot of work ... + # Needs a lot of work ... #______________________________________________________________________________ @@ -498,6 +549,7 @@ def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000): return [(A, test_agent(A, steps, copy.deepcopy(envs))) for A in AgentFactories] + def test_agent(AgentFactory, steps, envs): "Return the mean score of running an agent in each of the envs, for steps" def score(env): @@ -538,6 +590,3 @@ def score(env): >>> 0.5 < testv(RandomVacuumAgent) < 3 True """ - - - diff --git a/games.py b/games.py index 887de20ad..3141e6d51 100644 --- a/games.py +++ b/games.py @@ -8,6 +8,7 @@ #______________________________________________________________________________ # Minimax Search + def minimax_decision(state, game): """Given a state in a game, calculate the best move by searching forward all the way to the terminal states. [Fig. 5.3]""" @@ -36,13 +37,14 @@ def min_value(state): #______________________________________________________________________________ + def alphabeta_full_search(state, game): """Search game to determine best action; use alpha-beta pruning. As in [Fig. 5.7], this version searches all the way to the leaves.""" player = game.to_move(state) - #Functions used by alphabeta + # Functions used by alphabeta def max_value(state, alpha, beta): if game.terminal_test(state): return game.utility(state, player) @@ -68,13 +70,14 @@ def min_value(state, alpha, beta): # Body of alphabeta_search: return max_value(state, -infinity, infinity) + def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): """Search game to determine best action; use alpha-beta pruning. This version cuts off search and uses an evaluation function.""" player = game.to_move(state) - #Functions used by alphabeta + # Functions used by alphabeta def max_value(state, alpha, beta, depth): if cutoff_test(state, depth): return eval_fn(state) @@ -102,25 +105,29 @@ def min_value(state, alpha, beta, depth): # Body of alphabeta_search starts here: # The default test cuts off at depth d or at a terminal state cutoff_test = (cutoff_test or - (lambda state,depth: depth>d or game.terminal_test(state))) + (lambda state, depth: depth > d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) return max_value(state, -infinity, infinity, 0) #______________________________________________________________________________ # Players for Games + def query_player(game, state): "Make a move by querying standard input." game.display(state) return num_or_str(eval(input('Your move? '))) + def random_player(game, state): "A player that chooses a legal move at random." return random.choice(game.actions(state)) + def alphabeta_player(game, state): return alphabeta_search(state, game) + def play_game(game, *players): """Play an n-person, move-alternating game. >>> play_game(Fig52Game(), alphabeta_player, alphabeta_player) @@ -137,7 +144,9 @@ def play_game(game, *players): #______________________________________________________________________________ # Some Sample Games + class Game: + """A game is similar to a problem, but it has a utility for each state and a terminal test instead of a path cost and a goal test. To create a game, subclass this class and implement actions, @@ -173,7 +182,9 @@ def display(self, state): def __repr__(self): return '<%s>' % self.__class__.__name__ + class Fig52Game(Game): + """The game represented in [Fig. 5.2]. Serves as a simple test case. >>> g = Fig52Game() >>> minimax_decision('A', g) @@ -208,11 +219,14 @@ def terminal_test(self, state): def to_move(self, state): return ('MIN' if state in 'BCD' else 'MAX') + class TicTacToe(Game): + """Play TicTacToe on an h x v board, with Max (first player) playing 'X'. A state has the player to move, a cached utility, a list of moves in the form of a list of (x, y) positions, and a board, in the form of a dict of {(x, y): Player} entries, where Player is 'X' or 'O'.""" + def __init__(self, h=3, v=3, k=3): update(self, h=h, v=v, k=k) moves = [(x, y) for x in range(1, h+1) @@ -225,9 +239,11 @@ def actions(self, state): def result(self, state, move): if move not in state.moves: - return state # Illegal move has no effect - board = state.board.copy(); board[move] = state.to_move - moves = list(state.moves); moves.remove(move) + return state # Illegal move has no effect + board = state.board.copy() + board[move] = state.to_move + moves = list(state.moves) + moves.remove(move) return Struct(to_move=('O' if state.to_move == 'X' else 'X'), utility=self.compute_utility(board, move, state.to_move), board=board, moves=moves) @@ -250,9 +266,9 @@ def display(self, state): def compute_utility(self, board, move, player): "If X wins with this move, return 1; if O return -1; else return 0." if (self.k_in_row(board, move, player, (0, 1)) or - self.k_in_row(board, move, player, (1, 0)) or - self.k_in_row(board, move, player, (1, -1)) or - self.k_in_row(board, move, player, (1, 1))): + self.k_in_row(board, move, player, (1, 0)) or + self.k_in_row(board, move, player, (1, -1)) or + self.k_in_row(board, move, player, (1, 1))): return (+1 if player == 'X' else -1) else: return 0 @@ -261,7 +277,7 @@ def k_in_row(self, board, move, player, xxx_todo_changeme): "Return true if there is a line through move on board for player." (delta_x, delta_y) = xxx_todo_changeme x, y = move - n = 0 # n is number of moves in row + n = 0 # n is number of moves in row while board.get((x, y)) == player: n += 1 x, y = x + delta_x, y + delta_y @@ -269,10 +285,12 @@ def k_in_row(self, board, move, player, xxx_todo_changeme): while board.get((x, y)) == player: n += 1 x, y = x - delta_x, y - delta_y - n -= 1 # Because we counted move itself twice + n -= 1 # Because we counted move itself twice return n >= self.k + class ConnectFour(TicTacToe): + """A TicTacToe-like game in which you can only make a move on the bottom row, or in a square directly above an occupied square. Traditionally played on a 7x6 board and requiring 4 in a row.""" diff --git a/grid.py b/grid.py index 8fbbb8c46..45bce40f3 100644 --- a/grid.py +++ b/grid.py @@ -1,21 +1,25 @@ -## OK, the following are not as widely useful utilities as some of the other -## functions here, but they do show up wherever we have 2D grids: Wumpus and -## Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. -##__________________________________________________________________________ +# OK, the following are not as widely useful utilities as some of the other +# functions here, but they do show up wherever we have 2D grids: Wumpus and +# Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. +# __________________________________________________________________________ import math orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)] + def turn_heading(heading, inc, headings=orientations): return headings[(headings.index(heading) + inc) % len(headings)] + def turn_right(heading): return turn_heading(heading, -1) + def turn_left(heading): return turn_heading(heading, +1) + def distance(a, b): """The distance between two (x, y) points. >>> distance((1,2),(5,5)) @@ -23,17 +27,20 @@ def distance(a, b): """ return math.hypot((a[0] - b[0]), (a[1] - b[1])) + def distance_squared(a, b): """The square of the distance between two (x, y) points. >>> distance_squared((1,2),(5,5)) 25.0 """ - return (a[0]- b[0])**2 + (a[1] - b[1])**2 + return (a[0] - b[0])**2 + (a[1] - b[1])**2 + def distance2(a, b): "The square of the distance between two (x, y) points." return distance_squared(a, b) - + + def clip(x, lowest, highest): """Return x clipped to the range [lowest..highest]. >>> [clip(x, 0, 1) for x in [-1, 0.5, 10]] diff --git a/learning.py b/learning.py index e0fcc3639..247abd2e3 100644 --- a/learning.py +++ b/learning.py @@ -2,26 +2,35 @@ from utils import * -import copy, heapq, math, random +import copy +import heapq +import math +import random from collections import defaultdict #______________________________________________________________________________ + def rms_error(predictions, targets): return math.sqrt(ms_error(predictions, targets)) + def ms_error(predictions, targets): return mean([(p - t)**2 for p, t in zip(predictions, targets)]) + def mean_error(predictions, targets): return mean([abs(p - t) for p, t in zip(predictions, targets)]) + def mean_boolean_error(predictions, targets): - return mean([(p != t) for p, t in zip(predictions, targets)]) + return mean([(p != t) for p, t in zip(predictions, targets)]) #______________________________________________________________________________ + class DataSet: + """A data set for a machine learning problem. It has the following fields: d.examples A list of examples. Each one is a list of attribute values. @@ -53,7 +62,8 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, >>> DataSet(examples='1, 2, 3') """ - update(self, name=name, source=source, values=values, distance=distance) + update(self, name=name, source=source, + values=values, distance=distance) # Initialize .examples from string or list or data directory if isinstance(examples, str): self.examples = parse_csv(examples) @@ -120,9 +130,9 @@ def attrnum(self, attr): return attr def sanitize(self, example): - "Return a copy of example, with non-input attributes replaced by None." - return [attr_i if i in self.inputs else None - for i, attr_i in enumerate(example)] + "Return a copy of example, with non-input attributes replaced by None." + return [attr_i if i in self.inputs else None + for i, attr_i in enumerate(example)] def __repr__(self): return '' % ( @@ -130,6 +140,7 @@ def __repr__(self): #______________________________________________________________________________ + def parse_csv(input, delim=','): r"""Input is a string consisting of lines, each line has comma-delimited fields. Convert this into a list of lists. Blank lines are skipped. @@ -143,7 +154,9 @@ def parse_csv(input, delim=','): #______________________________________________________________________________ + class CountingProbDist: + """A probability distribution formed by observing and counting examples. If p is an instance of this class and o is an observed value, then there are 3 main operations: @@ -194,10 +207,12 @@ def sample(self): #______________________________________________________________________________ + def PluralityLearner(dataset): """A very dumb algorithm: always pick the result that was most popular in the training data. Makes a baseline for comparison.""" most_popular = mode([e[dataset.target] for e in dataset.examples]) + def predict(example): "Always return same result: the most popular from the training set." return most_popular @@ -205,6 +220,7 @@ def predict(example): #______________________________________________________________________________ + def NaiveBayesLearner(dataset): """Just count how many times each value of each input attribute occurs, conditional on the target value. Count the different @@ -234,6 +250,7 @@ def class_probability(targetval): #______________________________________________________________________________ + def NearestNeighborLearner(dataset, k=1): "k-NearestNeighbor: the k nearest neighbors vote." def predict(example): @@ -245,7 +262,9 @@ def predict(example): #______________________________________________________________________________ + class DecisionFork: + """A fork of a decision tree holds an attribute to test, and a dict of branches, one for each of the attribute's values.""" @@ -273,8 +292,10 @@ def display(self, indent=0): def __repr__(self): return ('DecisionFork(%r, %r, %r)' % (self.attr, self.attrname, self.branches)) - + + class DecisionLeaf: + "A leaf of a decision tree holds just a result." def __init__(self, result): @@ -288,9 +309,10 @@ def display(self, indent=0): def __repr__(self): return repr(self.result) - + #______________________________________________________________________________ + def DecisionTreeLearner(dataset): "[Fig. 18.5]" @@ -349,6 +371,7 @@ def split_by(attr, examples): return decision_tree_learning(dataset.examples, dataset.inputs) + def information_content(values): "Number of bits to represent the probability distribution in values." probabilities = normalize(removeall(0, values)) @@ -356,7 +379,8 @@ def information_content(values): #______________________________________________________________________________ -### A decision list is implemented as a list of (test, value) pairs. +# A decision list is implemented as a list of (test, value) pairs. + def DecisionListLearner(dataset): """[Fig. 18.11]""" @@ -389,37 +413,45 @@ def predict(example): #______________________________________________________________________________ + def NeuralNetLearner(dataset, sizes): - """Layered feed-forward network.""" + """Layered feed-forward network.""" - activations = [[0.0 for i in range(n)] for n in sizes] - weights = [] + activations = [[0.0 for i in range(n)] for n in sizes] + weights = [] - def predict(example): - unimplemented() + def predict(example): + unimplemented() + + return predict - return predict class NNUnit: - """Unit of a neural net.""" - def __init__(self): - unimplemented() + + """Unit of a neural net.""" + + def __init__(self): + unimplemented() + def PerceptronLearner(dataset, sizes): - def predict(example): - return sum([]) - unimplemented() + def predict(example): + return sum([]) + unimplemented() #______________________________________________________________________________ + def Linearlearner(dataset): - """Fit a linear model to the data.""" - unimplemented() + """Fit a linear model to the data.""" + unimplemented() #______________________________________________________________________________ + def EnsembleLearner(learners): """Given a list of learning algorithms, have them vote.""" def train(dataset): predictors = [learner(dataset) for learner in learners] + def predict(example): return mode(predictor(example) for predictor in predictors) return predict @@ -427,6 +459,7 @@ def predict(example): #______________________________________________________________________________ + def AdaBoost(L, K): """[Fig. 18.34]""" def train(dataset): @@ -450,6 +483,7 @@ def train(dataset): return WeightedMajority(h, z) return train + def WeightedMajority(predictors, weights): "Return a predictor that takes a weighted vote." def predict(example): @@ -457,6 +491,7 @@ def predict(example): weights) return predict + def weighted_mode(values, weights): """Return the value with the greatest total weight. >>> weighted_mode('abbaa', [1,2,3,1,2]) @@ -469,6 +504,7 @@ def weighted_mode(values, weights): #_____________________________________________________________________________ # Adapting an unweighted learner for AdaBoost + def WeightedLearner(unweighted_learner): """Given a learner that takes just an unweighted dataset, return one that takes also a weight for each example. [p. 749 footnote 14]""" @@ -476,6 +512,7 @@ def train(dataset, weights): return unweighted_learner(replicated_dataset(dataset, weights)) return train + def replicated_dataset(dataset, weights, n=None): "Copy dataset, replicating each example in proportion to its weight." n = n or len(dataset.examples) @@ -483,6 +520,7 @@ def replicated_dataset(dataset, weights, n=None): result.examples = weighted_replicate(dataset.examples, weights, n) return result + def weighted_replicate(seq, weights, n): """Return n selections from seq, with the count of each element of seq proportional to the corresponding weight (filling in fractions @@ -496,15 +534,19 @@ def weighted_replicate(seq, weights, n): return (flatten([x] * nx for x, nx in zip(seq, wholes)) + weighted_sample_with_replacement(seq, fractions, n - sum(wholes))) + def flatten(seqs): return sum(seqs, []) #_____________________________________________________________________________ # Functions for testing learners on examples + def test(predict, dataset, examples=None, verbose=0): "Return the proportion of the examples that are correctly predicted." - if examples is None: examples = dataset.examples - if len(examples) == 0: return 0.0 + if examples is None: + examples = dataset.examples + if len(examples) == 0: + return 0.0 right = 0.0 for example in examples: desired = example[dataset.target] @@ -512,12 +554,13 @@ def test(predict, dataset, examples=None, verbose=0): if output == desired: right += 1 if verbose >= 2: - print(' OK: got %s for %s' % (desired, example)) + print(' OK: got %s for %s' % (desired, example)) elif verbose: print('WRONG: got %s, expected %s for %s' % ( - output, desired, example)) + output, desired, example)) return right / len(examples) + def train_and_test(learner, dataset, start, end): """Reserve dataset.examples[start:end] for test; train on the remainder. Return the proportion of examples correct on the test examples.""" @@ -528,6 +571,7 @@ def train_and_test(learner, dataset, start, end): finally: dataset.examples = examples + def cross_validation(learner, dataset, k=10, trials=1): """Do k-fold cross_validate and return their mean. That is, keep out 1/k of the examples for testing on each of k runs. @@ -543,13 +587,16 @@ def cross_validation(learner, dataset, k=10, trials=1): return mean([train_and_test(learner, dataset, i*(n/k), (i+1)*(n/k)) for i in range(k)]) + def leave1out(learner, dataset): "Leave one out cross-validation over the dataset." return cross_validation(learner, dataset, k=len(dataset.examples)) + def learningcurve(learner, dataset, trials=10, sizes=None): if sizes is None: sizes = list(range(2, len(dataset.examples)-10, 2)) + def score(learner, size): random.shuffle(dataset.examples) return train_and_test(learner, dataset, 0, size) @@ -575,36 +622,38 @@ def score(learner, size): #______________________________________________________________________________ # The Restaurant example from Fig. 18.2 + def RestaurantDataSet(examples=None): "Build a DataSet of Restaurant waiting examples. [Fig. 18.3]" return DataSet(name='restaurant', target='Wait', examples=examples, attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' - + 'Raining Reservation Type WaitEstimate Wait') + + 'Raining Reservation Type WaitEstimate Wait') restaurant = RestaurantDataSet() + def T(attrname, branches): branches = dict((value, (child if isinstance(child, DecisionFork) else DecisionLeaf(child))) for value, child in list(branches.items())) return DecisionFork(restaurant.attrnum(attrname), attrname, branches) -Fig[18,2] = T('Patrons', - {'None': 'No', 'Some': 'Yes', 'Full': - T('WaitEstimate', - {'>60': 'No', '0-10': 'Yes', - '30-60': - T('Alternate', {'No': - T('Reservation', {'Yes': 'Yes', 'No': - T('Bar', {'No':'No', - 'Yes':'Yes'})}), - 'Yes': - T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})}), - '10-30': - T('Hungry', {'No': 'Yes', 'Yes': - T('Alternate', - {'No': 'Yes', 'Yes': - T('Raining', {'No': 'No', 'Yes': 'Yes'})})})})}) +Fig[18, 2] = T('Patrons', + {'None': 'No', 'Some': 'Yes', 'Full': + T('WaitEstimate', + {'>60': 'No', '0-10': 'Yes', + '30-60': + T('Alternate', {'No': + T('Reservation', {'Yes': 'Yes', 'No': + T('Bar', {'No': 'No', + 'Yes': 'Yes'})}), + 'Yes': + T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})}), + '10-30': + T('Hungry', {'No': 'Yes', 'Yes': + T('Alternate', + {'No': 'Yes', 'Yes': + T('Raining', {'No': 'No', 'Yes': 'Yes'})})})})}) __doc__ += """ [Fig. 18.6] @@ -625,17 +674,19 @@ def T(attrname, branches): Patrons = Some ==> RESULT = Yes """ + def SyntheticRestaurant(n=20): "Generate a DataSet with n examples." def gen(): example = list(map(random.choice, restaurant.values)) - example[restaurant.target] = Fig[18,2](example) + example[restaurant.target] = Fig[18, 2](example) return example return RestaurantDataSet([gen() for i in range(n)]) #______________________________________________________________________________ # Artificial, generated datasets. + def Majority(k, n): """Return a DataSet with n k-bit examples of the majority problem: k random bits followed by a 1 if more than half the bits are 1, else 0.""" @@ -646,6 +697,7 @@ def Majority(k, n): examples.append(bits) return DataSet(name="majority", examples=examples) + def Parity(k, n, name="parity"): """Return a DataSet with n k-bit examples of the parity problem: k random bits followed by a 1 if an odd number of bits are 1, else 0.""" @@ -656,10 +708,12 @@ def Parity(k, n, name="parity"): examples.append(bits) return DataSet(name=name, examples=examples) + def Xor(n): """Return a DataSet with n examples of 2-input xor.""" return Parity(2, n, name="xor") + def ContinuousXor(n): "2 inputs are chosen uniformly from (0.0 .. 2.0]; output is xor of ints." examples = [] @@ -670,6 +724,7 @@ def ContinuousXor(n): #______________________________________________________________________________ + def compare(algorithms=[PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, DecisionTreeLearner], datasets=[iris, orings, zoo, restaurant, SyntheticRestaurant(20), @@ -677,7 +732,7 @@ def compare(algorithms=[PluralityLearner, NaiveBayesLearner, k=10, trials=1): """Compare various learners on various datasets using cross-validation. Print results as a table.""" - print_table([[a.__name__.replace('Learner','')] + + print_table([[a.__name__.replace('Learner', '')] + [cross_validation(a, d, k, trials) for d in datasets] for a in algorithms], header=[''] + [d.name[0:7] for d in datasets], numfmt='%.2f') diff --git a/logic.py b/logic.py index 478725255..f5531ae99 100644 --- a/logic.py +++ b/logic.py @@ -32,7 +32,9 @@ #______________________________________________________________________________ + class KB: + """A knowledge base to which you can tell and ask sentences. To create a KB, first subclass this class and implement tell, ask_generator, and retract. Why ask_generator instead of ask? @@ -67,6 +69,7 @@ def retract(self, sentence): class PropKB(KB): + "A KB for propositional logic. Inefficient, with no indexing." def __init__(self, sentence=None): @@ -91,6 +94,7 @@ def retract(self, sentence): #______________________________________________________________________________ + def KB_AgentProgram(KB): """A generic logical knowledge-based agent program. [Fig. 7.1]""" steps = itertools.count() @@ -115,7 +119,9 @@ def make_action_sentence(self, action, t): #______________________________________________________________________________ + class Expr: + """A symbolic mathematical expression. We use this class for logical expressions, and for terms within logical expressions. In general, an Expr has an op (operator) and a list of args. The op can be: @@ -165,7 +171,7 @@ def __init__(self, op, *args): "Op is a string or number; args are Exprs (or are coerced to Exprs)." assert isinstance(op, str) or (isnumber(op) and not args) self.op = num_or_str(op) - self.args = list(map(expr, args)) ## Coerce args to Exprs + self.args = list(map(expr, args)) # Coerce args to Exprs def __call__(self, *args): """Self must be a symbol with no args, such as Expr('F'). Create a new @@ -179,7 +185,7 @@ def __repr__(self): return str(self.op) elif is_symbol(self.op): # Functional or propositional operator return '%s(%s)' % (self.op, ', '.join(map(repr, self.args))) - elif len(self.args) == 1: # Prefix operator + elif len(self.args) == 1: # Prefix operator return self.op + repr(self.args[0]) else: # Infix operator return '(%s)' % (' '+self.op+' ').join(map(repr, self.args)) @@ -187,7 +193,7 @@ def __repr__(self): def __eq__(self, other): """x and y are equal iff their ops and args are equal.""" return (other is self) or (isinstance(other, Expr) - and self.op == other.op and self.args == other.args) + and self.op == other.op and self.args == other.args) def __ne__(self, other): return not self.__eq__(other) @@ -198,25 +204,41 @@ def __hash__(self): # See http://www.python.org/doc/current/lib/module-operator.html # Not implemented: not, abs, pos, concat, contains, *item, *slice - def __lt__(self, other): return Expr('<', self, other) - def __le__(self, other): return Expr('<=', self, other) - def __ge__(self, other): return Expr('>=', self, other) - def __gt__(self, other): return Expr('>', self, other) - def __add__(self, other): return Expr('+', self, other) - def __sub__(self, other): return Expr('-', self, other) - def __and__(self, other): return Expr('&', self, other) - def __div__(self, other): return Expr('/', self, other) - def __truediv__(self, other):return Expr('/', self, other) - def __invert__(self): return Expr('~', self) + def __lt__(self, other): return Expr('<', self, other) + + def __le__(self, other): return Expr('<=', self, other) + + def __ge__(self, other): return Expr('>=', self, other) + + def __gt__(self, other): return Expr('>', self, other) + + def __add__(self, other): return Expr('+', self, other) + + def __sub__(self, other): return Expr('-', self, other) + + def __and__(self, other): return Expr('&', self, other) + + def __div__(self, other): return Expr('/', self, other) + + def __truediv__(self, other): return Expr('/', self, other) + + def __invert__(self): return Expr('~', self) + def __lshift__(self, other): return Expr('<<', self, other) + def __rshift__(self, other): return Expr('>>', self, other) - def __mul__(self, other): return Expr('*', self, other) - def __neg__(self): return Expr('-', self) - def __or__(self, other): return Expr('|', self, other) - def __pow__(self, other): return Expr('**', self, other) - def __xor__(self, other): return Expr('^', self, other) - def __mod__(self, other): return Expr('<=>', self, other) + def __mul__(self, other): return Expr('*', self, other) + + def __neg__(self): return Expr('-', self) + + def __or__(self, other): return Expr('|', self, other) + + def __pow__(self, other): return Expr('**', self, other) + + def __xor__(self, other): return Expr('^', self, other) + + def __mod__(self, other): return Expr('<=>', self, other) def expr(s): @@ -234,29 +256,35 @@ def expr(s): >>> expr('P & Q | ~R(x, F(x))') ((P & Q) | ~R(x, F(x))) """ - if isinstance(s, Expr): return s - if isnumber(s): return Expr(s) - ## Replace the alternative spellings of operators with canonical spellings + if isinstance(s, Expr): + return s + if isnumber(s): + return Expr(s) + # Replace the alternative spellings of operators with canonical spellings s = s.replace('==>', '>>').replace('<==', '<<') s = s.replace('<=>', '%').replace('=/=', '^') - ## Replace a symbol or number, such as 'P' with 'Expr("P")' + # Replace a symbol or number, such as 'P' with 'Expr("P")' s = re.sub(r'([a-zA-Z0-9_.]+)', r'Expr("\1")', s) - ## Now eval the string. (A security hole; do not use with an adversary.) - return eval(s, {'Expr':Expr}) + # Now eval the string. (A security hole; do not use with an adversary.) + return eval(s, {'Expr': Expr}) + def is_symbol(s): "A string s is a symbol if it starts with an alphabetic char." return isinstance(s, str) and s[:1].isalpha() + def is_var_symbol(s): "A logic variable symbol is an initial-lowercase string." return is_symbol(s) and s[0].islower() + def is_prop_symbol(s): """A proposition logic symbol is an initial-uppercase string other than TRUE or FALSE.""" return is_symbol(s) and s[0].isupper() and s != 'TRUE' and s != 'FALSE' + def variables(s): """Return a set of the variables in expression s. >>> ppset(variables(F(x, A, y))) @@ -267,6 +295,7 @@ def variables(s): set([x, y, z]) """ result = set([]) + def walk(s): if is_variable(s): result.add(s) @@ -276,6 +305,7 @@ def walk(s): walk(s) return result + def is_definite_clause(s): """returns True for exprs s of the form A & B & ... & C ==> D, where all literals are positive. In clause form, this is @@ -300,6 +330,7 @@ def is_definite_clause(s): else: return False + def parse_definite_clause(s): "Return the antecedents and the consequent of a definite clause." assert is_definite_clause(s) @@ -309,12 +340,13 @@ def parse_definite_clause(s): antecedent, consequent = s.args return conjuncts(antecedent), consequent -## Useful constant Exprs used in examples and code: +# Useful constant Exprs used in examples and code: TRUE, FALSE, ZERO, ONE, TWO = list(map(Expr, ['TRUE', 'FALSE', 0, 1, 2])) -A, B, C, D, E, F, G, P, Q, x, y, z = list(map(Expr, 'ABCDEFGPQxyz')) +A, B, C, D, E, F, G, P, Q, x, y, z = list(map(Expr, 'ABCDEFGPQxyz')) #______________________________________________________________________________ + def tt_entails(kb, alpha): """Does kb entail the sentence alpha? Use truth tables. For propositional kb's and sentences. [Fig. 7.10] @@ -324,6 +356,7 @@ def tt_entails(kb, alpha): assert not variables(alpha) return tt_check_all(kb, alpha, prop_symbols(kb & alpha), {}) + def tt_check_all(kb, alpha, symbols, model): "Auxiliary routine to implement tt_entails." if not symbols: @@ -338,6 +371,7 @@ def tt_check_all(kb, alpha, symbols, model): return (tt_check_all(kb, alpha, rest, extend(model, P, True)) and tt_check_all(kb, alpha, rest, extend(model, P, False))) + def prop_symbols(x): "Return a list of all propositional symbols in x." if not isinstance(x, Expr): @@ -348,6 +382,7 @@ def prop_symbols(x): return list(set(symbol for arg in x.args for symbol in prop_symbols(arg))) + def tt_true(alpha): """Is the propositional sentence alpha a tautology? (alpha will be coerced to an expr.) @@ -356,6 +391,7 @@ def tt_true(alpha): """ return tt_entails(TRUE, expr(alpha)) + def pl_true(exp, model={}): """Return True if the propositional logic expression is true in the model, and False if it is false. If the model does not specify the value for @@ -370,21 +406,27 @@ def pl_true(exp, model={}): return model.get(exp) elif op == '~': p = pl_true(args[0], model) - if p is None: return None - else: return not p + if p is None: + return None + else: + return not p elif op == '|': result = False for arg in args: p = pl_true(arg, model) - if p is True: return True - if p is None: result = None + if p is True: + return True + if p is None: + result = None return result elif op == '&': result = True for arg in args: p = pl_true(arg, model) - if p is False: return False - if p is None: result = None + if p is False: + return False + if p is None: + result = None return result p, q = args if op == '>>': @@ -392,9 +434,11 @@ def pl_true(exp, model={}): elif op == '<<': return pl_true(p | ~q, model) pt = pl_true(p, model) - if pt is None: return None + if pt is None: + return None qt = pl_true(q, model) - if qt is None: return None + if qt is None: + return None if op == '<=>': return pt == qt elif op == '^': @@ -404,7 +448,8 @@ def pl_true(exp, model={}): #______________________________________________________________________________ -## Convert to Conjunctive Normal Form (CNF) +# Convert to Conjunctive Normal Form (CNF) + def to_cnf(s): """Convert a propositional logical sentence s to conjunctive normal form. @@ -420,10 +465,12 @@ def to_cnf(s): >>> to_cnf("A | (B | (C | (D & E)))") ((D | A | B | C) & (E | A | B | C)) """ - if isinstance(s, str): s = expr(s) - s = eliminate_implications(s) # Steps 1, 2 from p. 253 - s = move_not_inwards(s) # Step 3 - return distribute_and_over_or(s) # Step 4 + if isinstance(s, str): + s = expr(s) + s = eliminate_implications(s) # Steps 1, 2 from p. 253 + s = move_not_inwards(s) # Step 3 + return distribute_and_over_or(s) # Step 4 + def eliminate_implications(s): """Change >>, <<, and <=> into &, |, and ~. That is, return an Expr @@ -433,7 +480,8 @@ def eliminate_implications(s): >>> eliminate_implications(A ^ B) ((A & ~B) | (~A & B)) """ - if not s.args or is_symbol(s.op): return s ## (Atoms are unchanged.) + if not s.args or is_symbol(s.op): + return s # (Atoms are unchanged.) args = list(map(eliminate_implications, s.args)) a, b = args[0], args[-1] if s.op == '>>': @@ -443,12 +491,13 @@ def eliminate_implications(s): elif s.op == '<=>': return (a | ~b) & (b | ~a) elif s.op == '^': - assert len(args) == 2 ## TODO: relax this restriction + assert len(args) == 2 # TODO: relax this restriction return (a & ~b) | (~a & b) else: assert s.op in ('&', '|', '~') return Expr(s.op, *args) + def move_not_inwards(s): """Rewrite sentence s by moving negation sign inward. >>> move_not_inwards(~(A | B)) @@ -461,15 +510,19 @@ def move_not_inwards(s): if s.op == '~': NOT = lambda b: move_not_inwards(~b) a = s.args[0] - if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A - if a.op =='&': return associate('|', list(map(NOT, a.args))) - if a.op =='|': return associate('&', list(map(NOT, a.args))) + if a.op == '~': + return move_not_inwards(a.args[0]) # ~~A ==> A + if a.op == '&': + return associate('|', list(map(NOT, a.args))) + if a.op == '|': + return associate('&', list(map(NOT, a.args))) return s elif is_symbol(s.op) or not s.args: return s else: return Expr(s.op, *list(map(move_not_inwards, s.args))) + def distribute_and_over_or(s): """Given a sentence s consisting of conjunctions and disjunctions of literals, return an equivalent sentence in CNF. @@ -489,13 +542,14 @@ def distribute_and_over_or(s): return s others = [a for a in s.args if a is not conj] rest = associate('|', others) - return associate('&', [distribute_and_over_or(c|rest) + return associate('&', [distribute_and_over_or(c | rest) for c in conj.args]) elif s.op == '&': return associate('&', list(map(distribute_and_over_or, s.args))) else: return s + def associate(op, args): """Given an associative op, return an expression with the same meaning as Expr(op, *args), but flattened -- that is, with nested @@ -513,19 +567,24 @@ def associate(op, args): else: return Expr(op, *args) -_op_identity = {'&':TRUE, '|':FALSE, '+':ZERO, '*':ONE} +_op_identity = {'&': TRUE, '|': FALSE, '+': ZERO, '*': ONE} + def dissociate(op, args): """Given an associative op, return a flattened list result such that Expr(op, *result) means the same as Expr(op, *args).""" result = [] + def collect(subargs): for arg in subargs: - if arg.op == op: collect(arg.args) - else: result.append(arg) + if arg.op == op: + collect(arg.args) + else: + result.append(arg) collect(args) return result + def conjuncts(s): """Return a list of the conjuncts in the sentence s. >>> conjuncts(A & B) @@ -535,6 +594,7 @@ def conjuncts(s): """ return dissociate('&', [s]) + def disjuncts(s): """Return a list of the disjuncts in the sentence s. >>> disjuncts(A | B) @@ -546,6 +606,7 @@ def disjuncts(s): #______________________________________________________________________________ + def pl_resolution(KB, alpha): "Propositional-logic resolution: say if alpha follows from KB. [Fig. 7.12]" clauses = KB.clauses + conjuncts(to_cnf(~alpha)) @@ -556,11 +617,15 @@ def pl_resolution(KB, alpha): for i in range(n) for j in range(i+1, n)] for (ci, cj) in pairs: resolvents = pl_resolve(ci, cj) - if FALSE in resolvents: return True + if FALSE in resolvents: + return True new = new.union(set(resolvents)) - if new.issubset(set(clauses)): return False + if new.issubset(set(clauses)): + return False for c in new: - if c not in clauses: clauses.append(c) + if c not in clauses: + clauses.append(c) + def pl_resolve(ci, cj): """Return all clauses that can be obtained by resolving clauses ci and cj. @@ -580,7 +645,9 @@ def pl_resolve(ci, cj): #______________________________________________________________________________ + class PropDefiniteKB(PropKB): + "A KB of propositional definite clauses." def tell(self, sentence): @@ -602,6 +669,7 @@ def clauses_with_premise(self, p): return [c for c in self.clauses if c.op == '>>' and p in conjuncts(c.args[0])] + def pl_fc_entails(KB, q): """Use forward chaining to see if a PropDefiniteKB entails symbol q. [Fig. 7.15] @@ -609,12 +677,13 @@ def pl_fc_entails(KB, q): True """ count = dict([(c, len(conjuncts(c.args[0]))) for c in KB.clauses - if c.op == '>>']) + if c.op == '>>']) inferred = defaultdict(bool) agenda = [s for s in KB.clauses if is_prop_symbol(s.op)] while agenda: p = agenda.pop() - if p == q: return True + if p == q: + return True if not inferred[p]: inferred[p] = True for c in KB.clauses_with_premise(p): @@ -623,17 +692,18 @@ def pl_fc_entails(KB, q): agenda.append(c.args[1]) return False -## Wumpus World example [Fig. 7.13] -Fig[7,13] = expr("(B11 <=> (P12 | P21)) & ~B11") +# Wumpus World example [Fig. 7.13] +Fig[7, 13] = expr("(B11 <=> (P12 | P21)) & ~B11") -## Propositional Logic Forward Chaining example [Fig. 7.16] -Fig[7,15] = PropDefiniteKB() +# Propositional Logic Forward Chaining example [Fig. 7.16] +Fig[7, 15] = PropDefiniteKB() for s in "P>>Q (L&M)>>P (B&L)>>M (A&P)>>L (A&B)>>L A B".split(): - Fig[7,15].tell(expr(s)) + Fig[7, 15].tell(expr(s)) #______________________________________________________________________________ # DPLL-Satisfiable [Fig. 7.17] + def dpll_satisfiable(s): """Check satisfiability of a propositional sentence. This differs from the book code in two ways: (1) it returns a model @@ -649,11 +719,12 @@ def dpll_satisfiable(s): symbols = prop_symbols(s) return dpll(clauses, symbols, {}) + def dpll(clauses, symbols, model): "See if the clauses are true in a partial model." - unknown_clauses = [] ## clauses with an unknown truth value + unknown_clauses = [] # clauses with an unknown truth value for c in clauses: - val = pl_true(c, model) + val = pl_true(c, model) if val == False: return False if val != True: @@ -672,6 +743,7 @@ def dpll(clauses, symbols, model): return (dpll(clauses, symbols, extend(model, P, True)) or dpll(clauses, symbols, extend(model, P, False))) + def find_pure_symbol(symbols, clauses): """Find a symbol and its value if it appears only as a positive literal (or only as a negative) in clauses. @@ -681,11 +753,15 @@ def find_pure_symbol(symbols, clauses): for s in symbols: found_pos, found_neg = False, False for c in clauses: - if not found_pos and s in disjuncts(c): found_pos = True - if not found_neg and ~s in disjuncts(c): found_neg = True - if found_pos != found_neg: return s, found_pos + if not found_pos and s in disjuncts(c): + found_pos = True + if not found_neg and ~s in disjuncts(c): + found_neg = True + if found_pos != found_neg: + return s, found_pos return None, None + def find_unit_clause(clauses, model): """Find a forced assignment if possible from a clause with only 1 variable not bound in the model. @@ -694,9 +770,11 @@ def find_unit_clause(clauses, model): """ for clause in clauses: P, value = unit_clause_assign(clause, model) - if P: return P, value + if P: + return P, value return None, None + def unit_clause_assign(clause, model): """Return a single variable/value pair that makes clause true in the model, if possible. @@ -719,6 +797,7 @@ def unit_clause_assign(clause, model): P, value = sym, positive return P, value + def inspect_literal(literal): """The symbol in this literal, and the value it should take to make the literal true. @@ -735,37 +814,44 @@ def inspect_literal(literal): #______________________________________________________________________________ # Walk-SAT [Fig. 7.18] + def WalkSAT(clauses, p=0.5, max_flips=10000): - ## model is a random assignment of true/false to the symbols in clauses - ## See ~/aima1e/print1/manual/knowledge+logic-answers.tex ??? + # model is a random assignment of true/false to the symbols in clauses + # See ~/aima1e/print1/manual/knowledge+logic-answers.tex ??? model = dict([(s, random.choice([True, False])) - for s in prop_symbols(clauses)]) + for s in prop_symbols(clauses)]) for i in range(max_flips): satisfied, unsatisfied = [], [] for clause in clauses: - (satisfied if pl_true(clause, model) else unsatisfied).append(clause) - if not unsatisfied: ## if model satisfies all the clauses + (satisfied if pl_true(clause, model) else unsatisfied).append( + clause) + if not unsatisfied: # if model satisfies all the clauses return model clause = random.choice(unsatisfied) if probability(p): sym = random.choice(prop_symbols(clause)) else: - ## Flip the symbol in clause that maximizes number of sat. clauses + # Flip the symbol in clause that maximizes number of sat. clauses raise NotImplementedError model[sym] = not model[sym] #______________________________________________________________________________ + class HybridWumpusAgent(agents.Agent): + "An agent for the wumpus world that does logical inference. [Fig. 7.19]""" + def __init__(self): unimplemented() + def plan_route(current, goals, allowed): unimplemented() #______________________________________________________________________________ + def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): "[Fig. 7.22]" for t in range(t_max): @@ -775,14 +861,17 @@ def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): return extract_solution(model) return None + def translate_to_SAT(init, transition, goal, t): unimplemented() + def extract_solution(model): unimplemented() #______________________________________________________________________________ + def unify(x, y, s): """Unify expressions x,y with substitution s; return a substitution that would make x,y equal, or None if x,y can not unify. x and y can be @@ -803,15 +892,18 @@ def unify(x, y, s): elif isinstance(x, str) or isinstance(y, str): return None elif issequence(x) and issequence(y) and len(x) == len(y): - if not x: return s + if not x: + return s return unify(x[1:], y[1:], unify(x[0], y[0], s)) else: return None + def is_variable(x): "A variable is an Expr with no args and a lowercase symbol as the op." return isinstance(x, Expr) and not x.args and is_var_symbol(x.op) + def unify_var(var, x, s): if var in s: return unify(s[var], x, s) @@ -820,6 +912,7 @@ def unify_var(var, x, s): else: return extend(s, var, x) + def occur_check(var, x, s): """Return true if variable var occurs anywhere in x (or in subst(s, x), if s has a binding for x).""" @@ -835,6 +928,7 @@ def occur_check(var, x, s): else: return False + def extend(s, var, val): """Copy the substitution s and extend it by setting var to val; return copy. @@ -845,6 +939,7 @@ def extend(s, var, val): s2[var] = val return s2 + def subst(s, x): """Substitute the substitution s into the expression x. >>> subst({x: 42, y:0}, F(x) + y) @@ -861,6 +956,7 @@ def subst(s, x): else: return Expr(x.op, *[subst(s, arg) for arg in x.args]) + def fol_fc_ask(KB, alpha): """Inefficient forward chaining for first-order logic. [Fig. 9.3] KB is a FolKB and alpha must be an atomic sentence.""" @@ -870,6 +966,7 @@ def fol_fc_ask(KB, alpha): ps, q = parse_definite_clause(standardize_variables(r)) raise NotImplementedError + def standardize_variables(sentence, dic=None): """Replace all the variables in sentence with new variables. >>> e = expr('F(a, b, c) & G(c, A, 23)') @@ -880,7 +977,8 @@ def standardize_variables(sentence, dic=None): >>> is_variable(standardize_variables(expr('x'))) True """ - if dic is None: dic = {} + if dic is None: + dic = {} if not isinstance(sentence, Expr): return sentence elif is_var_symbol(sentence.op): @@ -898,7 +996,9 @@ def standardize_variables(sentence, dic=None): #______________________________________________________________________________ + class FolKB(KB): + """A knowledge base consisting of first-order definite clauses. >>> kb0 = FolKB([expr('Farmer(Mac)'), expr('Rabbit(Pete)'), ... expr('(Rabbit(r) & Farmer(f)) ==> Hates(f, r)')]) @@ -909,8 +1009,9 @@ class FolKB(KB): >>> kb0.ask(expr('Wife(Pete, x)')) False """ + def __init__(self, initial_clauses=[]): - self.clauses = [] # inefficient: no indexing + self.clauses = [] # inefficient: no indexing for clause in initial_clauses: self.tell(clause) @@ -929,6 +1030,7 @@ def retract(self, sentence): def fetch_rules_for_goal(self, goal): return self.clauses + def test_ask(query, kb=None): q = expr(query) vars = variables(q) @@ -939,33 +1041,34 @@ def test_ask(query, kb=None): test_kb = FolKB( list(map(expr, ['Farmer(Mac)', - 'Rabbit(Pete)', - 'Mother(MrsMac, Mac)', - 'Mother(MrsRabbit, Pete)', - '(Rabbit(r) & Farmer(f)) ==> Hates(f, r)', - '(Mother(m, c)) ==> Loves(m, c)', - '(Mother(m, r) & Rabbit(r)) ==> Rabbit(m)', - '(Farmer(f)) ==> Human(f)', - # Note that this order of conjuncts - # would result in infinite recursion: - #'(Human(h) & Mother(m, h)) ==> Human(m)' - '(Mother(m, h) & Human(h)) ==> Human(m)' - ])) + 'Rabbit(Pete)', + 'Mother(MrsMac, Mac)', + 'Mother(MrsRabbit, Pete)', + '(Rabbit(r) & Farmer(f)) ==> Hates(f, r)', + '(Mother(m, c)) ==> Loves(m, c)', + '(Mother(m, r) & Rabbit(r)) ==> Rabbit(m)', + '(Farmer(f)) ==> Human(f)', + # Note that this order of conjuncts + # would result in infinite recursion: + #'(Human(h) & Mother(m, h)) ==> Human(m)' + '(Mother(m, h) & Human(h)) ==> Human(m)' + ])) ) crime_kb = FolKB( - list(map(expr, - ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', - 'Owns(Nono, M1)', - 'Missile(M1)', - '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)', - 'Missile(x) ==> Weapon(x)', - 'Enemy(x, America) ==> Hostile(x)', - 'American(West)', - 'Enemy(Nono, America)' - ])) + list(map(expr, + ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', + 'Owns(Nono, M1)', + 'Missile(M1)', + '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)', + 'Missile(x) ==> Weapon(x)', + 'Enemy(x, America) ==> Hostile(x)', + 'American(West)', + 'Enemy(Nono, America)' + ])) ) + def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] KB should be an instance of FolKB, and goals a list of literals. @@ -984,12 +1087,14 @@ def fol_bc_ask(KB, query): """ return fol_bc_or(KB, query, {}) + def fol_bc_or(KB, goal, theta): for rule in KB.fetch_rules_for_goal(goal): lhs, rhs = parse_definite_clause(standardize_variables(rule)) for theta1 in fol_bc_and(KB, lhs, unify(rhs, goal, theta)): yield theta1 + def fol_bc_and(KB, goals, theta): if theta is None: pass @@ -1007,6 +1112,7 @@ def fol_bc_and(KB, goals, theta): # You can use the Expr class to do symbolic differentiation. This used to be # a part of AI; now it is considered a separate field, Symbolic Algebra. + def diff(y, x): """Return the symbolic derivative, dy/dx, as an Expr. However, you probably want to simplify the results with simp. @@ -1015,74 +1121,115 @@ def diff(y, x): >>> simp(diff(x * x, x)) (2 * x) """ - if y == x: return ONE - elif not y.args: return ZERO + if y == x: + return ONE + elif not y.args: + return ZERO else: u, op, v = y.args[0], y.op, y.args[-1] - if op == '+': return diff(u, x) + diff(v, x) - elif op == '-' and len(args) == 1: return -diff(u, x) - elif op == '-': return diff(u, x) - diff(v, x) - elif op == '*': return u * diff(v, x) + v * diff(u, x) - elif op == '/': return (v*diff(u, x) - u*diff(v, x)) / (v * v) + if op == '+': + return diff(u, x) + diff(v, x) + elif op == '-' and len(args) == 1: + return -diff(u, x) + elif op == '-': + return diff(u, x) - diff(v, x) + elif op == '*': + return u * diff(v, x) + v * diff(u, x) + elif op == '/': + return (v*diff(u, x) - u*diff(v, x)) / (v * v) elif op == '**' and isnumber(x.op): return (v * u ** (v - 1) * diff(u, x)) - elif op == '**': return (v * u ** (v - 1) * diff(u, x) - + u ** v * Expr('log')(u) * diff(v, x)) - elif op == 'log': return diff(u, x) / u - else: raise ValueError("Unknown op: %s in diff(%s, %s)" % (op, y, x)) + elif op == '**': + return (v * u ** (v - 1) * diff(u, x) + + u ** v * Expr('log')(u) * diff(v, x)) + elif op == 'log': + return diff(u, x) / u + else: + raise ValueError("Unknown op: %s in diff(%s, %s)" % (op, y, x)) + def simp(x): - if not x.args: return x + if not x.args: + return x args = list(map(simp, x.args)) u, op, v = args[0], x.op, args[-1] if op == '+': - if v == ZERO: return u - if u == ZERO: return v - if u == v: return TWO * u - if u == -v or v == -u: return ZERO + if v == ZERO: + return u + if u == ZERO: + return v + if u == v: + return TWO * u + if u == -v or v == -u: + return ZERO elif op == '-' and len(args) == 1: - if u.op == '-' and len(u.args) == 1: return u.args[0] ## --y ==> y + if u.op == '-' and len(u.args) == 1: + return u.args[0] # --y ==> y elif op == '-': - if v == ZERO: return u - if u == ZERO: return -v - if u == v: return ZERO - if u == -v or v == -u: return ZERO + if v == ZERO: + return u + if u == ZERO: + return -v + if u == v: + return ZERO + if u == -v or v == -u: + return ZERO elif op == '*': - if u == ZERO or v == ZERO: return ZERO - if u == ONE: return v - if v == ONE: return u - if u == v: return u ** 2 + if u == ZERO or v == ZERO: + return ZERO + if u == ONE: + return v + if v == ONE: + return u + if u == v: + return u ** 2 elif op == '/': - if u == ZERO: return ZERO - if v == ZERO: return Expr('Undefined') - if u == v: return ONE - if u == -v or v == -u: return ZERO + if u == ZERO: + return ZERO + if v == ZERO: + return Expr('Undefined') + if u == v: + return ONE + if u == -v or v == -u: + return ZERO elif op == '**': - if u == ZERO: return ZERO - if v == ZERO: return ONE - if u == ONE: return ONE - if v == ONE: return u + if u == ZERO: + return ZERO + if v == ZERO: + return ONE + if u == ONE: + return ONE + if v == ONE: + return u elif op == 'log': - if u == ONE: return ZERO - else: raise ValueError("Unknown op: " + op) - ## If we fall through to here, we can not simplify further + if u == ONE: + return ZERO + else: + raise ValueError("Unknown op: " + op) + # If we fall through to here, we can not simplify further return Expr(op, *args) + def d(y, x): "Differentiate and then simplify." return simp(diff(y, x)) -#_______________________________________________________________________________ +#_________________________________________________________________________ # Utilities for doctest cases # These functions print their arguments in a standard order # to compensate for the random order in the standard representation + def pretty(x): t = type(x) - if t is dict: return pretty_dict(x) - elif t is set: return pretty_set(x) - else: return repr(x) + if t is dict: + return pretty_dict(x) + elif t is set: + return pretty_set(x) + else: + return repr(x) + def pretty_dict(d): """Return dictionary d's repr but with the items sorted. @@ -1094,6 +1241,7 @@ def pretty_dict(d): return '{%s}' % ', '.join('%r: %r' % (k, v) for k, v in sorted(list(d.items()), key=repr)) + def pretty_set(s): """Return set s's repr but with the items sorted. >>> pretty_set(set(['A', 'Q', 'F', 'K', 'Y', 'B'])) @@ -1103,22 +1251,29 @@ def pretty_set(s): """ return 'set(%r)' % sorted(s, key=repr) + def pp(x): print((pretty(x))) + def ppsubst(s): """Pretty-print substitution s""" ppdict(s) + def ppdict(d): print((pretty_dict(d))) + def ppset(s): print((pretty_set(s))) #________________________________________________________________________ -class logicTest: """ + +class logicTest: + + """ ### PropKB >>> kb = PropKB() >>> kb.tell(A & B) diff --git a/nlp.py b/nlp.py index 919862277..3687c2aed 100644 --- a/nlp.py +++ b/nlp.py @@ -9,6 +9,7 @@ #______________________________________________________________________________ # Grammars and Lexicons + def Rules(**rules): """Create a dictionary mapping symbols to alternative sequences. >>> Rules(A = "B C | D E") @@ -18,6 +19,7 @@ def Rules(**rules): rules[lhs] = [alt.strip().split() for alt in rhs.split('|')] return rules + def Lexicon(**rules): """Create a dictionary mapping symbols to alternative words. >>> Lexicon(Art = "the | a | an") @@ -27,7 +29,9 @@ def Lexicon(**rules): rules[lhs] = [word.strip() for word in rhs.split('|')] return rules + class Grammar: + def __init__(self, name, rules, lexicon): "A grammar has a set of rules and a lexicon." update(self, name=name, rules=rules, lexicon=lexicon) @@ -48,44 +52,45 @@ def __repr__(self): return '' % self.name E0 = Grammar('E0', - Rules( # Grammar for E_0 [Fig. 22.4] - S = 'NP VP | S Conjunction S', - NP = 'Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', - VP = 'Verb | VP NP | VP Adjective | VP PP | VP Adverb', - PP = 'Preposition NP', - RelClause = 'That VP'), - - Lexicon( # Lexicon for E_0 [Fig. 22.3] - Noun = "stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", - Verb = "is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", - Adjective = "right | left | east | south | back | smelly", - Adverb = "here | there | nearby | ahead | right | left | east | south | back", - Pronoun = "me | you | I | it", - Name = "John | Mary | Boston | Aristotle", - Article = "the | a | an", - Preposition = "to | in | on | near", - Conjunction = "and | or | but", - Digit = "0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9", - That = "that" - )) - -E_ = Grammar('E_', # Trivial Grammar and lexicon for testing - Rules( - S = 'NP VP', - NP = 'Art N | Pronoun', - VP = 'V NP'), - - Lexicon( - Art = 'the | a', - N = 'man | woman | table | shoelace | saw', - Pronoun = 'I | you | it', - V = 'saw | liked | feel' - )) - -E_NP_ = Grammar('E_NP_', # another trivial grammar for testing - Rules(NP = 'Adj NP | N'), - Lexicon(Adj = 'happy | handsome | hairy', - N = 'man')) + Rules( # Grammar for E_0 [Fig. 22.4] + S='NP VP | S Conjunction S', + NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', + VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb', + PP='Preposition NP', + RelClause='That VP'), + + Lexicon( # Lexicon for E_0 [Fig. 22.3] + Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", + Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", + Adjective="right | left | east | south | back | smelly", + Adverb="here | there | nearby | ahead | right | left | east | south | back", + Pronoun="me | you | I | it", + Name="John | Mary | Boston | Aristotle", + Article="the | a | an", + Preposition="to | in | on | near", + Conjunction="and | or | but", + Digit="0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9", + That="that" + )) + +E_ = Grammar('E_', # Trivial Grammar and lexicon for testing + Rules( + S='NP VP', + NP='Art N | Pronoun', + VP='V NP'), + + Lexicon( + Art='the | a', + N='man | woman | table | shoelace | saw', + Pronoun='I | you | it', + V='saw | liked | feel' + )) + +E_NP_ = Grammar('E_NP_', # another trivial grammar for testing + Rules(NP='Adj NP | N'), + Lexicon(Adj='happy | handsome | hairy', + N='man')) + def generate_random(grammar=E_, s='S'): """Replace each token in s by a random entry in grammar (recursively). @@ -109,6 +114,7 @@ def rewrite(tokens, into): class Chart: + """Class for parsing sentences using a chart data structure. [Fig 22.7] >>> chart = Chart(E0); >>> len(chart.parses('the stench is in 2 2')) @@ -180,10 +186,9 @@ def extender(self, edge): self.add_edge([i, k, A, alpha + [edge], B1b[1:]]) - -#### TODO: -#### 1. Parsing with augmentations -- requires unification, etc. -#### 2. Sequitor +# TODO: +# 1. Parsing with augmentations -- requires unification, etc. +# 2. Sequitor __doc__ += """ >>> chart = Chart(E0) diff --git a/planning.py b/planning.py index cc7604af4..d26350c4e 100644 --- a/planning.py +++ b/planning.py @@ -4,4 +4,9 @@ from utils import * import agents -import math, random, sys, time, bisect, string +import math +import random +import sys +import time +import bisect +import string diff --git a/probability.py b/probability.py index e80822751..6abb5ac3f 100644 --- a/probability.py +++ b/probability.py @@ -3,12 +3,13 @@ from utils import * from logic import extend -import random +import random from collections import defaultdict from functools import reduce #______________________________________________________________________________ + def DTAgentProgram(belief_state): "A decision-theoretic agent. [Fig. 13.1]" def program(percept): @@ -21,7 +22,9 @@ def program(percept): #______________________________________________________________________________ + class ProbDist: + """A discrete probability distribution. You name the random variable in the constructor, then assign and query probability of values. >>> P = ProbDist('Flip'); P['H'], P['T'] = 0.25, 0.75; P['H'] @@ -30,6 +33,7 @@ class ProbDist: >>> P['lo'], P['med'], P['hi'] (0.125, 0.375, 0.5) """ + def __init__(self, varname='?', freqs=None): """If freqs is given, it is a dictionary of value: frequency pairs, and the ProbDist then is normalized.""" @@ -41,8 +45,10 @@ def __init__(self, varname='?', freqs=None): def __getitem__(self, val): "Given a value, return P(value)." - try: return self.prob[val] - except KeyError: return 0 + try: + return self.prob[val] + except KeyError: + return 0 def __setitem__(self, val, p): "Set P(val) = p." @@ -73,7 +79,9 @@ def show_approx(self, numfmt='%.3g'): epsilon = 0.001 + class JointProbDist(ProbDist): + """A discrete probability distribute over a set of variables. >>> P = JointProbDist(['X', 'Y']); P[1, 1] = 0.25 >>> P[1, 1] @@ -81,6 +89,7 @@ class JointProbDist(ProbDist): >>> P[dict(X=0, Y=1)] = 0.5 >>> P[dict(X=0, Y=1)] 0.5""" + def __init__(self, variables): update(self, prob={}, variables=variables, vals=defaultdict(list)) @@ -106,6 +115,7 @@ def values(self, var): def __repr__(self): return "P(%s)" % self.variables + def event_values(event, vars): """Return a tuple of the values of variables vars in event. >>> event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) @@ -120,6 +130,7 @@ def event_values(event, vars): #______________________________________________________________________________ + def enumerate_joint_ask(X, e, P): """Return a probability distribution over the values of the variable X, given the {var:val} observations e, in the JointProbDist P. [Section 13.3] @@ -129,12 +140,13 @@ def enumerate_joint_ask(X, e, P): '0: 0.667, 1: 0.167, 2: 0.167' """ assert X not in e, "Query variable must be distinct from evidence" - Q = ProbDist(X) # probability distribution for X, initially empty - Y = [v for v in P.variables if v != X and v not in e] # hidden vars. + Q = ProbDist(X) # probability distribution for X, initially empty + Y = [v for v in P.variables if v != X and v not in e] # hidden vars. for xi in P.values(X): Q[xi] = enumerate_joint(Y, extend(e, X, xi), P) return Q.normalize() + def enumerate_joint(vars, e, P): """Return the sum of those entries in P consistent with e, provided vars is P's remaining variables (the ones not in e).""" @@ -146,7 +158,9 @@ def enumerate_joint(vars, e, P): #______________________________________________________________________________ + class BayesNet: + "Bayesian network containing only boolean-variable nodes." def __init__(self, node_specs=[]): @@ -182,7 +196,9 @@ def variable_values(self, var): def __repr__(self): return 'BayesNet(%r)' % self.nodes + class BayesNode: + """A conditional probability distribution for a boolean variable, P(X | parents). Part of a BayesNet.""" @@ -210,13 +226,15 @@ def __init__(self, X, parents, cpt): >>> Z = BayesNode('Z', 'P Q', ... {(T, T): 0.2, (T, F): 0.3, (F, T): 0.5, (F, F): 0.7}) """ - if isinstance(parents, str): parents = parents.split() + if isinstance(parents, str): + parents = parents.split() # We store the table always in the third form above. - if isinstance(cpt, (float, int)): # no parents, 0-tuple + if isinstance(cpt, (float, int)): # no parents, 0-tuple cpt = {(): cpt} elif isinstance(cpt, dict): - if cpt and isinstance(list(cpt.keys())[0], bool): # one parent, 1-tuple + # one parent, 1-tuple + if cpt and isinstance(list(cpt.keys())[0], bool): cpt = dict(((v,), p) for v, p in list(cpt.items())) assert isinstance(cpt, dict) @@ -257,13 +275,14 @@ def __repr__(self): ('Burglary', '', 0.001), ('Earthquake', '', 0.002), ('Alarm', 'Burglary Earthquake', - {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001}), + {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001}), ('JohnCalls', 'Alarm', {T: 0.90, F: 0.05}), ('MaryCalls', 'Alarm', {T: 0.70, F: 0.01}) - ]) +]) #______________________________________________________________________________ + def enumeration_ask(X, e, bn): """Return the conditional probability distribution of variable X given evidence e, from BayesNet bn. [Fig. 14.9] @@ -276,6 +295,7 @@ def enumeration_ask(X, e, bn): Q[xi] = enumerate_all(bn.vars, extend(e, X, xi), bn) return Q.normalize() + def enumerate_all(vars, e, bn): """Return the sum of those entries in P(vars | e{others}) consistent with e, where P is the joint distribution represented @@ -293,6 +313,7 @@ def enumerate_all(vars, e, bn): #______________________________________________________________________________ + def elimination_ask(X, e, bn): """Compute bn's P(X|e) by variable elimination. [Fig. 14.11] >>> elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary @@ -306,10 +327,12 @@ def elimination_ask(X, e, bn): factors = sum_out(var, factors, bn) return pointwise_product(factors, bn).normalize() + def is_hidden(var, X, e): "Is var a hidden variable when querying P(X|e)?" return var != X and var not in e + def make_factor(var, e, bn): """Return the factor for var in bn's joint distribution given e. That is, bn's full joint distribution, projected to accord with e, @@ -320,9 +343,11 @@ def make_factor(var, e, bn): for e1 in all_events(vars, bn, e)) return Factor(vars, cpt) + def pointwise_product(factors, bn): return reduce(lambda f, g: f.pointwise_product(g, bn), factors) + def sum_out(var, factors, bn): "Eliminate var from all factors by summing over its values." result, var_factors = [], [] @@ -331,7 +356,9 @@ def sum_out(var, factors, bn): result.append(pointwise_product(var_factors, bn).sum_out(var, bn)) return result + class Factor: + "A factor in a joint distribution." def __init__(self, vars, cpt): @@ -363,6 +390,7 @@ def p(self, e): "Look up my value tabulated for e." return self.cpt[event_values(e, self.vars)] + def all_events(vars, bn, e): "Yield every way of extending e with values for all vars." if not vars: @@ -382,10 +410,11 @@ def all_events(vars, bn, e): ('Sprinkler', 'Cloudy', {T: 0.10, F: 0.50}), ('Rain', 'Cloudy', {T: 0.80, F: 0.20}), ('WetGrass', 'Sprinkler Rain', - {(T, T): 0.99, (T, F): 0.90, (F, T): 0.90, (F, F): 0.00})]) + {(T, T): 0.99, (T, F): 0.90, (F, T): 0.90, (F, F): 0.00})]) #______________________________________________________________________________ + def prior_sample(bn): """Randomly sample from bn's full joint distribution. The result is a {variable: value} dict. [Fig. 14.13]""" @@ -394,7 +423,8 @@ def prior_sample(bn): event[node.variable] = node.sample(event) return event -#_______________________________________________________________________________ +#_________________________________________________________________________ + def rejection_sampling(X, e, bn, N): """Estimate the probability distribution of variable X given @@ -406,19 +436,22 @@ def rejection_sampling(X, e, bn, N): ... burglary, 10000).show_approx() 'False: 0.7, True: 0.3' """ - counts = dict((x, 0) for x in bn.variable_values(X)) # bold N in Fig. 14.14 + counts = dict((x, 0) + for x in bn.variable_values(X)) # bold N in Fig. 14.14 for j in range(N): - sample = prior_sample(bn) # boldface x in Fig. 14.14 + sample = prior_sample(bn) # boldface x in Fig. 14.14 if consistent_with(sample, e): counts[sample[X]] += 1 return ProbDist(X, counts) + def consistent_with(event, evidence): "Is event consistent with the given evidence?" return all(evidence.get(k, v) == v for k, v in list(event.items())) -#_______________________________________________________________________________ +#_________________________________________________________________________ + def likelihood_weighting(X, e, bn, N): """Estimate the probability distribution of variable X given @@ -430,16 +463,17 @@ def likelihood_weighting(X, e, bn, N): """ W = dict((x, 0) for x in bn.variable_values(X)) for j in range(N): - sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15 + sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15 W[sample[X]] += weight return ProbDist(X, W) + def weighted_sample(bn, e): """Sample an event from bn that's consistent with the evidence e; return the event and its weight, the likelihood that the event accords to the evidence.""" w = 1 - event = dict(e) # boldface x in Fig. 14.15 + event = dict(e) # boldface x in Fig. 14.15 for node in bn.nodes: Xi = node.variable if Xi in e: @@ -448,7 +482,8 @@ def weighted_sample(bn, e): event[Xi] = node.sample(event) return event, w -#_______________________________________________________________________________ +#_________________________________________________________________________ + def gibbs_ask(X, e, bn, N): """[Fig. 14.16] @@ -458,9 +493,10 @@ def gibbs_ask(X, e, bn, N): 'False: 0.738, True: 0.262' """ assert X not in e, "Query variable must be distinct from evidence" - counts = dict((x, 0) for x in bn.variable_values(X)) # bold N in Fig. 14.16 + counts = dict((x, 0) + for x in bn.variable_values(X)) # bold N in Fig. 14.16 Z = [var for var in bn.vars if var not in e] - state = dict(e) # boldface x in Fig. 14.16 + state = dict(e) # boldface x in Fig. 14.16 for Zi in Z: state[Zi] = random.choice(bn.variable_values(Zi)) for j in range(N): @@ -469,6 +505,7 @@ def gibbs_ask(X, e, bn, N): counts[state[X]] += 1 return ProbDist(X, counts) + def markov_blanket_sample(X, e, bn): """Return a sample from P(X | mb) where mb denotes that the variables in the Markov blanket of X take their values from event @@ -481,23 +518,27 @@ def markov_blanket_sample(X, e, bn): # [Equation 14.12:] Q[xi] = Xnode.p(xi, e) * product(Yj.p(ei[Yj.variable], ei) for Yj in Xnode.children) - return probability(Q.normalize()[True]) # (assuming a Boolean variable here) + # (assuming a Boolean variable here) + return probability(Q.normalize()[True]) + +#_________________________________________________________________________ -#_______________________________________________________________________________ def forward_backward(ev, prior): """[Fig. 15.4]""" unimplemented() + def fixed_lag_smoothing(e_t, hmm, d): """[Fig. 15.6]""" unimplemented() + def particle_filtering(e, N, dbn): """[Fig. 15.17]""" unimplemented() -#_______________________________________________________________________________ +#_________________________________________________________________________ __doc__ += """ # We can build up a probability distribution like this (p. 469): >>> P = ProbDist() diff --git a/probability_test.py b/probability_test.py index 3bc2dec55..13b00d310 100644 --- a/probability_test.py +++ b/probability_test.py @@ -1,13 +1,14 @@ import pytest from probability import * + def tests(): cpt = burglary.variable_node('Alarm') parents = ['Burglary', 'Earthquake'] event = {'Burglary': True, 'Earthquake': True} assert cpt.p(True, event) == 0.95 event = {'Burglary': False, 'Earthquake': True} - assert cpt.p(False, event) == 0.71 + assert cpt.p(False, event) == 0.71 # assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375 # assert BoolCPT(0.75).p(False, [], {}) == 0.25 # cpt = BoolCPT({True: 0.2, False: 0.7}) @@ -23,10 +24,12 @@ def tests(): assert not consistent_with(s, {'A': False}) assert not consistent_with(s, {'D': True}) - random.seed(21); p = rejection_sampling('Earthquake', {}, burglary, 1000) + random.seed(21) + p = rejection_sampling('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.001, 0.999) - random.seed(71); p = likelihood_weighting('Earthquake', {}, burglary, 1000) + random.seed(71) + p = likelihood_weighting('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.002, 0.998) if __name__ == '__main__': diff --git a/rl.py b/rl.py index fc0e2c9e9..f30e542ba 100644 --- a/rl.py +++ b/rl.py @@ -4,12 +4,16 @@ from utils import * import agents + class PassiveADPAgent(agents.Agent): + """Passive (non-learning) agent that uses adaptive dynamic programming on a given MDP and policy. [Fig. 21.2]""" NotImplemented + class PassiveTDAgent(agents.Agent): + """Passive (non-learning) agent that uses temporal differences to learn utility estimates. [Fig. 21.4]""" NotImplemented diff --git a/search.py b/search.py index c6b5b905a..4d4c9974a 100644 --- a/search.py +++ b/search.py @@ -6,11 +6,18 @@ from utils import * -import math, random, sys, time, bisect, string +import math +import random +import sys +import time +import bisect +import string #______________________________________________________________________________ + class Problem(object): + """The abstract class for a formal problem. You should subclass this and implement the methods actions and result, and possibly __init__, goal_test, and path_cost. Then you will create instances @@ -20,7 +27,8 @@ def __init__(self, initial, goal=None): """The constructor specifies the initial state, and possibly a goal state, if there is a unique goal. Your subclass's constructor can add other arguments.""" - self.initial = initial; self.goal = goal + self.initial = initial + self.goal = goal def actions(self, state): """Return the actions that can be executed in the given @@ -55,7 +63,9 @@ def value(self, state): raise NotImplementedError #______________________________________________________________________________ + class Node: + """A node in a search tree. Contains a pointer to the parent (the node that this is a successor of) and to the actual state for this node. Note that if a state is arrived at by two paths, then there are two nodes with @@ -111,8 +121,11 @@ def __hash__(self): #______________________________________________________________________________ + class SimpleProblemSolvingAgentProgram: + """Abstract framework for a problem-solving agent. [Fig. 3.1]""" + def __init__(self, initial_state=None): update(self, state=initial_state, seq=[]) @@ -122,7 +135,8 @@ def __call__(self, percept): goal = self.formulate_goal(self.state) problem = self.formulate_problem(self.state, goal) self.seq = self.search(problem) - if not self.seq: return None + if not self.seq: + return None return self.seq.pop(0) def update_state(self, percept): @@ -140,6 +154,7 @@ def search(self, problem): #______________________________________________________________________________ # Uninformed Search algorithms + def tree_search(problem, frontier): """Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. @@ -152,6 +167,7 @@ def tree_search(problem, frontier): frontier.extend(node.expand(problem)) return None + def graph_search(problem, frontier): """Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. @@ -168,18 +184,22 @@ def graph_search(problem, frontier): and child not in frontier) return None + def breadth_first_tree_search(problem): "Search the shallowest nodes in the search tree first." return tree_search(problem, FIFOQueue()) + def depth_first_tree_search(problem): "Search the deepest nodes in the search tree first." return tree_search(problem, Stack()) + def depth_first_graph_search(problem): "Search the deepest nodes in the search tree first." return graph_search(problem, Stack()) + def breadth_first_search(problem): "[Fig. 3.11]" node = Node(problem.initial) @@ -198,6 +218,7 @@ def breadth_first_search(problem): frontier.append(child) return None + def best_first_graph_search(problem, f): """Search the nodes with the lowest f scores first. You specify the function f(node) that you want to minimize; for example, @@ -228,10 +249,12 @@ def best_first_graph_search(problem, f): frontier.append(child) return None + def uniform_cost_search(problem): "[Fig. 3.14]" return best_first_graph_search(problem, lambda node: node.path_cost) + def depth_limited_search(problem, limit=50): "[Fig. 3.17]" def recursive_dls(node, problem, limit): @@ -252,6 +275,7 @@ def recursive_dls(node, problem, limit): # Body of depth_limited_search: return recursive_dls(Node(problem.initial), problem, limit) + def iterative_deepening_search(problem): "[Fig. 3.18]" for depth in range(sys.maxsize): @@ -263,7 +287,8 @@ def iterative_deepening_search(problem): # Informed (Heuristic) Search greedy_best_first_graph_search = best_first_graph_search - # Greedy best-first search is accomplished by specifying f(n) = h(n). +# Greedy best-first search is accomplished by specifying f(n) = h(n). + def astar_search(problem, h=None): """A* search is best-first graph search with f(n) = g(n)+h(n). @@ -275,6 +300,7 @@ def astar_search(problem, h=None): #______________________________________________________________________________ # Other search algorithms + def recursive_best_first_search(problem, h=None): "[Fig. 3.26]" h = memoize(h or problem.h, 'h') @@ -288,7 +314,8 @@ def RBFS(problem, node, flimit): for s in successors: s.f = max(s.path_cost + h(s), node.f) while True: - successors.sort(lambda x,y: cmp(x.f, y.f)) # Order by lowest f value + # Order by lowest f value + successors.sort(lambda x, y: cmp(x.f, y.f)) best = successors[0] if best.f > flimit: return None, best.f @@ -305,6 +332,7 @@ def RBFS(problem, node, flimit): result, bestf = RBFS(problem, node, infinity) return result + def hill_climbing(problem): """From the initial node, keep choosing the neighbor with highest value, stopping when no neighbor is better. [Fig. 4.2]""" @@ -320,10 +348,12 @@ def hill_climbing(problem): current = neighbor return current.state + def exp_schedule(k=20, lam=0.005, limit=100): "One possible schedule function for simulated annealing" return lambda t: (k * math.exp(-lam * t) if t < limit else 0) + def simulated_annealing(problem, schedule=exp_schedule()): "[Fig. 4.5]" current = Node(problem.initial) @@ -339,14 +369,17 @@ def simulated_annealing(problem, schedule=exp_schedule()): if delta_e > 0 or probability(math.exp(delta_e/T)): current = next + def and_or_graph_search(problem): "[Fig. 4.11]" unimplemented() + def online_dfs_agent(s1): "[Fig. 4.21]" unimplemented() + def lrta_star_agent(s1): "[Fig. 4.24]" unimplemented() @@ -354,6 +387,7 @@ def lrta_star_agent(s1): #______________________________________________________________________________ # Genetic Algorithm + def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): """Call genetic_algorithm on the appropriate parts of a problem. This requires the problem to have states that can mate and mutate, @@ -363,6 +397,7 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): random.shuffle(states) return genetic_algorithm(states[:n], problem.value, ngen, pmut) + def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): "[Fig. 4.8]" for i in range(ngen): @@ -377,8 +412,11 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): population = new_population return argmax(population, fitness_fn) + class GAState: + "Abstract class for individuals in a genetic search." + def __init__(self, genes): self.genes = genes @@ -397,7 +435,9 @@ def mutate(self): #______________________________________________________________________________ # Graphs and Graph Problems + class Graph: + """A graph connects nodes (verticies) by edges (links). Each edge can also have a length associated with it. The constructor call is something like: g = Graph({'A': {'B': 1, 'C': 2}) @@ -414,7 +454,8 @@ class Graph: def __init__(self, dict=None, directed=True): self.dict = dict or {} self.directed = directed - if not directed: self.make_undirected() + if not directed: + self.make_undirected() def make_undirected(self): "Make a digraph into an undirected graph by adding symmetric edges." @@ -426,30 +467,35 @@ def connect(self, A, B, distance=1): """Add a link from A and B of given distance, and also add the inverse link if the graph is undirected.""" self.connect1(A, B, distance) - if not self.directed: self.connect1(B, A, distance) + if not self.directed: + self.connect1(B, A, distance) def connect1(self, A, B, distance): "Add a link from A to B of given distance, in one direction only." - self.dict.setdefault(A,{})[B] = distance + self.dict.setdefault(A, {})[B] = distance def get(self, a, b=None): """Return a link distance or a dict of {node: distance} entries. .get(a,b) returns the distance or None; .get(a) returns a dict of {node: distance} entries, possibly {}.""" links = self.dict.setdefault(a, {}) - if b is None: return links - else: return links.get(b) + if b is None: + return links + else: + return links.get(b) def nodes(self): "Return a list of nodes in the graph." return list(self.dict.keys()) + def UndirectedGraph(dict=None): "Build a Graph where every edge (including future ones) goes both ways." return Graph(dict=dict, directed=False) + def RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300, - curvature=lambda: random.uniform(1.1, 1.5)): + curvature=lambda: random.uniform(1.1, 1.5)): """Construct a random graph, with the specified nodes, and random links. The nodes are laid out randomly on a (width x height) rectangle. Then each node is connected to the min_links nearest neighbors. @@ -458,16 +504,18 @@ def RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300, where curvature() defaults to a random number between 1.1 and 1.5.""" g = UndirectedGraph() g.locations = {} - ## Build the cities + # Build the cities for node in nodes: g.locations[node] = (random.randrange(width), random.randrange(height)) - ## Build roads from each city to at least min_links nearest neighbors. + # Build roads from each city to at least min_links nearest neighbors. for i in range(min_links): for node in nodes: if len(g.get(node)) < min_links: here = g.locations[node] + def distance_to_node(n): - if n is node or g.get(node,n): return infinity + if n is node or g.get(node, n): + return infinity return distance(g.locations[n], here) neighbor = argmin(nodes, distance_to_node) d = distance(g.locations[neighbor], here) * curvature() @@ -489,11 +537,11 @@ def distance_to_node(n): R=dict(S=80), U=dict(V=142))) romania.locations = dict( - A=( 91, 492), B=(400, 327), C=(253, 288), D=(165, 299), + A=(91, 492), B=(400, 327), C=(253, 288), D=(165, 299), E=(562, 293), F=(305, 449), G=(375, 270), H=(534, 350), I=(473, 506), L=(165, 379), M=(168, 339), N=(406, 537), O=(131, 571), P=(320, 368), R=(233, 410), S=(207, 457), - T=( 94, 410), U=(456, 350), V=(509, 444), Z=(108, 531)) + T=(94, 410), U=(456, 350), V=(509, 444), Z=(108, 531)) australia = UndirectedGraph(dict( T=dict(), @@ -503,8 +551,11 @@ def distance_to_node(n): australia.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) + class GraphProblem(Problem): + "The problem of searching a graph from one node to another." + def __init__(self, initial, goal, graph): Problem.__init__(self, initial, goal) self.graph = graph @@ -518,7 +569,7 @@ def result(self, state, action): return action def path_cost(self, cost_so_far, A, action, B): - return cost_so_far + (self.graph.get(A,B) or infinity) + return cost_so_far + (self.graph.get(A, B) or infinity) def h(self, node): "h function is straight-line distance from a node's state to goal." @@ -530,7 +581,9 @@ def h(self, node): #______________________________________________________________________________ + class NQueensProblem(Problem): + """The problem of placing N queens on an NxN board with none attacking each other. A state is represented as an N-element array, where a value of r in the c-th entry means there is a queen at column c, @@ -539,6 +592,7 @@ class NQueensProblem(Problem): >>> depth_first_tree_search(NQueensProblem(8)) """ + def __init__(self, N): self.N = N self.initial = [None] * N @@ -546,7 +600,7 @@ def __init__(self, N): def actions(self, state): "In the leftmost empty column, try all non-conflicting rows." if state[-1] is not None: - return [] # All columns filled; no successors + return [] # All columns filled; no successors else: col = state.index(None) return [row for row in range(self.N) @@ -566,10 +620,10 @@ def conflicted(self, state, row, col): def conflict(self, row1, col1, row2, col2): "Would putting two queens in (row1, col1) and (row2, col2) conflict?" - return (row1 == row2 ## same row - or col1 == col2 ## same column - or row1-col1 == row2-col2 ## same \ diagonal - or row1+col1 == row2+col2) ## same / diagonal + return (row1 == row2 # same row + or col1 == col2 # same column + or row1-col1 == row2-col2 # same \ diagonal + or row1+col1 == row2+col2) # same / diagonal def goal_test(self, state): "Check if all columns filled, no conflicts." @@ -589,6 +643,7 @@ def goal_test(self, state): 'NODESW', 'HEFIYE', 'ONUDTK', 'TEVIGN', 'ANEDVZ', 'PINESH', 'ABILYT', 'GKYLEU'] + def random_boggle(n=4): """Return a random Boggle board of size n x n. We represent a board as a linear list of letters.""" @@ -601,15 +656,21 @@ def random_boggle(n=4): boyan_best = list('RSTCSDEIAEGNLRPEATESMSSID') + def print_boggle(board): "Print the board in a 2-d array." - n2 = len(board); n = exact_sqrt(n2) + n2 = len(board) + n = exact_sqrt(n2) for i in range(n2): - if i % n == 0 and i > 0: print() - if board[i] == 'Q': print('Qu', end=' ') - else: print(str(board[i]) + ' ', end=' ') + if i % n == 0 and i > 0: + print() + if board[i] == 'Q': + print('Qu', end=' ') + else: + print(str(board[i]) + ' ', end=' ') print() + def boggle_neighbors(n2, cache={}): """Return a list of lists, where the i-th element is the list of indexes for the neighbors of square i.""" @@ -625,17 +686,24 @@ def boggle_neighbors(n2, cache={}): on_right = (i+1) % n == 0 if not on_top: neighbors[i].append(i - n) - if not on_left: neighbors[i].append(i - n - 1) - if not on_right: neighbors[i].append(i - n + 1) + if not on_left: + neighbors[i].append(i - n - 1) + if not on_right: + neighbors[i].append(i - n + 1) if not on_bottom: neighbors[i].append(i + n) - if not on_left: neighbors[i].append(i + n - 1) - if not on_right: neighbors[i].append(i + n + 1) - if not on_left: neighbors[i].append(i - 1) - if not on_right: neighbors[i].append(i + 1) + if not on_left: + neighbors[i].append(i + n - 1) + if not on_right: + neighbors[i].append(i + n + 1) + if not on_left: + neighbors[i].append(i - 1) + if not on_right: + neighbors[i].append(i + 1) cache[n2] = neighbors return neighbors + def exact_sqrt(n2): "If n2 is a perfect square, return its square root, else raise error." n = int(math.sqrt(n2)) @@ -644,10 +712,13 @@ def exact_sqrt(n2): #_____________________________________________________________________________ + class Wordlist: + """This class holds a list of words. You can use (word in wordlist) to check if a word is in the list, or wordlist.lookup(prefix) to see if prefix starts any of the words in the list.""" + def __init__(self, filename, min_len=3): lines = open(filename).read().upper().split() self.words = [word for word in lines if len(word) >= min_len] @@ -664,7 +735,8 @@ def lookup(self, prefix, lo=0, hi=None): words[i].startswith(prefix), or is None; the second is True iff prefix itself is in the Wordlist.""" words = self.words - if hi is None: hi = len(words) + if hi is None: + hi = len(words) i = bisect.bisect_left(words, prefix, lo, hi) if i < len(words) and words[i].startswith(prefix): return i, (words[i] == prefix) @@ -679,10 +751,12 @@ def __len__(self): #_____________________________________________________________________________ + class BoggleFinder: + """A class that allows you to find all the words in a Boggle board. """ - wordlist = None ## A class variable, holding a wordlist + wordlist = None # A class variable, holding a wordlist def __init__(self, board=None): if BoggleFinder.wordlist is None: @@ -715,7 +789,8 @@ def find(self, lo, hi, i, visited, prefix): self.found[prefix] = True visited.append(i) c = self.board[i] - if c == 'Q': c = 'QU' + if c == 'Q': + c = 'QU' prefix += c for j in self.neighbors[i]: self.find(wordpos, hi, j, visited, prefix) @@ -737,6 +812,7 @@ def __len__(self): #_____________________________________________________________________________ + def boggle_hill_climbing(board=None, ntimes=100, verbose=True): """Solve inverse Boggle by hill-climbing: find a high-scoring board by starting with a random one and changing it.""" @@ -749,24 +825,29 @@ def boggle_hill_climbing(board=None, ntimes=100, verbose=True): new = len(finder.set_board(board)) if new > best: best = new - if verbose: print(best, _, board) + if verbose: + print(best, _, board) else: - board[i] = oldc ## Change back + board[i] = oldc # Change back if verbose: print_boggle(board) return board, best + def mutate_boggle(board): i = random.randrange(len(board)) oldc = board[i] - board[i] = random.choice(random.choice(cubes16)) ##random.choice(boyan_best) + # random.choice(boyan_best) + board[i] = random.choice(random.choice(cubes16)) return i, oldc #______________________________________________________________________________ # Code to compare searchers on various problems. + class InstrumentedProblem(Problem): + """Delegates to a problem, and keeps statistics.""" def __init__(self, problem): @@ -802,6 +883,7 @@ def __repr__(self): return '<%4d/%4d/%4d/%s>' % (self.succs, self.goal_tests, self.states, str(self.found)[:4]) + def compare_searchers(problems, header, searchers=[breadth_first_tree_search, breadth_first_search, depth_first_graph_search, @@ -815,6 +897,7 @@ def do(searcher, problem): table = [[name(s)] + [do(s, p) for p in problems] for s in searchers] print_table(table, header) + def compare_graph_searchers(): """Prints a table of results like this: >>> compare_graph_searchers() @@ -828,7 +911,7 @@ def compare_graph_searchers(): compare_searchers(problems=[GraphProblem('A', 'B', romania), GraphProblem('O', 'N', romania), GraphProblem('Q', 'WA', australia)], - header=['Searcher', 'Romania(A, B)', 'Romania(O, N)', 'Australia']) + header=['Searcher', 'Romania(A, B)', 'Romania(O, N)', 'Australia']) #______________________________________________________________________________ diff --git a/text.py b/text.py index d087b841a..2f2059d7a 100644 --- a/text.py +++ b/text.py @@ -11,7 +11,9 @@ import re import search + class UnigramTextModel(CountingProbDist): + """This is a discrete probability distribution over words, so you can add, sample, or get P[word], just like with CountingProbDist. You can also generate a random text n words long with P.samples(n)""" @@ -20,21 +22,23 @@ def samples(self, n): "Return a string of n words, random according to the model." return ' '.join(self.sample() for i in range(n)) + class NgramTextModel(CountingProbDist): + """This is a discrete probability distribution over n-tuples of words. You can add, sample or get P[(word1, ..., wordn)]. The method P.samples(n) builds up an n-word sequence; P.add and P.add_sequence add data.""" def __init__(self, n, observation_sequence=[]): - ## In addition to the dictionary of n-tuples, cond_prob is a - ## mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1) + # In addition to the dictionary of n-tuples, cond_prob is a + # mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1) CountingProbDist.__init__(self) self.n = n self.cond_prob = defaultdict() self.add_sequence(observation_sequence) - ## __getitem__, top, sample inherited from CountingProbDist - ## Note they deal with tuples, not strings, as inputs + # __getitem__, top, sample inherited from CountingProbDist + # Note they deal with tuples, not strings, as inputs def add(self, ngram): """Count 1 for P[(w1, ..., wn)] and for P(wn | (w1, ..., wn-1)""" @@ -47,7 +51,7 @@ def add_sequence(self, words): """Add each of the tuple words[i:i+n], using a sliding window. Prefix some copies of the empty word, '', to make the start work.""" n = self.n - words = ['',] * (n-1) + words + words = ['', ] * (n-1) + words for i in range(len(words)-n): self.add(tuple(words[i:i+n])) @@ -59,7 +63,7 @@ def samples(self, nwords): output = [] for i in range(nwords): if nminus1gram not in self.cond_prob: - nminus1gram = ('',) * (n-1) # Cannot continue, so restart. + nminus1gram = ('',) * (n-1) # Cannot continue, so restart. wn = self.cond_prob[nminus1gram].sample() output.append(wn) nminus1gram = nminus1gram[1:] + (wn,) @@ -76,19 +80,20 @@ def viterbi_segment(text, P): n = len(text) words = [''] + list(text) best = [1.0] + [0.0] * n - ## Fill in the vectors best, words via dynamic programming + # Fill in the vectors best, words via dynamic programming for i in range(n+1): for j in range(0, i): w = text[j:i] if P[w] * best[i - len(w)] >= best[i]: best[i] = P[w] * best[i - len(w)] words[i] = w - ## Now recover the sequence of best words - sequence = []; i = len(words)-1 + # Now recover the sequence of best words + sequence = [] + i = len(words)-1 while i > 0: sequence[0:0] = [words[i]] i = i - len(words[i]) - ## Return sequence of best words and overall probability + # Return sequence of best words and overall probability return sequence, best[-1] @@ -97,6 +102,7 @@ def viterbi_segment(text, P): # TODO(tmrts): Expose raw index class IRSystem: + """A very simple Information Retrieval System, as discussed in Sect. 23.2. The constructor s = IRSystem('the a') builds an empty system with two stopwords. Next, index several documents with s.index_document(text, url). @@ -107,8 +113,8 @@ class IRSystem: def __init__(self, stopwords='the a of'): """Create an IR System. Optionally specify stopwords.""" - ## index is a map of {word: {docid: count}}, where docid is an int, - ## indicating the index into the documents list. + # index is a map of {word: {docid: count}}, where docid is an int, + # indicating the index into the documents list. update(self, index=defaultdict(lambda: defaultdict(int)), stopwords=set(words(stopwords)), documents=[]) @@ -119,7 +125,7 @@ def index_collection(self, filenames): def index_document(self, text, url): "Index the text of a document." - ## For now, use first line for title + # For now, use first line for title title = text[:text.index('\n')].strip() docwords = words(text) docid = len(self.documents) @@ -139,12 +145,13 @@ def query(self, query_text, n=10): shortest = argmin(qwords, lambda w: len(self.index[w])) docs = self.index[shortest] results = [(sum([self.score(w, d) for w in qwords]), d) for d in docs] - results.sort(); results.reverse() + results.sort() + results.reverse() return results[:n] def score(self, word, docid): "Compute a score for this word on this docid." - ## There are many options; here we take a very simple approach + # There are many options; here we take a very simple approach return (math.log(1 + self.index[word][docid]) / math.log(1 + self.documents[docid].nwords)) @@ -152,14 +159,18 @@ def present(self, results): "Present the results as a list." for (score, d) in results: doc = self.documents[d] - print(("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs()))) + print( + ("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs()))) def present_results(self, query_text, n=10): "Get results for the query and present them." self.present(self.query(query_text, n)) + class UnixConsultant(IRSystem): + """A trivial IR system over a small collection of Unix man pages.""" + def __init__(self): IRSystem.__init__(self, stopwords="how do i the a of") import os @@ -168,11 +179,15 @@ def __init__(self): if f.endswith('.txt')] self.index_collection(man_files) + class Document: + """Metadata for a document: title and url; maybe add others later.""" + def __init__(self, title, url, nwords): update(self, title=title, url=url, nwords=nwords) + def words(text, reg=re.compile('[a-z0-9]+')): """Return a list of the words in text, ignoring punctuation and converting everything to lowercase (to canonicalize). @@ -181,6 +196,7 @@ def words(text, reg=re.compile('[a-z0-9]+')): """ return reg.findall(text.lower()) + def canonicalize(text): """Return a canonical text: only lowercase letters and blanks. >>> canonicalize("``EGAD!'' Edgar cried.") @@ -191,14 +207,15 @@ def canonicalize(text): #______________________________________________________________________________ -## Example application (not in book): decode a cipher. -## A cipher is a code that substitutes one character for another. -## A shift cipher is a rotation of the letters in the alphabet, -## such as the famous rot13, which maps A to N, B to M, etc. +# Example application (not in book): decode a cipher. +# A cipher is a code that substitutes one character for another. +# A shift cipher is a rotation of the letters in the alphabet, +# such as the famous rot13, which maps A to N, B to M, etc. alphabet = 'abcdefghijklmnopqrstuvwxyz' -#### Encoding +# Encoding + def shift_encode(plaintext, n): """Encode text with a shift cipher that moves each letter up by n letters. @@ -207,6 +224,7 @@ def shift_encode(plaintext, n): """ return encode(plaintext, alphabet[n:] + alphabet[:n]) + def rot13(plaintext): """Encode text by rotating letters by 13 spaces in the alphabet. >>> rot13('hello') @@ -216,6 +234,7 @@ def rot13(plaintext): """ return shift_encode(plaintext, 13) + def translate(plaintext, function): """Translate chars of a plaintext with the given function.""" result = "" @@ -223,6 +242,7 @@ def translate(plaintext, function): result += function(char) return result + def maketrans(from_, to_): """Create a translation table and return the proper function.""" trans_table = {} @@ -231,12 +251,14 @@ def maketrans(from_, to_): return lambda char: trans_table.get(char, char) + def encode(plaintext, code): "Encodes text, using a code which is a permutation of the alphabet." trans = maketrans(alphabet + alphabet.upper(), code + code.upper()) return translate(plaintext, trans) + def bigrams(text): """Return a list of pairs in text (a sequence of letters or words). >>> bigrams('this') @@ -246,12 +268,15 @@ def bigrams(text): """ return [text[i:i+2] for i in range(len(text) - 1)] -#### Decoding a Shift (or Caesar) Cipher +# Decoding a Shift (or Caesar) Cipher + class ShiftDecoder: + """There are only 26 possible encodings, so we can try all of them, and return the one with the highest probability, according to a bigram probability distribution.""" + def __init__(self, training_text): training_text = canonicalize(training_text) self.P2 = CountingProbDist(bigrams(training_text), default=1) @@ -268,17 +293,21 @@ def score(self, plaintext): def decode(self, ciphertext): "Return the shift decoding of text with the best score." - list_ = [(self.score(shift), shift) for shift in all_shifts(ciphertext)] + list_ = [(self.score(shift), shift) + for shift in all_shifts(ciphertext)] return max(list_, key=lambda elm: elm[0])[1] + def all_shifts(text): "Return a list of all 26 possible encodings of text by a shift cipher." yield from (shift_encode(text, i) for i, _ in enumerate(alphabet)) -#### Decoding a General Permutation Cipher +# Decoding a General Permutation Cipher + class PermutationDecoder: + """This is a much harder problem than the shift decoder. There are 26! permutations, so we can't try them all. Instead we have to search. We want to search well, but there are many things to consider: @@ -292,10 +321,11 @@ class PermutationDecoder: represented as a letter-to-letter map; for example {'z': 'e'} to represent that 'z' will be translated to 'e'. """ + def __init__(self, training_text, ciphertext=None): self.Pwords = UnigramTextModel(words(training_text)) - self.P1 = UnigramTextModel(training_text) # By letter - self.P2 = NgramTextModel(2, training_text) # By letter pair + self.P1 = UnigramTextModel(training_text) # By letter + self.P2 = NgramTextModel(2, training_text) # By letter pair def decode(self, ciphertext): "Search for a decoding of the ciphertext." @@ -313,16 +343,18 @@ def score(self, code): sum([log(self.P2[b]) for b in bigrams(text)])) return exp(logP) + class PermutationDecoderProblem(search.Problem): + def __init__(self, initial=None, goal=None, decoder=None): self.initial = initial or {} self.decoder = decoder def actions(self, state): - ## Find the best + # Find the best p, plainchar = max([(self.decoder.P1[c], c) for c in alphabet if c not in state]) - succs = [extend(state, plainchar, cipherchar)] #???? + succs = [extend(state, plainchar, cipherchar)] # ???? def goal_test(self, state): "We're done when we get all 26 letters assigned." diff --git a/text_test.py b/text_test.py index 0c11ef5b8..cc9c64653 100644 --- a/text_test.py +++ b/text_test.py @@ -5,6 +5,7 @@ from random import choice from math import isclose + def test_unigram_text_model(): flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) @@ -12,13 +13,16 @@ def test_unigram_text_model(): s, p = viterbi_segment('itiseasytoreadwordswithoutspaces', P) - assert s == ['it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] + assert s == [ + 'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] + def test_shift_encoding(): code = shift_encode("This is a secret message.", 17) assert code == 'Kyzj zj r jvtivk dvjjrxv.' + def test_shift_decoding(): flatland = DataFile("EN-text/flatland.txt").read() ring = ShiftDecoder(flatland) @@ -26,6 +30,7 @@ def test_shift_decoding(): assert msg == 'This is a secret message.' + def test_rot13_decoding(): flatland = DataFile("EN-text/flatland.txt").read() ring = ShiftDecoder(flatland) @@ -33,6 +38,7 @@ def test_rot13_decoding(): assert msg == 'Hello, world!' + def test_counting_probability_distribution(): D = CountingProbDist() @@ -43,6 +49,7 @@ def test_counting_probability_distribution(): assert 1/7 <= min(ps) <= max(ps) <= 1/5 + def test_ngram_models(): flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) @@ -50,20 +57,23 @@ def test_ngram_models(): P2 = NgramTextModel(2, wordseq) P3 = NgramTextModel(3, wordseq) - ## The most frequent entries in each model - assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a'), - (722, 'i'), (640, 'in'), (478, 'that'), (399, 'is'), (348, 'you')] + # The most frequent entries in each model + assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a'), + (722, 'i'), (640, 'in'), (478, 'that'), (399, 'is'), (348, 'you')] - assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), - (80, ('it', 'is' )), (71, ('by', 'the' )), (68, ('for', 'the' )), - (68, ('and', 'the' )), (62, ('on', 'the' )), (60, ('to', 'be'))] - - assert P3.top(10) == [(30, ('a', 'straight', 'line')), (19, ('of', 'three', 'dimensions')), - (16, ('the', 'sense', 'of' )), (13, ('by', 'the', 'sense' )), - (13, ('as', 'well', 'as' )), (12, ('of', 'the', 'circles' )), - (12, ('of', 'sight', 'recognition' )), (11, ('the', 'number', 'of' )), - (11, ('that', 'i', 'had' )), (11, ('so', 'as', 'to'))] + assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), + (80, ('it', 'is')), (71, + ('by', 'the')), (68, ('for', 'the')), + (68, ('and', 'the')), (62, ('on', 'the')), (60, ('to', 'be'))] + assert P3.top(10) == [(30, ('a', 'straight', 'line')), (19, ('of', 'three', 'dimensions')), + (16, ('the', 'sense', 'of')), (13, + ('by', 'the', 'sense')), + (13, ('as', 'well', 'as')), (12, + ('of', 'the', 'circles')), + (12, ('of', 'sight', 'recognition') + ), (11, ('the', 'number', 'of')), + (11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))] assert isclose(P1['the'], 0.0611, rel_tol=0.001) @@ -75,7 +85,8 @@ def test_ngram_models(): assert P2.cond_prob.get(('went',)) is None - assert P3.cond_prob['in','order'].dictionary == {'to': 6} + assert P3.cond_prob['in', 'order'].dictionary == {'to': 6} + def test_ir_system(): from collections import namedtuple @@ -87,10 +98,12 @@ def verify_query(query, expected): assert len(expected) == len(query) for expected, (score, d) in zip(expected, query): - print(expected.url, "{0:.2f}".format(expected.score), "{0:.2f}".format(score * 100)) + print(expected.url, "{0:.2f}".format( + expected.score), "{0:.2f}".format(score * 100)) doc = uc.documents[d] print(doc.url) - assert "{0:.2f}".format(expected.score) == "{0:.2f}".format(score * 100) + assert "{0:.2f}".format( + expected.score) == "{0:.2f}".format(score * 100) assert expected.url == doc.url return True diff --git a/utils.py b/utils.py index 81f080301..6e20c9e1f 100644 --- a/utils.py +++ b/utils.py @@ -19,9 +19,12 @@ infinity = float('inf') + class Struct: + """Create an instance with argument=value slots. This is for making a lightweight object whose class doesn't matter.""" + def __init__(self, **entries): self.__dict__.update(entries) @@ -33,7 +36,8 @@ def __cmp__(self, other): def __repr__(self): args = ['{!s}={!s}'.format(k, repr(v)) - for (k, v) in list(vars(self).items())] + for (k, v) in list(vars(self).items())] + def update(x, **entries): """Update a dict or an object with slots according to entries.""" @@ -49,6 +53,7 @@ def update(x, **entries): # NOTE: Sequence functions (count_if, find_if, every, some) take function # argument first (like reduce, filter, and map). + def removeall(item, seq): """Return a copy of seq (or string) with all occurences of item removed.""" if isinstance(seq, str): @@ -56,10 +61,12 @@ def removeall(item, seq): else: return [x for x in seq if x != item] + def unique(seq): """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) + def product(numbers): """Return the product of the numbers, e.g. product([2, 3, 10]) == 60""" result = 1 @@ -67,10 +74,12 @@ def product(numbers): result *= x return result + def count_if(predicate, seq): """Count the number of elements of seq for which the predicate is true.""" return sum([bool(predicate(x)) for x in seq]) + def find_if(predicate, seq): """If there is an element of seq that satisfies predicate; return it.""" for x in seq: @@ -79,18 +88,23 @@ def find_if(predicate, seq): return None + def every(predicate, seq): """True if every element of seq satisfies predicate.""" return all(predicate(x) for x in seq) + def some(predicate, seq): """If some element x of seq satisfies predicate(x), return predicate(x).""" - elem = find_if(predicate,seq) + elem = find_if(predicate, seq) return predicate(elem) or False -# TODO: rename to is_in or possibily add 'identity' to function name to clarify intent +# TODO: rename to is_in or possibily add 'identity' to function name to +# clarify intent + + def isin(elt, seq): """Like (elt in seq), but compares with is, not ==.""" return any(x is elt for x in seq) @@ -103,15 +117,18 @@ def isin(elt, seq): # so there are three versions of argmin/argmax, depending on what you want to # do with ties: return the first one, return them all, or pick at random. + def argmin(seq, fn): return min(seq, key=fn) + def argmin_list(seq, fn): """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores.’""" smallest_score = len(min(seq, key=fn)) return [elem for elem in seq if fn(elem) == smallest_score] + def argmin_gen(seq, fn): """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores.""" @@ -119,15 +136,18 @@ def argmin_gen(seq, fn): yield from (elem for elem in seq if fn(elem) == smallest_score) + def argmin_random_tie(seq, fn): """Return an element with lowest fn(seq[i]) score; break ties at random. Thus, for all s,f: argmin_random_tie(s, f) in argmin_list(s, f)""" return random.choice(argmin_gen(seq, fn)) + def argmax(seq, fn): """Return an element with highest fn(seq[i]) score; tie goes to first one.""" return max(seq, key=fn) + def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method breaks if fn is len""" @@ -135,12 +155,14 @@ def argmax_list(seq, fn): return [elem for elem in seq if fn(elem) == largest_score] + def argmax_gen(seq, fn): """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores.""" largest_score = len(min(seq, key=fn)) yield from (elem for elem in seq if fn(elem) == largest_score) + def argmax_random_tie(seq, fn): "Return an element with highest fn(seq[i]) score; break ties at random." return argmin_random_tie(seq, lambda x: -fn(x)) @@ -148,6 +170,7 @@ def argmax_random_tie(seq, fn): #______________________________________________________________________________ # Statistical and mathematical functions + def histogram(values, mode=0, bin_function=None): """Return a list of (value, count) pairs, summarizing the input values. Sorted by increasing value, or if mode=1, by decreasing count. @@ -160,25 +183,29 @@ def histogram(values, mode=0, bin_function=None): bins[val] = bins.get(val, 0) + 1 if mode: - return sorted(list(bins.items()), key=lambda x: (x[1],x[0]), reverse=True) + return sorted(list(bins.items()), key=lambda x: (x[1], x[0]), reverse=True) else: return sorted(bins.items()) from math import log2 from statistics import mode, median, mean, stdev + def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y.""" return sum([x * y for x, y in zip(X, Y)]) + def vector_add(a, b): """Component-wise addition of two vectors.""" return tuple(map(operator.add, a, b)) + def probability(p): "Return true with probability p." return p > random.uniform(0.0, 1.0) + def weighted_sample_with_replacement(seq, weights, n): """Pick n samples from seq at random, with replacement, with the probability of each element in proportion to its corresponding @@ -187,6 +214,7 @@ def weighted_sample_with_replacement(seq, weights, n): return [sample() for _ in range(n)] + def weighted_sampler(seq, weights): "Return a random-sample function that picks from seq weighted by weights." totals = [] @@ -195,6 +223,7 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] + def num_or_str(x): """The argument is a string; convert to a number if possible, or strip it.""" try: @@ -205,52 +234,63 @@ def num_or_str(x): except ValueError: return str(x).strip() + def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) return [n / total for n in numbers] + def clip(x, lowest, highest): """Return x clipped to the range [lowest..highest].""" return max(lowest, min(x, highest)) #______________________________________________________________________________ -## OK, the following are not as widely useful utilities as some of the other -## functions here, but they do show up wherever we have 2D grids: Wumpus and -## Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. +# OK, the following are not as widely useful utilities as some of the other +# functions here, but they do show up wherever we have 2D grids: Wumpus and +# Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)] + def turn_heading(heading, inc, headings=orientations): return headings[(headings.index(heading) + inc) % len(headings)] + def turn_right(heading): return turn_heading(heading, -1) + def turn_left(heading): return turn_heading(heading, +1) + def Point(x, y): return (x, y) + def point_x(point): return point[0] + def point_y(point): return point[1] + def distance(a, b): "The distance between two (x, y) points." ax, ay = a bx, by = b return math.hypot((ax - bx), (ay - by)) + def distance2(a, b): "The square of the distance between two (x, y) points." ax, ay = a bx, by = b return (ax - bx)**2 + (ay - by)**2 + def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding value of lowest or more than the corresponding value of highest, clip to @@ -261,6 +301,7 @@ def vector_clip(vector, lowest, highest): #______________________________________________________________________________ # Misc Functions + def printf(format_str, *args): """Format args with the first argument as format string, and write. Return the last arg, or format itself if there are no args.""" @@ -268,6 +309,7 @@ def printf(format_str, *args): return args[-1] if args else format_str + def caller(n=1): """Return the name of the calling function n levels up in the frame stack.""" import inspect @@ -275,6 +317,8 @@ def caller(n=1): return inspect.getouterframes(inspect.currentframe())[n][3] # TODO: Use functools.lru_cache memoization decorator + + def memoize(fn, slot=None): """Memoize fn: make it remember the computed value for any argument list. If slot is specified, store result in that slot of first argument. @@ -304,14 +348,17 @@ def name(obj): or getattr(getattr(obj, '__class__', 0), '__name__', 0) or str(obj)) + def isnumber(x): "Is x a number? We say it is if it has a __int__ method." return hasattr(x, '__int__') + def issequence(x): "Is x a sequence? We say it is if it has a __getitem__ method." return hasattr(x, '__getitem__') + def print_table(table, header=None, sep=' ', numfmt='%g'): """Print a list of lists as a table, so that columns line up nicely. header, if specified, will be printed as the first row. @@ -324,15 +371,17 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): table.insert(0, header) table = [[numfmt.format(x) if isnumber(x) else x for x in row] - for row in table] + for row in table] maxlen = lambda seq: max(list(map(len, seq))) - sizes = list(map(maxlen, list(zip(*[list(map(str, row)) for row in table])))) + sizes = list( + map(maxlen, list(zip(*[list(map(str, row)) for row in table])))) for row in table: print((sep.join(getattr(str(x), j)(size) - for (j, size, x) in zip(justs, sizes, row)))) + for (j, size, x) in zip(justs, sizes, row)))) + def AIMAFile(components, mode='r'): "Open a file based at the AIMA root directory." @@ -343,10 +392,12 @@ def AIMAFile(components, mode='r'): return open(aima_file) + def DataFile(name, mode='r'): "Return a file in the AIMA /data directory." return AIMAFile(['aima-data', name], mode) + def unimplemented(): "Use this as a stub for not-yet-implemented functions." raise NotImplementedError @@ -355,7 +406,10 @@ def unimplemented(): # Queues: Stack, FIFOQueue, PriorityQueue # TODO: Use queue.Queue + + class Queue: + """Queue is an abstract class/interface. There are three types: Stack(): A Last In First Out Queue. FIFOQueue(): A First In First Out Queue. @@ -368,20 +422,27 @@ class Queue: item in q -- does q contain item? Note that isinstance(Stack(), Queue) is false, because we implement stacks as lists. If Python ever gets interfaces, Queue will be an interface.""" + def __init__(self): raise NotImplementedError def extend(self, items): - for item in items: self.append(item) + for item in items: + self.append(item) + def Stack(): """Return an empty list, suitable as a Last-In-First-Out Queue.""" return [] + class FIFOQueue(Queue): + """A First-In-First-Out Queue.""" + def __init__(self): - self.A = []; self.start = 0 + self.A = [] + self.start = 0 def append(self, item): self.A.append(item) @@ -404,11 +465,15 @@ def __contains__(self, item): return item in self.A[self.start:] # TODO: Use queue.PriorityQueue + + class PriorityQueue(Queue): + """A queue in which the minimum (or maximum) element (as determined by f and order) is returned first. If order is min, the item with minimum f(x) is returned first; if order is max, then it is the item with maximum f(x). Also supports dict-like lookup.""" + def __init__(self, order=min, f=lambda x: x): update(self, A=[], order=order, f=f) @@ -437,8 +502,7 @@ def __delitem__(self, key): if item == key: self.A.pop(i) -## Fig: The idea is we can define things like Fig[3,10] later. -## Alas, it is Fig[3,10] not Fig[3.10], because that would be the same -## as Fig[3.1] +# Fig: The idea is we can define things like Fig[3,10] later. +# Alas, it is Fig[3,10] not Fig[3.10], because that would be the same +# as Fig[3.1] Fig = {} - diff --git a/utils_test.py b/utils_test.py index af4a0a124..27e96a82e 100644 --- a/utils_test.py +++ b/utils_test.py @@ -1,104 +1,134 @@ import pytest from utils import * + def test_struct_initialization(): s = Struct(a=1, b=2) assert s.a == 1 assert s.b == 2 + def test_struct_assignment(): s = Struct(a=1) s.a = 3 assert s.a == 3 + def test_update_dict(): assert update({'a': 1}, a=10, b=20) == {'a': 10, 'b': 20} assert update({}, a=5) == {'a': 5} + def test_update_struct(): assert update(Struct(a=1), a=30, b=20).__cmp__(Struct(a=30, b=20)) assert update(Struct(), a=10).__cmp__(Struct(a=10)) + def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] assert removeall(4, [4, 1, 4, 2, 3, 4, 4]) == [1, 2, 3] + def test_removeall_string(): assert removeall('s', '') == '' - assert removeall('s', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' + assert removeall( + 's', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' + def test_unique(): assert unique([1, 2, 3, 2, 1]) == [1, 2, 3] assert unique([1, 5, 6, 7, 6, 5]) == [1, 5, 6, 7] + def test_product(): - assert product([1,2,3,4]) == 24 + assert product([1, 2, 3, 4]) == 24 assert product(list(range(1, 11))) == 3628800 + def test_find_if(): assert find_if(callable, [1, 2, 3]) == None assert find_if(callable, [3, min, max]) == min + def test_count_if(): assert count_if(callable, [42, None, max, min]) == 2 is_odd = lambda x: x % 2 assert count_if(is_odd, []) == 0 assert count_if(is_odd, [1, 2, 3, 4, 5]) == 3 + def test_every(): assert every(callable, [min, max]) == 1 assert every(callable, [min, 3]) == 0 + def test_some(): assert some(callable, [min, 3]) == 1 assert some(callable, [2, 3]) == 0 + def test_isin(): e = [] assert isin(e, [1, e, 3]) == True assert isin(e, [1, [], 3]) == False + def test_argmin(): assert argmin([-2, 1], lambda x: x**2) == 1 + def test_argmin_list(): assert argmin_list(['one', 'to', 'three', 'or'], len) == ['to', 'or'] + def test_argmin_gen(): - assert [i for i in argmin_gen(['one', 'to', 'three', 'or'], len)] == ['to', 'or'] + assert [i for i in argmin_gen(['one', 'to', 'three', 'or'], len)] == [ + 'to', 'or'] + def test_argmax(): assert argmax([-2, 1], lambda x: x**2) == -2 assert argmax(['one', 'to', 'three'], len) == 'three' + def test_argmax_list(): - assert argmax_list(['one', 'three', 'seven'], lambda x: len(x)) == ['three', 'seven'] + assert argmax_list(['one', 'three', 'seven'], lambda x: len(x)) == [ + 'three', 'seven'] + def test_argmax_gen(): assert argmax_list(['one', 'three', 'seven'], len) == ['three', 'seven'] + def test_dotproduct(): assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 + def test_vector_add(): assert vector_add((0, 1), (8, 9)) == (8, 10) + def test_num_or_str(): assert num_or_str('42') == 42 assert num_or_str(' 42x ') == '42x' + def test_normalize(): - assert normalize([1,2,1]) == [0.25, 0.5, 0.25] + assert normalize([1, 2, 1]) == [0.25, 0.5, 0.25] + def test_clip(): assert [clip(x, 0, 1) for x in [-1, 0.5, 10]] == [0, 0.5, 1] + def test_vector_clip(): assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) + def test_caller(): assert caller(0) == 'caller' + def f(): return caller() assert f() == 'f' From 6980ebfea0b50f636617c3d1da1b624278b22389 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 20:01:33 +0100 Subject: [PATCH 053/513] Convert into a python package with separate tests --- .gitmodules | 4 +-- MANIFEST.in | 2 ++ aimaPy/__init__.py | 14 ++++++++ agents.py => aimaPy/agents.py | 11 +++--- aimaPy/aima-data | 1 + csp.py => aimaPy/csp.py | 33 +++++++++++------- games.py => aimaPy/games.py | 2 +- grid.py => aimaPy/grid.py | 0 {images => aimaPy/images}/IMAGE-CREDITS | 0 {images => aimaPy/images}/dirt.svg | 0 {images => aimaPy/images}/dirt05-icon.jpg | Bin {images => aimaPy/images}/makefile | 0 {images => aimaPy/images}/vacuum-icon.jpg | Bin {images => aimaPy/images}/vacuum.svg | 0 {images => aimaPy/images}/wall-icon.jpg | Bin learning.py => aimaPy/learning.py | 2 +- logic.py => aimaPy/logic.py | 10 +++--- mdp.py => aimaPy/mdp.py | 2 +- nlp.py => aimaPy/nlp.py | 4 +-- planning.py => aimaPy/planning.py | 4 +-- probability.py => aimaPy/probability.py | 4 +-- rl.py => aimaPy/rl.py | 4 +-- search.py => aimaPy/search.py | 2 +- text.py => aimaPy/text.py | 12 ++++--- utils.py => aimaPy/utils.py | 9 +++-- setup.cfg | 2 ++ setup.py | 18 ++++++++++ .../probability_test.py | 2 +- text_test.py => tests/text_test.py | 5 +-- utils_test.py => tests/utils_test.py | 2 +- 30 files changed, 95 insertions(+), 54 deletions(-) create mode 100644 MANIFEST.in create mode 100644 aimaPy/__init__.py rename agents.py => aimaPy/agents.py (98%) create mode 160000 aimaPy/aima-data rename csp.py => aimaPy/csp.py (97%) rename games.py => aimaPy/games.py (99%) rename grid.py => aimaPy/grid.py (100%) rename {images => aimaPy/images}/IMAGE-CREDITS (100%) rename {images => aimaPy/images}/dirt.svg (100%) rename {images => aimaPy/images}/dirt05-icon.jpg (100%) rename {images => aimaPy/images}/makefile (100%) rename {images => aimaPy/images}/vacuum-icon.jpg (100%) rename {images => aimaPy/images}/vacuum.svg (100%) rename {images => aimaPy/images}/wall-icon.jpg (100%) rename learning.py => aimaPy/learning.py (99%) rename logic.py => aimaPy/logic.py (99%) rename mdp.py => aimaPy/mdp.py (99%) rename nlp.py => aimaPy/nlp.py (99%) rename planning.py => aimaPy/planning.py (72%) rename probability.py => aimaPy/probability.py (99%) rename rl.py => aimaPy/rl.py (90%) rename search.py => aimaPy/search.py (99%) rename text.py => aimaPy/text.py (97%) rename utils.py => aimaPy/utils.py (98%) create mode 100644 setup.cfg create mode 100644 setup.py rename probability_test.py => tests/probability_test.py (97%) rename text_test.py => tests/text_test.py (97%) rename utils_test.py => tests/utils_test.py (99%) diff --git a/.gitmodules b/.gitmodules index e15cc9e9a..b5b05a732 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ -[submodule "aima-data"] - path = aima-data +[submodule "aimaPy/aima-data"] + path = aimaPy/aima-data url = https://github.com/aimacode/aima-data diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000..57ce8b68e --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +graft aimaPy/aima-data +graft aimaPy/images \ No newline at end of file diff --git a/aimaPy/__init__.py b/aimaPy/__init__.py new file mode 100644 index 000000000..ae5bf6149 --- /dev/null +++ b/aimaPy/__init__.py @@ -0,0 +1,14 @@ +from . import agents +from . import csp +from . import games +from . import grid +from . import learning +from . import logic +from . import mdp +from . import nlp +from . import planning +from . import probability +from . import rl +from . import search +from . import text +from . import utils \ No newline at end of file diff --git a/agents.py b/aimaPy/agents.py similarity index 98% rename from agents.py rename to aimaPy/agents.py index 775ac1211..e04959837 100644 --- a/agents.py +++ b/aimaPy/agents.py @@ -35,7 +35,7 @@ # # Speed control in GUI does not have any effect -- fix it. -from utils import * +from . utils import * import random import copy import collections @@ -102,7 +102,7 @@ def TraceAgent(agent): def new_program(percept): action = old_program(percept) - print(('{} perceives {} and does {}'.format(agent, percept, action))) + print('{} perceives {} and does {}'.format(agent, percept, action)) return action agent.program = new_program return agent @@ -304,10 +304,9 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print( - (" Thing to be removed: {} at {}" .format(thing, thing.location))) - print((" from list: {}" .format([(thing, thing.location) - for thing in self.things]))) + print(" Thing to be removed: {} at {}" .format(thing, thing.location)) + print(" from list: {}" .format([(thing, thing.location) + for thing in self.things])) if thing in self.agents: self.agents.remove(thing) diff --git a/aimaPy/aima-data b/aimaPy/aima-data new file mode 160000 index 000000000..5b0526a5a --- /dev/null +++ b/aimaPy/aima-data @@ -0,0 +1 @@ +Subproject commit 5b0526a5a4d4312c3e65254c7e205a7ce327503b diff --git a/csp.py b/aimaPy/csp.py similarity index 97% rename from csp.py rename to aimaPy/csp.py index 0c9f50e64..25186ab07 100644 --- a/csp.py +++ b/aimaPy/csp.py @@ -1,9 +1,9 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from utils import * +from . utils import * from collections import defaultdict -import search +from . import search from functools import reduce @@ -519,6 +519,17 @@ def flatten(seqs): return sum(seqs, []) easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' +_R3 = list(range(3)) +_CELL = itertools.count().__next__ +_BGRID = [[[[_CELL() for x in _R3] for y in _R3] for bx in _R3] for by in _R3] +_BOXES = flatten([list(map(flatten, brow)) for brow in _BGRID]) +_ROWS = flatten([list(map(flatten, list(zip(*brow)))) for brow in _BGRID]) +_COLS = list(zip(*_ROWS)) + +_NEIGHBORS = dict([(v, set()) for v in flatten(_ROWS)]) +for unit in map(set, _BOXES + _ROWS + _COLS): + for v in unit: + _NEIGHBORS[v].update(unit - set([v])) class Sudoku(CSP): @@ -556,17 +567,13 @@ class Sudoku(CSP): >>> None != backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) True """ - R3 = list(range(3)) - Cell = itertools.count().__next__ - bgrid = [[[[Cell() for x in R3] for y in R3] for bx in R3] for by in R3] - boxes = flatten([list(map(flatten, brow)) for brow in bgrid]) - rows = flatten([list(map(flatten, list(zip(*brow)))) for brow in bgrid]) - cols = list(zip(*rows)) - - neighbors = dict([(v, set()) for v in flatten(rows)]) - for unit in map(set, boxes + rows + cols): - for v in unit: - neighbors[v].update(unit - set([v])) + R3 = _R3 + Cell = _CELL + bgrid = _BGRID + boxes = _BOXES + rows = _ROWS + cols = _COLS + neighbors = _NEIGHBORS def __init__(self, grid): """Build a Sudoku problem from a string representing the grid: diff --git a/games.py b/aimaPy/games.py similarity index 99% rename from games.py rename to aimaPy/games.py index 3141e6d51..14124f2f6 100644 --- a/games.py +++ b/aimaPy/games.py @@ -2,7 +2,7 @@ """ -from utils import * +from . utils import * import random #______________________________________________________________________________ diff --git a/grid.py b/aimaPy/grid.py similarity index 100% rename from grid.py rename to aimaPy/grid.py diff --git a/images/IMAGE-CREDITS b/aimaPy/images/IMAGE-CREDITS similarity index 100% rename from images/IMAGE-CREDITS rename to aimaPy/images/IMAGE-CREDITS diff --git a/images/dirt.svg b/aimaPy/images/dirt.svg similarity index 100% rename from images/dirt.svg rename to aimaPy/images/dirt.svg diff --git a/images/dirt05-icon.jpg b/aimaPy/images/dirt05-icon.jpg similarity index 100% rename from images/dirt05-icon.jpg rename to aimaPy/images/dirt05-icon.jpg diff --git a/images/makefile b/aimaPy/images/makefile similarity index 100% rename from images/makefile rename to aimaPy/images/makefile diff --git a/images/vacuum-icon.jpg b/aimaPy/images/vacuum-icon.jpg similarity index 100% rename from images/vacuum-icon.jpg rename to aimaPy/images/vacuum-icon.jpg diff --git a/images/vacuum.svg b/aimaPy/images/vacuum.svg similarity index 100% rename from images/vacuum.svg rename to aimaPy/images/vacuum.svg diff --git a/images/wall-icon.jpg b/aimaPy/images/wall-icon.jpg similarity index 100% rename from images/wall-icon.jpg rename to aimaPy/images/wall-icon.jpg diff --git a/learning.py b/aimaPy/learning.py similarity index 99% rename from learning.py rename to aimaPy/learning.py index 247abd2e3..0417b78e7 100644 --- a/learning.py +++ b/aimaPy/learning.py @@ -1,7 +1,7 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" -from utils import * +from . utils import * import copy import heapq import math diff --git a/logic.py b/aimaPy/logic.py similarity index 99% rename from logic.py rename to aimaPy/logic.py index f5531ae99..8981d6218 100644 --- a/logic.py +++ b/aimaPy/logic.py @@ -26,8 +26,8 @@ import itertools import re -import agents -from utils import * +from . import agents +from . utils import * from collections import defaultdict #______________________________________________________________________________ @@ -1253,7 +1253,7 @@ def pretty_set(s): def pp(x): - print((pretty(x))) + print(pretty(x)) def ppsubst(s): @@ -1262,11 +1262,11 @@ def ppsubst(s): def ppdict(d): - print((pretty_dict(d))) + print(pretty_dict(d)) def ppset(s): - print((pretty_set(s))) + print(pretty_set(s)) #________________________________________________________________________ diff --git a/mdp.py b/aimaPy/mdp.py similarity index 99% rename from mdp.py rename to aimaPy/mdp.py index b844fc749..8822f8260 100644 --- a/mdp.py +++ b/aimaPy/mdp.py @@ -7,7 +7,7 @@ and policy_iteration algorithms.""" -from utils import * +from . utils import * class MDP: diff --git a/nlp.py b/aimaPy/nlp.py similarity index 99% rename from nlp.py rename to aimaPy/nlp.py index 3687c2aed..8b6d3ca97 100644 --- a/nlp.py +++ b/aimaPy/nlp.py @@ -3,7 +3,7 @@ # (Written for the second edition of AIMA; expect some discrepanciecs # from the third edition until this gets reviewed.) -from utils import * +from . utils import * from collections import defaultdict #______________________________________________________________________________ @@ -158,7 +158,7 @@ def add_edge(self, edge): if edge not in self.chart[end]: self.chart[end].append(edge) if self.trace: - print(('%10s: added %s' % (caller(2), edge))) + print('%10s: added %s' % (caller(2), edge)) if not expects: self.extender(edge) else: diff --git a/planning.py b/aimaPy/planning.py similarity index 72% rename from planning.py rename to aimaPy/planning.py index d26350c4e..515ecf3ac 100644 --- a/planning.py +++ b/aimaPy/planning.py @@ -2,8 +2,8 @@ """ -from utils import * -import agents +from . utils import * +from . import agents import math import random import sys diff --git a/probability.py b/aimaPy/probability.py similarity index 99% rename from probability.py rename to aimaPy/probability.py index 6abb5ac3f..6975950e0 100644 --- a/probability.py +++ b/aimaPy/probability.py @@ -1,8 +1,8 @@ """Probability models. (Chapter 13-15) """ -from utils import * -from logic import extend +from . utils import * +from . logic import extend import random from collections import defaultdict from functools import reduce diff --git a/rl.py b/aimaPy/rl.py similarity index 90% rename from rl.py rename to aimaPy/rl.py index f30e542ba..67289e77d 100644 --- a/rl.py +++ b/aimaPy/rl.py @@ -1,8 +1,8 @@ """Reinforcement Learning (Chapter 21) """ -from utils import * -import agents +from . utils import * +from . import agents class PassiveADPAgent(agents.Agent): diff --git a/search.py b/aimaPy/search.py similarity index 99% rename from search.py rename to aimaPy/search.py index 4d4c9974a..4d9ce252f 100644 --- a/search.py +++ b/aimaPy/search.py @@ -5,7 +5,7 @@ functions.""" -from utils import * +from . utils import * import math import random import sys diff --git a/text.py b/aimaPy/text.py similarity index 97% rename from text.py rename to aimaPy/text.py index 2f2059d7a..5206b2de8 100644 --- a/text.py +++ b/aimaPy/text.py @@ -4,12 +4,12 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -from utils import * -from learning import CountingProbDist +from . utils import * +from . learning import CountingProbDist from math import log, exp from collections import defaultdict import re -import search +from . import search class UnigramTextModel(CountingProbDist): @@ -120,8 +120,9 @@ def __init__(self, stopwords='the a of'): def index_collection(self, filenames): "Index a whole collection of files." + prefix = os.path.dirname(__file__) for filename in filenames: - self.index_document(open(filename).read(), filename) + self.index_document(open(filename).read(), os.path.relpath(filename, prefix)) def index_document(self, text, url): "Index the text of a document." @@ -174,7 +175,8 @@ class UnixConsultant(IRSystem): def __init__(self): IRSystem.__init__(self, stopwords="how do i the a of") import os - mandir = 'aima-data/MAN/' + aima_root = os.path.dirname(__file__) + mandir = os.path.join(aima_root, 'aima-data/MAN/') man_files = [mandir + f for f in os.listdir(mandir) if f.endswith('.txt')] self.index_collection(man_files) diff --git a/utils.py b/aimaPy/utils.py similarity index 98% rename from utils.py rename to aimaPy/utils.py index 6e20c9e1f..df7cc09f2 100644 --- a/utils.py +++ b/aimaPy/utils.py @@ -305,7 +305,7 @@ def vector_clip(vector, lowest, highest): def printf(format_str, *args): """Format args with the first argument as format string, and write. Return the last arg, or format itself if there are no args.""" - print((str(format_str).format(*args, end=''))) + print(str(format_str).format(*args, end='')) return args[-1] if args else format_str @@ -379,14 +379,13 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): map(maxlen, list(zip(*[list(map(str, row)) for row in table])))) for row in table: - print((sep.join(getattr(str(x), j)(size) - for (j, size, x) in zip(justs, sizes, row)))) + print(sep.join(getattr(str(x), j)(size) + for (j, size, x) in zip(justs, sizes, row))) def AIMAFile(components, mode='r'): "Open a file based at the AIMA root directory." - import utils - aima_root = os.path.dirname(utils.__file__) + aima_root = os.path.dirname(__file__) aima_file = os.path.join(aima_root, *components) diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 000000000..9af7e6f11 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,2 @@ +[aliases] +test=pytest \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 000000000..4d9a11ede --- /dev/null +++ b/setup.py @@ -0,0 +1,18 @@ +from setuptools import setup + +setup( + name='aimaPy', + version='1.0', + description='Python code for the book Artificial Intelligence: A Modern Approach.', + long_description='Python code for the book Artificial Intelligence: A Modern Approach.', + author='Peter Norvig', + author_email='peter@norvig.com', + url='https://github.com/aimacode/aima-python', + license="MIT", + platforms="all", + packages=['aimaPy'], + include_package_data=True, + + setup_requires=['pytest-runner'], + tests_require=['pytest'], +) \ No newline at end of file diff --git a/probability_test.py b/tests/probability_test.py similarity index 97% rename from probability_test.py rename to tests/probability_test.py index 13b00d310..eb587dfa1 100644 --- a/probability_test.py +++ b/tests/probability_test.py @@ -1,5 +1,5 @@ import pytest -from probability import * +from aimaPy.probability import * def tests(): diff --git a/text_test.py b/tests/text_test.py similarity index 97% rename from text_test.py rename to tests/text_test.py index cc9c64653..4132682d2 100644 --- a/text_test.py +++ b/tests/text_test.py @@ -1,6 +1,6 @@ import pytest -from text import * +from aimaPy.text import * from random import choice from math import isclose @@ -98,10 +98,7 @@ def verify_query(query, expected): assert len(expected) == len(query) for expected, (score, d) in zip(expected, query): - print(expected.url, "{0:.2f}".format( - expected.score), "{0:.2f}".format(score * 100)) doc = uc.documents[d] - print(doc.url) assert "{0:.2f}".format( expected.score) == "{0:.2f}".format(score * 100) assert expected.url == doc.url diff --git a/utils_test.py b/tests/utils_test.py similarity index 99% rename from utils_test.py rename to tests/utils_test.py index 27e96a82e..d09587372 100644 --- a/utils_test.py +++ b/tests/utils_test.py @@ -1,5 +1,5 @@ import pytest -from utils import * +from aimaPy.utils import * def test_struct_initialization(): From cc3d995940d3e9cf1e5c3094e214a474f7eb0c59 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 20:05:48 +0100 Subject: [PATCH 054/513] Remove aims-data old link --- aima-data | 1 - 1 file changed, 1 deletion(-) delete mode 160000 aima-data diff --git a/aima-data b/aima-data deleted file mode 160000 index 75a59e53a..000000000 --- a/aima-data +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 75a59e53ab83773ac2838ddef4ac885d103f4511 From f100f01b52c03a205423512d7f86160fdd0bbfe4 Mon Sep 17 00:00:00 2001 From: MircoT Date: Sun, 6 Mar 2016 20:09:36 +0100 Subject: [PATCH 055/513] Add setup.py on travis install --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index ecf6ba14c..129b41668 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,6 +10,7 @@ before_install: install: - pip install flake8 - pip install -r requirements.txt + - python setup.py install script: - py.test From 88d498e378899a25073154ed122df57f44481d9a Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Sun, 6 Mar 2016 17:40:04 -0500 Subject: [PATCH 056/513] Added ipython notebooks and changed some syntax to python3 --- agents.py | 34 ++++++++-------- csp.py | 22 +++++----- iPython Notebooks/agents.ipynb | 46 +++++++++++++++++++++ iPython Notebooks/csp.ipynb | 63 +++++++++++++++++++++++++++++ iPython Notebooks/games.ipynb | 55 +++++++++++++++++++++++++ iPython Notebooks/grid.ipynb | 58 ++++++++++++++++++++++++++ iPython Notebooks/learning.ipynb | 34 ++++++++++++++++ iPython Notebooks/logic.ipynb | 34 ++++++++++++++++ iPython Notebooks/mdp.ipynb | 34 ++++++++++++++++ iPython Notebooks/nlp.ipynb | 34 ++++++++++++++++ iPython Notebooks/pathing.py | 11 +++++ iPython Notebooks/planning.ipynb | 34 ++++++++++++++++ iPython Notebooks/probability.ipynb | 34 ++++++++++++++++ iPython Notebooks/rl.ipynb | 34 ++++++++++++++++ iPython Notebooks/search.ipynb | 34 ++++++++++++++++ iPython Notebooks/text.ipynb | 34 ++++++++++++++++ search.py | 6 +-- 17 files changed, 570 insertions(+), 31 deletions(-) create mode 100644 iPython Notebooks/agents.ipynb create mode 100644 iPython Notebooks/csp.ipynb create mode 100644 iPython Notebooks/games.ipynb create mode 100644 iPython Notebooks/grid.ipynb create mode 100644 iPython Notebooks/learning.ipynb create mode 100644 iPython Notebooks/logic.ipynb create mode 100644 iPython Notebooks/mdp.ipynb create mode 100644 iPython Notebooks/nlp.ipynb create mode 100644 iPython Notebooks/pathing.py create mode 100644 iPython Notebooks/planning.ipynb create mode 100644 iPython Notebooks/probability.ipynb create mode 100644 iPython Notebooks/rl.ipynb create mode 100644 iPython Notebooks/search.ipynb create mode 100644 iPython Notebooks/text.ipynb diff --git a/agents.py b/agents.py index e45d0cbc0..b7756e74a 100644 --- a/agents.py +++ b/agents.py @@ -54,7 +54,7 @@ def is_alive(self): def show_state(self): "Display the agent's internal state. Subclasses should override." - print "I don't know how to show_state." + print("I don't know how to show_state.") def display(self, canvas, x, y, width, height): # Do we need this? @@ -94,7 +94,7 @@ def TraceAgent(agent): old_program = agent.program def new_program(percept): action = old_program(percept) - print '%s perceives %s and does %s' % (agent, percept, action) + print('%s perceives %s and does %s' % (agent, percept, action)) return action agent.program = new_program return agent @@ -172,7 +172,7 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): "A reflex agent for the two-state vacuum environment. [Fig. 2.8]" - def program((location, status)): + def program(location, status): if status == 'Dirty': return 'Suck' elif location == loc_A: return 'Right' elif location == loc_B: return 'Left' @@ -181,7 +181,7 @@ def program((location, status)): def ModelBasedVacuumAgent(): "An agent that keeps track of what locations are clean or dirty." model = {loc_A: None, loc_B: None} - def program((location, status)): + def program(location, status): "Same as ReflexVacuumAgent, except if everything is clean, do NoOp." model[location] = status ## Update the model here if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp' @@ -276,12 +276,12 @@ def delete_thing(self, thing): """Remove a thing from the environment.""" try: self.things.remove(thing) - except ValueError, e: - print e - print " in Environment delete_thing" - print " Thing to be removed: %s at %s" % (thing, thing.location) - print " from list: %s" % [(thing, thing.location) - for thing in self.things] + except ValueError as e: + print(e) + print(" in Environment delete_thing") + print(" Thing to be removed: %s at %s" % (thing, thing.location)) + print(" from list: %s" % [(thing, thing.location) + for thing in self.things]) if thing in self.agents: self.agents.remove(thing) @@ -544,7 +544,7 @@ def score(env): # (Tkinter is standard in all new releases), or delete the rest of this file # and muddle through without a GUI. -import Tkinter as tk +import tkinter as tk class EnvGUI(tk.Tk, object): @@ -592,12 +592,12 @@ def __init__(self, parent, env, canvas): scale.pack(side='left') def run(self): - print 'run' + print('run') self.running = True self.background_run() def stop(self): - print 'stop' + print('stop') self.running = False def background_run(self): @@ -610,14 +610,14 @@ def background_run(self): self.after(ms, self.background_run) def list_things(self): - print "Things in the environment:" + print("Things in the environment:") for thing in self.env.things: - print "%s at %s" % (thing, thing.location) + print("%s at %s" % (thing, thing.location)) def list_agents(self): - print "Agents in the environment:" + print("Agents in the environment:") for agt in self.env.agents: - print "%s at %s" % (agt, agt.location) + print("%s at %s" % (agt, agt.location)) def set_speed(self, speed): self.speed = float(speed) diff --git a/csp.py b/csp.py index 4c9b29459..ddf72e6bc 100644 --- a/csp.py +++ b/csp.py @@ -72,7 +72,7 @@ def conflict(var2): def display(self, assignment): "Show a human-readable representation of the CSP." # Subclasses can print in a prettier way, or display with a GUI - print 'CSP:', self, 'with assignment:', assignment + print('CSP:', self, 'with assignment:', assignment) ## These methods are for the tree- and graph-search interface: @@ -87,7 +87,8 @@ def actions(self, state): return [(var, val) for val in self.domains[var] if self.nconflicts(var, val, assignment) == 0] - def result(self, state, (var, val)): + def result(self, state, var_val): + var, val = var_val "Perform an action and return the new state." return state + ((var, val),) @@ -459,13 +460,13 @@ def display(self, assignment): if assignment.get(var,'') == val: ch = 'Q' elif (var+val) % 2 == 0: ch = '.' else: ch = '-' - print ch, - print ' ', + print(ch) + print(' ') for var in range(n): if assignment.get(var,'') == val: ch = '*' else: ch = ' ' - print str(self.nconflicts(var, val, assignment))+ch, - print + print(str(self.nconflicts(var, val, assignment))+ch) + #______________________________________________________________________________ # Sudoku @@ -540,8 +541,8 @@ def display(self, assignment): def show_box(box): return [' '.join(map(show_cell, row)) for row in box] def show_cell(cell): return str(assignment.get(cell, '.')) def abut(lines1, lines2): return map(' | '.join, zip(lines1, lines2)) - print '\n------+-------+------\n'.join( - '\n'.join(reduce(abut, map(show_box, brow))) for brow in self.bgrid) + print('\n------+-------+------\n'.join( + '\n'.join(reduce(abut, map(show_box, brow))) for brow in self.bgrid)) #______________________________________________________________________________ # The Zebra Puzzle @@ -598,10 +599,9 @@ def solve_zebra(algorithm=min_conflicts, **args): z = Zebra() ans = algorithm(z, **args) for h in range(1, 6): - print 'House', h, + print('House', h) for (var, val) in ans.items(): - if val == h: print var, - print + if val == h: print( var) return ans['Zebra'], ans['Water'], z.nassigns, ans diff --git a/iPython Notebooks/agents.ipynb b/iPython Notebooks/agents.ipynb new file mode 100644 index 000000000..b54caa06f --- /dev/null +++ b/iPython Notebooks/agents.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pathing\n", + "import agents\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/csp.ipynb b/iPython Notebooks/csp.ipynb new file mode 100644 index 000000000..41914188b --- /dev/null +++ b/iPython Notebooks/csp.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "dictionary changed size during iteration", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcsp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/csp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCSP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProblem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m97\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m U=Dict(V=142)))\n\u001b[0m\u001b[1;32m 490\u001b[0m romania.locations = Dict(\n\u001b[1;32m 491\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m91\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m492\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m327\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m253\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m288\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m165\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m299\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mUndirectedGraph\u001b[0;34m(dict)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mUndirectedGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\"Build a Graph where every edge (including future ones) goes both ways.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m def RandomGraph(nodes=range(10), min_links=2, width=400, height=300,\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dict, directed)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mmake_undirected\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;34m\"Make a digraph into an undirected graph by adding symmetric edges.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: dictionary changed size during iteration" + ] + } + ], + "source": [ + "import pathing\n", + "import csp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/games.ipynb b/iPython Notebooks/games.ipynb new file mode 100644 index 000000000..3ee1148c7 --- /dev/null +++ b/iPython Notebooks/games.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (games.py, line 152)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/games.py\"\u001b[0;36m, line \u001b[0;32m152\u001b[0m\n\u001b[0;31m raise NotImplementedError\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" + ] + } + ], + "source": [ + "import pathing\n", + "import games" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/grid.ipynb b/iPython Notebooks/grid.ipynb new file mode 100644 index 000000000..36f5d7b22 --- /dev/null +++ b/iPython Notebooks/grid.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ImportError", + "evalue": "No module named 'grid'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mImportError\u001b[0m: No module named 'grid'" + ] + } + ], + "source": [ + "import pathing\n", + "import grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/learning.ipynb b/iPython Notebooks/learning.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/learning.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/logic.ipynb b/iPython Notebooks/logic.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/logic.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/mdp.ipynb b/iPython Notebooks/mdp.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/mdp.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/nlp.ipynb b/iPython Notebooks/nlp.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/nlp.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/pathing.py b/iPython Notebooks/pathing.py new file mode 100644 index 000000000..fe3d14b4a --- /dev/null +++ b/iPython Notebooks/pathing.py @@ -0,0 +1,11 @@ +"""This small utility is used specifically for + iPython Notebooks to create a import module + path to the directory containing all of the + algorithms, namely the parent directory. +""" + +import os +import sys + +cwd = os.getcwd() +sys.path.insert(0, cwd.rstrip('iPython Notebooks')) diff --git a/iPython Notebooks/planning.ipynb b/iPython Notebooks/planning.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/planning.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/probability.ipynb b/iPython Notebooks/probability.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/probability.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/rl.ipynb b/iPython Notebooks/rl.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/rl.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/search.ipynb b/iPython Notebooks/search.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/search.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPython Notebooks/text.ipynb b/iPython Notebooks/text.ipynb new file mode 100644 index 000000000..f582c064c --- /dev/null +++ b/iPython Notebooks/text.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/search.py b/search.py index c0bbdd660..9e6d335ad 100644 --- a/search.py +++ b/search.py @@ -605,8 +605,8 @@ def print_boggle(board): n2 = len(board); n = exact_sqrt(n2) for i in range(n2): if i % n == 0 and i > 0: print - if board[i] == 'Q': print 'Qu', - else: print str(board[i]) + ' ', + if board[i] == 'Q': print ('Qu') + else: print(str(board[i]) + ' ') print def boggle_neighbors(n2, cache={}): @@ -748,7 +748,7 @@ def boggle_hill_climbing(board=None, ntimes=100, verbose=True): new = len(finder.set_board(board)) if new > best: best = new - if verbose: print best, _, board + if verbose: print(best, _, board) else: board[i] = oldc ## Change back if verbose: From ae09a5654c6e631d939c3fcd69a998c38c9d1263 Mon Sep 17 00:00:00 2001 From: SnShine Date: Mon, 7 Mar 2016 11:03:15 +0530 Subject: [PATCH 057/513] __init__.py in tests/ so build passes --- tests/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tests/__init__.py diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 000000000..e69de29bb From 79ece4217bb3887b5808cd8389fd1f15e3e49e78 Mon Sep 17 00:00:00 2001 From: SnShine Date: Mon, 7 Mar 2016 18:49:00 +0530 Subject: [PATCH 058/513] cleaned 2-d grid methods in utils.py as we got them in grid.py --- aimaPy/utils.py | 53 +------------------------------------------------ 1 file changed, 1 insertion(+), 52 deletions(-) diff --git a/aimaPy/utils.py b/aimaPy/utils.py index df7cc09f2..711eb0ea1 100644 --- a/aimaPy/utils.py +++ b/aimaPy/utils.py @@ -13,6 +13,7 @@ import bisect import re from functools import reduce +from . grid import * #______________________________________________________________________________ # Simple Data Structures: infinity, Dict, Struct @@ -245,58 +246,6 @@ def clip(x, lowest, highest): """Return x clipped to the range [lowest..highest].""" return max(lowest, min(x, highest)) -#______________________________________________________________________________ -# OK, the following are not as widely useful utilities as some of the other -# functions here, but they do show up wherever we have 2D grids: Wumpus and -# Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. - -orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)] - - -def turn_heading(heading, inc, headings=orientations): - return headings[(headings.index(heading) + inc) % len(headings)] - - -def turn_right(heading): - return turn_heading(heading, -1) - - -def turn_left(heading): - return turn_heading(heading, +1) - - -def Point(x, y): - return (x, y) - - -def point_x(point): - return point[0] - - -def point_y(point): - return point[1] - - -def distance(a, b): - "The distance between two (x, y) points." - ax, ay = a - bx, by = b - return math.hypot((ax - bx), (ay - by)) - - -def distance2(a, b): - "The square of the distance between two (x, y) points." - ax, ay = a - bx, by = b - return (ax - bx)**2 + (ay - by)**2 - - -def vector_clip(vector, lowest, highest): - """Return vector, except if any element is less than the corresponding - value of lowest or more than the corresponding value of highest, clip to - those values. - """ - return type(vector)(list(map(clip, vector, lowest, highest))) #______________________________________________________________________________ # Misc Functions From d06291611427e76bccd522c45e9d5524ffa8b6ab Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Mon, 7 Mar 2016 09:08:17 -0500 Subject: [PATCH 059/513] updated iPython notebooks --- iPython Notebooks/learning.ipynb | 21 ++++++++++++++++++++ iPython Notebooks/logic.ipynb | 29 ++++++++++++++++++++++++++++ iPython Notebooks/mdp.ipynb | 21 ++++++++++++++++++++ iPython Notebooks/nlp.ipynb | 21 ++++++++++++++++++++ iPython Notebooks/planning.ipynb | 12 ++++++++++++ iPython Notebooks/probability.ipynb | 30 +++++++++++++++++++++++++++++ iPython Notebooks/rl.ipynb | 12 ++++++++++++ iPython Notebooks/search.ipynb | 28 +++++++++++++++++++++++++++ iPython Notebooks/text.ipynb | 21 ++++++++++++++++++++ 9 files changed, 195 insertions(+) diff --git a/iPython Notebooks/learning.ipynb b/iPython Notebooks/learning.ipynb index f582c064c..9df30b878 100644 --- a/iPython Notebooks/learning.ipynb +++ b/iPython Notebooks/learning.ipynb @@ -1,5 +1,26 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print' (learning.py, line 267)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/learning.py\"\u001b[0;36m, line \u001b[0;32m267\u001b[0m\n\u001b[0;31m print 'Test', name\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" + ] + } + ], + "source": [ + "import pathing\n", + "import learning" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/logic.ipynb b/iPython Notebooks/logic.ipynb index f582c064c..4b8649230 100644 --- a/iPython Notebooks/logic.ipynb +++ b/iPython Notebooks/logic.ipynb @@ -1,5 +1,34 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlogic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;31m# would result in infinite recursion:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[0;31m#'(Human(h) & Mother(m, h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m \u001b[0;34m'(Mother(m, h) & Human(h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 953\u001b[0m ])\n\u001b[1;32m 954\u001b[0m )\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, initial_clauses)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclauses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# inefficient: no indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mclause\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36mexpr\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'([a-zA-Z0-9_.]+)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mr'Expr(\"\\1\")'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;31m## Now eval the string. (A security hole; do not use with an adversary.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'Expr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mExpr\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 171\u001b[0m \"\"\"Self must be a symbol with no args, such as Expr('F'). Create a new\n\u001b[1;32m 172\u001b[0m Expr with 'F' as op and the args as arguments.\"\"\"\n\u001b[0;32m--> 173\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mExpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "source": [ + "import pathing\n", + "import logic" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/mdp.ipynb b/iPython Notebooks/mdp.ipynb index f582c064c..b1b15bef9 100644 --- a/iPython Notebooks/mdp.ipynb +++ b/iPython Notebooks/mdp.ipynb @@ -1,5 +1,26 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndentationError", + "evalue": "unexpected indent (mdp.py, line 37)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/mdp.py\"\u001b[0;36m, line \u001b[0;32m37\u001b[0m\n\u001b[0;31m raise NotImplementedError\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" + ] + } + ], + "source": [ + "import pathing\n", + "import mdp" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/nlp.ipynb b/iPython Notebooks/nlp.ipynb index f582c064c..286c56ec7 100644 --- a/iPython Notebooks/nlp.ipynb +++ b/iPython Notebooks/nlp.ipynb @@ -1,5 +1,26 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (nlp.py, line 155)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/nlp.py\"\u001b[0;36m, line \u001b[0;32m155\u001b[0m\n\u001b[0;31m print '%10s: added %s' % (caller(2), edge)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "import pathing\n", + "import nlp" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/planning.ipynb b/iPython Notebooks/planning.ipynb index f582c064c..3efd385f0 100644 --- a/iPython Notebooks/planning.ipynb +++ b/iPython Notebooks/planning.ipynb @@ -1,5 +1,17 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pathing\n", + "import planning" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/probability.ipynb b/iPython Notebooks/probability.ipynb index f582c064c..f0735a189 100644 --- a/iPython Notebooks/probability.ipynb +++ b/iPython Notebooks/probability.ipynb @@ -1,5 +1,35 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mprobability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/probability.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlogic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;31m# would result in infinite recursion:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[0;31m#'(Human(h) & Mother(m, h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m \u001b[0;34m'(Mother(m, h) & Human(h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 953\u001b[0m ])\n\u001b[1;32m 954\u001b[0m )\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, initial_clauses)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclauses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# inefficient: no indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mclause\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36mexpr\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'([a-zA-Z0-9_.]+)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mr'Expr(\"\\1\")'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;31m## Now eval the string. (A security hole; do not use with an adversary.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'Expr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mExpr\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 171\u001b[0m \"\"\"Self must be a symbol with no args, such as Expr('F'). Create a new\n\u001b[1;32m 172\u001b[0m Expr with 'F' as op and the args as arguments.\"\"\"\n\u001b[0;32m--> 173\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mExpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], + "source": [ + "import pathing\n", + "import probability" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/rl.ipynb b/iPython Notebooks/rl.ipynb index f582c064c..faf460ad5 100644 --- a/iPython Notebooks/rl.ipynb +++ b/iPython Notebooks/rl.ipynb @@ -1,5 +1,17 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pathing\n", + "import rl" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/search.ipynb b/iPython Notebooks/search.ipynb index f582c064c..c16094a9e 100644 --- a/iPython Notebooks/search.ipynb +++ b/iPython Notebooks/search.ipynb @@ -1,5 +1,33 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "dictionary changed size during iteration", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m97\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m U=dict(V=142)))\n\u001b[0m\u001b[1;32m 490\u001b[0m romania.locations = dict(\n\u001b[1;32m 491\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m91\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m492\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m327\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m253\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m288\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m165\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m299\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mUndirectedGraph\u001b[0;34m(dict)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mUndirectedGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\"Build a Graph where every edge (including future ones) goes both ways.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m def RandomGraph(nodes=range(10), min_links=2, width=400, height=300,\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dict, directed)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mmake_undirected\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;34m\"Make a digraph into an undirected graph by adding symmetric edges.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: dictionary changed size during iteration" + ] + } + ], + "source": [ + "import pathing\n", + "import search" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/iPython Notebooks/text.ipynb b/iPython Notebooks/text.ipynb index f582c064c..3064cb661 100644 --- a/iPython Notebooks/text.ipynb +++ b/iPython Notebooks/text.ipynb @@ -1,5 +1,26 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print' (learning.py, line 267)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/learning.py\"\u001b[0;36m, line \u001b[0;32m267\u001b[0m\n\u001b[0;31m print 'Test', name\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" + ] + } + ], + "source": [ + "import pathing\n", + "import text" + ] + }, { "cell_type": "code", "execution_count": null, From ca10f73e554ee514cef97a7891e48ae94d73763f Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Mon, 7 Mar 2016 09:24:07 -0500 Subject: [PATCH 060/513] Fixed pathing to correct directory aimaPy --- {iPython Notebooks => iPythonNotebooks}/agents.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/csp.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/games.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/grid.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/learning.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/logic.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/mdp.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/nlp.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/pathing.py | 2 +- {iPython Notebooks => iPythonNotebooks}/planning.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/probability.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/rl.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/search.ipynb | 0 {iPython Notebooks => iPythonNotebooks}/text.ipynb | 0 14 files changed, 1 insertion(+), 1 deletion(-) rename {iPython Notebooks => iPythonNotebooks}/agents.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/csp.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/games.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/grid.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/learning.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/logic.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/mdp.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/nlp.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/pathing.py (79%) rename {iPython Notebooks => iPythonNotebooks}/planning.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/probability.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/rl.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/search.ipynb (100%) rename {iPython Notebooks => iPythonNotebooks}/text.ipynb (100%) diff --git a/iPython Notebooks/agents.ipynb b/iPythonNotebooks/agents.ipynb similarity index 100% rename from iPython Notebooks/agents.ipynb rename to iPythonNotebooks/agents.ipynb diff --git a/iPython Notebooks/csp.ipynb b/iPythonNotebooks/csp.ipynb similarity index 100% rename from iPython Notebooks/csp.ipynb rename to iPythonNotebooks/csp.ipynb diff --git a/iPython Notebooks/games.ipynb b/iPythonNotebooks/games.ipynb similarity index 100% rename from iPython Notebooks/games.ipynb rename to iPythonNotebooks/games.ipynb diff --git a/iPython Notebooks/grid.ipynb b/iPythonNotebooks/grid.ipynb similarity index 100% rename from iPython Notebooks/grid.ipynb rename to iPythonNotebooks/grid.ipynb diff --git a/iPython Notebooks/learning.ipynb b/iPythonNotebooks/learning.ipynb similarity index 100% rename from iPython Notebooks/learning.ipynb rename to iPythonNotebooks/learning.ipynb diff --git a/iPython Notebooks/logic.ipynb b/iPythonNotebooks/logic.ipynb similarity index 100% rename from iPython Notebooks/logic.ipynb rename to iPythonNotebooks/logic.ipynb diff --git a/iPython Notebooks/mdp.ipynb b/iPythonNotebooks/mdp.ipynb similarity index 100% rename from iPython Notebooks/mdp.ipynb rename to iPythonNotebooks/mdp.ipynb diff --git a/iPython Notebooks/nlp.ipynb b/iPythonNotebooks/nlp.ipynb similarity index 100% rename from iPython Notebooks/nlp.ipynb rename to iPythonNotebooks/nlp.ipynb diff --git a/iPython Notebooks/pathing.py b/iPythonNotebooks/pathing.py similarity index 79% rename from iPython Notebooks/pathing.py rename to iPythonNotebooks/pathing.py index fe3d14b4a..fff8b61b1 100644 --- a/iPython Notebooks/pathing.py +++ b/iPythonNotebooks/pathing.py @@ -8,4 +8,4 @@ import sys cwd = os.getcwd() -sys.path.insert(0, cwd.rstrip('iPython Notebooks')) +sys.path.insert(0, cwd.rstrip('iPythonNotebooks')+"aimaPy/") diff --git a/iPython Notebooks/planning.ipynb b/iPythonNotebooks/planning.ipynb similarity index 100% rename from iPython Notebooks/planning.ipynb rename to iPythonNotebooks/planning.ipynb diff --git a/iPython Notebooks/probability.ipynb b/iPythonNotebooks/probability.ipynb similarity index 100% rename from iPython Notebooks/probability.ipynb rename to iPythonNotebooks/probability.ipynb diff --git a/iPython Notebooks/rl.ipynb b/iPythonNotebooks/rl.ipynb similarity index 100% rename from iPython Notebooks/rl.ipynb rename to iPythonNotebooks/rl.ipynb diff --git a/iPython Notebooks/search.ipynb b/iPythonNotebooks/search.ipynb similarity index 100% rename from iPython Notebooks/search.ipynb rename to iPythonNotebooks/search.ipynb diff --git a/iPython Notebooks/text.ipynb b/iPythonNotebooks/text.ipynb similarity index 100% rename from iPython Notebooks/text.ipynb rename to iPythonNotebooks/text.ipynb From fe2cf09119e971d7dd4224301194caa6d5c725a2 Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Mon, 7 Mar 2016 09:49:18 -0500 Subject: [PATCH 061/513] Moved all ipython notebooks to aimaPy --- aimaPy/agents.ipynb | 59 +++++++++++++++++++ {iPythonNotebooks => aimaPy}/csp.ipynb | 0 {iPythonNotebooks => aimaPy}/games.ipynb | 0 {iPythonNotebooks => aimaPy}/grid.ipynb | 0 {iPythonNotebooks => aimaPy}/learning.ipynb | 0 {iPythonNotebooks => aimaPy}/logic.ipynb | 0 {iPythonNotebooks => aimaPy}/mdp.ipynb | 0 {iPythonNotebooks => aimaPy}/nlp.ipynb | 0 {iPythonNotebooks => aimaPy}/planning.ipynb | 0 .../probability.ipynb | 0 {iPythonNotebooks => aimaPy}/rl.ipynb | 0 {iPythonNotebooks => aimaPy}/search.ipynb | 0 {iPythonNotebooks => aimaPy}/text.ipynb | 0 iPythonNotebooks/agents.ipynb | 46 --------------- iPythonNotebooks/pathing.py | 11 ---- 15 files changed, 59 insertions(+), 57 deletions(-) create mode 100644 aimaPy/agents.ipynb rename {iPythonNotebooks => aimaPy}/csp.ipynb (100%) rename {iPythonNotebooks => aimaPy}/games.ipynb (100%) rename {iPythonNotebooks => aimaPy}/grid.ipynb (100%) rename {iPythonNotebooks => aimaPy}/learning.ipynb (100%) rename {iPythonNotebooks => aimaPy}/logic.ipynb (100%) rename {iPythonNotebooks => aimaPy}/mdp.ipynb (100%) rename {iPythonNotebooks => aimaPy}/nlp.ipynb (100%) rename {iPythonNotebooks => aimaPy}/planning.ipynb (100%) rename {iPythonNotebooks => aimaPy}/probability.ipynb (100%) rename {iPythonNotebooks => aimaPy}/rl.ipynb (100%) rename {iPythonNotebooks => aimaPy}/search.ipynb (100%) rename {iPythonNotebooks => aimaPy}/text.ipynb (100%) delete mode 100644 iPythonNotebooks/agents.ipynb delete mode 100644 iPythonNotebooks/pathing.py diff --git a/aimaPy/agents.ipynb b/aimaPy/agents.ipynb new file mode 100644 index 000000000..4daf8a32f --- /dev/null +++ b/aimaPy/agents.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/agents.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m# Speed control in GUI does not have any effect -- fix it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" + ] + } + ], + "source": [ + "import pathing\n", + "import agents\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/iPythonNotebooks/csp.ipynb b/aimaPy/csp.ipynb similarity index 100% rename from iPythonNotebooks/csp.ipynb rename to aimaPy/csp.ipynb diff --git a/iPythonNotebooks/games.ipynb b/aimaPy/games.ipynb similarity index 100% rename from iPythonNotebooks/games.ipynb rename to aimaPy/games.ipynb diff --git a/iPythonNotebooks/grid.ipynb b/aimaPy/grid.ipynb similarity index 100% rename from iPythonNotebooks/grid.ipynb rename to aimaPy/grid.ipynb diff --git a/iPythonNotebooks/learning.ipynb b/aimaPy/learning.ipynb similarity index 100% rename from iPythonNotebooks/learning.ipynb rename to aimaPy/learning.ipynb diff --git a/iPythonNotebooks/logic.ipynb b/aimaPy/logic.ipynb similarity index 100% rename from iPythonNotebooks/logic.ipynb rename to aimaPy/logic.ipynb diff --git a/iPythonNotebooks/mdp.ipynb b/aimaPy/mdp.ipynb similarity index 100% rename from iPythonNotebooks/mdp.ipynb rename to aimaPy/mdp.ipynb diff --git a/iPythonNotebooks/nlp.ipynb b/aimaPy/nlp.ipynb similarity index 100% rename from iPythonNotebooks/nlp.ipynb rename to aimaPy/nlp.ipynb diff --git a/iPythonNotebooks/planning.ipynb b/aimaPy/planning.ipynb similarity index 100% rename from iPythonNotebooks/planning.ipynb rename to aimaPy/planning.ipynb diff --git a/iPythonNotebooks/probability.ipynb b/aimaPy/probability.ipynb similarity index 100% rename from iPythonNotebooks/probability.ipynb rename to aimaPy/probability.ipynb diff --git a/iPythonNotebooks/rl.ipynb b/aimaPy/rl.ipynb similarity index 100% rename from iPythonNotebooks/rl.ipynb rename to aimaPy/rl.ipynb diff --git a/iPythonNotebooks/search.ipynb b/aimaPy/search.ipynb similarity index 100% rename from iPythonNotebooks/search.ipynb rename to aimaPy/search.ipynb diff --git a/iPythonNotebooks/text.ipynb b/aimaPy/text.ipynb similarity index 100% rename from iPythonNotebooks/text.ipynb rename to aimaPy/text.ipynb diff --git a/iPythonNotebooks/agents.ipynb b/iPythonNotebooks/agents.ipynb deleted file mode 100644 index b54caa06f..000000000 --- a/iPythonNotebooks/agents.ipynb +++ /dev/null @@ -1,46 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pathing\n", - "import agents\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/iPythonNotebooks/pathing.py b/iPythonNotebooks/pathing.py deleted file mode 100644 index fff8b61b1..000000000 --- a/iPythonNotebooks/pathing.py +++ /dev/null @@ -1,11 +0,0 @@ -"""This small utility is used specifically for - iPython Notebooks to create a import module - path to the directory containing all of the - algorithms, namely the parent directory. -""" - -import os -import sys - -cwd = os.getcwd() -sys.path.insert(0, cwd.rstrip('iPythonNotebooks')+"aimaPy/") From 92604944b7e21e81f4720560a58c43f17c8d50f8 Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Mon, 7 Mar 2016 11:44:42 -0500 Subject: [PATCH 062/513] fixed csp.py to pass test --- aimaPy/agents.ipynb | 3 +-- aimaPy/csp.ipynb | 19 +++++++------------ aimaPy/csp.py | 2 +- aimaPy/games.ipynb | 1 - aimaPy/grid.ipynb | 15 +-------------- aimaPy/learning.ipynb | 13 ++++++++----- aimaPy/logic.ipynb | 20 ++++++++------------ aimaPy/mdp.ipynb | 13 ++++++++----- aimaPy/nlp.ipynb | 11 +++++++---- aimaPy/planning.ipynb | 18 +++++++++++++++--- aimaPy/probability.ipynb | 20 +++++++------------- aimaPy/rl.ipynb | 18 +++++++++++++++--- aimaPy/search.ipynb | 16 ++++++---------- aimaPy/text.ipynb | 13 ++++++++----- 14 files changed, 92 insertions(+), 90 deletions(-) diff --git a/aimaPy/agents.ipynb b/aimaPy/agents.ipynb index 4daf8a32f..e66e246a6 100644 --- a/aimaPy/agents.ipynb +++ b/aimaPy/agents.ipynb @@ -14,14 +14,13 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/agents.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m# Speed control in GUI does not have any effect -- fix it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import agents\n" ] }, diff --git a/aimaPy/csp.ipynb b/aimaPy/csp.ipynb index 41914188b..18a45f205 100644 --- a/aimaPy/csp.ipynb +++ b/aimaPy/csp.ipynb @@ -2,30 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "RuntimeError", - "evalue": "dictionary changed size during iteration", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcsp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/csp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mCSP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProblem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m97\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m U=Dict(V=142)))\n\u001b[0m\u001b[1;32m 490\u001b[0m romania.locations = Dict(\n\u001b[1;32m 491\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m91\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m492\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m327\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m253\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m288\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m165\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m299\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mUndirectedGraph\u001b[0;34m(dict)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mUndirectedGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\"Build a Graph where every edge (including future ones) goes both ways.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m def RandomGraph(nodes=range(10), min_links=2, width=400, height=300,\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dict, directed)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mmake_undirected\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;34m\"Make a digraph into an undirected graph by adding symmetric edges.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: dictionary changed size during iteration" + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcsp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/csp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import csp" ] }, diff --git a/aimaPy/csp.py b/aimaPy/csp.py index 66a813052..ca25b04b7 100644 --- a/aimaPy/csp.py +++ b/aimaPy/csp.py @@ -596,7 +596,7 @@ def show_cell(cell): return str(assignment.get(cell, '.')) def abut(lines1, lines2): return list( map(' | '.join, list(zip(lines1, lines2)))) print('\n------+-------+------\n'.join( - '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgri + '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgri)) #______________________________________________________________________________ # The Zebra Puzzle diff --git a/aimaPy/games.ipynb b/aimaPy/games.ipynb index 3ee1148c7..35249cf01 100644 --- a/aimaPy/games.ipynb +++ b/aimaPy/games.ipynb @@ -17,7 +17,6 @@ } ], "source": [ - "import pathing\n", "import games" ] }, diff --git a/aimaPy/grid.ipynb b/aimaPy/grid.ipynb index 36f5d7b22..ba926a012 100644 --- a/aimaPy/grid.ipynb +++ b/aimaPy/grid.ipynb @@ -6,21 +6,8 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "No module named 'grid'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: No module named 'grid'" - ] - } - ], + "outputs": [], "source": [ - "import pathing\n", "import grid" ] }, diff --git a/aimaPy/learning.ipynb b/aimaPy/learning.ipynb index 9df30b878..35788415d 100644 --- a/aimaPy/learning.ipynb +++ b/aimaPy/learning.ipynb @@ -2,22 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "SyntaxError", - "evalue": "Missing parentheses in call to 'print' (learning.py, line 267)", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/learning.py\"\u001b[0;36m, line \u001b[0;32m267\u001b[0m\n\u001b[0;31m print 'Test', name\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlearning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/learning.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mheapq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import learning" ] }, diff --git a/aimaPy/logic.ipynb b/aimaPy/logic.ipynb index 4b8649230..6f021faea 100644 --- a/aimaPy/logic.ipynb +++ b/aimaPy/logic.ipynb @@ -2,30 +2,26 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "AssertionError", - "evalue": "", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlogic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;31m# would result in infinite recursion:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[0;31m#'(Human(h) & Mother(m, h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m \u001b[0;34m'(Mother(m, h) & Human(h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 953\u001b[0m ])\n\u001b[1;32m 954\u001b[0m )\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, initial_clauses)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclauses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# inefficient: no indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mclause\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36mexpr\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'([a-zA-Z0-9_.]+)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mr'Expr(\"\\1\")'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;31m## Now eval the string. (A security hole; do not use with an adversary.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'Expr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mExpr\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 171\u001b[0m \"\"\"Self must be a symbol with no args, such as Expr('F'). Create a new\n\u001b[1;32m 172\u001b[0m Expr with 'F' as op and the args as arguments.\"\"\"\n\u001b[0;32m--> 173\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mExpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAssertionError\u001b[0m: " + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlogic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", + "\n", "import logic" ] }, diff --git a/aimaPy/mdp.ipynb b/aimaPy/mdp.ipynb index b1b15bef9..073f1eb0f 100644 --- a/aimaPy/mdp.ipynb +++ b/aimaPy/mdp.ipynb @@ -2,22 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "IndentationError", - "evalue": "unexpected indent (mdp.py, line 37)", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/mdp.py\"\u001b[0;36m, line \u001b[0;32m37\u001b[0m\n\u001b[0;31m raise NotImplementedError\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmdp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/mdp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import mdp" ] }, diff --git a/aimaPy/nlp.ipynb b/aimaPy/nlp.ipynb index 286c56ec7..b2ae7e9fd 100644 --- a/aimaPy/nlp.ipynb +++ b/aimaPy/nlp.ipynb @@ -8,16 +8,19 @@ }, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (nlp.py, line 155)", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/nlp.py\"\u001b[0;36m, line \u001b[0;32m155\u001b[0m\n\u001b[0;31m print '%10s: added %s' % (caller(2), edge)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/nlp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# from the third edition until this gets reviewed.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import nlp" ] }, diff --git a/aimaPy/planning.ipynb b/aimaPy/planning.ipynb index 3efd385f0..4e5af6f03 100644 --- a/aimaPy/planning.ipynb +++ b/aimaPy/planning.ipynb @@ -4,11 +4,23 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplanning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/planning.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" + ] + } + ], "source": [ - "import pathing\n", "import planning" ] }, diff --git a/aimaPy/probability.ipynb b/aimaPy/probability.ipynb index f0735a189..f004f531c 100644 --- a/aimaPy/probability.ipynb +++ b/aimaPy/probability.ipynb @@ -2,31 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "AssertionError", - "evalue": "", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mprobability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/probability.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlogic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 950\u001b[0m \u001b[0;31m# would result in infinite recursion:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[0;31m#'(Human(h) & Mother(m, h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m \u001b[0;34m'(Mother(m, h) & Human(h)) ==> Human(m)'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 953\u001b[0m ])\n\u001b[1;32m 954\u001b[0m )\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, initial_clauses)\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclauses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# inefficient: no indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mclause\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minitial_clauses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 915\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclause\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36mexpr\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'([a-zA-Z0-9_.]+)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mr'Expr(\"\\1\")'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;31m## Now eval the string. (A security hole; do not use with an adversary.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'Expr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mExpr\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/logic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 171\u001b[0m \"\"\"Self must be a symbol with no args, such as Expr('F'). Create a new\n\u001b[1;32m 172\u001b[0m Expr with 'F' as op and the args as arguments.\"\"\"\n\u001b[0;32m--> 173\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mis_symbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mExpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAssertionError\u001b[0m: " + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mprobability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/probability.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \"\"\"\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mlogic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import probability" ] }, diff --git a/aimaPy/rl.ipynb b/aimaPy/rl.ipynb index faf460ad5..279fe50e0 100644 --- a/aimaPy/rl.ipynb +++ b/aimaPy/rl.ipynb @@ -4,11 +4,23 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mrl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/rl.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \"\"\"\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" + ] + } + ], "source": [ - "import pathing\n", "import rl" ] }, diff --git a/aimaPy/search.ipynb b/aimaPy/search.ipynb index c16094a9e..ce9b12412 100644 --- a/aimaPy/search.ipynb +++ b/aimaPy/search.ipynb @@ -8,23 +8,19 @@ }, "outputs": [ { - "ename": "RuntimeError", - "evalue": "dictionary changed size during iteration", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpathing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m97\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m U=dict(V=142)))\n\u001b[0m\u001b[1;32m 490\u001b[0m romania.locations = dict(\n\u001b[1;32m 491\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m91\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m492\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m327\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m253\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m288\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m165\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m299\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mUndirectedGraph\u001b[0;34m(dict)\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mUndirectedGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;34m\"Build a Graph where every edge (including future ones) goes both ways.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 450\u001b[0m def RandomGraph(nodes=range(10), min_links=2, width=400, height=300,\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dict, directed)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeff/git/aima-python/search.py\u001b[0m in \u001b[0;36mmake_undirected\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0;34m\"Make a digraph into an undirected graph by adding symmetric edges.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 421\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 422\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: dictionary changed size during iteration" + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import search" ] }, diff --git a/aimaPy/text.ipynb b/aimaPy/text.ipynb index 3064cb661..f15769f5f 100644 --- a/aimaPy/text.ipynb +++ b/aimaPy/text.ipynb @@ -2,22 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "SyntaxError", - "evalue": "Missing parentheses in call to 'print' (learning.py, line 267)", + "ename": "SystemError", + "evalue": "Parent module '' not loaded, cannot perform relative import", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/learning.py\"\u001b[0;36m, line \u001b[0;32m267\u001b[0m\n\u001b[0;31m print 'Test', name\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/text.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m working on a tiny sample of Unix manual pages.\"\"\"\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mlearning\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCountingProbDist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" ] } ], "source": [ - "import pathing\n", "import text" ] }, From 97bf5e390c85d14a1500734c2a0639d0954b5281 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Tue, 8 Mar 2016 14:08:05 +0530 Subject: [PATCH 063/513] renamed isin() to is_in() --- aimaPy/utils.py | 6 +++--- tests/utils_test.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/aimaPy/utils.py b/aimaPy/utils.py index 711eb0ea1..4a04dde22 100644 --- a/aimaPy/utils.py +++ b/aimaPy/utils.py @@ -102,12 +102,12 @@ def some(predicate, seq): return predicate(elem) or False -# TODO: rename to is_in or possibily add 'identity' to function name to +# TODO[COMPLETED]: rename to is_in or possibily add 'identity' to function name to # clarify intent -def isin(elt, seq): - """Like (elt in seq), but compares with is, not ==.""" +def is_in(elt, seq): + """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) #______________________________________________________________________________ diff --git a/tests/utils_test.py b/tests/utils_test.py index d09587372..d81b0b5b3 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -68,10 +68,10 @@ def test_some(): assert some(callable, [2, 3]) == 0 -def test_isin(): +def test_is_in(): e = [] - assert isin(e, [1, e, 3]) == True - assert isin(e, [1, [], 3]) == False + assert is_in(e, [1, e, 3]) == True + assert is_in(e, [1, [], 3]) == False def test_argmin(): From d616b9d3b9b93c03da187d2ff7534ed5f6e642aa Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Tue, 8 Mar 2016 14:13:44 +0530 Subject: [PATCH 064/513] Fixed bug in argmin and argmax functions where len() was used instead of fn --- aimaPy/utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/aimaPy/utils.py b/aimaPy/utils.py index 4a04dde22..2da0f3080 100644 --- a/aimaPy/utils.py +++ b/aimaPy/utils.py @@ -125,7 +125,7 @@ def argmin(seq, fn): def argmin_list(seq, fn): """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores.’""" - smallest_score = len(min(seq, key=fn)) + smallest_score = fn(min(seq, key=fn)) return [elem for elem in seq if fn(elem) == smallest_score] @@ -133,7 +133,7 @@ def argmin_list(seq, fn): def argmin_gen(seq, fn): """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores.""" - smallest_score = len(min(seq, key=fn)) + smallest_score = fn(min(seq, key=fn)) yield from (elem for elem in seq if fn(elem) == smallest_score) @@ -152,14 +152,14 @@ def argmax(seq, fn): def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method breaks if fn is len""" - largest_score = len(max(seq, key=fn)) + largest_score = fn(max(seq, key=fn)) return [elem for elem in seq if fn(elem) == largest_score] def argmax_gen(seq, fn): """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores.""" - largest_score = len(min(seq, key=fn)) + largest_score = fn(min(seq, key=fn)) yield from (elem for elem in seq if fn(elem) == largest_score) From b0fc4e8e3e5fcf2eeeb2fd617e6f0256debf503b Mon Sep 17 00:00:00 2001 From: MircoT Date: Tue, 8 Mar 2016 12:30:45 +0100 Subject: [PATCH 065/513] Add test_grid.py --- aimaPy/grid.py | 22 +++++----------------- tests/test_grid.py | 26 ++++++++++++++++++++++++++ 2 files changed, 31 insertions(+), 17 deletions(-) create mode 100644 tests/test_grid.py diff --git a/aimaPy/grid.py b/aimaPy/grid.py index 45bce40f3..cac6a5b9e 100644 --- a/aimaPy/grid.py +++ b/aimaPy/grid.py @@ -21,18 +21,12 @@ def turn_left(heading): def distance(a, b): - """The distance between two (x, y) points. - >>> distance((1,2),(5,5)) - 5.0 - """ + """The distance between two (x, y) points.""" return math.hypot((a[0] - b[0]), (a[1] - b[1])) def distance_squared(a, b): - """The square of the distance between two (x, y) points. - >>> distance_squared((1,2),(5,5)) - 25.0 - """ + """The square of the distance between two (x, y) points.""" return (a[0] - b[0])**2 + (a[1] - b[1])**2 @@ -42,18 +36,12 @@ def distance2(a, b): def clip(x, lowest, highest): - """Return x clipped to the range [lowest..highest]. - >>> [clip(x, 0, 1) for x in [-1, 0.5, 10]] - [0, 0.5, 1] - """ + """Return x clipped to the range [lowest..highest].""" return max(lowest, min(x, highest)) def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding value of lowest or more than the corresponding value of highest, clip to - those values. - >>> vector_clip((-1, 10), (0, 0), (9, 9)) - (0, 9) - """ - return type(vector)(list(map(clip, vector, lowest, highest))) + those values.""" + return type(vector)(map(clip, vector, lowest, highest)) diff --git a/tests/test_grid.py b/tests/test_grid.py new file mode 100644 index 000000000..f13170078 --- /dev/null +++ b/tests/test_grid.py @@ -0,0 +1,26 @@ +import pytest +from aimaPy.grid import * + +compare = lambda x, y: all([elm_x == y[i] for i, elm_x in enumerate(x)]) + + +def test_distance(): + assert distance((1, 2), (5, 5)) == 5.0 + + +def test_distance_squared(): + assert distance_squared((1, 2), (5, 5)) == 25.0 + + +def test_clip(): + list_ = [clip(x, 0, 1) for x in [-1, 0.5, 10]] + res = [0, 0.5, 1] + + assert compare(list_, res) + + +def test_vector_clip(): + assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) + +if __name__ == '__main__': + pytest.main() From 4b6756bd8305190a5d1dc1d2e8e9a0b94d5baa40 Mon Sep 17 00:00:00 2001 From: MircoT Date: Tue, 8 Mar 2016 12:33:23 +0100 Subject: [PATCH 066/513] Change name of compare function in test grid --- tests/test_grid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_grid.py b/tests/test_grid.py index f13170078..40d2c627a 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -1,7 +1,7 @@ import pytest from aimaPy.grid import * -compare = lambda x, y: all([elm_x == y[i] for i, elm_x in enumerate(x)]) +compare_list = lambda x, y: all([elm_x == y[i] for i, elm_x in enumerate(x)]) def test_distance(): @@ -16,7 +16,7 @@ def test_clip(): list_ = [clip(x, 0, 1) for x in [-1, 0.5, 10]] res = [0, 0.5, 1] - assert compare(list_, res) + assert compare_list(list_, res) def test_vector_clip(): From f5f38a2b737e716dba2ee4e98979411c9fcf32d2 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Wed, 9 Mar 2016 01:30:32 +0800 Subject: [PATCH 067/513] Add test cases for histogram --- tests/utils_test.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/utils_test.py b/tests/utils_test.py index d09587372..7a190845d 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -101,6 +101,18 @@ def test_argmax_gen(): assert argmax_list(['one', 'three', 'seven'], len) == ['three', 'seven'] +def test_histogram_no_function(): + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), (4, 2), (5, 1), (7, 1), (9, 1)] + + +def test_histogram_with_function(): + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, lambda x: x*x) == [(1, 2), (4, 3), (16, 2), (25, 1), (49, 1), (81, 1)] + + +def test_histogram_with_mode_one(): + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 1) == [(2, 3), (4, 2), (1, 2), (9, 1), (7, 1), (5, 1)] + + def test_dotproduct(): assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 From 9171ef849d46124b9eb2371c8c5295c29afe72f4 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Wed, 9 Mar 2016 02:44:48 +0800 Subject: [PATCH 068/513] Compress three functions into one --- tests/utils_test.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/tests/utils_test.py b/tests/utils_test.py index 7a190845d..bcc16757c 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -101,15 +101,9 @@ def test_argmax_gen(): assert argmax_list(['one', 'three', 'seven'], len) == ['three', 'seven'] -def test_histogram_no_function(): +def test_histogram(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), (4, 2), (5, 1), (7, 1), (9, 1)] - - -def test_histogram_with_function(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, lambda x: x*x) == [(1, 2), (4, 3), (16, 2), (25, 1), (49, 1), (81, 1)] - - -def test_histogram_with_mode_one(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 1) == [(2, 3), (4, 2), (1, 2), (9, 1), (7, 1), (5, 1)] From c0df02e6081c265cb74f5e148547683724b6e852 Mon Sep 17 00:00:00 2001 From: MircoT Date: Wed, 9 Mar 2016 10:42:19 +0100 Subject: [PATCH 069/513] Fix relative imports and not working notebooks - Fix relative imports in base of is a package or not - Fix not working notebooks - Add notebooks to packages sources --- MANIFEST.in | 3 ++- aimaPy/__init__.py | 29 +++++++++++++++-------------- aimaPy/agents.ipynb | 17 ++--------------- aimaPy/agents.py | 6 +++++- aimaPy/csp.ipynb | 17 ++--------------- aimaPy/csp.py | 9 +++++++-- aimaPy/games.ipynb | 11 +---------- aimaPy/games.py | 6 +++++- aimaPy/grid.ipynb | 14 ++++++++++++-- aimaPy/learning.ipynb | 15 +-------------- aimaPy/learning.py | 5 ++++- aimaPy/logic.ipynb | 16 +--------------- aimaPy/logic.py | 9 +++++++-- aimaPy/mdp.ipynb | 15 +-------------- aimaPy/mdp.py | 5 ++++- aimaPy/nlp.ipynb | 15 +-------------- aimaPy/nlp.py | 6 +++++- aimaPy/planning.ipynb | 15 +-------------- aimaPy/planning.py | 9 +++++++-- aimaPy/probability.ipynb | 17 ++--------------- aimaPy/probability.py | 9 +++++++-- aimaPy/rl.ipynb | 15 +-------------- aimaPy/rl.py | 8 ++++++-- aimaPy/search.ipynb | 15 +-------------- aimaPy/search.py | 5 ++++- aimaPy/text.ipynb | 15 +-------------- aimaPy/text.py | 13 ++++++++++--- aimaPy/utils.py | 6 +++++- 28 files changed, 120 insertions(+), 205 deletions(-) diff --git a/MANIFEST.in b/MANIFEST.in index 57ce8b68e..95bc5e3b4 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,3 @@ graft aimaPy/aima-data -graft aimaPy/images \ No newline at end of file +graft aimaPy/images +include aimaPy/*.ipynb \ No newline at end of file diff --git a/aimaPy/__init__.py b/aimaPy/__init__.py index ae5bf6149..85c43cec4 100644 --- a/aimaPy/__init__.py +++ b/aimaPy/__init__.py @@ -1,14 +1,15 @@ -from . import agents -from . import csp -from . import games -from . import grid -from . import learning -from . import logic -from . import mdp -from . import nlp -from . import planning -from . import probability -from . import rl -from . import search -from . import text -from . import utils \ No newline at end of file +if __name__ == 'aimaPy': + from . import agents + from . import csp + from . import games + from . import grid + from . import learning + from . import logic + from . import mdp + from . import nlp + from . import planning + from . import probability + from . import rl + from . import search + from . import text + from . import utils diff --git a/aimaPy/agents.ipynb b/aimaPy/agents.ipynb index e66e246a6..ee6807c1d 100644 --- a/aimaPy/agents.ipynb +++ b/aimaPy/agents.ipynb @@ -6,22 +6,9 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/agents.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m# Speed control in GUI does not have any effect -- fix it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ - "import agents\n" + "import agents" ] }, { diff --git a/aimaPy/agents.py b/aimaPy/agents.py index 3b99f461c..80a7064fb 100644 --- a/aimaPy/agents.py +++ b/aimaPy/agents.py @@ -35,7 +35,11 @@ # # Speed control in GUI does not have any effect -- fix it. -from . utils import * +if __name__ == "aimaPy.agents": + from . utils import * +else: + from utils import * + import random import copy import collections diff --git a/aimaPy/csp.ipynb b/aimaPy/csp.ipynb index 18a45f205..d82e1a6fd 100644 --- a/aimaPy/csp.ipynb +++ b/aimaPy/csp.ipynb @@ -2,24 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcsp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/csp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import csp" ] diff --git a/aimaPy/csp.py b/aimaPy/csp.py index ca25b04b7..414f69fb9 100644 --- a/aimaPy/csp.py +++ b/aimaPy/csp.py @@ -1,9 +1,14 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from . utils import * +if __name__ == "aimaPy.csp": + from . utils import * + from . import search +else: + from utils import * + import search + from collections import defaultdict -from . import search from functools import reduce diff --git a/aimaPy/games.ipynb b/aimaPy/games.ipynb index 35249cf01..34faa6563 100644 --- a/aimaPy/games.ipynb +++ b/aimaPy/games.ipynb @@ -6,16 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "IndentationError", - "evalue": "unexpected indent (games.py, line 152)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"/Users/jeff/git/aima-python/games.py\"\u001b[0;36m, line \u001b[0;32m152\u001b[0m\n\u001b[0;31m raise NotImplementedError\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" - ] - } - ], + "outputs": [], "source": [ "import games" ] diff --git a/aimaPy/games.py b/aimaPy/games.py index 14124f2f6..ed653c51e 100644 --- a/aimaPy/games.py +++ b/aimaPy/games.py @@ -2,7 +2,11 @@ """ -from . utils import * +if __name__ == "aimaPy.games": + from . utils import * +else: + from utils import * + import random #______________________________________________________________________________ diff --git a/aimaPy/grid.ipynb b/aimaPy/grid.ipynb index ba926a012..fe4e11a77 100644 --- a/aimaPy/grid.ipynb +++ b/aimaPy/grid.ipynb @@ -6,9 +6,19 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n" + ] + } + ], "source": [ - "import grid" + "import grid\n", + "\n", + "print(grid.distance_squared((1, 2), (5, 5)))" ] }, { diff --git a/aimaPy/learning.ipynb b/aimaPy/learning.ipynb index 35788415d..90d7746f7 100644 --- a/aimaPy/learning.ipynb +++ b/aimaPy/learning.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlearning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/learning.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mheapq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import learning" ] diff --git a/aimaPy/learning.py b/aimaPy/learning.py index 0417b78e7..638786215 100644 --- a/aimaPy/learning.py +++ b/aimaPy/learning.py @@ -1,7 +1,10 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" +if __name__ == "aimaPy.learning": + from . utils import * +else: + from utils import * -from . utils import * import copy import heapq import math diff --git a/aimaPy/logic.ipynb b/aimaPy/logic.ipynb index 6f021faea..a3f85d38b 100644 --- a/aimaPy/logic.ipynb +++ b/aimaPy/logic.ipynb @@ -6,22 +6,8 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlogic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/logic.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ - "\n", "import logic" ] }, diff --git a/aimaPy/logic.py b/aimaPy/logic.py index 8981d6218..8b1d8c9cf 100644 --- a/aimaPy/logic.py +++ b/aimaPy/logic.py @@ -24,10 +24,15 @@ diff, simp Symbolic differentiation and simplification """ +if __name__ == "aimaPy.logic": + from . utils import * + from . import agents +else: + from utils import * + import agents + import itertools import re -from . import agents -from . utils import * from collections import defaultdict #______________________________________________________________________________ diff --git a/aimaPy/mdp.ipynb b/aimaPy/mdp.ipynb index 073f1eb0f..d40f6030c 100644 --- a/aimaPy/mdp.ipynb +++ b/aimaPy/mdp.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmdp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/mdp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import mdp" ] diff --git a/aimaPy/mdp.py b/aimaPy/mdp.py index 8822f8260..4ade25ac3 100644 --- a/aimaPy/mdp.py +++ b/aimaPy/mdp.py @@ -7,7 +7,10 @@ and policy_iteration algorithms.""" -from . utils import * +if __name__ == "aimaPy.mdp": + from . utils import * +else: + from utils import * class MDP: diff --git a/aimaPy/nlp.ipynb b/aimaPy/nlp.ipynb index b2ae7e9fd..70bf2d974 100644 --- a/aimaPy/nlp.ipynb +++ b/aimaPy/nlp.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/nlp.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# from the third edition until this gets reviewed.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import nlp" ] diff --git a/aimaPy/nlp.py b/aimaPy/nlp.py index 8b6d3ca97..a55ef32fa 100644 --- a/aimaPy/nlp.py +++ b/aimaPy/nlp.py @@ -3,7 +3,11 @@ # (Written for the second edition of AIMA; expect some discrepanciecs # from the third edition until this gets reviewed.) -from . utils import * +if __name__ == "aimaPy.nlp": + from . utils import * +else: + from utils import * + from collections import defaultdict #______________________________________________________________________________ diff --git a/aimaPy/planning.ipynb b/aimaPy/planning.ipynb index 4e5af6f03..0c04c559b 100644 --- a/aimaPy/planning.ipynb +++ b/aimaPy/planning.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplanning\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/planning.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import planning" ] diff --git a/aimaPy/planning.py b/aimaPy/planning.py index 515ecf3ac..e6e0413b8 100644 --- a/aimaPy/planning.py +++ b/aimaPy/planning.py @@ -2,8 +2,13 @@ """ -from . utils import * -from . import agents +if __name__ == "aimaPy.planning": + from . utils import * + from . import agents +else: + from utils import * + import agents + import math import random import sys diff --git a/aimaPy/probability.ipynb b/aimaPy/probability.ipynb index f004f531c..48de9e035 100644 --- a/aimaPy/probability.ipynb +++ b/aimaPy/probability.ipynb @@ -2,24 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mprobability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/probability.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \"\"\"\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mlogic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import probability" ] diff --git a/aimaPy/probability.py b/aimaPy/probability.py index 6975950e0..c714b5b09 100644 --- a/aimaPy/probability.py +++ b/aimaPy/probability.py @@ -1,8 +1,13 @@ """Probability models. (Chapter 13-15) """ -from . utils import * -from . logic import extend +if __name__ == "aimaPy.probability": + from . utils import * + from . logic import extend +else: + from utils import * + from logic import extend + import random from collections import defaultdict from functools import reduce diff --git a/aimaPy/rl.ipynb b/aimaPy/rl.ipynb index 279fe50e0..d68ea9693 100644 --- a/aimaPy/rl.ipynb +++ b/aimaPy/rl.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mrl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/rl.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \"\"\"\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import rl" ] diff --git a/aimaPy/rl.py b/aimaPy/rl.py index 67289e77d..36dffb7af 100644 --- a/aimaPy/rl.py +++ b/aimaPy/rl.py @@ -1,8 +1,12 @@ """Reinforcement Learning (Chapter 21) """ -from . utils import * -from . import agents +if __name__ == "aimaPy.rl": + from . utils import * + from . import agents +else: + from utils import * + import agents class PassiveADPAgent(agents.Agent): diff --git a/aimaPy/search.ipynb b/aimaPy/search.ipynb index ce9b12412..0ec780587 100644 --- a/aimaPy/search.ipynb +++ b/aimaPy/search.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/search.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import search" ] diff --git a/aimaPy/search.py b/aimaPy/search.py index 93a2d44bb..dc29e9e85 100644 --- a/aimaPy/search.py +++ b/aimaPy/search.py @@ -4,8 +4,11 @@ then create problem instances and solve them with calls to the various search functions.""" +if __name__ == "aimaPy.search": + from . utils import * +else: + from utils import * -from . utils import * import math import random import sys diff --git a/aimaPy/text.ipynb b/aimaPy/text.ipynb index f15769f5f..a7b29bc44 100644 --- a/aimaPy/text.ipynb +++ b/aimaPy/text.ipynb @@ -6,20 +6,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "ename": "SystemError", - "evalue": "Parent module '' not loaded, cannot perform relative import", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Users/jeff/git/aima-python/aimaPy/text.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m working on a tiny sample of Unix manual pages.\"\"\"\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0mlearning\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCountingProbDist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSystemError\u001b[0m: Parent module '' not loaded, cannot perform relative import" - ] - } - ], + "outputs": [], "source": [ "import text" ] diff --git a/aimaPy/text.py b/aimaPy/text.py index 5206b2de8..86d91978d 100644 --- a/aimaPy/text.py +++ b/aimaPy/text.py @@ -4,12 +4,19 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -from . utils import * -from . learning import CountingProbDist +if __name__ == "aimaPy.text": + from . utils import * + from . learning import CountingProbDist + from . import search +else: + from utils import * + from learning import CountingProbDist + import search + from math import log, exp from collections import defaultdict import re -from . import search + class UnigramTextModel(CountingProbDist): diff --git a/aimaPy/utils.py b/aimaPy/utils.py index 2da0f3080..860b585dc 100644 --- a/aimaPy/utils.py +++ b/aimaPy/utils.py @@ -6,6 +6,11 @@ TODO: Priority queues may not belong here -- see treatment in search.py """ +if __name__ == "aimaPy.utils": + from . grid import * +else: + from grid import * + import operator import math import random @@ -13,7 +18,6 @@ import bisect import re from functools import reduce -from . grid import * #______________________________________________________________________________ # Simple Data Structures: infinity, Dict, Struct From 2d5ff1e8e289c97b2d4cddcc8225e5a7bac13626 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Thu, 10 Mar 2016 00:37:07 +0530 Subject: [PATCH 070/513] Implemented and_or_graph_search --- aimaPy/search.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/aimaPy/search.py b/aimaPy/search.py index 4d9ce252f..700ffcc1e 100644 --- a/aimaPy/search.py +++ b/aimaPy/search.py @@ -371,8 +371,35 @@ def simulated_annealing(problem, schedule=exp_schedule()): def and_or_graph_search(problem): + """Used when the environment is nondeterministic and completely observable + Contains OR nodes where the agent is free to choose any action + After every action there is an AND node which contains all possible states the agent may reach due to stochastic nature of environment + The agent must be able to handle all possible states of the AND node(as it may end up in any of them) + returns a conditional plan to reach goal state, or failure if the former is not possible""" "[Fig. 4.11]" - unimplemented() + + #functions used by and_or_search + def or_search(state, problem, path): + if problem.goal_test(state): + return {} + if state in path: + return None + for action in problem.action(state): + plan = and_search(problem.result(state, action), problem, path + [state,]) + if not plan == None: + return [action, plan] + + def and_search(states, problem, path): + "returns plan in form of dictionary where we take action plan[s] if we reach state s" + plan=dict() + for s in states: + plan[s] = or_search(s, problem, path) + if plan[s] == None: + return None + return plan + + #body of and or search + return or_search(problem.initial, problem, []) def online_dfs_agent(s1): From 5c7698921d8f8f0394c2e6c218a6b311bee10483 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Thu, 10 Mar 2016 03:20:30 +0800 Subject: [PATCH 071/513] Remove test_vector_clip in utils_test --- tests/utils_test.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tests/utils_test.py b/tests/utils_test.py index 2f5492e58..eb63448bb 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -128,10 +128,6 @@ def test_clip(): assert [clip(x, 0, 1) for x in [-1, 0.5, 10]] == [0, 0.5, 1] -def test_vector_clip(): - assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) - - def test_caller(): assert caller(0) == 'caller' From c248ef62017b08ccb4a6c04f4c5192850ca9271b Mon Sep 17 00:00:00 2001 From: jeff3456 Date: Wed, 9 Mar 2016 15:38:37 -0500 Subject: [PATCH 072/513] Fixed small error in csp.py where grid was spelt gri --- aimaPy/csp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aimaPy/csp.py b/aimaPy/csp.py index ca25b04b7..a482f0ace 100644 --- a/aimaPy/csp.py +++ b/aimaPy/csp.py @@ -596,7 +596,7 @@ def show_cell(cell): return str(assignment.get(cell, '.')) def abut(lines1, lines2): return list( map(' | '.join, list(zip(lines1, lines2)))) print('\n------+-------+------\n'.join( - '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgri)) + '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgrid)) #______________________________________________________________________________ # The Zebra Puzzle From 8ea630133c0c24e0a29e82eff6811a5d8eff230a Mon Sep 17 00:00:00 2001 From: MircoT Date: Thu, 10 Mar 2016 10:28:15 +0100 Subject: [PATCH 073/513] Rollback to one folder organization --- .gitmodules | 6 +++--- .travis.yml | 1 - MANIFEST.in | 3 --- aimaPy/agents.ipynb => agents.ipynb | 0 aimaPy/agents.py => agents.py | 5 +---- aimaPy/aima-data => aima-data | 0 aimaPy/__init__.py | 15 --------------- aimaPy/planning.py | 17 ----------------- aimaPy/csp.ipynb => csp.ipynb | 0 aimaPy/csp.py => csp.py | 9 ++------- aimaPy/games.ipynb => games.ipynb | 0 aimaPy/games.py => games.py | 6 +----- aimaPy/grid.ipynb => grid.ipynb | 0 aimaPy/grid.py => grid.py | 0 {aimaPy/images => images}/IMAGE-CREDITS | 0 {aimaPy/images => images}/dirt.svg | 0 {aimaPy/images => images}/dirt05-icon.jpg | Bin {aimaPy/images => images}/makefile | 0 {aimaPy/images => images}/vacuum-icon.jpg | Bin {aimaPy/images => images}/vacuum.svg | 0 {aimaPy/images => images}/wall-icon.jpg | Bin aimaPy/learning.ipynb => learning.ipynb | 0 aimaPy/learning.py => learning.py | 5 +---- aimaPy/logic.ipynb => logic.ipynb | 0 aimaPy/logic.py => logic.py | 8 ++------ aimaPy/mdp.ipynb => mdp.ipynb | 0 aimaPy/mdp.py => mdp.py | 6 +----- aimaPy/nlp.ipynb => nlp.ipynb | 0 aimaPy/nlp.py => nlp.py | 5 +---- aimaPy/planning.ipynb => planning.ipynb | 0 planning.py | 12 ++++++++++++ aimaPy/probability.ipynb => probability.ipynb | 0 aimaPy/probability.py => probability.py | 8 ++------ aimaPy/rl.ipynb => rl.ipynb | 0 aimaPy/rl.py => rl.py | 8 ++------ aimaPy/search.ipynb => search.ipynb | 0 aimaPy/search.py => search.py | 5 +---- setup.cfg | 2 -- setup.py | 18 ------------------ tests/test_grid.py | 2 +- ...robability_test.py => test_probability.py} | 2 +- tests/{text_test.py => test_text.py} | 2 +- tests/{utils_test.py => test_utils.py} | 2 +- aimaPy/text.ipynb => text.ipynb | 0 aimaPy/text.py => text.py | 11 +++-------- aimaPy/utils.py => utils.py | 5 +---- 46 files changed, 37 insertions(+), 126 deletions(-) delete mode 100644 MANIFEST.in rename aimaPy/agents.ipynb => agents.ipynb (100%) rename aimaPy/agents.py => agents.py (99%) rename aimaPy/aima-data => aima-data (100%) delete mode 100644 aimaPy/__init__.py delete mode 100644 aimaPy/planning.py rename aimaPy/csp.ipynb => csp.ipynb (100%) rename aimaPy/csp.py => csp.py (99%) rename aimaPy/games.ipynb => games.ipynb (100%) rename aimaPy/games.py => games.py (99%) rename aimaPy/grid.ipynb => grid.ipynb (100%) rename aimaPy/grid.py => grid.py (100%) rename {aimaPy/images => images}/IMAGE-CREDITS (100%) rename {aimaPy/images => images}/dirt.svg (100%) rename {aimaPy/images => images}/dirt05-icon.jpg (100%) rename {aimaPy/images => images}/makefile (100%) rename {aimaPy/images => images}/vacuum-icon.jpg (100%) rename {aimaPy/images => images}/vacuum.svg (100%) rename {aimaPy/images => images}/wall-icon.jpg (100%) rename aimaPy/learning.ipynb => learning.ipynb (100%) rename aimaPy/learning.py => learning.py (99%) rename aimaPy/logic.ipynb => logic.ipynb (100%) rename aimaPy/logic.py => logic.py (99%) rename aimaPy/mdp.ipynb => mdp.ipynb (100%) rename aimaPy/mdp.py => mdp.py (98%) rename aimaPy/nlp.ipynb => nlp.ipynb (100%) rename aimaPy/nlp.py => nlp.py (99%) rename aimaPy/planning.ipynb => planning.ipynb (100%) create mode 100644 planning.py rename aimaPy/probability.ipynb => probability.ipynb (100%) rename aimaPy/probability.py => probability.py (99%) rename aimaPy/rl.ipynb => rl.ipynb (100%) rename aimaPy/rl.py => rl.py (75%) rename aimaPy/search.ipynb => search.ipynb (100%) rename aimaPy/search.py => search.py (99%) delete mode 100644 setup.cfg delete mode 100644 setup.py rename tests/{probability_test.py => test_probability.py} (97%) rename tests/{text_test.py => test_text.py} (99%) rename tests/{utils_test.py => test_utils.py} (99%) rename aimaPy/text.ipynb => text.ipynb (100%) rename aimaPy/text.py => text.py (98%) rename aimaPy/utils.py => utils.py (99%) diff --git a/.gitmodules b/.gitmodules index b5b05a732..c1c16147f 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ -[submodule "aimaPy/aima-data"] - path = aimaPy/aima-data - url = https://github.com/aimacode/aima-data +[submodule "aima-data"] + path = aima-data + url = https://github.com/aimacode/aima-data.git diff --git a/.travis.yml b/.travis.yml index 129b41668..ecf6ba14c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,6 @@ before_install: install: - pip install flake8 - pip install -r requirements.txt - - python setup.py install script: - py.test diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 95bc5e3b4..000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,3 +0,0 @@ -graft aimaPy/aima-data -graft aimaPy/images -include aimaPy/*.ipynb \ No newline at end of file diff --git a/aimaPy/agents.ipynb b/agents.ipynb similarity index 100% rename from aimaPy/agents.ipynb rename to agents.ipynb diff --git a/aimaPy/agents.py b/agents.py similarity index 99% rename from aimaPy/agents.py rename to agents.py index 80a7064fb..746e83978 100644 --- a/aimaPy/agents.py +++ b/agents.py @@ -35,10 +35,7 @@ # # Speed control in GUI does not have any effect -- fix it. -if __name__ == "aimaPy.agents": - from . utils import * -else: - from utils import * +from utils import * import random import copy diff --git a/aimaPy/aima-data b/aima-data similarity index 100% rename from aimaPy/aima-data rename to aima-data diff --git a/aimaPy/__init__.py b/aimaPy/__init__.py deleted file mode 100644 index 85c43cec4..000000000 --- a/aimaPy/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -if __name__ == 'aimaPy': - from . import agents - from . import csp - from . import games - from . import grid - from . import learning - from . import logic - from . import mdp - from . import nlp - from . import planning - from . import probability - from . import rl - from . import search - from . import text - from . import utils diff --git a/aimaPy/planning.py b/aimaPy/planning.py deleted file mode 100644 index e6e0413b8..000000000 --- a/aimaPy/planning.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Planning (Chapters 10-11) -""" - - -if __name__ == "aimaPy.planning": - from . utils import * - from . import agents -else: - from utils import * - import agents - -import math -import random -import sys -import time -import bisect -import string diff --git a/aimaPy/csp.ipynb b/csp.ipynb similarity index 100% rename from aimaPy/csp.ipynb rename to csp.ipynb diff --git a/aimaPy/csp.py b/csp.py similarity index 99% rename from aimaPy/csp.py rename to csp.py index 59dfb8b1b..94469a935 100644 --- a/aimaPy/csp.py +++ b/csp.py @@ -1,12 +1,7 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" - -if __name__ == "aimaPy.csp": - from . utils import * - from . import search -else: - from utils import * - import search +from utils import * +import search from collections import defaultdict from functools import reduce diff --git a/aimaPy/games.ipynb b/games.ipynb similarity index 100% rename from aimaPy/games.ipynb rename to games.ipynb diff --git a/aimaPy/games.py b/games.py similarity index 99% rename from aimaPy/games.py rename to games.py index ed653c51e..fd3ebc4f0 100644 --- a/aimaPy/games.py +++ b/games.py @@ -1,11 +1,7 @@ """Games, or Adversarial Search. (Chapter 5) """ - -if __name__ == "aimaPy.games": - from . utils import * -else: - from utils import * +from utils import * import random diff --git a/aimaPy/grid.ipynb b/grid.ipynb similarity index 100% rename from aimaPy/grid.ipynb rename to grid.ipynb diff --git a/aimaPy/grid.py b/grid.py similarity index 100% rename from aimaPy/grid.py rename to grid.py diff --git a/aimaPy/images/IMAGE-CREDITS b/images/IMAGE-CREDITS similarity index 100% rename from aimaPy/images/IMAGE-CREDITS rename to images/IMAGE-CREDITS diff --git a/aimaPy/images/dirt.svg b/images/dirt.svg similarity index 100% rename from aimaPy/images/dirt.svg rename to images/dirt.svg diff --git a/aimaPy/images/dirt05-icon.jpg b/images/dirt05-icon.jpg similarity index 100% rename from aimaPy/images/dirt05-icon.jpg rename to images/dirt05-icon.jpg diff --git a/aimaPy/images/makefile b/images/makefile similarity index 100% rename from aimaPy/images/makefile rename to images/makefile diff --git a/aimaPy/images/vacuum-icon.jpg b/images/vacuum-icon.jpg similarity index 100% rename from aimaPy/images/vacuum-icon.jpg rename to images/vacuum-icon.jpg diff --git a/aimaPy/images/vacuum.svg b/images/vacuum.svg similarity index 100% rename from aimaPy/images/vacuum.svg rename to images/vacuum.svg diff --git a/aimaPy/images/wall-icon.jpg b/images/wall-icon.jpg similarity index 100% rename from aimaPy/images/wall-icon.jpg rename to images/wall-icon.jpg diff --git a/aimaPy/learning.ipynb b/learning.ipynb similarity index 100% rename from aimaPy/learning.ipynb rename to learning.ipynb diff --git a/aimaPy/learning.py b/learning.py similarity index 99% rename from aimaPy/learning.py rename to learning.py index 638786215..5812753bc 100644 --- a/aimaPy/learning.py +++ b/learning.py @@ -1,9 +1,6 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" -if __name__ == "aimaPy.learning": - from . utils import * -else: - from utils import * +from utils import * import copy import heapq diff --git a/aimaPy/logic.ipynb b/logic.ipynb similarity index 100% rename from aimaPy/logic.ipynb rename to logic.ipynb diff --git a/aimaPy/logic.py b/logic.py similarity index 99% rename from aimaPy/logic.py rename to logic.py index 8b1d8c9cf..88b1ea36e 100644 --- a/aimaPy/logic.py +++ b/logic.py @@ -24,12 +24,8 @@ diff, simp Symbolic differentiation and simplification """ -if __name__ == "aimaPy.logic": - from . utils import * - from . import agents -else: - from utils import * - import agents +from utils import * +import agents import itertools import re diff --git a/aimaPy/mdp.ipynb b/mdp.ipynb similarity index 100% rename from aimaPy/mdp.ipynb rename to mdp.ipynb diff --git a/aimaPy/mdp.py b/mdp.py similarity index 98% rename from aimaPy/mdp.py rename to mdp.py index 4ade25ac3..28ee611d9 100644 --- a/aimaPy/mdp.py +++ b/mdp.py @@ -6,11 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" - -if __name__ == "aimaPy.mdp": - from . utils import * -else: - from utils import * +from utils import * class MDP: diff --git a/aimaPy/nlp.ipynb b/nlp.ipynb similarity index 100% rename from aimaPy/nlp.ipynb rename to nlp.ipynb diff --git a/aimaPy/nlp.py b/nlp.py similarity index 99% rename from aimaPy/nlp.py rename to nlp.py index a55ef32fa..2077aec74 100644 --- a/aimaPy/nlp.py +++ b/nlp.py @@ -3,10 +3,7 @@ # (Written for the second edition of AIMA; expect some discrepanciecs # from the third edition until this gets reviewed.) -if __name__ == "aimaPy.nlp": - from . utils import * -else: - from utils import * +from utils import * from collections import defaultdict diff --git a/aimaPy/planning.ipynb b/planning.ipynb similarity index 100% rename from aimaPy/planning.ipynb rename to planning.ipynb diff --git a/planning.py b/planning.py new file mode 100644 index 000000000..89ef53fc6 --- /dev/null +++ b/planning.py @@ -0,0 +1,12 @@ +"""Planning (Chapters 10-11) +""" + +from utils import * +import agents + +import math +import random +import sys +import time +import bisect +import string diff --git a/aimaPy/probability.ipynb b/probability.ipynb similarity index 100% rename from aimaPy/probability.ipynb rename to probability.ipynb diff --git a/aimaPy/probability.py b/probability.py similarity index 99% rename from aimaPy/probability.py rename to probability.py index c714b5b09..c5bd76313 100644 --- a/aimaPy/probability.py +++ b/probability.py @@ -1,12 +1,8 @@ """Probability models. (Chapter 13-15) """ -if __name__ == "aimaPy.probability": - from . utils import * - from . logic import extend -else: - from utils import * - from logic import extend +from utils import * +from logic import extend import random from collections import defaultdict diff --git a/aimaPy/rl.ipynb b/rl.ipynb similarity index 100% rename from aimaPy/rl.ipynb rename to rl.ipynb diff --git a/aimaPy/rl.py b/rl.py similarity index 75% rename from aimaPy/rl.py rename to rl.py index 36dffb7af..f30e542ba 100644 --- a/aimaPy/rl.py +++ b/rl.py @@ -1,12 +1,8 @@ """Reinforcement Learning (Chapter 21) """ -if __name__ == "aimaPy.rl": - from . utils import * - from . import agents -else: - from utils import * - import agents +from utils import * +import agents class PassiveADPAgent(agents.Agent): diff --git a/aimaPy/search.ipynb b/search.ipynb similarity index 100% rename from aimaPy/search.ipynb rename to search.ipynb diff --git a/aimaPy/search.py b/search.py similarity index 99% rename from aimaPy/search.py rename to search.py index 9cd459391..f22b75d3d 100644 --- a/aimaPy/search.py +++ b/search.py @@ -4,10 +4,7 @@ then create problem instances and solve them with calls to the various search functions.""" -if __name__ == "aimaPy.search": - from . utils import * -else: - from utils import * +from utils import * import math import random diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 9af7e6f11..000000000 --- a/setup.cfg +++ /dev/null @@ -1,2 +0,0 @@ -[aliases] -test=pytest \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 4d9a11ede..000000000 --- a/setup.py +++ /dev/null @@ -1,18 +0,0 @@ -from setuptools import setup - -setup( - name='aimaPy', - version='1.0', - description='Python code for the book Artificial Intelligence: A Modern Approach.', - long_description='Python code for the book Artificial Intelligence: A Modern Approach.', - author='Peter Norvig', - author_email='peter@norvig.com', - url='https://github.com/aimacode/aima-python', - license="MIT", - platforms="all", - packages=['aimaPy'], - include_package_data=True, - - setup_requires=['pytest-runner'], - tests_require=['pytest'], -) \ No newline at end of file diff --git a/tests/test_grid.py b/tests/test_grid.py index 40d2c627a..b170a6321 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -1,5 +1,5 @@ import pytest -from aimaPy.grid import * +from grid import * compare_list = lambda x, y: all([elm_x == y[i] for i, elm_x in enumerate(x)]) diff --git a/tests/probability_test.py b/tests/test_probability.py similarity index 97% rename from tests/probability_test.py rename to tests/test_probability.py index eb587dfa1..13b00d310 100644 --- a/tests/probability_test.py +++ b/tests/test_probability.py @@ -1,5 +1,5 @@ import pytest -from aimaPy.probability import * +from probability import * def tests(): diff --git a/tests/text_test.py b/tests/test_text.py similarity index 99% rename from tests/text_test.py rename to tests/test_text.py index 4132682d2..0b01545b5 100644 --- a/tests/text_test.py +++ b/tests/test_text.py @@ -1,6 +1,6 @@ import pytest -from aimaPy.text import * +from text import * from random import choice from math import isclose diff --git a/tests/utils_test.py b/tests/test_utils.py similarity index 99% rename from tests/utils_test.py rename to tests/test_utils.py index eb63448bb..cddfff4d8 100644 --- a/tests/utils_test.py +++ b/tests/test_utils.py @@ -1,5 +1,5 @@ import pytest -from aimaPy.utils import * +from utils import * def test_struct_initialization(): diff --git a/aimaPy/text.ipynb b/text.ipynb similarity index 100% rename from aimaPy/text.ipynb rename to text.ipynb diff --git a/aimaPy/text.py b/text.py similarity index 98% rename from aimaPy/text.py rename to text.py index 86d91978d..7559474a2 100644 --- a/aimaPy/text.py +++ b/text.py @@ -4,14 +4,9 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -if __name__ == "aimaPy.text": - from . utils import * - from . learning import CountingProbDist - from . import search -else: - from utils import * - from learning import CountingProbDist - import search +from utils import * +from learning import CountingProbDist +import search from math import log, exp from collections import defaultdict diff --git a/aimaPy/utils.py b/utils.py similarity index 99% rename from aimaPy/utils.py rename to utils.py index 860b585dc..09808df65 100644 --- a/aimaPy/utils.py +++ b/utils.py @@ -6,10 +6,7 @@ TODO: Priority queues may not belong here -- see treatment in search.py """ -if __name__ == "aimaPy.utils": - from . grid import * -else: - from grid import * +from grid import * import operator import math From 25fc8c522e7320cee76b60fef5c655d89b6fc800 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Fri, 11 Mar 2016 00:39:18 +0530 Subject: [PATCH 074/513] Implemented Online DFS Agent --- search.py | 46 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 43 insertions(+), 3 deletions(-) diff --git a/search.py b/search.py index f22b75d3d..d21a21fb3 100644 --- a/search.py +++ b/search.py @@ -402,9 +402,49 @@ def and_search(states, problem, path): return or_search(problem.initial, problem, []) -def online_dfs_agent(s1): - "[Fig. 4.21]" - unimplemented() +class OnlineDFSAgent: + + """The abstract class for an OnlineDFSAgent. Override update_state + method to convert percept to state. While initilizing the subclass + a problem needs to be provided which is an instance of a subclass + of the Problem Class. [Fig. 4.21] """ + + def __init__(self, problem): + self.problem = problem + self.s = None + self.a = None + self.untried = defaultdict(list) + self.unbacktracked = defaultdict(list) + self.result = {} + + def update_state(self, percept): + raise NotImplementedError + + def run(self, percept): + current_state = self.update_state(percept) + if self.problem.goal_test(current_state): + self.a = None + else: + if current_state not in self.untried.keys(): + self.untried[current_state] = self.problem.actions(current_state) + if self.s is not None: + if current_state != self.result[(self.s, self.a)]: + self.result[(self.s, self.a)] = current_state + unbacktracked[current_state].insert(0, self.s) + if len(self.untried[current_state]) == 0: + if len(self.unbacktracked[current_state]) == 0: + self.a = None + else: + # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) + unbacktracked_pop = self.unbacktracked[current_state].pop(0) + for (s,b) in self.result.keys(): + if self.result[(s,b)] == unbacktracked_pop: + self.a = b + break + else: + self.a = self.untried[current_state].pop(0) + self.s = current_state + return self.a def lrta_star_agent(s1): From b83a15d81d87ff7e8295b47a1c43f9fa8f27525f Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Fri, 11 Mar 2016 01:57:46 +0530 Subject: [PATCH 075/513] Add tests for probability --- tests/test_probability.py | 56 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/tests/test_probability.py b/tests/test_probability.py index 13b00d310..903d511bf 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -32,5 +32,61 @@ def tests(): p = likelihood_weighting('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.002, 0.998) +def test_probdist_basic(): + P = ProbDist('Flip') + P['H'], P['T'] = 0.25, 0.75; + assert P['H'] == 0.25 + +def test_probdist_frequency(): + P = ProbDist('X', {'lo': 125, 'med': 375, 'hi': 500}) + assert (P['lo'], P['med'], P['hi']) == (0.125, 0.375, 0.5) + +def test_probdist_normalize(): + P = ProbDist('Flip') + P['H'], P['T'] = 35, 65 + P = P.normalize() + assert (P.prob['H'], P.prob['T']) == (0.350, 0.650) + +def test_jointprob(): + P = JointProbDist(['X', 'Y']) + P[1, 1] = 0.25 + assert P[1, 1] == 0.25 + P[dict(X=0, Y=1)] = 0.5 + assert P[dict(X=0, Y=1)] == 0.5 + +def test_event_values(): + assert event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) == (8, 10) + assert event_values ((1, 2), ['C', 'A']) == (1, 2) + +def test_enumerate_joint_ask(): + P = JointProbDist(['X', 'Y']) + P[0,0] = 0.25 + P[0,1] = 0.5 + P[1,1] = P[2,1] = 0.125 + assert enumerate_joint_ask('X', dict(Y=1), + P).show_approx() == '0: 0.667, 1: 0.167, 2: 0.167' + +def test_bayesnode_p(): + bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625}) + assert bn.p(False, {'Burglary': False, 'Earthquake': True}) == 0.375 + +def test_enumeration_ask(): + assert enumeration_ask('Burglary', + dict(JohnCalls=T, MaryCalls=T), burglary).show_approx() == 'False: 0.716, True: 0.284' + +def test_elemination_ask(): + elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary).show_approx() == 'False: 0.716, True: 0.284' + +def test_rejection_sampling(): + random.seed(47) + rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary, 10000).show_approx() == 'False: 0.7, True: 0.3' + +def test_likelihood_weighting(): + random.seed(1017) + assert likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary, 10000).show_approx() == 'False: 0.702, True: 0.298' + if __name__ == '__main__': pytest.main() From 94aedd8a9c1da5a1e708e3a996b09b7b35fed40b Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Fri, 11 Mar 2016 15:05:31 +0530 Subject: [PATCH 076/513] Added test for BFS --- tests/test_search.py | 73 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 tests/test_search.py diff --git a/tests/test_search.py b/tests/test_search.py new file mode 100644 index 000000000..43fc9f348 --- /dev/null +++ b/tests/test_search.py @@ -0,0 +1,73 @@ +import pytest +from search import * + +class Graph(Problem): + + """ + Graph class to test uninformed search algorithms which work on graphs with path costs. + """ + + def __init__(self, initial, goal=None, paths={}, bidirectional = False): + """ + The constructor takes as input the initial state, list of goal states and a dictionary representing a list of tuples which contiains the action and path cost + """ + #Make a dictionary of actions + action_dict = {} + for state in paths.keys(): + action_dict[state] = {} + for next_state, path_cost in paths[state]: + action_dict[state][next_state] = path_cost + if bidirectional: + if next_state not in action_dict.keys(): + action_dict[next_state]={} + action_dict[next_state][state] = path_cost + + update(self, initial=initial, goal=goal, action_dict=action_dict) + + def actions(self, state): + """ + returns the possible actions to take as a list of strings representing the state that action leads to + """ + return [ action for action in self.action_dict[state] ] + + def result(self, state, action): + """ + Return the state that results from executing the given action + """ + #Make sure the action is in actions(state) + assert is_in(action, self.actions(state)) + return action + + def goal_test(self, state): + """ + Return True if the state is a goal. + """ + return is_in(state, self.goal) + + def path_cost(self, c, state1, action, state2): + """Return the cost of a solution path that arrives at state2 from + state1 via action, assuming cost c to get up to state1. If the problem + is such that the path doesn't matter, this function will only look at + state2. If the path does matter, it will consider c and maybe state1 + and action. The default method costs 1 for every step in the path.""" + return c + self.action_dict[state1][state2] + +Fig[3, 12] = Graph('A', ['G'], {'A':[('B', 1), ('C', 1)], + 'B':[('D', 1), ('E', 1)], + 'C':[('F', 1), ('G', 1)], + 'D':[], + 'E':[], + 'F':[], + 'G':[]}) + +def test_breadth_first_tree_search(): + solution_node = breadth_first_tree_search(Fig[3, 12]) + assert solution_node.solution() == ['C', 'G'] + assert [node.action for node in solution_node.path()] == [None, 'C', 'G'] + #Test BFS if no goal is present + Fig[3, 12].goal = [] + assert breadth_first_tree_search(Fig[3, 12]) is None + Fig[3, 12].goal = ['G'] + +if __name__ == '__main__': + pytest.main() From ad42c6af7dcaafa188127f72ac205e5871982e9b Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Fri, 11 Mar 2016 22:00:23 +0530 Subject: [PATCH 077/513] Added tests for graph search algorithms and fixed a typo(?) in utils.py --- search.py | 73 ++++++++++++++++++++++---------------------- tests/test_search.py | 73 ++++++++------------------------------------ utils.py | 4 +-- 3 files changed, 51 insertions(+), 99 deletions(-) diff --git a/search.py b/search.py index f22b75d3d..8e0979c03 100644 --- a/search.py +++ b/search.py @@ -549,33 +549,34 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g -romania = UndirectedGraph(dict( - A=dict(Z=75, S=140, T=118), - B=dict(U=85, P=101, G=90, F=211), - C=dict(D=120, R=146, P=138), - D=dict(M=75), - E=dict(H=86), - F=dict(S=99), - H=dict(U=98), - I=dict(V=92, N=87), - L=dict(T=111, M=70), - O=dict(Z=71, S=151), - P=dict(R=97), - R=dict(S=80), - U=dict(V=142))) -romania.locations = dict( - A=(91, 492), B=(400, 327), C=(253, 288), D=(165, 299), - E=(562, 293), F=(305, 449), G=(375, 270), H=(534, 350), - I=(473, 506), L=(165, 379), M=(168, 339), N=(406, 537), - O=(131, 571), P=(320, 368), R=(233, 410), S=(207, 457), - T=(94, 410), U=(456, 350), V=(509, 444), Z=(108, 531)) - -australia = UndirectedGraph(dict( +#Simplified road map of Romania +Fig[3, 2] = UndirectedGraph(dict( + Arad=dict(Zerind=75, Sibiu=140, Timisoara=118), + Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211), + Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138), + Drobeta=dict(Mehadia=75), + Eforie=dict(Hirsova=86), + Fagaras=dict(Sibiu=99), + Hirsova=dict(Urziceni=98), + Iasi=dict(Vaslui=92, Neamt=87), + Lugoj=dict(Timisoara=111, Mehadia=70), + Oradea=dict(Zerind=71, Sibiu=151), + Pitesti=dict(Rimnicu=97), + Rimnicu=dict(Sibiu=80), + Urziceni=dict(Vaslui=142))) +Fig[3, 2].locations = dict( + Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288), Drobeta=(165, 299), + Eforie=(562, 293), Fagaras=(305, 449), Giurgiu=(375, 270), Hirsova=(534, 350), + Iasi=(473, 506), Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537), + Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410), Sibiu=(207, 457), + Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) + +Fig[6, 1] = UndirectedGraph(dict( T=dict(), SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), NT=dict(WA=1, Q=1), NSW=dict(Q=1, V=1))) -australia.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), +Fig[6, 1].locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) @@ -929,38 +930,38 @@ def do(searcher, problem): def compare_graph_searchers(): """Prints a table of results like this: >>> compare_graph_searchers() -Searcher Romania(A, B) Romania(O, N) Australia +Searcher Fig[3, 2](A, B) Fig[3, 2](O, N) Fig[6, 1] breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/ 12/ 43/WA>""" - compare_searchers(problems=[GraphProblem('A', 'B', romania), - GraphProblem('O', 'N', romania), - GraphProblem('Q', 'WA', australia)], - header=['Searcher', 'Romania(A, B)', 'Romania(O, N)', 'Australia']) + compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', Fig[3, 2]), + GraphProblem('Oradea', 'Neamt', Fig[3, 2]), + GraphProblem('Q', 'WA', Fig[6, 1])], + header=['Searcher', 'Fig[3, 2](Arad, Bucharest)', 'Fig[3, 2](Oradea, Neamt)', 'Fig[6, 1]']) #______________________________________________________________________________ __doc__ += """ ->>> ab = GraphProblem('A', 'B', romania) +>>> ab = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) >>> breadth_first_tree_search(ab).solution() -['S', 'F', 'B'] +['Sibiu', 'Fagaras', 'Bucharest'] >>> breadth_first_search(ab).solution() -['S', 'F', 'B'] +['Sibiu', 'Fagaras', 'Bucharest'] >>> uniform_cost_search(ab).solution() -['S', 'R', 'P', 'B'] +['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] >>> depth_first_graph_search(ab).solution() -['T', 'L', 'M', 'D', 'C', 'P', 'B'] +['Timisoara', 'Lugoj', 'Mehadia', 'Drobeta', 'Craiova', 'Pitesi', 'Bucharest'] >>> iterative_deepening_search(ab).solution() -['S', 'F', 'B'] +['Sibiu', 'Fagaras', 'Bucharest'] >>> len(depth_limited_search(ab).solution()) 50 >>> astar_search(ab).solution() -['S', 'R', 'P', 'B'] +['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] >>> recursive_best_first_search(ab).solution() -['S', 'R', 'P', 'B'] +['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] >>> board = list('SARTELNID') >>> print_boggle(board) diff --git a/tests/test_search.py b/tests/test_search.py index 43fc9f348..8dce793ea 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,73 +1,24 @@ import pytest from search import * -class Graph(Problem): - """ - Graph class to test uninformed search algorithms which work on graphs with path costs. - """ +romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) - def __init__(self, initial, goal=None, paths={}, bidirectional = False): - """ - The constructor takes as input the initial state, list of goal states and a dictionary representing a list of tuples which contiains the action and path cost - """ - #Make a dictionary of actions - action_dict = {} - for state in paths.keys(): - action_dict[state] = {} - for next_state, path_cost in paths[state]: - action_dict[state][next_state] = path_cost - if bidirectional: - if next_state not in action_dict.keys(): - action_dict[next_state]={} - action_dict[next_state][state] = path_cost - - update(self, initial=initial, goal=goal, action_dict=action_dict) - - def actions(self, state): - """ - returns the possible actions to take as a list of strings representing the state that action leads to - """ - return [ action for action in self.action_dict[state] ] - - def result(self, state, action): - """ - Return the state that results from executing the given action - """ - #Make sure the action is in actions(state) - assert is_in(action, self.actions(state)) - return action +def test_breadth_first_tree_search(): + assert breadth_first_tree_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] - def goal_test(self, state): - """ - Return True if the state is a goal. - """ - return is_in(state, self.goal) +def test_breadth_first_search(): + assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] - def path_cost(self, c, state1, action, state2): - """Return the cost of a solution path that arrives at state2 from - state1 via action, assuming cost c to get up to state1. If the problem - is such that the path doesn't matter, this function will only look at - state2. If the path does matter, it will consider c and maybe state1 - and action. The default method costs 1 for every step in the path.""" - return c + self.action_dict[state1][state2] +def test_uniform_cost_search(): + assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] -Fig[3, 12] = Graph('A', ['G'], {'A':[('B', 1), ('C', 1)], - 'B':[('D', 1), ('E', 1)], - 'C':[('F', 1), ('G', 1)], - 'D':[], - 'E':[], - 'F':[], - 'G':[]}) +def test_depth_first_graph_search(): + solution = depth_first_graph_search(romania).solution() + assert solution[-1] == 'Bucharest' -def test_breadth_first_tree_search(): - solution_node = breadth_first_tree_search(Fig[3, 12]) - assert solution_node.solution() == ['C', 'G'] - assert [node.action for node in solution_node.path()] == [None, 'C', 'G'] - #Test BFS if no goal is present - Fig[3, 12].goal = [] - assert breadth_first_tree_search(Fig[3, 12]) is None - Fig[3, 12].goal = ['G'] +def test_iterative_deepening_search(): + assert iterative_deepening_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] if __name__ == '__main__': pytest.main() diff --git a/utils.py b/utils.py index 09808df65..0fe21d7ab 100644 --- a/utils.py +++ b/utils.py @@ -101,7 +101,7 @@ def some(predicate, seq): """If some element x of seq satisfies predicate(x), return predicate(x).""" elem = find_if(predicate, seq) - return predicate(elem) or False + return predicate(elem) if elem is not None else False # TODO[COMPLETED]: rename to is_in or possibily add 'identity' to function name to # clarify intent @@ -439,7 +439,7 @@ def pop(self): return self.A.pop()[1] def __contains__(self, item): - return some(lambda _, x: x == item, self.A) + return some(lambda x: x == item, self.A) def __getitem__(self, key): for _, item in self.A: From c298aaada118d025115fdd69332f3045a1c510fd Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Fri, 11 Mar 2016 22:06:12 +0530 Subject: [PATCH 078/513] changed comments --- search.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/search.py b/search.py index 8e0979c03..cb49f5d37 100644 --- a/search.py +++ b/search.py @@ -571,6 +571,7 @@ def distance_to_node(n): Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410), Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) +#Principal states and territories of Australia Fig[6, 1] = UndirectedGraph(dict( T=dict(), SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), @@ -945,22 +946,22 @@ def compare_graph_searchers(): #______________________________________________________________________________ __doc__ += """ ->>> ab = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) ->>> breadth_first_tree_search(ab).solution() +>>> romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) +>>> breadth_first_tree_search(romania).solution() ['Sibiu', 'Fagaras', 'Bucharest'] ->>> breadth_first_search(ab).solution() +>>> breadth_first_search(romania).solution() ['Sibiu', 'Fagaras', 'Bucharest'] ->>> uniform_cost_search(ab).solution() +>>> uniform_cost_search(romania).solution() ['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] ->>> depth_first_graph_search(ab).solution() +>>> depth_first_graph_search(romania).solution() ['Timisoara', 'Lugoj', 'Mehadia', 'Drobeta', 'Craiova', 'Pitesi', 'Bucharest'] ->>> iterative_deepening_search(ab).solution() +>>> iterative_deepening_search(romania).solution() ['Sibiu', 'Fagaras', 'Bucharest'] ->>> len(depth_limited_search(ab).solution()) +>>> len(depth_limited_search(romania).solution()) 50 ->>> astar_search(ab).solution() +>>> astar_search(romania).solution() ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] ->>> recursive_best_first_search(ab).solution() +>>> recursive_best_first_search(romania).solution() ['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] >>> board = list('SARTELNID') From 52a4a0df1add30176526762d10aecf8a3f144048 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sat, 12 Mar 2016 00:46:41 +0530 Subject: [PATCH 079/513] Correct spelling mistake --- utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 09808df65..aaa15b74b 100644 --- a/utils.py +++ b/utils.py @@ -1,7 +1,7 @@ """Provide some widely useful utilities. Safe for "from utils import *". TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py -TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. +TODO: count_if and the like are leftovers from Common Lisp; let's make replace thenm with Pythonic alternatives. TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. TODO: Priority queues may not belong here -- see treatment in search.py """ From 960348c36384248ae59310c7344f5a83acdbb8f8 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 11 Mar 2016 12:00:45 -0800 Subject: [PATCH 080/513] Update README.md --- README.md | 94 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) diff --git a/README.md b/README.md index 2ef0d7185..31e6d2f63 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,100 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) def retry(url: Url) -> None: +# Index of Code # + +| **Fig** | **Page** | **Name (in book)** | **Code** | +|:--------|:---------|:-------------------|:---------| +| 2 | 32 | Environment | [Environment](../master/agents.py) | +| 2.1 | 33 | Agent | [Agent](../master/agents.py) | +| 2.3 | 34 | Table-Driven-Vacuum-Agent | [TableDrivenVacuumAgent](../master/agents.py) | +| 2.7 | 45 | Table-Driven-Agent | [TableDrivenAgent](../master/agents.py) | +| 2.8 | 46 | Reflex-Vacuum-Agent | [ReflexVacuumAgent](../master/agents.py) | +| 2.10 | 47 | Simple-Reflex-Agent | [SimpleReflexAgent](../master/agents.py) | +| 2.12 | 49 | Reflex-Agent-With-State | [ReflexAgentWithState](../master/agents.py) | +| 3.1 | 61 | Simple-Problem-Solving-Agent | [SimpleProblemSolvingAgent](../master/search.py) | +| 3 | 62 | Problem | [Problem](../master/search.py) | +| 3.2 | 63 | Romania | [romania](../master/search.py) | +| 3 | 69 | Node | [Node](../master/search.py) | +| 3.7 | 70 | Tree-Search | [tree\_search](../master/search.py) | +| 3 | 71 | Queue | [Queue](../master/utils.py) | +| 3.9 | 72 | Tree-Search | [tree\_search](../master/search.py) | +| 3.13 | 77 | Depth-Limited-Search | [depth\_limited\_search](../master/search.py) | +| 3.14 | 79 | Iterative-Deepening-Search | [iterative\_deepening\_search](../master/search.py) | +| 3.19 | 83 | Graph-Search | [graph\_search](../master/search.py) | +| 4 | 95 | Best-First-Search | [best\_first\_graph\_search](../master/search.py) | +| 4 | 97 | A`*`-Search | [astar\_search](../master/search.py) | +| 4.5 | 102 | Recursive-Best-First-Search | [recursive\_best\_first\_search](../master/search.py) | +| 4.11 | 112 | Hill-Climbing | [hill\_climbing](../master/search.py) | +| 4.14 | 116 | Simulated-Annealing | [simulated\_annealing](../master/search.py) | +| 4.17 | 119 | Genetic-Algorithm | [genetic\_algorithm](../master/search.py) | +| 4.20 | 126 | Online-DFS-Agent | [online\_dfs\_agent](../master/search.py) | +| 4.23 | 128 | LRTA`*`-Agent | [lrta\_star\_agent](../master/search.py) | +| 5 | 137 | CSP | [CSP](../master/csp.py) | +| 5.3 | 142 | Backtracking-Search | [backtracking\_search](../master/csp.py) | +| 5.7 | 146 | AC-3 | [AC3](../master/csp.py) | +| 5.8 | 151 | Min-Conflicts | [min\_conflicts](../master/csp.py) | +| 6.3 | 166 | Minimax-Decision | [minimax\_decision](../master/games.py) | +| 6.7 | 170 | Alpha-Beta-Search | [alphabeta\_search](../master/games.py) | +| 7 | 195 | KB | [KB](../master/logic.py) | +| 7.1 | 196 | KB-Agent | [KB\_Agent](../master/logic.py) | +| 7.7 | 205 | Propositional Logic Sentence | [Expr](../master/logic.py) | +| 7.10 | 209 | TT-Entails | [tt\_entials](../master/logic.py) | +| 7 | 215 | Convert to CNF | [to\_cnf](../master/logic.py) | +| 7.12 | 216 | PL-Resolution | [pl\_resolution](../master/logic.py) | +| 7.14 | 219 | PL-FC-Entails? | [pl\_fc\_resolution](../master/logic.py) | +| 7.16 | 222 | DPLL-Satisfiable? | [dpll\_satisfiable](../master/logic.py) | +| 7.17 | 223 | WalkSAT | [WalkSAT](../master/logic.py) | +| 7.19 | 226 | PL-Wumpus-Agent | [PLWumpusAgent](../master/logic.py) | +| 9 | 273 | Subst | [subst](../master/logic.py) | +| 9.1 | 278 | Unify | [unify](../master/logic.py) | +| 9.3 | 282 | FOL-FC-Ask | [fol\_fc\_ask](../master/logic.py) | +| 9.6 | 288 | FOL-BC-Ask | [fol\_bc\_ask](../master/logic.py) | +| 9.14 | 307 | Otter | | +| 11.2 | 380 | Airport-problem | | +| 11.3 | 381 | Spare-Tire-Problem | | +| 11.4 | 383 | Three-Block-Tower | | +| 11 | 390 | Partial-Order-Planner | | +| 11.11 | 396 | Cake-Problem | | +| 11.13 | 399 | Graphplan | | +| 11.15 | 403 | SATPlan | | +| 12.1 | 418 | Job-Shop-Problem | | +| 12.3 | 421 | Job-Shop-Problem-With-Resources | | +| 12.6 | 424 | House-Building-Problem | | +| 12.10 | 435 | And-Or-Graph-Search | [and\_or\_graph\_search](../master/search.py) | +| 12.22 | 449 | Continuous-POP-Agent | | +| 12.23 | 450 | Doubles-tennis | | +| 13.1 | 466 | DT-Agent | [DTAgent](../master/probability.py) | +| 13 | 469 | Discrete Probability Distribution | [DiscreteProbDist](../master/probability.py) | +| 13.4 | 477 | Enumerate-Joint-Ask | [enumerate\_joint\_ask](../master/probability.py) | +| 14.10 | 509 | Elimination-Ask | [elimination\_ask](../master/probability.py) | +| 14.12 | 512 | Prior-Sample | [prior\_sample](../master/probability.py) | +| 14.13 | 513 | Rejection-Sampling | [rejection\_sampling](../master/probability.py) | +| 14.14 | 515 | Likelihood-Weighting | [likelihood\_weighting](../master/probability.py) | +| 14.15 | 517 | MCMC-Ask | | +| 15.4 | 546 | Forward-Backward | [forward\_backward](../master/probability.py) | +| 15.6 | 552 | Fixed-Lag-Smoothing | [fixed\_lag\_smoothing](../master/probability.py) | +| 15.15 | 566 | Particle-Filtering | [particle\_filtering](../master/probability.py) | +| 16.8 | 603 | Information-Gathering-Agent | | +| 17.4 | 621 | Value-Iteration | [value\_iteration](../master/mdp.py) | +| 17.7 | 624 | Policy-Iteration | [policy\_iteration](../master/mdp.py) | +| 18.5 | 658 | Decision-Tree-Learning | [DecisionTreeLearner](../master/learning.py) | +| 18.10 | 667 | AdaBoost | [AdaBoost](../master/learning.py) | +| 18.14 | 672 | Decision-List-Learning | | +| 19.2 | 681 | Current-Best-Learning | | +| 19.3 | 683 | Version-Space-Learning | | +| 19.8 | 696 | Minimal-Consistent-Det | | +| 19.12 | 702 | FOIL | | +| 20.21 | 742 | Perceptron-Learning | [PerceptronLearner](../master/learning.py) | +| 20.25 | 746 | Back-Prop-Learning | | +| 21.2 | 768 | Passive-ADP-Agent | [PassiveADPAgent](../master/rl.py) | +| 21.4 | 769 | Passive-TD-Agent | [PassiveTDAgent](../master/rl.py) | +| 21.8 | 776 | Q-Learning-Agent | | +| 22.2 | 796 | Naive-Communicating-Agent | | +| 22.7 | 801 | Chart-Parse | [Chart](../master/nlp.py) | +| 23.1 | 837 | Viterbi-Segmentation | [viterbi\_segment](../master/text.py) | +| 24.21 | 892 | Align | | + # Choice of Programming Languages Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is From c3654f49907f389b32232e44a417c5d70d476fef Mon Sep 17 00:00:00 2001 From: Lucas Moura Date: Fri, 11 Mar 2016 03:05:20 -0300 Subject: [PATCH 081/513] Fix flake8 warning for source files --- agents.py | 32 +++++++----- csp.py | 55 +++++++++++--------- games.py | 13 ++--- learning.py | 128 ++++++++++++++++++++++++---------------------- logic.py | 66 ++++++++++++------------ mdp.py | 31 +++++++---- nlp.py | 31 ++++++----- planning.py | 2 + probability.py | 30 +++++------ rl.py | 2 +- search.py | 136 ++++++++++++++++++++++++++++--------------------- text.py | 25 ++++----- utils.py | 78 +++++++++++++++------------- 13 files changed, 347 insertions(+), 282 deletions(-) diff --git a/agents.py b/agents.py index 746e83978..4c7801430 100644 --- a/agents.py +++ b/agents.py @@ -35,13 +35,13 @@ # # Speed control in GUI does not have any effect -- fix it. -from utils import * +from utils import * # noqa import random import copy import collections -#______________________________________________________________________________ +# ______________________________________________________________________________ class Thing(object): @@ -51,7 +51,8 @@ class Thing(object): .__name__ slot (used for output only).""" def __repr__(self): - return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__)) + return '<{}>'.format(getattr(self, '__name__', + self.__class__.__name__)) def is_alive(self): "Things that are 'alive' should return true." @@ -108,7 +109,7 @@ def new_program(percept): agent.program = new_program return agent -#______________________________________________________________________________ +# ______________________________________________________________________________ def TableDrivenAgentProgram(table): @@ -129,7 +130,7 @@ def RandomAgentProgram(actions): "An agent that chooses an action at random, ignoring all percepts." return lambda percept: random.choice(actions) -#______________________________________________________________________________ +# ______________________________________________________________________________ def SimpleReflexAgentProgram(rules, interpret_input): @@ -159,7 +160,7 @@ def rule_match(state, rules): if rule.matches(state): return rule -#______________________________________________________________________________ +# ______________________________________________________________________________ loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world @@ -214,7 +215,7 @@ def program(location, status): return 'Left' return Agent(program) -#______________________________________________________________________________ +# ______________________________________________________________________________ class Environment(object): @@ -237,7 +238,10 @@ def thing_classes(self): return [] # List of classes that can go into environment def percept(self, agent): - "Return the percept that the agent sees at this point. (Implement this.)" + ''' + Return the percept that the agent sees at this point. + (Implement this.) + ''' raise NotImplementedError def execute_action(self, agent, action): @@ -305,7 +309,8 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print(" Thing to be removed: {} at {}" .format(thing, thing.location)) + print(" Thing to be removed: {} at {}" .format(thing, + thing.location)) print(" from list: {}" .format([(thing, thing.location) for thing in self.things])) if thing in self.agents: @@ -419,7 +424,7 @@ class Obstacle(Thing): class Wall(Obstacle): pass -#______________________________________________________________________________ +# ______________________________________________________________________________ # Vacuum environment @@ -502,7 +507,7 @@ def default_location(self, thing): "Agents start in either location at random." return random.choice([loc_A, loc_B]) -#______________________________________________________________________________ +# ______________________________________________________________________________ # The Wumpus World @@ -538,7 +543,7 @@ def thing_classes(self): # Needs a lot of work ... -#______________________________________________________________________________ +# ______________________________________________________________________________ def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000): """See how well each of several agents do in n instances of an environment. @@ -559,7 +564,7 @@ def score(env): return agent.performance return mean(list(map(score, envs))) -#_________________________________________________________________________ +# _________________________________________________________________________ __doc__ += """ >>> a = ReflexVacuumAgent() @@ -590,4 +595,3 @@ def score(env): >>> 0.5 < testv(RandomVacuumAgent) < 3 True """ - diff --git a/csp.py b/csp.py index 94469a935..c671f2f26 100644 --- a/csp.py +++ b/csp.py @@ -1,11 +1,14 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from utils import * +from utils import * # noqa import search from collections import defaultdict from functools import reduce +import itertools +import re + class CSP(search.Problem): @@ -44,7 +47,8 @@ class CSP(search.Problem): display(a) Print a human-readable representation >>> search.depth_first_graph_search(australia) - + """ def __init__(self, vars, domains, neighbors, constraints): @@ -70,8 +74,8 @@ def nconflicts(self, var, val, assignment): "Return the number of conflicts var=val has with other variables." # Subclasses may implement this more efficiently def conflict(var2): - return (var2 in assignment - and not self.constraints(var, val, var2, assignment[var2])) + return (var2 in assignment and + not self.constraints(var, val, var2, assignment[var2])) return count_if(conflict, self.neighbors[var]) def display(self, assignment): @@ -149,7 +153,7 @@ def conflicted_vars(self, current): return [var for var in self.vars if self.nconflicts(var, current[var], current) > 0] -#______________________________________________________________________________ +# ______________________________________________________________________________ # Constraint Propagation with AC-3 @@ -180,7 +184,7 @@ def revise(csp, Xi, Xj, removals): revised = True return revised -#______________________________________________________________________________ +# ______________________________________________________________________________ # CSP Backtracking Search # Variable ordering @@ -251,17 +255,21 @@ def backtracking_search(csp, """[Fig. 6.5] >>> backtracking_search(australia) is not None True - >>> backtracking_search(australia, select_unassigned_variable=mrv) is not None + >>> backtracking_search(australia, + >>> select_unassigned_variable=mrv) is not None True - >>> backtracking_search(australia, order_domain_values=lcv) is not None + >>> backtracking_search(australia, + >>> order_domain_values=lcv) is not None True - >>> backtracking_search(australia, select_unassigned_variable=mrv, order_domain_values=lcv) is not None + >>> backtracking_search(australia, select_unassigned_variable=mrv, + >>> order_domain_values=lcv) is not None True >>> backtracking_search(australia, inference=forward_checking) is not None True >>> backtracking_search(australia, inference=mac) is not None True - >>> backtracking_search(usa, select_unassigned_variable=mrv, order_domain_values=lcv, inference=mac) is not None + >>> backtracking_search(usa, select_unassigned_variable=mrv, + >>> order_domain_values=lcv, inference=mac) is not None True """ @@ -285,7 +293,7 @@ def backtrack(assignment): assert result is None or csp.goal_test(result) return result -#______________________________________________________________________________ +# ______________________________________________________________________________ # Min-conflicts hillclimbing search for CSPs @@ -313,12 +321,11 @@ def min_conflicts_value(csp, var, current): return argmin_random_tie(csp.domains[var], lambda val: csp.nconflicts(var, val, current)) -#______________________________________________________________________________ +# ______________________________________________________________________________ def tree_csp_solver(csp): "[Fig. 6.11]" - n = len(csp.vars) assignment = {} root = csp.vars[0] X, parent = topological_sort(csp.vars, root) @@ -339,7 +346,7 @@ def topological_sort(xs, x): def make_arc_consistent(Xj, Xk, csp): unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ # Map-Coloring Problems @@ -417,7 +424,7 @@ def parse_neighbors(neighbors, vars=[]): PI; PA: LR RA; PC: PL CE LI AQ; PI: NH NO CA IF; PL: BR NB CE PC; RA: AU BO FC PA LR""") -#______________________________________________________________________________ +# ______________________________________________________________________________ # n-Queens Problem @@ -507,17 +514,14 @@ def display(self, assignment): print(str(self.nconflicts(var, val, assignment))+ch, end=' ') print() -#______________________________________________________________________________ +# ______________________________________________________________________________ # Sudoku -import itertools -import re - def flatten(seqs): return sum(seqs, []) -easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' -harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' +easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' # noqa +harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' # noqa _R3 = list(range(3)) _CELL = itertools.count().__next__ @@ -531,6 +535,7 @@ def flatten(seqs): return sum(seqs, []) for v in unit: _NEIGHBORS[v].update(unit - set([v])) + class Sudoku(CSP): """A Sudoku problem. @@ -564,7 +569,8 @@ class Sudoku(CSP): 8 1 4 | 2 5 3 | 7 6 9 6 9 5 | 4 1 7 | 3 8 2 >>> h = Sudoku(harder1) - >>> None != backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) + >>> None != backtracking_search(h, select_unassigned_variable=mrv, + >>> inference=forward_checking) True """ R3 = _R3 @@ -596,8 +602,9 @@ def show_cell(cell): return str(assignment.get(cell, '.')) def abut(lines1, lines2): return list( map(' | '.join, list(zip(lines1, lines2)))) print('\n------+-------+------\n'.join( - '\n'.join(reduce(abut, list(map(show_box, brow)))) for brow in self.bgrid)) -#______________________________________________________________________________ + '\n'.join(reduce( + abut, list(map(show_box, brow)))) for brow in self.bgrid)) +# ______________________________________________________________________________ # The Zebra Puzzle diff --git a/games.py b/games.py index fd3ebc4f0..303ffd47e 100644 --- a/games.py +++ b/games.py @@ -1,11 +1,11 @@ """Games, or Adversarial Search. (Chapter 5) """ -from utils import * +from utils import * # noqa import random -#______________________________________________________________________________ +# ______________________________________________________________________________ # Minimax Search @@ -35,7 +35,7 @@ def min_value(state): return argmax(game.actions(state), lambda a: min_value(game.result(state, a))) -#______________________________________________________________________________ +# ______________________________________________________________________________ def alphabeta_full_search(state, game): @@ -105,11 +105,12 @@ def min_value(state, alpha, beta, depth): # Body of alphabeta_search starts here: # The default test cuts off at depth d or at a terminal state cutoff_test = (cutoff_test or - (lambda state, depth: depth > d or game.terminal_test(state))) + (lambda state, depth: depth > d or + game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) return max_value(state, -infinity, infinity, 0) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Players for Games @@ -141,7 +142,7 @@ def play_game(game, *players): if game.terminal_test(state): return game.utility(state, game.to_move(game.initial)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Some Sample Games diff --git a/learning.py b/learning.py index 5812753bc..b6741d1e8 100644 --- a/learning.py +++ b/learning.py @@ -1,6 +1,6 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" -from utils import * +from utils import * # noqa import copy import heapq @@ -8,7 +8,7 @@ import random from collections import defaultdict -#______________________________________________________________________________ +# ______________________________________________________________________________ def rms_error(predictions, targets): @@ -26,29 +26,29 @@ def mean_error(predictions, targets): def mean_boolean_error(predictions, targets): return mean([(p != t) for p, t in zip(predictions, targets)]) -#______________________________________________________________________________ +# ______________________________________________________________________________ class DataSet: """A data set for a machine learning problem. It has the following fields: - d.examples A list of examples. Each one is a list of attribute values. - d.attrs A list of integers to index into an example, so example[attr] - gives a value. Normally the same as range(len(d.examples[0])). - d.attrnames Optional list of mnemonic names for corresponding attrs. - d.target The attribute that a learning algorithm will try to predict. - By default the final attribute. - d.inputs The list of attrs without the target. - d.values A list of lists: each sublist is the set of possible - values for the corresponding attribute. If initially None, - it is computed from the known examples by self.setproblem. - If not None, an erroneous value raises ValueError. - d.distance A function from a pair of examples to a nonnegative number. - Should be symmetric, etc. Defaults to mean_boolean_error - since that can handle any field types. - d.name Name of the data set (for output display only). - d.source URL or other source where the data came from. + d.examples A list of examples. Each one is a list of attribute values. + d.attrs A list of integers to index into an example, so example[attr] + gives a value. Normally the same as range(len(d.examples[0])). + d.attrnames Optional list of mnemonic names for corresponding attrs. + d.target The attribute that a learning algorithm will try to predict. + By default the final attribute. + d.inputs The list of attrs without the target. + d.values A list of lists: each sublist is the set of possible + values for the corresponding attribute. If initially None, + it is computed from the known examples by self.setproblem. + If not None, an erroneous value raises ValueError. + d.distance A function from a pair of examples to a nonnegative number. + Should be symmetric, etc. Defaults to mean_boolean_error + since that can handle any field types. + d.name Name of the data set (for output display only). + d.source URL or other source where the data came from. Normally, you call the constructor and you're done; then you just access fields like d.examples and d.target and d.inputs.""" @@ -68,7 +68,7 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, if isinstance(examples, str): self.examples = parse_csv(examples) elif examples is None: - self.examples = parse_csv(DataFile(name+'.csv').read()) + self.examples = parse_csv(DataFile(name + '.csv').read()) else: self.examples = examples # Attrs are the indices of examples, unless otherwise stated. @@ -138,7 +138,7 @@ def __repr__(self): return '' % ( self.name, len(self.examples), len(self.attrs)) -#______________________________________________________________________________ +# ______________________________________________________________________________ def parse_csv(input, delim=','): @@ -152,7 +152,7 @@ def parse_csv(input, delim=','): lines = [line for line in input.splitlines() if line.strip()] return [list(map(num_or_str, line.split(delim))) for line in lines] -#______________________________________________________________________________ +# ______________________________________________________________________________ class CountingProbDist: @@ -196,7 +196,8 @@ def __getitem__(self, item): def top(self, n): "Return (count, obs) tuples for the n most frequent observations." - return heapq.nlargest(n, [(v, k) for (k, v) in list(self.dictionary.items())]) + return heapq.nlargest( + n, [(v, k) for (k, v) in list(self.dictionary.items())]) def sample(self): "Return a random sample from the distribution." @@ -205,7 +206,7 @@ def sample(self): list(self.dictionary.values())) return self.sampler() -#______________________________________________________________________________ +# ______________________________________________________________________________ def PluralityLearner(dataset): @@ -218,7 +219,7 @@ def predict(example): return most_popular return predict -#______________________________________________________________________________ +# ______________________________________________________________________________ def NaiveBayesLearner(dataset): @@ -241,14 +242,14 @@ def predict(example): """Predict the target value for example. Consider each possible value, and pick the most likely by looking at each attribute independently.""" def class_probability(targetval): - return (target_dist[targetval] - * product(attr_dists[targetval, attr][example[attr]] - for attr in dataset.inputs)) + return (target_dist[targetval] * + product(attr_dists[targetval, attr][example[attr]] + for attr in dataset.inputs)) return argmax(targetvals, class_probability) return predict -#______________________________________________________________________________ +# ______________________________________________________________________________ def NearestNeighborLearner(dataset, k=1): @@ -260,12 +261,12 @@ def predict(example): return mode(e[dataset.target] for (d, e) in best) return predict -#______________________________________________________________________________ +# ______________________________________________________________________________ class DecisionFork: - """A fork of a decision tree holds an attribute to test, and a dict + """A fork of a decision tree holds an attribute to test, and a dict of branches, one for each of the attribute's values.""" def __init__(self, attr, attrname=None, branches=None): @@ -286,8 +287,8 @@ def display(self, indent=0): name = self.attrname print('Test', name) for (val, subtree) in list(self.branches.items()): - print(' '*4*indent, name, '=', val, '==>', end=' ') - subtree.display(indent+1) + print(' ' * 4 * indent, name, '=', val, '==>', end=' ') + subtree.display(indent + 1) def __repr__(self): return ('DecisionFork(%r, %r, %r)' @@ -310,7 +311,7 @@ def display(self, indent=0): def __repr__(self): return repr(self.result) -#______________________________________________________________________________ +# ______________________________________________________________________________ def DecisionTreeLearner(dataset): @@ -377,7 +378,7 @@ def information_content(values): probabilities = normalize(removeall(0, values)) return sum(-p * log2(p) for p in probabilities) -#______________________________________________________________________________ +# ______________________________________________________________________________ # A decision list is implemented as a list of (test, value) pairs. @@ -411,14 +412,14 @@ def predict(example): return predict -#______________________________________________________________________________ +# ______________________________________________________________________________ def NeuralNetLearner(dataset, sizes): """Layered feed-forward network.""" - activations = [[0.0 for i in range(n)] for n in sizes] - weights = [] + activations = [[0.0 for i in range(n)] for n in sizes] # noqa + weights = [] # noqa def predict(example): unimplemented() @@ -438,13 +439,13 @@ def PerceptronLearner(dataset, sizes): def predict(example): return sum([]) unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ def Linearlearner(dataset): """Fit a linear model to the data.""" unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ def EnsembleLearner(learners): @@ -457,7 +458,7 @@ def predict(example): return predict return train -#______________________________________________________________________________ +# ______________________________________________________________________________ def AdaBoost(L, K): @@ -465,8 +466,8 @@ def AdaBoost(L, K): def train(dataset): examples, target = dataset.examples, dataset.target N = len(examples) - epsilon = 1./(2*N) - w = [1./N] * N + epsilon = 1. / (2 * N) + w = [1. / N] * N h, z = [], [] for k in range(K): h_k = L(dataset, w) @@ -474,7 +475,7 @@ def train(dataset): error = sum(weight for example, weight in zip(examples, w) if example[target] != h_k(example)) # Avoid divide-by-0 from either 0% or 100% error rates: - error = clip(error, epsilon, 1-epsilon) + error = clip(error, epsilon, 1 - epsilon) for j, example in enumerate(examples): if example[target] == h_k(example): w[j] *= error / (1. - error) @@ -501,7 +502,7 @@ def weighted_mode(values, weights): totals[v] += w return max(list(totals.keys()), key=totals.get) -#_____________________________________________________________________________ +# _____________________________________________________________________________ # Adapting an unweighted learner for AdaBoost @@ -529,15 +530,15 @@ def weighted_replicate(seq, weights, n): ['A', 'B', 'B', 'C']""" assert len(seq) == len(weights) weights = normalize(weights) - wholes = [int(w*n) for w in weights] - fractions = [(w*n) % 1 for w in weights] - return (flatten([x] * nx for x, nx in zip(seq, wholes)) - + weighted_sample_with_replacement(seq, fractions, n - sum(wholes))) + wholes = [int(w * n) for w in weights] + fractions = [(w * n) % 1 for w in weights] + return (flatten([x] * nx for x, nx in zip(seq, wholes)) + + weighted_sample_with_replacement(seq, fractions, n - sum(wholes))) def flatten(seqs): return sum(seqs, []) -#_____________________________________________________________________________ +# _____________________________________________________________________________ # Functions for testing learners on examples @@ -584,8 +585,9 @@ def cross_validation(learner, dataset, k=10, trials=1): else: n = len(dataset.examples) random.shuffle(dataset.examples) - return mean([train_and_test(learner, dataset, i*(n/k), (i+1)*(n/k)) - for i in range(k)]) + return mean( + [train_and_test(learner, dataset, i * (n / k), + (i + 1) * (n / k)) for i in range(k)]) def leave1out(learner, dataset): @@ -595,7 +597,7 @@ def leave1out(learner, dataset): def learningcurve(learner, dataset, trials=10, sizes=None): if sizes is None: - sizes = list(range(2, len(dataset.examples)-10, 2)) + sizes = list(range(2, len(dataset.examples) - 10, 2)) def score(learner, size): random.shuffle(dataset.examples) @@ -603,7 +605,7 @@ def score(learner, size): return [(size, mean([score(learner, size) for t in range(trials)])) for size in sizes] -#______________________________________________________________________________ +# ______________________________________________________________________________ # The rest of this file gives datasets for machine learning problems. orings = DataSet(name='orings', target='Distressed', @@ -619,15 +621,15 @@ def score(learner, size): iris = DataSet(name="iris", target="class", attrnames="sepal-len sepal-width petal-len petal-width class") -#______________________________________________________________________________ +# ______________________________________________________________________________ # The Restaurant example from Fig. 18.2 def RestaurantDataSet(examples=None): "Build a DataSet of Restaurant waiting examples. [Fig. 18.3]" return DataSet(name='restaurant', target='Wait', examples=examples, - attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' - + 'Raining Reservation Type WaitEstimate Wait') + attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' + + 'Raining Reservation Type WaitEstimate Wait') restaurant = RestaurantDataSet() @@ -646,14 +648,16 @@ def T(attrname, branches): T('Alternate', {'No': T('Reservation', {'Yes': 'Yes', 'No': T('Bar', {'No': 'No', - 'Yes': 'Yes'})}), + 'Yes': 'Yes' + })}), 'Yes': T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})}), '10-30': T('Hungry', {'No': 'Yes', 'Yes': T('Alternate', {'No': 'Yes', 'Yes': - T('Raining', {'No': 'No', 'Yes': 'Yes'})})})})}) + T('Raining', {'No': 'No', 'Yes': 'Yes'}) + })})})}) __doc__ += """ [Fig. 18.6] @@ -683,7 +687,7 @@ def gen(): return example return RestaurantDataSet([gen() for i in range(n)]) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Artificial, generated datasets. @@ -693,7 +697,7 @@ def Majority(k, n): examples = [] for i in range(n): bits = [random.choice([0, 1]) for i in range(k)] - bits.append(int(sum(bits) > k/2)) + bits.append(int(sum(bits) > k / 2)) examples.append(bits) return DataSet(name="majority", examples=examples) @@ -722,7 +726,7 @@ def ContinuousXor(n): examples.append([x, y, int(x) != int(y)]) return DataSet(name="continuous xor", examples=examples) -#______________________________________________________________________________ +# ______________________________________________________________________________ def compare(algorithms=[PluralityLearner, NaiveBayesLearner, diff --git a/logic.py b/logic.py index 88b1ea36e..4ae6fbeae 100644 --- a/logic.py +++ b/logic.py @@ -24,14 +24,14 @@ diff, simp Symbolic differentiation and simplification """ -from utils import * +from utils import * # noqa import agents import itertools import re from collections import defaultdict -#______________________________________________________________________________ +# ______________________________________________________________________________ class KB: @@ -93,7 +93,7 @@ def retract(self, sentence): if c in self.clauses: self.clauses.remove(c) -#______________________________________________________________________________ +# ______________________________________________________________________________ def KB_AgentProgram(KB): @@ -118,7 +118,7 @@ def make_action_sentence(self, action, t): return program -#______________________________________________________________________________ +# ______________________________________________________________________________ class Expr: @@ -193,8 +193,9 @@ def __repr__(self): def __eq__(self, other): """x and y are equal iff their ops and args are equal.""" - return (other is self) or (isinstance(other, Expr) - and self.op == other.op and self.args == other.args) + return (other is self) or (isinstance(other, Expr) and + self.op == other.op and + self.args == other.args) def __ne__(self, other): return not self.__eq__(other) @@ -326,8 +327,8 @@ def is_definite_clause(s): return True elif s.op == '>>': antecedent, consequent = s.args - return (is_symbol(consequent.op) - and every(lambda arg: is_symbol(arg.op), conjuncts(antecedent))) + return (is_symbol(consequent.op) and + every(lambda arg: is_symbol(arg.op), conjuncts(antecedent))) else: return False @@ -345,7 +346,7 @@ def parse_definite_clause(s): TRUE, FALSE, ZERO, ONE, TWO = list(map(Expr, ['TRUE', 'FALSE', 0, 1, 2])) A, B, C, D, E, F, G, P, Q, x, y, z = list(map(Expr, 'ABCDEFGPQxyz')) -#______________________________________________________________________________ +# ______________________________________________________________________________ def tt_entails(kb, alpha): @@ -447,7 +448,7 @@ def pl_true(exp, model={}): else: raise ValueError("illegal operator in logic expression" + str(exp)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Convert to Conjunctive Normal Form (CNF) @@ -509,7 +510,7 @@ def move_not_inwards(s): ((A | ~B) & ~C) """ if s.op == '~': - NOT = lambda b: move_not_inwards(~b) + def NOT(b): move_not_inwards(~b) # noqa a = s.args[0] if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A @@ -605,7 +606,7 @@ def disjuncts(s): """ return dissociate('|', [s]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def pl_resolution(KB, alpha): @@ -644,7 +645,7 @@ def pl_resolve(ci, cj): clauses.append(associate('|', dnew)) return clauses -#______________________________________________________________________________ +# ______________________________________________________________________________ class PropDefiniteKB(PropKB): @@ -701,7 +702,7 @@ def pl_fc_entails(KB, q): for s in "P>>Q (L&M)>>P (B&L)>>M (A&P)>>L (A&B)>>L A B".split(): Fig[7, 15].tell(expr(s)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # DPLL-Satisfiable [Fig. 7.17] @@ -726,9 +727,9 @@ def dpll(clauses, symbols, model): unknown_clauses = [] # clauses with an unknown truth value for c in clauses: val = pl_true(c, model) - if val == False: + if val is False: return False - if val != True: + if val is not True: unknown_clauses.append(c) if not unknown_clauses: return model @@ -812,7 +813,7 @@ def inspect_literal(literal): else: return literal, True -#______________________________________________________________________________ +# ______________________________________________________________________________ # Walk-SAT [Fig. 7.18] @@ -836,7 +837,7 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): raise NotImplementedError model[sym] = not model[sym] -#______________________________________________________________________________ +# ______________________________________________________________________________ class HybridWumpusAgent(agents.Agent): @@ -850,7 +851,7 @@ def __init__(self): def plan_route(current, goals, allowed): unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): @@ -870,7 +871,7 @@ def translate_to_SAT(init, transition, goal, t): def extract_solution(model): unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ def unify(x, y, s): @@ -962,7 +963,6 @@ def fol_fc_ask(KB, alpha): """Inefficient forward chaining for first-order logic. [Fig. 9.3] KB is a FolKB and alpha must be an atomic sentence.""" while True: - new = {} for r in KB.clauses: ps, q = parse_definite_clause(standardize_variables(r)) raise NotImplementedError @@ -995,7 +995,7 @@ def standardize_variables(sentence, dic=None): standardize_variables.counter = itertools.count() -#______________________________________________________________________________ +# ______________________________________________________________________________ class FolKB(KB): @@ -1036,9 +1036,9 @@ def test_ask(query, kb=None): q = expr(query) vars = variables(q) answers = fol_bc_ask(kb or test_kb, q) - return sorted([pretty(dict((x, v) for x, v in list(a.items()) if x in vars)) - for a in answers], - key=repr) + return sorted( + [pretty(dict((x, v) for x, v in list(a.items()) if x in vars)) + for a in answers], key=repr) test_kb = FolKB( list(map(expr, ['Farmer(Mac)', @@ -1051,14 +1051,14 @@ def test_ask(query, kb=None): '(Farmer(f)) ==> Human(f)', # Note that this order of conjuncts # would result in infinite recursion: - #'(Human(h) & Mother(m, h)) ==> Human(m)' + # '(Human(h) & Mother(m, h)) ==> Human(m)' '(Mother(m, h) & Human(h)) ==> Human(m)' ])) ) crime_kb = FolKB( list(map(expr, - ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', + ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', # noqa 'Owns(Nono, M1)', 'Missile(M1)', '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)', @@ -1107,7 +1107,7 @@ def fol_bc_and(KB, goals, theta): for theta2 in fol_bc_and(KB, rest, theta1): yield theta2 -#______________________________________________________________________________ +# ______________________________________________________________________________ # Example application (not in the book). # You can use the Expr class to do symbolic differentiation. This used to be @@ -1141,8 +1141,8 @@ def diff(y, x): elif op == '**' and isnumber(x.op): return (v * u ** (v - 1) * diff(u, x)) elif op == '**': - return (v * u ** (v - 1) * diff(u, x) - + u ** v * Expr('log')(u) * diff(v, x)) + return (v * u ** (v - 1) * diff(u, x) + + u ** v * Expr('log')(u) * diff(v, x)) elif op == 'log': return diff(u, x) / u else: @@ -1215,7 +1215,7 @@ def d(y, x): "Differentiate and then simplify." return simp(diff(y, x)) -#_________________________________________________________________________ +# _________________________________________________________________________ # Utilities for doctest cases # These functions print their arguments in a standard order @@ -1269,7 +1269,7 @@ def ppdict(d): def ppset(s): print(pretty_set(s)) -#________________________________________________________________________ +# ________________________________________________________________________ class logicTest: @@ -1331,7 +1331,7 @@ class logicTest: False ### An earlier version of the code failed on this: ->>> dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) +>>> dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) # noqa {B: False, C: True, A: True, F: False, D: True, E: False} ### [Fig. 7.13] diff --git a/mdp.py b/mdp.py index 28ee611d9..dbb5e4d54 100644 --- a/mdp.py +++ b/mdp.py @@ -6,7 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" -from utils import * +from utils import * # noqa class MDP: @@ -15,9 +15,9 @@ class MDP: and reward function. We also keep track of a gamma value, for use by algorithms. The transition model is represented somewhat differently from the text. Instead of P(s' | s, a) being a probability number for each - state/state/action triplet, we instead have T(s, a) return a list of (p, s') - pairs. We also keep track of the possible states, terminal states, and - actions for each state. [page 646]""" + state/state/action triplet, we instead have T(s, a) return a + list of (p, s') pairs. We also keep track of the possible states, + terminal states, and actions for each state. [page 646]""" def __init__(self, init, actlist, terminals, gamma=.9): self.init = init @@ -90,16 +90,17 @@ def to_grid(self, mapping): def to_arrows(self, policy): chars = { (1, 0): '>', (0, 1): '^', (-1, 0): '<', (0, -1): 'v', None: '.'} - return self.to_grid(dict([(s, chars[a]) for (s, a) in list(policy.items())])) + return self.to_grid( + dict([(s, chars[a]) for (s, a) in list(policy.items())])) -#______________________________________________________________________________ +# ______________________________________________________________________________ Fig[17, 1] = GridMDP([[-0.04, -0.04, -0.04, +1], [-0.04, None, -0.04, -1], [-0.04, -0.04, -0.04, -0.04]], terminals=[(3, 2), (3, 1)]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def value_iteration(mdp, epsilon=0.001): @@ -131,7 +132,7 @@ def expected_utility(a, s, U, mdp): "The expected utility of doing a in state s, according to the MDP and U." return sum([p * U[s1] for (p, s1) in mdp.T(s, a)]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def policy_iteration(mdp): @@ -180,12 +181,20 @@ def policy_evaluation(pi, U, mdp, k=20): __doc__ += """ Random tests: >>> pi -{(3, 2): None, (3, 1): None, (3, 0): (-1, 0), (2, 1): (0, 1), (0, 2): (1, 0), (1, 0): (1, 0), (0, 0): (0, 1), (1, 2): (1, 0), (2, 0): (0, 1), (0, 1): (0, 1), (2, 2): (1, 0)} +{(3, 2): None, (3, 1): None, (3, 0): (-1, 0), (2, 1): (0, 1), (0, 2): (1, 0), + (1, 0): (1, 0), (0, 0): (0, 1), (1, 2): (1, 0), (2, 0): (0, 1), + (0, 1): (0, 1), (2, 2): (1, 0)} >>> value_iteration(Fig[17,1], .01) -{(3, 2): 1.0, (3, 1): -1.0, (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, (0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537, (0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676, (2, 0): 0.34461306281476806, (2, 1): 0.48643676237737926, (2, 2): 0.79536093684710951} +{(3, 2): 1.0, (3, 1): -1.0, (3, 0): 0.12958868267972745, + (0, 1): 0.39810203830605462, (0, 2): 0.50928545646220924, + (1, 0): 0.25348746162470537, (0, 0): 0.29543540628363629, + (1, 2): 0.64958064617168676, (2, 0): 0.34461306281476806, + (2, 1): 0.48643676237737926, (2, 2): 0.79536093684710951} >>> policy_iteration(Fig[17,1]) -{(3, 2): None, (3, 1): None, (3, 0): (0, -1), (2, 1): (-1, 0), (0, 2): (1, 0), (1, 0): (1, 0), (0, 0): (1, 0), (1, 2): (1, 0), (2, 0): (1, 0), (0, 1): (1, 0), (2, 2): (1, 0)} +{(3, 2): None, (3, 1): None, (3, 0): (0, -1), (2, 1): (-1, 0), (0, 2): (1, 0), + (1, 0): (1, 0), (0, 0): (1, 0), (1, 2): (1, 0), (2, 0): (1, 0), + (0, 1): (1, 0), (2, 2): (1, 0)} """ diff --git a/nlp.py b/nlp.py index 2077aec74..ae418757e 100644 --- a/nlp.py +++ b/nlp.py @@ -3,11 +3,11 @@ # (Written for the second edition of AIMA; expect some discrepanciecs # from the third edition until this gets reviewed.) -from utils import * +from utils import * # noqa from collections import defaultdict -#______________________________________________________________________________ +# ______________________________________________________________________________ # Grammars and Lexicons @@ -55,16 +55,16 @@ def __repr__(self): E0 = Grammar('E0', Rules( # Grammar for E_0 [Fig. 22.4] S='NP VP | S Conjunction S', - NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', + NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', # noqa VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb', PP='Preposition NP', RelClause='That VP'), Lexicon( # Lexicon for E_0 [Fig. 22.3] - Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", - Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", + Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", # noqa + Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", # noqa Adjective="right | left | east | south | back | smelly", - Adverb="here | there | nearby | ahead | right | left | east | south | back", + Adverb="here | there | nearby | ahead | right | left | east | south | back", # noqa Pronoun="me | you | I | it", Name="John | Mary | Boston | Aristotle", Article="the | a | an", @@ -110,7 +110,7 @@ def rewrite(tokens, into): return ' '.join(rewrite(s.split(), [])) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Chart Parsing @@ -132,7 +132,8 @@ def parses(self, words, S='S'): """Return a list of parses; words can be a list or string. >>> chart = Chart(E_NP_) >>> chart.parses('happy man', 'NP') - [[0, 2, 'NP', [('Adj', 'happy'), [1, 2, 'NP', [('N', 'man')], []]], []]] + [[0, 2, 'NP', [('Adj', 'happy'), + [1, 2, 'NP', [('N', 'man')], []]], []]] """ if isinstance(words, str): words = words.split() @@ -166,7 +167,7 @@ def add_edge(self, edge): self.predictor(edge) def scanner(self, j, word): - "For each edge expecting a word of this category here, extend the edge." + "For each edge expecting a word of this category here, extend the edge." # noqa for (i, j, A, alpha, Bb) in self.chart[j]: if Bb and self.grammar.isa(word, Bb[0]): self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]]) @@ -195,9 +196,15 @@ def extender(self, edge): >>> chart = Chart(E0) >>> chart.parses('the wumpus that is smelly is near 2 2') -[[0, 9, 'S', [[0, 5, 'NP', [[0, 2, 'NP', [('Article', 'the'), ('Noun', 'wumpus')], []], [2, 5, 'RelClause', [('That', 'that'), [3, 5, 'VP', [[3, 4, 'VP', [('Verb', 'is')], []], ('Adjective', 'smelly')], []]], []]], []], [5, 9, 'VP', [[5, 6, 'VP', [('Verb', 'is')], []], [6, 9, 'PP', [('Preposition', 'near'), [7, 9, 'NP', [('Digit', '2'), ('Digit', '2')], []]], []]], []]], []]] - -### There is a built-in trace facility (compare [Fig. 22.9]) +[[0, 9, 'S', [[0, 5, 'NP', [[0, 2, 'NP', + [('Article', 'the'), ('Noun', 'wumpus')], []], + [2, 5, 'RelClause', [('That', 'that'), [3, 5, 'VP', + [[3, 4, 'VP', [('Verb', 'is')], []], ('Adjective', 'smelly')], []]], + []]], []], [5, 9, 'VP', [[5, 6, 'VP', [('Verb', 'is')], []], + [6, 9, 'PP', [('Preposition', 'near'), [7, 9, 'NP', [('Digit', '2'), + ('Digit', '2')], []]], []]], []]], []]] + +### There is a built-in trace facility (compare [Fig. 22.9]) # noqa >>> Chart(E_, trace=True).parses('I feel it') parse: added [0, 0, 'S_', [], ['S']] predictor: added [0, 0, 'S', [], ['NP', 'VP']] diff --git a/planning.py b/planning.py index 89ef53fc6..c939b9808 100644 --- a/planning.py +++ b/planning.py @@ -1,6 +1,8 @@ """Planning (Chapters 10-11) """ +# flake8: noqa + from utils import * import agents diff --git a/probability.py b/probability.py index c5bd76313..a582d128d 100644 --- a/probability.py +++ b/probability.py @@ -1,14 +1,14 @@ """Probability models. (Chapter 13-15) """ -from utils import * +from utils import * # noqa from logic import extend import random from collections import defaultdict from functools import reduce -#______________________________________________________________________________ +# ______________________________________________________________________________ def DTAgentProgram(belief_state): @@ -21,7 +21,7 @@ def program(percept): program.action = None return program -#______________________________________________________________________________ +# ______________________________________________________________________________ class ProbDist: @@ -129,7 +129,7 @@ def event_values(event, vars): else: return tuple([event[var] for var in vars]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def enumerate_joint_ask(X, e, P): @@ -157,7 +157,7 @@ def enumerate_joint(vars, e, P): return sum([enumerate_joint(rest, extend(e, Y, y), P) for y in P.values(Y)]) -#______________________________________________________________________________ +# ______________________________________________________________________________ class BayesNet: @@ -281,7 +281,7 @@ def __repr__(self): ('MaryCalls', 'Alarm', {T: 0.70, F: 0.01}) ]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def enumeration_ask(X, e, bn): @@ -312,7 +312,7 @@ def enumerate_all(vars, e, bn): return sum(Ynode.p(y, e) * enumerate_all(rest, extend(e, Y, y), bn) for y in bn.variable_values(Y)) -#______________________________________________________________________________ +# ______________________________________________________________________________ def elimination_ask(X, e, bn): @@ -402,7 +402,7 @@ def all_events(vars, bn, e): for x in bn.variable_values(X): yield extend(e1, X, x) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Fig. 14.12a: sprinkler network @@ -413,7 +413,7 @@ def all_events(vars, bn, e): ('WetGrass', 'Sprinkler Rain', {(T, T): 0.99, (T, F): 0.90, (F, T): 0.90, (F, F): 0.00})]) -#______________________________________________________________________________ +# ______________________________________________________________________________ def prior_sample(bn): @@ -424,7 +424,7 @@ def prior_sample(bn): event[node.variable] = node.sample(event) return event -#_________________________________________________________________________ +# _________________________________________________________________________ def rejection_sampling(X, e, bn, N): @@ -451,7 +451,7 @@ def consistent_with(event, evidence): return all(evidence.get(k, v) == v for k, v in list(event.items())) -#_________________________________________________________________________ +# _________________________________________________________________________ def likelihood_weighting(X, e, bn, N): @@ -483,7 +483,7 @@ def weighted_sample(bn, e): event[Xi] = node.sample(event) return event, w -#_________________________________________________________________________ +# _________________________________________________________________________ def gibbs_ask(X, e, bn, N): @@ -522,7 +522,7 @@ def markov_blanket_sample(X, e, bn): # (assuming a Boolean variable here) return probability(Q.normalize()[True]) -#_________________________________________________________________________ +# _________________________________________________________________________ def forward_backward(ev, prior): @@ -539,7 +539,7 @@ def particle_filtering(e, N, dbn): """[Fig. 15.17]""" unimplemented() -#_________________________________________________________________________ +# _________________________________________________________________________ __doc__ += """ # We can build up a probability distribution like this (p. 469): >>> P = ProbDist() @@ -553,7 +553,7 @@ def particle_filtering(e, N, dbn): >>> P['rain'] #doctest:+ELLIPSIS 0.2... -# A Joint Probability Distribution is dealt with like this (Fig. 13.3): +# A Joint Probability Distribution is dealt with like this (Fig. 13.3): # noqa >>> P = JointProbDist(['Toothache', 'Cavity', 'Catch']) >>> T, F = True, False >>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008 diff --git a/rl.py b/rl.py index f30e542ba..d74062be4 100644 --- a/rl.py +++ b/rl.py @@ -1,7 +1,7 @@ """Reinforcement Learning (Chapter 21) """ -from utils import * +from utils import * # noqa import agents diff --git a/search.py b/search.py index 40816722b..66e2fdbba 100644 --- a/search.py +++ b/search.py @@ -4,16 +4,14 @@ then create problem instances and solve them with calls to the various search functions.""" -from utils import * +from utils import * # noqa import math import random import sys -import time import bisect -import string -#______________________________________________________________________________ +# ______________________________________________________________________________ class Problem(object): @@ -61,7 +59,7 @@ def value(self, state): """For optimization problems, each state has a value. Hill-climbing and related algorithms try to maximize this value.""" raise NotImplementedError -#______________________________________________________________________________ +# ______________________________________________________________________________ class Node: @@ -94,7 +92,8 @@ def child_node(self, problem, action): "Fig. 3.10" next = problem.result(self.state, action) return Node(next, self, action, - problem.path_cost(self.path_cost, self.state, action, next)) + problem.path_cost(self.path_cost, self.state, + action, next)) def solution(self): "Return the sequence of actions to go from the root to this node." @@ -119,7 +118,7 @@ def __eq__(self, other): def __hash__(self): return hash(self.state) -#______________________________________________________________________________ +# ______________________________________________________________________________ class SimpleProblemSolvingAgentProgram: @@ -151,7 +150,7 @@ def formulate_problem(self, state, goal): def search(self, problem): raise NotImplementedError -#______________________________________________________________________________ +# ______________________________________________________________________________ # Uninformed Search algorithms @@ -180,8 +179,8 @@ def graph_search(problem, frontier): return node explored.add(node.state) frontier.extend(child for child in node.expand(problem) - if child.state not in explored - and child not in frontier) + if child.state not in explored and + child not in frontier) return None @@ -283,7 +282,7 @@ def iterative_deepening_search(problem): if result != 'cutoff': return result -#______________________________________________________________________________ +# ______________________________________________________________________________ # Informed (Heuristic) Search greedy_best_first_graph_search = best_first_graph_search @@ -297,7 +296,7 @@ def astar_search(problem, h=None): h = memoize(h or problem.h, 'h') return best_first_graph_search(problem, lambda n: n.path_cost + h(n)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Other search algorithms @@ -373,32 +372,35 @@ def simulated_annealing(problem, schedule=exp_schedule()): def and_or_graph_search(problem): """Used when the environment is nondeterministic and completely observable Contains OR nodes where the agent is free to choose any action - After every action there is an AND node which contains all possible states the agent may reach due to stochastic nature of environment - The agent must be able to handle all possible states of the AND node(as it may end up in any of them) - returns a conditional plan to reach goal state, or failure if the former is not possible""" + After every action there is an AND node which contains all possible states + the agent may reach due to stochastic nature of environment + The agent must be able to handle all possible states of the AND node(as it + may end up in any of them) returns a conditional plan to reach goal state, + or failure if the former is not possible""" "[Fig. 4.11]" - #functions used by and_or_search + # functions used by and_or_search def or_search(state, problem, path): if problem.goal_test(state): return {} if state in path: return None for action in problem.action(state): - plan = and_search(problem.result(state, action), problem, path + [state,]) - if not plan == None: + plan = and_search(problem.result(state, action), + problem, path + [state, ]) + if plan is not None: return [action, plan] def and_search(states, problem, path): - "returns plan in form of dictionary where we take action plan[s] if we reach state s" - plan=dict() + "returns plan in form of dictionary where we take action plan[s] if we reach state s" # noqa + plan = dict() for s in states: plan[s] = or_search(s, problem, path) - if plan[s] == None: + if plan[s] is None: return None return plan - #body of and or search + # body of and or search return or_search(problem.initial, problem, []) @@ -426,7 +428,8 @@ def run(self, percept): self.a = None else: if current_state not in self.untried.keys(): - self.untried[current_state] = self.problem.actions(current_state) + self.untried[current_state] = self.problem.actions( + current_state) if self.s is not None: if current_state != self.result[(self.s, self.a)]: self.result[(self.s, self.a)] = current_state @@ -435,10 +438,10 @@ def run(self, percept): if len(self.unbacktracked[current_state]) == 0: self.a = None else: - # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) - unbacktracked_pop = self.unbacktracked[current_state].pop(0) - for (s,b) in self.result.keys(): - if self.result[(s,b)] == unbacktracked_pop: + # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) # noqa + unbacktracked_pop = self.unbacktracked[current_state].pop(0) # noqa + for (s, b) in self.result.keys(): + if self.result[(s, b)] == unbacktracked_pop: self.a = b break else: @@ -451,7 +454,7 @@ def lrta_star_agent(s1): "[Fig. 4.24]" unimplemented() -#______________________________________________________________________________ +# ______________________________________________________________________________ # Genetic Algorithm @@ -496,10 +499,10 @@ def mutate(self): "Change a few of my genes." raise NotImplementedError -#_____________________________________________________________________________ +# _____________________________________________________________________________ # The remainder of this file implements examples for the search algorithms. -#______________________________________________________________________________ +# ______________________________________________________________________________ # Graphs and Graph Problems @@ -589,7 +592,7 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g -#Simplified road map of Romania +# Simplified road map of Romania Fig[3, 2] = UndirectedGraph(dict( Arad=dict(Zerind=75, Sibiu=140, Timisoara=118), Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211), @@ -605,20 +608,24 @@ def distance_to_node(n): Rimnicu=dict(Sibiu=80), Urziceni=dict(Vaslui=142))) Fig[3, 2].locations = dict( - Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288), Drobeta=(165, 299), - Eforie=(562, 293), Fagaras=(305, 449), Giurgiu=(375, 270), Hirsova=(534, 350), - Iasi=(473, 506), Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537), - Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410), Sibiu=(207, 457), - Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) - -#Principal states and territories of Australia + Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288), + Drobeta=(165, 299), Eforie=(562, 293), Fagaras=(305, 449), + Giurgiu=(375, 270), Hirsova=(534, 350), Iasi=(473, 506), + Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537), + Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410), + Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), + Vaslui=(509, 444), Zerind=(108, 531)) + +# Principal states and territories of Australia Fig[6, 1] = UndirectedGraph(dict( T=dict(), SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), NT=dict(WA=1, Q=1), NSW=dict(Q=1, V=1))) + Fig[6, 1].locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), - Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) + Q=(145, 20), NSW=(145, 32), T=(145, 42), + V=(145, 37)) class GraphProblem(Problem): @@ -648,7 +655,7 @@ def h(self, node): else: return infinity -#______________________________________________________________________________ +# ______________________________________________________________________________ class NQueensProblem(Problem): @@ -689,10 +696,10 @@ def conflicted(self, state, row, col): def conflict(self, row1, col1, row2, col2): "Would putting two queens in (row1, col1) and (row2, col2) conflict?" - return (row1 == row2 # same row - or col1 == col2 # same column - or row1-col1 == row2-col2 # same \ diagonal - or row1+col1 == row2+col2) # same / diagonal + return (row1 == row2 or # same row + col1 == col2 or # same column + row1-col1 == row2-col2 or # same \ diagonal + row1+col1 == row2+col2) # same / diagonal def goal_test(self, state): "Check if all columns filled, no conflicts." @@ -701,7 +708,7 @@ def goal_test(self, state): return not any(self.conflicted(state, state[col], col) for col in range(len(state))) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Inverse Boggle: Search for a high-scoring Boggle board. A good domain for # iterative-repair and related search techniques, as suggested by Justin Boyan. @@ -780,7 +787,7 @@ def exact_sqrt(n2): assert n * n == n2 return n -#_____________________________________________________________________________ +# _____________________________________________________________________________ class Wordlist: @@ -819,7 +826,7 @@ def __contains__(self, word): def __len__(self): return len(self.words) -#_____________________________________________________________________________ +# _____________________________________________________________________________ class BoggleFinder: @@ -880,7 +887,7 @@ def __len__(self): "The number of words found." return len(self.found) -#_____________________________________________________________________________ +# _____________________________________________________________________________ def boggle_hill_climbing(board=None, ntimes=100, verbose=True): @@ -911,7 +918,7 @@ def mutate_boggle(board): board[i] = random.choice(random.choice(cubes16)) return i, oldc -#______________________________________________________________________________ +# ______________________________________________________________________________ # Code to compare searchers on various problems. @@ -956,7 +963,8 @@ def __repr__(self): def compare_searchers(problems, header, searchers=[breadth_first_tree_search, - breadth_first_search, depth_first_graph_search, + breadth_first_search, + depth_first_graph_search, iterative_deepening_search, depth_limited_search, recursive_best_first_search]): @@ -977,13 +985,14 @@ def compare_graph_searchers(): depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> -recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/ 12/ 43/WA>""" +recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/12/ 43/WA>""" # noqa compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', Fig[3, 2]), GraphProblem('Oradea', 'Neamt', Fig[3, 2]), GraphProblem('Q', 'WA', Fig[6, 1])], - header=['Searcher', 'Fig[3, 2](Arad, Bucharest)', 'Fig[3, 2](Oradea, Neamt)', 'Fig[6, 1]']) + header=['Searcher', 'Fig[3, 2](Arad, Bucharest)', + 'Fig[3, 2](Oradea, Neamt)', 'Fig[6, 1]']) -#______________________________________________________________________________ +# ______________________________________________________________________________ __doc__ += """ >>> romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) @@ -1006,9 +1015,9 @@ def compare_graph_searchers(): >>> board = list('SARTELNID') >>> print_boggle(board) -S A R -T E L -N I D +S A R +T E L +N I D >>> f = BoggleFinder(board) >>> len(f) 206 @@ -1017,7 +1026,20 @@ def compare_graph_searchers(): __doc__ += """ Random tests >>> ' '.join(f.words()) -'LID LARES DEAL LIE DIETS LIN LINT TIL TIN RATED ERAS LATEN DEAR TIE LINE INTER STEAL LATED LAST TAR SAL DITES RALES SAE RETS TAE RAT RAS SAT IDLE TILDES LEAST IDEAS LITE SATED TINED LEST LIT RASE RENTS TINEA EDIT EDITS NITES ALES LATE LETS RELIT TINES LEI LAT ELINT LATI SENT TARED DINE STAR SEAR NEST LITAS TIED SEAT SERAL RATE DINT DEL DEN SEAL TIER TIES NET SALINE DILATE EAST TIDES LINTER NEAR LITS ELINTS DENI RASED SERA TILE NEAT DERAT IDLEST NIDE LIEN STARED LIER LIES SETA NITS TINE DITAS ALINE SATIN TAS ASTER LEAS TSAR LAR NITE RALE LAS REAL NITER ATE RES RATEL IDEA RET IDEAL REI RATS STALE DENT RED IDES ALIEN SET TEL SER TEN TEA TED SALE TALE STILE ARES SEA TILDE SEN SEL ALINES SEI LASE DINES ILEA LINES ELD TIDE RENT DIEL STELA TAEL STALED EARL LEA TILES TILER LED ETA TALI ALE LASED TELA LET IDLER REIN ALIT ITS NIDES DIN DIE DENTS STIED LINER LASTED RATINE ERA IDLES DIT RENTAL DINER SENTI TINEAL DEIL TEAR LITER LINTS TEAL DIES EAR EAT ARLES SATE STARE DITS DELI DENTAL REST DITE DENTIL DINTS DITA DIET LENT NETS NIL NIT SETAL LATS TARE ARE SATI' +'LID LARES DEAL LIE DIETS LIN LINT TIL TIN RATED ERAS LATEN DEAR TIE LINE INTER +STEAL LATED LAST TAR SAL DITES RALES SAE RETS TAE RAT RAS SAT IDLE TILDES LEAST +IDEAS LITE SATED TINED LEST LIT RASE RENTS TINEA EDIT EDITS NITES ALES LATE +LETS RELIT TINES LEI LAT ELINT LATI SENT TARED DINE STAR SEAR NEST LITAS TIED +SEAT SERAL RATE DINT DEL DEN SEAL TIER TIES NET SALINE DILATE EAST TIDES LINTER +NEAR LITS ELINTS DENI RASED SERA TILE NEAT DERAT IDLEST NIDE LIEN STARED LIER +LIES SETA NITS TINE DITAS ALINE SATIN TAS ASTER LEAS TSAR LAR NITE RALE LAS +REAL NITER ATE RES RATEL IDEA RET IDEAL REI RATS STALE DENT RED IDES ALIEN SET +TEL SER TEN TEA TED SALE TALE STILE ARES SEA TILDE SEN SEL ALINES SEI LASE +DINES ILEA LINES ELD TIDE RENT DIEL STELA TAEL STALED EARL LEA TILES TILER LED +ETA TALI ALE LASED TELA LET IDLER REIN ALIT ITS NIDES DIN DIE DENTS STIED LINER +LASTED RATINE ERA IDLES DIT RENTAL DINER SENTI TINEAL DEIL TEAR LITER LINTS +TEAL DIES EAR EAT ARLES SATE STARE DITS DELI DENTAL REST DITE DENTIL DINTS DITA +DIET LENT NETS NIL NIT SETAL LATS TARE ARE SATI' >>> boggle_hill_climbing(list('ABCDEFGHI'), verbose=False) (['E', 'P', 'R', 'D', 'O', 'A', 'G', 'S', 'T'], 123) diff --git a/text.py b/text.py index 7559474a2..d4e48aa65 100644 --- a/text.py +++ b/text.py @@ -4,7 +4,7 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -from utils import * +from utils import * # noqa from learning import CountingProbDist import search @@ -13,7 +13,6 @@ import re - class UnigramTextModel(CountingProbDist): """This is a discrete probability distribution over words, so you @@ -71,7 +70,7 @@ def samples(self, nwords): nminus1gram = nminus1gram[1:] + (wn,) return ' '.join(output) -#______________________________________________________________________________ +# ______________________________________________________________________________ def viterbi_segment(text, P): @@ -99,7 +98,7 @@ def viterbi_segment(text, P): return sequence, best[-1] -#______________________________________________________________________________ +# ______________________________________________________________________________ # TODO(tmrts): Expose raw index @@ -124,7 +123,8 @@ def index_collection(self, filenames): "Index a whole collection of files." prefix = os.path.dirname(__file__) for filename in filenames: - self.index_document(open(filename).read(), os.path.relpath(filename, prefix)) + self.index_document(open(filename).read(), + os.path.relpath(filename, prefix)) def index_document(self, text, url): "Index the text of a document." @@ -155,15 +155,16 @@ def query(self, query_text, n=10): def score(self, word, docid): "Compute a score for this word on this docid." # There are many options; here we take a very simple approach - return (math.log(1 + self.index[word][docid]) - / math.log(1 + self.documents[docid].nwords)) + return (math.log(1 + self.index[word][docid]) / + math.log(1 + self.documents[docid].nwords)) def present(self, results): "Present the results as a list." for (score, d) in results: doc = self.documents[d] print( - ("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs()))) + ("{:5.2}|{:25} | {}".format(100 * score, doc.url, + doc.title[:45].expandtabs()))) def present_results(self, query_text, n=10): "Get results for the query and present them." @@ -209,7 +210,7 @@ def canonicalize(text): return ' '.join(words(text)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Example application (not in book): decode a cipher. # A cipher is a code that substitutes one character for another. @@ -358,19 +359,19 @@ def actions(self, state): # Find the best p, plainchar = max([(self.decoder.P1[c], c) for c in alphabet if c not in state]) - succs = [extend(state, plainchar, cipherchar)] # ???? + succs = [extend(state, plainchar, cipherchar)] # ???? # noqa def goal_test(self, state): "We're done when we get all 26 letters assigned." return len(state) >= 26 -#______________________________________________________________________________ +# ______________________________________________________________________________ # TODO(tmrts): Set RNG seed to test random functions __doc__ += """ Random tests: -## Generate random text from the N-gram models +## Generate random text from the N-gram models # noqa >>> P1.samples(20) 'you thought known but were insides of see in depend by us dodecahedrons just but i words are instead degrees' diff --git a/utils.py b/utils.py index 0fe21d7ab..766aabbdf 100644 --- a/utils.py +++ b/utils.py @@ -1,4 +1,4 @@ -"""Provide some widely useful utilities. Safe for "from utils import *". +"""Provide some widely useful utilities. Safe for "from utils import *". # noqa TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. @@ -6,17 +6,14 @@ TODO: Priority queues may not belong here -- see treatment in search.py """ -from grid import * +from grid import * # noqa import operator -import math import random import os.path import bisect -import re -from functools import reduce -#______________________________________________________________________________ +# ______________________________________________________________________________ # Simple Data Structures: infinity, Dict, Struct infinity = float('inf') @@ -37,7 +34,7 @@ def __cmp__(self, other): return self.__dict__ == other def __repr__(self): - args = ['{!s}={!s}'.format(k, repr(v)) + return ['{!s}={!s}'.format(k, repr(v)) for (k, v) in list(vars(self).items())] @@ -50,7 +47,7 @@ def update(x, **entries): return x -#______________________________________________________________________________ +# ______________________________________________________________________________ # Functions on Sequences (mostly inspired by Common Lisp) # NOTE: Sequence functions (count_if, find_if, every, some) take function # argument first (like reduce, filter, and map). @@ -103,15 +100,15 @@ def some(predicate, seq): return predicate(elem) if elem is not None else False -# TODO[COMPLETED]: rename to is_in or possibily add 'identity' to function name to -# clarify intent +# TODO[COMPLETED]: rename to is_in or possibily add 'identity' to function +# name to clarify intent def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Functions on sequences of numbers # NOTE: these take the sequence argument first, like min and max, # and like standard math notation: \sigma (i = 1..n) fn(i) @@ -125,14 +122,18 @@ def argmin(seq, fn): def argmin_list(seq, fn): - """Return a list of elements of seq[i] with the lowest fn(seq[i]) scores.’""" + """Return a list of elements of seq[i] with + the lowest fn(seq[i]) scores.’ + """ smallest_score = fn(min(seq, key=fn)) return [elem for elem in seq if fn(elem) == smallest_score] def argmin_gen(seq, fn): - """Return a generator of elements of seq[i] with the lowest fn(seq[i]) scores.""" + """Return a generator of elements of seq[i] with the + lowest fn(seq[i]) scores. + """ smallest_score = fn(min(seq, key=fn)) @@ -146,20 +147,26 @@ def argmin_random_tie(seq, fn): def argmax(seq, fn): - """Return an element with highest fn(seq[i]) score; tie goes to first one.""" + """Return an element with highest fn(seq[i]) score; + tie goes to first one. + """ return max(seq, key=fn) def argmax_list(seq, fn): """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. - Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method breaks if fn is len""" + Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method + breaks if fn is len + """ largest_score = fn(max(seq, key=fn)) return [elem for elem in seq if fn(elem) == largest_score] def argmax_gen(seq, fn): - """Return a generator of elements of seq[i] with the highest fn(seq[i]) scores.""" + """Return a generator of elements of seq[i] with + the highest fn(seq[i]) scores. + """ largest_score = fn(min(seq, key=fn)) yield from (elem for elem in seq if fn(elem) == largest_score) @@ -169,7 +176,7 @@ def argmax_random_tie(seq, fn): "Return an element with highest fn(seq[i]) score; break ties at random." return argmin_random_tie(seq, lambda x: -fn(x)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Statistical and mathematical functions @@ -185,13 +192,11 @@ def histogram(values, mode=0, bin_function=None): bins[val] = bins.get(val, 0) + 1 if mode: - return sorted(list(bins.items()), key=lambda x: (x[1], x[0]), reverse=True) + return sorted(list(bins.items()), key=lambda x: (x[1], x[0]), + reverse=True) else: return sorted(bins.items()) -from math import log2 -from statistics import mode, median, mean, stdev - def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y.""" @@ -227,7 +232,9 @@ def weighted_sampler(seq, weights): def num_or_str(x): - """The argument is a string; convert to a number if possible, or strip it.""" + """The argument is a string; convert to a number if + possible, or strip it. + """ try: return int(x) except ValueError: @@ -248,7 +255,7 @@ def clip(x, lowest, highest): return max(lowest, min(x, highest)) -#______________________________________________________________________________ +# ______________________________________________________________________________ # Misc Functions @@ -261,7 +268,9 @@ def printf(format_str, *args): def caller(n=1): - """Return the name of the calling function n levels up in the frame stack.""" + """Return the name of the calling function n levels up + in the frame stack. + """ import inspect return inspect.getouterframes(inspect.currentframe())[n][3] @@ -294,9 +303,9 @@ def memoized_fn(*args): def name(obj): "Try to find some reasonable name for the object." - return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) - or getattr(getattr(obj, '__class__', 0), '__name__', 0) - or str(obj)) + return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) or + getattr(getattr(obj, '__class__', 0), '__name__', 0) or + str(obj)) def isnumber(x): @@ -313,8 +322,8 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): """Print a list of lists as a table, so that columns line up nicely. header, if specified, will be printed as the first row. numfmt is the format for all numbers; you might want e.g. '%6.2f'. - (If you want different formats in different columns, don't use print_table.) - sep is the separator between columns.""" + (If you want different formats in different columns, + don't use print_table.) sep is the separator between columns.""" justs = ['rjust' if isnumber(x) else 'ljust' for x in table[0]] if header: @@ -323,14 +332,13 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): table = [[numfmt.format(x) if isnumber(x) else x for x in row] for row in table] - maxlen = lambda seq: max(list(map(len, seq))) - sizes = list( - map(maxlen, list(zip(*[list(map(str, row)) for row in table])))) + map(lambda seq: max(list(map(len, seq))), + list(zip(*[list(map(str, row)) for row in table])))) for row in table: - print(sep.join(getattr(str(x), j)(size) - for (j, size, x) in zip(justs, sizes, row))) + print(sep.join(getattr( + str(x), j)(size) for (j, size, x) in zip(justs, sizes, row))) def AIMAFile(components, mode='r'): @@ -351,7 +359,7 @@ def unimplemented(): "Use this as a stub for not-yet-implemented functions." raise NotImplementedError -#______________________________________________________________________________ +# ______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue # TODO: Use queue.Queue From 13e893f03286267cab24ed58b89e6c99919a33d2 Mon Sep 17 00:00:00 2001 From: Lucas Moura Date: Fri, 11 Mar 2016 03:05:44 -0300 Subject: [PATCH 082/513] Fix flake8 warnings for test files --- tests/test_grid.py | 6 +++-- tests/test_probability.py | 50 +++++++++++++++++++++++++-------------- tests/test_search.py | 21 ++++++++++++---- tests/test_text.py | 37 ++++++++++++++++------------- tests/test_utils.py | 25 ++++++++++++-------- 5 files changed, 87 insertions(+), 52 deletions(-) diff --git a/tests/test_grid.py b/tests/test_grid.py index b170a6321..d160ca6e9 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -1,7 +1,9 @@ import pytest -from grid import * +from grid import * # noqa -compare_list = lambda x, y: all([elm_x == y[i] for i, elm_x in enumerate(x)]) + +def compare_list(x, y): + return all([elm_x == y[i] for i, elm_x in enumerate(x)]) def test_distance(): diff --git a/tests/test_probability.py b/tests/test_probability.py index 903d511bf..40bdca660 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -1,10 +1,9 @@ import pytest -from probability import * +from probability import * # noqa def tests(): cpt = burglary.variable_node('Alarm') - parents = ['Burglary', 'Earthquake'] event = {'Burglary': True, 'Earthquake': True} assert cpt.p(True, event) == 0.95 event = {'Burglary': False, 'Earthquake': True} @@ -32,21 +31,25 @@ def tests(): p = likelihood_weighting('Earthquake', {}, burglary, 1000) assert p[True], p[False] == (0.002, 0.998) + def test_probdist_basic(): P = ProbDist('Flip') - P['H'], P['T'] = 0.25, 0.75; + P['H'], P['T'] = 0.25, 0.75 assert P['H'] == 0.25 + def test_probdist_frequency(): P = ProbDist('X', {'lo': 125, 'med': 375, 'hi': 500}) assert (P['lo'], P['med'], P['hi']) == (0.125, 0.375, 0.5) + def test_probdist_normalize(): P = ProbDist('Flip') P['H'], P['T'] = 35, 65 P = P.normalize() assert (P.prob['H'], P.prob['T']) == (0.350, 0.650) + def test_jointprob(): P = JointProbDist(['X', 'Y']) P[1, 1] = 0.25 @@ -54,39 +57,50 @@ def test_jointprob(): P[dict(X=0, Y=1)] = 0.5 assert P[dict(X=0, Y=1)] == 0.5 + def test_event_values(): - assert event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) == (8, 10) - assert event_values ((1, 2), ['C', 'A']) == (1, 2) + assert event_values({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) == (8, 10) + assert event_values((1, 2), ['C', 'A']) == (1, 2) + def test_enumerate_joint_ask(): P = JointProbDist(['X', 'Y']) - P[0,0] = 0.25 - P[0,1] = 0.5 - P[1,1] = P[2,1] = 0.125 - assert enumerate_joint_ask('X', dict(Y=1), - P).show_approx() == '0: 0.667, 1: 0.167, 2: 0.167' + P[0, 0] = 0.25 + P[0, 1] = 0.5 + P[1, 1] = P[2, 1] = 0.125 + assert enumerate_joint_ask( + 'X', dict(Y=1), P).show_approx() == '0: 0.667, 1: 0.167, 2: 0.167' + def test_bayesnode_p(): bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625}) assert bn.p(False, {'Burglary': False, 'Earthquake': True}) == 0.375 + def test_enumeration_ask(): - assert enumeration_ask('Burglary', - dict(JohnCalls=T, MaryCalls=T), burglary).show_approx() == 'False: 0.716, True: 0.284' + assert enumeration_ask( + 'Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary).show_approx() == 'False: 0.716, True: 0.284' + def test_elemination_ask(): - elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), - burglary).show_approx() == 'False: 0.716, True: 0.284' + elimination_ask( + 'Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary).show_approx() == 'False: 0.716, True: 0.284' + def test_rejection_sampling(): random.seed(47) - rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T), - burglary, 10000).show_approx() == 'False: 0.7, True: 0.3' + rejection_sampling( + 'Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary, 10000).show_approx() == 'False: 0.7, True: 0.3' + def test_likelihood_weighting(): random.seed(1017) - assert likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T), - burglary, 10000).show_approx() == 'False: 0.702, True: 0.298' + assert likelihood_weighting( + 'Burglary', dict(JohnCalls=T, MaryCalls=T), + burglary, 10000).show_approx() == 'False: 0.702, True: 0.298' if __name__ == '__main__': pytest.main() diff --git a/tests/test_search.py b/tests/test_search.py index 8dce793ea..4c0eb9ed3 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,24 +1,35 @@ import pytest -from search import * +from search import * # noqa romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) + def test_breadth_first_tree_search(): - assert breadth_first_tree_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert breadth_first_tree_search(romania).solution() == ['Sibiu', + 'Fagaras', + 'Bucharest'] + def test_breadth_first_search(): - assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', + 'Bucharest'] + def test_uniform_cost_search(): - assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', + 'Pitesti', 'Bucharest'] + def test_depth_first_graph_search(): solution = depth_first_graph_search(romania).solution() assert solution[-1] == 'Bucharest' + def test_iterative_deepening_search(): - assert iterative_deepening_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert iterative_deepening_search(romania).solution() == ['Sibiu', + 'Fagaras', + 'Bucharest'] if __name__ == '__main__': pytest.main() diff --git a/tests/test_text.py b/tests/test_text.py index 0b01545b5..391a381e0 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -1,8 +1,7 @@ import pytest -from text import * +from text import * # noqa -from random import choice from math import isclose @@ -58,21 +57,25 @@ def test_ngram_models(): P3 = NgramTextModel(3, wordseq) # The most frequent entries in each model - assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a'), - (722, 'i'), (640, 'in'), (478, 'that'), (399, 'is'), (348, 'you')] - - assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), - (80, ('it', 'is')), (71, - ('by', 'the')), (68, ('for', 'the')), - (68, ('and', 'the')), (62, ('on', 'the')), (60, ('to', 'be'))] - - assert P3.top(10) == [(30, ('a', 'straight', 'line')), (19, ('of', 'three', 'dimensions')), - (16, ('the', 'sense', 'of')), (13, - ('by', 'the', 'sense')), - (13, ('as', 'well', 'as')), (12, - ('of', 'the', 'circles')), - (12, ('of', 'sight', 'recognition') - ), (11, ('the', 'number', 'of')), + assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), + (1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'), + (478, 'that'), (399, 'is'), (348, 'you')] + + assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), + (152, ('in', 'the')), (86, ('of', 'a')), + (80, ('it', 'is')), + (71, ('by', 'the')), (68, ('for', 'the')), + (68, ('and', 'the')), (62, ('on', 'the')), + (60, ('to', 'be'))] + + assert P3.top(10) == [(30, ('a', 'straight', 'line')), + (19, ('of', 'three', 'dimensions')), + (16, ('the', 'sense', 'of')), + (13, ('by', 'the', 'sense')), + (13, ('as', 'well', 'as')), + (12, ('of', 'the', 'circles')), + (12, ('of', 'sight', 'recognition')), + (11, ('the', 'number', 'of')), (11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))] assert isclose(P1['the'], 0.0611, rel_tol=0.001) diff --git a/tests/test_utils.py b/tests/test_utils.py index cddfff4d8..6fa9ba5f4 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,5 +1,5 @@ import pytest -from utils import * +from utils import * # noqa def test_struct_initialization(): @@ -47,15 +47,14 @@ def test_product(): def test_find_if(): - assert find_if(callable, [1, 2, 3]) == None + assert find_if(callable, [1, 2, 3]) is None assert find_if(callable, [3, min, max]) == min def test_count_if(): assert count_if(callable, [42, None, max, min]) == 2 - is_odd = lambda x: x % 2 - assert count_if(is_odd, []) == 0 - assert count_if(is_odd, [1, 2, 3, 4, 5]) == 3 + assert count_if(lambda x: x, []) == 0 + assert count_if(lambda x: x % 2, [1, 2, 3, 4, 5]) == 3 def test_every(): @@ -70,8 +69,8 @@ def test_some(): def test_is_in(): e = [] - assert is_in(e, [1, e, 3]) == True - assert is_in(e, [1, [], 3]) == False + assert is_in(e, [1, e, 3]) is True + assert is_in(e, [1, [], 3]) is False def test_argmin(): @@ -102,9 +101,15 @@ def test_argmax_gen(): def test_histogram(): - assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), (4, 2), (5, 1), (7, 1), (9, 1)] - assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, lambda x: x*x) == [(1, 2), (4, 3), (16, 2), (25, 1), (49, 1), (81, 1)] - assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 1) == [(2, 3), (4, 2), (1, 2), (9, 1), (7, 1), (5, 1)] + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), + (4, 2), (5, 1), + (7, 1), (9, 1)] + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, + lambda x: x*x) == [(1, 2), (4, 3), (16, 2), (25, 1), + (49, 1), (81, 1)] + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 1) == [(2, 3), (4, 2), + (1, 2), (9, 1), + (7, 1), (5, 1)] def test_dotproduct(): From 3fdb931ca0117d998598e8f69e9306e5e857b038 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 12 Mar 2016 02:03:39 +0530 Subject: [PATCH 083/513] Fixed alphabeta_search --- games.py | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/games.py b/games.py index fd3ebc4f0..14e069401 100644 --- a/games.py +++ b/games.py @@ -68,7 +68,17 @@ def min_value(state, alpha, beta): return v # Body of alphabeta_search: - return max_value(state, -infinity, infinity) + v = -infinity + best_action = None + for a in game.actions(state): + val = min_value(game.result(state, a), alpha, beta) + if val > v: + v = val + best_action = a + if v >= beta: + break + alpha = max(alpha, v) + return best_action def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): @@ -107,7 +117,17 @@ def min_value(state, alpha, beta, depth): cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) - return max_value(state, -infinity, infinity, 0) + v = -infinity + best_action = None + for a in game.actions(state): + val = min_value(game.result(state, a), alpha, beta, 1) + if val > v: + v = val + best_action = a + if v >= beta: + break + alpha = max(alpha, v) + return best_action #______________________________________________________________________________ # Players for Games From 04dd8e2872e14eb2112f85103ca2042f91c5f43e Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 12 Mar 2016 02:19:09 +0530 Subject: [PATCH 084/513] Make alphabeta_search similar to algorithm in the book --- games.py | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/games.py b/games.py index 14e069401..a55e91c34 100644 --- a/games.py +++ b/games.py @@ -68,16 +68,13 @@ def min_value(state, alpha, beta): return v # Body of alphabeta_search: - v = -infinity + best_score = -infinity best_action = None for a in game.actions(state): - val = min_value(game.result(state, a), alpha, beta) - if val > v: - v = val + v = min_value(game.result(state, a), best_score, beta) + if v > best_score: + best_score = v best_action = a - if v >= beta: - break - alpha = max(alpha, v) return best_action @@ -117,16 +114,13 @@ def min_value(state, alpha, beta, depth): cutoff_test = (cutoff_test or (lambda state, depth: depth > d or game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) - v = -infinity + best_score = -infinity best_action = None for a in game.actions(state): - val = min_value(game.result(state, a), alpha, beta, 1) - if val > v: - v = val + v = min_value(game.result(state, a), best_score, beta, 1) + if v > best_score: + best_score = v best_action = a - if v >= beta: - break - alpha = max(alpha, v) return best_action #______________________________________________________________________________ From 9c5b13cf3cc6117b9b16f0580e34125c715701ae Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 11 Mar 2016 12:49:49 -0800 Subject: [PATCH 085/513] Update README.md --- README.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 31e6d2f63..57e3f8165 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,19 @@ -# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python` (Python 3.5) [![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) +# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. (And we're always looking for solid contributors who are not affiliated with GSoC.) +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. And we're always looking for solid contributors who are not affiliated with GSoC. A big thank you to everyone who has contributed! + +## Python 3.5 + +This code is in Pythoin 3.5. If you don't have that version, you should [install it](https://www.python.org/downloads), and if you can't install it, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project -When complete, this project will have [Python 3.5](https://www.python.org/downloads/release/python-350/) code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following files in the main branch: +When complete, this project will have Python code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following three files in the main branch: - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. -- `logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). - `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. +- `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken: @@ -164,3 +168,7 @@ various dates. However, I don't have much confidence in these figures... |[lisp](http://www.google.com/search?q=lisp+norvig+russell+%22Modern+Approach%22)|844|974|30,100|19,000|14,000| |[prolog](http://www.google.com/search?q=prolog+norvig+russell+%22Modern+Approach%22)|789|2,010|23,200|17,000|16,000| |[python](http://www.google.com/search?q=python+norvig+russell+%22Modern+Approach%22)|785|1,240|18,400|11,000|12,000| + +# Acknowledgements + +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is in Githib, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are actively improving the project. Thanks! From c2c815876a75e01d21435d90ed33829f9ecd8b3b Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 11 Mar 2016 13:01:51 -0800 Subject: [PATCH 086/513] Update README.md --- README.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 57e3f8165..72a86ad7b 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Python code for the book *Artificial Intelligence: A Modern Approach.* We're loo ## Python 3.5 -This code is in Pythoin 3.5. If you don't have that version, you should [install it](https://www.python.org/downloads), and if you can't install it, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.5. (I believe any version from 3.4 on will work.) You can [install the latest version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project @@ -21,14 +21,13 @@ Until we get there, we will support a legacy branch, `aima3python2` (for the thi - Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. - Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. -- Create a `_test.py` file, and define functions that use `assert` to make tests. Remove any old `doctest` tests. -In other words, replace the ">>> 2 + 2 \n 4" in a docstring with "assert 2 + 2 == 4" in `filename_test.py`. +- Add more tests in `_test.py` files. Strive for terseness; it is ok to group multiple asserts into one `def test_something():` function. Move most tests to `_test.py`, but it is fine to have a single `doctest` example in the docstring of a function in the `.py` file, if the purpose of the doctest is to explain how to use the function, rather than test the implementation. ## New and Improved Algorithms - Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](http://aima.cs.berkeley.edu/algorithms.pdf) to see what's missing. -- As we finish chapters for the new fourth edition, we will share the new pseudocode, and describe what changes are necessary. - +- As we finish chapters for the new fourth edition, we will share the new pseudocode in the [`aima-pseudocode`](https://github.com/aimacode/aima-pseudocode) repository, and describe what changes are necessary. +We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. - Create a `.ipynb` notebook, and give examples of how to use the code. # Style Guide @@ -46,11 +45,8 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) - Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). - I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). - Not all constants have to be UPPERCASE. -- [Pep 484](https://www.python.org/dev/peps/pep-0484/) type annotations are allowed but not required. If your - parameter name is already suggestive of the name of a type, such as `url` below, then i don't think the type annotation is useful. - Return type annotations, such as `-> None` below, can be very useful. - - def retry(url: Url) -> None: +- At some point I may add [Pep 484](https://www.python.org/dev/peps/pep-0484/) type annotations, but I think I'll hold off for now; + I want to get more experience with them, and some people may still be in Python 3.4. # Index of Code # @@ -171,4 +167,4 @@ various dates. However, I don't have much confidence in these figures... # Acknowledgements -Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is in Githib, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are actively improving the project. Thanks! +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is in Githib, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Thanks to all! From fcead672efac178e98ed88ea69880cf4f1168923 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 11 Mar 2016 13:21:57 -0800 Subject: [PATCH 087/513] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 72a86ad7b..0b166ab77 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ # ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code (GSoC) to work on this project; if you want to be that student, make some good contributions here (by looking throush the "Issues" and resolving some), and submit an application. And we're always looking for solid contributors who are not affiliated with GSoC. A big thank you to everyone who has contributed! +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code ([GSoC](https://summerofcode.withgoogle.com/)) to work on this project; if you want to be that student, make some good contributions here (by looking through the [Issues](https://github.com/aimacode/aima-python/issues) and resolving some), and submit an [application](https://summerofcode.withgoogle.com/terms/student). (However, be warned that we've had over 150 students express interest, so competition will be tough.) And we're always looking for solid contributors who are not affiliated with GSoC. A big thank you to everyone who has contributed! ## Python 3.5 -This code is in Python 3.5. (I believe any version from 3.4 on will work.) You can [install the latest version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.5. (I believe any version from 3.4 on will work.) You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project @@ -25,10 +25,10 @@ Until we get there, we will support a legacy branch, `aima3python2` (for the thi ## New and Improved Algorithms -- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](http://aima.cs.berkeley.edu/algorithms.pdf) to see what's missing. +- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](https://github.com/aimacode/pseudocode/blob/master/algorithms.pdf) to see what's missing. - As we finish chapters for the new fourth edition, we will share the new pseudocode in the [`aima-pseudocode`](https://github.com/aimacode/aima-pseudocode) repository, and describe what changes are necessary. We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. -- Create a `.ipynb` notebook, and give examples of how to use the code. +- Give examples of how to use the code in the `.ipynb` file. # Style Guide From f633d0c462ed0fc8951e32af6e3a9194c95923e5 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Sat, 12 Mar 2016 03:01:34 +0530 Subject: [PATCH 088/513] Renamed run to __call__ --- search.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/search.py b/search.py index 66e2fdbba..5a2fa0032 100644 --- a/search.py +++ b/search.py @@ -419,10 +419,7 @@ def __init__(self, problem): self.unbacktracked = defaultdict(list) self.result = {} - def update_state(self, percept): - raise NotImplementedError - - def run(self, percept): + def __call__(self, percept): current_state = self.update_state(percept) if self.problem.goal_test(current_state): self.a = None @@ -449,6 +446,8 @@ def run(self, percept): self.s = current_state return self.a + def update_state(self, percept): + raise NotImplementedError def lrta_star_agent(s1): "[Fig. 4.24]" From e7479db972754479f177f8283ab77f54c6ab064e Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 12 Mar 2016 04:48:54 +0530 Subject: [PATCH 089/513] Resolved conflicts --- search.py | 48 ++++++++++++++++++++++++++++++++++++++++---- tests/test_search.py | 14 +++++++++++++ 2 files changed, 58 insertions(+), 4 deletions(-) diff --git a/search.py b/search.py index 66e2fdbba..e5f0de30c 100644 --- a/search.py +++ b/search.py @@ -43,9 +43,12 @@ def result(self, state, action): def goal_test(self, state): """Return True if the state is a goal. The default method compares the - state to self.goal, as specified in the constructor. Override this + state to self.goal or checks for state in self.goal if it is a list, as specified in the constructor. Override this method if checking against a single self.goal is not enough.""" - return state == self.goal + if isinstance(self.goal, list): + return is_in(state, self.goal) + else: + return state == self.goal def path_cost(self, c, state1, action, state2): """Return the cost of a solution path that arrives at state2 from @@ -382,10 +385,10 @@ def and_or_graph_search(problem): # functions used by and_or_search def or_search(state, problem, path): if problem.goal_test(state): - return {} + return [] if state in path: return None - for action in problem.action(state): + for action in problem.actions(state): plan = and_search(problem.result(state, action), problem, path + [state, ]) if plan is not None: @@ -616,6 +619,29 @@ def distance_to_node(n): Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) +""" +Eight possible states of the vacumm world +Each state is represented as "State if the left room" "State of the right room" "Room in which the agent is present" +1 Dirty Dirty Left - DDL +2 Dirty Dirty Right - DDR +3 Dirty Clean Left - DCL +4 Dirty Clean Right - DCR +5 Clean Dirty Left - CDL +6 Clean Dirty Right - CDR +7 Clean Clean Left - CCL +8 Clean Clean Right - CCR +""" +Fig[4, 9] = Graph(dict( + State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']), + State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']), + State_3 = dict(Suck = ['State_7'], Right = ['State_4']), + State_4 = dict(Suck = ['State_4', 'State_2'], Left = ['State_3']), + State_5 = dict(Suck = ['State_5', 'State_1'], Right = ['State_6']), + State_6 = dict(Suck = ['State_8'], Left = ['State_5']), + State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']), + State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) +)) + # Principal states and territories of Australia Fig[6, 1] = UndirectedGraph(dict( T=dict(), @@ -655,6 +681,20 @@ def h(self, node): else: return infinity +class GraphProblemStochastic(GraphProblem): + """ + A version of Graph Problem where an action can lead to undeterministic output i.e. multiple possible states + Define the graph as dict(A = dict(Action = [[, , ...],], ...), ...) + A the dictionary format is different, make sure the graph is created as a directed graph + """ + + def result(self, state, action): + return self.graph.get(state, action) + + def path_cost(): + raise NotImplementedError + + # ______________________________________________________________________________ diff --git a/tests/test_search.py b/tests/test_search.py index 4c0eb9ed3..5cfcb4991 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,8 +1,10 @@ import pytest from search import * # noqa +from random import choice #noqa romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) +vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], Fig[4, 9]) def test_breadth_first_tree_search(): @@ -31,5 +33,17 @@ def test_iterative_deepening_search(): 'Fagaras', 'Bucharest'] +def test_and_or_graph_search(): + def run_plan(state, problem, plan): + if problem.goal_test(state): + return True + if len(plan) is not 2: + return False + next_state = choice(problem.result(state, plan[0])) + return run_plan(next_state, problem, plan[1][next_state]) + plan = and_or_graph_search(vacumm_world) + assert run_plan('State_1', vacumm_world, plan) + + if __name__ == '__main__': pytest.main() From 6f764c933aa0c1f14634c569c999e579b22ad611 Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 11 Mar 2016 15:43:39 -0800 Subject: [PATCH 090/513] Update README.md --- README.md | 139 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 72 insertions(+), 67 deletions(-) diff --git a/README.md b/README.md index 0b166ab77..1e6e502e5 100644 --- a/README.md +++ b/README.md @@ -50,53 +50,58 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) # Index of Code # -| **Fig** | **Page** | **Name (in book)** | **Code** | -|:--------|:---------|:-------------------|:---------| -| 2 | 32 | Environment | [Environment](../master/agents.py) | -| 2.1 | 33 | Agent | [Agent](../master/agents.py) | -| 2.3 | 34 | Table-Driven-Vacuum-Agent | [TableDrivenVacuumAgent](../master/agents.py) | -| 2.7 | 45 | Table-Driven-Agent | [TableDrivenAgent](../master/agents.py) | -| 2.8 | 46 | Reflex-Vacuum-Agent | [ReflexVacuumAgent](../master/agents.py) | -| 2.10 | 47 | Simple-Reflex-Agent | [SimpleReflexAgent](../master/agents.py) | -| 2.12 | 49 | Reflex-Agent-With-State | [ReflexAgentWithState](../master/agents.py) | -| 3.1 | 61 | Simple-Problem-Solving-Agent | [SimpleProblemSolvingAgent](../master/search.py) | -| 3 | 62 | Problem | [Problem](../master/search.py) | -| 3.2 | 63 | Romania | [romania](../master/search.py) | -| 3 | 69 | Node | [Node](../master/search.py) | -| 3.7 | 70 | Tree-Search | [tree\_search](../master/search.py) | -| 3 | 71 | Queue | [Queue](../master/utils.py) | -| 3.9 | 72 | Tree-Search | [tree\_search](../master/search.py) | -| 3.13 | 77 | Depth-Limited-Search | [depth\_limited\_search](../master/search.py) | -| 3.14 | 79 | Iterative-Deepening-Search | [iterative\_deepening\_search](../master/search.py) | -| 3.19 | 83 | Graph-Search | [graph\_search](../master/search.py) | -| 4 | 95 | Best-First-Search | [best\_first\_graph\_search](../master/search.py) | -| 4 | 97 | A`*`-Search | [astar\_search](../master/search.py) | -| 4.5 | 102 | Recursive-Best-First-Search | [recursive\_best\_first\_search](../master/search.py) | -| 4.11 | 112 | Hill-Climbing | [hill\_climbing](../master/search.py) | -| 4.14 | 116 | Simulated-Annealing | [simulated\_annealing](../master/search.py) | -| 4.17 | 119 | Genetic-Algorithm | [genetic\_algorithm](../master/search.py) | -| 4.20 | 126 | Online-DFS-Agent | [online\_dfs\_agent](../master/search.py) | -| 4.23 | 128 | LRTA`*`-Agent | [lrta\_star\_agent](../master/search.py) | -| 5 | 137 | CSP | [CSP](../master/csp.py) | -| 5.3 | 142 | Backtracking-Search | [backtracking\_search](../master/csp.py) | -| 5.7 | 146 | AC-3 | [AC3](../master/csp.py) | -| 5.8 | 151 | Min-Conflicts | [min\_conflicts](../master/csp.py) | -| 6.3 | 166 | Minimax-Decision | [minimax\_decision](../master/games.py) | -| 6.7 | 170 | Alpha-Beta-Search | [alphabeta\_search](../master/games.py) | -| 7 | 195 | KB | [KB](../master/logic.py) | -| 7.1 | 196 | KB-Agent | [KB\_Agent](../master/logic.py) | -| 7.7 | 205 | Propositional Logic Sentence | [Expr](../master/logic.py) | -| 7.10 | 209 | TT-Entails | [tt\_entials](../master/logic.py) | -| 7 | 215 | Convert to CNF | [to\_cnf](../master/logic.py) | -| 7.12 | 216 | PL-Resolution | [pl\_resolution](../master/logic.py) | -| 7.14 | 219 | PL-FC-Entails? | [pl\_fc\_resolution](../master/logic.py) | -| 7.16 | 222 | DPLL-Satisfiable? | [dpll\_satisfiable](../master/logic.py) | -| 7.17 | 223 | WalkSAT | [WalkSAT](../master/logic.py) | -| 7.19 | 226 | PL-Wumpus-Agent | [PLWumpusAgent](../master/logic.py) | -| 9 | 273 | Subst | [subst](../master/logic.py) | -| 9.1 | 278 | Unify | [unify](../master/logic.py) | -| 9.3 | 282 | FOL-FC-Ask | [fol\_fc\_ask](../master/logic.py) | -| 9.6 | 288 | FOL-BC-Ask | [fol\_bc\_ask](../master/logic.py) | +Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an iassue. + + +| **Fig** | **Page** | **Name (in book)** | **Name (in code)** | **File** +|:--------|:---------|:-------------------|:---------|:-----------| +| 2 | 32 | Environment | `Environment` | [`agents.py`](../master/agents.py) | +| 2.1 | 33 | Agent | `Agent` | [`agents.py`](../master/agents.py) | +| 2.3 | 34 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.7 | 45 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | +| 2.8 | 46 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.10 | 47 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | +| 2.12 | 49 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | +| 3.1 | 61 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | +| 3 | 62 | Problem | `Problem` | [`search.py`](../master/search.py) | +| 3.2 | 63 | Romania | `romania` | [`search.py`](../master/search.py) | +| 3 | 69 | Node | `Node` | [`search.py`](../master/search.py) | +| 3 | 71 | Queue | `Queue` | [`utils.py`](../master/utils.py) | +| 3.7 | 70 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | +| 3.7 | 72 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | +| 3.11 | 72 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | +| 3.13 | 72 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | +| 3.16 | 77 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | +| 3.14 | 79 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | +| 3.19 | 83 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | +| 4 | 95 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | +| 4 | 97 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | +| 4.5 | 102 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | +| 4.11 | 112 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | +| 4.14 | 116 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | +| 4.17 | 119 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | +| 4.20 | 126 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | +| 4.23 | 128 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | +| 5 | 137 | CSP | `CSP` | [`csp.py`](../master/csp.py) | +| 5.3 | 142 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | +| 5.7 | 146 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | +| 5.8 | 151 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | +| 6.3 | 166 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | +| 6.7 | 170 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | +| 7 | 195 | KB | `KB` | [`logic.py`](../master/logic.py) | +| 7.1 | 196 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | +| 7.7 | 205 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | +| 7.10 | 209 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | +| 7 | 215 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | +| 7.12 | 216 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | +| 7.14 | 219 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | +| 7.16 | 222 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | +| 7.17 | 223 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | +| 7.19 | 226 | PL-Wumpus-Agent | `PLWumpusAgent` | [`logic.py`](../master/logic.py) | +| 9 | 273 | Subst | `subst` | [`logic.py`](../master/logic.py) | +| 9.1 | 278 | Unify | `unify` | [`logic.py`](../master/logic.py) | +| 9.3 | 282 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | +| 9.6 | 288 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | | 9.14 | 307 | Otter | | | 11.2 | 380 | Airport-problem | | | 11.3 | 381 | Spare-Tire-Problem | | @@ -108,38 +113,38 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) | 12.1 | 418 | Job-Shop-Problem | | | 12.3 | 421 | Job-Shop-Problem-With-Resources | | | 12.6 | 424 | House-Building-Problem | | -| 12.10 | 435 | And-Or-Graph-Search | [and\_or\_graph\_search](../master/search.py) | +| 12.10 | 435 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | | 12.22 | 449 | Continuous-POP-Agent | | | 12.23 | 450 | Doubles-tennis | | -| 13.1 | 466 | DT-Agent | [DTAgent](../master/probability.py) | -| 13 | 469 | Discrete Probability Distribution | [DiscreteProbDist](../master/probability.py) | -| 13.4 | 477 | Enumerate-Joint-Ask | [enumerate\_joint\_ask](../master/probability.py) | -| 14.10 | 509 | Elimination-Ask | [elimination\_ask](../master/probability.py) | -| 14.12 | 512 | Prior-Sample | [prior\_sample](../master/probability.py) | -| 14.13 | 513 | Rejection-Sampling | [rejection\_sampling](../master/probability.py) | -| 14.14 | 515 | Likelihood-Weighting | [likelihood\_weighting](../master/probability.py) | +| 13.1 | 466 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | +| 13 | 469 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | +| 13.4 | 477 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | +| 14.10 | 509 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | +| 14.12 | 512 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | +| 14.13 | 513 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | +| 14.14 | 515 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | | 14.15 | 517 | MCMC-Ask | | -| 15.4 | 546 | Forward-Backward | [forward\_backward](../master/probability.py) | -| 15.6 | 552 | Fixed-Lag-Smoothing | [fixed\_lag\_smoothing](../master/probability.py) | -| 15.15 | 566 | Particle-Filtering | [particle\_filtering](../master/probability.py) | +| 15.4 | 546 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | +| 15.6 | 552 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | +| 15.15 | 566 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | | 16.8 | 603 | Information-Gathering-Agent | | -| 17.4 | 621 | Value-Iteration | [value\_iteration](../master/mdp.py) | -| 17.7 | 624 | Policy-Iteration | [policy\_iteration](../master/mdp.py) | -| 18.5 | 658 | Decision-Tree-Learning | [DecisionTreeLearner](../master/learning.py) | -| 18.10 | 667 | AdaBoost | [AdaBoost](../master/learning.py) | +| 17.4 | 621 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | +| 17.7 | 624 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | +| 18.5 | 658 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | +| 18.10 | 667 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | | 18.14 | 672 | Decision-List-Learning | | | 19.2 | 681 | Current-Best-Learning | | | 19.3 | 683 | Version-Space-Learning | | | 19.8 | 696 | Minimal-Consistent-Det | | | 19.12 | 702 | FOIL | | -| 20.21 | 742 | Perceptron-Learning | [PerceptronLearner](../master/learning.py) | +| 20.21 | 742 | Perceptron-Learning | `PerceptronLearner` | [`learning.py`](../master/learning.py) | | 20.25 | 746 | Back-Prop-Learning | | -| 21.2 | 768 | Passive-ADP-Agent | [PassiveADPAgent](../master/rl.py) | -| 21.4 | 769 | Passive-TD-Agent | [PassiveTDAgent](../master/rl.py) | +| 21.2 | 768 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | +| 21.4 | 769 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | 776 | Q-Learning-Agent | | | 22.2 | 796 | Naive-Communicating-Agent | | -| 22.7 | 801 | Chart-Parse | [Chart](../master/nlp.py) | -| 23.1 | 837 | Viterbi-Segmentation | [viterbi\_segment](../master/text.py) | +| 22.7 | 801 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | +| 23.1 | 837 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | | 24.21 | 892 | Align | | # Choice of Programming Languages From 7e34aeda275cbbceb2dae8d695a6b6630b477b38 Mon Sep 17 00:00:00 2001 From: Stuart Russell Date: Fri, 11 Mar 2016 22:24:39 -0800 Subject: [PATCH 091/513] Remove legacy lisp functions Remove count_if, find_if, every, some, Struct; replace them with modern Python equivalents. --- csp.py | 8 +++--- games.py | 9 +++--- learning.py | 3 +- tests/test_utils.py | 44 +++++------------------------ utils.py | 67 ++++++++++----------------------------------- 5 files changed, 32 insertions(+), 99 deletions(-) diff --git a/csp.py b/csp.py index c671f2f26..1b80ed4be 100644 --- a/csp.py +++ b/csp.py @@ -76,7 +76,7 @@ def nconflicts(self, var, val, assignment): def conflict(var2): return (var2 in assignment and not self.constraints(var, val, var2, assignment[var2])) - return count_if(conflict, self.neighbors[var]) + return count(conflict(v) for v in self.neighbors[var]) def display(self, assignment): "Show a human-readable representation of the CSP." @@ -92,7 +92,7 @@ def actions(self, state): return [] else: assignment = dict(state) - var = find_if(lambda v: v not in assignment, self.vars) + var = first(v for v in self.vars if v not in assignment) return [(var, val) for val in self.domains[var] if self.nconflicts(var, val, assignment) == 0] @@ -206,8 +206,8 @@ def num_legal_values(csp, var, assignment): if csp.curr_domains: return len(csp.curr_domains[var]) else: - return count_if(lambda val: csp.nconflicts(var, val, assignment) == 0, - csp.domains[var]) + return count(csp.nconflicts(var, val, assignment) == 0 + for val in csp.domains[var]) # Value ordering diff --git a/games.py b/games.py index 53b6adafd..04d3cfe14 100644 --- a/games.py +++ b/games.py @@ -234,6 +234,7 @@ def terminal_test(self, state): def to_move(self, state): return ('MIN' if state in 'BCD' else 'MAX') +GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') class TicTacToe(Game): @@ -246,7 +247,7 @@ def __init__(self, h=3, v=3, k=3): update(self, h=h, v=v, k=k) moves = [(x, y) for x in range(1, h+1) for y in range(1, v+1)] - self.initial = Struct(to_move='X', utility=0, board={}, moves=moves) + self.initial = GameState(to_move='X', utility=0, board={}, moves=moves) def actions(self, state): "Legal moves are any square not yet taken." @@ -259,9 +260,9 @@ def result(self, state, move): board[move] = state.to_move moves = list(state.moves) moves.remove(move) - return Struct(to_move=('O' if state.to_move == 'X' else 'X'), - utility=self.compute_utility(board, move, state.to_move), - board=board, moves=moves) + return GameState(to_move=('O' if state.to_move == 'X' else 'X'), + utility=self.compute_utility(board, move, state.to_move), + board=board, moves=moves) def utility(self, state, player): "Return the value to player; 1 for win, -1 for loss, 0 otherwise." diff --git a/learning.py b/learning.py index b6741d1e8..497dea8c0 100644 --- a/learning.py +++ b/learning.py @@ -343,7 +343,8 @@ def plurality_value(examples): return DecisionLeaf(popular) def count(attr, val, examples): - return count_if(lambda e: e[attr] == val, examples) + "Count the number of examples that have attr = val." + return count(e[attr] == val for e in examples) def all_same_class(examples): "Are all these examples in the same target class?" diff --git a/tests/test_utils.py b/tests/test_utils.py index 6fa9ba5f4..b6dd089a1 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,28 +2,11 @@ from utils import * # noqa -def test_struct_initialization(): - s = Struct(a=1, b=2) - assert s.a == 1 - assert s.b == 2 - - -def test_struct_assignment(): - s = Struct(a=1) - s.a = 3 - assert s.a == 3 - - def test_update_dict(): assert update({'a': 1}, a=10, b=20) == {'a': 10, 'b': 20} assert update({}, a=5) == {'a': 5} -def test_update_struct(): - assert update(Struct(a=1), a=30, b=20).__cmp__(Struct(a=30, b=20)) - assert update(Struct(), a=10).__cmp__(Struct(a=10)) - - def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] @@ -46,26 +29,13 @@ def test_product(): assert product(list(range(1, 11))) == 3628800 -def test_find_if(): - assert find_if(callable, [1, 2, 3]) is None - assert find_if(callable, [3, min, max]) == min - - -def test_count_if(): - assert count_if(callable, [42, None, max, min]) == 2 - assert count_if(lambda x: x, []) == 0 - assert count_if(lambda x: x % 2, [1, 2, 3, 4, 5]) == 3 - - -def test_every(): - assert every(callable, [min, max]) == 1 - assert every(callable, [min, 3]) == 0 - - -def test_some(): - assert some(callable, [min, 3]) == 1 - assert some(callable, [2, 3]) == 0 - +def test_first(): + assert first('word') == 'w' + assert first('') == None + assert first('', 'empty') == 'empty' + assert first(range(10)) == 0 + assert first(x for x in range(10) if x > 3) == 4 + assert first(x for x in range(10) if x > 100) == None def test_is_in(): e = [] diff --git a/utils.py b/utils.py index 766aabbdf..44e040a48 100644 --- a/utils.py +++ b/utils.py @@ -1,7 +1,6 @@ """Provide some widely useful utilities. Safe for "from utils import *". # noqa TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py -TODO: count_if and the like are leftovers from COmmon Lisp; let's make replace thenm with Pythonic alternatives. TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. TODO: Priority queues may not belong here -- see treatment in search.py """ @@ -13,31 +12,6 @@ import os.path import bisect -# ______________________________________________________________________________ -# Simple Data Structures: infinity, Dict, Struct - -infinity = float('inf') - - -class Struct: - - """Create an instance with argument=value slots. - This is for making a lightweight object whose class doesn't matter.""" - - def __init__(self, **entries): - self.__dict__.update(entries) - - def __cmp__(self, other): - if isinstance(other, Struct): - return self.__dict__ == other.__dict__ - else: - return self.__dict__ == other - - def __repr__(self): - return ['{!s}={!s}'.format(k, repr(v)) - for (k, v) in list(vars(self).items())] - - def update(x, **entries): """Update a dict or an object with slots according to entries.""" if isinstance(x, dict): @@ -49,8 +23,6 @@ def update(x, **entries): # ______________________________________________________________________________ # Functions on Sequences (mostly inspired by Common Lisp) -# NOTE: Sequence functions (count_if, find_if, every, some) take function -# argument first (like reduce, filter, and map). def removeall(item, seq): @@ -65,6 +37,10 @@ def unique(seq): """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) +def count(seq): + """Count the number of items in sequence that are interpreted as true.""" + return sum(bool(x) for x in seq) + def product(numbers): """Return the product of the numbers, e.g. product([2, 3, 10]) == 60""" @@ -73,19 +49,14 @@ def product(numbers): result *= x return result - -def count_if(predicate, seq): - """Count the number of elements of seq for which the predicate is true.""" - return sum([bool(predicate(x)) for x in seq]) - - -def find_if(predicate, seq): - """If there is an element of seq that satisfies predicate; return it.""" - for x in seq: - if predicate(x): - return x - - return None +def first(iterable, default=None): + "Return the first element of an iterable or sequence; or default." + try: + return iterable[0] + except IndexError: + return default + except TypeError: + return next(iterable, default) def every(predicate, seq): @@ -94,16 +65,6 @@ def every(predicate, seq): return all(predicate(x) for x in seq) -def some(predicate, seq): - """If some element x of seq satisfies predicate(x), return predicate(x).""" - elem = find_if(predicate, seq) - - return predicate(elem) if elem is not None else False - -# TODO[COMPLETED]: rename to is_in or possibily add 'identity' to function -# name to clarify intent - - def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) @@ -200,7 +161,7 @@ def histogram(values, mode=0, bin_function=None): def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y.""" - return sum([x * y for x, y in zip(X, Y)]) + return sum(x * y for x, y in zip(X, Y)) def vector_add(a, b): @@ -447,7 +408,7 @@ def pop(self): return self.A.pop()[1] def __contains__(self, item): - return some(lambda x: x == item, self.A) + return any(item == pair[1] for pair in self.A) def __getitem__(self, key): for _, item in self.A: From aad5a8a386a9819bf74a95de2a49c48efbd470a8 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 12 Mar 2016 16:01:57 +0530 Subject: [PATCH 092/513] Improved test of and_or_search --- tests/test_search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_search.py b/tests/test_search.py index 5cfcb4991..6585aab1a 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -39,8 +39,8 @@ def run_plan(state, problem, plan): return True if len(plan) is not 2: return False - next_state = choice(problem.result(state, plan[0])) - return run_plan(next_state, problem, plan[1][next_state]) + predicate = lambda x : run_plan(x, problem, plan[1][x]) + return every(predicate, problem.result(state, plan[0])) plan = and_or_graph_search(vacumm_world) assert run_plan('State_1', vacumm_world, plan) From 1695bbd277017f32d1db25d9829bcd8ed89f14f6 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sat, 12 Mar 2016 16:29:56 +0530 Subject: [PATCH 093/513] Fix alphabeta_full_search and alphabeta_search, remove docstring tests --- games.py | 36 +++++++++--------------------------- 1 file changed, 9 insertions(+), 27 deletions(-) diff --git a/games.py b/games.py index 53b6adafd..c9c57ebfd 100644 --- a/games.py +++ b/games.py @@ -1,5 +1,4 @@ -"""Games, or Adversarial Search. (Chapter 5) -""" +"""Games, or Adversarial Search. (Chapter 5)""" from utils import * # noqa @@ -69,6 +68,7 @@ def min_value(state, alpha, beta): # Body of alphabeta_search: best_score = -infinity + beta = infinity best_action = None for a in game.actions(state): v = min_value(game.result(state, a), best_score, beta) @@ -77,7 +77,6 @@ def min_value(state, alpha, beta): best_action = a return best_action - def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): """Search game to determine best action; use alpha-beta pruning. This version cuts off search and uses an evaluation function.""" @@ -116,6 +115,7 @@ def min_value(state, alpha, beta, depth): game.terminal_test(state))) eval_fn = eval_fn or (lambda state: game.utility(state, player)) best_score = -infinity + beta = infinity best_action = None for a in game.actions(state): v = min_value(game.result(state, a), best_score, beta, 1) @@ -131,7 +131,7 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): "Make a move by querying standard input." game.display(state) - return num_or_str(eval(input('Your move? '))) + return eval(input('Your move? ')) def random_player(game, state): @@ -140,14 +140,12 @@ def random_player(game, state): def alphabeta_player(game, state): - return alphabeta_search(state, game) + return alphabeta_full_search(state, game) def play_game(game, *players): - """Play an n-person, move-alternating game. - >>> play_game(Fig52Game(), alphabeta_player, alphabeta_player) - 3 - """ + """Play an n-person, move-alternating game.""" + state = game.initial while True: for player in players: @@ -199,16 +197,8 @@ def __repr__(self): class Fig52Game(Game): + """The game represented in [Fig. 5.2]. Serves as a simple test case.""" - """The game represented in [Fig. 5.2]. Serves as a simple test case. - >>> g = Fig52Game() - >>> minimax_decision('A', g) - 'a1' - >>> alphabeta_full_search('A', g) - 'a1' - >>> alphabeta_search('A', g) - 'a1' - """ succs = dict(A=dict(a1='B', a2='C', a3='D'), B=dict(b1='B1', b2='B2', b3='B3'), C=dict(c1='C1', c2='C2', c3='C3'), @@ -243,7 +233,7 @@ class TicTacToe(Game): a dict of {(x, y): Player} entries, where Player is 'X' or 'O'.""" def __init__(self, h=3, v=3, k=3): - update(self, h=h, v=v, k=k) + update(self, h=h, v=v, k=k) # What is this exactly? moves = [(x, y) for x in range(1, h+1) for y in range(1, v+1)] self.initial = Struct(to_move='X', utility=0, board={}, moves=moves) @@ -316,11 +306,3 @@ def __init__(self, h=7, v=6, k=4): def actions(self, state): return [(x, y) for (x, y) in state.moves if y == 1 or (x, y-1) in state.board] - -__doc__ += """ -Random tests: ->>> play_game(Fig52Game(), random_player, random_player) -6 ->>> play_game(TicTacToe(), random_player, random_player) -0 -""" From 8d43217c3757488f57cc81bf47f11303a560a34f Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sat, 12 Mar 2016 16:30:56 +0530 Subject: [PATCH 094/513] Replace deprecated __cmp__ with __eq__ for Struct --- utils.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/utils.py b/utils.py index 06471af40..d8d9a7c21 100644 --- a/utils.py +++ b/utils.py @@ -1,7 +1,7 @@ """Provide some widely useful utilities. Safe for "from utils import *". # noqa TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py -TODO: count_if and the like are leftovers from Common Lisp; let's make replace thenm with Pythonic alternatives. +TODO: count_if and the like are leftovers from Common Lisp; let's make replace them with Pythonic alternatives. TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. TODO: Priority queues may not belong here -- see treatment in search.py """ @@ -27,15 +27,12 @@ class Struct: def __init__(self, **entries): self.__dict__.update(entries) - def __cmp__(self, other): - if isinstance(other, Struct): - return self.__dict__ == other.__dict__ - else: - return self.__dict__ == other + def __eq__(self, other): + return self.__dict__ == other.__dict__ def __repr__(self): - return ['{!s}={!s}'.format(k, repr(v)) - for (k, v) in list(vars(self).items())] + return str(['{!s}={!s}'.format(k, repr(v)) + for (k, v) in list(vars(self).items())]) def update(x, **entries): From 199f594a335b477a1dee7627a6b2734de34e6936 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sat, 12 Mar 2016 16:31:44 +0530 Subject: [PATCH 095/513] Edit tests/test_utils.py to work with modified Struct --- tests/test_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 6fa9ba5f4..aa892813c 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -20,8 +20,8 @@ def test_update_dict(): def test_update_struct(): - assert update(Struct(a=1), a=30, b=20).__cmp__(Struct(a=30, b=20)) - assert update(Struct(), a=10).__cmp__(Struct(a=10)) + assert update(Struct(a=1), a=30, b=20) == (Struct(a=30, b=20)) + assert update(Struct(), a=10) == (Struct(a=10)) def test_removeall_list(): From 37320029aab65274c20756a061996610163c6896 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sat, 12 Mar 2016 16:32:28 +0530 Subject: [PATCH 096/513] Add the test module tests/test_games.py --- tests/test_games.py | 56 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 tests/test_games.py diff --git a/tests/test_games.py b/tests/test_games.py new file mode 100644 index 000000000..4fffade4a --- /dev/null +++ b/tests/test_games.py @@ -0,0 +1,56 @@ +import pytest +from games import * + +# Creating the games +f52 = Fig52Game() +ttt = TicTacToe() + +# State generating function for TicTacToe +def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3): + moves = set([(x, y) for x in range(1, h+1) for y in range(1, v+1)]) \ + - set(x_positions) - set(o_positions) + moves = list(moves) + board = {} + for pos in x_positions: + board[pos] = 'X' + for pos in o_positions: + board[pos] = 'O' + return Struct(to_move=to_move, utility=0, board=board, moves=moves) + +def test_minimax_decision(): + assert minimax_decision('A', f52) == 'a1' + assert minimax_decision('B', f52) == 'b1' + assert minimax_decision('C', f52) == 'c1' + assert minimax_decision('D', f52) == 'd3' + +def test_alphabeta_full_search(): + assert alphabeta_full_search('A', f52) == 'a1' + assert alphabeta_full_search('B', f52) == 'b1' + assert alphabeta_full_search('C', f52) == 'c1' + assert alphabeta_full_search('D', f52) == 'd3' + + state = gen_state(to_move='X', x_positions=[(1,1), (3,3)], + o_positions=[(1,2),(3,2)]) + assert alphabeta_full_search(state, ttt) == (2,2) + + state = gen_state(to_move='O', x_positions=[(1,1), (3,1), (3,3)], + o_positions=[(1,2),(3,2)]) + assert alphabeta_full_search(state, ttt) == (2,2) + + state = gen_state(to_move='O', x_positions=[(1,1)], + o_positions=[]) + assert alphabeta_full_search(state, ttt) == (2,2) + + state = gen_state(to_move='X', x_positions=[(1,1), (3,1)], + o_positions=[(2,2), (3,1)]) + assert alphabeta_full_search(state, ttt) == (1,3) + +def test_random_tests(): + assert play_game(Fig52Game(), alphabeta_player, alphabeta_player) == 3 + + # The player 'X' (one who plays first) in TicTacToe never loses: + assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 + + # The player 'X' (one who plays first) in TicTacToe never loses: + for i in range(10): + assert play_game(ttt, alphabeta_player, random_player) >= 0 From eb61b3241d3eaa468858a0ee777ee3228589d893 Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Sun, 13 Mar 2016 05:07:40 +0530 Subject: [PATCH 097/513] adds sigmoid, step, scalar_vector_product --- utils.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/utils.py b/utils.py index 44e040a48..485629a43 100644 --- a/utils.py +++ b/utils.py @@ -169,6 +169,11 @@ def vector_add(a, b): return tuple(map(operator.add, a, b)) +def scalar_vector_product(X, Y): + """Return vector as a product of a scalar and a vector""" + return [X*y for y in Y] + + def probability(p): "Return true with probability p." return p > random.uniform(0.0, 1.0) @@ -216,6 +221,15 @@ def clip(x, lowest, highest): return max(lowest, min(x, highest)) +def sigmoid(x): + """Return activation value of x with sigmoid function""" + return 1/(1 + math.exp(-x)) + + +def step(x): + """Return activation value of x with sign function""" + return 1 if x >= 0 else 0 + # ______________________________________________________________________________ # Misc Functions From 45bcc83f738d6a5e122cc59d2f65ed0d66907009 Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Sun, 13 Mar 2016 05:09:04 +0530 Subject: [PATCH 098/513] implements backprop and neural net learner --- learning.py | 144 +++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 136 insertions(+), 8 deletions(-) diff --git a/learning.py b/learning.py index 497dea8c0..94c33b5d5 100644 --- a/learning.py +++ b/learning.py @@ -416,24 +416,152 @@ def predict(example): # ______________________________________________________________________________ -def NeuralNetLearner(dataset, sizes): - """Layered feed-forward network.""" +def NeuralNetLearner(dataset, hidden_layer_sizes=[3], + learning_rate=0.01, epoches=100): + """ + Layered feed-forward network. + hidden_layer_sizes: List of number of hidden units per hidden layer + learning_rate: Learning rate of gradient decent + epoches: Number of passes over the dataset + """ - activations = [[0.0 for i in range(n)] for n in sizes] # noqa - weights = [] # noqa + examples = dataset.examples + i_units = len(dataset.inputs) + o_units = 1 # As of now, dataset.target gives only one index. + + # construct a network + raw_net = network(i_units, hidden_layer_sizes, o_units) + learned_net = BackPropagationLearner(dataset, raw_net, + learning_rate, epoches) def predict(example): - unimplemented() + + # Input nodes + i_nodes = learned_net[0] + + # Activate input layer + for v, n in zip(example, i_nodes): + n.value = v + + # Forward pass + for layer in learned_net[1:]: + for node in layer: + inc = [n.value for n in node.inputs] + in_val = dotproduct(inc, node.weights) + node.value = node.activation(in_val) + + # Hypothesis + o_nodes = learned_net[-1] + pred = [o_nodes[i].value for i in range(o_units)] + return pred[0] return predict class NNUnit: + """ + Single Unit of Multiple Layer Neural Network + inputs: Incoming connections + weights: weights to incoming connections + """ - """Unit of a neural net.""" + def __init__(self, weights=None, inputs=None): + self.weights = [] + self.inputs = [] + self.value = None + self.activation = sigmoid - def __init__(self): - unimplemented() + +def network(input_units, hidden_layer_sizes, output_units): + """ + Create of Directed Acyclic Network of given number layers + hidden_layers_sizes : list number of neuron units in each hidden layer + excluding input and output layers. + """ + layers_sizes = [input_units] + hidden_layer_sizes + [output_units] + net = [[NNUnit() for n in range(size)] + for size in layers_sizes] + n_layers = len(net) + + # Make Connection + for i in range(1, n_layers): + for n in net[i]: + for k in net[i-1]: + n.inputs.append(k) + n.weights.append(0) + return net + + +def BackPropagationLearner(dataset, network, learning_rate, epoches): + "[Fig. 18.23] The back-propagation algorithm for multilayer network" + # Initialise weights + for layer in network: + for node in layer: + node.weights = [random.uniform(-0.5, 0.5) + for i in range(len(node.weights))] + + examples = dataset.examples + ''' + As of now dataset.target gives an int instead of list, + Changing dataset class will have effect on all the learners. + Will be taken care of later + ''' + idx_t = [dataset.target] + idx_i = dataset.inputs + n_layers = len(network) + o_nodes = network[-1] + i_nodes = network[0] + + for epoch in range(epoches): + # Iterate over each example + for e in examples: + i_val = [e[i] for i in idx_i] + t_val = [e[i] for i in idx_t] + # Activate input layer + for v, n in zip(i_val, i_nodes): + n.value = v + + # Forward pass + for layer in network[1:]: + for node in layer: + inc = [n.value for n in node.inputs] + in_val = dotproduct(inc, node.weights) + node.value = node.activation(in_val) + + # Initialize delta + delta = [[] for i in range(n_layers)] + + # Compute outer layer delta + o_units = len(o_nodes) + err = [t_val[i] - o_nodes[i].value + for i in range(o_units)] + delta[-1] = [(o_nodes[i].value)*(1 - o_nodes[i].value) * + (err[i]) for i in range(o_units)] + + # Backward pass + h_layers = n_layers - 2 + for i in range(h_layers, 0, -1): + layer = network[i] + h_units = len(layer) + nx_layer = network[i+1] + # weights from each ith layer node to each i + 1th layer node + w = [[node.weights[k] for node in nx_layer] + for k in range(h_units)] + + delta[i] = [(layer[j].value) * (1 - layer[j].value) * + dotproduct(w[j], delta[i+1]) + for j in range(h_units)] + + # Update weights + for i in range(1, n_layers): + layer = network[i] + inc = [node.value for node in network[i-1]] + units = len(layer) + for j in range(units): + layer[j].weights = vector_add(layer[j].weights, + scalar_vector_product(learning_rate * delta[i][j], inc)) + + return network def PerceptronLearner(dataset, sizes): From 1c1075a3e557509e7fc96163b8172e15bd93ca5c Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Sun, 13 Mar 2016 05:10:08 +0530 Subject: [PATCH 099/513] adds test for new util funcs --- tests/test_utils.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index b6dd089a1..5ce5969c5 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -31,11 +31,12 @@ def test_product(): def test_first(): assert first('word') == 'w' - assert first('') == None + assert first('') is None assert first('', 'empty') == 'empty' assert first(range(10)) == 0 - assert first(x for x in range(10) if x > 3) == 4 - assert first(x for x in range(10) if x > 100) == None + assert first(x for x in range(10) if x > 3) == 4 + assert first(x for x in range(10) if x > 100) is None + def test_is_in(): e = [] @@ -90,6 +91,10 @@ def test_vector_add(): assert vector_add((0, 1), (8, 9)) == (8, 10) +def test_scalar_vector_product(): + assert scalar_vector_product(2, [1, 2, 3]) == [2, 4, 6] + + def test_num_or_str(): assert num_or_str('42') == 42 assert num_or_str(' 42x ') == '42x' @@ -111,5 +116,17 @@ def f(): assert f() == 'f' +def test_sigmoid(): + assert math.isclose(0.5, sigmoid(0)) is True + assert math.isclose(0.7310585786300049, sigmoid(1)) is True + assert math.isclose(0.2689414213699951, sigmoid(-1)) is True + + +def test_step(): + assert step(1) == 1 + assert step(0) == 1 + assert step(-1) == 0 + + if __name__ == '__main__': pytest.main() From 171782d6f961f33ff9243b6f530c351394875b54 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 17:31:37 -0800 Subject: [PATCH 100/513] Update test_and_or_graph -- replace every with all --- tests/test_search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_search.py b/tests/test_search.py index 6585aab1a..c6ab3ac87 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -40,7 +40,7 @@ def run_plan(state, problem, plan): if len(plan) is not 2: return False predicate = lambda x : run_plan(x, problem, plan[1][x]) - return every(predicate, problem.result(state, plan[0])) + return all(predicate(r) for r in problem.result(state, plan[0])) plan = and_or_graph_search(vacumm_world) assert run_plan('State_1', vacumm_world, plan) From 709e0ae02b55ccfac89624c40ca3ffaed3153dfd Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 17:34:09 -0800 Subject: [PATCH 101/513] Test with Python 3.4, not 3.5 in .travis.yml --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ecf6ba14c..ca78cc3ef 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,7 +2,7 @@ language: - python python: - - "3.5" + - "3.4" before_install: - git submodule update --remote From 727b174ab3f2c85a06ee830791ae81138e7b9f3a Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 17:52:34 -0800 Subject: [PATCH 102/513] define isclose, to allow Python 3.4 --- utils.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/utils.py b/utils.py index 485629a43..71aa9d140 100644 --- a/utils.py +++ b/utils.py @@ -229,6 +229,13 @@ def sigmoid(x): def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 + +try: # math.isclose was added in Python 3.5; + from math import isclose +except ImportError: + def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): + "Return true if numbers a and b are close to each other." + return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) # ______________________________________________________________________________ # Misc Functions From 49be8ac7c2dce777c5915188cb8bc48e157bc55d Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 17:53:37 -0800 Subject: [PATCH 103/513] Update test_utils.py --- tests/test_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 5ce5969c5..dac3808a9 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -117,9 +117,9 @@ def f(): def test_sigmoid(): - assert math.isclose(0.5, sigmoid(0)) is True - assert math.isclose(0.7310585786300049, sigmoid(1)) is True - assert math.isclose(0.2689414213699951, sigmoid(-1)) is True + assert isclose(0.5, sigmoid(0)) + assert isclose(0.7310585786300049, sigmoid(1)) + assert isclose(0.2689414213699951, sigmoid(-1)) def test_step(): From 828371d27c222a29ce875cd0cbb567bfba1d1b72 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 17:56:47 -0800 Subject: [PATCH 104/513] Update test_text.py --- tests/test_text.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tests/test_text.py b/tests/test_text.py index 391a381e0..6ef534583 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -1,8 +1,6 @@ import pytest - from text import * # noqa - -from math import isclose +from utils import isclose def test_unigram_text_model(): From 0a0d1ff4cc8b96b40d8e2e0d07ec3596734b684d Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 18:02:22 -0800 Subject: [PATCH 105/513] Update .travis.yml --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ca78cc3ef..76739f356 100644 --- a/.travis.yml +++ b/.travis.yml @@ -15,7 +15,7 @@ script: - py.test after_success: - - flake8 . + - flake8 --max-line-length 100 . notifications: email: false From bbd732a1236820b72c0e420abd2974c1616f357e Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 12 Mar 2016 18:05:49 -0800 Subject: [PATCH 106/513] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 1e6e502e5..7d2fce254 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,9 @@ Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code ([GSoC](https://summerofcode.withgoogle.com/)) to work on this project; if you want to be that student, make some good contributions here (by looking through the [Issues](https://github.com/aimacode/aima-python/issues) and resolving some), and submit an [application](https://summerofcode.withgoogle.com/terms/student). (However, be warned that we've had over 150 students express interest, so competition will be tough.) And we're always looking for solid contributors who are not affiliated with GSoC. A big thank you to everyone who has contributed! -## Python 3.5 +## Python 3.4 -This code is in Python 3.5. (I believe any version from 3.4 on will work.) You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.4. (Of course, the current version, Python 3.5, also works.) You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project @@ -40,7 +40,7 @@ There are a few style rules that are unique to this project: Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008), with a few minor exceptions: -- I'm not too worried about an occasional line longer than 79 characters. +- I have set `--max-line-length 100`, not 79. - You don't need two spaces after a sentence-ending period. - Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). - I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). From 4664b633fa1d1833901c1d16952419f0ef97f547 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sun, 13 Mar 2016 16:46:25 +0530 Subject: [PATCH 107/513] Get branch even with latest changes in master --- games.py | 6 ++++-- tests/test_utils.py | 8 -------- utils.py | 29 ----------------------------- 3 files changed, 4 insertions(+), 39 deletions(-) diff --git a/games.py b/games.py index d18b5f731..ef366b09b 100644 --- a/games.py +++ b/games.py @@ -1,4 +1,5 @@ -"""Games, or Adversarial Search. (Chapter 5)""" +"""Games, or Adversarial Search. (Chapter 5) +""" from utils import * # noqa @@ -77,6 +78,7 @@ def min_value(state, alpha, beta): best_action = a return best_action + def alphabeta_search(state, game, d=4, cutoff_test=None, eval_fn=None): """Search game to determine best action; use alpha-beta pruning. This version cuts off search and uses an evaluation function.""" @@ -234,7 +236,7 @@ class TicTacToe(Game): a dict of {(x, y): Player} entries, where Player is 'X' or 'O'.""" def __init__(self, h=3, v=3, k=3): - update(self, h=h, v=v, k=k) # What is this exactly? + update(self, h=h, v=v, k=k) moves = [(x, y) for x in range(1, h+1) for y in range(1, v+1)] self.initial = GameState(to_move='X', utility=0, board={}, moves=moves) diff --git a/tests/test_utils.py b/tests/test_utils.py index 9b48c1a17..dac3808a9 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -7,14 +7,6 @@ def test_update_dict(): assert update({}, a=5) == {'a': 5} -<<<<<<< HEAD -def test_update_struct(): - assert update(Struct(a=1), a=30, b=20) == (Struct(a=30, b=20)) - assert update(Struct(), a=10) == (Struct(a=10)) - - -======= ->>>>>>> master def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] diff --git a/utils.py b/utils.py index cd0b8c882..71aa9d140 100644 --- a/utils.py +++ b/utils.py @@ -1,10 +1,6 @@ """Provide some widely useful utilities. Safe for "from utils import *". # noqa TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py -<<<<<<< HEAD -TODO: count_if and the like are leftovers from Common Lisp; let's make replace them with Pythonic alternatives. -======= ->>>>>>> master TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. TODO: Priority queues may not belong here -- see treatment in search.py """ @@ -16,31 +12,6 @@ import os.path import bisect -<<<<<<< HEAD -# ______________________________________________________________________________ -# Simple Data Structures: infinity, Dict, Struct - -infinity = float('inf') - - -class Struct: - - """Create an instance with argument=value slots. - This is for making a lightweight object whose class doesn't matter.""" - - def __init__(self, **entries): - self.__dict__.update(entries) - - def __eq__(self, other): - return self.__dict__ == other.__dict__ - - def __repr__(self): - return str(['{!s}={!s}'.format(k, repr(v)) - for (k, v) in list(vars(self).items())]) - - -======= ->>>>>>> master def update(x, **entries): """Update a dict or an object with slots according to entries.""" if isinstance(x, dict): From 5d56cf54d5b0a3e3586b7d301c0c8a72dbb486c8 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sun, 13 Mar 2016 17:23:20 +0530 Subject: [PATCH 108/513] Modify test_games.py to work with collection.namedtuple instead of Struct --- games.py | 10 ++++++++-- tests/test_games.py | 9 ++++++++- 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/games.py b/games.py index ef366b09b..4861f6e9e 100644 --- a/games.py +++ b/games.py @@ -1,9 +1,13 @@ """Games, or Adversarial Search. (Chapter 5) """ +import collections +import math +import random + from utils import * # noqa -import random +infinity = math.inf # ______________________________________________________________________________ # Minimax Search @@ -236,7 +240,9 @@ class TicTacToe(Game): a dict of {(x, y): Player} entries, where Player is 'X' or 'O'.""" def __init__(self, h=3, v=3, k=3): - update(self, h=h, v=v, k=k) + self.h = h + self.v = v + self.k = k moves = [(x, y) for x in range(1, h+1) for y in range(1, v+1)] self.initial = GameState(to_move='X', utility=0, board={}, moves=moves) diff --git a/tests/test_games.py b/tests/test_games.py index 4fffade4a..3418672eb 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -1,10 +1,14 @@ import pytest +import collections +import math from games import * # Creating the games f52 = Fig52Game() ttt = TicTacToe() +GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') + # State generating function for TicTacToe def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3): moves = set([(x, y) for x in range(1, h+1) for y in range(1, v+1)]) \ @@ -15,7 +19,7 @@ def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3): board[pos] = 'X' for pos in o_positions: board[pos] = 'O' - return Struct(to_move=to_move, utility=0, board=board, moves=moves) + return GameState(to_move=to_move, utility=0, board=board, moves=moves) def test_minimax_decision(): assert minimax_decision('A', f52) == 'a1' @@ -54,3 +58,6 @@ def test_random_tests(): # The player 'X' (one who plays first) in TicTacToe never loses: for i in range(10): assert play_game(ttt, alphabeta_player, random_player) >= 0 + +if __name__ == '__main__': + pytest.main() From 2eb8d7761b89b6e2b81d34bd04b54f0dfce11269 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Sun, 13 Mar 2016 17:32:14 +0530 Subject: [PATCH 109/513] Replace math.inf with float('inf') --- games.py | 2 +- tests/test_games.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/games.py b/games.py index 4861f6e9e..dda86053d 100644 --- a/games.py +++ b/games.py @@ -7,7 +7,7 @@ from utils import * # noqa -infinity = math.inf +infinity = float('inf') # ______________________________________________________________________________ # Minimax Search diff --git a/tests/test_games.py b/tests/test_games.py index 3418672eb..07e8a956f 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -1,6 +1,5 @@ import pytest import collections -import math from games import * # Creating the games From ac59cd1a9847dd64045fea202e6320265de4dc6c Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Sun, 13 Mar 2016 21:58:28 +0530 Subject: [PATCH 110/513] Implements cross_validation_wrapper, modify train_test --- learning.py | 89 ++++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 65 insertions(+), 24 deletions(-) diff --git a/learning.py b/learning.py index 94c33b5d5..5e1343dbf 100644 --- a/learning.py +++ b/learning.py @@ -559,7 +559,8 @@ def BackPropagationLearner(dataset, network, learning_rate, epoches): units = len(layer) for j in range(units): layer[j].weights = vector_add(layer[j].weights, - scalar_vector_product(learning_rate * delta[i][j], inc)) + scalar_vector_product( + learning_rate * delta[i][j], inc)) return network @@ -671,8 +672,8 @@ def flatten(seqs): return sum(seqs, []) # Functions for testing learners on examples -def test(predict, dataset, examples=None, verbose=0): - "Return the proportion of the examples that are correctly predicted." +def test(predict, dataset, examples, verbose=0): + "Return the proportion of the examples that are NOT correctly predicted." if examples is None: examples = dataset.examples if len(examples) == 0: @@ -688,40 +689,80 @@ def test(predict, dataset, examples=None, verbose=0): elif verbose: print('WRONG: got %s, expected %s for %s' % ( output, desired, example)) - return right / len(examples) + return 1 - (right / len(examples)) -def train_and_test(learner, dataset, start, end): - """Reserve dataset.examples[start:end] for test; train on the remainder. - Return the proportion of examples correct on the test examples.""" +def train_and_test(dataset, start, end): + """Reserve dataset.examples[start:end] for test; train on the remainder.""" + start = int(start) + end = int(end) examples = dataset.examples - try: - dataset.examples = examples[:start] + examples[end:] - return test(learner(dataset), dataset, examples[start:end]) - finally: - dataset.examples = examples + train = examples[:start] + examples[end:] + val = examples[start:end] + return train, val -def cross_validation(learner, dataset, k=10, trials=1): +def cross_validation(learner, size, dataset, k=10, trials=1): """Do k-fold cross_validate and return their mean. That is, keep out 1/k of the examples for testing on each of k runs. - Shuffle the examples first; If trials>1, average over several shuffles.""" + Shuffle the examples first; If trials>1, average over several shuffles. + Returns Training error, Validataion error""" if k is None: k = len(dataset.examples) if trials > 1: - return mean([cross_validation(learner, dataset, k, trials=1) - for t in range(trials)]) + trial_errT = 0 + trial_errV = 0 + for t in range(trials): + errT, errV = cross_validation(learner, size, dataset, + k=10, trials=1) + trial_errT += errT + trial_errV += errV + return trial_errT/trials, trial_errV/trials else: + fold_errT = 0 + fold_errV = 0 n = len(dataset.examples) - random.shuffle(dataset.examples) - return mean( - [train_and_test(learner, dataset, i * (n / k), - (i + 1) * (n / k)) for i in range(k)]) - - -def leave1out(learner, dataset): + examples = dataset.examples + for fold in range(k): + random.shuffle(dataset.examples) + train_data, val_data = train_and_test(dataset, fold * (n/k), + (fold + 1) * (n/k)) + dataset.examples = train_data + h = learner(dataset, size) + fold_errT += test(h, dataset, train_data) + fold_errV += test(h, dataset, val_data) + # Reverting back to original once test is completed + dataset.examples = examples + return fold_errT/k, fold_errV/k + + +def cross_validation_wrapper(learner, dataset, k=10, trials=1): + """ + Fig 18.8 + Return the optimal value of size having minimum error + on validataion set + err_train: a training error array, indexed by size + err_val: a validataion error array, indexed by size + """ + err_val = [] + err_train = [] + size = 1 + while True: + errT, errV = cross_validation(learner, size, dataset, k) + # Check for convergence provided err_val is not empty + if (err_val and math.isclose(err_val[-1], errV, rel_tol=1e-6)): + best_size = size + return learner(dataset, best_size) + + err_val.append(errV) + err_train.append(errT) + print(err_val) + size += 1 + + +def leave_one_out(learner, dataset): "Leave one out cross-validation over the dataset." - return cross_validation(learner, dataset, k=len(dataset.examples)) + return cross_validation(learner, size, dataset, k=len(dataset.examples)) def learningcurve(learner, dataset, trials=10, sizes=None): From cbdd47f8a6ecaaa6b9d389da641309d24308576a Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 12:27:24 -0700 Subject: [PATCH 111/513] Update CONTRIBUTING.md --- CONTRIBUTING.md | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 595f3c9f1..ce96f003a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,7 +1,32 @@ How to Contribute to aima-python ========================== -Thanks for considering contributing to aima-python. +Thanks for considering contributing to `aima-python`! Here is some of the work that needs to be done: + +## Port to Python 3; Pythonic Idioms; py.test + +- Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. +- Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. +- Add more tests in `_test.py` files. Strive for terseness; it is ok to group multiple asserts into one `def test_something():` function. Move most tests to `_test.py`, but it is fine to have a single `doctest` example in the docstring of a function in the `.py` file, if the purpose of the doctest is to explain how to use the function, rather than test the implementation. + +# Style Guide + +There are a few style rules that are unique to this project: + +- The first rule is that the code should correspond directly to the pseudocode in the book. When possible this will be almost one-to-one, just allowing for the syntactic differences between Python and pseudocode, and for different library functions. +- Don't make a function more complicated than the pseudocode in the book, even if the complication would add a nice feature, or give an efficiency gain. Instead, remain faithful to the pseudocode, and if you must, add a new function (not in the book) with the added feature. +- I use functional programming (functions with no side effects) in many cases, but not exclusively (sometimes classes and/or functions with side effects are used). Let the book's pseudocode be the guide. + +Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008), with a few minor exceptions: + +- I have set `--max-line-length 100`, not 79. +- You don't need two spaces after a sentence-ending period. +- Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). +- I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). +- Not all constants have to be UPPERCASE. +- At some point I may add [Pep 484](https://www.python.org/dev/peps/pep-0484/) type annotations, but I think I'll hold off for now; + I want to get more experience with them, and some people may still be in Python 3.4. + Contributing a Patch ==================== @@ -21,14 +46,13 @@ Reporting Issues Patch Rules =========== -- Ensure that the patch is python 3.5 compliant. +- Ensure that the patch is python 3.4 compliant. - Include tests if your patch is supposed to solve a bug, and explain clearly under which circumstances the bug happens. Make sure the test fails without your patch. -- Try to follow `PEP8 `_, but you - may ignore the line-length-limit if following it would make the code uglier. +- Follw the style guidelines described above. Running the Test-Suite ===================== From 0cce5dfaa477433c40e1020d0127dd42d955c72b Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 12:30:15 -0700 Subject: [PATCH 112/513] Update CONTRIBUTING.md --- CONTRIBUTING.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ce96f003a..5e6d1a4ec 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -69,3 +69,26 @@ Clone this repository:: Then you can run the testsuite with:: py.test + +# Choice of Programming Languages + +Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is +fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: + + Java, C, C++, C#, Python + +So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. And finally, Javascript is the language of the browser; it would be nice to have code that runs in the browser, in Javascript or a variant such as Typescript. + +There is also a `aima-lisp` project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular. + +What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [norvig russell "Modern Approach"](https://www.google.com/webhp#q=russell%20norvig%20%22modern%20approach%22%20java) along with the names of various languages and looked at the estimated counts of results on +various dates. However, I don't have much confidence in these figures... + +|Language |2004 |2005 |2007 |2010 |2016 | +|-------- |----: |----: |----: |----: |----: | +|[none](http://www.google.com/search?q=norvig+russell+%22Modern+Approach%22)|8,080|20,100|75,200|150,000|132,000| +|[java](http://www.google.com/search?q=java+norvig+russell+%22Modern+Approach%22)|1,990|4,930|44,200|37,000|50,000| +|[c++](http://www.google.com/search?q=c%2B%2B+norvig+russell+%22Modern+Approach%22)|875|1,820|35,300|105,000|35,000| +|[lisp](http://www.google.com/search?q=lisp+norvig+russell+%22Modern+Approach%22)|844|974|30,100|19,000|14,000| +|[prolog](http://www.google.com/search?q=prolog+norvig+russell+%22Modern+Approach%22)|789|2,010|23,200|17,000|16,000| +|[python](http://www.google.com/search?q=python+norvig+russell+%22Modern+Approach%22)|785|1,240|18,400|11,000|12,000| From 2995fc5850a46ea53a4061ba39e58fbce3ae11fd Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 12:30:45 -0700 Subject: [PATCH 113/513] README.md -- moved parts to CONTRIBUTING.md --- README.md | 46 +--------------------------------------------- 1 file changed, 1 insertion(+), 45 deletions(-) diff --git a/README.md b/README.md index 7d2fce254..1c29088b1 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code ([GSoC](https://summerofcode.withgoogle.com/)) to work on this project; if you want to be that student, make some good contributions here (by looking through the [Issues](https://github.com/aimacode/aima-python/issues) and resolving some), and submit an [application](https://summerofcode.withgoogle.com/terms/student). (However, be warned that we've had over 150 students express interest, so competition will be tough.) And we're always looking for solid contributors who are not affiliated with GSoC. A big thank you to everyone who has contributed! +Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code ([GSoC](https://summerofcode.withgoogle.com/)) to work on this project; if you want to be that student, make some good [contributions](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) here by looking through the [Issues](https://github.com/aimacode/aima-python/issues) and resolving some), and submit an [application](https://summerofcode.withgoogle.com/terms/student). (However, be warned that we've had over 150 students express interest, so competition will be tough.) And we're always [looking for solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) who are not affiliated with GSoC. A big thank you to everyone who has contributed! ## Python 3.4 @@ -17,11 +17,6 @@ When complete, this project will have Python code for all the pseudocode algorit Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken: -## Port to Python 3; Pythonic Idioms; py.test - -- Check for common problems in [porting to Python 3](http://python3porting.com/problems.html), such as: `print` is now a function; `range` and `map` and other functions no longer produce `list`s; objects of different types can no longer be compared with `<`; strings are now Unicode; it would be nice to move `%` string formating to `.format`; there is a new `next` function for generators; integer division now returns a float; we can now use set literals. -- Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. -- Add more tests in `_test.py` files. Strive for terseness; it is ok to group multiple asserts into one `def test_something():` function. Move most tests to `_test.py`, but it is fine to have a single `doctest` example in the docstring of a function in the `.py` file, if the purpose of the doctest is to explain how to use the function, rather than test the implementation. ## New and Improved Algorithms @@ -30,23 +25,6 @@ Until we get there, we will support a legacy branch, `aima3python2` (for the thi We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. - Give examples of how to use the code in the `.ipynb` file. -# Style Guide - -There are a few style rules that are unique to this project: - -- The first rule is that the code should correspond directly to the pseudocode in the book. When possible this will be almost one-to-one, just allowing for the syntactic differences between Python and pseudocode, and for different library functions. -- Don't make a function more complicated than the pseudocode in the book, even if the complication would add a nice feature, or give an efficiency gain. Instead, remain faithful to the pseudocode, and if you must, add a new function (not in the book) with the added feature. -- I use functional programming (functions with no side effects) in many cases, but not exclusively (sometimes classes and/or functions with side effects are used). Let the book's pseudocode be the guide. - -Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008), with a few minor exceptions: - -- I have set `--max-line-length 100`, not 79. -- You don't need two spaces after a sentence-ending period. -- Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). -- I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). -- Not all constants have to be UPPERCASE. -- At some point I may add [Pep 484](https://www.python.org/dev/peps/pep-0484/) type annotations, but I think I'll hold off for now; - I want to get more experience with them, and some people may still be in Python 3.4. # Index of Code # @@ -147,28 +125,6 @@ Here is a table of algorithms, the figure and page where they appear in the book | 23.1 | 837 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | | 24.21 | 892 | Align | | -# Choice of Programming Languages - -Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is -fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: - - Java, C, C++, C#, Python - -So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. And finally, Javascript is the language of the browser; it would be nice to have code that runs in the browser, in Javascript or a variant such as Typescript. - -There is also a `aima-lisp` project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular. - -What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [norvig russell "Modern Approach"](https://www.google.com/webhp#q=russell%20norvig%20%22modern%20approach%22%20java) along with the names of various languages and looked at the estimated counts of results on -various dates. However, I don't have much confidence in these figures... - -|Language |2004 |2005 |2007 |2010 |2016 | -|-------- |----: |----: |----: |----: |----: | -|[none](http://www.google.com/search?q=norvig+russell+%22Modern+Approach%22)|8,080|20,100|75,200|150,000|132,000| -|[java](http://www.google.com/search?q=java+norvig+russell+%22Modern+Approach%22)|1,990|4,930|44,200|37,000|50,000| -|[c++](http://www.google.com/search?q=c%2B%2B+norvig+russell+%22Modern+Approach%22)|875|1,820|35,300|105,000|35,000| -|[lisp](http://www.google.com/search?q=lisp+norvig+russell+%22Modern+Approach%22)|844|974|30,100|19,000|14,000| -|[prolog](http://www.google.com/search?q=prolog+norvig+russell+%22Modern+Approach%22)|789|2,010|23,200|17,000|16,000| -|[python](http://www.google.com/search?q=python+norvig+russell+%22Modern+Approach%22)|785|1,240|18,400|11,000|12,000| # Acknowledgements From 89a7bd8242dea7a93ea040fb404860964d10a383 Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 12:35:20 -0700 Subject: [PATCH 114/513] Update README.md --- README.md | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/README.md b/README.md index 1c29088b1..5c4cf9e14 100644 --- a/README.md +++ b/README.md @@ -12,18 +12,9 @@ This code is in Python 3.4. (Of course, the current version, Python 3.5, also wo When complete, this project will have Python code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following three files in the main branch: - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. -- `logic.ipynb`: A Jupyter notebook, with examples of usage. Does a `from logic import *` to get the code. +- `logic.ipynb`: A Jupyter notebook that explains and gives examples of how to use the code. - `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). -Until we get there, we will support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). To prepare code for the new master branch, the following two steps should be taken: - - -## New and Improved Algorithms - -- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](https://github.com/aimacode/pseudocode/blob/master/algorithms.pdf) to see what's missing. -- As we finish chapters for the new fourth edition, we will share the new pseudocode in the [`aima-pseudocode`](https://github.com/aimacode/aima-pseudocode) repository, and describe what changes are necessary. -We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. -- Give examples of how to use the code in the `.ipynb` file. # Index of Code # From 5e54a042902e5b9fa6a87062e40e5ca201112e5d Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 12:35:45 -0700 Subject: [PATCH 115/513] Update CONTRIBUTING.md --- CONTRIBUTING.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5e6d1a4ec..2ee837edb 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,6 +9,15 @@ Thanks for considering contributing to `aima-python`! Here is some of the work t - Replace old Lisp-based idioms with proper Python idioms. For example, we have many functions that were taken directly from Common Lisp, such as the `every` function: `every(callable, items)` returns true if every element of `items` is callable. This is good Lisp style, but good Python style would be to use `all` and a generator expression: `all(callable(f) for f in items)`. Eventually, fix all calls to these legacy Lisp functions and then remove the functions. - Add more tests in `_test.py` files. Strive for terseness; it is ok to group multiple asserts into one `def test_something():` function. Move most tests to `_test.py`, but it is fine to have a single `doctest` example in the docstring of a function in the `.py` file, if the purpose of the doctest is to explain how to use the function, rather than test the implementation. +## New and Improved Algorithms + +- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](https://github.com/aimacode/pseudocode/blob/master/algorithms.pdf) to see what's missing. +- As we finish chapters for the new fourth edition, we will share the new pseudocode in the [`aima-pseudocode`](https://github.com/aimacode/aima-pseudocode) repository, and describe what changes are necessary. +We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. +- Give examples of how to use the code in the `.ipynb` file. + +We still support a legacy branch, `aima3python2` (for the third edition of the textbook and for Python 2 code). + # Style Guide There are a few style rules that are unique to this project: From 128cf360508a7089df0920a3d81666dda73c72b6 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Mon, 14 Mar 2016 01:26:08 +0530 Subject: [PATCH 116/513] Modify query_player to work with move representations that are strings --- games.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/games.py b/games.py index dda86053d..b0414152e 100644 --- a/games.py +++ b/games.py @@ -137,7 +137,12 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): "Make a move by querying standard input." game.display(state) - return eval(input('Your move? ')) + move_string = input('Your move? ') + try: + move = eval(move_string) + except NameError: + move = move_string + return move def random_player(game, state): From d52205cf35e972e1c2e30ff65641d171a4dbcf77 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Mon, 14 Mar 2016 01:26:23 +0530 Subject: [PATCH 117/513] Finalize content in games.ipynb --- games.ipynb | 839 ++++++++++++++++++++++++++++++++++++++++++++- images/fig_5_2.png | Bin 0 -> 112166 bytes 2 files changed, 836 insertions(+), 3 deletions(-) create mode 100644 images/fig_5_2.png diff --git a/games.ipynb b/games.ipynb index 34faa6563..b39f0ddcc 100644 --- a/games.ipynb +++ b/games.ipynb @@ -1,5 +1,49 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explaining the games.py module\n", + "*Author: Chirag Vartak*
\n", + "*Date: 12th March 2016*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An Introduction, and some other (un)essential information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Hello all! \n", + " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book 'Artificial Intelligence: A Modern Approach'. The code in this IPython notebook, and the entire [aima-python](https://github.com/aimacode/aima-python) repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. If you do not have Python 3 installed, you might want to get that done first. Or even better, install [Anaconda](https://www.continuum.io/downloads) and you will get Jupyter Notebook and IPython along with it. This way you will be able to run both Python 2 and Python 3 using what they call 'virtual environments'. This is the way to go if you don't yet want to let go of your dear old Python 2.7. And this is what I do anyways. \n", + " \n", + " What we will do to learn to use the code in this module is simply dive in! I feel this is the correct approach as I assume you must have already read Chapter 5 of AIMA. If you haven't, you might want to go back and do that first. If you are tired (or just lazy), at least read the chapter upto Sec. 5.3 because this module covers the algorithms only till that section anyway. So, I will start by explaining what the class `Game` is and then we will immediately start implementing the `TicTacToe` game. After we define the rules of the `TicTacToe` game, we will create AI players who use different search strategies, namely Minimax Search and Alpha-Beta Search. We will make these players play among themselves, and later on we ourselves will play against these AI players (Yay!). \n", + " \n", + "The reason I chose the `TicTacToe` game for demonstration of this module should be obvious to you. Everyone knows it and has played it, it is analyzed in quite some detail in AIMA, and most importantly, it has comparatively few states (fewer than 362,880) so that we can explore the search tree completely. \n", + " \n", + " So let's begin." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementing TicTacToe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use the code in `games.py` let's import everything from it:" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -8,17 +52,806 @@ }, "outputs": [], "source": [ - "import games" + "from games import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, here, as the module `games.py` does a `from utils import *`, all the names (global variables, functions etc.) available in `utils.py` are directly available to us now." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### The class `Game` \n", + " \n", + "Let's have a look at the class `Game` in our module. We see that it has six functions, namely `actions`, `result`, `utility`, `terminal_test`, `to_move` and `display`. We see that these functions have not actually been implemented. This class is actually just a template class; we are supposed to create the class for our game, `TicTacToe` by inheriting this `Game` class and implement all the methods mentioned in `Game`. If you forget to implement any one of those, a `NotImplementedError` will be raised. So, in this sense, the `Game` class is what you might call an abstract class in Java: it implements nothing, just tells you all that you are supposed to implement and screams at you if forget to implement what it asks. \n", + " \n", + " Now let's get into some details of all these methods in our `Game` class. You have to implement these methods when you create the new class that would represent your game.\n", + " \n", + "* `__init__(self, )` : When you create a class inherited from the `Game` class (class `TicTacToe` in our case), you'll have to create an object of this inherited class to initialize the game. This initialization might require some additional information which would be passed to `__init__` as variables. For the case of our `TicTacToe` game, this additional information would be the number of rows `h`, number of columns `v` and how many consecutive X's or O's are needed in a row, column or diagonal for a win `k`. Also, the initial game state has to be defined here in `__init__`.\n", + "* `actions(self, state)` : Given a game state, this method should generates all the legal actions possible from this state, as a list or a generator. Returning a generator rather than a list has the advantage that it saves space and you can still operate on it as a list.\n", + "* `result(self, state, move)` : Given a game state and a move, this method returns the game state that you get by making that move on this game state.\n", + "* `utility(self, state, player)` : Given a terminal game state and a player, this method returns the utility for that player in the given terminal game state. While implementing this method assume that the game state is a terminal game state. The logic in this module is such that this method will be called only on terminal game states.\n", + "* `terminal_test(self, state)` : Given a game state, this method should return `True` if this game state is a terminal state, and `False` otherwise.\n", + "* `to_move(self, state)` : Given a game state, this method returns the player who is to play next. This information is typically stored in the game state, so all this method does is extract this information and return it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deciding the game state representation\n", + " \n", + " Now, before we start implementing our `TicTacToe` game, we need to decide how we will be representing our game state. Typically, a game state will give you all the current information about the game at any point in time. Yes, all of it. When you are given a game state, you should be able to tell whose turn it is next, how the game will look like on a real-life board (if it has one) etc. A game state need not include the history of the game. If you can play the game further given a game state, you game state representation is acceptable. While we might like to include all kinds of information in our game state, we wouldn't want to put too much information into it. Modifying this game state to generate a new one would be a real pain then.\n", + " \n", + " Now, as for our `TicTacToe` game state, would storing only the positions of all the X's and O's be sufficient to represent all the game information at that point in time? Well, does it tell us whose turn it is next? Looking at the 'X's and O's on the board and counting them should tell us that. But that would mean extra computing. To avoid this, we will also store whose move it is next in the game state. \n", + " \n", + " Think about what we've done here. We have reduced extra computation by storing additional information in a game state. Now, this information might not be absolutely essential to tell us about the state of the game, but it does save us additional computation time. We'll do more of this later on. \n", + " \n", + " The `TicTacToe` game defines its game state as:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The game state is called, quite appropriately, `GameState`, and it has 4 variables, namely, `to_move`, `utility`, `board` and `moves`. \n", + " \n", + " I'll describe these variables in some more detail:\n", + " \n", + "* `to_move` : It represents whose turn it is to move next. This will be a string of a single character, either 'X' or 'O'.\n", + "* `utility` : It stores the utility of the game state. Storing this utility is a good idea, because, when you do a Minimax Search or an Alphabeta Search, you generate many recursive calls, which travel all the way down to the terminal states. When these recursive calls go back up to the original callee, we have calculated utilities for many game states. We store these utilities in their respective `GameState`s to avoid calculating them all over again.\n", + "* `board` : A dict that stores all the positions of X's and O's on the board\n", + "* `moves` : It stores the list of legal moves possible from the current position. Note here, that storing the moves as a list, as it is done here, increases the space complexity of Minimax Search from `O(m)` to `O(bm)`. Refer to Sec. 5.2.1 of the book." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Representing a move \n", + " \n", + " Now that we have decided how our game state will be represented, it's time to decide how our move will be represented. My advice on this: keep it simple. Becomes easy to use this move to modify a current game state to generate a new one. \n", + " \n", + " For our `TicTacToe` game, we'll just represent a move by a tuple, where the first and the second elements of the tuple will represent the row and column, respectively, where the next X or O is to be made. Whether to make an X or an O will be decided by the `to_move` variable in the `GameState`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### The class `TicTacToe` \n", + " \n", + " Take a look at the class `TicTacToe`. All the methods mentioned in the class `Game` have been implemented here. Some points to note in this class might be: \n", + " \n", + "* The class `TicTacToe` has been inherited from the class `Game`. As I mentioned earlier, you really want to do this. Catching bugs and errors becomes a whole lot easier.\n", + "* A `display` function has been implemented. This function prints the given game state on the console. This might come in handy for debugging and is great when we play ourselves against AIs that we will be creating.\n", + "* Additional functions `compute_utility` and `k_in_a_row` are created, which are used by other functions. Well, no one said that you can't do this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating players to play the games " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `random_player` and `alphabeta_player` \n", + " \n", + " So, we have finished implementation of the `TicTacToe` class. What this class does is that, it just defines the rules of the game. We need more to create an AI that can actually play the game. This is where `random_player` and `alphabeta_player` come in. \n", + " \n", + " The `random_player` is a function that plays random moves in the game. That's it. There isn't much more to this guy. \n", + " \n", + " The `alphabeta_player`, on the other hand, calls the `alphabeta_full_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `query_player` and `play_game` \n", + " \n", + " The `query_player` function allows you, a human opponent, to play the game. This function requires a `display` method to be implemented in your game class, so that successive game states can be displayed on the terminal, making it easier for you to visualize the game and play accordingly. \n", + " \n", + " The `play_game` function will be the one that will actually be used to play the game. You pass as arguments to it, an instance of the game you want to play and the players you want in this game. Use it to play AI vs AI, AI vs human, or even human vs human matches!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Examples \n", + " \n", + " I will show some code examples below that you can run. The games' classes which I will use are `TicTacToe` and the `Fig52Game`. The `Fig52Game` is already implemented (actually both are) in the module. This is that small game in Fig 5.2 of the book. \n", + " \n", + " Have fun executing and modifying these examples!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by experimenting with the `Fig52Game` first. For that we'll first create an instance of this game:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from games import *\n", + "game52 = Fig52Game()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we try out our `random_player`. Given a game state it will give us a random move every time:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a2'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_player(game52, 'A')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_player(game52, 'A')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `alphabeta_player` will always give us the best move:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a1\n", + "b1\n", + "c1\n" + ] + } + ], + "source": [ + "print( alphabeta_player(game52, 'A') )\n", + "print( alphabeta_player(game52, 'B') )\n", + "print( alphabeta_player(game52, 'C') )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What the `alphabeta_player` does is, it simply calls the method `alphabeta_full_search`. They both are essentially the same. In the module, both `alphabeta_full_search` and `minimax_decision` have been implemented. They both do the same job and return the same thing, which is, the best move in the current state. It's just that `alphabeta_full_search` is more efficient w.r.t time because it prunes the search tree and hence, explores lesser number of states." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minimax_decision('A', game52)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphabeta_full_search('A', game52)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's play `TicTacToe`. First we initialize the game:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "ttt = TicTacToe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print a state using the display method:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". . . \n", + ". . . \n", + ". . . \n" + ] + } + ], + "source": [ + "ttt.display(ttt.initial)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmm, so that's the initial state of the game; no X's and no O's. \n", + " \n", + " Let us create a new game state by ourselves to experiment:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "my_state = GameState(\n", + " to_move = 'X',\n", + " utility = '0',\n", + " board = {(1,1): 'X', (1,2): 'O', (1,3): 'X',\n", + " (2,1): 'O', (2,3): 'O',\n", + " (3,1): 'X',\n", + " },\n", + " moves = [(2,2), (3,2), (3,3)]\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, how does this game state look like?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X O X \n", + "O . O \n", + "X . . \n" + ] + } + ], + "source": [ + "ttt.display(my_state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `random_player` will behave how he is supposed to:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 3)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_player(ttt, my_state)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_player(ttt, my_state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But the `alphabeta_player` will always give the best move, as expected:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphabeta_player(ttt, my_state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's make 2 players play against each other. We use the `play_game` function for this. The `play_game` function makes players play the match against each other and returns the utility for the first player, of the terminal state reached when the game ends. Hence, for our `TicTacToe` game, if we get the output +1, the first player wins, -1 if the second player wins, and 0 if the match ends in a draw." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(ttt, alphabeta_player, random_player)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output is +1, hence `alphabeta_player` wins. \n", + " \n", + " Since, an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n" + ] + } + ], + "source": [ + "for _ in range(10):\n", + " print(play_game(ttt, alphabeta_player, alphabeta_player))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `random_player` should never win against an `alphabeta_player`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1\n", + "-1\n", + "-1\n", + "0\n", + "-1\n", + "0\n", + "-1\n", + "0\n", + "-1\n", + "-1\n" + ] + } + ], + "source": [ + "for _ in range(10):\n", + " print(play_game(ttt, random_player, alphabeta_player))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's play a game ourselves against a `random_player`:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". . . \n", + ". . . \n", + ". . . \n", + "Your move? (3,1)\n", + ". . . \n", + ". . O \n", + "X . . \n", + "Your move? (2,2)\n", + ". . . \n", + ". X O \n", + "X O . \n", + "Your move? (1,3)\n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(ttt, query_player, random_player)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yay! We win. But we cannot win against an `alphabeta_player`, however hard we try." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". . . \n", + ". . . \n", + ". . . \n", + "Your move? (1,1)\n", + "X . . \n", + ". O . \n", + ". . . \n", + "Your move? (3,3)\n", + "X O . \n", + ". O . \n", + ". . X \n", + "Your move? (3,2)\n", + "X O . \n", + ". O . \n", + "O X X \n", + "Your move? (1,3)\n", + "X O X \n", + ". O O \n", + "O X X \n", + "Your move? (2,1)\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(ttt, query_player, alphabeta_player)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Demonstrating the `play_game` function on the `game52`:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(game52, alphabeta_player, alphabeta_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(game52, alphabeta_player, random_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A\n", + "Your move? a3\n" + ] + }, + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(game52, query_player, alphabeta_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B\n", + "Your move? b2\n" + ] + }, + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(game52, alphabeta_player, query_player)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that, here, if you are the first player, the `alphabeta_player` plays as MIN, and if you are the second player, the `alphabeta_player` plays as MAX. This happens because that's the way the game is defined in the class `Fig52Game`. Having a look at the code of this class should make it clear." + ] } ], "metadata": { diff --git a/images/fig_5_2.png b/images/fig_5_2.png new file mode 100644 index 0000000000000000000000000000000000000000..e561b07272449c3b48cb9f7601cad38d6c5f422a GIT binary patch literal 112166 zcmbq)WmFaK7p{PSv~)^JNQi)RNh96eol?>b(yda`-Q9WUlcWFq8OuU?^jl@wKa^$HI3>J<$54FYh)wqP+1_yyyn zBq98&e3WDl`10CZNLJ|8tEw232Sa$^JCePmrqioe=snLrF!2oN#IIgK^S+7-skrGK zuE2Y#^y49%Fh?UaY#igbx+cDF#Mg`9?QLtMX~a+bV(*%m9RxlmC71MCOCQuFB1eLu zY)OGYecXRErHtDgr}6HLrn2Gj@V~`;`6RPQAIVex_o?XxCx*U!{*ngj|96bl6V)f| z<=DU>%MJDaPVE1GpQyQeQ9BlBYHM5OEiU@5xc&Hs~zZwD0b#)2fNB(!60kCMHML-9T-sb{xdgj!T3b_zdxA8bTa>M5BvzA z|6S&a-~sF9;Qz;o-cv2EklHc8LK*Cr+i;g(p2o9zRi?eT_IwcIk_yKRzz61MEr0dod6v``uE*=0*^T#Y8i^QGaw4 z(q^Bf#PgCU|1FsS0twPYXZYzW<%^CiIxF;dVuoOlc7<)y)~W-~D;fR2Wf&XktQ`)v zWj|Xj;>^y@7AltDVZE31J2mdNsvrjpdG}(-F2m~Ts(msaJRzH@@F^Cxs&*s+3wSz{ zVl;y{WTnNWhy%Fe%AGGyvc1}7P>Ijyfk0Nf_D4`q&@8e_sS^C)Pg?ciEI3%$S~cL* 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Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Mon, 14 Mar 2016 04:57:34 +0530 Subject: [PATCH 118/513] Fix verify_query in tests/test_text.py to work with Windows file-paths --- tests/test_text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_text.py b/tests/test_text.py index 6ef534583..8c23cc223 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -102,7 +102,7 @@ def verify_query(query, expected): doc = uc.documents[d] assert "{0:.2f}".format( expected.score) == "{0:.2f}".format(score * 100) - assert expected.url == doc.url + assert os.path.basename(expected.url) == os.path.basename(doc.url) return True From 9c8170c77cddb1967746921dd6a145a358881e6c Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Mon, 14 Mar 2016 05:28:27 +0530 Subject: [PATCH 119/513] Implements Perceptron Learner --- learning.py | 67 +++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 49 insertions(+), 18 deletions(-) diff --git a/learning.py b/learning.py index 5e1343dbf..b964911e8 100644 --- a/learning.py +++ b/learning.py @@ -453,7 +453,7 @@ def predict(example): # Hypothesis o_nodes = learned_net[-1] pred = [o_nodes[i].value for i in range(o_units)] - return pred[0] + return 1 if pred[0] >= 0.5 else 0 return predict @@ -478,7 +478,12 @@ def network(input_units, hidden_layer_sizes, output_units): hidden_layers_sizes : list number of neuron units in each hidden layer excluding input and output layers. """ - layers_sizes = [input_units] + hidden_layer_sizes + [output_units] + # Check for PerceptronLearner + if hidden_layer_sizes: + layers_sizes = [input_units] + hidden_layer_sizes + [output_units] + else: + layers_sizes = [input_units] + [output_units] + net = [[NNUnit() for n in range(size)] for size in layers_sizes] n_layers = len(net) @@ -492,10 +497,10 @@ def network(input_units, hidden_layer_sizes, output_units): return net -def BackPropagationLearner(dataset, network, learning_rate, epoches): +def BackPropagationLearner(dataset, net, learning_rate, epoches): "[Fig. 18.23] The back-propagation algorithm for multilayer network" # Initialise weights - for layer in network: + for layer in net: for node in layer: node.weights = [random.uniform(-0.5, 0.5) for i in range(len(node.weights))] @@ -508,9 +513,9 @@ def BackPropagationLearner(dataset, network, learning_rate, epoches): ''' idx_t = [dataset.target] idx_i = dataset.inputs - n_layers = len(network) - o_nodes = network[-1] - i_nodes = network[0] + n_layers = len(net) + o_nodes = net[-1] + i_nodes = net[0] for epoch in range(epoches): # Iterate over each example @@ -522,7 +527,7 @@ def BackPropagationLearner(dataset, network, learning_rate, epoches): n.value = v # Forward pass - for layer in network[1:]: + for layer in net[1:]: for node in layer: inc = [n.value for n in node.inputs] in_val = dotproduct(inc, node.weights) @@ -541,9 +546,9 @@ def BackPropagationLearner(dataset, network, learning_rate, epoches): # Backward pass h_layers = n_layers - 2 for i in range(h_layers, 0, -1): - layer = network[i] + layer = net[i] h_units = len(layer) - nx_layer = network[i+1] + nx_layer = net[i+1] # weights from each ith layer node to each i + 1th layer node w = [[node.weights[k] for node in nx_layer] for k in range(h_units)] @@ -554,21 +559,47 @@ def BackPropagationLearner(dataset, network, learning_rate, epoches): # Update weights for i in range(1, n_layers): - layer = network[i] - inc = [node.value for node in network[i-1]] + layer = net[i] + inc = [node.value for node in net[i-1]] units = len(layer) for j in range(units): layer[j].weights = vector_add(layer[j].weights, scalar_vector_product( learning_rate * delta[i][j], inc)) - return network + return net -def PerceptronLearner(dataset, sizes): +def PerceptronLearner(dataset, learning_rate=0.01, epoches=100): + """Logistic Regression, NO hidden layer""" + examples = dataset.examples + i_units = len(dataset.inputs) + o_units = 1 # As of now, dataset.target gives only one index. + hidden_layer_sizes = [] + raw_net = network(i_units, hidden_layer_sizes, o_units) + learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epoches) + def predict(example): - return sum([]) - unimplemented() + # Input nodes + i_nodes = learned_net[0] + + # Activate input layer + for v, n in zip(example, i_nodes): + n.value = v + + # Forward pass + for layer in learned_net[1:]: + for node in layer: + inc = [n.value for n in node.inputs] + in_val = dotproduct(inc, node.weights) + node.value = node.activation(in_val) + + # Hypothesis + o_nodes = learned_net[-1] + pred = [o_nodes[i].value for i in range(o_units)] + return 1 if pred[0] >= 0.5 else 0 + + return predict # ______________________________________________________________________________ @@ -672,7 +703,7 @@ def flatten(seqs): return sum(seqs, []) # Functions for testing learners on examples -def test(predict, dataset, examples, verbose=0): +def test(predict, dataset, examples=None, verbose=0): "Return the proportion of the examples that are NOT correctly predicted." if examples is None: examples = dataset.examples @@ -695,7 +726,7 @@ def test(predict, dataset, examples, verbose=0): def train_and_test(dataset, start, end): """Reserve dataset.examples[start:end] for test; train on the remainder.""" start = int(start) - end = int(end) + end = int(end) examples = dataset.examples train = examples[:start] + examples[end:] val = examples[start:end] From 0ee73d0744e4d8fb40296f560b464aeece1a6ebf Mon Sep 17 00:00:00 2001 From: norvig Date: Sun, 13 Mar 2016 18:12:41 -0700 Subject: [PATCH 120/513] Update test_text.py --- tests/test_text.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_text.py b/tests/test_text.py index 8c23cc223..5e0d47bee 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -1,4 +1,5 @@ import pytest +import os from text import * # noqa from utils import isclose From 8165fd09b5c095f12a9f01ab9eaa99d0a0b5641e Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Mon, 14 Mar 2016 17:33:02 +0530 Subject: [PATCH 121/513] Shorten introduction in games.ipynb --- games.ipynb | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/games.ipynb b/games.ipynb index b39f0ddcc..ab333022a 100644 --- a/games.ipynb +++ b/games.ipynb @@ -13,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## An Introduction, and some other (un)essential information" + "## An Introduction" ] }, { @@ -21,7 +21,9 @@ "metadata": {}, "source": [ " Hello all! \n", - " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book 'Artificial Intelligence: A Modern Approach'. The code in this IPython notebook, and the entire [aima-python](https://github.com/aimacode/aima-python) repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. If you do not have Python 3 installed, you might want to get that done first. Or even better, install [Anaconda](https://www.continuum.io/downloads) and you will get Jupyter Notebook and IPython along with it. This way you will be able to run both Python 2 and Python 3 using what they call 'virtual environments'. This is the way to go if you don't yet want to let go of your dear old Python 2.7. And this is what I do anyways. \n", + " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book 'Artificial Intelligence: A Modern Approach'. \n", + " \n", + " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first.\n", " \n", " What we will do to learn to use the code in this module is simply dive in! I feel this is the correct approach as I assume you must have already read Chapter 5 of AIMA. If you haven't, you might want to go back and do that first. If you are tired (or just lazy), at least read the chapter upto Sec. 5.3 because this module covers the algorithms only till that section anyway. So, I will start by explaining what the class `Game` is and then we will immediately start implementing the `TicTacToe` game. After we define the rules of the `TicTacToe` game, we will create AI players who use different search strategies, namely Minimax Search and Alpha-Beta Search. We will make these players play among themselves, and later on we ourselves will play against these AI players (Yay!). \n", " \n", @@ -870,7 +872,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.4" } }, "nbformat": 4, From 43f4435fbbf4dd72d3a74cf9c733f32833163f8d Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Mon, 14 Mar 2016 17:33:34 +0530 Subject: [PATCH 122/513] Add intro.ipynb --- intro.ipynb | 63 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 intro.ipynb diff --git a/intro.ipynb b/intro.ipynb new file mode 100644 index 000000000..1b2b3663c --- /dev/null +++ b/intro.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# An Introduction To Using `aima-python` \n", + "*Author: Chirag Vartak* \n", + "*Date: 14th March 2016* \n", + " \n", + "## About `aima-python` \n", + " \n", + " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. \n", + " \n", + "## What version of Python?\n", + " \n", + " The version of Python using which we have written and tested the code is Python 3.4. While running the code using Python 3.4 would be ideal, it should run fine on any Python 3.x. If you find that some function or module gives rise to errors on a different version of Python 3 which you are using, we would be glad if you could report it as an [Issue](https://github.com/aimacode/aima-python/issues). As far as Python 2 is concerned, the code simply will not work and produce too many errors. So, please *do not use the code in this repository with Python 2*. If, for some reason, you cannot obtain access to Python 3, we do have a [legacy branch](https://github.com/aimacode/aima-python/tree/aima3python2) that was developed a long time ago and was intended to work with Python 2. Not all modules have been implemented in this branch and its development and maintainence have been stopped. \n", + " \n", + "## Installing Anaconda\n", + " \n", + " If you have Python installed on your computer directly from python.org, you should be able to get the code in this repository to run just fine. But what we prefer is that you get [Anaconda](https://www.continuum.io/downloads) installed on your computer. Anaconda is a completely free Python distribution and has recently gotten quite popular in the Python scientific computing community. Plus, it comes with additional tools like the powerful IPython interpreter, the Jupyter Notebook App and many essential software packages. After installing Anaconda, you will be good to go to use these IPython notebooks. Also, you can run code with multiple versions of Python using what they call [virtual environments](http://conda.pydata.org/docs/py2or3.html).\n", + "\n", + "## Using these IPython notebooks \n", + " \n", + " An IPython notebook in this repository explains how to use a particular module and gives examples of its usage. An IPython notebook explains the module with the same name. For example, `games.ipynb` helps you with using the `games.py` module. A notebook has some content telling you more about the code in the module and some examples at the end which you can run in the notebook itself. \n", + " \n", + " You can use these IPython notebook in two ways: either you can view them as static HTML pages in your browser by clicking on their links in this repository on Gitub, or, you can download these notebooks and use them with a notebook app like Jupyter. (If you plan to use these notebooks with a notebook app, download the entire repository and then do so; a notebook might have some files it needs in the repo.) A notebook app allows you to run the code interactively in the browser, but if you just want to take a fleeting look at the notebook, viewing it as a static HTML page would be great too. \n", + " \n", + " If you don't know what IPython notebooks or the Jupyter Notebook App are or have never used them before, then I suggest [you read a bit](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) about them. Then, you might want to get your hands dirty and [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb). If you want to explore IPython notebooks some more before you get started with this repository, [this wiki page](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) has some truly amazing example notebooks. If you want to work with a specific version of Python, [virtual environments](http://conda.pydata.org/docs/py2or3.html) might be what you are looking for. To run the IPython interpreter or the Jupyter Notebook App with a specific version of Python, just create the particular virtual environment in the terminal and proceed as you normally would." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From c5d5de1bb45e04106d069898da14fe4e20346158 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 15 Mar 2016 17:38:18 +0530 Subject: [PATCH 123/513] Make games.py PEP8 compliant --- games.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/games.py b/games.py index b0414152e..ba842e2f5 100644 --- a/games.py +++ b/games.py @@ -1,13 +1,12 @@ -"""Games, or Adversarial Search. (Chapter 5) -""" +"""Games, or Adversarial Search (Chapter 5)""" import collections -import math import random from utils import * # noqa infinity = float('inf') +GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') # ______________________________________________________________________________ # Minimax Search @@ -156,7 +155,7 @@ def alphabeta_player(game, state): def play_game(game, *players): """Play an n-person, move-alternating game.""" - + state = game.initial while True: for player in players: @@ -170,7 +169,6 @@ def play_game(game, *players): class Game: - """A game is similar to a problem, but it has a utility for each state and a terminal test instead of a path cost and a goal test. To create a game, subclass this class and implement actions, @@ -235,10 +233,8 @@ def terminal_test(self, state): def to_move(self, state): return ('MIN' if state in 'BCD' else 'MAX') -GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') class TicTacToe(Game): - """Play TicTacToe on an h x v board, with Max (first player) playing 'X'. A state has the player to move, a cached utility, a list of moves in the form of a list of (x, y) positions, and a board, in the form of From bcf734cc854b08fad701452c25b1b1c2201eebeb Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 15 Mar 2016 17:38:33 +0530 Subject: [PATCH 124/513] Make utils.py PEP8 compliant --- utils.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/utils.py b/utils.py index 71aa9d140..242da4fe7 100644 --- a/utils.py +++ b/utils.py @@ -1,17 +1,18 @@ -"""Provide some widely useful utilities. Safe for "from utils import *". # noqa +"""Provides some utilities widely used by other modules""" -TODO[COMPLETED]: Let's take the >>> doctest examples out of the docstrings, and put them in utils_test.py -TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. -TODO: Priority queues may not belong here -- see treatment in search.py -""" +# This module is safe for: from utils import * -from grid import * # noqa +# TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. +# TODO: Priority queues may not belong here -- see treatment in search.py import operator import random import os.path import bisect +from grid import * # noqa + + def update(x, **entries): """Update a dict or an object with slots according to entries.""" if isinstance(x, dict): @@ -37,6 +38,7 @@ def unique(seq): """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) + def count(seq): """Count the number of items in sequence that are interpreted as true.""" return sum(bool(x) for x in seq) @@ -49,6 +51,7 @@ def product(numbers): result *= x return result + def first(iterable, default=None): "Return the first element of an iterable or sequence; or default." try: @@ -229,8 +232,8 @@ def sigmoid(x): def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 - -try: # math.isclose was added in Python 3.5; + +try: # math.isclose was added in Python 3.5 from math import isclose except ImportError: def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): From 53a206db0771b922db4a0d589b124b6143166549 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 15 Mar 2016 18:05:55 +0530 Subject: [PATCH 125/513] Make tests/test_games.py PEP8 compliant and resolve flake8 warnings --- tests/test_games.py | 92 +++++++++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 40 deletions(-) diff --git a/tests/test_games.py b/tests/test_games.py index 07e8a956f..8e6443c10 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -1,62 +1,74 @@ +"""A lightweight test suite for games.py""" + +# You can run this test suite by doing: py.test tests/games.py +# Of course you need to have py.test installed to do this. + import pytest -import collections -from games import * -# Creating the games +from games import * # noqa + +# Creating the game instances f52 = Fig52Game() ttt = TicTacToe() -GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') -# State generating function for TicTacToe def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3): - moves = set([(x, y) for x in range(1, h+1) for y in range(1, v+1)]) \ - - set(x_positions) - set(o_positions) - moves = list(moves) - board = {} - for pos in x_positions: - board[pos] = 'X' - for pos in o_positions: - board[pos] = 'O' - return GameState(to_move=to_move, utility=0, board=board, moves=moves) + """Given whose turn it is to move, the positions of X's on the board, the + positions of O's on the board, and, (optionally) number of rows, columns + and how many consecutive X's or O's required to win, return the corresponding + game state""" + + moves = set([(x, y) for x in range(1, h+1) for y in range(1, v+1)]) \ + - set(x_positions) - set(o_positions) + moves = list(moves) + board = {} + for pos in x_positions: + board[pos] = 'X' + for pos in o_positions: + board[pos] = 'O' + return GameState(to_move=to_move, utility=0, board=board, moves=moves) + def test_minimax_decision(): - assert minimax_decision('A', f52) == 'a1' - assert minimax_decision('B', f52) == 'b1' - assert minimax_decision('C', f52) == 'c1' - assert minimax_decision('D', f52) == 'd3' + assert minimax_decision('A', f52) == 'a1' + assert minimax_decision('B', f52) == 'b1' + assert minimax_decision('C', f52) == 'c1' + assert minimax_decision('D', f52) == 'd3' + def test_alphabeta_full_search(): - assert alphabeta_full_search('A', f52) == 'a1' - assert alphabeta_full_search('B', f52) == 'b1' - assert alphabeta_full_search('C', f52) == 'c1' - assert alphabeta_full_search('D', f52) == 'd3' + assert alphabeta_full_search('A', f52) == 'a1' + assert alphabeta_full_search('B', f52) == 'b1' + assert alphabeta_full_search('C', f52) == 'c1' + assert alphabeta_full_search('D', f52) == 'd3' - state = gen_state(to_move='X', x_positions=[(1,1), (3,3)], - o_positions=[(1,2),(3,2)]) - assert alphabeta_full_search(state, ttt) == (2,2) + state = gen_state(to_move='X', x_positions=[(1, 1), (3, 3)], + o_positions=[(1, 2), (3, 2)]) + assert alphabeta_full_search(state, ttt) == (2, 2) - state = gen_state(to_move='O', x_positions=[(1,1), (3,1), (3,3)], - o_positions=[(1,2),(3,2)]) - assert alphabeta_full_search(state, ttt) == (2,2) + state = gen_state(to_move='O', x_positions=[(1, 1), (3, 1), (3, 3)], + o_positions=[(1, 2), (3, 2)]) + assert alphabeta_full_search(state, ttt) == (2, 2) - state = gen_state(to_move='O', x_positions=[(1,1)], - o_positions=[]) - assert alphabeta_full_search(state, ttt) == (2,2) + state = gen_state(to_move='O', x_positions=[(1, 1)], + o_positions=[]) + assert alphabeta_full_search(state, ttt) == (2, 2) + + state = gen_state(to_move='X', x_positions=[(1, 1), (3, 1)], + o_positions=[(2, 2), (3, 1)]) + assert alphabeta_full_search(state, ttt) == (1, 3) - state = gen_state(to_move='X', x_positions=[(1,1), (3,1)], - o_positions=[(2,2), (3,1)]) - assert alphabeta_full_search(state, ttt) == (1,3) def test_random_tests(): - assert play_game(Fig52Game(), alphabeta_player, alphabeta_player) == 3 + assert play_game(Fig52Game(), alphabeta_player, alphabeta_player) == 3 + + # The player 'X' (one who plays first) in TicTacToe never loses: + assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 - # The player 'X' (one who plays first) in TicTacToe never loses: - assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 + # The player 'X' (one who plays first) in TicTacToe never loses: + for i in range(10): + assert play_game(ttt, alphabeta_player, random_player) >= 0 - # The player 'X' (one who plays first) in TicTacToe never loses: - for i in range(10): - assert play_game(ttt, alphabeta_player, random_player) >= 0 if __name__ == '__main__': pytest.main() From 90709b5d8cbe17eaf2f4aa39cc7621d2594c4e01 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Tue, 15 Mar 2016 18:41:11 +0530 Subject: [PATCH 126/513] Added BFS and UCS to search.ipynb --- search.ipynb | 240 +++++++++++++++++++++++++++++++++++++++++++++++++-- search.py | 3 + 2 files changed, 238 insertions(+), 5 deletions(-) diff --git a/search.ipynb b/search.ipynb index 0ec780587..fb76f5ff4 100644 --- a/search.ipynb +++ b/search.ipynb @@ -1,24 +1,254 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# The search.py module\n", + "*Date: 14 March 2016*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "Hello!\n", + " In this IPython notebook, we study different kinds of search techniques used in [search.py](https://github.com/aimacode/aima-python/blob/master/search.py) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", + "\n", + " The code in this IPython notebook, and the entire [aima-python](https://github.com/aimacode/aima-python) repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the 'intro' IPython notebook. Now that you have all that sorted out, lets get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uninformed Search Strategies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also called `blind search`, in such search strategies with all the information we have about any state all we can do is generate its successors and check whether it's a `goal state` or not. THAT'S IT. NOTHING MORE(Well ....not really. See the `value` method defined in the following section).\n", + "\n", + "First let's formulate the problem we intend to solve. So let's import everything from our module." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import search" + "from search import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing we observe is '`from utils import *`'. This means that everything in utils.py is imported for use in this module. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n", + " \n", + "The `Problem` class is an abstract class on which we define our problems(*duh*).\n", + "Again, if you are confused about what `abstract class` means have a look at the 'Intro' notebook.\n", + "The `Problem` class has six methods.\n", + "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. In this and all of the below methods `self` refers to the object itself- the object whose method is called. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins his task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", + "* `actions(self, state)` : This method returns all the possible actions our agent can make in state `state`.\n", + "* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n", + "* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else , ofcourse, `False` is returned.\n", + "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n", + "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now using the above abstract class as a parent there is another named called `GraphProblem` in our module. It creates a graph problem from an instance of the `Graph` class. To create a graph, simply do `graph = Graph(dict(...))`. The dictionary must contain nodes of the graph as keys, so make sure they are `hashable`. If you don't know what that means just use strings or numbers. Each node in the dictionary should correspond to another dictionary which contain the adjacent nodes as keys and the edge length as its value. The `Graph` class creates a directed(edges allow only one way traffic) by default.If you want to make an undirected graph, use `UndirectedGraph` instead, but make sure to mention any edge in only one of its nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you didn't understand the above paragraph, `Fret not!`. Just think of the below code as a magicical method to create a simple undirected graph. I'll explain what it is about later." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "museum_graph = UndirectedGraph(dict(\n", + " Start = dict(Dog = 3, Cat = 9, Mouse = 4),\n", + " Dog = dict(Bear = 7),\n", + " Cat = dict(Monkey = 9, Fish = 8, Penguin = 3),\n", + " Mouse = dict(Penguin = 2),\n", + " Bear = dict(Monkey = 7),\n", + " Monkey = dict(Giraffe = 11, Fish = 6),\n", + " Fish = dict(Giraffe = 8, Parrot = 3),\n", + " Penguin = dict(Parrot = 4, Elephant = 6),\n", + " Giraffe = dict(Pig = 5),\n", + " Parrot = dict(Pig = 10),\n", + " Elephant = dict(Pig = 9)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose we are in a museum showcasing statues of various animals. To navigate through the museum there are paths between some statues and the entrance. We define the entrance and the statues as nodes in our graph with the path connecting them as edges. The cost/weight of an edge specifies its length. So `Start = dict(Dog = 3, Cat = 9, Mouse = 4)` means that there are paths from `Start` to `Dog`, `Cat` and `Mouse` with path costs 3, 9 and 4 respectively. See the image below to better understand the graph." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "TODO - ADD image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Breadth First Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In breadth first search the `shallowest` unexpanded node is chosen for expansion. That means that all nodes of a given depth must be expanded before any node of the next depth level. It accomplishes this by using a `FIFO` meaning 'First In First Out' queue. Any thing thats gets in the queue first also gets out first just like the checkout queue in a supermarket. To use the algorithm, first we need to define our problem. Say we want to find the statue of `Monkey` and we start from the entrance which is the `Start` state. We define our problem using the `GraphProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "monkey_problem = GraphProblem('Start', 'Monkey', museum_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's find the solution for our problem using the `breadth_first_search` method. Note that it returns a `Node` from which we can find the solution by looking at the path that was taken to reach there." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Cat', 'Monkey']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bfs_node = breadth_first_search(monkey_problem)\n", + "bfs_node.solution()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get the output as `['Cat', 'Monkey']`. That is because the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that in breadth first search the goal test is done when it is being added to the queue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Uniform-cost Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In uniform cost search instead of expanding the shallowest node we expand the node with the lowest path cost(cost to reach upto that node from the start). Instead of a `FIFO` queue we use something called a `priority queue` which selects the element with the highest `priority` of all elements in the queue. For our problem lower path cost means higher priority. Whenever we need to enqueue a node already in the queue we update its path cost if the newer path is better. This is a very important step and it means that the path cost to a node may keep getting better until it is selected for expansion. This is the reason that we do a goal check only when a node is selected for expanion." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Dog', 'Bear', 'Monkey']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ucs_node = uniform_cost_search(monkey_problem)\n", + "ucs_node.solution()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We got `['Dog', 'Bear', 'Monkey']` instead of `['Cat', 'Monkey']` because the path cost is lower! We can also see the path cost with the path_cost attribute. Lets compare the path cost of the Breadth first search solution and Uniform cost search solution" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 17)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bfs_node.path_cost, ucs_node.path_cost" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We were right! The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former has only two roads compared to three roads in `ucs_node`." + ] } ], "metadata": { @@ -37,7 +267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.0" } }, "nbformat": 4, diff --git a/search.py b/search.py index 1cd5dc962..6c772e4f8 100644 --- a/search.py +++ b/search.py @@ -86,6 +86,9 @@ def __init__(self, state, parent=None, action=None, path_cost=0): def __repr__(self): return "" % (self.state,) + def __lt__(self, node): + return self.state < node.state + def expand(self, problem): "List the nodes reachable in one step from this node." return [self.child_node(problem, action) From 8406e0470a9faf6fc40d533affe54e0d15069d09 Mon Sep 17 00:00:00 2001 From: norvig Date: Tue, 15 Mar 2016 16:44:38 -0700 Subject: [PATCH 127/513] ignore some errors in flake8 in .travis.yml --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 76739f356..3404bacf4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -15,7 +15,7 @@ script: - py.test after_success: - - flake8 --max-line-length 100 . + - flake8 --max-line-length 100 ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . notifications: email: false From 250864785568c0ad29149d38017422c14686a71f Mon Sep 17 00:00:00 2001 From: norvig Date: Tue, 15 Mar 2016 17:22:19 -0700 Subject: [PATCH 128/513] Update logic.py - remove pretty; % => .format --- logic.py | 57 +++++++------------------------------------------------- 1 file changed, 7 insertions(+), 50 deletions(-) diff --git a/logic.py b/logic.py index 4ae6fbeae..b28e76856 100644 --- a/logic.py +++ b/logic.py @@ -111,7 +111,7 @@ def make_percept_sentence(self, percept, t): return Expr("Percept")(percept, t) def make_action_query(self, t): - return expr("ShouldDo(action, %d)" % t) + return expr("ShouldDo(action, {})".format(t)) def make_action_sentence(self, action, t): return Expr("Did")(action[expr('action')], t) @@ -185,11 +185,11 @@ def __repr__(self): if not self.args: # Constant or proposition with arity 0 return str(self.op) elif is_symbol(self.op): # Functional or propositional operator - return '%s(%s)' % (self.op, ', '.join(map(repr, self.args))) + return '{}({})'.format(self.op, ', '.join(map(repr, self.args))) elif len(self.args) == 1: # Prefix operator return self.op + repr(self.args[0]) else: # Infix operator - return '(%s)' % (' '+self.op+' ').join(map(repr, self.args)) + return '({})'.format((' '+self.op+' ').join(map(repr, self.args))) def __eq__(self, other): """x and y are equal iff their ops and args are equal.""" @@ -986,7 +986,7 @@ def standardize_variables(sentence, dic=None): if sentence in dic: return dic[sentence] else: - v = Expr('v_%d' % next(standardize_variables.counter)) + v = Expr('v_{}'.format(next(standardize_variables.counter))) dic[sentence] = v return v else: @@ -1020,7 +1020,7 @@ def tell(self, sentence): if is_definite_clause(sentence): self.clauses.append(sentence) else: - raise Exception("Not a definite clause: %s" % sentence) + raise Exception("Not a definite clause: {}".format(sentence)) def ask_generator(self, query): return fol_bc_ask(self, query) @@ -1037,7 +1037,7 @@ def test_ask(query, kb=None): vars = variables(q) answers = fol_bc_ask(kb or test_kb, q) return sorted( - [pretty(dict((x, v) for x, v in list(a.items()) if x in vars)) + [dict((x, v) for x, v in list(a.items()) if x in vars) for a in answers], key=repr) test_kb = FolKB( @@ -1146,7 +1146,7 @@ def diff(y, x): elif op == 'log': return diff(u, x) / u else: - raise ValueError("Unknown op: %s in diff(%s, %s)" % (op, y, x)) + raise ValueError("Unknown op: {} in diff({}, {})".format(op, y, x)) def simp(x): @@ -1222,52 +1222,9 @@ def d(y, x): # to compensate for the random order in the standard representation -def pretty(x): - t = type(x) - if t is dict: - return pretty_dict(x) - elif t is set: - return pretty_set(x) - else: - return repr(x) - - -def pretty_dict(d): - """Return dictionary d's repr but with the items sorted. - >>> pretty_dict({'m': 'M', 'a': 'A', 'r': 'R', 'k': 'K'}) - "{'a': 'A', 'k': 'K', 'm': 'M', 'r': 'R'}" - >>> pretty_dict({z: C, y: B, x: A}) - '{x: A, y: B, z: C}' - """ - return '{%s}' % ', '.join('%r: %r' % (k, v) - for k, v in sorted(list(d.items()), key=repr)) - - -def pretty_set(s): - """Return set s's repr but with the items sorted. - >>> pretty_set(set(['A', 'Q', 'F', 'K', 'Y', 'B'])) - "set(['A', 'B', 'F', 'K', 'Q', 'Y'])" - >>> pretty_set(set([z, y, x])) - 'set([x, y, z])' - """ - return 'set(%r)' % sorted(s, key=repr) - - -def pp(x): - print(pretty(x)) - - -def ppsubst(s): - """Pretty-print substitution s""" - ppdict(s) - -def ppdict(d): - print(pretty_dict(d)) -def ppset(s): - print(pretty_set(s)) # ________________________________________________________________________ From 1f7f4c8a519e3d89b44dd428c6f7440f6acc4d8f Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 11:51:10 +0530 Subject: [PATCH 129/513] changes index of code to reflex pseudo code in 3rd edition --- README.md | 137 +++++++++++++++++++++++++++--------------------------- logic.py | 2 +- 2 files changed, 70 insertions(+), 69 deletions(-) diff --git a/README.md b/README.md index 5c4cf9e14..15a32f8f9 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This code is in Python 3.4. (Of course, the current version, Python 3.5, also wo When complete, this project will have Python code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following three files in the main branch: - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. -- `logic.ipynb`: A Jupyter notebook that explains and gives examples of how to use the code. +- `logic.ipynb`: A Jupyter notebook that explains and gives examples of how to use the code. - `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). @@ -22,99 +22,100 @@ When complete, this project will have Python code for all the pseudocode algorit Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an iassue. -| **Fig** | **Page** | **Name (in book)** | **Name (in code)** | **File** +| **Fig** | **Page** | **Name (in 3rd edition)** | **Name (in code)** | **File** |:--------|:---------|:-------------------|:---------|:-----------| -| 2 | 32 | Environment | `Environment` | [`agents.py`](../master/agents.py) | -| 2.1 | 33 | Agent | `Agent` | [`agents.py`](../master/agents.py) | -| 2.3 | 34 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.7 | 45 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | -| 2.8 | 46 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.10 | 47 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | -| 2.12 | 49 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | -| 3.1 | 61 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | +| 2.1 | 36 | Environment | `Environment` | [`agents.py`](../master/agents.py) | +| 2.1 | 36 | Agent | `Agent` | [`agents.py`](../master/agents.py) | +| 2.3 | 37 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.7 | 48 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | +| 2.8 | 49 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.10 | 50 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | +| 2.12 | 52 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | +| 3.1 | 69 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | | 3 | 62 | Problem | `Problem` | [`search.py`](../master/search.py) | -| 3.2 | 63 | Romania | `romania` | [`search.py`](../master/search.py) | +| 3.2 | 70 | Romania | `romania` | [`search.py`](../master/search.py) | | 3 | 69 | Node | `Node` | [`search.py`](../master/search.py) | | 3 | 71 | Queue | `Queue` | [`utils.py`](../master/utils.py) | -| 3.7 | 70 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | -| 3.7 | 72 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | -| 3.11 | 72 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | -| 3.13 | 72 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | -| 3.16 | 77 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | -| 3.14 | 79 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | +| 3.7 | 79 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | +| 3.7 | 79 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | +| 3.11 | 84 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | +| 3.14 | 86 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | +| 3.17 | 90 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | +| 3.18 | 91 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | | 3.19 | 83 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | | 4 | 95 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | | 4 | 97 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | -| 4.5 | 102 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | -| 4.11 | 112 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | -| 4.14 | 116 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | -| 4.17 | 119 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | -| 4.20 | 126 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.23 | 128 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | +| 3.26 | 101 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | +| 4.2 | 125 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | +| 4.5 | 129 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | +| 4.8 | 132 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | +| 4.21 | 153 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | +| 4.24 | 155 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | | 5 | 137 | CSP | `CSP` | [`csp.py`](../master/csp.py) | -| 5.3 | 142 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | -| 5.7 | 146 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | -| 5.8 | 151 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | -| 6.3 | 166 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | -| 6.7 | 170 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | +| 5.3 | 169 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | +| 5.7 | 173 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | | 7 | 195 | KB | `KB` | [`logic.py`](../master/logic.py) | -| 7.1 | 196 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | -| 7.7 | 205 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | -| 7.10 | 209 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | +| 6.1 | 208 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | +| 6.7 | 216 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | +| 6.10 | 220 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | | 7 | 215 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | -| 7.12 | 216 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | -| 7.14 | 219 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | -| 7.16 | 222 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | -| 7.17 | 223 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | -| 7.19 | 226 | PL-Wumpus-Agent | `PLWumpusAgent` | [`logic.py`](../master/logic.py) | +| 6.12 | 227 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | +| 6.15 | 230 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | +| 6.17 | 233 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | +| 6.18 | 235 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | +| 6.20 | 242 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | +| 6.22 | 244 | SATPlan | | +| 7.3 | 265 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | +| 7.5 | 271 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | +| 7.8 | 277 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | | 9 | 273 | Subst | `subst` | [`logic.py`](../master/logic.py) | -| 9.1 | 278 | Unify | `unify` | [`logic.py`](../master/logic.py) | -| 9.3 | 282 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | -| 9.6 | 288 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | +| 9.1 | 334 | Unify | `unify` | [`logic.py`](../master/logic.py) | +| 9.3 | 338 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | +| 9.6 | 344 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | | 9.14 | 307 | Otter | | -| 11.2 | 380 | Airport-problem | | -| 11.3 | 381 | Spare-Tire-Problem | | -| 11.4 | 383 | Three-Block-Tower | | +| 10.1 | 376 | Air-Cargo-problem | | +| 10.2 | 377 | Spare-Tire-Problem | | +| 10.3 | 378 | Three-Block-Tower | | | 11 | 390 | Partial-Order-Planner | | -| 11.11 | 396 | Cake-Problem | | -| 11.13 | 399 | Graphplan | | -| 11.15 | 403 | SATPlan | | +| 10.7 | 387 | Cake-Problem | | +| 10.9 | 390 | Graphplan | | | 12.1 | 418 | Job-Shop-Problem | | -| 12.3 | 421 | Job-Shop-Problem-With-Resources | | +| 11.1 | 409 | Job-Shop-Problem-With-Resources | | | 12.6 | 424 | House-Building-Problem | | | 12.10 | 435 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | | 12.22 | 449 | Continuous-POP-Agent | | | 12.23 | 450 | Doubles-tennis | | -| 13.1 | 466 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | +| 13.1 | 492 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | | 13 | 469 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | | 13.4 | 477 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | -| 14.10 | 509 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | -| 14.12 | 512 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | -| 14.13 | 513 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | -| 14.14 | 515 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | +| 14.11 | 537 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | +| 14.13 | 540 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | +| 14.14 | 542 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | +| 14.15 | 543 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | | 14.15 | 517 | MCMC-Ask | | -| 15.4 | 546 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | -| 15.6 | 552 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | -| 15.15 | 566 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | -| 16.8 | 603 | Information-Gathering-Agent | | -| 17.4 | 621 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | -| 17.7 | 624 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | -| 18.5 | 658 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | -| 18.10 | 667 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | -| 18.14 | 672 | Decision-List-Learning | | -| 19.2 | 681 | Current-Best-Learning | | -| 19.3 | 683 | Version-Space-Learning | | -| 19.8 | 696 | Minimal-Consistent-Det | | -| 19.12 | 702 | FOIL | | +| 15.4 | 586 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | +| 15.6 | 590 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | +| 15.17 | 608 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | +| 16.9 | 643 | Information-Gathering-Agent | | +| 17.4 | 664 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | +| 17.7 | 668 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | +| 18.5 | 713 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | +| 18.11 | 728 | Decision-List-Learning | | +| 18.24 | 745 | Back-Prop-Learning | | +| 18.34 | 762 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | +| 19.2 | 783 | Current-Best-Learning | | +| 19.3 | 785 | Version-Space-Learning | | +| 19.8 | 798 | Minimal-Consistent-Det | | +| 19.12 | 805 | FOIL | | | 20.21 | 742 | Perceptron-Learning | `PerceptronLearner` | [`learning.py`](../master/learning.py) | -| 20.25 | 746 | Back-Prop-Learning | | -| 21.2 | 768 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | -| 21.4 | 769 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | -| 21.8 | 776 | Q-Learning-Agent | | +| 22.2 | 877 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | +| 22.4 | 880 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | +| 22.8 | 887 | Q-Learning-Agent | | | 22.2 | 796 | Naive-Communicating-Agent | | | 22.7 | 801 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | | 23.1 | 837 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | | 24.21 | 892 | Align | | +| 25.9 | 999 | Monte-Carlo-Localization| | # Acknowledgements diff --git a/logic.py b/logic.py index b28e76856..a19b84f1e 100644 --- a/logic.py +++ b/logic.py @@ -842,7 +842,7 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): class HybridWumpusAgent(agents.Agent): - "An agent for the wumpus world that does logical inference. [Fig. 7.19]""" + "An agent for the wumpus world that does logical inference. [Fig. 7.20]""" def __init__(self): unimplemented() From 1239cff1a4cc69d6ccc12d740da4f166104798a6 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Wed, 16 Mar 2016 11:53:38 +0530 Subject: [PATCH 130/513] fixed markdown typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 15a32f8f9..4cf72e473 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ When complete, this project will have Python code for all the pseudocode algorit Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an iassue. -| **Fig** | **Page** | **Name (in 3rd edition)** | **Name (in code)** | **File** +| **Fig** | **Page** | **Name (in 3rd edition)** | **Name (in code)** | **File** |:--------|:---------|:-------------------|:---------|:-----------| | 2.1 | 36 | Environment | `Environment` | [`agents.py`](../master/agents.py) | | 2.1 | 36 | Agent | `Agent` | [`agents.py`](../master/agents.py) | From e2ed73f6d926fa58d9169fcb9e483d885ffb7eac Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Wed, 16 Mar 2016 17:07:36 +0530 Subject: [PATCH 131/513] Made code easier to read reverting from 79 charaters per line to 100 characters --- agents.py | 9 +++------ csp.py | 13 ++++--------- tests/test_search.py | 14 ++++---------- tests/test_utils.py | 6 +++--- 4 files changed, 14 insertions(+), 28 deletions(-) diff --git a/agents.py b/agents.py index 4c7801430..baf769951 100644 --- a/agents.py +++ b/agents.py @@ -51,8 +51,7 @@ class Thing(object): .__name__ slot (used for output only).""" def __repr__(self): - return '<{}>'.format(getattr(self, '__name__', - self.__class__.__name__)) + return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__)) def is_alive(self): "Things that are 'alive' should return true." @@ -309,10 +308,8 @@ def delete_thing(self, thing): except(ValueError, e): print(e) print(" in Environment delete_thing") - print(" Thing to be removed: {} at {}" .format(thing, - thing.location)) - print(" from list: {}" .format([(thing, thing.location) - for thing in self.things])) + print(" Thing to be removed: {} at {}" .format(thing, thing.location)) + print(" from list: {}" .format([(thing, thing.location) for thing in self.things])) if thing in self.agents: self.agents.remove(thing) diff --git a/csp.py b/csp.py index 1b80ed4be..5fa953b7f 100644 --- a/csp.py +++ b/csp.py @@ -105,9 +105,7 @@ def goal_test(self, state): "The goal is to assign all vars, with all constraints satisfied." assignment = dict(state) return (len(assignment) == len(self.vars) and - every(lambda var: self.nconflicts(var, assignment[var], - assignment) == 0, - self.vars)) + every(lambda x: self.nconflicts(x, assignment[x], assignment) == 0, self.x)) # These are for constraint propagation @@ -115,8 +113,7 @@ def support_pruning(self): """Make sure we can prune values from domains. (We want to pay for this only if we use it.)""" if self.curr_domains is None: - self.curr_domains = dict((v, list(self.domains[v])) - for v in self.vars) + self.curr_domains = dict((v, list(self.domains[v])) for v in self.vars) def suppose(self, var, value): "Start accumulating inferences from assuming var=value." @@ -590,12 +587,10 @@ def __init__(self, grid): for var, ch in zip(flatten(self.rows), squares)) for _ in squares: raise ValueError("Not a Sudoku grid", grid) # Too many squares - CSP.__init__(self, None, domains, self.neighbors, - different_values_constraint) + CSP.__init__(self, None, domains, self.neighbors, different_values_constraint) def display(self, assignment): - def show_box(box): return [ - ' '.join(map(show_cell, row)) for row in box] + def show_box(box): return [' '.join(map(show_cell, row)) for row in box] def show_cell(cell): return str(assignment.get(cell, '.')) diff --git a/tests/test_search.py b/tests/test_search.py index c6ab3ac87..20dc1ce96 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -8,19 +8,15 @@ def test_breadth_first_tree_search(): - assert breadth_first_tree_search(romania).solution() == ['Sibiu', - 'Fagaras', - 'Bucharest'] + assert breadth_first_tree_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_breadth_first_search(): - assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', - 'Bucharest'] + assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_uniform_cost_search(): - assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', - 'Pitesti', 'Bucharest'] + assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] def test_depth_first_graph_search(): @@ -29,9 +25,7 @@ def test_depth_first_graph_search(): def test_iterative_deepening_search(): - assert iterative_deepening_search(romania).solution() == ['Sibiu', - 'Fagaras', - 'Bucharest'] + assert iterative_deepening_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_and_or_graph_search(): def run_plan(state, problem, plan): diff --git a/tests/test_utils.py b/tests/test_utils.py index dac3808a9..b809e1dc9 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -75,9 +75,9 @@ def test_histogram(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), (4, 2), (5, 1), (7, 1), (9, 1)] - assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, - lambda x: x*x) == [(1, 2), (4, 3), (16, 2), (25, 1), - (49, 1), (81, 1)] + assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 0, lambda x: x*x) == [(1, 2), (4, 3), + (16, 2), (25, 1), + (49, 1), (81, 1)] assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1], 1) == [(2, 3), (4, 2), (1, 2), (9, 1), (7, 1), (5, 1)] From c1295f2ddf4c547ade59085f33cfa1fae196611a Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 17:53:09 +0530 Subject: [PATCH 132/513] changed the name of function to sum_dotproduct --- learning.py | 8 ++++---- tests/test_utils.py | 10 +++++----- utils.py | 8 +++++++- 3 files changed, 16 insertions(+), 10 deletions(-) diff --git a/learning.py b/learning.py index b964911e8..d60bbbd03 100644 --- a/learning.py +++ b/learning.py @@ -447,7 +447,7 @@ def predict(example): for layer in learned_net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = dotproduct(inc, node.weights) + in_val = sum_dotproduct(inc, node.weights) node.value = node.activation(in_val) # Hypothesis @@ -530,7 +530,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): for layer in net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = dotproduct(inc, node.weights) + in_val = sum_dotproduct(inc, node.weights) node.value = node.activation(in_val) # Initialize delta @@ -554,7 +554,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): for k in range(h_units)] delta[i] = [(layer[j].value) * (1 - layer[j].value) * - dotproduct(w[j], delta[i+1]) + sum_dotproduct(w[j], delta[i+1]) for j in range(h_units)] # Update weights @@ -591,7 +591,7 @@ def predict(example): for layer in learned_net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = dotproduct(inc, node.weights) + in_val = sum_dotproduct(inc, node.weights) node.value = node.activation(in_val) # Hypothesis diff --git a/tests/test_utils.py b/tests/test_utils.py index dac3808a9..141cb1cd3 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -83,8 +83,8 @@ def test_histogram(): (7, 1), (5, 1)] -def test_dotproduct(): - assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 +def test_sum_dotproduct(): + assert sum_dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 def test_vector_add(): @@ -117,9 +117,9 @@ def f(): def test_sigmoid(): - assert isclose(0.5, sigmoid(0)) - assert isclose(0.7310585786300049, sigmoid(1)) - assert isclose(0.2689414213699951, sigmoid(-1)) + assert isclose(0.5, sigmoid(0)) + assert isclose(0.7310585786300049, sigmoid(1)) + assert isclose(0.2689414213699951, sigmoid(-1)) def test_step(): diff --git a/utils.py b/utils.py index 242da4fe7..606c0efc8 100644 --- a/utils.py +++ b/utils.py @@ -162,11 +162,17 @@ def histogram(values, mode=0, bin_function=None): return sorted(bins.items()) -def dotproduct(X, Y): +def sum_dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y.""" return sum(x * y for x, y in zip(X, Y)) +def dotproduct(X, Y): + """Return element-wise product of vectors x and y""" + assert len(X) == len(Y) + return(list(x * y for x, y in zip(X, Y))) + + def vector_add(a, b): """Component-wise addition of two vectors.""" return tuple(map(operator.add, a, b)) From fffe727f04694118d8e1c5655d541f7b2b52a880 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 17:53:54 +0530 Subject: [PATCH 133/513] implemented forward-backward with new HiddenMarkovModel class --- probability.py | 69 ++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 67 insertions(+), 2 deletions(-) diff --git a/probability.py b/probability.py index a582d128d..685581361 100644 --- a/probability.py +++ b/probability.py @@ -524,11 +524,76 @@ def markov_blanket_sample(X, e, bn): # _________________________________________________________________________ +""" +umbrella_evidence = [T, T, F, T, T] +umbrella_prior = [0.5, 0.5] +umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] +umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] +umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + +print(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) +""" + +class HiddenMarkovModel: + + """ A Hidden markov model which takes Transition model and Sensor model as inputs""" + + def __init__(self, transition_model, sensor_model): + self.transition_model = transition_model + self.sensor_model = sensor_model + + def transition_model(self): + return self.transition_model + + def sensor_dist(self, ev): + if ev is True: + return self.sensor_model[0] + else: + return self.sensor_model[1] + + +def forward(HMM, fv, ev): + prediction = vector_add(scalar_vector_product(fv[0], HMM.transition_model[0]), + scalar_vector_product(fv[1], HMM.transition_model[1])) + sensor_dist = HMM.sensor_dist(ev) -def forward_backward(ev, prior): + return(normalize(dotproduct(sensor_dist, prediction))) + +def backward(HMM, b, ev): + sensor_dist = HMM.sensor_dist(ev) + prediction = dotproduct(sensor_dist, b) + + return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), + scalar_vector_product(prediction[1], HMM.transition_model[1])))) + + +def forward_backward(HMM, ev, prior): """[Fig. 15.4]""" - unimplemented() + t = len(ev) + ev.insert(0, None) # to make the code look similar to pseudo code + + fv = [[0.0, 0.0] for i in range(len(ev))] + b = [1.0, 1.0] + bv = [b] # we don't need bv; but we will have a list of all backward messages here + sv = [[0, 0] for i in range(len(ev))] + fv[0] = prior + + for i in range(1, t+ 1): + fv[i] = forward(HMM, fv[i- 1], ev[i]) + for i in range(t, -1, -1): + sv[i- 1] = normalize(dotproduct(fv[i], b)) + b = backward(HMM, b, ev[i]) + bv.append(b) + + sv = sv[::-1] + for i in range(len(sv)): + for j in range(len(sv[i])): + sv[i][j] = float("{0:.4f}".format(sv[i][j])) + + return(sv) + +# _________________________________________________________________________ def fixed_lag_smoothing(e_t, hmm, d): """[Fig. 15.6]""" From 4075725a829e1beabe873ab90aa3478bb7f7b942 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 21:18:32 +0530 Subject: [PATCH 134/513] fall back to the original method name and adds element_wise_product method --- learning.py | 8 ++++---- probability.py | 6 +++--- tests/test_utils.py | 4 ++-- utils.py | 6 +++--- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/learning.py b/learning.py index d60bbbd03..b964911e8 100644 --- a/learning.py +++ b/learning.py @@ -447,7 +447,7 @@ def predict(example): for layer in learned_net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = sum_dotproduct(inc, node.weights) + in_val = dotproduct(inc, node.weights) node.value = node.activation(in_val) # Hypothesis @@ -530,7 +530,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): for layer in net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = sum_dotproduct(inc, node.weights) + in_val = dotproduct(inc, node.weights) node.value = node.activation(in_val) # Initialize delta @@ -554,7 +554,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): for k in range(h_units)] delta[i] = [(layer[j].value) * (1 - layer[j].value) * - sum_dotproduct(w[j], delta[i+1]) + dotproduct(w[j], delta[i+1]) for j in range(h_units)] # Update weights @@ -591,7 +591,7 @@ def predict(example): for layer in learned_net[1:]: for node in layer: inc = [n.value for n in node.inputs] - in_val = sum_dotproduct(inc, node.weights) + in_val = dotproduct(inc, node.weights) node.value = node.activation(in_val) # Hypothesis diff --git a/probability.py b/probability.py index 685581361..1a22db4d9 100644 --- a/probability.py +++ b/probability.py @@ -557,11 +557,11 @@ def forward(HMM, fv, ev): scalar_vector_product(fv[1], HMM.transition_model[1])) sensor_dist = HMM.sensor_dist(ev) - return(normalize(dotproduct(sensor_dist, prediction))) + return(normalize(element_wise_product(sensor_dist, prediction))) def backward(HMM, b, ev): sensor_dist = HMM.sensor_dist(ev) - prediction = dotproduct(sensor_dist, b) + prediction = element_wise_product(sensor_dist, b) return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), scalar_vector_product(prediction[1], HMM.transition_model[1])))) @@ -582,7 +582,7 @@ def forward_backward(HMM, ev, prior): for i in range(1, t+ 1): fv[i] = forward(HMM, fv[i- 1], ev[i]) for i in range(t, -1, -1): - sv[i- 1] = normalize(dotproduct(fv[i], b)) + sv[i- 1] = normalize(element_wise_product(fv[i], b)) b = backward(HMM, b, ev[i]) bv.append(b) diff --git a/tests/test_utils.py b/tests/test_utils.py index 141cb1cd3..d8ed78de9 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -83,8 +83,8 @@ def test_histogram(): (7, 1), (5, 1)] -def test_sum_dotproduct(): - assert sum_dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 +def test_dotproduct(): + assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 def test_vector_add(): diff --git a/utils.py b/utils.py index 606c0efc8..4c4d5950e 100644 --- a/utils.py +++ b/utils.py @@ -162,13 +162,13 @@ def histogram(values, mode=0, bin_function=None): return sorted(bins.items()) -def sum_dotproduct(X, Y): +def dotproduct(X, Y): """Return the sum of the element-wise product of vectors x and y.""" return sum(x * y for x, y in zip(X, Y)) -def dotproduct(X, Y): - """Return element-wise product of vectors x and y""" +def element_wise_product(X, Y): + """Return vector as an element-wise product of vectors x and y""" assert len(X) == len(Y) return(list(x * y for x, y in zip(X, Y))) From bbf6ecea41ccaad3c54719ed953be83012051432 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 21:43:51 +0530 Subject: [PATCH 135/513] removes page numbers in index of code, readme.md --- README.md | 188 +++++++++++++++++++++++++++--------------------------- 1 file changed, 94 insertions(+), 94 deletions(-) diff --git a/README.md b/README.md index 4cf72e473..45cee6cde 100644 --- a/README.md +++ b/README.md @@ -22,100 +22,100 @@ When complete, this project will have Python code for all the pseudocode algorit Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an iassue. -| **Fig** | **Page** | **Name (in 3rd edition)** | **Name (in code)** | **File** -|:--------|:---------|:-------------------|:---------|:-----------| -| 2.1 | 36 | Environment | `Environment` | [`agents.py`](../master/agents.py) | -| 2.1 | 36 | Agent | `Agent` | [`agents.py`](../master/agents.py) | -| 2.3 | 37 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.7 | 48 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | -| 2.8 | 49 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.10 | 50 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | -| 2.12 | 52 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | -| 3.1 | 69 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | -| 3 | 62 | Problem | `Problem` | [`search.py`](../master/search.py) | -| 3.2 | 70 | Romania | `romania` | [`search.py`](../master/search.py) | -| 3 | 69 | Node | `Node` | [`search.py`](../master/search.py) | -| 3 | 71 | Queue | `Queue` | [`utils.py`](../master/utils.py) | -| 3.7 | 79 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | -| 3.7 | 79 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | -| 3.11 | 84 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | -| 3.14 | 86 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | -| 3.17 | 90 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | -| 3.18 | 91 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | -| 3.19 | 83 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | -| 4 | 95 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | -| 4 | 97 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | -| 3.26 | 101 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | -| 4.2 | 125 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | -| 4.5 | 129 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | -| 4.8 | 132 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | -| 4.21 | 153 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.24 | 155 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | -| 5 | 137 | CSP | `CSP` | [`csp.py`](../master/csp.py) | -| 5.3 | 169 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | -| 5.7 | 173 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | -| 7 | 195 | KB | `KB` | [`logic.py`](../master/logic.py) | -| 6.1 | 208 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | -| 6.7 | 216 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | -| 6.10 | 220 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | -| 7 | 215 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | -| 6.12 | 227 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | -| 6.15 | 230 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | -| 6.17 | 233 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | -| 6.18 | 235 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | -| 6.20 | 242 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | -| 6.22 | 244 | SATPlan | | -| 7.3 | 265 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | -| 7.5 | 271 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | -| 7.8 | 277 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | -| 9 | 273 | Subst | `subst` | [`logic.py`](../master/logic.py) | -| 9.1 | 334 | Unify | `unify` | [`logic.py`](../master/logic.py) | -| 9.3 | 338 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | -| 9.6 | 344 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | -| 9.14 | 307 | Otter | | -| 10.1 | 376 | Air-Cargo-problem | | -| 10.2 | 377 | Spare-Tire-Problem | | -| 10.3 | 378 | Three-Block-Tower | | -| 11 | 390 | Partial-Order-Planner | | -| 10.7 | 387 | Cake-Problem | | -| 10.9 | 390 | Graphplan | | -| 12.1 | 418 | Job-Shop-Problem | | -| 11.1 | 409 | Job-Shop-Problem-With-Resources | | -| 12.6 | 424 | House-Building-Problem | | -| 12.10 | 435 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | -| 12.22 | 449 | Continuous-POP-Agent | | -| 12.23 | 450 | Doubles-tennis | | -| 13.1 | 492 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | -| 13 | 469 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | -| 13.4 | 477 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | -| 14.11 | 537 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | -| 14.13 | 540 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | -| 14.14 | 542 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | -| 14.15 | 543 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | -| 14.15 | 517 | MCMC-Ask | | -| 15.4 | 586 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | -| 15.6 | 590 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | -| 15.17 | 608 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | -| 16.9 | 643 | Information-Gathering-Agent | | -| 17.4 | 664 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | -| 17.7 | 668 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | -| 18.5 | 713 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | -| 18.11 | 728 | Decision-List-Learning | | -| 18.24 | 745 | Back-Prop-Learning | | -| 18.34 | 762 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | -| 19.2 | 783 | Current-Best-Learning | | -| 19.3 | 785 | Version-Space-Learning | | -| 19.8 | 798 | Minimal-Consistent-Det | | -| 19.12 | 805 | FOIL | | -| 20.21 | 742 | Perceptron-Learning | `PerceptronLearner` | [`learning.py`](../master/learning.py) | -| 22.2 | 877 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | -| 22.4 | 880 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | -| 22.8 | 887 | Q-Learning-Agent | | -| 22.2 | 796 | Naive-Communicating-Agent | | -| 22.7 | 801 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | -| 23.1 | 837 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | -| 24.21 | 892 | Align | | -| 25.9 | 999 | Monte-Carlo-Localization| | +| **Fig** | **Name (in 3rd edition)** | **Name (in code)** | **File** +|:--------|:-------------------|:---------|:-----------| +| 2.1 | Environment | `Environment` | [`agents.py`](../master/agents.py) | +| 2.1 | Agent | `Agent` | [`agents.py`](../master/agents.py) | +| 2.3 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.7 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | +| 2.8 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | +| 2.10 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | +| 2.12 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | +| 3.1 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | +| 3 | Problem | `Problem` | [`search.py`](../master/search.py) | +| 3.2 | Romania | `romania` | [`search.py`](../master/search.py) | +| 3 | Node | `Node` | [`search.py`](../master/search.py) | +| 3 | Queue | `Queue` | [`utils.py`](../master/utils.py) | +| 3.7 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | +| 3.7 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | +| 3.11 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | +| 3.14 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | +| 3.17 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | +| 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | +| 3.19 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | +| 4 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | +| 4 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | +| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | +| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | +| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | +| 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | +| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | +| 4.24 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | +| 5 | CSP | `CSP` | [`csp.py`](../master/csp.py) | +| 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | +| 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | +| 7 | KB | `KB` | [`logic.py`](../master/logic.py) | +| 6.1 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | +| 6.7 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | +| 6.10 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | +| 7 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | +| 6.12 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | +| 6.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | +| 6.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | +| 6.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | +| 6.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | +| 6.22 | SATPlan | | +| 7.3 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | +| 7.5 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | +| 7.8 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | +| 9 | Subst | `subst` | [`logic.py`](../master/logic.py) | +| 9.1 | Unify | `unify` | [`logic.py`](../master/logic.py) | +| 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | +| 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | +| 9.14 | Otter | | +| 10.1 | Air-Cargo-problem | | +| 10.2 | Spare-Tire-Problem | | +| 10.3 | Three-Block-Tower | | +| 11 | Partial-Order-Planner | | +| 10.7 | Cake-Problem | | +| 10.9 | Graphplan | | +| 12.1 | Job-Shop-Problem | | +| 11.1 | Job-Shop-Problem-With-Resources | | +| 12.6 | House-Building-Problem | | +| 12.10 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | +| 12.22 | Continuous-POP-Agent | | +| 12.23 | Doubles-tennis | | +| 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | +| 13 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | +| 13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | +| 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | +| 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | +| 14.14 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | +| 14.15 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | +| 14.15 | MCMC-Ask | | +| 15.4 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | +| 15.6 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | +| 15.17 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | +| 16.9 | Information-Gathering-Agent | | +| 17.4 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | +| 17.7 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | +| 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | +| 18.11 | Decision-List-Learning | | +| 18.24 | Back-Prop-Learning | | +| 18.34 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | +| 19.2 | Current-Best-Learning | | +| 19.3 | Version-Space-Learning | | +| 19.8 | Minimal-Consistent-Det | | +| 19.12 | FOIL | | +| 20.21 | Perceptron-Learning | `PerceptronLearner` | [`learning.py`](../master/learning.py) | +| 22.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | +| 22.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | +| 22.8 | Q-Learning-Agent | | +| 22.2 | Naive-Communicating-Agent | | +| 22.7 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | +| 23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | +| 24.21 | Align | | +| 25.9 | Monte-Carlo-Localization| | # Acknowledgements From e99fa292a901d8a522ceaf0175741e8d941244c8 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Wed, 16 Mar 2016 22:02:55 +0530 Subject: [PATCH 136/513] Replaced old find_if() with first() method --- logic.py | 102 +++---------------------------------------- tests/test_search.py | 1 - 2 files changed, 5 insertions(+), 98 deletions(-) diff --git a/logic.py b/logic.py index a19b84f1e..a5df00237 100644 --- a/logic.py +++ b/logic.py @@ -160,12 +160,6 @@ class Expr: (3) (x % y) and (x ^ y). It is very ugly to have (x % y) mean (x <=> y), but we need SOME operator to make (2) work, and this seems the best choice. - - WARNING: if x is an Expr, then so is x + 1, because the int 1 gets - coerced to an Expr by the constructor. But 1 + x is an error, because - 1 doesn't know how to add an Expr. (Adding an __radd__ method to Expr - wouldn't help, because int.__add__ is still called first.) Therefore, - you should use Expr(1) + x instead, or ONE + x, or expr('1 + x'). """ def __init__(self, op, *args): @@ -216,6 +210,8 @@ def __gt__(self, other): return Expr('>', self, other) def __add__(self, other): return Expr('+', self, other) + def __radd__(self, other): return Expr('+', other, self) + def __sub__(self, other): return Expr('-', self, other) def __and__(self, other): return Expr('&', self, other) @@ -253,10 +249,6 @@ def expr(s): 'x =/= y' parses as (x ^ y) # Logical disequality (xor) But BE CAREFUL; precedence of implication is wrong. expr('P & Q ==> R & S') is ((P & (Q >> R)) & S); so you must use expr('(P & Q) ==> (R & S)'). - >>> expr('P <=> Q(1)') - (P <=> Q(1)) - >>> expr('P & Q | ~R(x, F(x))') - ((P & Q) | ~R(x, F(x))) """ if isinstance(s, Expr): return s @@ -539,7 +531,7 @@ def distribute_and_over_or(s): return FALSE if len(s.args) == 1: return distribute_and_over_or(s.args[0]) - conj = find_if((lambda d: d.op == '&'), s.args) + conj = first(arg for arg in s.args if arg.op == '&') if not conj: return s others = [a for a in s.args if a is not conj] @@ -1031,7 +1023,7 @@ def retract(self, sentence): def fetch_rules_for_goal(self, goal): return self.clauses - +""" TODO Rename test_ask to remove test from the name(or tell pytest to ignore it) def test_ask(query, kb=None): q = expr(query) vars = variables(q) @@ -1039,6 +1031,7 @@ def test_ask(query, kb=None): return sorted( [dict((x, v) for x, v in list(a.items()) if x in vars) for a in answers], key=repr) +""" test_kb = FolKB( list(map(expr, ['Farmer(Mac)', @@ -1229,88 +1222,3 @@ def d(y, x): # ________________________________________________________________________ -class logicTest: - - """ -### PropKB ->>> kb = PropKB() ->>> kb.tell(A & B) ->>> kb.tell(B >> C) ->>> kb.ask(C) ## The result {} means true, with no substitutions -{} ->>> kb.ask(P) -False ->>> kb.retract(B) ->>> kb.ask(C) -False - ->>> pl_true(P, {}) ->>> pl_true(P | Q, {P: True}) -True - -# Notice that the function pl_true cannot reason by cases: ->>> pl_true(P | ~P) - -# However, tt_true can: ->>> tt_true(P | ~P) -True - -# The following are tautologies from [Fig. 7.11]: ->>> tt_true("(A & B) <=> (B & A)") -True ->>> tt_true("(A | B) <=> (B | A)") -True ->>> tt_true("((A & B) & C) <=> (A & (B & C))") -True ->>> tt_true("((A | B) | C) <=> (A | (B | C))") -True ->>> tt_true("~~A <=> A") -True ->>> tt_true("(A >> B) <=> (~B >> ~A)") -True ->>> tt_true("(A >> B) <=> (~A | B)") -True ->>> tt_true("(A <=> B) <=> ((A >> B) & (B >> A))") -True ->>> tt_true("~(A & B) <=> (~A | ~B)") -True ->>> tt_true("~(A | B) <=> (~A & ~B)") -True ->>> tt_true("(A & (B | C)) <=> ((A & B) | (A & C))") -True ->>> tt_true("(A | (B & C)) <=> ((A | B) & (A | C))") -True - -# The following are not tautologies: ->>> tt_true(A & ~A) -False ->>> tt_true(A & B) -False - -### An earlier version of the code failed on this: ->>> dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) # noqa -{B: False, C: True, A: True, F: False, D: True, E: False} - -### [Fig. 7.13] ->>> alpha = expr("~P12") ->>> to_cnf(Fig[7,13] & ~alpha) -((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12) ->>> tt_entails(Fig[7,13], alpha) -True ->>> pl_resolution(PropKB(Fig[7,13]), alpha) -True - -### [Fig. 7.15] ->>> pl_fc_entails(Fig[7,15], expr('SomethingSilly')) -False - -### Unification: ->>> unify(x, x, {}) -{} ->>> unify(x, 3, {}) -{x: 3} - - ->>> to_cnf((P&Q) | (~P & ~Q)) -((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q)) -""" diff --git a/tests/test_search.py b/tests/test_search.py index c6ab3ac87..de2824c28 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,6 +1,5 @@ import pytest from search import * # noqa -from random import choice #noqa romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) From 71950013af0e1a06adfd2614a4453606790f156d Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Wed, 16 Mar 2016 22:53:12 +0530 Subject: [PATCH 137/513] added some tests for logic.py --- tests/test_logic.py | 72 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 tests/test_logic.py diff --git a/tests/test_logic.py b/tests/test_logic.py new file mode 100644 index 000000000..5aae637a9 --- /dev/null +++ b/tests/test_logic.py @@ -0,0 +1,72 @@ +import pytest +from logic import * + + +def test_expr(): + assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' + assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' + +def test_PropKB(): + kb = PropKB() + assert count(kb.ask(expr) for expr in [A, B, C, P, Q]) is 0 + kb.tell(A & B) + assert kb.ask(A) == kb.ask(B) == {} + kb.tell(B >> C) + assert kb.ask(C) == {} + kb.retract(B) + assert kb.ask(B) is False + assert kb.ask(C) is False + +def test_pl_true(): + assert pl_true(P, {}) is None + assert pl_true(P, {P: False}) is False + assert pl_true(P | Q, {P: True}) is True + assert pl_true((A|B)&(C|D), {A: False, B: True, D: True}) is True + assert pl_true((A&B)&(C|D), {A: False, B: True, D: True}) is False + assert pl_true((A&B)|(A&C), {A: False, B: True, C: True}) is False + assert pl_true((A|B)&(C|D), {A: True, D: False}) is None + assert pl_true(P | P, {}) is None + +def test_tt_true(): + assert tt_true(P | ~P) + assert tt_true('~~P <=> P') + assert not tt_true('(P | ~Q)&(~P | Q)') + assert not tt_true(P & ~P) + assert not tt_true(P & Q) + assert tt_true('(P | ~Q)|(~P | Q)') + assert tt_true('(A & B) ==> (A | B)') + assert tt_true('((A & B) & C) <=> (A & (B & C))') + assert tt_true('((A | B) | C) <=> (A | (B | C))') + assert tt_true('(A >> B) <=> (~B >> ~A)') + assert tt_true('(A >> B) <=> (~A | B)') + assert tt_true('(A <=> B) <=> ((A >> B) & (B >> A))') + assert tt_true('~(A & B) <=> (~A | ~B)') + assert tt_true('~(A | B) <=> (~A & ~B)') + assert tt_true('(A & (B | C)) <=> ((A & B) | (A & C))') + assert tt_true('(A | (B & C)) <=> ((A | B) & (A | C))') + +def test_dpll(): + assert dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) == {B: False, C: True, A: True, F: False, D: True, E: False} #noqa + +def test_unify(): + assert unify(x, x, {}) == {} + assert unify(x, 3, {}) == {x: 3} + +def test_to_cnf(): + #assert to_cnf(Fig[7, 13] & ~expr('~P12')) BUG - FAILING THIS TEST DUE TO AN ERROR + assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' + pass + +def test_pl_fc_entails(): + assert pl_fc_entails(Fig[7,15], expr('Q')) + assert not pl_fc_entails(Fig[7,15], expr('SomethingSilly')) + +def tt_entails(): + assert tt_entails(P & Q, Q) + assert not tt_entails(P | Q, Q) + assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) + assert tt_entails(Fig[7,13], alpha) + + +if __name__ == '__main__': + pytest.main() From 406bef8ac876bd132fa35e137951df7ece348c57 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 23:12:27 +0530 Subject: [PATCH 138/513] added all the pseudo codes in 3rd edition to index in readme.md --- README.md | 81 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 43 insertions(+), 38 deletions(-) diff --git a/README.md b/README.md index 45cee6cde..0c617fd81 100644 --- a/README.md +++ b/README.md @@ -31,75 +31,79 @@ Here is a table of algorithms, the figure and page where they appear in the book | 2.8 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | | 2.10 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | | 2.12 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | -| 3.1 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | | 3 | Problem | `Problem` | [`search.py`](../master/search.py) | -| 3.2 | Romania | `romania` | [`search.py`](../master/search.py) | | 3 | Node | `Node` | [`search.py`](../master/search.py) | | 3 | Queue | `Queue` | [`utils.py`](../master/utils.py) | +| 3.1 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | +| 3.2 | Romania | `romania` | [`search.py`](../master/search.py) | | 3.7 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | | 3.7 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | | 3.11 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | | 3.14 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | | 3.17 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | | 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | -| 3.19 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | -| 4 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | -| 4 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | +| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | +| 3.24 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | | 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | | 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | | 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | | 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | +| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | | 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.24 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | -| 5 | CSP | `CSP` | [`csp.py`](../master/csp.py) | +| 4.24 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | | 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | | 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | +| 6 | CSP | `CSP` | [`csp.py`](../master/csp.py) | +| 6.3 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | +| 6.5 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | +| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | +| 6.11 | Tree-CSP-Solver | `tree_csp_solver` | [`csp.py`](../master/csp.py) | | 7 | KB | `KB` | [`logic.py`](../master/logic.py) | -| 6.1 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | -| 6.7 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | -| 6.10 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | -| 7 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | -| 6.12 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | -| 6.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | -| 6.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | -| 6.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | -| 6.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | -| 6.22 | SATPlan | | -| 7.3 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | -| 7.5 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | -| 7.8 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | +| 7.1 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | +| 7.7 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | +| 7.10 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | +| 7.12 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | +| 7.14 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | +| 7.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | +| 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | +| 7.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | +| 7.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | +| 7.22 | SATPlan | | | 9 | Subst | `subst` | [`logic.py`](../master/logic.py) | | 9.1 | Unify | `unify` | [`logic.py`](../master/logic.py) | | 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | | 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | -| 9.14 | Otter | | +| 9.8 | Append | | | | 10.1 | Air-Cargo-problem | | | 10.2 | Spare-Tire-Problem | | | 10.3 | Three-Block-Tower | | -| 11 | Partial-Order-Planner | | | 10.7 | Cake-Problem | | | 10.9 | Graphplan | | -| 12.1 | Job-Shop-Problem | | +| 10.13 | Partial-Order-Planner | | | 11.1 | Job-Shop-Problem-With-Resources | | -| 12.6 | House-Building-Problem | | -| 12.10 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | -| 12.22 | Continuous-POP-Agent | | -| 12.23 | Doubles-tennis | | -| 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | +| 11.5 | Hierarchical-Search | | +| 11.8 | Angelic-Search | | +| \*12.6 | House-Building-Problem | | +| \*12.22 | Continuous-POP-Agent | | +| 11.10 | Doubles-tennis | | | 13 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | -| 13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | +| 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | +| \*13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | +| 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`](../master/probability.py) | | 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | | 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | | 14.14 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | | 14.15 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | -| 14.15 | MCMC-Ask | | +| 14.16 | Gibbs-Ask | | | 15.4 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | | 15.6 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | | 15.17 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | | 16.9 | Information-Gathering-Agent | | | 17.4 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | | 17.7 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | +| 17.7 | POMDP-Value-Iteration | | | | 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | +| 18.8 | Cross-Validation | `cross_validation` | [`learning.py`](../master/learning.py) | | 18.11 | Decision-List-Learning | | | 18.24 | Back-Prop-Learning | | | 18.34 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | @@ -107,14 +111,15 @@ Here is a table of algorithms, the figure and page where they appear in the book | 19.3 | Version-Space-Learning | | | 19.8 | Minimal-Consistent-Det | | | 19.12 | FOIL | | -| 20.21 | Perceptron-Learning | `PerceptronLearner` | [`learning.py`](../master/learning.py) | -| 22.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | -| 22.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | -| 22.8 | Q-Learning-Agent | | -| 22.2 | Naive-Communicating-Agent | | -| 22.7 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | -| 23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | -| 24.21 | Align | | +| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | +| 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | +| 21.8 | Q-Learning-Agent | | +| \*21.2 | Naive-Communicating-Agent | | +| 22.1 | HITS | | | +| 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | +| 23.5 | CYK-Parse | | | +| \*23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | +| \*24.21 | Align | | | 25.9 | Monte-Carlo-Localization| | From 4c64a2c3007fffa7c9630a8d7ca0dae530eb805f Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 16 Mar 2016 23:49:54 +0530 Subject: [PATCH 139/513] adds tests for element_wise_product and forward_backward --- probability.py | 25 +++++++++++++++---------- tests/test_probability.py | 16 ++++++++++++++++ tests/test_utils.py | 7 +++++-- 3 files changed, 36 insertions(+), 12 deletions(-) diff --git a/probability.py b/probability.py index 1a22db4d9..b7619036f 100644 --- a/probability.py +++ b/probability.py @@ -524,15 +524,7 @@ def markov_blanket_sample(X, e, bn): # _________________________________________________________________________ -""" -umbrella_evidence = [T, T, F, T, T] -umbrella_prior = [0.5, 0.5] -umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] -umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] -umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) - -print(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) -""" +# Umbrella Example [Fig. 15.2] class HiddenMarkovModel: @@ -568,7 +560,19 @@ def backward(HMM, b, ev): def forward_backward(HMM, ev, prior): - """[Fig. 15.4]""" + """[Fig. 15.4] + Forward-Backward algorithm for smoothing. Computes posterior probabilities + of a sequence of states given a sequence of observations. + + umbrella_evidence = [T, T, F, T, T] + umbrella_prior = [0.5, 0.5] + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + >>> forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) + [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] + """ t = len(ev) ev.insert(0, None) # to make the code look similar to pseudo code @@ -587,6 +591,7 @@ def forward_backward(HMM, ev, prior): bv.append(b) sv = sv[::-1] + # to have only 4 digits after decimal point for i in range(len(sv)): for j in range(len(sv[i])): sv[i][j] = float("{0:.4f}".format(sv[i][j])) diff --git a/tests/test_probability.py b/tests/test_probability.py index 40bdca660..04791d835 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -102,5 +102,21 @@ def test_likelihood_weighting(): 'Burglary', dict(JohnCalls=T, MaryCalls=T), burglary, 10000).show_approx() == 'False: 0.702, True: 0.298' + +def test_forward_backward(): + umbrella_prior = [0.5, 0.5] + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + umbrella_evidence = [T, T, F, T, T] + assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.6469, 0.3531], + [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] + + umbrella_evidence = [T, F, T, F, T] + assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.5871, 0.4129], + [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + + if __name__ == '__main__': pytest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py index d8ed78de9..dbfcba3b5 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -15,8 +15,7 @@ def test_removeall_list(): def test_removeall_string(): assert removeall('s', '') == '' - assert removeall( - 's', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' + assert removeall('s', 'This is a test. Was a test.') == 'Thi i a tet. Wa a tet.' def test_unique(): @@ -86,6 +85,10 @@ def test_histogram(): def test_dotproduct(): assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 +def test_element_wise_product(): + assert element_wise_product([1, 2, 5], [7, 10, 0]) == [7, 20, 0] + assert element_wise_product([1, 6, 3, 0], [9, 12, 0, 0]) == [9, 72, 0, 0] + def test_vector_add(): assert vector_add((0, 1), (8, 9)) == (8, 10) From d1e934858871df541e95c740fe32775ef25110d6 Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Thu, 17 Mar 2016 05:04:07 +0530 Subject: [PATCH 140/513] adds multivariate linear reg --- learning.py | 42 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 39 insertions(+), 3 deletions(-) diff --git a/learning.py b/learning.py index b964911e8..d491cf0a5 100644 --- a/learning.py +++ b/learning.py @@ -603,9 +603,45 @@ def predict(example): # ______________________________________________________________________________ -def Linearlearner(dataset): - """Fit a linear model to the data.""" - unimplemented() +def Linearlearner(dataset, learning_rate=0.01, epochs=100): + """ + >>> learner = Linearlearner(data) + >>> learner(x) + y + """ + idx_i = dataset.inputs + idx_t = dataset.target # As of now, dataset.target gives only one index. + examples = dataset.examples + + # X transpose + X_col = [dataset.values[i] for i in idx_i] # vertical columns of X + + # Add dummy + ones = [1 for i in range(len(examples))] + X_col = ones + X_col + + # Initialize random weigts + w = [random(-0.5, 0.5) for i in range(len(idx_i) + 1)] + + for epoch in range(epochs): + err = [] + # Pass over all examples + for example in examples: + x = [example[i] for i in range(idx_i)] + x = [1] + x + y = dotproduct(w, x) + t = example[idx_t] + err.append(t - y) + + # update weights + for i in range(len(w)): + w[i] = w[i] - dotproduct(err, X_col[i]) + + def predict(example): + x = [1] + example + return dotproduct(w, x) + return predict + # ______________________________________________________________________________ From 6bf771723a500dfc3d45e9f3fc676e2491ce20e5 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Thu, 17 Mar 2016 22:06:26 +0530 Subject: [PATCH 141/513] Fixed wrong name in test_logic --- logic.py | 6 ++++-- tests/test_logic.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index a5df00237..ade9ba207 100644 --- a/logic.py +++ b/logic.py @@ -54,7 +54,7 @@ def tell(self, sentence): raise NotImplementedError def ask(self, query): - """Return a substitution that makes the query true, or, + """Return ma substitution that makes the query true, or, failing that, return False.""" for result in self.ask_generator(query): return result @@ -130,7 +130,7 @@ class Expr: A number, representing the number itself. (e.g. Expr(42) => 42) A symbol, representing a variable or constant (e.g. Expr('F') => F) Unary (1 arg) op: - '~', '-', representing NOT, negation (e.g. Expr('~', Expr('P')) => ~P) + '~', '-', representing NOT, negation (e.g. Expr('~', Expr('P')) => ~P) Binary (2 arg) op: '>>', '<<', representing forward and backward implication '+', '-', '*', '/', '**', representing arithmetic operators @@ -214,6 +214,8 @@ def __radd__(self, other): return Expr('+', other, self) def __sub__(self, other): return Expr('-', self, other) + def __rsub__(self, other): return Expr('-', other, self) + def __and__(self, other): return Expr('&', self, other) def __div__(self, other): return Expr('/', self, other) diff --git a/tests/test_logic.py b/tests/test_logic.py index 5aae637a9..a7a145aaf 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -61,7 +61,7 @@ def test_pl_fc_entails(): assert pl_fc_entails(Fig[7,15], expr('Q')) assert not pl_fc_entails(Fig[7,15], expr('SomethingSilly')) -def tt_entails(): +def test_tt_entails(): assert tt_entails(P & Q, Q) assert not tt_entails(P | Q, Q) assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) From e03f8d788c7faac6467d7dec470b796a56a234e4 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Thu, 17 Mar 2016 22:15:06 +0530 Subject: [PATCH 142/513] added a new test and fixed an old test --- logic.py | 4 ++-- tests/test_logic.py | 5 ++++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index ade9ba207..110235cfa 100644 --- a/logic.py +++ b/logic.py @@ -54,7 +54,7 @@ def tell(self, sentence): raise NotImplementedError def ask(self, query): - """Return ma substitution that makes the query true, or, + """Return a substitution that makes the query true, or, failing that, return False.""" for result in self.ask_generator(query): return result @@ -130,7 +130,7 @@ class Expr: A number, representing the number itself. (e.g. Expr(42) => 42) A symbol, representing a variable or constant (e.g. Expr('F') => F) Unary (1 arg) op: - '~', '-', representing NOT, negation (e.g. Expr('~', Expr('P')) => ~P) + '~', '-', representing NOT, negation (e.g. Expr('~', Expr('P')) => ~P) Binary (2 arg) op: '>>', '<<', representing forward and backward implication '+', '-', '*', '/', '**', representing arithmetic operators diff --git a/tests/test_logic.py b/tests/test_logic.py index a7a145aaf..7d7ebeda2 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -65,7 +65,10 @@ def test_tt_entails(): assert tt_entails(P & Q, Q) assert not tt_entails(P | Q, Q) assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) - assert tt_entails(Fig[7,13], alpha) + +def test_eliminate_implications(): + assert repr(eliminate_implications(A >> (~B << C))) == '((~B | ~C) | ~A)' + assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' if __name__ == '__main__': From 7455357993019c11e5b465d6595c1a19e5635c16 Mon Sep 17 00:00:00 2001 From: Mort Yao Date: Thu, 17 Mar 2016 19:56:28 +0100 Subject: [PATCH 143/513] Fix a few typos in README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0c617fd81..439e1d579 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ When complete, this project will have Python code for all the pseudocode algorit # Index of Code # -Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an iassue. +Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an issue. | **Fig** | **Name (in 3rd edition)** | **Name (in code)** | **File** @@ -125,4 +125,4 @@ Here is a table of algorithms, the figure and page where they appear in the book # Acknowledgements -Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is in Githib, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Thanks to all! +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Thanks to all! From 12a4e92bbf5da0f764cdd6162121d3519d5bc474 Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Fri, 18 Mar 2016 02:56:13 +0530 Subject: [PATCH 144/513] Particle filtering --- probability.py | 69 +++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 66 insertions(+), 3 deletions(-) diff --git a/probability.py b/probability.py index b7619036f..02fd71b39 100644 --- a/probability.py +++ b/probability.py @@ -605,9 +605,72 @@ def fixed_lag_smoothing(e_t, hmm, d): unimplemented() -def particle_filtering(e, N, dbn): - """[Fig. 15.17]""" - unimplemented() +def particle_filtering(e, N, HMM): + """ + Particle filtering considering two states variables + N = 10 + umbrella_evidence = T + umbrella_prior = [0.5, 0.5] + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + >>> particle_filtering(umbrella_evidence, N, umbrellaHMM) + ['A', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'B'] + + NOTE: Output is an probabilistic answer, therfore can vary + """ + s = [] + dist = [0.5, 0.5] + # State Initialization + s = ['A' if probability(dist[0]) else 'B' for i in range(N)] + # Weight Initialization + w = [0 for i in range(N)] + # STEP 1 + # Propagate one step using transition model given prior state + dist = vector_add(scalar_vector_product(dist[0], HMM.transition_model[0]), + scalar_vector_product(dist[1], HMM.transition_model[1])) + # Assign state according to probability + s = ['A' if probability(dist[0]) else 'B' for i in range(N)] + w_tot = 0 + # Calculate importance weight given evidence e + for i in range(N): + if s[i] == 'A': + # P(U|A)*P(A) + w_i = HMM.sensor_dist(e)[0]*dist[0] + if s[i] == 'B': + # P(U|B)*P(B) + w_i = HMM.sensor_dist(e)[1]*dist[1] + w[i] = w_i + w_tot += w_i + + # Normalize all the weights + for i in range(N): + w[i] = w[i]/w_tot + + # Limit weights to 4 digits + for i in range(N): + w[i] = float("{0:.4f}".format(w[i])) + + # STEP 2 + s = weighted_sample_with_replacement(N, s, w) + return s + + +def weighted_sample_with_replacement(N, s, w): + """ + Performs Weighted sampling over the paricles given weights of each particle. + We keep on picking random states unitll we fill N number states in new distribution + """ + s_wtd = [] + cnt = 0 + while (cnt <= N): + # Generate a random number from 0 to N-1 + i = random.randint(0, N-1) + if (probability(w[i])): + s_wtd.append(s[i]) + cnt += 1 + return s_wtd # _________________________________________________________________________ __doc__ += """ From a2449928e47ae454b7f6785521c93f7652cc5466 Mon Sep 17 00:00:00 2001 From: Sidharth Sindhra Date: Fri, 18 Mar 2016 03:14:18 +0530 Subject: [PATCH 145/513] pep8 compliance --- probability.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/probability.py b/probability.py index 02fd71b39..21d93bb14 100644 --- a/probability.py +++ b/probability.py @@ -526,6 +526,7 @@ def markov_blanket_sample(X, e, bn): # Umbrella Example [Fig. 15.2] + class HiddenMarkovModel: """ A Hidden markov model which takes Transition model and Sensor model as inputs""" @@ -546,17 +547,18 @@ def sensor_dist(self, ev): def forward(HMM, fv, ev): prediction = vector_add(scalar_vector_product(fv[0], HMM.transition_model[0]), - scalar_vector_product(fv[1], HMM.transition_model[1])) + scalar_vector_product(fv[1], HMM.transition_model[1])) sensor_dist = HMM.sensor_dist(ev) return(normalize(element_wise_product(sensor_dist, prediction))) + def backward(HMM, b, ev): sensor_dist = HMM.sensor_dist(ev) prediction = element_wise_product(sensor_dist, b) return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), - scalar_vector_product(prediction[1], HMM.transition_model[1])))) + scalar_vector_product(prediction[1], HMM.transition_model[1])))) def forward_backward(HMM, ev, prior): @@ -571,7 +573,8 @@ def forward_backward(HMM, ev, prior): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) >>> forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) - [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] + [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], + [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] """ t = len(ev) ev.insert(0, None) # to make the code look similar to pseudo code @@ -583,10 +586,10 @@ def forward_backward(HMM, ev, prior): fv[0] = prior - for i in range(1, t+ 1): - fv[i] = forward(HMM, fv[i- 1], ev[i]) + for i in range(1, t + 1): + fv[i] = forward(HMM, fv[i - 1], ev[i]) for i in range(t, -1, -1): - sv[i- 1] = normalize(element_wise_product(fv[i], b)) + sv[i - 1] = normalize(element_wise_product(fv[i], b)) b = backward(HMM, b, ev[i]) bv.append(b) @@ -600,6 +603,7 @@ def forward_backward(HMM, ev, prior): # _________________________________________________________________________ + def fixed_lag_smoothing(e_t, hmm, d): """[Fig. 15.6]""" unimplemented() @@ -617,7 +621,7 @@ def particle_filtering(e, N, HMM): >>> particle_filtering(umbrella_evidence, N, umbrellaHMM) ['A', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'B'] - + NOTE: Output is an probabilistic answer, therfore can vary """ s = [] @@ -629,9 +633,9 @@ def particle_filtering(e, N, HMM): # STEP 1 # Propagate one step using transition model given prior state dist = vector_add(scalar_vector_product(dist[0], HMM.transition_model[0]), - scalar_vector_product(dist[1], HMM.transition_model[1])) + scalar_vector_product(dist[1], HMM.transition_model[1])) # Assign state according to probability - s = ['A' if probability(dist[0]) else 'B' for i in range(N)] + s = ['A' if probability(dist[0]) else 'B' for i in range(N)] w_tot = 0 # Calculate importance weight given evidence e for i in range(N): @@ -643,7 +647,7 @@ def particle_filtering(e, N, HMM): w_i = HMM.sensor_dist(e)[1]*dist[1] w[i] = w_i w_tot += w_i - + # Normalize all the weights for i in range(N): w[i] = w[i]/w_tot @@ -656,14 +660,14 @@ def particle_filtering(e, N, HMM): s = weighted_sample_with_replacement(N, s, w) return s - + def weighted_sample_with_replacement(N, s, w): """ Performs Weighted sampling over the paricles given weights of each particle. We keep on picking random states unitll we fill N number states in new distribution """ s_wtd = [] - cnt = 0 + cnt = 0 while (cnt <= N): # Generate a random number from 0 to N-1 i = random.randint(0, N-1) From a38098f4d4f7e7c20e21fb8924398dcd95d5dfb1 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Fri, 18 Mar 2016 14:20:36 +0530 Subject: [PATCH 146/513] Fixed bug in logic.py and added more tests --- logic.py | 2 +- tests/test_logic.py | 19 ++++++++++++++----- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/logic.py b/logic.py index 110235cfa..224051ae0 100644 --- a/logic.py +++ b/logic.py @@ -504,7 +504,7 @@ def move_not_inwards(s): ((A | ~B) & ~C) """ if s.op == '~': - def NOT(b): move_not_inwards(~b) # noqa + def NOT(b): return move_not_inwards(~b) # noqa a = s.args[0] if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A diff --git a/tests/test_logic.py b/tests/test_logic.py index 7d7ebeda2..cf5a527f2 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -52,11 +52,6 @@ def test_unify(): assert unify(x, x, {}) == {} assert unify(x, 3, {}) == {x: 3} -def test_to_cnf(): - #assert to_cnf(Fig[7, 13] & ~expr('~P12')) BUG - FAILING THIS TEST DUE TO AN ERROR - assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' - pass - def test_pl_fc_entails(): assert pl_fc_entails(Fig[7,15], expr('Q')) assert not pl_fc_entails(Fig[7,15], expr('SomethingSilly')) @@ -69,6 +64,20 @@ def test_tt_entails(): def test_eliminate_implications(): assert repr(eliminate_implications(A >> (~B << C))) == '((~B | ~C) | ~A)' assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' + assert repr(eliminate_implications(A & B | C & ~D)) == '((A & B) | (C & ~D))' + +def test_associate(): + assert repr(associate('&', [(A&B),(B|C),(B&C)])) == '(A & B & (B | C) & B & C)' + assert repr(associate('|', [A|(B|(C|(A&B)))])) == '(A | B | C | (A & B))' + +def test_move_not_inwards(): + assert repr(move_not_inwards(~(A | B))) == '(~A & ~B)' + assert repr(move_not_inwards(~(A & B))) == '(~A | ~B)' + assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' + +def test_to_cnf(): + assert repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == '((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)' + assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' if __name__ == '__main__': From bdeab0c9fd5177ea549c638eb352845b5ab0ea2b Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Fri, 18 Mar 2016 15:23:58 +0530 Subject: [PATCH 147/513] replaced old undefined function and added new test --- logic.py | 11 +---------- tests/test_logic.py | 21 ++++++++++++++++++++- 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/logic.py b/logic.py index 224051ae0..01c101674 100644 --- a/logic.py +++ b/logic.py @@ -920,7 +920,7 @@ def occur_check(var, x, s): return (occur_check(var, x.op, s) or occur_check(var, x.args, s)) elif isinstance(x, (list, tuple)): - return some(lambda element: occur_check(var, element, s), x) + return first([e for e in x if occur_check(var, e, s)]) else: return False @@ -1025,15 +1025,6 @@ def retract(self, sentence): def fetch_rules_for_goal(self, goal): return self.clauses -""" TODO Rename test_ask to remove test from the name(or tell pytest to ignore it) -def test_ask(query, kb=None): - q = expr(query) - vars = variables(q) - answers = fol_bc_ask(kb or test_kb, q) - return sorted( - [dict((x, v) for x, v in list(a.items()) if x in vars) - for a in answers], key=repr) -""" test_kb = FolKB( list(map(expr, ['Farmer(Mac)', diff --git a/tests/test_logic.py b/tests/test_logic.py index cf5a527f2..f680ebbb5 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -66,6 +66,11 @@ def test_eliminate_implications(): assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' assert repr(eliminate_implications(A & B | C & ~D)) == '((A & B) | (C & ~D))' +def test_dissociate(): + assert dissociate('&', [A & B]) == [A, B] + assert dissociate('|', [A, B, C & D, P | Q]) == [A, B, C & D, P, Q] + assert dissociate('&', [A, B, C & D, P | Q]) == [A, B, C, D, P | Q] + def test_associate(): assert repr(associate('&', [(A&B),(B|C),(B&C)])) == '(A & B & (B | C) & B & C)' assert repr(associate('|', [A|(B|(C|(A&B)))])) == '(A | B | C | (A & B))' @@ -76,9 +81,23 @@ def test_move_not_inwards(): assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' def test_to_cnf(): - assert repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == '((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)' + assert repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == \ + "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)" assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' +def test_fol_bc_ask(): + def test_ask(query, kb=None): + q = expr(query) + vars = variables(q) + answers = fol_bc_ask(kb or test_kb, q) + return sorted( + [dict((x, v) for x, v in list(a.items()) if x in vars) + for a in answers], key=repr) + assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' + assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' + assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' + assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' + if __name__ == '__main__': pytest.main() From cf1db88e61ab8bef59a004065dbc99f003e5514f Mon Sep 17 00:00:00 2001 From: norvig Date: Fri, 18 Mar 2016 11:28:59 -0700 Subject: [PATCH 148/513] Update logic.py --- logic.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/logic.py b/logic.py index 01c101674..74f7f5589 100644 --- a/logic.py +++ b/logic.py @@ -56,9 +56,7 @@ def tell(self, sentence): def ask(self, query): """Return a substitution that makes the query true, or, failing that, return False.""" - for result in self.ask_generator(query): - return result - return False + for first(self.ask_generator(query), default=False) def ask_generator(self, query): "Yield all the substitutions that make query true." @@ -920,7 +918,7 @@ def occur_check(var, x, s): return (occur_check(var, x.op, s) or occur_check(var, x.args, s)) elif isinstance(x, (list, tuple)): - return first([e for e in x if occur_check(var, e, s)]) + return first(e for e in x if occur_check(var, e, s)) else: return False From b02a583a8c9b5420b64471d9099b1f778e60e5db Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 19 Mar 2016 11:46:59 +0530 Subject: [PATCH 149/513] cleaned the outputs of games notebook and modified games.py --- games.ipynb | 368 +++++++--------------------------------------------- games.py | 9 +- 2 files changed, 53 insertions(+), 324 deletions(-) diff --git a/games.ipynb b/games.ipynb index ab333022a..5ee06a113 100644 --- a/games.ipynb +++ b/games.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, @@ -227,44 +227,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a2'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(game52, 'A')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(game52, 'A')" ] @@ -278,21 +256,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a1\n", - "b1\n", - "c1\n" - ] - } - ], + "outputs": [], "source": [ "print( alphabeta_player(game52, 'A') )\n", "print( alphabeta_player(game52, 'B') )\n", @@ -308,44 +276,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "minimax_decision('A', game52)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alphabeta_full_search('A', game52)" ] @@ -359,9 +305,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -377,21 +323,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ". . . \n", - ". . . \n", - ". . . \n" - ] - } - ], + "outputs": [], "source": [ "ttt.display(ttt.initial)" ] @@ -407,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, @@ -433,21 +369,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X O X \n", - "O . O \n", - "X . . \n" - ] - } - ], + "outputs": [], "source": [ "ttt.display(my_state)" ] @@ -461,44 +387,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 3)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(ttt, my_state)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 2)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(ttt, my_state)" ] @@ -512,22 +416,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 2)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alphabeta_player(ttt, my_state)" ] @@ -541,22 +434,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(ttt, alphabeta_player, random_player)" ] @@ -572,28 +454,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "0\n", - "0\n", - "0\n", - "0\n", - "0\n", - "0\n", - "0\n", - "0\n", - "0\n" - ] - } - ], + "outputs": [], "source": [ "for _ in range(10):\n", " print(play_game(ttt, alphabeta_player, alphabeta_player))" @@ -608,28 +473,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-1\n", - "-1\n", - "-1\n", - "0\n", - "-1\n", - "0\n", - "-1\n", - "0\n", - "-1\n", - "-1\n" - ] - } - ], + "outputs": [], "source": [ "for _ in range(10):\n", " print(play_game(ttt, random_player, alphabeta_player))" @@ -644,40 +492,11 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ". . . \n", - ". . . \n", - ". . . \n", - "Your move? (3,1)\n", - ". . . \n", - ". . O \n", - "X . . \n", - "Your move? (2,2)\n", - ". . . \n", - ". X O \n", - "X O . \n", - "Your move? (1,3)\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(ttt, query_player, random_player)" ] @@ -691,48 +510,11 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ". . . \n", - ". . . \n", - ". . . \n", - "Your move? (1,1)\n", - "X . . \n", - ". O . \n", - ". . . \n", - "Your move? (3,3)\n", - "X O . \n", - ". O . \n", - ". . X \n", - "Your move? (3,2)\n", - "X O . \n", - ". O . \n", - "O X X \n", - "Your move? (1,3)\n", - "X O X \n", - ". O O \n", - "O X X \n", - "Your move? (2,1)\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(ttt, query_player, alphabeta_player)" ] @@ -746,104 +528,44 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, alphabeta_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, alphabeta_player, random_player)" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A\n", - "Your move? a3\n" - ] - }, - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, query_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "B\n", - "Your move? b2\n" - ] - }, - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, alphabeta_player, query_player)" ] @@ -872,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.4" + "version": "3.5.0" } }, "nbformat": 4, diff --git a/games.py b/games.py index ba842e2f5..689caaa27 100644 --- a/games.py +++ b/games.py @@ -135,7 +135,7 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): "Make a move by querying standard input." - game.display(state) + # game.display(state) move_string = input('Your move? ') try: move = eval(move_string) @@ -157,11 +157,18 @@ def play_game(game, *players): """Play an n-person, move-alternating game.""" state = game.initial + print("Initial state:") + game.display(state) while True: for player in players: move = player(game, state) state = game.result(state, move) + print("State after %s's move:" % player.__name__) + game.display(state) if game.terminal_test(state): + print("\nGame's over!") + print("Final state:") + game.display(state) return game.utility(state, game.to_move(game.initial)) # ______________________________________________________________________________ From a4b688e8043b79023f2c8cfb4628104d2a6c235f Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 19 Mar 2016 11:49:54 +0530 Subject: [PATCH 150/513] fixed a typo in logic.py --- logic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logic.py b/logic.py index 74f7f5589..7b196db3e 100644 --- a/logic.py +++ b/logic.py @@ -56,7 +56,7 @@ def tell(self, sentence): def ask(self, query): """Return a substitution that makes the query true, or, failing that, return False.""" - for first(self.ask_generator(query), default=False) + return first(self.ask_generator(query), default=False) def ask_generator(self, query): "Yield all the substitutions that make query true." From 78051e02aaf3fe34bfe76812ddbd9a5d9925388c Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 19 Mar 2016 13:45:56 +0530 Subject: [PATCH 151/513] cleared the outputs in all notebooks --- agents.ipynb | 2 +- csp.ipynb | 2 +- games.ipynb | 2 +- grid.ipynb | 12 ++--------- intro.ipynb | 2 +- learning.ipynb | 2 +- logic.ipynb | 2 +- mdp.ipynb | 2 +- nlp.ipynb | 2 +- planning.ipynb | 2 +- probability.ipynb | 2 +- rl.ipynb | 2 +- search.ipynb | 53 +++++++++-------------------------------------- text.ipynb | 2 +- 14 files changed, 24 insertions(+), 65 deletions(-) diff --git a/agents.ipynb b/agents.ipynb index ee6807c1d..e6e185e06 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/csp.ipynb b/csp.ipynb index d82e1a6fd..0d2aa513a 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/games.ipynb b/games.ipynb index 5ee06a113..3aec144ab 100644 --- a/games.ipynb +++ b/games.ipynb @@ -594,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/grid.ipynb b/grid.ipynb index fe4e11a77..4e3bbd7e5 100644 --- a/grid.ipynb +++ b/grid.ipynb @@ -2,19 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25\n" - ] - } - ], + "outputs": [], "source": [ "import grid\n", "\n", diff --git a/intro.ipynb b/intro.ipynb index 1b2b3663c..ce9020e95 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -55,7 +55,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.4" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/learning.ipynb b/learning.ipynb index 90d7746f7..4798f2914 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/logic.ipynb b/logic.ipynb index a3f85d38b..79595ac5d 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/mdp.ipynb b/mdp.ipynb index d40f6030c..ed0bd9783 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/nlp.ipynb b/nlp.ipynb index 70bf2d974..1a2da9488 100644 --- a/nlp.ipynb +++ b/nlp.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/planning.ipynb b/planning.ipynb index 0c04c559b..d5a5eb25d 100644 --- a/planning.ipynb +++ b/planning.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/probability.ipynb b/probability.ipynb index 48de9e035..446fc11fb 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/rl.ipynb b/rl.ipynb index d68ea9693..005e7b7f9 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, diff --git a/search.ipynb b/search.ipynb index fb76f5ff4..18b22c045 100644 --- a/search.ipynb +++ b/search.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": true }, @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, @@ -149,22 +149,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Cat', 'Monkey']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bfs_node = breadth_first_search(monkey_problem)\n", "bfs_node.solution()" @@ -193,22 +182,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Dog', 'Bear', 'Monkey']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ucs_node = uniform_cost_search(monkey_problem)\n", "ucs_node.solution()" @@ -223,22 +201,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(18, 17)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bfs_node.path_cost, ucs_node.path_cost" ] @@ -267,7 +234,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/text.ipynb b/text.ipynb index a7b29bc44..37e4d0b63 100644 --- a/text.ipynb +++ b/text.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, From 7e65653c3d004f588d09c8cfa17e13dcf49fbbbc Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 19 Mar 2016 20:02:23 +0530 Subject: [PATCH 152/513] implemented matrix multiplication in utils.py and added tests --- logic.py | 2 +- tests/test_utils.py | 17 +++++++++++++++++ utils.py | 32 ++++++++++++++++++++++++++++++-- 3 files changed, 48 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index 74f7f5589..7b196db3e 100644 --- a/logic.py +++ b/logic.py @@ -56,7 +56,7 @@ def tell(self, sentence): def ask(self, query): """Return a substitution that makes the query true, or, failing that, return False.""" - for first(self.ask_generator(query), default=False) + return first(self.ask_generator(query), default=False) def ask_generator(self, query): "Yield all the substitutions that make query true." diff --git a/tests/test_utils.py b/tests/test_utils.py index a453045ed..a881b2aeb 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -89,6 +89,23 @@ def test_element_wise_product(): assert element_wise_product([1, 2, 5], [7, 10, 0]) == [7, 20, 0] assert element_wise_product([1, 6, 3, 0], [9, 12, 0, 0]) == [9, 72, 0, 0] +def test_matrix_multiplication(): + assert matrix_multiplication([[1, 2, 3], + [2, 3, 4]], + [[3, 4], + [1, 2], + [1, 0]]) == [[8, 8],[13, 14]] + + assert matrix_multiplication([[1, 2, 3], + [2, 3, 4]], + [[3, 4, 8, 1], + [1, 2, 5, 0], + [1, 0, 0, 3]], + [[1,2], + [3,4], + [5,6], + [1,2]]) == [[132, 176], [224, 296]] + def test_vector_add(): assert vector_add((0, 1), (8, 9)) == (8, 10) diff --git a/utils.py b/utils.py index 4c4d5950e..d44eecae5 100644 --- a/utils.py +++ b/utils.py @@ -163,15 +163,43 @@ def histogram(values, mode=0, bin_function=None): def dotproduct(X, Y): - """Return the sum of the element-wise product of vectors x and y.""" + """Return the sum of the element-wise product of vectors X and Y.""" return sum(x * y for x, y in zip(X, Y)) def element_wise_product(X, Y): - """Return vector as an element-wise product of vectors x and y""" + """Return vector as an element-wise product of vectors X and Y""" assert len(X) == len(Y) return(list(x * y for x, y in zip(X, Y))) +def _mat_mult(X_M, Y_M): + """Return a matrix as a matrix-multiplication of two matrices X_M and Y_M + >>> matrix_multiplication([[1, 2, 3], + [2, 3, 4]], + [[3, 4], + [1, 2], + [1, 0]]) + [[8, 8],[13, 14]] + """ + assert len(X_M[0]) == len(Y_M) + + result = [[0 for i in range(len(Y_M[0]))] for j in range(len(X_M))] + for i in range(len(X_M)): + for j in range(len(Y_M[0])): + for k in range(len(Y_M)): + result[i][j] += X_M[i][k] * Y_M[k][j] + + return(result) + + +def matrix_multiplication(X_M, *Y_M): + """Return a matrix as a matrix-multiplication of X_M and arbitary number of matrices *Y_M""" + result = X_M + for Y in Y_M: + result = _mat_mult(result, Y) + + return(result) + def vector_add(a, b): """Component-wise addition of two vectors.""" From 96e8e8e33fd434e2fb3fb7ad07d0ec6922adb272 Mon Sep 17 00:00:00 2001 From: SnShine Date: Sat, 19 Mar 2016 21:04:19 +0530 Subject: [PATCH 153/513] moved _mat_mult function into matrix_multiplication --- utils.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/utils.py b/utils.py index d44eecae5..429694ff4 100644 --- a/utils.py +++ b/utils.py @@ -172,28 +172,28 @@ def element_wise_product(X, Y): assert len(X) == len(Y) return(list(x * y for x, y in zip(X, Y))) -def _mat_mult(X_M, Y_M): - """Return a matrix as a matrix-multiplication of two matrices X_M and Y_M - >>> matrix_multiplication([[1, 2, 3], - [2, 3, 4]], - [[3, 4], - [1, 2], - [1, 0]]) - [[8, 8],[13, 14]] - """ - assert len(X_M[0]) == len(Y_M) - result = [[0 for i in range(len(Y_M[0]))] for j in range(len(X_M))] - for i in range(len(X_M)): - for j in range(len(Y_M[0])): - for k in range(len(Y_M)): - result[i][j] += X_M[i][k] * Y_M[k][j] +def matrix_multiplication(X_M, *Y_M): + """Return a matrix as a matrix-multiplication of X_M and arbitary number of matrices *Y_M""" - return(result) + def _mat_mult(X_M, Y_M): + """Return a matrix as a matrix-multiplication of two matrices X_M and Y_M + >>> matrix_multiplication([[1, 2, 3], + [2, 3, 4]], + [[3, 4], + [1, 2], + [1, 0]]) + [[8, 8],[13, 14]] + """ + assert len(X_M[0]) == len(Y_M) + result = [[0 for i in range(len(Y_M[0]))] for j in range(len(X_M))] + for i in range(len(X_M)): + for j in range(len(Y_M[0])): + for k in range(len(Y_M)): + result[i][j] += X_M[i][k] * Y_M[k][j] + return(result) -def matrix_multiplication(X_M, *Y_M): - """Return a matrix as a matrix-multiplication of X_M and arbitary number of matrices *Y_M""" result = X_M for Y in Y_M: result = _mat_mult(result, Y) From 62473f71ed6b13e37534cd81ef42ae68956fdab9 Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 20 Mar 2016 00:53:21 +0530 Subject: [PATCH 154/513] added helper methods to inverse matrices to utils.py and tests --- tests/test_utils.py | 12 ++++++++++++ utils.py | 25 +++++++++++++++++++++++-- 2 files changed, 35 insertions(+), 2 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index a881b2aeb..8145d2ce8 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -105,6 +105,9 @@ def test_matrix_multiplication(): [3,4], [5,6], [1,2]]) == [[132, 176], [224, 296]] +def test_vector_to_diagonal(): + assert vector_to_diagonal([1, 2, 3]) == [[1, 0, 0], [0, 2, 0], [0, 0, 3]] + assert vector_to_diagonal([0, 3, 6]) == [[0, 0, 0], [0, 3, 0], [0, 0, 6]] def test_vector_add(): @@ -114,6 +117,15 @@ def test_vector_add(): def test_scalar_vector_product(): assert scalar_vector_product(2, [1, 2, 3]) == [2, 4, 6] +def test_scalar_matrix_product(): + assert scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]]) == [[-5, -10], [-15, -20], [0, -30]] + assert scalar_matrix_product(0.2, [[1, 2], [2, 3]]) == [[0.2, 0.4], [0.4, 0.6]] + + +def test_inverse_matrix(): + assert inverse_matrix([[1, 0], [0, 1]]) == [[1, 0], [0, 1]] + assert inverse_matrix([[2, 1], [4, 3]]) == [[1.5, -0.5], [-2.0, 1.0]] + assert inverse_matrix([[4, 7], [2, 6]]) == [[0.6, -0.7], [-0.2, 0.4]] def test_num_or_str(): assert num_or_str('42') == 42 diff --git a/utils.py b/utils.py index 429694ff4..f97e5f0b9 100644 --- a/utils.py +++ b/utils.py @@ -198,8 +198,16 @@ def _mat_mult(X_M, Y_M): for Y in Y_M: result = _mat_mult(result, Y) - return(result) + return([[float("{0:.4f}".format(i)) for i in row] for row in result]) +def vector_to_diagonal(v): + """Converts a vector to a diagonal matrix with vector elements + as the diagonal elements of the matrix""" + diag_matrix = [[0 for i in range(len(v))] for j in range(len(v))] + for i in range(len(v)): + diag_matrix[i][i] = v[i] + + return diag_matrix def vector_add(a, b): """Component-wise addition of two vectors.""" @@ -210,6 +218,19 @@ def scalar_vector_product(X, Y): """Return vector as a product of a scalar and a vector""" return [X*y for y in Y] +def scalar_matrix_product(X, Y): + return([[float("{0:.4f}".format(i)) for i in scalar_vector_product(X, y)] for y in Y]) + +def inverse_matrix(X): + """Inverse a given square matrix of size 2x2""" + assert len(X) == 2 + assert len(X[0]) == 2 + det = X[0][0] * X[1][1] - X[0][1] * X[1][0] + assert det != 0 + inv_mat = scalar_matrix_product(1.0/det, [[X[1][1], -X[0][1]], [-X[1][0], X[0][0]]]) + + return([[float("{0:.4f}".format(i)) for i in row] for row in inv_mat]) + def probability(p): "Return true with probability p." @@ -250,7 +271,7 @@ def num_or_str(x): def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) - return [n / total for n in numbers] + return([float("{0:.4f}".format(n / total)) for n in numbers]) def clip(x, lowest, highest): From 4363d02fb148ef290849b1606ec56ea9efa4740b Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 20 Mar 2016 00:53:56 +0530 Subject: [PATCH 155/513] implemented fixed_lag_smoothing in probability.py --- probability.py | 55 ++++++++++++++++++++++++++++++++------- tests/test_probability.py | 24 +++++++++++++++-- 2 files changed, 68 insertions(+), 11 deletions(-) diff --git a/probability.py b/probability.py index 21d93bb14..cd7ee2897 100644 --- a/probability.py +++ b/probability.py @@ -524,16 +524,15 @@ def markov_blanket_sample(X, e, bn): # _________________________________________________________________________ -# Umbrella Example [Fig. 15.2] - class HiddenMarkovModel: """ A Hidden markov model which takes Transition model and Sensor model as inputs""" - def __init__(self, transition_model, sensor_model): + def __init__(self, transition_model, sensor_model, prior= [0.5, 0.5]): self.transition_model = transition_model self.sensor_model = sensor_model + self.prior = prior def transition_model(self): return self.transition_model @@ -550,15 +549,16 @@ def forward(HMM, fv, ev): scalar_vector_product(fv[1], HMM.transition_model[1])) sensor_dist = HMM.sensor_dist(ev) - return(normalize(element_wise_product(sensor_dist, prediction))) + return([float("{0:.4f}".format(i)) for i in normalize(element_wise_product(sensor_dist, prediction))]) def backward(HMM, b, ev): sensor_dist = HMM.sensor_dist(ev) prediction = element_wise_product(sensor_dist, b) - return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), - scalar_vector_product(prediction[1], HMM.transition_model[1])))) + return([float("{0:.4f}".format(i)) for i in normalize(vector_add( + scalar_vector_product(prediction[0], HMM.transition_model[0]), + scalar_vector_product(prediction[1], HMM.transition_model[1])))]) def forward_backward(HMM, ev, prior): @@ -604,9 +604,46 @@ def forward_backward(HMM, ev, prior): # _________________________________________________________________________ -def fixed_lag_smoothing(e_t, hmm, d): - """[Fig. 15.6]""" - unimplemented() +def fixed_lag_smoothing(e_t, HMM, d, ev, t): + """[Fig. 15.6] + Smoothing algorithm with a fixed time lag of 'd' steps. + Online algorithm that outputs the new smoothed estimate if observation + for new time step is given. + + umbrella_evidence = [T, T, F, T, T] + e_t = T + t = 4 + d = 3 + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + >>> fixed_lag_smoothing(T, umbrellaHMM, d) + """ + ev.insert(0, None) + + T_model = HMM.transition_model + f = HMM.prior + B = [[1, 0], [0, 1]] + evidence = [] + + evidence.append(e_t) + O_t = vector_to_diagonal(HMM.sensor_dist(e_t)) + if t > d: + f = forward(HMM, f, e_t) + O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t- d])) + B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t) + else: + B = matrix_multiplication(B, T_model, O_t) + t = t + 1 + + if t > d: + # always returns a 1x2 matrix + return([normalize(i) for i in matrix_multiplication([f], B)][0]) + else: + return None + +# _________________________________________________________________________ def particle_filtering(e, N, HMM): diff --git a/tests/test_probability.py b/tests/test_probability.py index 04791d835..ef978373a 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -111,11 +111,31 @@ def test_forward_backward(): umbrella_evidence = [T, T, F, T, T] assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.6469, 0.3531], - [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] + [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8205, 0.1795], [0.8673, 0.1327]] umbrella_evidence = [T, F, T, F, T] assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.5871, 0.4129], - [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + [0.7177, 0.2823], [0.2325, 0.7675], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + +def test_fixed_lag_smoothing(): + umbrella_evidence = [T, F, T, F, T] + e_t = F + t = 4 + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + d = 2 + assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) == [0.1111, 0.8889] + d = 5 + assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) is None + + umbrella_evidence = [T, T, F, T, T] + # t = 4 + e_t = T + + d = 1 + assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) == [0.9939, 0.0061] if __name__ == '__main__': From 476baf526cbe876b0f054137bb3e2d9ff88cc945 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 19 Mar 2016 17:09:25 -0700 Subject: [PATCH 156/513] Update utils.py --- utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/utils.py b/utils.py index f97e5f0b9..0f383f393 100644 --- a/utils.py +++ b/utils.py @@ -2,7 +2,6 @@ # This module is safe for: from utils import * -# TODO: Create a separate grid.py file for 2D grid environments; move headings, etc there. # TODO: Priority queues may not belong here -- see treatment in search.py import operator @@ -170,7 +169,7 @@ def dotproduct(X, Y): def element_wise_product(X, Y): """Return vector as an element-wise product of vectors X and Y""" assert len(X) == len(Y) - return(list(x * y for x, y in zip(X, Y))) + return [x * y for x, y in zip(X, Y)] def matrix_multiplication(X_M, *Y_M): From f94fc0cf17693126909b36f01c94857c00d5f62d Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sun, 20 Mar 2016 22:43:16 +0530 Subject: [PATCH 157/513] Converted png to svg to save space --- games.ipynb | 4 +- images/fig_5_2.png | Bin 112166 -> 0 bytes images/fig_5_2.svg | 662 +++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 664 insertions(+), 2 deletions(-) delete mode 100644 images/fig_5_2.png create mode 100644 images/fig_5_2.svg diff --git a/games.ipynb b/games.ipynb index 3aec144ab..196171db5 100644 --- a/games.ipynb +++ 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100644 index 000000000..4f53217f1 --- /dev/null +++ b/images/fig_5_2.svg @@ -0,0 +1,662 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + MAX + MIN + 3 + + + a1 + a3 + a2 + b1 + b2 + b3 + c1 + c2 + c3 + d1 + d2 + d3 + 3 + 2 + 2 + 2 + 5 + 14 + 6 + 4 + 2 + 8 + 12 + 3 + + Figure 5.2 A two-ply game tree. The Δ nodes are "MAX nodes", in which it is MAX'sturn to move, and the ∇ nodes are "MIN nodes." The terminal nodes show the utility valuesfor MAX; the other nodes are labeled with their minimax values. MAX's best move at the rootis a1, because it leads to the state with the highest minimax value, and MIN's best reply is b1,beacuse it leads to the state with the lowest minimax value. + + + + + + + + + + + + + + + + + + A + B + C + D + + + + + + + + From 0b69fce86cad8ea73a9926c034e0bf3197fc066f Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sun, 20 Mar 2016 23:17:49 +0530 Subject: [PATCH 158/513] Added image to search.ipynb --- images/search_animal.svg | 1533 ++++++++++++++++++++++++++++++++++++++ search.ipynb | 67 +- 2 files changed, 1583 insertions(+), 17 deletions(-) create mode 100644 images/search_animal.svg diff --git a/images/search_animal.svg b/images/search_animal.svg new file mode 100644 index 000000000..e3c3105c8 --- /dev/null +++ b/images/search_animal.svg @@ -0,0 +1,1533 @@ + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Start + 3 + 7 + 7 + 9 + 6 + 11 + 8 + 5 + 9 + 10 + 6 + 4 + 3 + 2 + 4 + 9 + 8 + diff --git a/search.ipynb b/search.ipynb index 18b22c045..ef0ac2c55 100644 --- a/search.ipynb +++ b/search.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -94,11 +94,11 @@ " Mouse = dict(Penguin = 2),\n", " Bear = dict(Monkey = 7),\n", " Monkey = dict(Giraffe = 11, Fish = 6),\n", - " Fish = dict(Giraffe = 8, Parrot = 3),\n", + " Fish = dict(Giraffe = 8),\n", " Penguin = dict(Parrot = 4, Elephant = 6),\n", - " Giraffe = dict(Pig = 5),\n", - " Parrot = dict(Pig = 10),\n", - " Elephant = dict(Pig = 9)))" + " Giraffe = dict(Hen = 5),\n", + " Parrot = dict(Hen = 10),\n", + " Elephant = dict(Hen = 9)))" ] }, { @@ -109,10 +109,10 @@ ] }, { - "cell_type": "raw", + "cell_type": "markdown", "metadata": {}, "source": [ - "TODO - ADD image" + "" ] }, { @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -149,11 +149,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['Cat', 'Monkey']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bfs_node = breadth_first_search(monkey_problem)\n", "bfs_node.solution()" @@ -163,7 +174,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We get the output as `['Cat', 'Monkey']`. That is because the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that in breadth first search the goal test is done when it is being added to the queue." + "We get the output as `['Cat', 'Monkey']`. That is because first the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order when we are explanding the `Start` node. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that in breadth first search the goal test is done when it is being added to the queue." ] }, { @@ -182,11 +193,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['Dog', 'Bear', 'Monkey']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ucs_node = uniform_cost_search(monkey_problem)\n", "ucs_node.solution()" @@ -201,11 +223,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(18, 17)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bfs_node.path_cost, ucs_node.path_cost" ] @@ -234,7 +267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.0" } }, "nbformat": 4, From 389725def81f77307bcb6abc2365518aa2ce9925 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sun, 20 Mar 2016 23:45:33 +0530 Subject: [PATCH 159/513] Fixed hyperlink and added image source --- search.ipynb | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/search.ipynb b/search.ipynb index ef0ac2c55..80c9743c5 100644 --- a/search.ipynb +++ b/search.ipynb @@ -16,9 +16,9 @@ "source": [ "## Introduction\n", "Hello!\n", - " In this IPython notebook, we study different kinds of search techniques used in [search.py](https://github.com/aimacode/aima-python/blob/master/search.py) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", + " In this IPython notebook, we study different kinds of search techniques used in [ search.py ]( https://github.com/aimacode/aima-python/blob/master/search.py ) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", "\n", - " The code in this IPython notebook, and the entire [aima-python](https://github.com/aimacode/aima-python) repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the 'intro' IPython notebook. Now that you have all that sorted out, lets get started!" + " The code in this IPython notebook, and the entire aima-python repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the 'intro' IPython notebook. Now that you have all that sorted out, lets get started!" ] }, { @@ -112,7 +112,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "" + "\n", + "\n" ] }, { From fa85706f79dfc1c78652d8f1370ba8e4a0efbfc4 Mon Sep 17 00:00:00 2001 From: tolusalako Date: Mon, 21 Mar 2016 23:32:04 -0700 Subject: [PATCH 160/513] Fixed ussue where an agent's location set to 0 returned None --- agents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.py b/agents.py index baf769951..60ba0f393 100644 --- a/agents.py +++ b/agents.py @@ -295,7 +295,7 @@ def add_thing(self, thing, location=None): if not isinstance(thing, Thing): thing = Agent(thing) assert thing not in self.things, "Don't add the same thing twice" - thing.location = location or self.default_location(thing) + thing.location = location if location is not None else self.default_location(thing) self.things.append(thing) if isinstance(thing, Agent): thing.performance = 0 From 60501ddc6b49ee90885f472ad1f9be745a770a2d Mon Sep 17 00:00:00 2001 From: tolusalako Date: Tue, 22 Mar 2016 00:34:14 -0700 Subject: [PATCH 161/513] 1D agent sample --- agents.ipynb | 226 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 224 insertions(+), 2 deletions(-) diff --git a/agents.ipynb b/agents.ipynb index e6e185e06..78d3ffb1f 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -1,14 +1,236 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AGENT #\n", + "\n", + "An agent, as defined in 2.1 is anything that can perceive its environment through sensors, and act upon that environment through actuators based on its agent program. This can be a dog, robot, or even you. As long as you can perceive the environment and act on it, you are an agent. This notebook will explain how to implement a simple agent, create an environment, and create a program that helps the agent act on the environment based on its percepts.\n", + "\n", + "Before moving on, review the Agent and Environment classes in [agents.py](https://github.com/aimacode/aima-python/blob/master/agents.py).\n", + "\n", + "Let's begin by importing all the functions from the agents.py module and creating our first agent - a blind dog." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "from agents import *\n", + "\n", + "class BlindDog(Agent):\n", + " def eat(self, thing):\n", + " print(\"Dog: Ate food at {}.\".format(self.location))\n", + " \n", + " def drink(self, thing):\n", + " print(\"Dog: Drank water at {}.\".format( self.location))\n", + "\n", + "dog = BlindDog()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What we have just done is create a dog who can only feel what's in his location (since he's blind), and can eat or drink. Let's see if he's alive..." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "print(dog.alive)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Cool dog](https://gifgun.files.wordpress.com/2015/07/wpid-wp-1435860392895.gif)\n", + "This is our dog. How cool is he? Well, he's hungry and needs to go search for food. For him to do this, we need to give him a program. But before that, let's create a park for our dog to play in." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ENVIRONMENT #\n", + "\n", + "A park is an example of an environment because our dog can perceive and act upon it. The Environment class in agents.py is an abstract class, so we will have to create our own subclass from it before we can use it. The abstract class must contain the following methods:\n", + "\n", + "

  • percept(self, agent) - returns what the agent perceives
    • \n", + "
  • execute_action(self, agent, action) - changes the state of the environment based on what the agent does.
    • " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class Food(Thing):\n", + " pass\n", + "\n", + "class Water(Thing):\n", + " pass\n", + "\n", + "class Park(Environment):\n", + " '''prints & return a list of things that are in our dog's location'''\n", + " def percept(self, agent):\n", + " things = self.list_things_at(agent.location)\n", + " print(things)\n", + " return things\n", + " \n", + " def execute_action(self, agent, action):\n", + " '''changes the state of the environment based on what the agent does.'''\n", + " if action == \"move down\":\n", + " agent.movedown()\n", + " elif action == \"eat\":\n", + " items = self.list_things_at(agent.location, tclass=Food)\n", + " if len(items) != 0:\n", + " if agent.eat(items[0]): #Have the dog pick eat the first item\n", + " self.delete_thing(items[0]) #Delete it from the Park after.\n", + " elif action == \"drink\":\n", + " items = self.list_things_at(agent.location, tclass=Water)\n", + " if len(items) != 0:\n", + " if agent.drink(items[0]): #Have the dog drink the first item\n", + " self.delete_thing(items[0]) #Delete it from the Park after.\n", + " \n", + " def is_done(self):\n", + " '''By default, we're done when we can't find a live agent, \n", + " but to prevent killing our cute dog, we will or it with when there is no more food or water'''\n", + " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", + " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", + " return dead_agents or no_edibles\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# PROGRAM #\n", + "Now that we have a Park Class, we need to implement a program module for our dog. A program controls how the dog acts upon it's environment. Our program will be very simple, and is shown in the table below.\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Percept: Feel Food Feel WaterFeel Nothing
    Action: eatdrinkmove up
    \n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import agents" + "class BlindDog(Agent):\n", + " location = 1\n", + " \n", + " def movedown(self):\n", + " self.location += 1\n", + " \n", + " def eat(self, thing):\n", + " '''returns True upon success or False otherwise'''\n", + " if isinstance(thing, Food):\n", + " print(\"Dog: Ate food at {}.\".format(self.location))\n", + " return True\n", + " return False\n", + " \n", + " def drink(self, thing):\n", + " ''' returns True upon success or False otherwise'''\n", + " if isinstance(thing, Water):\n", + " print(\"Dog: Drank water at {}.\".format(self.location))\n", + " return True\n", + " return False\n", + " \n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " for p in percepts:\n", + " if isinstance(p, Food):\n", + " return 'eat'\n", + " elif isinstance(p, Water):\n", + " return 'drink'\n", + " return 'move down'\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n", + "[]\n", + "[]\n", + "[]\n", + "[]\n", + "[, ]\n", + "Dog: Ate food at 5.\n", + "[]\n", + "[]\n", + "[, ]\n", + "Dog: Drank water at 7.\n" + ] + } + ], + "source": [ + "park = Park()\n", + "dog = BlindDog(program)\n", + "dogfood = Food()\n", + "water = Water()\n", + "park.add_thing(dog, 0)\n", + "park.add_thing(dogfood, 5)\n", + "park.add_thing(water, 7)\n", + "\n", + "park.run(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's how easy it is to implement an agent, its program, and environment. But that was a very simple case. What if our environment was 2-Dimentional instead of 1? And what if we had multiple agents?" ] }, { From fb2c84bd4d27ec0387f6aa87503e5bfff14c15bb Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 22 Mar 2016 14:08:10 +0530 Subject: [PATCH 162/513] modified the variable name 'vars' which is built in name --- csp.py | 64 +++++++++++++++++++++++------------------------ tests/test_csp.py | 8 ++++++ 2 files changed, 40 insertions(+), 32 deletions(-) create mode 100644 tests/test_csp.py diff --git a/csp.py b/csp.py index 5fa953b7f..99c65ffcf 100644 --- a/csp.py +++ b/csp.py @@ -14,7 +14,7 @@ class CSP(search.Problem): """This class describes finite-domain Constraint Satisfaction Problems. A CSP is specified by the following inputs: - vars A list of variables; each is atomic (e.g. int or string). + variables A list of variables; each is atomic (e.g. int or string). domains A dict of {var:[possible_value, ...]} entries. neighbors A dict of {var:[var,...]} that for each variable lists the other variables that participate in constraints. @@ -51,10 +51,10 @@ class CSP(search.Problem): ('NT', 'R'), ('NSW', 'R'))> """ - def __init__(self, vars, domains, neighbors, constraints): - "Construct a CSP problem. If vars is empty, it becomes domains.keys()." - vars = vars or list(domains.keys()) - update(self, vars=vars, domains=domains, + def __init__(self, variables, domains, neighbors, constraints): + "Construct a CSP problem. If variables is empty, it becomes domains.keys()." + variables = variables or list(domains.keys()) + update(self, variables=variables, domains=domains, neighbors=neighbors, constraints=constraints, initial=(), curr_domains=None, nassigns=0) @@ -88,11 +88,11 @@ def display(self, assignment): def actions(self, state): """Return a list of applicable actions: nonconflicting assignments to an unassigned variable.""" - if len(state) == len(self.vars): + if len(state) == len(self.variables): return [] else: assignment = dict(state) - var = first(v for v in self.vars if v not in assignment) + var = first([v for v in self.variables if v not in assignment]) return [(var, val) for val in self.domains[var] if self.nconflicts(var, val, assignment) == 0] @@ -102,10 +102,10 @@ def result(self, state, xxx_todo_changeme): return state + ((var, val),) def goal_test(self, state): - "The goal is to assign all vars, with all constraints satisfied." + "The goal is to assign all variables, with all constraints satisfied." assignment = dict(state) - return (len(assignment) == len(self.vars) and - every(lambda x: self.nconflicts(x, assignment[x], assignment) == 0, self.x)) + return (len(assignment) == len(self.variables) and + every(lambda variables: self.nconflicts(variables, assignment[variables], assignment) == 0, self.variables)) # These are for constraint propagation @@ -113,7 +113,7 @@ def support_pruning(self): """Make sure we can prune values from domains. (We want to pay for this only if we use it.)""" if self.curr_domains is None: - self.curr_domains = dict((v, list(self.domains[v])) for v in self.vars) + self.curr_domains = dict((v, list(self.domains[v])) for v in self.variables) def suppose(self, var, value): "Start accumulating inferences from assuming var=value." @@ -136,7 +136,7 @@ def infer_assignment(self): "Return the partial assignment implied by the current inferences." self.support_pruning() return dict((v, self.curr_domains[v][0]) - for v in self.vars if 1 == len(self.curr_domains[v])) + for v in self.variables if 1 == len(self.curr_domains[v])) def restore(self, removals): "Undo a supposition and all inferences from it." @@ -147,7 +147,7 @@ def restore(self, removals): def conflicted_vars(self, current): "Return a list of variables in current assignment that are in conflict" - return [var for var in self.vars + return [var for var in self.variables if self.nconflicts(var, current[var], current) > 0] # ______________________________________________________________________________ @@ -157,7 +157,7 @@ def conflicted_vars(self, current): def AC3(csp, queue=None, removals=None): """[Fig. 6.3]""" if queue is None: - queue = [(Xi, Xk) for Xi in csp.vars for Xk in csp.neighbors[Xi]] + queue = [(Xi, Xk) for Xi in csp.variables for Xk in csp.neighbors[Xi]] csp.support_pruning() while queue: (Xi, Xj) = queue.pop() @@ -189,13 +189,13 @@ def revise(csp, Xi, Xj, removals): def first_unassigned_variable(assignment, csp): "The default variable order." - return find_if(lambda var: var not in assignment, csp.vars) + return first([var for var in csp.variables if var not in assignment]) def mrv(assignment, csp): "Minimum-remaining-values heuristic." return argmin_random_tie( - [v for v in csp.vars if v not in assignment], + [v for v in csp.variables if v not in assignment], lambda var: num_legal_values(csp, var, assignment)) @@ -271,7 +271,7 @@ def backtracking_search(csp, """ def backtrack(assignment): - if len(assignment) == len(csp.vars): + if len(assignment) == len(csp.variables): return assignment var = select_unassigned_variable(assignment, csp) for value in order_domain_values(var, assignment, csp): @@ -296,9 +296,9 @@ def backtrack(assignment): def min_conflicts(csp, max_steps=100000): """Solve a CSP by stochastic hillclimbing on the number of conflicts.""" - # Generate a complete assignment for all vars (probably with conflicts) + # Generate a complete assignment for all variables (probably with conflicts) csp.current = current = {} - for var in csp.vars: + for var in csp.variables: val = min_conflicts_value(csp, var, current) csp.assign(var, val, current) # Now repeatedly choose a random conflicted variable and change it @@ -324,8 +324,8 @@ def min_conflicts_value(csp, var, current): def tree_csp_solver(csp): "[Fig. 6.11]" assignment = {} - root = csp.vars[0] - X, parent = topological_sort(csp.vars, root) + root = csp.variables[0] + X, parent = topological_sort(csp.variables, root) for Xj in reversed(X): if not make_arc_consistent(parent[Xj], Xj, csp): return None @@ -350,7 +350,7 @@ def make_arc_consistent(Xj, Xk, csp): class UniversalDict: """A universal dict maps any key to the same value. We use it here - as the domains dict for CSPs in which all vars have the same domain. + as the domains dict for CSPs in which all variables have the same domain. >>> d = UniversalDict(42) >>> d['life'] 42 @@ -379,7 +379,7 @@ def MapColoringCSP(colors, neighbors): different_values_constraint) -def parse_neighbors(neighbors, vars=[]): +def parse_neighbors(neighbors, variables=[]): """Convert a string of the form 'X: Y Z; Y: Z' into a dict mapping regions to neighbors. The syntax is a region name followed by a ':' followed by zero or more region names, followed by ';', repeated for @@ -388,7 +388,7 @@ def parse_neighbors(neighbors, vars=[]): {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} """ dict = defaultdict(list) - for var in vars: + for var in variables: dict[var] = [] specs = [spec.split(':') for spec in neighbors.split(';')] for (A, Aneighbors) in specs: @@ -446,7 +446,7 @@ class NQueensCSP(CSP): We increment/decrement these counts each time a queen is placed/moved from a row/diagonal. So moving is O(1), as is nconflicts. But choosing a variable, and a best value for the variable, are each O(n). - If you want, you can keep track of conflicted vars, then variable + If you want, you can keep track of conflicted variables, then variable selection will also be O(1). >>> len(backtracking_search(NQueensCSP(8))) 8 @@ -462,7 +462,7 @@ def nconflicts(self, var, val, assignment): """The number of conflicts, as recorded with each assignment. Count conflicts in row and in up, down diagonals. If there is a queen there, it can't conflict with itself, so subtract 3.""" - n = len(self.vars) + n = len(self.variables) c = self.rows[val] + self.downs[var+val] + self.ups[var-val+n-1] if assignment.get(var, None) == val: c -= 3 @@ -485,14 +485,14 @@ def unassign(self, var, assignment): def record_conflict(self, assignment, var, val, delta): "Record conflicts caused by addition or deletion of a Queen." - n = len(self.vars) + n = len(self.variables) self.rows[val] += delta self.downs[var + val] += delta self.ups[var - val + n - 1] += delta def display(self, assignment): "Print the queens and the nconflicts values (for debugging)." - n = len(self.vars) + n = len(self.variables) for val in range(n): for var in range(n): if assignment.get(var, '') == val: @@ -610,9 +610,9 @@ def Zebra(): Drinks = 'OJ Tea Coffee Milk Water'.split() Countries = 'Englishman Spaniard Norwegian Ukranian Japanese'.split() Smokes = 'Kools Chesterfields Winston LuckyStrike Parliaments'.split() - vars = Colors + Pets + Drinks + Countries + Smokes + variables = Colors + Pets + Drinks + Countries + Smokes domains = {} - for var in vars: + for var in variables: domains[var] = list(range(1, 6)) domains['Norwegian'] = [1] domains['Milk'] = [3] @@ -620,7 +620,7 @@ def Zebra(): Spaniard: Dog; Kools: Yellow; Chesterfields: Fox; Norwegian: Blue; Winston: Snails; LuckyStrike: OJ; Ukranian: Tea; Japanese: Parliaments; Kools: Horse; - Coffee: Green; Green: Ivory""", vars) + Coffee: Green; Green: Ivory""", variables) for type in [Colors, Pets, Drinks, Countries, Smokes]: for A in type: for B in type: @@ -666,7 +666,7 @@ def zebra_constraint(A, a, B, b, recurse=0): (A in Smokes and B in Smokes)): return not same raise Exception('error') - return CSP(vars, domains, neighbors, zebra_constraint) + return CSP(variables, domains, neighbors, zebra_constraint) def solve_zebra(algorithm=min_conflicts, **args): diff --git a/tests/test_csp.py b/tests/test_csp.py new file mode 100644 index 000000000..aaab1c219 --- /dev/null +++ b/tests/test_csp.py @@ -0,0 +1,8 @@ +import pytest +from csp import * #noqa + +def test_backtracking_search(): + assert (backtracking_search(australia) is not None) == True + +if __name__ == "__main__": + pytest.main() From be90698e223b2081e5b7306a3c6f2a8c60518680 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 22 Mar 2016 14:23:46 +0530 Subject: [PATCH 163/513] random.choice takes only lists not generators --- utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 0f383f393..faca12b43 100644 --- a/utils.py +++ b/utils.py @@ -106,7 +106,7 @@ def argmin_gen(seq, fn): def argmin_random_tie(seq, fn): """Return an element with lowest fn(seq[i]) score; break ties at random. Thus, for all s,f: argmin_random_tie(s, f) in argmin_list(s, f)""" - return random.choice(argmin_gen(seq, fn)) + return random.choice(argmin_list(seq, fn)) def argmax(seq, fn): From 3a8318bfc8fe9dd3ed21541366393e1a0a75aa9d Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 22 Mar 2016 14:24:38 +0530 Subject: [PATCH 164/513] added unittests for csp.py --- csp.py | 18 ------------------ search.py | 4 ++-- tests/test_csp.py | 19 +++++++++++++++++++ 3 files changed, 21 insertions(+), 20 deletions(-) diff --git a/csp.py b/csp.py index 99c65ffcf..422887388 100644 --- a/csp.py +++ b/csp.py @@ -250,24 +250,6 @@ def backtracking_search(csp, order_domain_values=unordered_domain_values, inference=no_inference): """[Fig. 6.5] - >>> backtracking_search(australia) is not None - True - >>> backtracking_search(australia, - >>> select_unassigned_variable=mrv) is not None - True - >>> backtracking_search(australia, - >>> order_domain_values=lcv) is not None - True - >>> backtracking_search(australia, select_unassigned_variable=mrv, - >>> order_domain_values=lcv) is not None - True - >>> backtracking_search(australia, inference=forward_checking) is not None - True - >>> backtracking_search(australia, inference=mac) is not None - True - >>> backtracking_search(usa, select_unassigned_variable=mrv, - >>> order_domain_values=lcv, inference=mac) is not None - True """ def backtrack(assignment): diff --git a/search.py b/search.py index 6c772e4f8..831adb4da 100644 --- a/search.py +++ b/search.py @@ -689,7 +689,7 @@ class GraphProblemStochastic(GraphProblem): Define the graph as dict(A = dict(Action = [[, , ...],], ...), ...) A the dictionary format is different, make sure the graph is created as a directed graph """ - + def result(self, state, action): return self.graph.get(state, action) @@ -1021,7 +1021,7 @@ def do(searcher, problem): def compare_graph_searchers(): """Prints a table of results like this: >>> compare_graph_searchers() -Searcher Fig[3, 2](A, B) Fig[3, 2](O, N) Fig[6, 1] +Searcher Fig[3, 2](A, B) Fig[3, 2](O, N) Fig[6, 1] breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> diff --git a/tests/test_csp.py b/tests/test_csp.py index aaab1c219..ee7645f6d 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -3,6 +3,25 @@ def test_backtracking_search(): assert (backtracking_search(australia) is not None) == True + assert (backtracking_search(australia, select_unassigned_variable=mrv) is not None) == True + assert (backtracking_search(australia, order_domain_values=lcv) is not None) == True + assert (backtracking_search(australia, select_unassigned_variable=mrv, + order_domain_values=lcv) is not None) == True + assert (backtracking_search(australia, inference=forward_checking) is not None) == True + assert (backtracking_search(australia, inference=mac) is not None) == True + assert (backtracking_search(usa, select_unassigned_variable=mrv, + order_domain_values=lcv, inference=mac) is not None) == True + +def test_universal_dict(): + d = UniversalDict(42) + assert d['life'] == 42 + +def test_parse_neighbours(): + assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} + +def test_sudoku(): + + if __name__ == "__main__": pytest.main() From 4bd39938bcb252a1728b69bd40ae971e6c615990 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 22 Mar 2016 14:36:16 +0530 Subject: [PATCH 165/513] modified typo in test_csp.py --- tests/test_csp.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/test_csp.py b/tests/test_csp.py index ee7645f6d..f22383f82 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -19,8 +19,6 @@ def test_universal_dict(): def test_parse_neighbours(): assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} -def test_sudoku(): - if __name__ == "__main__": From 42627d584a32505a70091fd17295534fcf619234 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 22 Mar 2016 16:53:04 +0530 Subject: [PATCH 166/513] Just studying the code --- logic.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index 7b196db3e..44fddccf9 100644 --- a/logic.py +++ b/logic.py @@ -53,12 +53,12 @@ def tell(self, sentence): "Add the sentence to the KB." raise NotImplementedError - def ask(self, query): + def ask(self, query): # not sure what this means or does """Return a substitution that makes the query true, or, failing that, return False.""" return first(self.ask_generator(query), default=False) - def ask_generator(self, query): + def ask_generator(self, query): # Still not sure what this means or does "Yield all the substitutions that make query true." raise NotImplementedError @@ -152,7 +152,7 @@ class Expr: equalities and disequalities. We concentrate on logical equality (or equivalence) and logical disequality (or XOR). You have 3 choices: (1) Expr('<=>', x, y) and Expr('^', x, y) - Note that ^ is bitwose XOR in Python (and Java and C++) + Note that ^ is bitwise XOR in Python (and Java and C++) (2) expr('x <=> y') and expr('x =/= y'). See the doc string for the function expr. (3) (x % y) and (x ^ y). @@ -239,6 +239,7 @@ def __xor__(self, other): return Expr('^', self, other) def __mod__(self, other): return Expr('<=>', self, other) +# TODO: Fix the precedence of connectives def expr(s): """Create an Expr representing a logic expression by parsing the input string. Symbols and numbers are automatically converted to Exprs. From c2fa32a255e756f36f736ffea47df7c4486851ee Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 22 Mar 2016 21:08:56 +0530 Subject: [PATCH 167/513] Make some docstrings somewhat more clearer --- logic.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/logic.py b/logic.py index 44fddccf9..7146845e4 100644 --- a/logic.py +++ b/logic.py @@ -31,6 +31,8 @@ import re from collections import defaultdict +# TODO: Fix the precedence of connectives in expr() + # ______________________________________________________________________________ @@ -53,12 +55,11 @@ def tell(self, sentence): "Add the sentence to the KB." raise NotImplementedError - def ask(self, query): # not sure what this means or does - """Return a substitution that makes the query true, or, - failing that, return False.""" + def ask(self, query): + """Return a substitution that makes the query true, or, failing that, return False.""" return first(self.ask_generator(query), default=False) - def ask_generator(self, query): # Still not sure what this means or does + def ask_generator(self, query): "Yield all the substitutions that make query true." raise NotImplementedError @@ -81,9 +82,10 @@ def tell(self, sentence): self.clauses.extend(conjuncts(to_cnf(sentence))) def ask_generator(self, query): - "Yield the empty substitution if KB implies query; else nothing." + "Return the empty substitution {} if KB entails query; else return False." if tt_entails(Expr('&', *self.clauses), query): - yield {} + yield {} # Why use yield when you are not returning a generator? + # Or for that purpose, not even an iterable. def retract(self, sentence): "Remove the sentence's clauses from the KB." @@ -239,7 +241,6 @@ def __xor__(self, other): return Expr('^', self, other) def __mod__(self, other): return Expr('<=>', self, other) -# TODO: Fix the precedence of connectives def expr(s): """Create an Expr representing a logic expression by parsing the input string. Symbols and numbers are automatically converted to Exprs. @@ -344,7 +345,8 @@ def parse_definite_clause(s): def tt_entails(kb, alpha): """Does kb entail the sentence alpha? Use truth tables. For propositional - kb's and sentences. [Fig. 7.10] + kb's and sentences. [Fig. 7.10]. Note that the 'kb' that has to be passed should actually be an + Expr which is a conjunction of clauses. >>> tt_entails(expr('P & Q'), expr('Q')) True """ From 4f34339082a951cf25784edd84665c161f37f058 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 22 Mar 2016 21:09:59 +0530 Subject: [PATCH 168/513] Add tests for the conditions in Fig. 7.2 of the Wumpus World --- tests/test_logic.py | 52 +++++++++++++++++++++++++++++++++++++++------ 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/tests/test_logic.py b/tests/test_logic.py index f680ebbb5..5dc941943 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -8,15 +8,55 @@ def test_expr(): def test_PropKB(): kb = PropKB() - assert count(kb.ask(expr) for expr in [A, B, C, P, Q]) is 0 - kb.tell(A & B) - assert kb.ask(A) == kb.ask(B) == {} - kb.tell(B >> C) + assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 + kb.tell(A & E) + assert kb.ask(A) == kb.ask(E) == {} + kb.tell(E >> C) assert kb.ask(C) == {} - kb.retract(B) - assert kb.ask(B) is False + kb.retract(E) + assert kb.ask(E) is False assert kb.ask(C) is False + # A simple KB that defines the relevant conditions of the Wumpus World as in Fig 7.4. + # See Sec. 7.4.3 + kb_wumpus = PropKB() + + # Creating the relevant expressions + P = {} + B = {} + P[1,1] = Expr("P[1,1]") + P[1,2] = Expr("P[1,2]") + P[2,1] = Expr("P[2,1]") + P[2,2] = Expr("P[2,2]") + P[3,1] = Expr("P[3,1]") + B[1,1] = Expr("B[1,1]") + B[2,1] = Expr("B[2,1]") + + kb_wumpus.tell(~P[1,1]) + kb_wumpus.tell(B[1,1] % ((P[1,2] | P[2,1]))) + kb_wumpus.tell(B[2,1] % ((P[1,1] | P[2,2] | P[3,1]))) + kb_wumpus.tell(~B[1,1]) + kb_wumpus.tell(B[2,1]) + + # Statement: There is no pit in [1,1]. + assert kb_wumpus.ask(~P[1,1]) == {} + + # Statement: There is no pit in [1,2]. + assert kb_wumpus.ask(~P[1,2]) == {} + + # Statement: There is a pit in [2,2]. + assert kb_wumpus.ask(P[2,2]) == False + + # Statement: There is a pit in [3,1]. + assert kb_wumpus.ask(P[3,1]) == False + + # Statement: Neither [1,2] nor [2,1] contains a pit. + assert kb_wumpus.ask(~P[1,2] & ~P[2,1]) == {} + + # Statement: There is a pit in either [2,2] or [3,1]. + assert kb_wumpus.ask(P[2,2] | P[3,1]) == {} + + def test_pl_true(): assert pl_true(P, {}) is None assert pl_true(P, {P: False}) is False From 76e3b923dcf95c0b8e81378c6fedefafd707e686 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Tue, 22 Mar 2016 21:10:57 +0530 Subject: [PATCH 169/513] A small change: explain a docstring to resolve an ambiguity --- utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 0f383f393..50addacd1 100644 --- a/utils.py +++ b/utils.py @@ -52,7 +52,7 @@ def product(numbers): def first(iterable, default=None): - "Return the first element of an iterable or sequence; or default." + "Return the first element of an iterable or the next element of a generator; or default." try: return iterable[0] except IndexError: From 815bae49a941d463f988acafe39ac5a4d3dab9ef Mon Sep 17 00:00:00 2001 From: Toluwanimi Salako Date: Tue, 22 Mar 2016 09:26:34 -0700 Subject: [PATCH 170/513] Update agents.ipynb --- agents.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.ipynb b/agents.ipynb index 78d3ffb1f..4aa6cc71c 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![Cool dog](https://gifgun.files.wordpress.com/2015/07/wpid-wp-1435860392895.gif)\n", + "![Cool dog](https://gifgun.files.wordpress.com/2015/07/wpid-wp-1435860392895.gif = 50x50)\n", "This is our dog. How cool is he? Well, he's hungry and needs to go search for food. For him to do this, we need to give him a program. But before that, let's create a park for our dog to play in." ] }, From f43981f146b1c7f9b9144614eead7c837862e4fb Mon Sep 17 00:00:00 2001 From: Toluwanimi Salako Date: Tue, 22 Mar 2016 09:27:09 -0700 Subject: [PATCH 171/513] Update agents.ipynb --- agents.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.ipynb b/agents.ipynb index 4aa6cc71c..78d3ffb1f 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -64,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![Cool dog](https://gifgun.files.wordpress.com/2015/07/wpid-wp-1435860392895.gif = 50x50)\n", + "![Cool dog](https://gifgun.files.wordpress.com/2015/07/wpid-wp-1435860392895.gif)\n", "This is our dog. How cool is he? Well, he's hungry and needs to go search for food. For him to do this, we need to give him a program. But before that, let's create a park for our dog to play in." ] }, From 10a911c21f142ddd6c619af27c75e97d6268a668 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 01:22:19 +0530 Subject: [PATCH 172/513] Add ask_if_true() method and fix minor errors --- logic.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index 7146845e4..d6de373ec 100644 --- a/logic.py +++ b/logic.py @@ -82,10 +82,16 @@ def tell(self, sentence): self.clauses.extend(conjuncts(to_cnf(sentence))) def ask_generator(self, query): - "Return the empty substitution {} if KB entails query; else return False." + "Return the empty substitution {} if KB entails query; else return None." if tt_entails(Expr('&', *self.clauses), query): - yield {} # Why use yield when you are not returning a generator? - # Or for that purpose, not even an iterable. + yield {} + + def ask_if_true(self, query): + "Return True if the KB entails query, else return False." + if self.ask_generator(query) == {}: + return True + else: + return False def retract(self, sentence): "Remove the sentence's clauses from the KB." From 35d035e724d380e2522c7ef60edad63de27b130f Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 23 Mar 2016 03:52:48 +0530 Subject: [PATCH 173/513] Implemented Passive Temporal Difference Agent --- rl.py | 43 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 39 insertions(+), 4 deletions(-) diff --git a/rl.py b/rl.py index d74062be4..76f1f79ea 100644 --- a/rl.py +++ b/rl.py @@ -12,8 +12,43 @@ class PassiveADPAgent(agents.Agent): NotImplemented -class PassiveTDAgent(agents.Agent): +class PassiveTDAgent: + """The abstract class for a Passive (non-learning) agent that uses + temporal differences to learn utility estimates. Override update_state + method to convert percept to state and reward. The mdp being probided + should be an instance of a subclass of the MDP Class.[Fig. 21.4] + """ - """Passive (non-learning) agent that uses temporal differences to learn - utility estimates. [Fig. 21.4]""" - NotImplemented + def __init__(self, pi, mdp, alpha=None): + + self.pi = pi + self.U = {s: 0. for s in mdp.states} + self.Ns = {s: 0 for s in mdp.states} + self.s = None + self.a = None + self.r = None + self.gamma = mdp.gamma + self.terminals = mdp.terminals + + if alpha: + self.alpha = alpha + else: + self.alpha = lambda n: 1./(1+n) # udacity video + + def __call__(self, percept): + s_prime, r_prime = self.update_state(percept) + pi, U, Ns, s, a, r = self.pi, self.U, self.Ns, self.s, self.a, self.r + alpha, gamma, terminals = self.alpha, self.gamma, self.terminals + if not Ns[s_prime]: + U[s_prime] = r_prime + if s is not None: + Ns[s] += 1 + U[s] += alpha(Ns[s]) * (r + gamma * U[s_prime] - U[s]) + if s_prime in terminals: + self.s = self.a = self.r = None + else: + self.s, self.a, self.r = s_prime, pi[s_prime], r_prime + return self.a + + def update_state(self, percept): + raise NotImplementedError From 1ea0aef4609c7c0bda6ed787739a268f596cdb40 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 23 Mar 2016 06:09:53 +0530 Subject: [PATCH 174/513] Moved Tests for MDP --- tests/test_mdp.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 tests/test_mdp.py diff --git a/tests/test_mdp.py b/tests/test_mdp.py new file mode 100644 index 000000000..e60f73df9 --- /dev/null +++ b/tests/test_mdp.py @@ -0,0 +1,23 @@ +import pytest +from mdp import * # noqa + +def test_value_iteration(): + assert value_iteration(Fig[17, 1], .01) == {(3, 2): 1.0, (3, 1): -1.0, + (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, + (0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537, + (0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676, + (2, 0): 0.34461306281476806, (2, 1): 0.48643676237737926, + (2, 2): 0.79536093684710951} + + +def test_policy_iteration(): + assert policy_iteration(Fig[17, 1]) == {(0, 1): (0, 1), (1, 2): (1, 0), (3, 2): None, + (0, 0): (0, 1), (2, 0): (0, 1), (3, 0): (-1, 0), + (1, 0): (1, 0), (3, 1): None, (2, 2): (1, 0), + (2, 1): (0, 1), (0, 2): (1, 0)} + + +def test_best_policy(): + pi = best_policy(Fig[17, 1], value_iteration(Fig[17, 1], .01)) + assert Fig[17, 1].to_arrows(pi) == [['>', '>', '>', '.'], ['^', None, '^', '.'], + ['^', '>', '^', '<']] From 9b660a9a12857e7d77fa0a42b323a70f79d1b875 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 23 Mar 2016 06:10:56 +0530 Subject: [PATCH 175/513] Removed Moved Tests --- mdp.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/mdp.py b/mdp.py index dbb5e4d54..442a92de6 100644 --- a/mdp.py +++ b/mdp.py @@ -177,24 +177,3 @@ def policy_evaluation(pi, U, mdp, k=20): ^ None ^ . ^ > ^ < """ - -__doc__ += """ -Random tests: ->>> pi -{(3, 2): None, (3, 1): None, (3, 0): (-1, 0), (2, 1): (0, 1), (0, 2): (1, 0), - (1, 0): (1, 0), (0, 0): (0, 1), (1, 2): (1, 0), (2, 0): (0, 1), - (0, 1): (0, 1), (2, 2): (1, 0)} - ->>> value_iteration(Fig[17,1], .01) -{(3, 2): 1.0, (3, 1): -1.0, (3, 0): 0.12958868267972745, - (0, 1): 0.39810203830605462, (0, 2): 0.50928545646220924, - (1, 0): 0.25348746162470537, (0, 0): 0.29543540628363629, - (1, 2): 0.64958064617168676, (2, 0): 0.34461306281476806, - (2, 1): 0.48643676237737926, (2, 2): 0.79536093684710951} - ->>> policy_iteration(Fig[17,1]) -{(3, 2): None, (3, 1): None, (3, 0): (0, -1), (2, 1): (-1, 0), (0, 2): (1, 0), - (1, 0): (1, 0), (0, 0): (1, 0), (1, 2): (1, 0), (2, 0): (1, 0), - (0, 1): (1, 0), (2, 2): (1, 0)} - -""" From c6308b837aa06763a71c545e9f54bdaedf95674b Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 12:12:42 +0530 Subject: [PATCH 176/513] Fix a minor error to arguments of flake8 in Travis configuration file --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 3404bacf4..b7f66bfb6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -15,7 +15,7 @@ script: - py.test after_success: - - flake8 --max-line-length 100 ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . + - flake8 --max-line-length 100 --ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . notifications: email: false From 4c5ae6851c60be84bff222065a802268fa33d42d Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Tue, 22 Mar 2016 23:44:39 -0700 Subject: [PATCH 177/513] Update test_mdp.py --- tests/test_mdp.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tests/test_mdp.py b/tests/test_mdp.py index e60f73df9..0f5bb656c 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -11,13 +11,14 @@ def test_value_iteration(): def test_policy_iteration(): - assert policy_iteration(Fig[17, 1]) == {(0, 1): (0, 1), (1, 2): (1, 0), (3, 2): None, - (0, 0): (0, 1), (2, 0): (0, 1), (3, 0): (-1, 0), - (1, 0): (1, 0), (3, 1): None, (2, 2): (1, 0), - (2, 1): (0, 1), (0, 2): (1, 0)} + assert policy_iteration(Fig[17, 1]) == {(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0), + (1, 0): (1, 0), (1, 2): (1, 0), + (2, 0): (0, 1), (2, 1): (0, 1), (2, 2): (1, 0), + (3, 0): (-1, 0), (3, 1): None, (3, 2): None} def test_best_policy(): pi = best_policy(Fig[17, 1], value_iteration(Fig[17, 1], .01)) - assert Fig[17, 1].to_arrows(pi) == [['>', '>', '>', '.'], ['^', None, '^', '.'], + assert Fig[17, 1].to_arrows(pi) == [['>', '>', '>', '.'], + ['^', None, '^', '.'], ['^', '>', '^', '<']] From 6392897bc105c642869533b455187c482a051f5a Mon Sep 17 00:00:00 2001 From: tolusalako Date: Wed, 23 Mar 2016 00:54:32 -0700 Subject: [PATCH 178/513] Created a Directions class to for 2D-Environments and started work on the WumpusEnvironment --- agents.ipynb | 49 +++++++++++++++++---- agents.py | 119 ++++++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 152 insertions(+), 16 deletions(-) diff --git a/agents.ipynb b/agents.ipynb index 78d3ffb1f..d7cd85484 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -95,8 +95,8 @@ " pass\n", "\n", "class Park(Environment):\n", - " '''prints & return a list of things that are in our dog's location'''\n", " def percept(self, agent):\n", + " '''prints & return a list of things that are in our agent's location'''\n", " things = self.list_things_at(agent.location)\n", " print(things)\n", " return things\n", @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -230,17 +230,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "That's how easy it is to implement an agent, its program, and environment. But that was a very simple case. What if our environment was 2-Dimentional instead of 1? And what if we had multiple agents?" + "That's how easy it is to implement an agent, its program, and environment. But that was a very simple case. What if our environment was 2-Dimentional instead of 1? And what if we had multiple agents?\n", + "\n", + "To make our Park 2D, we will need to make it a subclass of XYEnvironment instead of Environment. Also, let's add a person to play fetch with the dog." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "class Park(XYEnvironment):\n", + " def percept(self, agent):\n", + " '''prints & return a list of things that are in our agent's location'''\n", + " things = self.list_things_at(agent.location)\n", + " print(things)\n", + " return things\n", + " \n", + " def execute_action(self, agent, action):\n", + " '''changes the state of the environment based on what the agent does.'''\n", + " if action == \"move down\":\n", + " agent.movedown()\n", + " elif action == \"eat\":\n", + " items = self.list_things_at(agent.location, tclass=Food)\n", + " if len(items) != 0:\n", + " if agent.eat(items[0]): #Have the dog pick eat the first item\n", + " self.delete_thing(items[0]) #Delete it from the Park after.\n", + " elif action == \"drink\":\n", + " items = self.list_things_at(agent.location, tclass=Water)\n", + " if len(items) != 0:\n", + " if agent.drink(items[0]): #Have the dog drink the first item\n", + " self.delete_thing(items[0]) #Delete it from the Park after.\n", + " \n", + " def is_done(self):\n", + " '''By default, we're done when we can't find a live agent, \n", + " but to prevent killing our cute dog, we will or it with when there is no more food or water'''\n", + " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", + " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", + " return dead_agents or no_edibles" + ] } ], "metadata": { diff --git a/agents.py b/agents.py index 60ba0f393..0e4c69c88 100644 --- a/agents.py +++ b/agents.py @@ -313,6 +313,46 @@ def delete_thing(self, thing): if thing in self.agents: self.agents.remove(thing) +class Direction(): + '''A direction class for agents that want to move in a 2D plane + Usage: + d = Direction("Down") + To change directions: + d = d + "right" or d = d + Direction.R #Both do the same thing + Note that the argument to __add__ must be a string and not a Direction object. + Also, it (the argument) can only be right or left. ''' + + R = "right" + L = "left" + U = "up" + D = "down" + + def __init__(self, direction): + self.direction = direction + + def __add__(self, heading): + print(heading, self.direction, heading == self.direction) + if self.direction == self.R: + return{ + self.R: Direction(self.D), + self.L: Direction(self.U), + }.get(heading, None) + elif self.direction == self.L: + return{ + self.R: Direction(U), + self.L: Direction(L), + }.get(heading, None) + elif self.direction == self.U: + return{ + self.R: Direction(R), + self.L: Direction(L), + }.get(heading, None) + elif self.direction == self.D: + return{ + self.R: Direction(self.L), + self.L: Direction(self.R), + }.get(heading, None) + class XYEnvironment(Environment): @@ -327,7 +367,8 @@ class XYEnvironment(Environment): def __init__(self, width=10, height=10): super(XYEnvironment, self).__init__() update(self, width=width, height=height, observers=[]) - + + perceptible_distance = 1 def things_near(self, location, radius=None): "Return all things within radius of location." if radius is None: @@ -336,8 +377,6 @@ def things_near(self, location, radius=None): return [thing for thing in self.things if distance2(location, thing.location) <= radius2] - perceptible_distance = 1 - def percept(self, agent): "By default, agent perceives things within a default radius." return [self.thing_percept(thing, agent) @@ -387,7 +426,8 @@ def delete_thing(self, thing): # Any more to do? Thing holding anything or being held? for obs in self.observers: obs.thing_deleted(thing) - + + has_walls = False def add_walls(self): "Put walls around the entire perimeter of the grid." for x in range(self.width): @@ -396,6 +436,7 @@ def add_walls(self): for y in range(self.height): self.add_thing(Wall(), (0, y)) self.add_thing(Wall(), (self.width-1, y)) + self.has_walls = True def add_observer(self, observer): """Adds an observer to the list of observers. @@ -515,6 +556,9 @@ class Gold(Thing): class Pit(Thing): pass +class Breeze(Thing): + pass + class Arrow(Thing): pass @@ -523,17 +567,78 @@ class Arrow(Thing): class Wumpus(Agent): pass +class Stench(Thing): + pass class Explorer(Agent): - pass + direction = Direction("right") class WumpusEnvironment(XYEnvironment): - + pit_probability = 0.2 #Probability to spawn a pit in a location + def __init__(self, width=10, height=10): super(WumpusEnvironment, self).__init__(width, height) self.add_walls() - + self.init_world() + + def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items = True): + '''Adds thing to the world''' + if (isinstance(thing, Wall) or self.is_inbounds(location)): + if (exclude_duplicate_class_items and + any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): + return + super(WumpusEnvironment, self).add_thing(thing, location) + + def init_world(self): + '''Spawn items to the world based on probabilities from the book''' + "PITS" + for x in range(self.width): + for y in range(self.height): + if random.random() < self.pit_probability: + self.add_thing(Pit(), (x,y)) + self.add_thing(Breeze(), (x - 1,y)) + self.add_thing(Breeze(), (x,y - 1)) + self.add_thing(Breeze(), (x + 1,y)) + self.add_thing(Breeze(), (x,y + 1)) + + "WUMPUS" + w_x, w_y = self.random_location(exclude = (1,1)) + self.add_thing(Wumpus(), (w_x, w_y)) + self.add_thing(Stench(), (w_x - 1, w_y)) + self.add_thing(Stench(), (w_x + 1, w_y)) + self.add_thing(Stench(), (w_x, w_y - 1)) + self.add_thing(Stench(), (w_x, w_y + 1)) + + "GOLD" + self.add_thing(Gold(), self.random_location(exclude = (1,1))) + + "AGENT" + self.add_thing(Explorer(), (1,1)) + + def is_inbounds(self, location): + '''Checks to make sure that the location is inbounds (within walls)''' + x,y = location + return not (x < 0 or x >= self.width or y < 0 or y >= self.height) + + def random_location(self, exclude = None): + '''Returns a random location that is inbounds''' + location = (random.randint(1, self.width), random.randint(1, self.height)) + if exclude is not None: + while(location == exclude): + location = (random.randint(1, self.width), random.randint(1, self.height)) + return location + + def print_world(self, show_walls = False): + '''Prints the world''' + x_start,y_start = (1,1) if not show_walls else (0,0) + x_end,y_end = (self.width-1, self.height-1) if not show_walls else (self.width, self.height) + for x in range(x_start, x_end): + for y in range(y_start, y_end): + print(self.list_things_at((x,y)), end = "") + print() + + def thing_classes(self): return [Wall, Gold, Pit, Arrow, Wumpus, Explorer] From f58cde94c71437c9bf73c3ed87876fe73f3f9780 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 19:24:20 +0530 Subject: [PATCH 179/513] Make a few minor changes, add link to aima-java --- games.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/games.ipynb b/games.ipynb index 196171db5..e2e3bdc41 100644 --- a/games.ipynb +++ b/games.ipynb @@ -21,9 +21,9 @@ "metadata": {}, "source": [ " Hello all! \n", - " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book 'Artificial Intelligence: A Modern Approach'. \n", + " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book *Artificial Intelligence: A Modern Approach*. \n", " \n", - " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first.\n", + " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository.\n", " \n", " What we will do to learn to use the code in this module is simply dive in! I feel this is the correct approach as I assume you must have already read Chapter 5 of AIMA. If you haven't, you might want to go back and do that first. If you are tired (or just lazy), at least read the chapter upto Sec. 5.3 because this module covers the algorithms only till that section anyway. So, I will start by explaining what the class `Game` is and then we will immediately start implementing the `TicTacToe` game. After we define the rules of the `TicTacToe` game, we will create AI players who use different search strategies, namely Minimax Search and Alpha-Beta Search. We will make these players play among themselves, and later on we ourselves will play against these AI players (Yay!). \n", " \n", @@ -594,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.4.4" } }, "nbformat": 4, From 67e1f88f9322468a936ba19f54dc7c528acbfacb Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 19:27:49 +0530 Subject: [PATCH 180/513] Add link to aima-java --- intro.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/intro.ipynb b/intro.ipynb index ce9020e95..c7492aa5c 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -10,7 +10,7 @@ " \n", "## About `aima-python` \n", " \n", - " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. \n", + " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before you proceedif you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository. \n", " \n", "## What version of Python?\n", " \n", @@ -55,7 +55,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.4" } }, "nbformat": 4, From 62094849e39d2e3fe56873bfcb3665349b0ca668 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 19:29:14 +0530 Subject: [PATCH 181/513] Add link to aima-java --- intro.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/intro.ipynb b/intro.ipynb index c7492aa5c..72e6ca0de 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -10,7 +10,7 @@ " \n", "## About `aima-python` \n", " \n", - " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before you proceedif you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository. \n", + " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before we proceed, I should let you know that if you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository. \n", " \n", "## What version of Python?\n", " \n", From 3e8b857e09009590b9e2e2e4a80b9fd8e7e09c15 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 19:55:24 +0530 Subject: [PATCH 182/513] Add the introduction section to logic.ipynb --- games.ipynb | 2 +- intro.ipynb | 2 +- logic.ipynb | 30 ++++++++++++++++++++++++------ 3 files changed, 26 insertions(+), 8 deletions(-) diff --git a/games.ipynb b/games.ipynb index e2e3bdc41..d66c158a4 100644 --- a/games.ipynb +++ b/games.ipynb @@ -23,7 +23,7 @@ " Hello all! \n", " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book *Artificial Intelligence: A Modern Approach*. \n", " \n", - " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository.\n", + " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository.\n", " \n", " What we will do to learn to use the code in this module is simply dive in! I feel this is the correct approach as I assume you must have already read Chapter 5 of AIMA. If you haven't, you might want to go back and do that first. If you are tired (or just lazy), at least read the chapter upto Sec. 5.3 because this module covers the algorithms only till that section anyway. So, I will start by explaining what the class `Game` is and then we will immediately start implementing the `TicTacToe` game. After we define the rules of the `TicTacToe` game, we will create AI players who use different search strategies, namely Minimax Search and Alpha-Beta Search. We will make these players play among themselves, and later on we ourselves will play against these AI players (Yay!). \n", " \n", diff --git a/intro.ipynb b/intro.ipynb index 72e6ca0de..2b5d55b2c 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -10,7 +10,7 @@ " \n", "## About `aima-python` \n", " \n", - " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before we proceed, I should let you know that if you are more comfortable with Java than you are with Python we also have [aima-java](https://github.com/aimacode/aima-java) repository. \n", + " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before we proceed, I should let you know that if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository. \n", " \n", "## What version of Python?\n", " \n", diff --git a/logic.ipynb b/logic.ipynb index 79595ac5d..e8642b922 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -1,14 +1,32 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": true }, - "outputs": [], "source": [ - "import logic" + "# Explaining the logic.py module\n", + "*Author: Chirag Vartak* \n", + "*Date: 23rd March 2016*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An Introduction\n", + " \n", + " Hello reader. \n", + " In this IPython notebook, I will help you a little so that you will be more comfortable using the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module. As you might already know, the `logic.py` module implements the algorithms given in Chapter 6 (Logical Agents), Chapter 7 (First-Order Logic) and Chapter 8 (Inference in First-Order Logic) of the book *Artificial Intelligence: A Modern Approach*. \n", + " \n", + " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository. \n", + " \n", + " I am assuming that you have read at least Chapter 7 (Logical Agents). You really want to do this if you intend to make sense of anything I tell you in this notebook, or any code in the `logic.py` module, for that matter. If you haven't you should go back and read this chapter first, at least till Sec. 7.5. As a side note, be sure to keep the `logic.py` module open and keep referring to it as you read this notebook. The docstrings of most classes and function are well-written and will give you more insight and in some cases, even examples, of how to use that particular class or function. \n", + " \n", + " To briefly outline how I will proceed in this notebook, I will start by telling you more about the classes `KB` and `ProbKB`, the classes for the Knowledge Bases that we will be using. Next, we will begin with Propositional Logic; only after we are mostly done with it, we will be getting into First-Order Logic. In Propositional Logic, we will have a look at the class `Expr` and the `expr` function, and try to get more comfortable with using them to create and manipulate logical expressions. We will also play a little with other utility functions created to make working with statements easy. Then, we will construct a knowledge base of a specific situation in the Wumpus World. We will next go through the `tt_entails` function and experiment with it a bit. The `pl_resolution` and `pl_fc_entails` functions will come next. \n", + " \n", + " So let's get started." ] }, { @@ -37,7 +55,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.4" } }, "nbformat": 4, From 16c9658457a664806db9e96808453ca1ed5a09d4 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Wed, 23 Mar 2016 20:17:31 +0530 Subject: [PATCH 183/513] Add some content explaining knowledge bases --- logic.ipynb | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/logic.ipynb b/logic.ipynb index e8642b922..1e5a0c1e4 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -29,6 +29,22 @@ " So let's get started." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Knowledge Bases: `KB` and `PropKB` \n", + " \n", + " The class `KB` is just a template class which you have to inherit to create a knowledge base class that you plan to use. This class reminds you to implement all the methods mentioned here and will scream at you if you forget to. It is, what you might call in Java, an abstract class. The class `PropKB` has been derived from the class `KB` and all the methods have been implemented in here. Let's have a look at these classes in somewhat more detail. \n", + " \n", + " We see that the class `KB` 4 methods, apart from `__init__`. A point to note here: the `ask` method simply calls the `ask_generator` method. Thus, this one has already been implemented and what you'll have to actually implement when you create your own knowledge base class (if you want to, though I doubt you'll ever need to), will be the `ask_generator` function and not the `ask` function itself. \n", + " \n", + " The class `PropKB` now. \n", + "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base, in their CNF form. \n", + "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method. \n", + "* `ask_generator(self, query)` : " + ] + }, { "cell_type": "code", "execution_count": null, From a2502be8e20ac11c3217f835ad10fcb997c9a044 Mon Sep 17 00:00:00 2001 From: Larry He Date: Wed, 23 Mar 2016 22:36:16 -0400 Subject: [PATCH 184/513] Fixed Problems in Python 3 Port Problems fixed: Replaced cmp: 1 instance in line 323 of search.py Convert set() to {}: 3 instances in line 29 of MDP.py, line 181 and 215 of search.py Convert .sort() to sorted(): 1 instance in line 150 of text.py Cast map to list: 1 instance in line 47 of grid.py --- grid.py | 2 +- mdp.py | 2 +- search.py | 6 +++--- text.py | 3 +-- 4 files changed, 6 insertions(+), 7 deletions(-) diff --git a/grid.py b/grid.py index cac6a5b9e..fb34cfc2f 100644 --- a/grid.py +++ b/grid.py @@ -44,4 +44,4 @@ def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding value of lowest or more than the corresponding value of highest, clip to those values.""" - return type(vector)(map(clip, vector, lowest, highest)) + return type(vector)(list(map(clip, vector, lowest, highest))) diff --git a/mdp.py b/mdp.py index 442a92de6..9be1615db 100644 --- a/mdp.py +++ b/mdp.py @@ -26,7 +26,7 @@ def __init__(self, init, actlist, terminals, gamma=.9): if not (0 <= gamma < 1): raise ValueError("An MDP must have 0 <= gamma < 1") self.gamma = gamma - self.states = set() + self.states = {} self.reward = {} def R(self, state): diff --git a/search.py b/search.py index 831adb4da..951377489 100644 --- a/search.py +++ b/search.py @@ -212,7 +212,7 @@ def breadth_first_search(problem): return node frontier = FIFOQueue() frontier.append(node) - explored = set() + explored = {} while frontier: node = frontier.pop() explored.add(node.state) @@ -238,7 +238,7 @@ def best_first_graph_search(problem, f): return node frontier = PriorityQueue(min, f) frontier.append(node) - explored = set() + explored = {} while frontier: node = frontier.pop() if problem.goal_test(node.state): @@ -320,7 +320,7 @@ def RBFS(problem, node, flimit): s.f = max(s.path_cost + h(s), node.f) while True: # Order by lowest f value - successors.sort(lambda x, y: cmp(x.f, y.f)) + successors.sort(key=lambda x: x.f) best = successors[0] if best.f > flimit: return None, best.f diff --git a/text.py b/text.py index d4e48aa65..71691e1ce 100644 --- a/text.py +++ b/text.py @@ -147,8 +147,7 @@ def query(self, query_text, n=10): qwords = [w for w in words(query_text) if w not in self.stopwords] shortest = argmin(qwords, lambda w: len(self.index[w])) docs = self.index[shortest] - results = [(sum([self.score(w, d) for w in qwords]), d) for d in docs] - results.sort() + results = sorted([(sum([self.score(w, d) for w in qwords]), d) for d in docs]) results.reverse() return results[:n] From 43c2faf437333e5f729125b6749daade46afed1d Mon Sep 17 00:00:00 2001 From: Larry He Date: Wed, 23 Mar 2016 23:04:10 -0400 Subject: [PATCH 185/513] Reverted set changes Apparently, set() does not equal {} --- mdp.py | 2 +- search.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/mdp.py b/mdp.py index 9be1615db..442a92de6 100644 --- a/mdp.py +++ b/mdp.py @@ -26,7 +26,7 @@ def __init__(self, init, actlist, terminals, gamma=.9): if not (0 <= gamma < 1): raise ValueError("An MDP must have 0 <= gamma < 1") self.gamma = gamma - self.states = {} + self.states = set() self.reward = {} def R(self, state): diff --git a/search.py b/search.py index 951377489..268d7b7bc 100644 --- a/search.py +++ b/search.py @@ -212,7 +212,7 @@ def breadth_first_search(problem): return node frontier = FIFOQueue() frontier.append(node) - explored = {} + explored = set() while frontier: node = frontier.pop() explored.add(node.state) @@ -238,7 +238,7 @@ def best_first_graph_search(problem, f): return node frontier = PriorityQueue(min, f) frontier.append(node) - explored = {} + explored = set() while frontier: node = frontier.pop() if problem.goal_test(node.state): From 3df865acc8cde1a0f8d1edcd570fe9abc2b60a11 Mon Sep 17 00:00:00 2001 From: tolusalako Date: Thu, 24 Mar 2016 02:39:06 -0700 Subject: [PATCH 186/513] Fully implemented WumpusEnvironment following details from the book. (WumpusEnvironment) Fixed bug where dead agents still get to act (Environment) Improved Direction class to ease 2D Movements (Direction) Implemented an easier way to iterate with and without walls (XYEnvironment) Added is_inbounds() method to check if location is within the width and height. Works within walls also. (XYEnvironment) Multiple Minor Fixes(agents.py) --- agents.py | 291 +++++++++++++++++++++++++----------- tolusalako wumpustest.ipynb | 130 ++++++++++++++++ 2 files changed, 335 insertions(+), 86 deletions(-) create mode 100755 tolusalako wumpustest.ipynb diff --git a/agents.py b/agents.py index 0e4c69c88..776df123e 100644 --- a/agents.py +++ b/agents.py @@ -84,6 +84,8 @@ class Agent(Thing): def __init__(self, program=None): self.alive = True self.bump = False + self.holding = [] + self.performance = 0 if program is None: def program(percept): return eval(input('Percept={}; action? ' .format(percept))) @@ -266,7 +268,7 @@ def step(self): override this method.""" if not self.is_done(): actions = [agent.program(self.percept(agent)) - for agent in self.agents] + for agent in self.agents if agent.alive] for (agent, action) in zip(self.agents, actions): self.execute_action(agent, action) self.exogenous_change() @@ -331,7 +333,6 @@ def __init__(self, direction): self.direction = direction def __add__(self, heading): - print(heading, self.direction, heading == self.direction) if self.direction == self.R: return{ self.R: Direction(self.D), @@ -339,13 +340,13 @@ def __add__(self, heading): }.get(heading, None) elif self.direction == self.L: return{ - self.R: Direction(U), - self.L: Direction(L), + self.R: Direction(self.U), + self.L: Direction(self.L), }.get(heading, None) elif self.direction == self.U: return{ - self.R: Direction(R), - self.L: Direction(L), + self.R: Direction(self.R), + self.L: Direction(self.L), }.get(heading, None) elif self.direction == self.D: return{ @@ -353,6 +354,17 @@ def __add__(self, heading): self.L: Direction(self.R), }.get(heading, None) + def move_forward(self, from_location): + x,y = from_location + if self.direction == self.R: + return (x+1, y) + elif self.direction == self.L: + return (x-1, y) + elif self.direction == self.U: + return (x, y-1) + elif self.direction == self.D: + return (x, y+1) + class XYEnvironment(Environment): @@ -368,28 +380,31 @@ def __init__(self, width=10, height=10): super(XYEnvironment, self).__init__() update(self, width=width, height=height, observers=[]) + #Sets iteration start and end (no walls). + self.x_start,self.y_start = (0,0) + self.x_end,self.y_end = (self.width, self.height) + perceptible_distance = 1 def things_near(self, location, radius=None): "Return all things within radius of location." if radius is None: radius = self.perceptible_distance radius2 = radius * radius - return [thing for thing in self.things + return [(thing, radius2 - distance2(location, thing.location)) for thing in self.things if distance2(location, thing.location) <= radius2] def percept(self, agent): - "By default, agent perceives things within a default radius." - return [self.thing_percept(thing, agent) - for thing in self.things_near(agent.location)] + '''By default, agent perceives things within a default radius.''' + return self.things_near(agent.location) def execute_action(self, agent, action): agent.bump = False if action == 'TurnRight': - agent.heading = self.turn_heading(agent.heading, -1) + agent.direction = agent.direction + Direction.R elif action == 'TurnLeft': - agent.heading = self.turn_heading(agent.heading, +1) + agent.direction = agent.direction + Direction.L elif action == 'Forward': - self.move_to(agent, vector_add(agent.heading, agent.location)) + agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location)) # elif action == 'Grab': # things = [thing for thing in self.list_things_at(agent.location) # if agent.can_grab(thing)] @@ -399,45 +414,72 @@ def execute_action(self, agent, action): if agent.holding: agent.holding.pop() - def thing_percept(self, thing, agent): # ??? Should go to thing? - "Return the percept for this thing." - return thing.__class__.__name__ - def default_location(self, thing): return (random.choice(self.width), random.choice(self.height)) def move_to(self, thing, destination): - "Move a thing to a new location." + '''Move a thing to a new location. Returns True on success or False if there is an Obstacle + If thing is grabbing anything, they move with him ''' thing.bump = self.some_things_at(destination, Obstacle) if not thing.bump: thing.location = destination for o in self.observers: o.thing_moved(thing) - - def add_thing(self, thing, location=(1, 1)): - super(XYEnvironment, self).add_thing(thing, location) - thing.holding = [] - thing.held = None - for obs in self.observers: - obs.thing_added(thing) + for t in thing.holding: + self.delete_thing(t) + self.add_thing(t, destination) + t.location = destination + return thing.bump + + # def add_thing(self, thing, location=(1, 1)): + # super(XYEnvironment, self).add_thing(thing, location) + # thing.holding = [] + # thing.held = None + # for obs in self.observers: + # obs.thing_added(thing) + + def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items = False): + '''Adds things to the world. + If (exclude_duplicate_class_items) then the item won't be added if the location + has at least one item of the same class''' + if (self.is_inbounds(location)): + if (exclude_duplicate_class_items and + any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): + return + super(XYEnvironment, self).add_thing(thing, location) + + def is_inbounds(self, location): + '''Checks to make sure that the location is inbounds (within walls if we have walls)''' + x,y = location + return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end) + + def random_location_inbounds(self, exclude = None): + '''Returns a random location that is inbounds (within walls if we have walls)''' + location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) + if exclude is not None: + while(location == exclude): + location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) + return location def delete_thing(self, thing): super(XYEnvironment, self).delete_thing(thing) # Any more to do? Thing holding anything or being held? for obs in self.observers: obs.thing_deleted(thing) - - has_walls = False + def add_walls(self): - "Put walls around the entire perimeter of the grid." + '''Put walls around the entire perimeter of the grid.''' for x in range(self.width): self.add_thing(Wall(), (x, 0)) self.add_thing(Wall(), (x, self.height-1)) for y in range(self.height): self.add_thing(Wall(), (0, y)) self.add_thing(Wall(), (self.width-1, y)) - self.has_walls = True - + + #Updates iteration start and end (with walls). + self.x_start,self.y_start = (1,1) + self.x_end,self.y_end = (self.width-1, self.height-1) + def add_observer(self, observer): """Adds an observer to the list of observers. An observer is typically an EnvGUI. @@ -550,8 +592,17 @@ def default_location(self, thing): class Gold(Thing): + + def __eq__(self, rhs): + '''All Gold are equal''' + return rhs.__class__ == Gold pass +class Bump(Thing): + pass + +class Glitter(Thing): + pass class Pit(Thing): pass @@ -563,88 +614,156 @@ class Breeze(Thing): class Arrow(Thing): pass +class Scream(Thing): + pass + -class Wumpus(Agent): +class Wumpus(Thing): pass class Stench(Thing): pass class Explorer(Agent): + holding = [] + arrow_count = 1 + killed_by = "" direction = Direction("right") + + def can_grab(self, thing): + '''Explorer can only grab gold''' + return thing.__class__ == Gold class WumpusEnvironment(XYEnvironment): - pit_probability = 0.2 #Probability to spawn a pit in a location - - def __init__(self, width=10, height=10): + pit_probability = 0.2 #Probability to spawn a pit in a location. (From Chapter 7.2) + #Room should be 4x4 grid of rooms. The extra 2 for walls + def __init__(self, agent_program, width=6, height=6): super(WumpusEnvironment, self).__init__(width, height) - self.add_walls() - self.init_world() + self.init_world(agent_program) - def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items = True): - '''Adds thing to the world''' - if (isinstance(thing, Wall) or self.is_inbounds(location)): - if (exclude_duplicate_class_items and - any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): - return - super(WumpusEnvironment, self).add_thing(thing, location) - - def init_world(self): + def init_world(self, program): '''Spawn items to the world based on probabilities from the book''' + + "WALLS" + self.add_walls() + "PITS" - for x in range(self.width): - for y in range(self.height): + for x in range(self.x_start, self.x_end): + for y in range(self.y_start, self.y_end): if random.random() < self.pit_probability: - self.add_thing(Pit(), (x,y)) - self.add_thing(Breeze(), (x - 1,y)) - self.add_thing(Breeze(), (x,y - 1)) - self.add_thing(Breeze(), (x + 1,y)) - self.add_thing(Breeze(), (x,y + 1)) + self.add_thing(Pit(), (x,y), True) + self.add_thing(Breeze(), (x - 1,y), True) + self.add_thing(Breeze(), (x,y - 1), True) + self.add_thing(Breeze(), (x + 1,y), True) + self.add_thing(Breeze(), (x,y + 1), True) "WUMPUS" - w_x, w_y = self.random_location(exclude = (1,1)) - self.add_thing(Wumpus(), (w_x, w_y)) - self.add_thing(Stench(), (w_x - 1, w_y)) - self.add_thing(Stench(), (w_x + 1, w_y)) - self.add_thing(Stench(), (w_x, w_y - 1)) - self.add_thing(Stench(), (w_x, w_y + 1)) + w_x, w_y = self.random_location_inbounds(exclude = (1,1)) + self.add_thing(Wumpus(), (w_x, w_y), True) + self.add_thing(Stench(), (w_x - 1, w_y), True) + self.add_thing(Stench(), (w_x + 1, w_y), True) + self.add_thing(Stench(), (w_x, w_y - 1), True) + self.add_thing(Stench(), (w_x, w_y + 1), True) "GOLD" - self.add_thing(Gold(), self.random_location(exclude = (1,1))) - + self.add_thing(Gold(), self.random_location_inbounds(exclude = (1,1)), True) + #self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier + "AGENT" - self.add_thing(Explorer(), (1,1)) + self.add_thing(Explorer(program), (1,1), True) - def is_inbounds(self, location): - '''Checks to make sure that the location is inbounds (within walls)''' - x,y = location - return not (x < 0 or x >= self.width or y < 0 or y >= self.height) - - def random_location(self, exclude = None): - '''Returns a random location that is inbounds''' - location = (random.randint(1, self.width), random.randint(1, self.height)) - if exclude is not None: - while(location == exclude): - location = (random.randint(1, self.width), random.randint(1, self.height)) - return location - - def print_world(self, show_walls = False): - '''Prints the world''' - x_start,y_start = (1,1) if not show_walls else (0,0) - x_end,y_end = (self.width-1, self.height-1) if not show_walls else (self.width, self.height) + def get_world(self, show_walls = True): + '''returns the items in the world''' + result = [] + x_start,y_start = (0,0) if show_walls else (1,1) + x_end,y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) for x in range(x_start, x_end): + row = [] for y in range(y_start, y_end): - print(self.list_things_at((x,y)), end = "") - print() + row.append(self.list_things_at((x,y))) + result.append(row) + return result + + def percepts_from(self, agent, location, tclass = Thing): + '''Returns percepts from a given location, and replaces some items with percepts from chapter 7.''' + thing_percepts = { + Gold: Glitter(), + Wall: Bump(), + Wumpus: Stench(), + Pit: Breeze() + } + '''Agents don't need to get their percepts''' + thing_percepts[agent.__class__] = None + + '''Gold only glitters in its cell''' + if location != agent.location: + thing_percepts[Gold] = None + result = [thing_percepts.get(thing.__class__, thing) for thing in self.things + if thing.location == location and isinstance(thing, tclass)] + return result if len(result) else [None] + + def percept(self, agent): + '''Returns things in adjacent (not diagonal) cells of the agent. + Result format: [Left, Right, Up, Down, Center / Current location]''' + x,y = agent.location + result = [] + result.append(self.percepts_from(agent, (x - 1,y))) + result.append(self.percepts_from(agent, (x + 1,y))) + result.append(self.percepts_from(agent, (x,y - 1))) + result.append(self.percepts_from(agent, (x,y + 1))) + result.append(self.percepts_from(agent, (x,y))) + return result - def thing_classes(self): - return [Wall, Gold, Pit, Arrow, Wumpus, Explorer] - - # Needs a lot of work ... - - + def execute_action(self, agent, action): + '''Modify the state of the environment based on the agent's actions + Performance score taken directly out of the book''' + + agent.bump = False + if action == 'TurnRight': + agent.direction = agent.direction + Direction.R + agent.performance -= 1 + elif action == 'TurnLeft': + agent.direction = agent.direction + Direction.L + agent.performance -= 1 + elif action == 'Forward': + agent.bump = self.move_to(agent, agent.direction.move_forward(agent.location)) + agent.performance -= 1 + elif action == 'Grab': + things = [thing for thing in self.list_things_at(agent.location) + if agent.can_grab(thing)] + print("Grabbing", things[0].__class__.__name__) + if len(things): + agent.holding.append(things[0]) + agent.performance -= 1 + elif action == 'Climb': + if agent.location == (1,1): #Agent can only climb out of (1,1) + agent.performance += 1000 if Gold() in agent.holding else 0 + self.delete_thing(agent) + + '''Check if agent is in danger, if he is, kill him''' + for thing in self.list_things_at(agent.location): + if isinstance(thing, Wumpus) or isinstance(thing, Pit): + agent.alive = False + agent.performance -= 1000 + agent.killed_by = thing.__class__.__name__ + + def is_done(self): + '''The game is over when the Explorer is killed + or if he climbs out of the cave only at (1,1)''' + explorer = [agent for agent in self.agents if isinstance(agent, Explorer) ] + if len(explorer): + if explorer[0].alive: + return False + else: + print("Death by {} [-1000].".format(explorer[0].killed_by)) + else: + print("Explorer climbed out {}." + .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]")) + return True + + #Almost done. Arrow needs to be implemented # ______________________________________________________________________________ def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000): diff --git a/tolusalako wumpustest.ipynb b/tolusalako wumpustest.ipynb new file mode 100755 index 000000000..6fedf930b --- /dev/null +++ b/tolusalako wumpustest.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from ipythonblocks import BlockGrid\n", + "from agents import *\n", + "\n", + "color = {\"Breeze\": (225, 225, 225),\n", + " \"Pit\": (0,0,0),\n", + " \"Gold\": (253, 208, 23),\n", + " \"Glitter\": (253, 208, 23),\n", + " \"Wumpus\": (43, 27, 23),\n", + " \"Stench\": (128, 128, 128),\n", + " \"Explorer\": (0, 0, 255),\n", + " \"Wall\": (44, 53, 57)\n", + " }\n", + "\n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " print(percepts)\n", + " return input()\n", + "\n", + "w = WumpusEnvironment(program, 7, 7) \n", + "grid = BlockGrid(w.width, w.height, fill=(123, 234, 123))\n", + "\n", + "def draw_grid(world):\n", + " global grid\n", + " grid[:] = (123, 234, 123)\n", + " for x in range(0, len(world)):\n", + " for y in range(0, len(world[x])):\n", + " if len(world[x][y]):\n", + " grid[y, x] = color[world[x][y][-1].__class__.__name__]\n", + "\n", + "def step():\n", + " global grid, w\n", + " draw_grid(w.get_world())\n", + " grid.show()\n", + " w.step()\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Explorer climbed out without Gold [+0].\n" + ] + } + ], + "source": [ + "step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From ad78b805f37d8932e18f92fcbc595ccba4810b41 Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Thu, 24 Mar 2016 18:54:40 +0530 Subject: [PATCH 187/513] Add more content about knowledge bases --- logic.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index 1e5a0c1e4..6b1868779 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -42,16 +42,15 @@ " The class `PropKB` now. \n", "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base, in their CNF form. \n", "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method. \n", - "* `ask_generator(self, query)` : " + "* `ask_generator(self, query)` : The `ask_generator` function is to be used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns and empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem weird to you a little bit. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is to maintain consistency with how things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true in a dict. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`. \n", + "* `retract(self, sentence)` : This function removes all the clauses of the sentence given from the knowledge base. Like the `tell` function, you don't have to explicitly pass only clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses. " ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [] } ], From 4ed9716daa8e226880c7f938056d2fe07ab02afb Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Thu, 24 Mar 2016 19:13:51 +0530 Subject: [PATCH 188/513] Improve markdown formatting; correct minor mistakes --- logic.ipynb | 57 ++++++++++++++++++++++++++++++++--------------------- 1 file changed, 34 insertions(+), 23 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index 6b1868779..b238f7f48 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -7,7 +7,7 @@ }, "source": [ "# Explaining the logic.py module\n", - "*Author: Chirag Vartak* \n", + "*Author: Chirag Vartak*
    \n", "*Date: 23rd March 2016*" ] }, @@ -16,34 +16,34 @@ "metadata": {}, "source": [ "## An Introduction\n", - " \n", - " Hello reader. \n", - " In this IPython notebook, I will help you a little so that you will be more comfortable using the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module. As you might already know, the `logic.py` module implements the algorithms given in Chapter 6 (Logical Agents), Chapter 7 (First-Order Logic) and Chapter 8 (Inference in First-Order Logic) of the book *Artificial Intelligence: A Modern Approach*. \n", - " \n", - " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository. \n", - " \n", - " I am assuming that you have read at least Chapter 7 (Logical Agents). You really want to do this if you intend to make sense of anything I tell you in this notebook, or any code in the `logic.py` module, for that matter. If you haven't you should go back and read this chapter first, at least till Sec. 7.5. As a side note, be sure to keep the `logic.py` module open and keep referring to it as you read this notebook. The docstrings of most classes and function are well-written and will give you more insight and in some cases, even examples, of how to use that particular class or function. \n", - " \n", - " To briefly outline how I will proceed in this notebook, I will start by telling you more about the classes `KB` and `ProbKB`, the classes for the Knowledge Bases that we will be using. Next, we will begin with Propositional Logic; only after we are mostly done with it, we will be getting into First-Order Logic. In Propositional Logic, we will have a look at the class `Expr` and the `expr` function, and try to get more comfortable with using them to create and manipulate logical expressions. We will also play a little with other utility functions created to make working with statements easy. Then, we will construct a knowledge base of a specific situation in the Wumpus World. We will next go through the `tt_entails` function and experiment with it a bit. The `pl_resolution` and `pl_fc_entails` functions will come next. \n", - " \n", - " So let's get started." + "\n", + "Hello reader.
    \n", + "In this IPython notebook, I will help you a little so that you will become more comfortable with using the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module. The `logic.py` module implements the algorithms given in Chapter 6 (Logical Agents), Chapter 7 (First-Order Logic) and Chapter 8 (Inference in First-Order Logic) of the book *Artificial Intelligence: A Modern Approach*.\n", + "\n", + "Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository.\n", + "\n", + "I am assuming that you have read at least Chapter 7 (Logical Agents). You really want to do this if you intend to make sense of anything I tell you in this notebook, or any code in the `logic.py` module, for that matter. If you haven't you should go back and read this chapter first, at least upto Sec. 7.5. As a side note, be sure to keep the `logic.py` module open and keep referring to it as you read this notebook. The docstrings of most classes and functions are well-written and will give you more insight and in some cases, even examples, of how to use that particular class or function.\n", + "\n", + "To briefly outline how I will proceed in this notebook, I will start by telling you more about the classes `KB` and `ProbKB`, the classes for the Knowledge Bases that we will be using. Next, we will begin with Propositional Logic; only after we are mostly done with it, we will be getting into First-Order Logic. In Propositional Logic, we will have a look at the class `Expr` and the `expr` function, and try to get more comfortable with using them to create and manipulate logical expressions. We will also play a little with other utility functions created to make working with statements easy. Then, we will construct a knowledge base of a specific situation in the Wumpus World. We will next go through the `tt_entails` function and experiment with it a bit. The `pl_resolution` and `pl_fc_entails` functions will come next.\n", + "\n", + "So let's get started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Knowledge Bases: `KB` and `PropKB` \n", - " \n", - " The class `KB` is just a template class which you have to inherit to create a knowledge base class that you plan to use. This class reminds you to implement all the methods mentioned here and will scream at you if you forget to. It is, what you might call in Java, an abstract class. The class `PropKB` has been derived from the class `KB` and all the methods have been implemented in here. Let's have a look at these classes in somewhat more detail. \n", - " \n", - " We see that the class `KB` 4 methods, apart from `__init__`. A point to note here: the `ask` method simply calls the `ask_generator` method. Thus, this one has already been implemented and what you'll have to actually implement when you create your own knowledge base class (if you want to, though I doubt you'll ever need to), will be the `ask_generator` function and not the `ask` function itself. \n", - " \n", - " The class `PropKB` now. \n", - "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base, in their CNF form. \n", - "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method. \n", - "* `ask_generator(self, query)` : The `ask_generator` function is to be used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns and empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem weird to you a little bit. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is to maintain consistency with how things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true in a dict. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`. \n", - "* `retract(self, sentence)` : This function removes all the clauses of the sentence given from the knowledge base. Like the `tell` function, you don't have to explicitly pass only clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses. " + "## Knowledge Bases: `KB` and `PropKB`\n", + "\n", + "The class `KB` is just a template class which you have to inherit to create a knowledge base class that you plan to use. This class reminds you to implement all the methods mentioned here and will scream at you if you forget to. It is, what you might call in Java, an abstract class. The class `PropKB` has been derived from the class `KB` and all the methods have been implemented in here. Let's have a look at these classes in somewhat more detail.\n", + "\n", + "We see that the class `KB` has four methods, apart from `__init__`. A point to note here: the `ask` method simply calls the `ask_generator` method. Thus, this one has already been implemented and what you'll have to actually implement when you create your own knowledge base class (if you want to, though I doubt you'll ever need to; just use the ones we've created for you), will be the `ask_generator` function and not the `ask` function itself.\n", + "\n", + "The class `PropKB` now.\n", + "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base. Note that each one of these sentences will be a 'clause' i.e. a sentence which is made up of only literals and `or`s.\n", + "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method.\n", + "* `ask_generator(self, query)` : The `ask_generator` function is to be used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns and empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem weird to you a little bit. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is to maintain consistency with how things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true in a dict. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`.\n", + "* `retract(self, sentence)` : This function removes all the clauses of the sentence given from the knowledge base. Like the `tell` function, you don't have to explicitly pass only clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses." ] }, { @@ -51,6 +51,17 @@ "metadata": { "collapsed": true }, + "source": [ + "## Getting started with Propositional Logic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [] } ], From 65636ca3ecd28ec32c319da81faf15565c811551 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 12:50:38 -0400 Subject: [PATCH 189/513] Revert cast, improved query function Cast was not needed since the return value was already being cast. Query function changed to use heapq.nlargest() for simplicity --- grid.py | 2 +- text.py | 14 ++++++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/grid.py b/grid.py index fb34cfc2f..cac6a5b9e 100644 --- a/grid.py +++ b/grid.py @@ -44,4 +44,4 @@ def vector_clip(vector, lowest, highest): """Return vector, except if any element is less than the corresponding value of lowest or more than the corresponding value of highest, clip to those values.""" - return type(vector)(list(map(clip, vector, lowest, highest))) + return type(vector)(map(clip, vector, lowest, highest)) diff --git a/text.py b/text.py index 71691e1ce..e1a4ecde0 100644 --- a/text.py +++ b/text.py @@ -147,15 +147,17 @@ def query(self, query_text, n=10): qwords = [w for w in words(query_text) if w not in self.stopwords] shortest = argmin(qwords, lambda w: len(self.index[w])) docs = self.index[shortest] - results = sorted([(sum([self.score(w, d) for w in qwords]), d) for d in docs]) - results.reverse() - return results[:n] + return heapq.nlargest(n, ((total_score(qwords, doc), doc) for doc in docs)) - def score(self, word, docid): + def score(self, word, doc): "Compute a score for this word on this docid." # There are many options; here we take a very simple approach - return (math.log(1 + self.index[word][docid]) / - math.log(1 + self.documents[docid].nwords)) + return (math.log(1 + self.index[word][doc]) / + math.log(1 + self.documents[doc].nwords)) + + def total_score(qwords, doc): + "Compute the sum of the scores of the queried words on this doc." + return sum(self.score(qword, doc) for qword in qwords) def present(self, results): "Present the results as a list." From c75a3353c5f06a7c5c9aab34367be325d25e35e1 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 12:55:16 -0400 Subject: [PATCH 190/513] Change all mentions of docid to doc in text.py This change is for consistency. --- text.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/text.py b/text.py index e1a4ecde0..78ab2bb74 100644 --- a/text.py +++ b/text.py @@ -131,11 +131,11 @@ def index_document(self, text, url): # For now, use first line for title title = text[:text.index('\n')].strip() docwords = words(text) - docid = len(self.documents) + doc = len(self.documents) self.documents.append(Document(title, url, len(docwords))) for word in docwords: if word not in self.stopwords: - self.index[word][docid] += 1 + self.index[word][doc] += 1 def query(self, query_text, n=10): """Return a list of n (score, docid) pairs for the best matches. @@ -150,7 +150,7 @@ def query(self, query_text, n=10): return heapq.nlargest(n, ((total_score(qwords, doc), doc) for doc in docs)) def score(self, word, doc): - "Compute a score for this word on this docid." + "Compute a score for this word on this doc." # There are many options; here we take a very simple approach return (math.log(1 + self.index[word][doc]) / math.log(1 + self.documents[doc].nwords)) From d23d95c9301fa0c5337fc25c8fe6c285fe2e8006 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:00:56 -0400 Subject: [PATCH 191/513] Import heapq --- text.py | 1 + 1 file changed, 1 insertion(+) diff --git a/text.py b/text.py index 78ab2bb74..63c9710d7 100644 --- a/text.py +++ b/text.py @@ -10,6 +10,7 @@ from math import log, exp from collections import defaultdict +import heapq import re From e914051933854915585d7aee20317a6a4354cead Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:02:12 -0400 Subject: [PATCH 192/513] Change more docid instance to doc --- text.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/text.py b/text.py index 63c9710d7..2bc669ad4 100644 --- a/text.py +++ b/text.py @@ -115,7 +115,7 @@ class IRSystem: def __init__(self, stopwords='the a of'): """Create an IR System. Optionally specify stopwords.""" - # index is a map of {word: {docid: count}}, where docid is an int, + # index is a map of {word: {doc: count}}, where doc is an int, # indicating the index into the documents list. update(self, index=defaultdict(lambda: defaultdict(int)), stopwords=set(words(stopwords)), documents=[]) @@ -139,7 +139,7 @@ def index_document(self, text, url): self.index[word][doc] += 1 def query(self, query_text, n=10): - """Return a list of n (score, docid) pairs for the best matches. + """Return a list of n (score, doc) pairs for the best matches. Also handle the special syntax for 'learn: command'.""" if query_text.startswith("learn:"): doctext = os.popen(query_text[len("learn:"):], 'r').read() From d58c2b00dc1eee5394e47e861015e474e507ecf0 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:04:46 -0400 Subject: [PATCH 193/513] Fix total_score Added self to argument --- text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text.py b/text.py index 2bc669ad4..717c885a3 100644 --- a/text.py +++ b/text.py @@ -156,7 +156,7 @@ def score(self, word, doc): return (math.log(1 + self.index[word][doc]) / math.log(1 + self.documents[doc].nwords)) - def total_score(qwords, doc): + def total_score(self, qwords, doc): "Compute the sum of the scores of the queried words on this doc." return sum(self.score(qword, doc) for qword in qwords) From 20b42f93872b43b12f43cc91ed025c212ae10bfd Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:07:58 -0400 Subject: [PATCH 194/513] Fixed the other missing self --- text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text.py b/text.py index 717c885a3..c8c8c865f 100644 --- a/text.py +++ b/text.py @@ -148,7 +148,7 @@ def query(self, query_text, n=10): qwords = [w for w in words(query_text) if w not in self.stopwords] shortest = argmin(qwords, lambda w: len(self.index[w])) docs = self.index[shortest] - return heapq.nlargest(n, ((total_score(qwords, doc), doc) for doc in docs)) + return heapq.nlargest(n, ((self.total_score(qwords, doc), doc) for doc in docs)) def score(self, word, doc): "Compute a score for this word on this doc." From 3e70af2c58278cda563f14354b5d4e763a29bfb4 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:28:41 -0400 Subject: [PATCH 195/513] Fix variable names in total_score --- text.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/text.py b/text.py index c8c8c865f..f28454434 100644 --- a/text.py +++ b/text.py @@ -156,9 +156,9 @@ def score(self, word, doc): return (math.log(1 + self.index[word][doc]) / math.log(1 + self.documents[doc].nwords)) - def total_score(self, qwords, doc): + def total_score(self, words, doc): "Compute the sum of the scores of the queried words on this doc." - return sum(self.score(qword, doc) for qword in qwords) + return sum(self.score(word, doc) for word in words) def present(self, results): "Present the results as a list." From 5d27561622238b055ee14c68817b4a92129d4181 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:29:10 -0400 Subject: [PATCH 196/513] Change description of total_score --- text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text.py b/text.py index f28454434..ba074b7fe 100644 --- a/text.py +++ b/text.py @@ -157,7 +157,7 @@ def score(self, word, doc): math.log(1 + self.documents[doc].nwords)) def total_score(self, words, doc): - "Compute the sum of the scores of the queried words on this doc." + "Compute the sum of the scores of these words on this doc." return sum(self.score(word, doc) for word in words) def present(self, results): From 5f96f3bb1cf8e08e34a0478a3ff43f990c33ca35 Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:34:57 -0400 Subject: [PATCH 197/513] Change all mentions of doc to docid In hindsight, the variable name "doc" implies we are manipulating the document itself when, in reality, we are manipulating the index of the document. "docid" better expresses this concept. --- text.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/text.py b/text.py index ba074b7fe..b4478034c 100644 --- a/text.py +++ b/text.py @@ -115,7 +115,7 @@ class IRSystem: def __init__(self, stopwords='the a of'): """Create an IR System. Optionally specify stopwords.""" - # index is a map of {word: {doc: count}}, where doc is an int, + # index is a map of {word: {docid: count}}, where docid is an int, # indicating the index into the documents list. update(self, index=defaultdict(lambda: defaultdict(int)), stopwords=set(words(stopwords)), documents=[]) @@ -132,14 +132,14 @@ def index_document(self, text, url): # For now, use first line for title title = text[:text.index('\n')].strip() docwords = words(text) - doc = len(self.documents) + docid = len(self.documents) self.documents.append(Document(title, url, len(docwords))) for word in docwords: if word not in self.stopwords: - self.index[word][doc] += 1 + self.index[word][docid] += 1 def query(self, query_text, n=10): - """Return a list of n (score, doc) pairs for the best matches. + """Return a list of n (score, docid) pairs for the best matches. Also handle the special syntax for 'learn: command'.""" if query_text.startswith("learn:"): doctext = os.popen(query_text[len("learn:"):], 'r').read() @@ -147,23 +147,23 @@ def query(self, query_text, n=10): return [] qwords = [w for w in words(query_text) if w not in self.stopwords] shortest = argmin(qwords, lambda w: len(self.index[w])) - docs = self.index[shortest] - return heapq.nlargest(n, ((self.total_score(qwords, doc), doc) for doc in docs)) + docids = self.index[shortest] + return heapq.nlargest(n, ((self.total_score(qwords, docid), docid) for docid in docids)) - def score(self, word, doc): - "Compute a score for this word on this doc." + def score(self, word, docid): + "Compute a score for this word on the document with this docid." # There are many options; here we take a very simple approach - return (math.log(1 + self.index[word][doc]) / - math.log(1 + self.documents[doc].nwords)) + return (math.log(1 + self.index[word][docid]) / + math.log(1 + self.documents[docid].nwords)) - def total_score(self, words, doc): - "Compute the sum of the scores of these words on this doc." - return sum(self.score(word, doc) for word in words) + def total_score(self, words, docid): + "Compute the sum of the scores of these words on the doccument with this docid." + return sum(self.score(word, docid) for word in words) def present(self, results): "Present the results as a list." - for (score, d) in results: - doc = self.documents[d] + for (score, docid) in results: + doc = self.documents[docid] print( ("{:5.2}|{:25} | {}".format(100 * score, doc.url, doc.title[:45].expandtabs()))) From 65907a328ea5ece07bfb1fd42c0e4821b2bde9ab Mon Sep 17 00:00:00 2001 From: Larry He Date: Thu, 24 Mar 2016 13:45:16 -0400 Subject: [PATCH 198/513] Fix typo in comment doccument -> document --- text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text.py b/text.py index b4478034c..d637d6757 100644 --- a/text.py +++ b/text.py @@ -157,7 +157,7 @@ def score(self, word, docid): math.log(1 + self.documents[docid].nwords)) def total_score(self, words, docid): - "Compute the sum of the scores of these words on the doccument with this docid." + "Compute the sum of the scores of these words on the document with this docid." return sum(self.score(word, docid) for word in words) def present(self, results): From f367a16514c2a17521829bdeec5075c381cc0ddc Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Fri, 25 Mar 2016 01:33:45 +0530 Subject: [PATCH 199/513] The notebook at an itermediate stage --- logic.ipynb | 58 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 49 insertions(+), 9 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index b238f7f48..bb448c08f 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -8,7 +8,9 @@ "source": [ "# Explaining the logic.py module\n", "*Author: Chirag Vartak*
    \n", - "*Date: 23rd March 2016*" + "*Date: 23rd March 2016*\n", + "\n", + "---" ] }, { @@ -42,8 +44,8 @@ "The class `PropKB` now.\n", "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base. Note that each one of these sentences will be a 'clause' i.e. a sentence which is made up of only literals and `or`s.\n", "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method.\n", - "* `ask_generator(self, query)` : The `ask_generator` function is to be used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns and empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem weird to you a little bit. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is to maintain consistency with how things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true in a dict. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`.\n", - "* `retract(self, sentence)` : This function removes all the clauses of the sentence given from the knowledge base. Like the `tell` function, you don't have to explicitly pass only clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses." + "* `ask_generator(self, query)` : The `ask_generator` function is used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns an empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem a little bit weird to you. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is done to maintain consistency with the way things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true. Hence the dict, to return all these substitutions. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`.\n", + "* `retract(self, sentence)` : This function removes all the clauses of the sentence given, from the knowledge base. Like the `tell` function, you don't have to pass clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses and then remove those." ] }, { @@ -55,6 +57,42 @@ "## Getting started with Propositional Logic" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A note to delete later on\n", + "\n", + "1. Markdown is converted to HTML. It's closer to HTML than it is to Latex. Hence, to add special symbols, unicode characters etc., use HTML character reference rather than Latex symbols.\n", + "2. Propositional symbols in markdown\n", + " * ¬   `¬`\n", + " * ∧   `∧`\n", + " * ∨   `∨`\n", + " * →   `→`\n", + " * ↔   `↔`\n", + "3. And some others\n", + " * ⊨   `⊨`\n", + " * ≡   `≡`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "def my_function(string1, string2):\n", + " for in in range(42):\n", + " print string1 + string2\n", + "```\n", + "\n", + "```python def __init__(self, sentence)```" + ] + }, { "cell_type": "code", "execution_count": null, @@ -62,26 +100,28 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "dcsdfdsf" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.4" + "pygments_lexer": "ipython2", + "version": "2.7.11" } }, "nbformat": 4, From c114cc798c9e76e4d50ea7bcace05e25d048afcd Mon Sep 17 00:00:00 2001 From: Chirag Vartak Date: Fri, 25 Mar 2016 14:35:35 +0530 Subject: [PATCH 200/513] Complete explanation of the Expr class --- logic.ipynb | 268 +++++++++++++++++++++++++++++++++++++++++++++++----- logic.py | 2 +- 2 files changed, 245 insertions(+), 25 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index bb448c08f..097cfc804 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -60,37 +60,259 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "### The `Expr` class\n", + "\n", + "The `Expr` class is the one that enables us to work with propositional logic. This class, combined with the `expr` function will enable us to work with propositional logic with much ease.\n", + "\n", + "An instance of the `Expr` class, an `Expr` object represents a symbolic mathematical expression. Truth be told, this class can handle not just Propositional Logic but also First-Order Logic. (As a matter of fact, you can also do arithmetic using this class but you would just be introducing unnecessary complication for a simple task). For the case of our Propositional Logic, an `Expr` object represents a propositional sentence. If you will have a look at its `__init__`, you will see that an `Expr` object just stores the operator and the arguments of a propositional sentence. This is important to note. The `Expr` class does not define the *logic* of Propositional Logic; nor will we be defining it ourselves. It just gives you a way to *represent* expressions. You won't be able to do any propositional math using `Expr`; you won't be be able assign a value of `True` to `P` and `False` to `Q` and then do a `P` ∧ `Q` to get `False`. No, you won't be able to do that. What you will be able to do is to create a representation of sentence and assign it to `P`. Something like,\n", + "\n", + "```python\n", + "sent = Expr(\"==>\", \"A & B\", \"C\")\n", + "```\n", + "\n", + "which is represents the sentence\n", + "\n", + "> (A ∧ B) → C\n", + "\n", + "That's not much, you say. We can create representations of sentences using strings, you continue. Well, we manipulate the `Expr` objects to convert a sentence to its CNF (`to_cnf`), check satisfiability of a sentence (`dpll_satisfiable`), use resolution to find out if a knowledge base entails a sentence (`pl_resolution`) and whatnot. Best of luck doing that with your string representations!\n", + "\n", + "So, the point to take away from the last two paragraphs: The `Expr` class just allows you to create good, easily manipulable representations of propositional sentences. It does a little more than that though. Before I get into that let us create a few expressions of our own to experiment with them later on." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from logic import *\n", + "\n", + "P = Expr(\"P\")\n", + "Q = Expr(\"Q\")\n", + "R1 = Expr(\"&\", \"A\", \"B\")\n", + "R2 = Expr(\"==>\", \"C | D\", \"E\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note here that you can create expressions that have no operators (like the literals `P` and `Q`), simple expressions of literals (like the sentence R1 which represents `A` ∧ `B`) and also expressions that have, as their arguments, complex sentences represented as strings. But, these strings that are allowed as arguments, can use only certain symbols in them. This is the list of symbols that you should use when you want to put complex sentences as arguments to the `Expr` constructor:\n", + "\n", + "| Operation | Propositional Symbol | Operator to use in Code |\n", + "|--------------------------|----------------------|-------------------------|\n", + "| Negation | ¬ | ~ |\n", + "| And | ∧ | & |\n", + "| Or | ∨ | | |\n", + "| Implies | → | >> or ==> |\n", + "| Biconditional | ↔ | % or <=> |\n", + "| **Some additional ones** | | |\n", + "| Inequality (Xor) | (Dunno) | =/= or ^ |\n", + "| Reverse Implication | ← | << or <== |\n", + "\n", + "Also, this is the precedence sequence with which the operators will be evaluated in code. The highest precedence operators are at the top:\n", + "\n", + " ~\n", + " % <=>\n", + " << <== >> ==>\n", + " &\n", + " ^\n", + " |\n", + " \n", + "Note that the `<=>` and the implication operators are quite at the top. So make sure to use parenthesis correctly when using them with others like `&`, `^` and `|`. You might note that the precedence of these operators is the same as that in Python language. This is not just a coincidence. More about this later.\n", + "\n", + "Getting back to the `Expr` class and the expressions that we have created, lets create a more complex expression from the ones we have already created:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Cell: Creating complicated sentences\n", + "R3 = Expr(\"<=>\", R1, Q)\n", + "R4 = Expr(\"==>\", R2, P & ~Q)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, these are the expressions that we've created now. To display these expressions in a nice, intuitive form, the `__repr__` method has been implemented accordingly. It called when we put the variable in the interpreter or when we use the `print` function. Let's try both:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "P" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(A & B)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "R1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(((C | D) ==> E) ==> (P & ~Q))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "R4" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P\n", + "(A & B)\n", + "(((C | D) ==> E) ==> (P & ~Q))\n" + ] + } + ], + "source": [ + "print(P)\n", + "print(R1)\n", + "print(R4)" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### A note to delete later on\n", + "So, that's how it works. Now scroll above a little and have a look at the cell titled \"Creating complicated sentences\". Do you notice something amiss? Now note that, the third argument in the 2nd line is `P & ~Q`. Now, how is that done? It's a statement, for sure, but it's not in the form of a string. As a matter of fact, `P` and `Q` are both `Expr`s themselves.\n", "\n", - "1. Markdown is converted to HTML. It's closer to HTML than it is to Latex. Hence, to add special symbols, unicode characters etc., use HTML character reference rather than Latex symbols.\n", - "2. Propositional symbols in markdown\n", - " * ¬   `¬`\n", - " * ∧   `∧`\n", - " * ∨   `∨`\n", - " * →   `→`\n", - " * ↔   `↔`\n", - "3. And some others\n", - " * ⊨   `⊨`\n", - " * ≡   `≡`" + "This is made possible because the `Expr` class overloads many operators. (It actually overloads mostly all the operators available in Python, but don't use them all; not, at least, for Propositional Logic.) Hence, you can do things like `P & ~Q` to *create `Expr`s by directly combining existing `Exprs`*. You might not immediately recognize the power and ease that this grants you, but I'll explain. Once, you have created some small, rudimentary expressions, you can use these overloaded operators to directly get your desired expressions; no need of using the `Expr` constructor each time. See how simple doing all that we did above becomes:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P\n", + "(A & B)\n", + "(((C | D) >> E) >> (P & ~Q))\n" + ] + } + ], + "source": [ + "# Some simple, rudimentary sentences\n", + "P = Expr(\"P\")\n", + "Q = Expr(\"Q\")\n", + "A = Expr(\"A\")\n", + "B = Expr(\"B\")\n", + "C = Expr(\"C\")\n", + "D = Expr(\"D\")\n", + "E = Expr(\"E\")\n", + "\n", + "# Now for our complex expressions\n", + "R1 = A & B\n", + "R2 = (C | D) >> E\n", + "\n", + "# And the more complex expressions\n", + "R3 = R1 % Q\n", + "R4 = R2 >> (P & ~Q)\n", + "\n", + "# Let's print them and see if they are the same as before\n", + "print(P)\n", + "print(R1)\n", + "print(R4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "Yes, yes they are. We cannot use `==>` and `<=>` when we use operator overloading, because those are not operators in Python. Instead, we have to use `>>` and `%` operators in their place. (Actually, `==>` and `<=>` are converted to `>>` and `%` internally.) Did you just cringe at using `%` for biconditionals? I am not too happy with that either. Ugly, I know, but for many reasons we *had to* implement it that way. But hey, it works like a charm.\n", + "\n", + "Before we move on, I would like to point out something that might cause you some confusion. The `==` and `!=` operators for `Expr`s. They do not logically evaluate two expressions and then check if they are equal. So don't do something like\n", + "\n", "```python\n", - "def my_function(string1, string2):\n", - " for in in range(42):\n", - " print string1 + string2\n", + "A & (B | C) == (A & B) | (A & C)\n", "```\n", "\n", - "```python def __init__(self, sentence)```" + "or even\n", + "\n", + "```python\n", + "A & B == B & A\n", + "```\n", + "\n", + "and expect it to return `True`. That's not how the `==` operator is intended to work. If you to know what it is supposed to do, have a look at the implementation of `__eq__`; that should tell you enough." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `expr` function\n", + "\n" ] }, { @@ -100,28 +322,26 @@ "collapsed": true }, "outputs": [], - "source": [ - "dcsdfdsf" - ] + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.4.4" } }, "nbformat": 4, diff --git a/logic.py b/logic.py index d6de373ec..b52711c0a 100644 --- a/logic.py +++ b/logic.py @@ -169,7 +169,7 @@ class Expr: """ def __init__(self, op, *args): - "Op is a string or number; args are Exprs (or are coerced to Exprs)." + "op is a string or number; args are Exprs (or are coerced to Exprs)." assert isinstance(op, str) or (isnumber(op) and not args) self.op = num_or_str(op) self.args = list(map(expr, args)) # Coerce args to Exprs From b2a9dc77b17a3b429e46498c043bf519bc57710f Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 26 Mar 2016 16:18:27 +0530 Subject: [PATCH 201/513] Added WalkSAT --- logic.py | 24 +++++++++++++++--------- tests/test_games.py | 3 +-- tests/test_logic.py | 19 +++++++++++++++++++ 3 files changed, 35 insertions(+), 11 deletions(-) diff --git a/logic.py b/logic.py index d6de373ec..a6dd6e65d 100644 --- a/logic.py +++ b/logic.py @@ -15,7 +15,7 @@ tt_entails Say if a statement is entailed by a KB pl_resolution Do resolution on propositional sentences dpll_satisfiable See if a propositional sentence is satisfiable - WalkSAT (not yet implemented) + WalkSAT Try to find a solution for a set of clauses And a few other functions: @@ -382,8 +382,7 @@ def prop_symbols(x): elif is_prop_symbol(x.op): return [x] else: - return list(set(symbol for arg in x.args - for symbol in prop_symbols(arg))) + return list(set(symbol for arg in x.args for symbol in prop_symbols(arg))) def tt_true(alpha): @@ -819,15 +818,16 @@ def inspect_literal(literal): def WalkSAT(clauses, p=0.5, max_flips=10000): + """Checks for satisfiability of all clauses by randomly flipping values of variables + """ + # set of all symbols in all clauses + symbols = set(sym for clause in clauses for sym in prop_symbols(clause)) # model is a random assignment of true/false to the symbols in clauses - # See ~/aima1e/print1/manual/knowledge+logic-answers.tex ??? - model = dict([(s, random.choice([True, False])) - for s in prop_symbols(clauses)]) + model = dict([(s, random.choice([True, False])) for s in symbols]) for i in range(max_flips): satisfied, unsatisfied = [], [] for clause in clauses: - (satisfied if pl_true(clause, model) else unsatisfied).append( - clause) + (satisfied if pl_true(clause, model) else unsatisfied).append(clause) if not unsatisfied: # if model satisfies all the clauses return model clause = random.choice(unsatisfied) @@ -835,7 +835,13 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): sym = random.choice(prop_symbols(clause)) else: # Flip the symbol in clause that maximizes number of sat. clauses - raise NotImplementedError + def sat_count(sym): + #returns the the number of clauses satisfied after flipping the symbol + model[sym] = not model[sym] + count = len([clause for clause in clauses if pl_true(clause, model)]) + model[sym] = not model[sym] + return count + sym = argmax(prop_symbols(clause), sat_count) model[sym] = not model[sym] # ______________________________________________________________________________ diff --git a/tests/test_games.py b/tests/test_games.py index 8e6443c10..5603270cd 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -66,8 +66,7 @@ def test_random_tests(): assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 # The player 'X' (one who plays first) in TicTacToe never loses: - for i in range(10): - assert play_game(ttt, alphabeta_player, random_player) >= 0 + assert play_game(ttt, alphabeta_player, random_player) >= 0 if __name__ == '__main__': diff --git a/tests/test_logic.py b/tests/test_logic.py index 5dc941943..0fe4b458e 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -138,6 +138,25 @@ def test_ask(query, kb=None): assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' +def test_WalkSAT(): + def check_SAT(clauses, single_solution = {}): + #Make sure the solution is correct if it is returned by WalkSat + #Sometimes WalkSat may run out of flips before finding a solution + soln = WalkSAT(clauses) + if soln: + assert every(lambda x: pl_true(x, soln), clauses) + if single_solution: #Cross check the solution if only one exists + assert every(lambda x: pl_true(x, single_solution), clauses) + assert soln == single_solution + #Test WalkSat for problems with solution + check_SAT([A & B, A & C]) + check_SAT([A | B, P & Q, P & B]) + check_SAT([A & B, C | D, ~(D | P)], {A: True, B: True, C: True, D: False, P: False}) + #Test WalkSat for problems without solution + assert WalkSAT([A & ~A], 0.5, 100) is None + assert WalkSAT([A | B, ~A, ~(B | C), C | D, P | Q], 0.5, 100) is None + assert WalkSAT([A | B, B & C, C | D, D & A, P, ~P], 0.5, 100) is None + if __name__ == '__main__': pytest.main() From 1021286b1bfc6aa7fa7e0fb47b21a42d104ff563 Mon Sep 17 00:00:00 2001 From: Chipe1 Date: Sat, 26 Mar 2016 16:33:29 +0530 Subject: [PATCH 202/513] Added explicit failure return --- logic.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/logic.py b/logic.py index a6dd6e65d..38abf060d 100644 --- a/logic.py +++ b/logic.py @@ -843,6 +843,8 @@ def sat_count(sym): return count sym = argmax(prop_symbols(clause), sat_count) model[sym] = not model[sym] + #If no solution is found within the flip limit, we return failure + return None # ______________________________________________________________________________ From 474fe8784606a0fc274da399196584ee454c1fdb Mon Sep 17 00:00:00 2001 From: tolusalako Date: Sun, 27 Mar 2016 16:54:27 -0700 Subject: [PATCH 203/513] Fixed issue where, if there are multiple agents, dead agents get the actions of alive ones. Implemented arrow shooting in Wumpus World. Wumpus Environment is now complete. --- agents.py | 60 +++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 49 insertions(+), 11 deletions(-) diff --git a/agents.py b/agents.py index 776df123e..743f7836d 100644 --- a/agents.py +++ b/agents.py @@ -267,8 +267,12 @@ def step(self): do. If there are interactions between them, you'll need to override this method.""" if not self.is_done(): - actions = [agent.program(self.percept(agent)) - for agent in self.agents if agent.alive] + actions = [] + for agent in self.agents: + if agent.alive: + actions.append(agent.program(self.percept(agent))) + else: + actions.append("") for (agent, action) in zip(self.agents, actions): self.execute_action(agent, action) self.exogenous_change() @@ -462,8 +466,14 @@ def random_location_inbounds(self, exclude = None): return location def delete_thing(self, thing): + '''Deletes thing, and everything it is holding (if thing is an agent)''' + if isinstance(thing, Agent): + for obj in thing.holding: + super(XYEnvironment, self).delete_thing(obj) + for obs in self.observers: + obs.thing_deleted(obj) + super(XYEnvironment, self).delete_thing(thing) - # Any more to do? Thing holding anything or being held? for obs in self.observers: obs.thing_deleted(thing) @@ -618,7 +628,8 @@ class Scream(Thing): pass -class Wumpus(Thing): +class Wumpus(Agent): + screamed = False pass class Stench(Thing): @@ -626,7 +637,7 @@ class Stench(Thing): class Explorer(Agent): holding = [] - arrow_count = 1 + has_arrow = True killed_by = "" direction = Direction("right") @@ -660,7 +671,7 @@ def init_world(self, program): "WUMPUS" w_x, w_y = self.random_location_inbounds(exclude = (1,1)) - self.add_thing(Wumpus(), (w_x, w_y), True) + self.add_thing(Wumpus(lambda x: ""), (w_x, w_y), True) self.add_thing(Stench(), (w_x - 1, w_y), True) self.add_thing(Stench(), (w_x + 1, w_y), True) self.add_thing(Stench(), (w_x, w_y - 1), True) @@ -700,6 +711,7 @@ def percepts_from(self, agent, location, tclass = Thing): if location != agent.location: thing_percepts[Gold] = None + result = [thing_percepts.get(thing.__class__, thing) for thing in self.things if thing.location == location and isinstance(thing, tclass)] return result if len(result) else [None] @@ -714,12 +726,22 @@ def percept(self, agent): result.append(self.percepts_from(agent, (x,y - 1))) result.append(self.percepts_from(agent, (x,y + 1))) result.append(self.percepts_from(agent, (x,y))) + + '''The wumpus gives out a a loud scream once it's killed.''' + wumpus = [thing for thing in self.things if isinstance(thing, Wumpus)] + if len(wumpus) and not wumpus[0].alive and not wumpus[0].screamed: + result[-1].append(Scream()) + wumpus[0].screamed = True + return result def execute_action(self, agent, action): '''Modify the state of the environment based on the agent's actions Performance score taken directly out of the book''' + if isinstance(agent, Explorer) and self.in_danger(agent): + return + agent.bump = False if action == 'TurnRight': agent.direction = agent.direction + Direction.R @@ -733,21 +755,37 @@ def execute_action(self, agent, action): elif action == 'Grab': things = [thing for thing in self.list_things_at(agent.location) if agent.can_grab(thing)] - print("Grabbing", things[0].__class__.__name__) if len(things): - agent.holding.append(things[0]) + print("Grabbing", things[0].__class__.__name__) + if len(things): + agent.holding.append(things[0]) agent.performance -= 1 elif action == 'Climb': if agent.location == (1,1): #Agent can only climb out of (1,1) agent.performance += 1000 if Gold() in agent.holding else 0 self.delete_thing(agent) - - '''Check if agent is in danger, if he is, kill him''' + elif action == 'Shoot': + '''The arrow travels straight down the path the agent is facing''' + if agent.has_arrow: + arrow_travel = agent.direction.move_forward(agent.location) + while(self.is_inbounds(arrow_travel)): + wumpus = [thing for thing in self.list_things_at(arrow_travel) + if isinstance(thing, Wumpus)] + if len(wumpus): + wumpus[0].alive = False + break + arrow_travel = agent.direction.move_forward(agent.location) + agent.has_arrow = False + + def in_danger(self, agent): + '''Checks if Explorer is in danger (Pit or Wumpus), if he is, kill him''' for thing in self.list_things_at(agent.location): - if isinstance(thing, Wumpus) or isinstance(thing, Pit): + if isinstance(thing, Pit) or (isinstance(thing, Wumpus) and thing.alive): agent.alive = False agent.performance -= 1000 agent.killed_by = thing.__class__.__name__ + return True + return False def is_done(self): '''The game is over when the Explorer is killed From c4e8ec3c76b7555bf010bd14cf415f10928e1b87 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 29 Mar 2016 12:17:36 +0530 Subject: [PATCH 204/513] added truncate method and removed {0:.4f} to truncate floats to 4 decimal places --- probability.py | 11 +++-------- tests/test_probability.py | 14 +++++++------- tests/test_utils.py | 18 +++++++++++++----- utils.py | 18 ++++++++++++++---- 4 files changed, 37 insertions(+), 24 deletions(-) diff --git a/probability.py b/probability.py index cd7ee2897..d9655242a 100644 --- a/probability.py +++ b/probability.py @@ -549,16 +549,15 @@ def forward(HMM, fv, ev): scalar_vector_product(fv[1], HMM.transition_model[1])) sensor_dist = HMM.sensor_dist(ev) - return([float("{0:.4f}".format(i)) for i in normalize(element_wise_product(sensor_dist, prediction))]) + return(normalize(element_wise_product(sensor_dist, prediction))) def backward(HMM, b, ev): sensor_dist = HMM.sensor_dist(ev) prediction = element_wise_product(sensor_dist, b) - return([float("{0:.4f}".format(i)) for i in normalize(vector_add( - scalar_vector_product(prediction[0], HMM.transition_model[0]), - scalar_vector_product(prediction[1], HMM.transition_model[1])))]) + return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), + scalar_vector_product(prediction[1], HMM.transition_model[1])))) def forward_backward(HMM, ev, prior): @@ -594,10 +593,6 @@ def forward_backward(HMM, ev, prior): bv.append(b) sv = sv[::-1] - # to have only 4 digits after decimal point - for i in range(len(sv)): - for j in range(len(sv[i])): - sv[i][j] = float("{0:.4f}".format(sv[i][j])) return(sv) diff --git a/tests/test_probability.py b/tests/test_probability.py index ef978373a..03da667e0 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -110,12 +110,12 @@ def test_forward_backward(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) umbrella_evidence = [T, T, F, T, T] - assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.6469, 0.3531], - [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8205, 0.1795], [0.8673, 0.1327]] + assert truncate(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.6469, 0.3531], + [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] umbrella_evidence = [T, F, T, F, T] - assert forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) == [[0.5871, 0.4129], - [0.7177, 0.2823], [0.2325, 0.7675], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + assert truncate(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], + [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] def test_fixed_lag_smoothing(): umbrella_evidence = [T, F, T, F, T] @@ -126,16 +126,16 @@ def test_fixed_lag_smoothing(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) d = 2 - assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) == [0.1111, 0.8889] + assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.1111, 0.8889] d = 5 - assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) is None + assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) is None umbrella_evidence = [T, T, F, T, T] # t = 4 e_t = T d = 1 - assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) == [0.9939, 0.0061] + assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] if __name__ == '__main__': diff --git a/tests/test_utils.py b/tests/test_utils.py index 8145d2ce8..438d9eb7d 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -118,14 +118,22 @@ def test_scalar_vector_product(): assert scalar_vector_product(2, [1, 2, 3]) == [2, 4, 6] def test_scalar_matrix_product(): - assert scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]]) == [[-5, -10], [-15, -20], [0, -30]] - assert scalar_matrix_product(0.2, [[1, 2], [2, 3]]) == [[0.2, 0.4], [0.4, 0.6]] + assert truncate(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] + assert truncate(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] def test_inverse_matrix(): - assert inverse_matrix([[1, 0], [0, 1]]) == [[1, 0], [0, 1]] - assert inverse_matrix([[2, 1], [4, 3]]) == [[1.5, -0.5], [-2.0, 1.0]] - assert inverse_matrix([[4, 7], [2, 6]]) == [[0.6, -0.7], [-0.2, 0.4]] + assert truncate(inverse_matrix([[1, 0], [0, 1]])) == [[1, 0], [0, 1]] + assert truncate(inverse_matrix([[2, 1], [4, 3]])) == [[1.5, -0.5], [-2.0, 1.0]] + assert truncate(inverse_matrix([[4, 7], [2, 6]])) == [[0.6, -0.7], [-0.2, 0.4]] + +def test_truncate(): + assert truncate(5.3330000300330) == 5.3330 + assert truncate(10.234566) == 10.2346 + assert truncate([1.234566, 0.555555, 6.010101]) == [1.2346, 0.5556, 6.0101] + assert truncate([[1.234566, 0.555555, 6.010101], + [10.505050, 12.121212, 6.030303]]) == [[1.2346, 0.5556, 6.0101], + [10.5051, 12.1212, 6.0303]] def test_num_or_str(): assert num_or_str('42') == 42 diff --git a/utils.py b/utils.py index b3beeb6f7..17bc19c01 100644 --- a/utils.py +++ b/utils.py @@ -197,7 +197,7 @@ def _mat_mult(X_M, Y_M): for Y in Y_M: result = _mat_mult(result, Y) - return([[float("{0:.4f}".format(i)) for i in row] for row in result]) + return(result) def vector_to_diagonal(v): """Converts a vector to a diagonal matrix with vector elements @@ -218,7 +218,7 @@ def scalar_vector_product(X, Y): return [X*y for y in Y] def scalar_matrix_product(X, Y): - return([[float("{0:.4f}".format(i)) for i in scalar_vector_product(X, y)] for y in Y]) + return([scalar_vector_product(X, y) for y in Y]) def inverse_matrix(X): """Inverse a given square matrix of size 2x2""" @@ -228,7 +228,7 @@ def inverse_matrix(X): assert det != 0 inv_mat = scalar_matrix_product(1.0/det, [[X[1][1], -X[0][1]], [-X[1][0], X[0][0]]]) - return([[float("{0:.4f}".format(i)) for i in row] for row in inv_mat]) + return(inv_mat) def probability(p): @@ -253,6 +253,16 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] +def truncate(x, n = 4): + """Truncates floats, vectors, matrices to n decimal values""" + if isinstance(x, float): + return(float("{0:.{1}f}".format(x, n))) + elif isinstance(x, list) and not isinstance(x[0], list): + return([float("{0:.{1}f}".format(i, n)) for i in x]) + elif isinstance(x, list) and isinstance(x[0], list): + return([[float("{0:.{1}f}".format(i, n)) for i in row] for row in x]) + else: + return x def num_or_str(x): """The argument is a string; convert to a number if @@ -270,7 +280,7 @@ def num_or_str(x): def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) - return([float("{0:.4f}".format(n / total)) for n in numbers]) + return([(n / total) for n in numbers]) def clip(x, lowest, highest): From 81f50a6460b86d1628306d0ee7e99864165066d5 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 29 Mar 2016 13:41:39 +0530 Subject: [PATCH 205/513] added wumpus environment to agents.ipynb --- agents.ipynb | 109 ++++++++++++++++++++++++------ tolusalako wumpustest.ipynb | 130 ------------------------------------ 2 files changed, 87 insertions(+), 152 deletions(-) delete mode 100755 tolusalako wumpustest.ipynb diff --git a/agents.ipynb b/agents.ipynb index d7cd85484..db42f8d33 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -124,6 +124,89 @@ " return dead_agents or no_edibles\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wumpus Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from ipythonblocks import BlockGrid\n", + "from agents import *\n", + "\n", + "color = {\"Breeze\": (225, 225, 225),\n", + " \"Pit\": (0,0,0),\n", + " \"Gold\": (253, 208, 23),\n", + " \"Glitter\": (253, 208, 23),\n", + " \"Wumpus\": (43, 27, 23),\n", + " \"Stench\": (128, 128, 128),\n", + " \"Explorer\": (0, 0, 255),\n", + " \"Wall\": (44, 53, 57)\n", + " }\n", + "\n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " print(percepts)\n", + " return input()\n", + "\n", + "w = WumpusEnvironment(program, 7, 7) \n", + "grid = BlockGrid(w.width, w.height, fill=(123, 234, 123))\n", + "\n", + "def draw_grid(world):\n", + " global grid\n", + " grid[:] = (123, 234, 123)\n", + " for x in range(0, len(world)):\n", + " for y in range(0, len(world[x])):\n", + " if len(world[x][y]):\n", + " grid[y, x] = color[world[x][y][-1].__class__.__name__]\n", + "\n", + "def step():\n", + " global grid, w\n", + " draw_grid(w.get_world())\n", + " grid.show()\n", + " w.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[], [None], [], [], [None]]\n", + "2\n" + ] + } + ], + "source": [ + "step()" + ] + }, { "cell_type": "markdown", "metadata": { @@ -151,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, @@ -191,29 +274,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[]\n", - "[]\n", - "[]\n", - "[]\n", - "[]\n", - "[, ]\n", - "Dog: Ate food at 5.\n", - "[]\n", - "[]\n", - "[, ]\n", - "Dog: Drank water at 7.\n" - ] - } - ], + "outputs": [], "source": [ "park = Park()\n", "dog = BlindDog(program)\n", @@ -237,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": true }, diff --git a/tolusalako wumpustest.ipynb b/tolusalako wumpustest.ipynb deleted file mode 100755 index 6fedf930b..000000000 --- a/tolusalako wumpustest.ipynb +++ /dev/null @@ -1,130 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from ipythonblocks import BlockGrid\n", - "from agents import *\n", - "\n", - "color = {\"Breeze\": (225, 225, 225),\n", - " \"Pit\": (0,0,0),\n", - " \"Gold\": (253, 208, 23),\n", - " \"Glitter\": (253, 208, 23),\n", - " \"Wumpus\": (43, 27, 23),\n", - " \"Stench\": (128, 128, 128),\n", - " \"Explorer\": (0, 0, 255),\n", - " \"Wall\": (44, 53, 57)\n", - " }\n", - "\n", - "def program(percepts):\n", - " '''Returns an action based on it's percepts'''\n", - " print(percepts)\n", - " return input()\n", - "\n", - "w = WumpusEnvironment(program, 7, 7) \n", - "grid = BlockGrid(w.width, w.height, fill=(123, 234, 123))\n", - "\n", - "def draw_grid(world):\n", - " global grid\n", - " grid[:] = (123, 234, 123)\n", - " for x in range(0, len(world)):\n", - " for y in range(0, len(world[x])):\n", - " if len(world[x][y]):\n", - " grid[y, x] = color[world[x][y][-1].__class__.__name__]\n", - "\n", - "def step():\n", - " global grid, w\n", - " draw_grid(w.get_world())\n", - " grid.show()\n", - " w.step()\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Explorer climbed out without Gold [+0].\n" - ] - } - ], - "source": [ - "step()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 3394e44bfc12f8882b818bd1015cce3dc4709377 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 29 Mar 2016 14:09:27 +0530 Subject: [PATCH 206/513] modified index of code by removing unimplemented pseudo codes --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 439e1d579..992b58f14 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ Here is a table of algorithms, the figure and page where they appear in the book | 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | | 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | | 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.24 | LRTA\*-Agent | `lrta_star_agent` | [`search.py`](../master/search.py) | +| 4.24 | LRTA\*-Agent | | | | 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | | 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | | 6 | CSP | `CSP` | [`csp.py`](../master/csp.py) | @@ -67,7 +67,7 @@ Here is a table of algorithms, the figure and page where they appear in the book | 7.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | | 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | | 7.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | -| 7.20 | Hybrid-Wumpus-Agent | `HybridWumpusAgent` | [`logic.py`](../master/logic.py) | +| 7.20 | Hybrid-Wumpus-Agent | | | | 7.22 | SATPlan | | | 9 | Subst | `subst` | [`logic.py`](../master/logic.py) | | 9.1 | Unify | `unify` | [`logic.py`](../master/logic.py) | @@ -83,12 +83,12 @@ Here is a table of algorithms, the figure and page where they appear in the book | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | -| \*12.6 | House-Building-Problem | | -| \*12.22 | Continuous-POP-Agent | | +| \* 12.6 | House-Building-Problem | | +| \* 12.22 | Continuous-POP-Agent | | | 11.10 | Doubles-tennis | | -| 13 | Discrete Probability Distribution | `DiscreteProbDist` | [`probability.py`](../master/probability.py) | +| 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`](../master/probability.py) | | 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | -| \*13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | +| \* 13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | | 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`](../master/probability.py) | | 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | | 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | @@ -114,12 +114,12 @@ Here is a table of algorithms, the figure and page where they appear in the book | 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | Q-Learning-Agent | | -| \*21.2 | Naive-Communicating-Agent | | +| \* 21.2 | Naive-Communicating-Agent | | | 22.1 | HITS | | | | 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | | 23.5 | CYK-Parse | | | -| \*23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | -| \*24.21 | Align | | +| \* 23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | +| \* 24.21 | Align | | | 25.9 | Monte-Carlo-Localization| | From 21d9136f12f94b9c90b24714a77eefdb3c64b4a6 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 30 Mar 2016 00:10:20 +0530 Subject: [PATCH 207/513] Added function to simulate iterations --- rl.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/rl.py b/rl.py index 76f1f79ea..311d08860 100644 --- a/rl.py +++ b/rl.py @@ -52,3 +52,32 @@ def __call__(self, percept): def update_state(self, percept): raise NotImplementedError + + +def run_single_trial(agent_program, mdp): + ''' Execute trial for given agent_program + and mdp. mdp should be an instance of subclass + of mdp.MDP ''' + + def take_single_action(mdp, s, a): + ''' + Selects outcome of taking action a + in state s. Weighted Sampling. + ''' + x = random.uniform(0, 1) + cumulative_probability = 0.0 + for probabilty_state in mdp.T(s, a): + probabilty, state = probabilty_state + cumulative_probability += probabilty + if x < cumulative_probability: + break + return state + + current_state = mdp.init + while True: + current_reward = mdp.R(current_state) + percept = (current_state, current_reward) + next_action = agent_program(percept) + if next_action is None: + break + current_state = take_single_action(mdp, current_state, next_action) From 9ebf2047f93cc31d1aec88a3dfc02d9da94d6674 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 30 Mar 2016 01:07:40 +0530 Subject: [PATCH 208/513] Added sane default for update_state --- rl.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/rl.py b/rl.py index 311d08860..44673b528 100644 --- a/rl.py +++ b/rl.py @@ -51,7 +51,9 @@ def __call__(self, percept): return self.a def update_state(self, percept): - raise NotImplementedError + ''' To be overriden in most cases. The default case + assumes th percept to be of type (state, reward)''' + return percept def run_single_trial(agent_program, mdp): From 645dd42b661f2be64253be14e1984778cda8fd33 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Wed, 30 Mar 2016 02:20:36 +0530 Subject: [PATCH 209/513] Added Details on how to view source of imported implementations --- intro.ipynb | 96 ++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 95 insertions(+), 1 deletion(-) diff --git a/intro.ipynb b/intro.ipynb index 2b5d55b2c..af23f6787 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -29,6 +29,26 @@ " If you don't know what IPython notebooks or the Jupyter Notebook App are or have never used them before, then I suggest [you read a bit](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) about them. Then, you might want to get your hands dirty and [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb). If you want to explore IPython notebooks some more before you get started with this repository, [this wiki page](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) has some truly amazing example notebooks. If you want to work with a specific version of Python, [virtual environments](http://conda.pydata.org/docs/py2or3.html) might be what you are looking for. To run the IPython interpreter or the Jupyter Notebook App with a specific version of Python, just create the particular virtual environment in the terminal and proceed as you normally would." ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Helpful Tips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing the Source and Function Definitions\n", + "\n", + "The general work-flow of these notebooks includes importing implementations from corresponding python files and then illustrating the use of the imported Classes and Functions.\n", + "\n", + "Sometimes it might be really helpful to view the source of these implementation to gain a better understanding to their working. One can obviously do this by opening the python file but this can also be done inside IPy notebooks in a very easy way. The example below illustrates this." + ] + }, { "cell_type": "code", "execution_count": null, @@ -36,7 +56,81 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "from rl import PassiveTDAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to view the source of PassiveTDAgent we can use IPy magic funtion %psource. One can do this by adding a cell at any place using the Cell menu." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource PassiveTDAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are only interested in the definition / Class Constructor instead of the full source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%pdef PassiveTDAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The docstring can be viewed by using the %pdoc magic function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%pdoc PassiveTDAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use Object? to get both the definition and the docstring together." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "PassiveTDAgent?" + ] } ], "metadata": { From e74239d7371dbee109382fcf8d39e31d89cbf365 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 30 Mar 2016 13:08:35 +0530 Subject: [PATCH 210/513] removed test for deprecated update_dict method --- tests/test_utils.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 438d9eb7d..0f084ea00 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,11 +2,6 @@ from utils import * # noqa -def test_update_dict(): - assert update({'a': 1}, a=10, b=20) == {'a': 10, 'b': 20} - assert update({}, a=5) == {'a': 5} - - def test_removeall_list(): assert removeall(4, []) == [] assert removeall(4, [1, 2, 3, 4]) == [1, 2, 3] From b01f82260208184b7d7650b0aee8f95bfb9f5b16 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 30 Mar 2016 13:40:34 +0530 Subject: [PATCH 211/513] removes the method 'update' in utils.py and changes remaining files accordingly --- agents.py | 86 ++++++++++++++++++++++++++------------------------ csp.py | 16 +++++++--- learning.py | 19 ++++++++--- nlp.py | 7 ++-- probability.py | 19 ++++++++--- search.py | 10 ++++-- text.py | 10 +++--- utils.py | 14 ++------ 8 files changed, 105 insertions(+), 76 deletions(-) diff --git a/agents.py b/agents.py index 743f7836d..6968bf84b 100644 --- a/agents.py +++ b/agents.py @@ -327,15 +327,15 @@ class Direction(): d = d + "right" or d = d + Direction.R #Both do the same thing Note that the argument to __add__ must be a string and not a Direction object. Also, it (the argument) can only be right or left. ''' - + R = "right" L = "left" U = "up" D = "down" - + def __init__(self, direction): self.direction = direction - + def __add__(self, heading): if self.direction == self.R: return{ @@ -357,7 +357,7 @@ def __add__(self, heading): self.R: Direction(self.L), self.L: Direction(self.R), }.get(heading, None) - + def move_forward(self, from_location): x,y = from_location if self.direction == self.R: @@ -368,7 +368,7 @@ def move_forward(self, from_location): return (x, y-1) elif self.direction == self.D: return (x, y+1) - + class XYEnvironment(Environment): @@ -382,12 +382,14 @@ class XYEnvironment(Environment): def __init__(self, width=10, height=10): super(XYEnvironment, self).__init__() - update(self, width=width, height=height, observers=[]) - + + self.width = width + self.height = height + self.observers = [] #Sets iteration start and end (no walls). self.x_start,self.y_start = (0,0) self.x_end,self.y_end = (self.width, self.height) - + perceptible_distance = 1 def things_near(self, location, radius=None): "Return all things within radius of location." @@ -441,22 +443,22 @@ def move_to(self, thing, destination): # thing.held = None # for obs in self.observers: # obs.thing_added(thing) - + def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items = False): '''Adds things to the world. - If (exclude_duplicate_class_items) then the item won't be added if the location + If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class''' if (self.is_inbounds(location)): - if (exclude_duplicate_class_items and + if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): return super(XYEnvironment, self).add_thing(thing, location) - + def is_inbounds(self, location): '''Checks to make sure that the location is inbounds (within walls if we have walls)''' x,y = location return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end) - + def random_location_inbounds(self, exclude = None): '''Returns a random location that is inbounds (within walls if we have walls)''' location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) @@ -472,11 +474,11 @@ def delete_thing(self, thing): super(XYEnvironment, self).delete_thing(obj) for obs in self.observers: obs.thing_deleted(obj) - + super(XYEnvironment, self).delete_thing(thing) for obs in self.observers: obs.thing_deleted(thing) - + def add_walls(self): '''Put walls around the entire perimeter of the grid.''' for x in range(self.width): @@ -485,11 +487,11 @@ def add_walls(self): for y in range(self.height): self.add_thing(Wall(), (0, y)) self.add_thing(Wall(), (self.width-1, y)) - + #Updates iteration start and end (with walls). self.x_start,self.y_start = (1,1) self.x_end,self.y_end = (self.width-1, self.height-1) - + def add_observer(self, observer): """Adds an observer to the list of observers. An observer is typically an EnvGUI. @@ -602,7 +604,7 @@ def default_location(self, thing): class Gold(Thing): - + def __eq__(self, rhs): '''All Gold are equal''' return rhs.__class__ == Gold @@ -640,7 +642,7 @@ class Explorer(Agent): has_arrow = True killed_by = "" direction = Direction("right") - + def can_grab(self, thing): '''Explorer can only grab gold''' return thing.__class__ == Gold @@ -652,13 +654,13 @@ class WumpusEnvironment(XYEnvironment): def __init__(self, agent_program, width=6, height=6): super(WumpusEnvironment, self).__init__(width, height) self.init_world(agent_program) - + def init_world(self, program): '''Spawn items to the world based on probabilities from the book''' - - "WALLS" + + "WALLS" self.add_walls() - + "PITS" for x in range(self.x_start, self.x_end): for y in range(self.y_start, self.y_end): @@ -668,7 +670,7 @@ def init_world(self, program): self.add_thing(Breeze(), (x,y - 1), True) self.add_thing(Breeze(), (x + 1,y), True) self.add_thing(Breeze(), (x,y + 1), True) - + "WUMPUS" w_x, w_y = self.random_location_inbounds(exclude = (1,1)) self.add_thing(Wumpus(lambda x: ""), (w_x, w_y), True) @@ -676,14 +678,14 @@ def init_world(self, program): self.add_thing(Stench(), (w_x + 1, w_y), True) self.add_thing(Stench(), (w_x, w_y - 1), True) self.add_thing(Stench(), (w_x, w_y + 1), True) - + "GOLD" self.add_thing(Gold(), self.random_location_inbounds(exclude = (1,1)), True) - #self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier - + #self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier + "AGENT" self.add_thing(Explorer(program), (1,1), True) - + def get_world(self, show_walls = True): '''returns the items in the world''' result = [] @@ -693,9 +695,9 @@ def get_world(self, show_walls = True): row = [] for y in range(y_start, y_end): row.append(self.list_things_at((x,y))) - result.append(row) + result.append(row) return result - + def percepts_from(self, agent, location, tclass = Thing): '''Returns percepts from a given location, and replaces some items with percepts from chapter 7.''' thing_percepts = { @@ -706,16 +708,16 @@ def percepts_from(self, agent, location, tclass = Thing): } '''Agents don't need to get their percepts''' thing_percepts[agent.__class__] = None - + '''Gold only glitters in its cell''' if location != agent.location: thing_percepts[Gold] = None - - + + result = [thing_percepts.get(thing.__class__, thing) for thing in self.things if thing.location == location and isinstance(thing, tclass)] return result if len(result) else [None] - + def percept(self, agent): '''Returns things in adjacent (not diagonal) cells of the agent. Result format: [Left, Right, Up, Down, Center / Current location]''' @@ -726,22 +728,22 @@ def percept(self, agent): result.append(self.percepts_from(agent, (x,y - 1))) result.append(self.percepts_from(agent, (x,y + 1))) result.append(self.percepts_from(agent, (x,y))) - + '''The wumpus gives out a a loud scream once it's killed.''' wumpus = [thing for thing in self.things if isinstance(thing, Wumpus)] if len(wumpus) and not wumpus[0].alive and not wumpus[0].screamed: result[-1].append(Scream()) wumpus[0].screamed = True - + return result - + def execute_action(self, agent, action): '''Modify the state of the environment based on the agent's actions Performance score taken directly out of the book''' - + if isinstance(agent, Explorer) and self.in_danger(agent): return - + agent.bump = False if action == 'TurnRight': agent.direction = agent.direction + Direction.R @@ -776,7 +778,7 @@ def execute_action(self, agent, action): break arrow_travel = agent.direction.move_forward(agent.location) agent.has_arrow = False - + def in_danger(self, agent): '''Checks if Explorer is in danger (Pit or Wumpus), if he is, kill him''' for thing in self.list_things_at(agent.location): @@ -786,7 +788,7 @@ def in_danger(self, agent): agent.killed_by = thing.__class__.__name__ return True return False - + def is_done(self): '''The game is over when the Explorer is killed or if he climbs out of the cave only at (1,1)''' @@ -800,7 +802,7 @@ def is_done(self): print("Explorer climbed out {}." .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]")) return True - + #Almost done. Arrow needs to be implemented # ______________________________________________________________________________ diff --git a/csp.py b/csp.py index 422887388..018ecf435 100644 --- a/csp.py +++ b/csp.py @@ -54,9 +54,14 @@ class CSP(search.Problem): def __init__(self, variables, domains, neighbors, constraints): "Construct a CSP problem. If variables is empty, it becomes domains.keys()." variables = variables or list(domains.keys()) - update(self, variables=variables, domains=domains, - neighbors=neighbors, constraints=constraints, - initial=(), curr_domains=None, nassigns=0) + + self.variables = variables + self.domains = domains + self.neighbors = neighbors + self.constraints = constraints + self.initial = () + self.curr_domains = None + self.nassigns = 0 def assign(self, var, val, assignment): "Add {var: val} to assignment; Discard the old value if any." @@ -438,7 +443,10 @@ def __init__(self, n): """Initialize data structures for n Queens.""" CSP.__init__(self, list(range(n)), UniversalDict(list(range(n))), UniversalDict(list(range(n))), queen_constraint) - update(self, rows=[0]*n, ups=[0]*(2*n - 1), downs=[0]*(2*n - 1)) + + self.rows = [0]*n + self.ups = [0]*(2*n - 1) + self.downs = [0]*(2*n - 1) def nconflicts(self, var, val, assignment): """The number of conflicts, as recorded with each assignment. diff --git a/learning.py b/learning.py index d491cf0a5..a5534868e 100644 --- a/learning.py +++ b/learning.py @@ -62,8 +62,11 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, >>> DataSet(examples='1, 2, 3') """ - update(self, name=name, source=source, - values=values, distance=distance) + self.name = name + self.source = source + self.values = values + self.distance = distance + # Initialize .examples from string or list or data directory if isinstance(examples, str): self.examples = parse_csv(examples) @@ -168,7 +171,11 @@ def __init__(self, observations=[], default=0): """Create a distribution, and optionally add in some observations. By default this is an unsmoothed distribution, but saying default=1, for example, gives you add-one smoothing.""" - update(self, dictionary={}, n_obs=0.0, default=default, sampler=None) + self.dictionary = {} + self.n_obs = 0.0 + self.default = default + self.sampler = None + for o in observations: self.add(o) @@ -271,8 +278,10 @@ class DecisionFork: def __init__(self, attr, attrname=None, branches=None): "Initialize by saying what attribute this node tests." - update(self, attr=attr, attrname=attrname or attr, - branches=branches or {}) + self.attr = attr + self.attrname = attrname or attr + self.branches = branches or {} + def __call__(self, example): "Given an example, classify it using the attribute and the branches." diff --git a/nlp.py b/nlp.py index ae418757e..cc6d63b80 100644 --- a/nlp.py +++ b/nlp.py @@ -35,7 +35,9 @@ class Grammar: def __init__(self, name, rules, lexicon): "A grammar has a set of rules and a lexicon." - update(self, name=name, rules=rules, lexicon=lexicon) + self.name = name + self.rules = rules + self.lexicon = lexicon self.categories = defaultdict(list) for lhs in lexicon: for word in lexicon[lhs]: @@ -126,7 +128,8 @@ def __init__(self, grammar, trace=False): """A datastructure for parsing a string; and methods to do the parse. self.chart[i] holds the edges that end just before the i'th word. Edges are 5-element lists of [start, end, lhs, [found], [expects]].""" - update(self, grammar=grammar, trace=trace) + self.grammar = grammar + self.trace = trace def parses(self, words, S='S'): """Return a list of parses; words can be a list or string. diff --git a/probability.py b/probability.py index d9655242a..ac942d81b 100644 --- a/probability.py +++ b/probability.py @@ -38,7 +38,9 @@ class ProbDist: def __init__(self, varname='?', freqs=None): """If freqs is given, it is a dictionary of value: frequency pairs, and the ProbDist then is normalized.""" - update(self, prob={}, varname=varname, values=[]) + self.prob = {} + self.varname = varname + self.values = [] if freqs: for (v, p) in list(freqs.items()): self[v] = p @@ -92,7 +94,9 @@ class JointProbDist(ProbDist): 0.5""" def __init__(self, variables): - update(self, prob={}, variables=variables, vals=defaultdict(list)) + self.prob = {} + self.variables = variables + self.vals = defaultdict(list) def __getitem__(self, values): "Given a tuple or dict of values, return P(values)." @@ -166,7 +170,8 @@ class BayesNet: def __init__(self, node_specs=[]): "nodes must be ordered with parents before children." - update(self, nodes=[], vars=[]) + self.nodes = [] + self.vars = [] for node_spec in node_specs: self.add(node_spec) @@ -244,7 +249,10 @@ def __init__(self, X, parents, cpt): assert every(lambda v: isinstance(v, bool), vs) assert 0 <= p <= 1 - update(self, variable=X, parents=parents, cpt=cpt, children=[]) + self.variable = X + self.parents = parents + self.cpt = cpt + self.children = [] def p(self, value, event): """Return the conditional probability @@ -363,7 +371,8 @@ class Factor: "A factor in a joint distribution." def __init__(self, vars, cpt): - update(self, vars=vars, cpt=cpt) + self.vars = vars + self.cpt = cpt def pointwise_product(self, other, bn): "Multiply two factors, combining their variables." diff --git a/search.py b/search.py index 268d7b7bc..7aff1550b 100644 --- a/search.py +++ b/search.py @@ -78,8 +78,11 @@ class Node: def __init__(self, state, parent=None, action=None, path_cost=0): "Create a search tree Node, derived from a parent by an action." - update(self, state=state, parent=parent, action=action, - path_cost=path_cost, depth=0) + self.state = state + self.parent = parent + self.action = action + self.path_cost = path_cost + self.depth = 0 if parent: self.depth = parent.depth + 1 @@ -132,7 +135,8 @@ class SimpleProblemSolvingAgentProgram: """Abstract framework for a problem-solving agent. [Fig. 3.1]""" def __init__(self, initial_state=None): - update(self, state=initial_state, seq=[]) + self.state = initial_state + self.seq = [] def __call__(self, percept): self.state = self.update_state(self.state, percept) diff --git a/text.py b/text.py index d637d6757..b8f06d899 100644 --- a/text.py +++ b/text.py @@ -117,8 +117,9 @@ def __init__(self, stopwords='the a of'): """Create an IR System. Optionally specify stopwords.""" # index is a map of {word: {docid: count}}, where docid is an int, # indicating the index into the documents list. - update(self, index=defaultdict(lambda: defaultdict(int)), - stopwords=set(words(stopwords)), documents=[]) + self.index = defaultdict(lambda: defaultdict(int)) + self.stopwords = set(words(stopwords)) + self.documents = [] def index_collection(self, filenames): "Index a whole collection of files." @@ -192,8 +193,9 @@ class Document: """Metadata for a document: title and url; maybe add others later.""" def __init__(self, title, url, nwords): - update(self, title=title, url=url, nwords=nwords) - + self.title = title + self.url = url + self.nwords = nwords def words(text, reg=re.compile('[a-z0-9]+')): """Return a list of the words in text, ignoring punctuation and diff --git a/utils.py b/utils.py index 17bc19c01..248f7f562 100644 --- a/utils.py +++ b/utils.py @@ -11,16 +11,6 @@ from grid import * # noqa - -def update(x, **entries): - """Update a dict or an object with slots according to entries.""" - if isinstance(x, dict): - x.update(entries) - else: - x.__dict__.update(entries) - - return x - # ______________________________________________________________________________ # Functions on Sequences (mostly inspired by Common Lisp) @@ -481,7 +471,9 @@ class PriorityQueue(Queue): Also supports dict-like lookup.""" def __init__(self, order=min, f=lambda x: x): - update(self, A=[], order=order, f=f) + self.A = [] + self.order = order + self.f = f def append(self, item): bisect.insort(self.A, (self.f(item), item)) From 3658b5ba60d0e603f76c5d8e8a2129bbb719a131 Mon Sep 17 00:00:00 2001 From: SnShine Date: Thu, 31 Mar 2016 10:20:38 +0530 Subject: [PATCH 212/513] modified the variable name 'vars' which is builtin name to 'variables' through out the repo --- probability.py | 88 ++++++++++++++++++++++----------------------- tests/test_logic.py | 6 ++-- 2 files changed, 47 insertions(+), 47 deletions(-) diff --git a/probability.py b/probability.py index ac942d81b..b73ddfb09 100644 --- a/probability.py +++ b/probability.py @@ -121,17 +121,17 @@ def __repr__(self): return "P(%s)" % self.variables -def event_values(event, vars): - """Return a tuple of the values of variables vars in event. +def event_values(event, variables): + """Return a tuple of the values of variables variables in event. >>> event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) (8, 10) >>> event_values ((1, 2), ['C', 'A']) (1, 2) """ - if isinstance(event, tuple) and len(event) == len(vars): + if isinstance(event, tuple) and len(event) == len(variables): return event else: - return tuple([event[var] for var in vars]) + return tuple([event[var] for var in variables]) # ______________________________________________________________________________ @@ -146,18 +146,18 @@ def enumerate_joint_ask(X, e, P): """ assert X not in e, "Query variable must be distinct from evidence" Q = ProbDist(X) # probability distribution for X, initially empty - Y = [v for v in P.variables if v != X and v not in e] # hidden vars. + Y = [v for v in P.variables if v != X and v not in e] # hidden variables. for xi in P.values(X): Q[xi] = enumerate_joint(Y, extend(e, X, xi), P) return Q.normalize() -def enumerate_joint(vars, e, P): +def enumerate_joint(variables, e, P): """Return the sum of those entries in P consistent with e, - provided vars is P's remaining variables (the ones not in e).""" - if not vars: + provided variables is P's remaining variables (the ones not in e).""" + if not variables: return P[e] - Y, rest = vars[0], vars[1:] + Y, rest = variables[0], variables[1:] return sum([enumerate_joint(rest, extend(e, Y, y), P) for y in P.values(Y)]) @@ -171,7 +171,7 @@ class BayesNet: def __init__(self, node_specs=[]): "nodes must be ordered with parents before children." self.nodes = [] - self.vars = [] + self.variables = [] for node_spec in node_specs: self.add(node_spec) @@ -179,10 +179,10 @@ def add(self, node_spec): """Add a node to the net. Its parents must already be in the net, and its variable must not.""" node = BayesNode(*node_spec) - assert node.variable not in self.vars - assert every(lambda parent: parent in self.vars, node.parents) + assert node.variable not in self.variables + assert every(lambda parent: parent in self.variables, node.parents) self.nodes.append(node) - self.vars.append(node.variable) + self.variables.append(node.variable) for parent in node.parents: self.variable_node(parent).children.append(node) @@ -268,7 +268,7 @@ def p(self, value, event): def sample(self, event): """Sample from the distribution for this variable conditioned - on event's values for parent_vars. That is, return True/False + on event's values for parent_variables. That is, return True/False at random according with the conditional probability given the parents.""" return probability(self.p(True, event)) @@ -301,18 +301,18 @@ def enumeration_ask(X, e, bn): assert X not in e, "Query variable must be distinct from evidence" Q = ProbDist(X) for xi in bn.variable_values(X): - Q[xi] = enumerate_all(bn.vars, extend(e, X, xi), bn) + Q[xi] = enumerate_all(bn.variables, extend(e, X, xi), bn) return Q.normalize() -def enumerate_all(vars, e, bn): - """Return the sum of those entries in P(vars | e{others}) +def enumerate_all(variables, e, bn): + """Return the sum of those entries in P(variables | e{others}) consistent with e, where P is the joint distribution represented by bn, and e{others} means e restricted to bn's other variables - (the ones other than vars). Parents must precede children in vars.""" - if not vars: + (the ones other than variables). Parents must precede children in variables.""" + if not variables: return 1.0 - Y, rest = vars[0], vars[1:] + Y, rest = variables[0], variables[1:] Ynode = bn.variable_node(Y) if Y in e: return Ynode.p(e[Y], e) * enumerate_all(rest, e, bn) @@ -330,7 +330,7 @@ def elimination_ask(X, e, bn): 'False: 0.716, True: 0.284'""" assert X not in e, "Query variable must be distinct from evidence" factors = [] - for var in reversed(bn.vars): + for var in reversed(bn.variables): factors.append(make_factor(var, e, bn)) if is_hidden(var, X, e): factors = sum_out(var, factors, bn) @@ -347,10 +347,10 @@ def make_factor(var, e, bn): That is, bn's full joint distribution, projected to accord with e, is the pointwise product of these factors for bn's variables.""" node = bn.variable_node(var) - vars = [X for X in [var] + node.parents if X not in e] - cpt = dict((event_values(e1, vars), node.p(e1[var], e1)) - for e1 in all_events(vars, bn, e)) - return Factor(vars, cpt) + variables = [X for X in [var] + node.parents if X not in e] + cpt = dict((event_values(e1, variables), node.p(e1[var], e1)) + for e1 in all_events(variables, bn, e)) + return Factor(variables, cpt) def pointwise_product(factors, bn): @@ -361,7 +361,7 @@ def sum_out(var, factors, bn): "Eliminate var from all factors by summing over its values." result, var_factors = [], [] for f in factors: - (var_factors if var in f.vars else result).append(f) + (var_factors if var in f.variables else result).append(f) result.append(pointwise_product(var_factors, bn).sum_out(var, bn)) return result @@ -370,43 +370,43 @@ class Factor: "A factor in a joint distribution." - def __init__(self, vars, cpt): - self.vars = vars + def __init__(self, variables, cpt): + self.variables = variables self.cpt = cpt def pointwise_product(self, other, bn): "Multiply two factors, combining their variables." - vars = list(set(self.vars) | set(other.vars)) - cpt = dict((event_values(e, vars), self.p(e) * other.p(e)) - for e in all_events(vars, bn, {})) - return Factor(vars, cpt) + variables = list(set(self.variables) | set(other.variables)) + cpt = dict((event_values(e, variables), self.p(e) * other.p(e)) + for e in all_events(variables, bn, {})) + return Factor(variables, cpt) def sum_out(self, var, bn): "Make a factor eliminating var by summing over its values." - vars = [X for X in self.vars if X != var] - cpt = dict((event_values(e, vars), + variables = [X for X in self.variables if X != var] + cpt = dict((event_values(e, variables), sum(self.p(extend(e, var, val)) for val in bn.variable_values(var))) - for e in all_events(vars, bn, {})) - return Factor(vars, cpt) + for e in all_events(variables, bn, {})) + return Factor(variables, cpt) def normalize(self): "Return my probabilities; must be down to one variable." - assert len(self.vars) == 1 - return ProbDist(self.vars[0], + assert len(self.variables) == 1 + return ProbDist(self.variables[0], dict((k, v) for ((k,), v) in list(self.cpt.items()))) def p(self, e): "Look up my value tabulated for e." - return self.cpt[event_values(e, self.vars)] + return self.cpt[event_values(e, self.variables)] -def all_events(vars, bn, e): - "Yield every way of extending e with values for all vars." - if not vars: +def all_events(variables, bn, e): + "Yield every way of extending e with values for all variables." + if not variables: yield e else: - X, rest = vars[0], vars[1:] + X, rest = variables[0], variables[1:] for e1 in all_events(rest, bn, e): for x in bn.variable_values(X): yield extend(e1, X, x) @@ -505,7 +505,7 @@ def gibbs_ask(X, e, bn, N): assert X not in e, "Query variable must be distinct from evidence" counts = dict((x, 0) for x in bn.variable_values(X)) # bold N in Fig. 14.16 - Z = [var for var in bn.vars if var not in e] + Z = [var for var in bn.variables if var not in e] state = dict(e) # boldface x in Fig. 14.16 for Zi in Z: state[Zi] = random.choice(bn.variable_values(Zi)) diff --git a/tests/test_logic.py b/tests/test_logic.py index 0fe4b458e..ffd1dc78f 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -125,13 +125,13 @@ def test_to_cnf(): "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)" assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' -def test_fol_bc_ask(): +def test_fol_bc_ask(): def test_ask(query, kb=None): q = expr(query) - vars = variables(q) + test_variables = variables(q) answers = fol_bc_ask(kb or test_kb, q) return sorted( - [dict((x, v) for x, v in list(a.items()) if x in vars) + [dict((x, v) for x, v in list(a.items()) if x in test_variables) for a in answers], key=repr) assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' From 9da4ccd6a75357552b2843f5e7d5d6b2f1a864db Mon Sep 17 00:00:00 2001 From: SnShine Date: Thu, 31 Mar 2016 10:36:16 +0530 Subject: [PATCH 213/513] enhances the truncate() method in utils.py --- utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/utils.py b/utils.py index 248f7f562..660670f24 100644 --- a/utils.py +++ b/utils.py @@ -247,9 +247,9 @@ def truncate(x, n = 4): """Truncates floats, vectors, matrices to n decimal values""" if isinstance(x, float): return(float("{0:.{1}f}".format(x, n))) - elif isinstance(x, list) and not isinstance(x[0], list): + elif isinstance(x, list) and isinstance(x[0], float): return([float("{0:.{1}f}".format(i, n)) for i in x]) - elif isinstance(x, list) and isinstance(x[0], list): + elif isinstance(x, list) and isinstance(x[0], list) and isinstance(x[0][0], float): return([[float("{0:.{1}f}".format(i, n)) for i in row] for row in x]) else: return x From 2d0d765e5ec66a303c15244e29200855e8eb7a46 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Thu, 31 Mar 2016 21:41:35 +0530 Subject: [PATCH 214/513] Intro for RL Notebook --- rl.ipynb | 28 +++++++++++++++++++++++----- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/rl.ipynb b/rl.ipynb index 005e7b7f9..13a4bcc03 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -1,14 +1,14 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": false }, - "outputs": [], "source": [ - "import rl" + "# Reinforcement Learning\n", + "\n", + "This IPy notebook acts as supporting material for **Chapter 21 Reinforcement Learning** of the book* Artificial Intelligence: A Modern Approach*. This notebook makes use of the implementations in rl.py module. We also make use of implementation of MDPs in the mdp.py module to test our agents. It might be helpful if you have already gone through the IPy notebook dealing with Markov decision process. Let us import everything from the rl module. It might be helpful to view the source of some of our implementations. Please refer to the Introductory IPy file for more details." ] }, { @@ -19,6 +19,24 @@ }, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Review\n", + "Before we start playing with the actual implementations let us review a couple of things about RL.\n", + "\n", + "1. Reinforcement Learning is concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. \n", + "\n", + "2. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).\n", + "\n", + "-- Source: [Wikipedia](https://en.wikipedia.org/wiki/Reinforcement_learning)\n", + "\n", + "In summary we have a sequence of state action transitions with rewards associated with some states. Our goal is to find the optimal policy (pi) which tells us what action to take in each state." + ] } ], "metadata": { @@ -37,7 +55,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From b80f62c78889556678724540f1de673e77a6068f Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Thu, 31 Mar 2016 21:52:03 +0530 Subject: [PATCH 215/513] Usage examples for PassiveTDAgent --- images/mdp.png | Bin 0 -> 824 bytes rl.ipynb | 183 ++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 181 insertions(+), 2 deletions(-) create mode 100644 images/mdp.png diff --git a/images/mdp.png b/images/mdp.png new file mode 100644 index 0000000000000000000000000000000000000000..e874130ee7bee4523a7ae02280ad73420919825f GIT binary patch literal 824 zcmV-81IPS{P)004Lh0{{R3LVO8b0001TP)t-s|Ns90 z004e|e&FEXI5;@Bx3}iz=8TMthK7dx{QO8rNL^iB`uh63yu9}I_Ur5GJv}{dZ*RrL z#iF93m6eq$Dk^w*c(Ssx9v&WviHVz=o65?{(b3Uza&lr~Vl*@~MMXtVPfr;c84V2$ zUteEURaFSp`i1}i0)a_HK~#90?VQ_^qA(CZ!!3Y@gi91ez(L3V|HTci)=ai6+fB*P zyY_j|Ky@d_M3ia~#t4ESeu!=rLgXVDat0&9F^3`!)jh!|178DAz>pB0iG+jW`1kfk z5!iKbH__q=K|gRChXiks;6q5zA<=>a9TF`_&>_*{e-)HGPN8v@6cqd82TnoJU7n;o zuW$+m;ni!6Rq%ve@Q7Kk>;1yp`r8_-Am3@1z%Gl2njkQT9BYaq6G;$BwD<= zpiXwBXtlr7m1L||Q%$1RamTpU+c>W8e~JH?VAc(+)1plKR%Vg~;m+(W4~!pquxCvz zs-*>a6NxE#JN+puEvrL12}*UC&W|=Vf+eoUg5LyHEeG?iERXHw8dDsCjQ8VBkPq=V z7{R>uE_JcP@`72mXW7QI3T+;=>E=|L9cg`<3>yT3oi1B*-mj9@93s9sj{8>BeZaKY zRzoe87L1ru(_E#~)tX|ucGEPZ6sB#qm0WxV|A7x`w?qpPbV#%yL5D;O5_CwkAVG&j zi?jFv5gvppXV$DS{w~x8WB;KQWYU?iK<70000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fig[17,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Fig[17,1]** is a GridMDP object and is similar to the grid shown in **Fig 21.1**. The rewards in the terminal states are **+1** and **-1** and **-0.04** in rest of the states. Now we define a policy similar to **Fig 21.1** in the book." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "policy = {(0, 0): (0, 1),\n", + " (0, 1): (0, 1),\n", + " (0, 2): (1, 0),\n", + " (1, 0): (-1, 0),\n", + " (1, 2): (1, 0),\n", + " (2, 0): (-1, 0),\n", + " (2, 1): (0, 1),\n", + " (2, 2): (1, 0),\n", + " (3, 0): (-1, 0),\n", + " (3, 1): None,\n", + " (3, 2): None,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us create our object now. We also use the **same alpha** as given in the footnote of the book on **page 837**." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "our_agent = PassiveTDAgent(policy, Fig[17,1], alpha=lambda n: 60./(59+n))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The rl module also has a simple implementation to simulate iterations. The function is called **run_single_trial**. Now we can try our implementation. We can also compare the utility estimates learned by our agent to those obtained via **value iteration**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mdp import value_iteration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The values calculated by value iteration:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(0, 1): 0.3984432178350045, (1, 2): 0.649585681261095, (3, 2): 1.0, (0, 0): 0.2962883154554812, (3, 0): 0.12987274656746342, (3, 1): -1.0, (2, 1): 0.48644001739269643, (2, 0): 0.3447542300124158, (2, 2): 0.7953620878466678, (1, 0): 0.25386699846479516, (0, 2): 0.5093943765842497}\n" + ] + } + ], + "source": [ + "print(value_iteration(Fig[17,1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the values estimated by our agent after 200 trials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { From 870f11d06efb2fa5c8de4d22c9d00d497ec949dc Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Thu, 31 Mar 2016 21:57:04 +0530 Subject: [PATCH 216/513] Added Plot corresponding to Fig 21.5a and Usage Examples --- rl.ipynb | 139 +++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 129 insertions(+), 10 deletions(-) diff --git a/rl.ipynb b/rl.ipynb index 2f2842f20..29d69c032 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -98,10 +98,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -205,7 +205,126 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now the values estimated by our agent after 200 trials." + "Now the values estimated by our agent after **200 trials**." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(0, 1): 0.45317974152528395, (1, 2): 0.6904748397040797, (3, 2): 1, (0, 0): 0.32814758326189863, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.2173891183100952, (3, 1): -1, (2, 2): 0.8413461352306864, (2, 1): 0.5466536540971639, (0, 2): 0.5623108119928993}\n" + ] + } + ], + "source": [ + "for i in range(200):\n", + " run_single_trial(our_agent,Fig[17,1])\n", + "print(our_agent.U)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also explore how these estimates vary with time by using plots similar to **Fig 21.5a**. To do so we define a function to help us with the same." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline\n", + "def graph_utility_estimates(agent_program, mdp, no_of_iterations, states_to_graph):\n", + " graphs = {state:[] for state in states_to_graph}\n", + " for iteration in range(1,no_of_iterations+1):\n", + " run_single_trial(agent_program, mdp)\n", + " for state in states_to_graph:\n", + " graphs[state].append((iteration, agent_program.U[state]))\n", + " for state, value in graphs.items():\n", + " state_x, state_y = zip(*value)\n", + " pylab.plot(state_x, state_y, label=str(state))\n", + " pylab.ylim([0,1.2])\n", + " pylab.legend(loc='lower right')\n", + " pylab.xlabel('Iterations')\n", + " pylab.ylabel('U')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a plot of state (2,2)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "agent = PassiveTDAgent(policy, Fig[17,1], alpha=lambda n: 60./(59+n))\n", + "graph_utility_estimates(agent, Fig[17,1], 500, [(2,2)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to plot multiple states on the same plot." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PwUsHU6ZkGR686kHKlSrHyfiTuWrn/pP7eWjsQ/S9uy/htcJz9NwZm2fw9E9Pc2XFK1l/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\nRERERERERERERERERESKsv8HNGsHZ8bQ4KMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph_utility_estimates(agent, Fig[17,1], 500, [(2,2), (3,2)])" ] }, { From 2913298bd7b6bba1b7a5d9144b78d0a5eb4e87ee Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Fri, 1 Apr 2016 08:15:14 +0530 Subject: [PATCH 217/513] Better Representation of Policy in Notebook --- rl.ipynb | 35 +++++++++++++++++------------------ 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/rl.ipynb b/rl.ipynb index 29d69c032..1e0f7a646 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -98,7 +98,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -125,18 +125,17 @@ }, "outputs": [], "source": [ - "policy = {(0, 0): (0, 1),\n", - " (0, 1): (0, 1),\n", - " (0, 2): (1, 0),\n", - " (1, 0): (-1, 0),\n", - " (1, 2): (1, 0),\n", - " (2, 0): (-1, 0),\n", - " (2, 1): (0, 1),\n", - " (2, 2): (1, 0),\n", - " (3, 0): (-1, 0),\n", - " (3, 1): None,\n", - " (3, 2): None,\n", - " }" + "# Action Directions\n", + "north = (0, 1)\n", + "south = (0,-1)\n", + "west = (-1, 0)\n", + "east = (1, 0)\n", + "\n", + "policy = {\n", + " (0, 2): east, (1, 2): east, (2, 2): east, (3, 2): None,\n", + " (0, 1): north, (2, 1): north, (3, 1): None,\n", + " (0, 0): north, (1, 0): west, (2, 0): west, (3, 0): west, \n", + "}\n" ] }, { @@ -219,7 +218,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{(0, 1): 0.45317974152528395, (1, 2): 0.6904748397040797, (3, 2): 1, (0, 0): 0.32814758326189863, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.2173891183100952, (3, 1): -1, (2, 2): 0.8413461352306864, (2, 1): 0.5466536540971639, (0, 2): 0.5623108119928993}\n" + "{(0, 1): 0.45349055962264184, (1, 2): 0.6127179535526855, (3, 2): 1, (0, 0): 0.3686271642983948, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.2327528230992705, (3, 1): -1, (2, 2): 0.7269851866778488, (2, 1): 0.5227571939134159, (0, 2): 0.5160077721580049}\n" ] } ], @@ -284,9 +283,9 @@ "outputs": [ { "data": { - "image/png": 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vg8QDBoMnY/Pr8keOJ56axHcDijRVDT44+EqzqMgCQLrMwQ9ef/GF3U8WHIqK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\nj3/k008//XqsoG3btrRr1+7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WInKhn/nzk2PtjmuvNRFxoaCgOLjWXv/+4e9FhZVmzrQ4taNTJ7uJZ882QzZ5srUkx461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\nIV2BJaSPBxYBbxNOSO8MvAy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -314,9 +313,9 @@ "outputs": [ { "data": { - "image/png": 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PwUsHU6ZkGR686kHKlSrHyfiTuWrn/pP7eWjsQ/S9uy/htcJz9NwZm2fw9E9Pc2XFK1l/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\nkQKkcJDiJJZzlzgG56Y5L3J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P7WVL9BbXAJ/IhapcujKx8bF0n9Gd7Ue2s/BfC7P1gKILUdTvUJbCrci0HDzm\nYXHkYm6pe4t3X2q30vqo9d59P//xM1dWu5LDpw47LY0GTkujfKnyzNs5j+trX5/pXaoiF6JkiZIY\nxqLIRSz810ItvihFXpEJDluit1CpdKV0j5ZM7VZa96czjzz6ZDSbozdzV6O7WLp/KRUuqeBdjbJ8\nqfLM3TmXm+vcXCDll+Lh3ib36opeLgpFJjgsilxE27pt0+0rE1CGzdGbKe1fmpByIczdOZera1xN\nzcCa/LjrR26sfXaQuXxAefYd36cuJfGZL+/7koHtMn92tkhRU2SCw9J9S10zikr7l2b1wdW0qdWG\noDJBzN05lzahbQguE0xcQlz64FCqPAElAmgV2iq/iy7FxP2X3+9dVVWkqCsywWHlgZW0Dm2dbl8Z\n/zJ4zEOb0DZULl2ZH3f9SKvQVgSXDQZIF0zKlyrPNTWvUZNfRCQbikxw+DPuT5pUaZJuX5mAMgDe\nlkNMfAyta7UmuEwwFS6pwOVVL/emrVOxjuv+BhERyVyRmcraskZL19TA0v6l8S/hz9U1riaoTBA1\nA2tSq0ItqpevzsyHZqZL3/va3vldZBGRIqvItByurn61a1/tCrXp3qI7ZQLKULl0ZW+3k38Jf9rW\na+tKLyIi2VNkWg6pa9mnFVohlAmdJwDO+ILHPPldLBGRi1JReaSZbYzaSPOQ5gVdDhGRIiPlqZW5\nOs8XmeCQmJyopYhFRHKgWASH1OfjiohI9lxIcCgyA9IiIpJ/FBxERMRFwUFERFx8HRw6ANuAHUBW\nD9MNA34DNgERPi6PiIhkgy8HpEsCvwO3AQeAVUBXYGuaNJWAJcCdwH6gCnAYNw1Ii4jk0IUMSJ9r\nbuh/M2wbEA38CvyRjbxbATuBPSnb04DOpA8O3YCvcQIDZB4YREQkn52rWykQKJ/mJxC4DpiH0wI4\nn1BgX5rt/Sn70roMCAJ+AVYDPbJVahER8alztRzCs9gfBPwEfH6evLPTDxQAXA3cCpQFlgHLccYo\n0hcm/GxxwsLCCAsLy0b2IiLFR0REBBEREXmSV27HHH4DWp4nTRucAJO6TnZ/wAMMSpOmH1CGs4Fo\nPE7L5KsMeWnMQUQkh/L7Jrh2QGw20q3G6TaqB5QCHgRmZkjzHXATzuB1WaA1sCUXZRIRkTx0rm6l\njZnsqwwcAnpmI+8k4CngB5yT/8c4g9GpD1YYgzPNdR6wAadVMQ4FBxGRAneu5ka9DNsGHAHifFaa\nrKlbSUSGWd2+AAANp0lEQVQkh7TwnoiIuGjhPRERyVMKDiIi4qLgICIiLgoOIiLiouAgIiIuCg4i\nIuKi4CAiIi4KDiIi4qLgICIiLgoOIiLiouAgIiIuCg4iIuKi4CAiIi4KDiIi4qLgICIiLgoOIiLi\nouAgIiIuCg4iIuKi4CAiIi4KDiIi4qLgICIiLgoOIiLiouAgIiIuCg4iIuKi4CAiIi4KDiIi4qLg\nICIiLr4ODh2AbcAOoN850l0HJAFdfFweERHJBl8Gh5LACJwA0QzoCjTNIt0gYB7g58PyiIhINvky\nOLQCdgJ7gERgGtA5k3RPA18B0T4si4iI5IAvg0MosC/N9v6UfRnTdAZGpWybD8sjIiLZ5O/DvLNz\nov8AeDElrR/n6FYKDw/3vg4LCyMsLOzCSicicpGJiIggIiIiT/LyZR9/GyAcZ8wBoD/gwRlfSLU7\nTRmqAKeAx4CZGfIyMzUqRERyws/PD3J5nvdlcPAHfgduBQ4CK3EGpbdmkX4CMAv4JpNjCg4iIjl0\nIcHBl91KScBTwA84M5I+xgkMvVOOj/HhZ4uIyAUoKlNH1XIQEcmhC2k56A5pERFxUXAQEREXBQcR\nEXFRcBARERcFBxERcVFwEBERFwUHERFxUXAQEREXBQcREXFRcBARERcFBxERcVFwEBERFwUHERFx\nUXAQEREXBQcREXFRcBARERcFBxERcVFwEBERFwUHERFxUXAQEREXBQcREXFRcBARERf/gi6AiEhm\ngoKCiI2NLehiFAmVK1cmJiYmT/P0y9PcfMfMrKDLICL5yM/PD/3dZ09WdeXn5we5PM+rW0lERFwU\nHERExEXBQUREXBQcRETEJT+CQwdgG7AD6JfJ8X8C64ENwBKgRT6USUTkgvXv359hw4b5/HNmzZrF\nQw895PPPScvXwaEkMAInQDQDugJNM6TZDdyCExQGAmN9XCYRkQsWHR3N5MmT6dOnDwBbtmzh2muv\nJSgoiEqVKnHjjTfy66+/Zjuvrl27EhoaSqVKlbjppptYuXKl9/jdd9/N5s2b2bhxo0++S2Z8HRxa\nATuBPUAiMA3onCHNMuBYyusVQC0fl0lE5IJNnDiRTp06cckllwAQGhrK9OnTOXLkCLGxsTz00EPc\nd9992corLi6O1q1bs3btWmJjY+nVqxedOnXi5MmT3jRdu3Zl7Nj8u3b2dXAIBfal2d6fsi8rjwBz\nfFoiEZE8MG/ePNq2bevdrlixIvXr18fPz4/k5GRKlChBjRo1spVX/fr16du3L9WqVcPPz4/HHnuM\nhIQEtm/f7k0TFhbG7Nmz8/x7ZMXXd0jn5A6WdsC/gRt9VBYRkTyzceNGGjdu7NpfqVIlTp48Sc2a\nNfn5559zlfe6detISEigYcOG3n1NmjRhz549xMXFUb58+VyXO7t8HRwOALXTbNfGaT1k1AIYhzM2\nken98uHh4d7XYWFhhIWF5VUZRaSI8sujNR5ycyP20aNHCQwMzHT/qVOneP3117n//vtZs2ZN6p3K\n2XL8+HF69OhBeHh4uvxTXx89ejTL4BAREUFERETOvkgWfL18hj/wO3ArcBBYiTMovTVNmjrAz0B3\nYHkW+Wj5DJFiprAvn1GtWjXmzJnDNddck+lxMyMwMJClS5fSokX2JmHGx8fToUMHmjRpwpgxY9Id\ni4mJoUqVKhw/ftwVHIri8hlJwFPAD8AW4AucwNA75QfgVaAyMAr4DSeAiIgUai1atOD333/P8nhy\ncjIej4eyZctmK78zZ85wzz33UKdOHVdgANi6dSv16tXLly4lyJ/7HOYCjYGGwNsp+8ak/AA8CgQD\nLVN+WuVDmURELkjHjh1ZuHChd3vBggWsW7eO5ORkjh8/zvPPP0/jxo294wYTJ06kfv36meaVmJjI\nfffdR9myZZk4cWKmaRYuXEjHjh3z/HtkRXdIi4jkQs+ePZkzZw6nT58GnLGArl27UqlSJRo3bkx0\ndDQzZ870pt+3bx833XRTpnktXbqU2bNnM3/+fCpVqkRgYCCBgYEsWbLEm2batGn07t070/f7gpbs\nFpFCqbCPOQC8/PLLhISE8Oyzz5437Z133snw4cMzneF0PrNmzWLq1KlMmzYt0+O+GHNQcBCRQqko\nBIfCoigOSIuISBGk4CAiIi4KDiIi4qLgICIiLgoOIiLiouAgIiIuCg4iIuKi4CAikkt6TKiIiKST\n8TGhy5cv5/bbbyc4OJiQkBAeeOAB/vzzz2znVdweEyoiclHK+JjQo0eP0qdPHyIjI4mMjCQwMJCH\nH344W3kVxseEavkMESmUCvvyGbfeeiuPPPII3bp1y/T42rVrCQsL4/jx47nKv2LFikRERNCyZUvA\nWZyve/fu7N6925VWy2eIiBQSWT0mNNWiRYto3rx5rvI+32NC84OvHxMqIuIzfq/nTeeHvZbzFkpW\njwkF2LBhAwMHDky3ZHd2XchjQvOSgoOIFFm5OannlcqVK3PixAnX/p07d9KxY0eGDx/OjTfemKM8\n4+Pjufvuu7nhhhvo169fumOpn1WpUqXcFzoH1K0kIpILmT0mNDIykttvv51XX32Vf/7znznKrzg+\nJlRE5KKT8TGhBw4coH379jz11FM8/vjjrvR6TKiISDGQ8TGh48eP548//vCOFQQGBlKhQgVvej0m\n1Dc0lVWkmCnsU1lBjwktDBQcRIqZohAcCgvd5yAiIvlCwUFERFwUHERExEXBQUREXBQcRETERctn\niEihVLly5dTZNnIelStXzvM8fV3zHYAPgJLAeGBQJmmGA38DTgH/An7LJI2msoqI5FBhncpaEhiB\nEyCaAV2BphnSdAQaApcBjwOjfFiei0JERERBF6HQUF2cpbo4S3WRN3wZHFoBO4E9QCIwDeicIc3f\ngU9TXq8AKgHVfFimIk+/+GepLs5SXZylusgbvgwOocC+NNv7U/adL00tH5ZJRESywZfBIbuDBBn7\nwzS4ICJSwHw5IN0GCMcZcwDoD3hIPyg9GojA6XIC2Aa0BaIy5LUTuNRH5RQRuVjtwhnXLVT8cQpW\nDygFrCPzAek5Ka/bAMvzq3AiIlJw/gb8jnPl3z9lX++Un1QjUo6vB67O19KJiIiIiMjFoQPOOMQO\noN950l4MPsEZb9mYZl8QMB/YDvyIM903VX+cutkG3JFPZcwvtYFfgM3AJuCZlP3FsT5K40z1Xgds\nAd5O2V8c6yJVSZwbZmelbBfXutgDbMCpi5Up+y76uiiJ091UDwgg8zGLi83NQEvSB4fBwP+lvO4H\nvJPyuhlOnQTg1NFOLq61sqoDV6W8Lo/TPdmU4lsfZVP+9ccZm7uJ4lsXAM8DU4GZKdvFtS7+wAkG\naV30dXE9MC/N9ospPxe7eqQPDts4e2Ng9ZRtcK4A0ram5uEM6l+svgVuQ/VRFlgFXE7xrYtawAKg\nHWdbDsW1Lv4AgjPsy5O6KMxRIzs30RUH1Tg7tTeKs//pNXHqJNXFXD/1cFpUKyi+9VEC56ovirPd\nbcW1LoYCL+BMjU9VXOvCcALlauCxlH15UheFeVVW3QznZpy7Xi7GOisPfA08C5zIcKw41YcHp5ut\nIvADzlVzWsWlLu4C/sLpYw/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"text/plain": [ - "" + "" ] }, "metadata": {}, From 7866408a0740988981496d9c64890f2ba23a1095 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Date: Fri, 1 Apr 2016 09:10:23 +0530 Subject: [PATCH 218/513] Replaced inline pylab with standard matlab inline magic --- rl.ipynb | 40 +++++++++++++++++----------------------- 1 file changed, 17 insertions(+), 23 deletions(-) diff --git a/rl.ipynb b/rl.ipynb index 1e0f7a646..cc0c0b59e 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -98,7 +98,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -218,7 +218,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{(0, 1): 0.45349055962264184, (1, 2): 0.6127179535526855, (3, 2): 1, (0, 0): 0.3686271642983948, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.2327528230992705, (3, 1): -1, (2, 2): 0.7269851866778488, (2, 1): 0.5227571939134159, (0, 2): 0.5160077721580049}\n" + "{(0, 1): 0.43655093803808254, (1, 2): 0.7111433090760988, (3, 2): 1, (0, 0): 0.3220542204171776, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.20098994088292488, (3, 1): 0.0, (2, 2): 0.8560074788087413, (2, 1): 0.6639270026362584, (0, 2): 0.5629080090683166}\n" ] } ], @@ -232,26 +232,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also explore how these estimates vary with time by using plots similar to **Fig 21.5a**. To do so we define a function to help us with the same." + "We can also explore how these estimates vary with time by using plots similar to **Fig 21.5a**. To do so we define a function to help us with the same. We will first enable matplotlib using the inline backend." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], + "outputs": [], "source": [ - "%pylab inline\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", "def graph_utility_estimates(agent_program, mdp, no_of_iterations, states_to_graph):\n", " graphs = {state:[] for state in states_to_graph}\n", " for iteration in range(1,no_of_iterations+1):\n", @@ -260,11 +254,11 @@ " graphs[state].append((iteration, agent_program.U[state]))\n", " for state, value in graphs.items():\n", " state_x, state_y = zip(*value)\n", - " pylab.plot(state_x, state_y, label=str(state))\n", - " pylab.ylim([0,1.2])\n", - " pylab.legend(loc='lower right')\n", - " pylab.xlabel('Iterations')\n", - " pylab.ylabel('U')" + " plt.plot(state_x, state_y, label=str(state))\n", + " plt.ylim([0,1.2])\n", + " plt.legend(loc='lower right')\n", + " plt.xlabel('Iterations')\n", + " plt.ylabel('U')" ] }, { @@ -283,9 +277,9 @@ "outputs": [ { "data": { - "image/png": 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9qbKVc9asgou7hXYhDlG9lSoqbDm1eyhk9hziDCutWmUt26hpC8DioYMHW0Ks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\nIV2BJaSPBxYBbxNOSO8MvAy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -313,9 +307,9 @@ "outputs": [ { "data": { - "image/png": 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P7WVL9BbXAJ/IhapcujKx8bF0n9Gd7Ue2s/BfC7P1gKILUdTvUJbCrci0HDzm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\nIvADzlVzWsWlLu4C/sLpYw/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0c2sHG0sb2FrZ6oVDyL0Q1LSriba12mL31d1lam6/UfVGGNF2RKFPHEhEZIjZ\nhwOgTC1NPTRV93mAnCOHvVF78Yr7K3Cr6gaNaNCtQdk4DTMANK7eGPN6zyvtZhBROWOW51bKKTYp\nFrFJsTg47CAAdTgEXQvCsOeHwc3eDQDKzDn6iYhMhSMHrTY12+hOu509HEQER28dRad6nVC3Wl3M\neG0G6lStU5pNJSIyOY4ctDxqeuhuZw+HS7GXYG9lr7vS2tiXx5ZK+4iIShJHDgA61+uMEW2zrslg\na5l1Guxjt46hfZ32pdU0IqJSwZEDgANDD+jdzz5yOHrrKNq7MRyIyLxw5GBAzlNqdKjToZRbRERU\nshgOBthY2iAxNRFJaUkIiwlDm5ptSrtJREQliuFgQA2bGohNikXo/VA0rdFUdRpsIqLyjuFggFVF\nK9hZ2SH4WrDeUUxEROaC4ZALF1sX7Lq6C61dWpd2U4iIShzDIReudq44cP0ARw5EZJZMHQ69AEQA\nuARgfC5lPAGcAXAeQLCJ22M0V1tXpGSkwMOV4UBE5seUn3OoCGAegFcBRAM4AWArgPBsZRwAzAfQ\nE8AtAE4mbE+BuNi6oKZdTTjbOpd2U4iISlxe4fBVjvsCIAbAQQBRRtTdDsBlANe099cC6Af9cBgI\nYCOUYACAB0bUWyJcbV05aiAis5XXtJI9ALtsX/YAXgIQAGCAEXW7AbiZ7f4t7bbsmgCoDiAIwEkA\ng41qdQl4teGrGNZmWGk3g4ioVOQ1cvDOZXt1AHsArMmnbjHi+S0BvACgOwAbAEcAHIWyRqHfGO+s\n5nh6esLT09OI6gvv5bov42W8bNLnICIqTsHBwQgODi6WuiwK+bgzAPL72HAHKAGTef3K7wBoAEzN\nVmY8gCrICqKlUEYmG3LUJSLGZA0REWWysLAACvk6X5ijlboBiDOi3Eko00YNAFgB+CeUBenstgDo\nDGXx2gZAewBhhWgTEREVo7ymlUINbHMEcAfAR0bUnQ5gDIBAKC/+y6AsRmeeG3sRlMNcAwCcgzKq\nWAKGAxFRqctruNEgx30B8BDAE5O1JnecViIiKqCiTCsVds2hpDEciIgKqKTXHIiIqJxjOBARkQrD\ngYiIVBgORESkwnAgIiIVhgMREakwHIiISIXhQEREKgwHIiJSYTgQEZEKw4GIiFQYDkREpMJwICIi\nFYYDERGpMByIiEiF4UBERCoMByIiUmE4EBGRCsOBiIhUGA5ERKTCcCAiIhWGAxERqTAciIhIheFA\nREQqDAci7p36AAANXUlEQVQiIlJhOBARkQrDgYiIVEwdDr0ARAC4BGB8HuVeApAO4B0Tt4eIiIxg\nynCoCGAelIBoCWAAgBa5lJsKIACAhQnbQ0RERjJlOLQDcBnANQBpANYC6Geg3L8AbAAQY8K2EBFR\nAZgyHNwA3Mx2/5Z2W84y/QAs1N4XE7aHiIiMVMmEdRvzQj8LwLfashbIY1rJ29tbd9vT0xOenp5F\nax0RUTkTHByM4ODgYqnLlHP8HQB4Q1lzAIDvAGigrC9kupqtDU4AngL4DMDWHHWJCAcVREQFYWFh\nARTydd6U4VAJwEUA3QHcBnAcyqJ0eC7lVwDYBmCTgX0MByKiAipKOJhyWikdwBgAgVCOSFoGJRhG\naPcvMuFzExFRETwrh45y5EBEVEBFGTnwE9JERKTCcCAiIhWGAxERqTAciIhIheFAREQqDAciIlJh\nOBARkQrDgYiIVBgORESkwnAgIiIVhgMREakwHIiISIXhQEREKgwHIiJSYTgQEZEKw4GIiFQYDkRE\npMJwICIiFYYDERGpMByIiEiF4UBERCoMByIiUqlU2g0gIjKkevXqiIuLK+1mPBMcHR0RGxtbrHVa\nFGttpiMiUtptIKISZGFhAf7fGye3vrKwsAAK+TrPaSUiIlJhOBARkQrDgYiIVBgORESkUhLh0AtA\nBIBLAMYb2D8IQAiAcwAOAWhdAm0iIiqy7777DrNnzzb582zbtg0ffPCByZ8nO1OHQ0UA86AEREsA\nAwC0yFHmKoD/gxIKEwAsNnGbiIiKLCYmBqtWrcLIkSMBAGFhYXjxxRdRvXp1ODg4oFOnTjh48KDR\ndQ0YMABubm5wcHBA586dcfz4cd3+vn374sKFCwgNDTXJz2KIqcOhHYDLAK4BSAOwFkC/HGWOAHik\nvX0MQB0Tt4mIqMhWrlyJN954A9bW1gAANzc3rF+/Hg8fPkRcXBw++OADvPfee0bV9eTJE7Rv3x6n\nT59GXFwchgwZgjfeeAOJiYm6MgMGDMDixSX33tnU4eAG4Ga2+7e023LzCYAdJm0REVExCAgIQNeu\nXXX3q1WrBnd3d1hYWCAjIwMVKlRArVq1jKrL3d0dX375JVxdXWFhYYHPPvsMqampiIyM1JXx9PSE\nv79/sf8cuTH1J6QL8gmWbgCGAehkorYQERWb0NBQNGvWTLXdwcEBiYmJqF27Nvbu3Vuous+ePYvU\n1FQ0btxYt6158+a4du0anjx5Ajs7u0K321imDodoAHWz3a8LZfSQU2sAS6CsTRj8vLy3t7futqen\nJzw9PYurjUT0jLIopnM8FOaD2PHx8bC3tze4/enTp/jpp5/w/vvv49SpU5mfVDbK48ePMXjwYHh7\ne+vVn3k7Pj4+13AIDg5GcHBwwX6QXJj69BmVAFwE0B3AbQDHoSxKh2crUw/AXgAfAjiaSz08fQaR\nmSnrp89wdXXFjh070LZtW4P7RQT29vY4fPgwWrc27iDMpKQk9OrVC82bN8eiRYv09sXGxsLJyQmP\nHz9WhcOzePqMdABjAAQCCAOwDkowjNB+AcCPABwBLARwBkqAEBGVaa1bt8bFixdz3Z+RkQGNRgMb\nGxuj6ktJScFbb72FevXqqYIBAMLDw9GgQYMSmVICSuZzDn8DaAagMYDJ2m2LtF8A8CmAGgDaaL/a\nlUCbiIiKpHfv3ti3b5/u/u7du3H27FlkZGTg8ePHGDduHJo1a6ZbN1i5ciXc3d0N1pWWlob33nsP\nNjY2WLlypcEy+/btQ+/evYv958gNPyFNRFQIH330EXbs2IHk5GQAylrAgAED4ODggGbNmiEmJgZb\nt27Vlb958yY6d+5ssK7Dhw/D398fu3btgoODA+zt7WFvb49Dhw7pyqxduxYjRoww+HhT4Cm7iahM\nKutrDgDw3//+Fy4uLvjiiy/yLduzZ0/MmTPH4BFO+dm2bRv+/PNPrF271uB+U6w5MByIqEx6FsKh\nrHgWF6SJiOgZxHAgIiIVhgMREakwHIiISIXhQEREKgwHIiJSYTgQEZEKw4GIqJB4mVAiItKT8zKh\nR48eRY8ePVCjRg24uLigf//+uHv3rtF1mdtlQomIyqWclwmNj4/HyJEjcf36dVy/fh329vYYOnSo\nUXWVxcuE8vQZRFQmlfXTZ3Tv3h2ffPIJBg4caHD/6dOn4enpicePHxeq/mrVqiE4OBht2rQBoJyc\n78MPP8TVq1dVZXn6DCKiMiK3y4Rm2r9/P1q1alWouvO7TGhJMPVlQomITMbip+KZ/BCvgo9QcrtM\nKACcO3cOEyZM0Dtlt7GKcpnQ4sRwIKJnVmFe1IuLo6MjEhISVNsvX76M3r17Y86cOejUqVOB6kxK\nSkLfvn3RsWNHjB8/Xm9f5nM5ODgUvtEFwGklIqJCMHSZ0OvXr6NHjx748ccfMWjQoALVZ46XCSUi\nKndyXiY0Ojoar7zyCsaMGYPhw4eryvMyoUREZiDnZUKXLl2KqKgo3VqBvb09qlatqivPy4SaBg9l\nJTIzZf1QVoCXCS0LGA5EZuZZCIeygp9zICKiEsFwICIiFYYDERGpMByIiEiF4UBERCo8fQYRlUmO\njo6ZR9tQPhwdHYu9TlP3fC8AswBUBLAUwFQDZeYAeB3AUwAfAzhjoAwPZSUiKqCyeihrRQDzoARE\nSwADALTIUaY3gMYAmgAYDmChCdtTLgQHB5d2E8oM9kUW9kUW9kXxMGU4tANwGcA1AGkA1gLol6PM\nmwB+094+BsABgKsJ2/TM4x9+FvZFFvZFFvZF8TBlOLgBuJnt/i3ttvzK1DFhm4iIyAimDAdjFwly\nzodxcYGIqJSZckG6AwBvKGsOAPAdAA30F6V/BRAMZcoJACIAdAVwL0ddlwE0MlE7iYjKqytQ1nXL\nlEpQGtYAgBWAszC8IL1De7sDgKMl1TgiIio9rwO4COWd/3fabSO0X5nmafeHAHihRFtHRERERETl\nQy8o6xCXAIzPp2x5sBzKektotm3VAewCEAlgJ5TDfTN9B6VvIgC8VkJtLCl1AQQBuADgPIB/a7eb\nY39UhnKo91kAYQAma7ebY19kqgjlA7PbtPfNtS+uATgHpS+Oa7eV+76oCGW6qQEASxhesyhvugBo\nA/1w8AHwjfb2eABTtLdbQukTSyh9dBnl61xZNQE8r71tB2V6sgXMtz9stN8rQVmb6wzz7QsAGAfg\nTwBbtffNtS+ioIRBduW+L14GEJDt/rfar/KuAfTDIQJZHwysqb0PKO8Aso+mAqAs6pdXfgBeBfvD\nBsAJAM/BfPuiDoDdALoha+Rgrn0RBaBGjm3F0hdlOTWM+RCdOXBF1qG995D1S68NpU8ylef+aQBl\nRHUM5tsfFaC867uHrOk2c+2LmQC+hnJofCZz7QuBEpQnAXym3VYsfVGWz8rKD8OpCfLul/LYZ3YA\nNgL4AkBCjn3m1B8aKNNs1QAEQnnXnJ259EUfAPehzLF75lLGXPoCADoBuAPAGco6Q0SO/YXui7I8\ncoiGsiiZqS70U89c3IMyNASAWlD+MQB1/9TRbitPLKEEwyoo00qAefcHADwC4A+gLcyzLzpCOSdb\nFIA1AF6B8vdhjn0BKMEAADE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"text/plain": [ - "" + "" ] }, "metadata": {}, From 7ca09a70b96c048fd94488dbc19ee44dff9500ac Mon Sep 17 00:00:00 2001 From: SnShine Date: Fri, 1 Apr 2016 10:44:39 +0530 Subject: [PATCH 219/513] added rounder() instead of truncate() --- utils.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/utils.py b/utils.py index 660670f24..de3bb65a6 100644 --- a/utils.py +++ b/utils.py @@ -243,16 +243,13 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] -def truncate(x, n = 4): - """Truncates floats, vectors, matrices to n decimal values""" - if isinstance(x, float): - return(float("{0:.{1}f}".format(x, n))) - elif isinstance(x, list) and isinstance(x[0], float): - return([float("{0:.{1}f}".format(i, n)) for i in x]) - elif isinstance(x, list) and isinstance(x[0], list) and isinstance(x[0][0], float): - return([[float("{0:.{1}f}".format(i, n)) for i in row] for row in x]) +def rounder(numbers, d = 4): + "Round a single number, or sequence of numbers, to d decimal places." + if isinstance(numbers, (int, float)): + return round(numbers, d) else: - return x + constructor = type(numbers) # Can be list, set, tuple, etc. + return constructor(rounder(n, d) for n in numbers) def num_or_str(x): """The argument is a string; convert to a number if From 273a47bb0daf78dad3b95655fb69da89cb05af4a Mon Sep 17 00:00:00 2001 From: SnShine Date: Fri, 1 Apr 2016 10:45:41 +0530 Subject: [PATCH 220/513] modified tests which uses deprecated truncate() method --- tests/test_probability.py | 10 +++++----- tests/test_utils.py | 22 +++++++++++----------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/tests/test_probability.py b/tests/test_probability.py index 03da667e0..21219682f 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -110,11 +110,11 @@ def test_forward_backward(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) umbrella_evidence = [T, T, F, T, T] - assert truncate(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.6469, 0.3531], + assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] umbrella_evidence = [T, F, T, F, T] - assert truncate(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], + assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] def test_fixed_lag_smoothing(): @@ -126,16 +126,16 @@ def test_fixed_lag_smoothing(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) d = 2 - assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.1111, 0.8889] + assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.1111, 0.8889] d = 5 - assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) is None + assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) is None umbrella_evidence = [T, T, F, T, T] # t = 4 e_t = T d = 1 - assert truncate(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] + assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] if __name__ == '__main__': diff --git a/tests/test_utils.py b/tests/test_utils.py index 0f084ea00..2392afb47 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -113,20 +113,20 @@ def test_scalar_vector_product(): assert scalar_vector_product(2, [1, 2, 3]) == [2, 4, 6] def test_scalar_matrix_product(): - assert truncate(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] - assert truncate(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] + assert rounder(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] + assert rounder(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] def test_inverse_matrix(): - assert truncate(inverse_matrix([[1, 0], [0, 1]])) == [[1, 0], [0, 1]] - assert truncate(inverse_matrix([[2, 1], [4, 3]])) == [[1.5, -0.5], [-2.0, 1.0]] - assert truncate(inverse_matrix([[4, 7], [2, 6]])) == [[0.6, -0.7], [-0.2, 0.4]] - -def test_truncate(): - assert truncate(5.3330000300330) == 5.3330 - assert truncate(10.234566) == 10.2346 - assert truncate([1.234566, 0.555555, 6.010101]) == [1.2346, 0.5556, 6.0101] - assert truncate([[1.234566, 0.555555, 6.010101], + assert rounder(inverse_matrix([[1, 0], [0, 1]])) == [[1, 0], [0, 1]] + assert rounder(inverse_matrix([[2, 1], [4, 3]])) == [[1.5, -0.5], [-2.0, 1.0]] + assert rounder(inverse_matrix([[4, 7], [2, 6]])) == [[0.6, -0.7], [-0.2, 0.4]] + +def test_rounder(): + assert rounder(5.3330000300330) == 5.3330 + assert rounder(10.234566) == 10.2346 + assert rounder([1.234566, 0.555555, 6.010101]) == [1.2346, 0.5556, 6.0101] + assert rounder([[1.234566, 0.555555, 6.010101], [10.505050, 12.121212, 6.030303]]) == [[1.2346, 0.5556, 6.0101], [10.5051, 12.1212, 6.0303]] From b4016e941f8f73626f890bebee97042f86e467b0 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sat, 2 Apr 2016 10:00:46 +0530 Subject: [PATCH 221/513] Implementation of Continuous World * Model for ContinuousWorld * Added PolygonObstacle Class * Added HTML for Continuos World * Added JS for Continuous World * Implemented ContinuousWorldView Class --- agents.py | 22 ++++++++++++++ ipyviews.py | 62 +++++++++++++++++++++++++++++++++++++ js/continuousworld.js | 71 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 155 insertions(+) create mode 100644 ipyviews.py create mode 100644 js/continuousworld.js diff --git a/agents.py b/agents.py index 6968bf84b..79a910908 100644 --- a/agents.py +++ b/agents.py @@ -516,6 +516,28 @@ class Obstacle(Thing): class Wall(Obstacle): pass + + +# ______________________________________________________________________________ +# Continuous environment + +class ContinuousWorld(Environment): + """ Model for Continuous World. """ + def __init__(self, width=10, height=10): + super(ContinuousWorld, self).__init__() + self.width = width + self.height = height + + def add_obstacle(self, coordinates): + self.things.append(PolygonObstacle(coordinates)) + + +class PolygonObstacle(Obstacle): + def __init__(self, coordinates): + """ Coordinates is a list of tuples. """ + super(PolygonObstacle, self).__init__() + self.coordinates = coordinates + # ______________________________________________________________________________ # Vacuum environment diff --git a/ipyviews.py b/ipyviews.py new file mode 100644 index 000000000..75939ea27 --- /dev/null +++ b/ipyviews.py @@ -0,0 +1,62 @@ +from IPython.display import HTML, display, clear_output +from agents import PolygonObstacle +import time +import __main__ + + +# ______________________________________________________________________________ +# Continuous environment + + +_CONTINUOUS_WORLD_HTML = ''' +
    + +
    + + +''' + +with open('js/continuousworld.js', 'r') as js_file: + _JS_CONTINUOUS_WORLD = js_file.read() + + +class ContinuousWorldView: + ''' View for continuousworld Implementation in agents.py ''' + + def __init__(self, world, fill="#AAA"): + self.time = time.time() + self.world = world + self.width = world.width + self.height = world.height + + def object_name(self): + globals_in_main = {x: getattr(__main__, x) for x in dir(__main__)} + for x in globals_in_main: + if isinstance(globals_in_main[x], type(self)): + if globals_in_main[x].time == self.time: + return x + + def handle_add_obstacle(self, vertices): + """ Vertices must be a nestedtuple. This method + is called from kernel.execute on completion of + a polygon. """ + self.world.add_obstacle(vertices) + self.show() + + def handle_remove_obstacle(self): + return NotImplementedError + + def get_polygon_obstacles_coordinates(self): + obstacle_coordiantes = [] + for thing in self.world.things: + if isinstance(thing, PolygonObstacle): + obstacle_coordiantes.append(thing.coordinates) + return obstacle_coordiantes + + def show(self): + clear_output() + total_html = _CONTINUOUS_WORLD_HTML.format(self.width, self.height, self.object_name(), str(self.get_polygon_obstacles_coordinates()), _JS_CONTINUOUS_WORLD) + display(HTML(total_html)) diff --git a/js/continuousworld.js b/js/continuousworld.js new file mode 100644 index 000000000..ab589f6d1 --- /dev/null +++ b/js/continuousworld.js @@ -0,0 +1,71 @@ +var latest_output_area ="NONE"; // Jquery object for the DOM element of output area which was used most recently +function handle_output(out, block){ + var output = out.content.data["text/html"]; + latest_output_area.html(output); +} +function polygon_complete(canvas, vertices){ + latest_output_area = $(canvas).parents('.output_subarea'); + var world_object_name = canvas.dataset.world_name; + var command = world_object_name + ".handle_add_obstacle(" + JSON.stringify(vertices) + ")"; + console.log("Executing Command: " + command); + var kernel = IPython.notebook.kernel; + var callbacks = { 'iopub' : {'output' : handle_output}}; + kernel.execute(command,callbacks); +} +var canvas , ctx; +function drawPolygon(array) { + ctx.fillStyle = '#f00'; + ctx.beginPath(); + ctx.moveTo(array[0][0],array[0][1]); + for(var i = 1;i1) + { + drawPoint(pArray[0][0],pArray[0][1]); + } + //check overlap + if(ctx.isPointInPath(x, y) && (pArray.length>1)) { + //Do something + drawPolygon(pArray); + polygon_complete(canvas,pArray); + } + else { + var point = new Array(); + point.push(x,y); + pArray.push(point); + } +} +function drawPoint(x, y) { + ctx.beginPath(); + ctx.arc(x, y, 5, 0, Math.PI*2); + ctx.fillStyle = '#00f'; + ctx.fill(); + ctx.closePath(); +} +function initalizeObstacles(objects) { + canvas = $('canvas.main-robo-world').get(0); + ctx = canvas.getContext('2d'); + $('canvas.main-robo-world').removeClass('main-robo-world'); + for(var i=0;i Date: Mon, 4 Apr 2016 12:19:56 +0530 Subject: [PATCH 222/513] Fully implemented LRTA* agent with tests * adds Fig[4.23] graph which is 1-dim state space problem * adds LRTA star agent class * adds fully implemented LRTA star agent * adds tests for LRTA star agent --- search.py | 122 +++++++++++++++++++++++++++++++++++++++++-- tests/test_search.py | 14 +++++ 2 files changed, 131 insertions(+), 5 deletions(-) diff --git a/search.py b/search.py index 7aff1550b..361c4aac9 100644 --- a/search.py +++ b/search.py @@ -459,9 +459,97 @@ def __call__(self, percept): def update_state(self, percept): raise NotImplementedError -def lrta_star_agent(s1): - "[Fig. 4.24]" - unimplemented() +# ______________________________________________________________________________ + +class OnlineSearchProblem(Problem): + """ Fig. [4.23] + """ + def __init__(self, initial, goal, graph): + self.initial = initial + self.goal = goal + self.graph = graph + + def actions(self, state): + return self.graph.dict[state].keys() + + def output(self, state, action): + return self.graph.dict[state][action] + + def h(self, state): + """ + returns least possible cost for the given state + """ + return self.graph.least_costs[state] + + def c(self, s, a, s1): + """ + returns a cost estimate to move from state 's' to state 's1' + """ + return 1 + + def update_state(self, percept): + raise NotImplementedError + + def goal_test(self, state): + if state == self.goal: + return True + return False + + +class LRTAStarAgent: + + """Fig. [4.24] + Abstract class for LRTA*-Agent. A problem needs to be + provided which is an instanace of a subclass of Problem Class. + + Takes a OneDimStateSpaceProblem Fig. [4.23] as a problem + """ + + def __init__(self, problem): + self.problem = problem + # self.result = {} # no need as we are using problem.result + self.H = {} + self.s = None + self.a = None + + def __call__(self, s1): # as of now s1 is a state rather than a percept + if self.problem.goal_test(s1): + self.a = None + return(self.a) + else: + if s1 not in self.H: + self.H[s1] = self.problem.h(s1) + if self.s is not None: + # self.result[(self.s, self.a)] = s1 # no need as we are using problem.output + + # minimum cost for action b in problem.actions(s) + self.H[self.s] = min([self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) + for b in self.problem.actions(self.s)]) + + # costs for action b in problem.actions(s1) + costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H) + for b in self.problem.actions(s1)] + # an action b in problem.actions(s1) that minimizes costs + self.a = list(self.problem.actions(s1))[costs.index(min(costs))] + + self.s = s1 + return self.a + + def LRTA_cost(self, s, a, s1, H): + """ + returns cost to move from state 's' to state 's1' plus + estimated cost to get to goal from s1 + """ + print(s, a, s1) + if s1 is None: + return(self.problem.h(s)) + else: + # sometimes we need to get H[s1] which we haven't yet added to H + # to replace this try, except: we can initialize H with values from problem.h + try: + return(self.problem.c(s, a, s1) + self.H[s1]) + except: + return(self.problem.c(s, a, s1) + self.problem.h(s1)) # ______________________________________________________________________________ # Genetic Algorithm @@ -646,7 +734,32 @@ def distance_to_node(n): State_6 = dict(Suck = ['State_8'], Left = ['State_5']), State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']), State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) -)) + )) + +""" +Fig. [4.23] +One-dimensional state space Graph + +""" + +# TODO: It's better to use some meaningful names rather +# than Fig[4, 9] or Fig[6, 1] to represent graphs in figures + +one_dim_state_space = Graph(dict( + State_1 = dict(Right = 'State_2'), + State_2 = dict(Right = 'State_3', Left = 'State_1'), + State_3 = dict(Right = 'State_4', Left = 'State_2'), + State_4 = dict(Right = 'State_5', Left = 'State_3'), + State_5 = dict(Right = 'State_6', Left = 'State_4'), + State_6 = dict(Left = 'State_5') + )) +one_dim_state_space.least_costs = dict( + State_1 = 8, + State_2 = 9, + State_3 = 2, + State_4 = 2, + State_5 = 4, + State_6 = 3) # Principal states and territories of Australia Fig[6, 1] = UndirectedGraph(dict( @@ -654,7 +767,6 @@ def distance_to_node(n): SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), NT=dict(WA=1, Q=1), NSW=dict(Q=1, V=1))) - Fig[6, 1].locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) diff --git a/tests/test_search.py b/tests/test_search.py index aafa66c6a..d388bbbb0 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -4,6 +4,7 @@ romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], Fig[4, 9]) +LRTA_world = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space) def test_breadth_first_tree_search(): @@ -37,6 +38,19 @@ def run_plan(state, problem, plan): plan = and_or_graph_search(vacumm_world) assert run_plan('State_1', vacumm_world, plan) +def test_LRTAStarAgent(): + my_agent = LRTAStarAgent(LRTA_world) + assert my_agent('State_3') == 'Right' + assert my_agent('State_4') == 'Left' + assert my_agent('State_3') == 'Right' + assert my_agent('State_4') == 'Right' + assert my_agent('State_5') is None + + my_agent = LRTAStarAgent(LRTA_world) + assert my_agent('State_4') == 'Left' + + my_agent = LRTAStarAgent(LRTA_world) + assert my_agent('State_5') is None if __name__ == '__main__': pytest.main() From a8fd7e39014e2e35cd11fe73c2cefa6c10ebf58d Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 5 Apr 2016 00:30:10 +0530 Subject: [PATCH 223/513] Modified search.py * changes a typo of file name to /aima-data * modified some documentaion and removed doc tests * changed the names like Fig[2, 3] and added unit tests for search.py --- search.py | 129 ++++++++++++++++++------------------------- tests/test_search.py | 45 +++++++++++---- utils.py | 2 +- 3 files changed, 87 insertions(+), 89 deletions(-) diff --git a/search.py b/search.py index 361c4aac9..2c3eecd4a 100644 --- a/search.py +++ b/search.py @@ -11,6 +11,8 @@ import sys import bisect +infinity = float('inf') + # ______________________________________________________________________________ @@ -98,7 +100,7 @@ def expand(self, problem): for action in problem.actions(self.state)] def child_node(self, problem, action): - "Fig. 3.10" + "[Fig. 3.10]" next = problem.result(self.state, action) return Node(next, self, action, problem.path_cost(self.path_cost, self.state, @@ -462,7 +464,10 @@ def update_state(self, percept): # ______________________________________________________________________________ class OnlineSearchProblem(Problem): - """ Fig. [4.23] + """ + A problem which is solved by an agent executing + actions, rather than by just computation. + Carried in a deterministic and a fully observable environment. """ def __init__(self, initial, goal, graph): self.initial = initial @@ -477,13 +482,13 @@ def output(self, state, action): def h(self, state): """ - returns least possible cost for the given state + Returns least possible cost to reach a goal for the given state. """ return self.graph.least_costs[state] def c(self, s, a, s1): """ - returns a cost estimate to move from state 's' to state 's1' + Returns a cost estimate for an agent to move from state 's' to state 's1' """ return 1 @@ -498,11 +503,11 @@ def goal_test(self, state): class LRTAStarAgent: - """Fig. [4.24] + """ [Fig. 4.24] Abstract class for LRTA*-Agent. A problem needs to be provided which is an instanace of a subclass of Problem Class. - Takes a OneDimStateSpaceProblem Fig. [4.23] as a problem + Takes a OnlineSearchProblem [Fig. 4.23] as a problem """ def __init__(self, problem): @@ -537,7 +542,7 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept def LRTA_cost(self, s, a, s1, H): """ - returns cost to move from state 's' to state 's1' plus + Returns cost to move from state 's' to state 's1' plus estimated cost to get to goal from s1 """ print(s, a, s1) @@ -556,7 +561,8 @@ def LRTA_cost(self, s, a, s1, H): def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): - """Call genetic_algorithm on the appropriate parts of a problem. + """ + Call genetic_algorithm on the appropriate parts of a problem. This requires the problem to have states that can mate and mutate, plus a value method that scores states.""" s = problem.initial_state @@ -689,8 +695,10 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g -# Simplified road map of Romania -Fig[3, 2] = UndirectedGraph(dict( +""" [Fig. 3.2] +Simplified road map of Romania +""" +romania_map = UndirectedGraph(dict( Arad=dict(Zerind=75, Sibiu=140, Timisoara=118), Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211), Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138), @@ -704,7 +712,7 @@ def distance_to_node(n): Pitesti=dict(Rimnicu=97), Rimnicu=dict(Sibiu=80), Urziceni=dict(Vaslui=142))) -Fig[3, 2].locations = dict( +romania_map.locations = dict( Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288), Drobeta=(165, 299), Eforie=(562, 293), Fagaras=(305, 449), Giurgiu=(375, 270), Hirsova=(534, 350), Iasi=(473, 506), @@ -713,19 +721,20 @@ def distance_to_node(n): Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) -""" +""" [Fig. 4.9] Eight possible states of the vacumm world -Each state is represented as "State if the left room" "State of the right room" "Room in which the agent is present" -1 Dirty Dirty Left - DDL -2 Dirty Dirty Right - DDR -3 Dirty Clean Left - DCL -4 Dirty Clean Right - DCR -5 Clean Dirty Left - CDL -6 Clean Dirty Right - CDR -7 Clean Clean Left - CCL -8 Clean Clean Right - CCR +Each state is represented as + * "State of the left room" "State of the right room" "Room in which the agent is present" +1 - DDL Dirty Dirty Left +2 - DDR Dirty Dirty Right +3 - DCL Dirty Clean Left +4 - DCR Dirty Clean Right +5 - CDL Clean Dirty Left +6 - CDR Clean Dirty Right +7 - CCL Clean Clean Left +8 - CCR Clean Clean Right """ -Fig[4, 9] = Graph(dict( +vacumm_world = Graph(dict( State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']), State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']), State_3 = dict(Suck = ['State_7'], Right = ['State_4']), @@ -736,15 +745,10 @@ def distance_to_node(n): State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) )) -""" -Fig. [4.23] +""" [Fig. 4.23] One-dimensional state space Graph """ - -# TODO: It's better to use some meaningful names rather -# than Fig[4, 9] or Fig[6, 1] to represent graphs in figures - one_dim_state_space = Graph(dict( State_1 = dict(Right = 'State_2'), State_2 = dict(Right = 'State_3', Left = 'State_1'), @@ -762,12 +766,12 @@ def distance_to_node(n): State_6 = 3) # Principal states and territories of Australia -Fig[6, 1] = UndirectedGraph(dict( +australia_map = UndirectedGraph(dict( T=dict(), SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), NT=dict(WA=1, Q=1), NSW=dict(Q=1, V=1))) -Fig[6, 1].locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), +australia_map.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), Q=(145, 20), NSW=(145, 32), T=(145, 42), V=(145, 37)) @@ -954,8 +958,8 @@ class Wordlist: to check if a word is in the list, or wordlist.lookup(prefix) to see if prefix starts any of the words in the list.""" - def __init__(self, filename, min_len=3): - lines = open(filename).read().upper().split() + def __init__(self, file, min_len=3): + lines = file.read().upper().split() self.words = [word for word in lines if len(word) >= min_len] self.words.sort() self.bounds = {} @@ -995,7 +999,7 @@ class BoggleFinder: def __init__(self, board=None): if BoggleFinder.wordlist is None: - BoggleFinder.wordlist = Wordlist("../data/EN-text/wordlist") + BoggleFinder.wordlist = Wordlist(DataFile("EN-text/wordlist")) self.found = {} if board: self.set_board(board) @@ -1135,52 +1139,25 @@ def do(searcher, problem): def compare_graph_searchers(): - """Prints a table of results like this: ->>> compare_graph_searchers() -Searcher Fig[3, 2](A, B) Fig[3, 2](O, N) Fig[6, 1] -breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> -breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> -depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> -iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> -depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> -recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/12/ 43/WA>""" # noqa - compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', Fig[3, 2]), - GraphProblem('Oradea', 'Neamt', Fig[3, 2]), - GraphProblem('Q', 'WA', Fig[6, 1])], - header=['Searcher', 'Fig[3, 2](Arad, Bucharest)', - 'Fig[3, 2](Oradea, Neamt)', 'Fig[6, 1]']) + """ + Prints a table of results like this: + >>> compare_graph_searchers() + Searcher romania_map(A, B) romania_map(O, N) australia_map + breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> + breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> + depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> + iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> + depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> + recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/12/ 43/WA> + """ # noqa + compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', romania_map), + GraphProblem('Oradea', 'Neamt', romania_map), + GraphProblem('Q', 'WA', australia_map)], + header=['Searcher', 'romania_map(Arad, Bucharest)', + 'romania_map(Oradea, Neamt)', 'australia_map']) # ______________________________________________________________________________ -__doc__ += """ ->>> romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) ->>> breadth_first_tree_search(romania).solution() -['Sibiu', 'Fagaras', 'Bucharest'] ->>> breadth_first_search(romania).solution() -['Sibiu', 'Fagaras', 'Bucharest'] ->>> uniform_cost_search(romania).solution() -['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] ->>> depth_first_graph_search(romania).solution() -['Timisoara', 'Lugoj', 'Mehadia', 'Drobeta', 'Craiova', 'Pitesi', 'Bucharest'] ->>> iterative_deepening_search(romania).solution() -['Sibiu', 'Fagaras', 'Bucharest'] ->>> len(depth_limited_search(romania).solution()) -50 ->>> astar_search(romania).solution() -['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] ->>> recursive_best_first_search(romania).solution() -['Sibiu', 'Rimnicu', 'Pitesi', 'Bucharest'] - ->>> board = list('SARTELNID') ->>> print_boggle(board) -S A R -T E L -N I D ->>> f = BoggleFinder(board) ->>> len(f) -206 -""" - __doc__ += """ Random tests >>> ' '.join(f.words()) diff --git a/tests/test_search.py b/tests/test_search.py index d388bbbb0..299fefba7 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -2,30 +2,51 @@ from search import * # noqa -romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2]) -vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], Fig[4, 9]) -LRTA_world = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space) +romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) +vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], vacumm_world) +LRTA_problem = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space) def test_breadth_first_tree_search(): - assert breadth_first_tree_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert breadth_first_tree_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_breadth_first_search(): - assert breadth_first_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert breadth_first_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_uniform_cost_search(): - assert uniform_cost_search(romania).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + assert uniform_cost_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] def test_depth_first_graph_search(): - solution = depth_first_graph_search(romania).solution() + solution = depth_first_graph_search(romania_problem).solution() assert solution[-1] == 'Bucharest' - def test_iterative_deepening_search(): - assert iterative_deepening_search(romania).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert iterative_deepening_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + +def test_depth_limited_search(): + # output flickers between 49 and 50 + # assert len(depth_limited_search(romania_problem).solution()) == 50 + pass + +def test_astar_search(): + assert astar_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + +def test_recursive_best_first_search(): + assert recursive_best_first_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + +def test_BoggleFinder(): + board = list('SARTELNID') + """ + >>> print_boggle(board) + S A R + T E L + N I D + """ + f = BoggleFinder(board) + assert len(f) == 206 def test_and_or_graph_search(): def run_plan(state, problem, plan): @@ -39,17 +60,17 @@ def run_plan(state, problem, plan): assert run_plan('State_1', vacumm_world, plan) def test_LRTAStarAgent(): - my_agent = LRTAStarAgent(LRTA_world) + my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_3') == 'Right' assert my_agent('State_4') == 'Left' assert my_agent('State_3') == 'Right' assert my_agent('State_4') == 'Right' assert my_agent('State_5') is None - my_agent = LRTAStarAgent(LRTA_world) + my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_4') == 'Left' - my_agent = LRTAStarAgent(LRTA_world) + my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_5') is None if __name__ == '__main__': diff --git a/utils.py b/utils.py index de3bb65a6..7a81930c7 100644 --- a/utils.py +++ b/utils.py @@ -387,7 +387,7 @@ def AIMAFile(components, mode='r'): def DataFile(name, mode='r'): - "Return a file in the AIMA /data directory." + "Return a file in the AIMA /aima-data directory." return AIMAFile(['aima-data', name], mode) From fd51088a8d4790de4d7c185a52719bd5454a8ef3 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 4 Apr 2016 17:05:56 -0700 Subject: [PATCH 224/513] Run doctests --- agents.py | 19 ++---- csp.py | 34 ++++------- games.py | 2 +- learning.py | 31 ++-------- logic.py | 109 +++++++--------------------------- mdp.py | 6 +- nlp.py | 42 +------------- probability.py | 102 ++++---------------------------- search.py | 40 ++----------- tests/test_logic.py | 38 +++++++++--- tests/test_probability.py | 50 +++++++++++++++- tests/test_search.py | 31 ++++++++++ tests/test_text.py | 13 +++++ tests/test_utils.py | 36 ++---------- text.py | 16 +---- utils.py | 119 ++++++++++---------------------------- 16 files changed, 217 insertions(+), 471 deletions(-) diff --git a/agents.py b/agents.py index 79a910908..df853103b 100644 --- a/agents.py +++ b/agents.py @@ -189,7 +189,8 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): "A reflex agent for the two-state vacuum environment. [Fig. 2.8]" - def program(location, status): + def program(percept): + location, status = percept if status == 'Dirty': return 'Suck' elif location == loc_A: @@ -203,8 +204,9 @@ def ModelBasedVacuumAgent(): "An agent that keeps track of what locations are clean or dirty." model = {loc_A: None, loc_B: None} - def program(location, status): + def program(percept): "Same as ReflexVacuumAgent, except if everything is clean, do NoOp." + location, status = percept model[location] = status # Update the model here if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp' @@ -864,17 +866,4 @@ def score(env): >>> e.add_thing(ModelBasedVacuumAgent()) >>> e.run(5) -## Environments, and some agents, are randomized, so the best we can -## give is a range of expected scores. If this test fails, it does -## not necessarily mean something is wrong. ->>> envs = [TrivialVacuumEnvironment() for i in range(100)] ->>> def testv(A): return test_agent(A, 4, copy.deepcopy(envs)) ->>> 7 < testv(ModelBasedVacuumAgent) < 11 -True ->>> 5 < testv(ReflexVacuumAgent) < 9 -True ->>> 2 < testv(TableDrivenVacuumAgent) < 6 -True ->>> 0.5 < testv(RandomVacuumAgent) < 3 -True """ diff --git a/csp.py b/csp.py index 018ecf435..54b09a2f9 100644 --- a/csp.py +++ b/csp.py @@ -45,10 +45,6 @@ class CSP(search.Problem): The following are just for debugging purposes: nassigns Slot: tracks the number of assignments made display(a) Print a human-readable representation - - >>> search.depth_first_graph_search(australia) - """ def __init__(self, variables, domains, neighbors, constraints): @@ -201,7 +197,7 @@ def mrv(assignment, csp): "Minimum-remaining-values heuristic." return argmin_random_tie( [v for v in csp.variables if v not in assignment], - lambda var: num_legal_values(csp, var, assignment)) + key=lambda var: num_legal_values(csp, var, assignment)) def num_legal_values(csp, var, assignment): @@ -303,7 +299,7 @@ def min_conflicts_value(csp, var, current): """Return the value that will give var the least number of conflicts. If there is a tie, choose at random.""" return argmin_random_tie(csp.domains[var], - lambda val: csp.nconflicts(var, val, current)) + key=lambda val: csp.nconflicts(var, val, current)) # ______________________________________________________________________________ @@ -371,20 +367,19 @@ def parse_neighbors(neighbors, variables=[]): regions to neighbors. The syntax is a region name followed by a ':' followed by zero or more region names, followed by ';', repeated for each region name. If you say 'X: Y' you don't need 'Y: X'. - >>> parse_neighbors('X: Y Z; Y: Z') - {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} + >>> parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} + True """ - dict = defaultdict(list) + dic = defaultdict(list) for var in variables: - dict[var] = [] + dic[var] = [] specs = [spec.split(':') for spec in neighbors.split(';')] for (A, Aneighbors) in specs: A = A.strip() - dict.setdefault(A, []) for B in Aneighbors.split(): - dict[A].append(B) - dict[B].append(A) - return dict + dic[A].append(B) + dic[B].append(A) + return dic australia = MapColoringCSP(list('RGB'), 'SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: ') @@ -556,8 +551,7 @@ class Sudoku(CSP): 8 1 4 | 2 5 3 | 7 6 9 6 9 5 | 4 1 7 | 3 8 2 >>> h = Sudoku(harder1) - >>> None != backtracking_search(h, select_unassigned_variable=mrv, - >>> inference=forward_checking) + >>> backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) is not None True """ R3 = _R3 @@ -670,11 +664,3 @@ def solve_zebra(algorithm=min_conflicts, **args): print() return ans['Zebra'], ans['Water'], z.nassigns, ans - -__doc__ += """ -Random tests: ->>> min_conflicts(australia) -{'WA': 'B', 'Q': 'B', 'T': 'G', 'V': 'B', 'SA': 'R', 'NT': 'G', 'NSW': 'G'} ->>> min_conflicts(NQueensCSP(8), max_steps=10000) -{0: 5, 1: 0, 2: 4, 3: 1, 4: 7, 5: 2, 6: 6, 7: 3} -""" diff --git a/games.py b/games.py index 689caaa27..980a3fd43 100644 --- a/games.py +++ b/games.py @@ -36,7 +36,7 @@ def min_value(state): # Body of minimax_decision: return argmax(game.actions(state), - lambda a: min_value(game.result(state, a))) + key=lambda a: min_value(game.result(state, a))) # ______________________________________________________________________________ diff --git a/learning.py b/learning.py index a5534868e..eb319a524 100644 --- a/learning.py +++ b/learning.py @@ -252,7 +252,7 @@ def class_probability(targetval): return (target_dist[targetval] * product(attr_dists[targetval, attr][example[attr]] for attr in dataset.inputs)) - return argmax(targetvals, class_probability) + return argmax(targetvals, key=class_probability) return predict @@ -348,7 +348,7 @@ def plurality_value(examples): """Return the most popular target value for this set of examples. (If target is binary, this is the majority; otherwise plurality.)""" popular = argmax_random_tie(values[target], - lambda v: count(target, v, examples)) + key=lambda v: count(target, v, examples)) return DecisionLeaf(popular) def count(attr, val, examples): @@ -363,7 +363,7 @@ def all_same_class(examples): def choose_attribute(attrs, examples): "Choose the attribute with the highest information gain." return argmax_random_tie(attrs, - lambda a: information_gain(a, examples)) + key=lambda a: information_gain(a, examples)) def information_gain(attr, examples): "Return the expected reduction in entropy from splitting by attr." @@ -613,11 +613,7 @@ def predict(example): def Linearlearner(dataset, learning_rate=0.01, epochs=100): - """ - >>> learner = Linearlearner(data) - >>> learner(x) - y - """ + """Define with learner = Linearlearner(data); infer with learner(x).""" idx_i = dataset.inputs idx_t = dataset.target # As of now, dataset.target gives only one index. examples = dataset.examples @@ -905,25 +901,6 @@ def T(attrname, branches): T('Raining', {'No': 'No', 'Yes': 'Yes'}) })})})}) -__doc__ += """ -[Fig. 18.6] ->>> random.seed(437) ->>> restaurant_tree = DecisionTreeLearner(restaurant) ->>> restaurant_tree.display() -Test Patrons - Patrons = None ==> RESULT = No - Patrons = Full ==> Test Hungry - Hungry = Yes ==> Test Type - Type = Burger ==> RESULT = Yes - Type = Thai ==> Test Fri/Sat - Fri/Sat = Yes ==> RESULT = Yes - Fri/Sat = No ==> RESULT = No - Type = French ==> RESULT = Yes - Type = Italian ==> RESULT = No - Hungry = No ==> RESULT = No - Patrons = Some ==> RESULT = Yes -""" - def SyntheticRestaurant(n=20): "Generate a DataSet with n examples." diff --git a/logic.py b/logic.py index 0aa4d094c..2f9408124 100644 --- a/logic.py +++ b/logic.py @@ -289,12 +289,8 @@ def is_prop_symbol(s): def variables(s): """Return a set of the variables in expression s. - >>> ppset(variables(F(x, A, y))) - set([x, y]) - >>> ppset(variables(F(G(x), z))) - set([x, z]) - >>> ppset(variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, z)'))) - set([x, y, z]) + >>> variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, z)')) == {x, y, z} + True """ result = set([]) @@ -314,14 +310,6 @@ def is_definite_clause(s): ~A | ~B | ... | ~C | D, where exactly one clause is positive. >>> is_definite_clause(expr('Farmer(Mac)')) True - >>> is_definite_clause(expr('~Farmer(Mac)')) - False - >>> is_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) - True - >>> is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) ==> Hates(f, r)')) - False - >>> is_definite_clause(expr('(Farmer(f) | Rabbit(r)) ==> Hates(f, r)')) - False """ if is_symbol(s.op): return True @@ -343,15 +331,16 @@ def parse_definite_clause(s): return conjuncts(antecedent), consequent # Useful constant Exprs used in examples and code: -TRUE, FALSE, ZERO, ONE, TWO = list(map(Expr, ['TRUE', 'FALSE', 0, 1, 2])) -A, B, C, D, E, F, G, P, Q, x, y, z = list(map(Expr, 'ABCDEFGPQxyz')) +TRUE, FALSE = Expr('TRUE'), Expr('FALSE') +ZERO, ONE, TWO = 0, 1, 2 +A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') # ______________________________________________________________________________ def tt_entails(kb, alpha): """Does kb entail the sentence alpha? Use truth tables. For propositional - kb's and sentences. [Fig. 7.10]. Note that the 'kb' that has to be passed should actually be an + kb's and sentences. [Fig. 7.10]. Note that the 'kb' should be an Expr which is a conjunction of clauses. >>> tt_entails(expr('P & Q'), expr('Q')) True @@ -458,14 +447,6 @@ def to_cnf(s): That is, to the form ((A | ~B | ...) & (B | C | ...) & ...) [p. 253] >>> to_cnf("~(B|C)") (~B & ~C) - >>> to_cnf("B <=> (P1|P2)") - ((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B)) - >>> to_cnf("a | (b & c) | d") - ((b | a | d) & (c | a | d)) - >>> to_cnf("A & (B | (D & E))") - (A & (D | B) & (E | B)) - >>> to_cnf("A | (B | (C | (D & E)))") - ((D | A | B | C) & (E | A | B | C)) """ if isinstance(s, str): s = expr(s) @@ -475,24 +456,18 @@ def to_cnf(s): def eliminate_implications(s): - """Change >>, <<, and <=> into &, |, and ~. That is, return an Expr - that is equivalent to s, but has only &, |, and ~ as logical operators. - >>> eliminate_implications(A >> (~B << C)) - ((~B | ~C) | ~A) - >>> eliminate_implications(A ^ B) - ((A & ~B) | (~A & B)) - """ + "Change implications into equivalent form with only &, |, and ~ as logical operators." if not s.args or is_symbol(s.op): - return s # (Atoms are unchanged.) + return s # Atoms are unchanged. args = list(map(eliminate_implications, s.args)) a, b = args[0], args[-1] - if s.op == '>>': + if s.op == '>>' or s.op == '==>': return (b | ~a) - elif s.op == '<<': + elif s.op == '<<' or s.op == '<==': return (a | ~b) elif s.op == '<=>': return (a | ~b) & (b | ~a) - elif s.op == '^': + elif s.op == '^' or s.op == '<=/=>': assert len(args) == 2 # TODO: relax this restriction return (a & ~b) | (~a & b) else: @@ -503,12 +478,7 @@ def eliminate_implications(s): def move_not_inwards(s): """Rewrite sentence s by moving negation sign inward. >>> move_not_inwards(~(A | B)) - (~A & ~B) - >>> move_not_inwards(~(A & B)) - (~A | ~B) - >>> move_not_inwards(~(~(A | ~B) | ~~C)) - ((A | ~B) & ~C) - """ + (~A & ~B)""" if s.op == '~': def NOT(b): return move_not_inwards(~b) # noqa a = s.args[0] @@ -630,12 +600,7 @@ def pl_resolution(KB, alpha): def pl_resolve(ci, cj): - """Return all clauses that can be obtained by resolving clauses ci and cj. - >>> for res in pl_resolve(to_cnf(A|B|C), to_cnf(~B|~C|F)): - ... ppset(disjuncts(res)) - set([A, C, F, ~C]) - set([A, B, F, ~B]) - """ + """Return all clauses that can be obtained by resolving clauses ci and cj.""" clauses = [] for di in disjuncts(ci): for dj in disjuncts(cj): @@ -711,12 +676,7 @@ def dpll_satisfiable(s): This differs from the book code in two ways: (1) it returns a model rather than True when it succeeds; this is more useful. (2) The function find_pure_symbol is passed a list of unknown clauses, rather - than a list of all clauses and the model; this is more efficient. - >>> ppsubst(dpll_satisfiable(A&~B)) - {A: True, B: False} - >>> dpll_satisfiable(P&~P) - False - """ + than a list of all clauses and the model; this is more efficient.""" clauses = conjuncts(to_cnf(s)) symbols = prop_symbols(s) return dpll(clauses, symbols, {}) @@ -841,7 +801,7 @@ def sat_count(sym): count = len([clause for clause in clauses if pl_true(clause, model)]) model[sym] = not model[sym] return count - sym = argmax(prop_symbols(clause), sat_count) + sym = argmax(prop_symbols(clause), key=sat_count) model[sym] = not model[sym] #If no solution is found within the flip limit, we return failure return None @@ -886,10 +846,7 @@ def extract_solution(model): def unify(x, y, s): """Unify expressions x,y with substitution s; return a substitution that would make x,y equal, or None if x,y can not unify. x and y can be - variables (e.g. Expr('x')), constants, lists, or Exprs. [Fig. 9.1] - >>> ppsubst(unify(x + y, y + C, {})) - {x: y, y: C} - """ + variables (e.g. Expr('x')), constants, lists, or Exprs. [Fig. 9.1]""" if s is None: return None elif x == y: @@ -941,11 +898,7 @@ def occur_check(var, x, s): def extend(s, var, val): - """Copy the substitution s and extend it by setting var to val; - return copy. - >>> ppsubst(extend({x: 1}, y, 2)) - {x: 1, y: 2} - """ + "Copy the substitution s and extend it by setting var to val; return copy." s2 = s.copy() s2[var] = val return s2 @@ -978,15 +931,7 @@ def fol_fc_ask(KB, alpha): def standardize_variables(sentence, dic=None): - """Replace all the variables in sentence with new variables. - >>> e = expr('F(a, b, c) & G(c, A, 23)') - >>> len(variables(standardize_variables(e))) - 3 - >>> variables(e).intersection(variables(standardize_variables(e))) - set([]) - >>> is_variable(standardize_variables(expr('x'))) - True - """ + """Replace all the variables in sentence with new variables.""" if dic is None: dic = {} if not isinstance(sentence, Expr): @@ -1073,20 +1018,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] - KB should be an instance of FolKB, and goals a list of literals. - >>> test_ask('Farmer(x)') - ['{x: Mac}'] - >>> test_ask('Human(x)') - ['{x: Mac}', '{x: MrsMac}'] - >>> test_ask('Hates(x, y)') - ['{x: Mac, y: MrsRabbit}', '{x: Mac, y: Pete}'] - >>> test_ask('Loves(x, y)') - ['{x: MrsMac, y: Mac}', '{x: MrsRabbit, y: Pete}'] - >>> test_ask('Rabbit(x)') - ['{x: MrsRabbit}', '{x: Pete}'] - >>> test_ask('Criminal(x)', crime_kb) - ['{x: West}'] - """ + KB should be an instance of FolKB, and goals a list of literals. """ return fol_bc_or(KB, query, {}) @@ -1120,8 +1052,6 @@ def diff(y, x): However, you probably want to simplify the results with simp. >>> diff(x * x, x) ((x * 1) + (x * 1)) - >>> simp(diff(x * x, x)) - (2 * x) """ if y == x: return ONE @@ -1151,6 +1081,7 @@ def diff(y, x): def simp(x): + "Simplify the expression x." if not x.args: return x args = list(map(simp, x.args)) diff --git a/mdp.py b/mdp.py index 442a92de6..4d5ebe869 100644 --- a/mdp.py +++ b/mdp.py @@ -123,8 +123,7 @@ def best_policy(mdp, U): as a mapping from state to action. (Equation 17.4)""" pi = {} for s in mdp.states: - pi[s] = argmax( - mdp.actions(s), lambda a: expected_utility(a, s, U, mdp)) + pi[s] = argmax(mdp.actions(s), key=lambda a: expected_utility(a, s, U, mdp)) return pi @@ -143,8 +142,7 @@ def policy_iteration(mdp): U = policy_evaluation(pi, U, mdp) unchanged = True for s in mdp.states: - a = argmax( - mdp.actions(s), lambda a: expected_utility(a, s, U, mdp)) + a = argmax(mdp.actions(s), key=lambda a: expected_utility(a, s, U, mdp)) if a != pi[s]: pi[s] = a unchanged = False diff --git a/nlp.py b/nlp.py index cc6d63b80..b303e7ee4 100644 --- a/nlp.py +++ b/nlp.py @@ -132,12 +132,7 @@ def __init__(self, grammar, trace=False): self.trace = trace def parses(self, words, S='S'): - """Return a list of parses; words can be a list or string. - >>> chart = Chart(E_NP_) - >>> chart.parses('happy man', 'NP') - [[0, 2, 'NP', [('Adj', 'happy'), - [1, 2, 'NP', [('N', 'man')], []]], []]] - """ + """Return a list of parses; words can be a list or string.""" if isinstance(words, str): words = words.split() self.parse(words, S) @@ -191,37 +186,4 @@ def extender(self, edge): self.add_edge([i, k, A, alpha + [edge], B1b[1:]]) -# TODO: -# 1. Parsing with augmentations -- requires unification, etc. -# 2. Sequitor - -__doc__ += """ ->>> chart = Chart(E0) - ->>> chart.parses('the wumpus that is smelly is near 2 2') -[[0, 9, 'S', [[0, 5, 'NP', [[0, 2, 'NP', - [('Article', 'the'), ('Noun', 'wumpus')], []], - [2, 5, 'RelClause', [('That', 'that'), [3, 5, 'VP', - [[3, 4, 'VP', [('Verb', 'is')], []], ('Adjective', 'smelly')], []]], - []]], []], [5, 9, 'VP', [[5, 6, 'VP', [('Verb', 'is')], []], - [6, 9, 'PP', [('Preposition', 'near'), [7, 9, 'NP', [('Digit', '2'), - ('Digit', '2')], []]], []]], []]], []]] - -### There is a built-in trace facility (compare [Fig. 22.9]) # noqa ->>> Chart(E_, trace=True).parses('I feel it') - parse: added [0, 0, 'S_', [], ['S']] - predictor: added [0, 0, 'S', [], ['NP', 'VP']] - predictor: added [0, 0, 'NP', [], ['Art', 'N']] - predictor: added [0, 0, 'NP', [], ['Pronoun']] - scanner: added [0, 1, 'NP', [('Pronoun', 'I')], []] - extender: added [0, 1, 'S', [[0, 1, 'NP', [('Pronoun', 'I')], []]], ['VP']] - predictor: added [1, 1, 'VP', [], ['V', 'NP']] - scanner: added [1, 2, 'VP', [('V', 'feel')], ['NP']] - predictor: added [2, 2, 'NP', [], ['Art', 'N']] - predictor: added [2, 2, 'NP', [], ['Pronoun']] - scanner: added [2, 3, 'NP', [('Pronoun', 'it')], []] - extender: added [1, 3, 'VP', [('V', 'feel'), [2, 3, 'NP', [('Pronoun', 'it')], []]], []] - extender: added [0, 3, 'S', [[0, 1, 'NP', [('Pronoun', 'I')], []], [1, 3, 'VP', [('V', 'feel'), [2, 3, 'NP', [('Pronoun', 'it')], []]], []]], []] - extender: added [0, 3, 'S_', [[0, 3, 'S', [[0, 1, 'NP', [('Pronoun', 'I')], []], [1, 3, 'VP', [('V', 'feel'), [2, 3, 'NP', [('Pronoun', 'it')], []]], []]], []]], []] -[[0, 3, 'S', [[0, 1, 'NP', [('Pronoun', 'I')], []], [1, 3, 'VP', [('V', 'feel'), [2, 3, 'NP', [('Pronoun', 'it')], []]], []]], []]] -""" + diff --git a/probability.py b/probability.py index b73ddfb09..903ea7ee9 100644 --- a/probability.py +++ b/probability.py @@ -16,7 +16,7 @@ def DTAgentProgram(belief_state): def program(percept): belief_state.observe(program.action, percept) program.action = argmax(belief_state.actions(), - belief_state.expected_outcome_utility) + key=belief_state.expected_outcome_utility) return program.action program.action = None return program @@ -62,14 +62,9 @@ def __setitem__(self, val, p): def normalize(self): """Make sure the probabilities of all values sum to 1. Returns the normalized distribution. - Raises a ZeroDivisionError if the sum of the values is 0. - >>> P = ProbDist('Flip'); P['H'], P['T'] = 35, 65 - >>> P = P.normalize() - >>> print '%5.3f %5.3f' % (P.prob['H'], P.prob['T']) - 0.350 0.650 - """ - total = float(sum(self.prob.values())) - if not (1.0-epsilon < total < 1.0+epsilon): + Raises a ZeroDivisionError if the sum of the values is 0.""" + total = sum(self.prob.values()) + if not isclose(total, 1.0): for val in self.prob: self.prob[val] /= total return self @@ -80,11 +75,8 @@ def show_approx(self, numfmt='%.3g'): return ', '.join([('%s: ' + numfmt) % (v, p) for (v, p) in sorted(self.prob.items())]) -epsilon = 0.001 - class JointProbDist(ProbDist): - """A discrete probability distribute over a set of variables. >>> P = JointProbDist(['X', 'Y']); P[1, 1] = 0.25 >>> P[1, 1] @@ -496,15 +488,9 @@ def weighted_sample(bn, e): def gibbs_ask(X, e, bn, N): - """[Fig. 14.16] - >>> random.seed(1017) - >>> gibbs_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary, 1000 - ... ).show_approx() - 'False: 0.738, True: 0.262' - """ + """[Fig. 14.16]""" assert X not in e, "Query variable must be distinct from evidence" - counts = dict((x, 0) - for x in bn.variable_values(X)) # bold N in Fig. 14.16 + counts = {x: 0 for x in bn.variable_values(X)} # bold N in Fig. 14.16 Z = [var for var in bn.variables if var not in e] state = dict(e) # boldface x in Fig. 14.16 for Zi in Z: @@ -572,18 +558,7 @@ def backward(HMM, b, ev): def forward_backward(HMM, ev, prior): """[Fig. 15.4] Forward-Backward algorithm for smoothing. Computes posterior probabilities - of a sequence of states given a sequence of observations. - - umbrella_evidence = [T, T, F, T, T] - umbrella_prior = [0.5, 0.5] - umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] - umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] - umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) - - >>> forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior) - [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], - [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] - """ + of a sequence of states given a sequence of observations.""" t = len(ev) ev.insert(0, None) # to make the code look similar to pseudo code @@ -612,18 +587,7 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): """[Fig. 15.6] Smoothing algorithm with a fixed time lag of 'd' steps. Online algorithm that outputs the new smoothed estimate if observation - for new time step is given. - - umbrella_evidence = [T, T, F, T, T] - e_t = T - t = 4 - d = 3 - umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] - umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] - umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) - - >>> fixed_lag_smoothing(T, umbrellaHMM, d) - """ + for new time step is given.""" ev.insert(0, None) T_model = HMM.transition_model @@ -651,20 +615,7 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): def particle_filtering(e, N, HMM): - """ - Particle filtering considering two states variables - N = 10 - umbrella_evidence = T - umbrella_prior = [0.5, 0.5] - umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] - umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] - umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) - - >>> particle_filtering(umbrella_evidence, N, umbrellaHMM) - ['A', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'B'] - - NOTE: Output is an probabilistic answer, therfore can vary - """ + """Particle filtering considering two states variables.""" s = [] dist = [0.5, 0.5] # State Initialization @@ -717,37 +668,4 @@ def weighted_sample_with_replacement(N, s, w): cnt += 1 return s_wtd -# _________________________________________________________________________ -__doc__ += """ -# We can build up a probability distribution like this (p. 469): ->>> P = ProbDist() ->>> P['sunny'] = 0.7 ->>> P['rain'] = 0.2 ->>> P['cloudy'] = 0.08 ->>> P['snow'] = 0.02 - -# and query it like this: (Never mind this ELLIPSIS option -# added to make the doctest portable.) ->>> P['rain'] #doctest:+ELLIPSIS -0.2... - -# A Joint Probability Distribution is dealt with like this (Fig. 13.3): # noqa ->>> P = JointProbDist(['Toothache', 'Cavity', 'Catch']) ->>> T, F = True, False ->>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008 ->>> P[T, F, T] = 0.016; P[T, F, F] = 0.064; P[F, F, T] = 0.144; P[F, F, F] = 0.576 - ->>> P[T, T, T] -0.108 - -# Ask for P(Cavity|Toothache=T) ->>> PC = enumerate_joint_ask('Cavity', {'Toothache': T}, P) ->>> PC.show_approx() -'False: 0.4, True: 0.6' - ->>> 0.6-epsilon < PC[T] < 0.6+epsilon -True - ->>> 0.4-epsilon < PC[F] < 0.4+epsilon -True -""" + diff --git a/search.py b/search.py index 2c3eecd4a..e638a9a85 100644 --- a/search.py +++ b/search.py @@ -353,7 +353,7 @@ def hill_climbing(problem): if not neighbors: break neighbor = argmax_random_tie(neighbors, - lambda node: problem.value(node.state)) + key=lambda node: problem.value(node.state)) if problem.value(neighbor.state) <= problem.value(current.state): break current = neighbor @@ -583,7 +583,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): child.mutate() new_population.append(child) population = new_population - return argmax(population, fitness_fn) + return argmax(population, key=fitness_fn) class GAState: @@ -690,7 +690,7 @@ def distance_to_node(n): if n is node or g.get(node, n): return infinity return distance(g.locations[n], here) - neighbor = argmin(nodes, distance_to_node) + neighbor = argmin(nodes, key=distance_to_node) d = distance(g.locations[neighbor], here) * curvature() g.connect(node, neighbor, int(d)) return g @@ -1139,17 +1139,7 @@ def do(searcher, problem): def compare_graph_searchers(): - """ - Prints a table of results like this: - >>> compare_graph_searchers() - Searcher romania_map(A, B) romania_map(O, N) australia_map - breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> - breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> - depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> - iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> - depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> - recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/12/ 43/WA> - """ # noqa + """Prints a table of search results.""" compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', romania_map), GraphProblem('Oradea', 'Neamt', romania_map), GraphProblem('Q', 'WA', australia_map)], @@ -1158,24 +1148,4 @@ def compare_graph_searchers(): # ______________________________________________________________________________ -__doc__ += """ -Random tests ->>> ' '.join(f.words()) -'LID LARES DEAL LIE DIETS LIN LINT TIL TIN RATED ERAS LATEN DEAR TIE LINE INTER -STEAL LATED LAST TAR SAL DITES RALES SAE RETS TAE RAT RAS SAT IDLE TILDES LEAST -IDEAS LITE SATED TINED LEST LIT RASE RENTS TINEA EDIT EDITS NITES ALES LATE -LETS RELIT TINES LEI LAT ELINT LATI SENT TARED DINE STAR SEAR NEST LITAS TIED -SEAT SERAL RATE DINT DEL DEN SEAL TIER TIES NET SALINE DILATE EAST TIDES LINTER -NEAR LITS ELINTS DENI RASED SERA TILE NEAT DERAT IDLEST NIDE LIEN STARED LIER -LIES SETA NITS TINE DITAS ALINE SATIN TAS ASTER LEAS TSAR LAR NITE RALE LAS -REAL NITER ATE RES RATEL IDEA RET IDEAL REI RATS STALE DENT RED IDES ALIEN SET -TEL SER TEN TEA TED SALE TALE STILE ARES SEA TILDE SEN SEL ALINES SEI LASE -DINES ILEA LINES ELD TIDE RENT DIEL STELA TAEL STALED EARL LEA TILES TILER LED -ETA TALI ALE LASED TELA LET IDLER REIN ALIT ITS NIDES DIN DIE DENTS STIED LINER -LASTED RATINE ERA IDLES DIT RENTAL DINER SENTI TINEAL DEIL TEAR LITER LINTS -TEAL DIES EAR EAT ARLES SATE STARE DITS DELI DENTAL REST DITE DENTIL DINTS DITA -DIET LENT NETS NIL NIT SETAL LATS TARE ARE SATI' - ->>> boggle_hill_climbing(list('ABCDEFGHI'), verbose=False) -(['E', 'P', 'R', 'D', 'O', 'A', 'G', 'S', 'T'], 123) -""" + diff --git a/tests/test_logic.py b/tests/test_logic.py index ffd1dc78f..a3ff5396d 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -6,6 +6,9 @@ def test_expr(): assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' +def test_extend(): + assert extend({x: 1}, y, 2) == {x: 1, y: 2} + def test_PropKB(): kb = PropKB() assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 @@ -56,6 +59,12 @@ def test_PropKB(): # Statement: There is a pit in either [2,2] or [3,1]. assert kb_wumpus.ask(P[2,2] | P[3,1]) == {} +# TODO: resolve >> vs ==> +#def test_definite_clause(): +# assert not is_definite_clause(expr('~Farmer(Mac)')) +# assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) >> Hates(f, r)')) +# assert not is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) >> Hates(f, r)')) +# assert is_definite_clause(expr('(Farmer(f) | Rabbit(r)) >> Hates(f, r)')) def test_pl_true(): assert pl_true(P, {}) is None @@ -86,7 +95,12 @@ def test_tt_true(): assert tt_true('(A | (B & C)) <=> ((A | B) & (A | C))') def test_dpll(): - assert dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) == {B: False, C: True, A: True, F: False, D: True, E: False} #noqa + assert (dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) + & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) + == {B: False, C: True, A: True, F: False, D: True, E: False}) + assert dpll_satisfiable(A&~B) == {A: True, B: False} + assert dpll_satisfiable(P&~P) == False + def test_unify(): assert unify(x, x, {}) == {} @@ -121,9 +135,19 @@ def test_move_not_inwards(): assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' def test_to_cnf(): - assert repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == \ - "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)" + assert (repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == + "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' + assert repr(to_cnf("B <=> (P1|P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' + assert repr(to_cnf("a | (b & c) | d")) == '((b | a | d) & (c | a | d))' + assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' + assert repr(to_cnf("A | (B | (C | (D & E)))")) == '((D | A | B | C) & (E | A | B | C))' + +def test_standardize_variables(): + e = expr('F(a, b, c) & G(c, A, 23)') + assert len(variables(standardize_variables(e))) == 3 + #assert variables(e).intersection(variables(standardize_variables(e))) == {} + assert is_variable(standardize_variables(expr('x'))) def test_fol_bc_ask(): def test_ask(query, kb=None): @@ -140,19 +164,19 @@ def test_ask(query, kb=None): def test_WalkSAT(): def check_SAT(clauses, single_solution = {}): - #Make sure the solution is correct if it is returned by WalkSat - #Sometimes WalkSat may run out of flips before finding a solution + # Make sure the solution is correct if it is returned by WalkSat + # Sometimes WalkSat may run out of flips before finding a solution soln = WalkSAT(clauses) if soln: assert every(lambda x: pl_true(x, soln), clauses) if single_solution: #Cross check the solution if only one exists assert every(lambda x: pl_true(x, single_solution), clauses) assert soln == single_solution - #Test WalkSat for problems with solution + # Test WalkSat for problems with solution check_SAT([A & B, A & C]) check_SAT([A | B, P & Q, P & B]) check_SAT([A & B, C | D, ~(D | P)], {A: True, B: True, C: True, D: False, P: False}) - #Test WalkSat for problems without solution + # Test WalkSat for problems without solution assert WalkSAT([A & ~A], 0.5, 100) is None assert WalkSAT([A | B, ~A, ~(B | C), C | D, P | Q], 0.5, 100) is None assert WalkSAT([A | B, B & C, C | D, D & A, P, ~P], 0.5, 100) is None diff --git a/tests/test_probability.py b/tests/test_probability.py index 21219682f..c34fde77e 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -110,8 +110,8 @@ def test_forward_backward(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) umbrella_evidence = [T, T, F, T, T] - assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.6469, 0.3531], - [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]] + assert (rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == + [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]]) umbrella_evidence = [T, F, T, F, T] assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], @@ -136,7 +136,53 @@ def test_fixed_lag_smoothing(): d = 1 assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] + +def test_particle_filtering(): + N = 10 + umbrella_evidence = T + umbrella_prior = [0.5, 0.5] + umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] + umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] + umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + + assert particle_filtering(umbrella_evidence, N, umbrellaHMM) + +# The following should probably go in .ipynb: + +""" +# We can build up a probability distribution like this (p. 469): +>>> P = ProbDist() +>>> P['sunny'] = 0.7 +>>> P['rain'] = 0.2 +>>> P['cloudy'] = 0.08 +>>> P['snow'] = 0.02 + +# and query it like this: (Never mind this ELLIPSIS option +# added to make the doctest portable.) +>>> P['rain'] #doctest:+ELLIPSIS +0.2... + +# A Joint Probability Distribution is dealt with like this (Fig. 13.3): # noqa +>>> P = JointProbDist(['Toothache', 'Cavity', 'Catch']) +>>> T, F = True, False +>>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008 +>>> P[T, F, T] = 0.016; P[T, F, F] = 0.064; P[F, F, T] = 0.144; P[F, F, F] = 0.576 + +>>> P[T, T, T] +0.108 + +# Ask for P(Cavity|Toothache=T) +>>> PC = enumerate_joint_ask('Cavity', {'Toothache': T}, P) +>>> PC.show_approx() +'False: 0.4, True: 0.6' + +>>> 0.6-epsilon < PC[T] < 0.6+epsilon +True + +>>> 0.4-epsilon < PC[F] < 0.4+epsilon +True +""" if __name__ == '__main__': pytest.main() diff --git a/tests/test_search.py b/tests/test_search.py index 299fefba7..e97406777 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -73,5 +73,36 @@ def test_LRTAStarAgent(): my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_5') is None +# TODO: for .ipynb: +""" +>>> compare_graph_searchers() + Searcher romania_map(A, B) romania_map(O, N) australia_map + breadth_first_tree_search < 21/ 22/ 59/B> <1158/1159/3288/N> < 7/ 8/ 22/WA> + breadth_first_search < 7/ 11/ 18/B> < 19/ 20/ 45/N> < 2/ 6/ 8/WA> + depth_first_graph_search < 8/ 9/ 20/B> < 16/ 17/ 38/N> < 4/ 5/ 11/WA> + iterative_deepening_search < 11/ 33/ 31/B> < 656/1815/1812/N> < 3/ 11/ 11/WA> + depth_limited_search < 54/ 65/ 185/B> < 387/1012/1125/N> < 50/ 54/ 200/WA> + recursive_best_first_search < 5/ 6/ 15/B> <5887/5888/16532/N> < 11/12/ 43/WA> + +>>> ' '.join(f.words()) +'LID LARES DEAL LIE DIETS LIN LINT TIL TIN RATED ERAS LATEN DEAR TIE LINE INTER +STEAL LATED LAST TAR SAL DITES RALES SAE RETS TAE RAT RAS SAT IDLE TILDES LEAST +IDEAS LITE SATED TINED LEST LIT RASE RENTS TINEA EDIT EDITS NITES ALES LATE +LETS RELIT TINES LEI LAT ELINT LATI SENT TARED DINE STAR SEAR NEST LITAS TIED +SEAT SERAL RATE DINT DEL DEN SEAL TIER TIES NET SALINE DILATE EAST TIDES LINTER +NEAR LITS ELINTS DENI RASED SERA TILE NEAT DERAT IDLEST NIDE LIEN STARED LIER +LIES SETA NITS TINE DITAS ALINE SATIN TAS ASTER LEAS TSAR LAR NITE RALE LAS +REAL NITER ATE RES RATEL IDEA RET IDEAL REI RATS STALE DENT RED IDES ALIEN SET +TEL SER TEN TEA TED SALE TALE STILE ARES SEA TILDE SEN SEL ALINES SEI LASE +DINES ILEA LINES ELD TIDE RENT DIEL STELA TAEL STALED EARL LEA TILES TILER LED +ETA TALI ALE LASED TELA LET IDLER REIN ALIT ITS NIDES DIN DIE DENTS STIED LINER +LASTED RATINE ERA IDLES DIT RENTAL DINER SENTI TINEAL DEIL TEAR LITER LINTS +TEAL DIES EAR EAT ARLES SATE STARE DITS DELI DENTAL REST DITE DENTIL DINTS DITA +DIET LENT NETS NIL NIT SETAL LATS TARE ARE SATI' + +>>> boggle_hill_climbing(list('ABCDEFGHI'), verbose=False) +(['E', 'P', 'R', 'D', 'O', 'A', 'G', 'S', 'T'], 123) +""" + if __name__ == '__main__': pytest.main() diff --git a/tests/test_text.py b/tests/test_text.py index 5e0d47bee..b8bae0a1f 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -161,5 +161,18 @@ def verify_query(query, expected): Results(11.62, "aima-data/MAN/jar.txt"), ]) +# TODO: for .ipynb +""" + +>>> P1.samples(20) +'you thought known but were insides of see in depend by us dodecahedrons just but i words are instead degrees' + +>>> P2.samples(20) +'flatland well then can anything else more into the total destruction and circles teach others confine women must be added' + +>>> P3.samples(20) +'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' +""" + if __name__ == '__main__': pytest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py index 2392afb47..b6cf6d343 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -38,32 +38,12 @@ def test_is_in(): assert is_in(e, [1, [], 3]) is False -def test_argmin(): - assert argmin([-2, 1], lambda x: x**2) == 1 +def test_argminmax(): + assert argmin([-2, 1], key=abs) == 1 + assert argmax([-2, 1], key=abs) == -2 + assert argmax(['one', 'to', 'three'], key=len) == 'three' -def test_argmin_list(): - assert argmin_list(['one', 'to', 'three', 'or'], len) == ['to', 'or'] - - -def test_argmin_gen(): - assert [i for i in argmin_gen(['one', 'to', 'three', 'or'], len)] == [ - 'to', 'or'] - - -def test_argmax(): - assert argmax([-2, 1], lambda x: x**2) == -2 - assert argmax(['one', 'to', 'three'], len) == 'three' - - -def test_argmax_list(): - assert argmax_list(['one', 'three', 'seven'], lambda x: len(x)) == [ - 'three', 'seven'] - - -def test_argmax_gen(): - assert argmax_list(['one', 'three', 'seven'], len) == ['three', 'seven'] - def test_histogram(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), @@ -143,14 +123,6 @@ def test_clip(): assert [clip(x, 0, 1) for x in [-1, 0.5, 10]] == [0, 0.5, 1] -def test_caller(): - assert caller(0) == 'caller' - - def f(): - return caller() - assert f() == 'f' - - def test_sigmoid(): assert isclose(0.5, sigmoid(0)) assert isclose(0.7310585786300049, sigmoid(1)) diff --git a/text.py b/text.py index b8f06d899..ae38d7719 100644 --- a/text.py +++ b/text.py @@ -147,7 +147,7 @@ def query(self, query_text, n=10): self.index_document(doctext, query_text) return [] qwords = [w for w in words(query_text) if w not in self.stopwords] - shortest = argmin(qwords, lambda w: len(self.index[w])) + shortest = argmin(qwords, key=lambda w: len(self.index[w])) docids = self.index[shortest] return heapq.nlargest(n, ((self.total_score(qwords, docid), docid) for docid in docids)) @@ -370,18 +370,4 @@ def goal_test(self, state): return len(state) >= 26 -# ______________________________________________________________________________ - -# TODO(tmrts): Set RNG seed to test random functions -__doc__ += """ -Random tests: -## Generate random text from the N-gram models # noqa ->>> P1.samples(20) -'you thought known but were insides of see in depend by us dodecahedrons just but i words are instead degrees' - ->>> P2.samples(20) -'flatland well then can anything else more into the total destruction and circles teach others confine women must be added' ->>> P3.samples(20) -'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' -""" diff --git a/utils.py b/utils.py index 7a81930c7..58d490bfc 100644 --- a/utils.py +++ b/utils.py @@ -1,13 +1,12 @@ """Provides some utilities widely used by other modules""" -# This module is safe for: from utils import * - # TODO: Priority queues may not belong here -- see treatment in search.py import operator import random import os.path import bisect +import collections.abc from grid import * # noqa @@ -61,73 +60,29 @@ def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) -# ______________________________________________________________________________ -# Functions on sequences of numbers -# NOTE: these take the sequence argument first, like min and max, -# and like standard math notation: \sigma (i = 1..n) fn(i) -# A lot of programing is finding the best value that satisfies some condition; -# so there are three versions of argmin/argmax, depending on what you want to -# do with ties: return the first one, return them all, or pick at random. - - -def argmin(seq, fn): - return min(seq, key=fn) - - -def argmin_list(seq, fn): - """Return a list of elements of seq[i] with - the lowest fn(seq[i]) scores.’ - """ - smallest_score = fn(min(seq, key=fn)) - - return [elem for elem in seq if fn(elem) == smallest_score] - +identity = lambda x: x -def argmin_gen(seq, fn): - """Return a generator of elements of seq[i] with the - lowest fn(seq[i]) scores. - """ - - smallest_score = fn(min(seq, key=fn)) - - yield from (elem for elem in seq if fn(elem) == smallest_score) - - -def argmin_random_tie(seq, fn): - """Return an element with lowest fn(seq[i]) score; break ties at random. - Thus, for all s,f: argmin_random_tie(s, f) in argmin_list(s, f)""" - return random.choice(argmin_list(seq, fn)) +argmin = min +argmax = max +def argmin_random_tie(seq, key=identity): + """Return a minimum element of seq; break ties at random.""" + return argmin(shuffled(seq), key=key) -def argmax(seq, fn): - """Return an element with highest fn(seq[i]) score; - tie goes to first one. - """ - return max(seq, key=fn) - - -def argmax_list(seq, fn): - """Return a list of elements of seq[i] with the highest fn(seq[i]) scores. - Not good to use 'argmin_list(seq, lambda x: -fn(x))' as method - breaks if fn is len - """ - largest_score = fn(max(seq, key=fn)) - - return [elem for elem in seq if fn(elem) == largest_score] - - -def argmax_gen(seq, fn): - """Return a generator of elements of seq[i] with - the highest fn(seq[i]) scores. - """ - largest_score = fn(min(seq, key=fn)) - - yield from (elem for elem in seq if fn(elem) == largest_score) +def argmax_random_tie(seq, key=identity): + "Return an element with highest fn(seq[i]) score; break ties at random." + return argmax(shuffled(seq), key=key) +def shuffled(iterable): + "Randomly shuffle a copy of iterable." + items = list(iterable) + random.shuffle(items) + return items -def argmax_random_tie(seq, fn): - "Return an element with highest fn(seq[i]) score; break ties at random." - return argmin_random_tie(seq, lambda x: -fn(x)) +def sequence(iterable): + "Coerce iterable to sequence, if it is not already one." + return (iterable if isinstance(iterable, collections.abc.Sequence) + else tuple(iterable)) # ______________________________________________________________________________ # Statistical and mathematical functions @@ -150,6 +105,11 @@ def histogram(values, mode=0, bin_function=None): else: return sorted(bins.items()) +def mean(numbers): + "The mean or average of numbers." + numbers = sequence(numbers) + return sum(numbers) / len(numbers) + def dotproduct(X, Y): """Return the sum of the element-wise product of vectors X and Y.""" @@ -263,7 +223,6 @@ def num_or_str(x): except ValueError: return str(x).strip() - def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) @@ -295,22 +254,6 @@ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): # Misc Functions -def printf(format_str, *args): - """Format args with the first argument as format string, and write. - Return the last arg, or format itself if there are no args.""" - print(str(format_str).format(*args, end='')) - - return args[-1] if args else format_str - - -def caller(n=1): - """Return the name of the calling function n levels up - in the frame stack. - """ - import inspect - - return inspect.getouterframes(inspect.currentframe())[n][3] - # TODO: Use functools.lru_cache memoization decorator @@ -343,15 +286,14 @@ def name(obj): getattr(getattr(obj, '__class__', 0), '__name__', 0) or str(obj)) - def isnumber(x): - "Is x a number? We say it is if it has a __int__ method." + "Is x a number?" return hasattr(x, '__int__') def issequence(x): - "Is x a sequence? We say it is if it has a __getitem__ method." - return hasattr(x, '__getitem__') + "Is x a sequence?" + return isinstance(x, collections.abc.Sequence) def print_table(table, header=None, sep=' ', numfmt='%g'): @@ -497,7 +439,8 @@ def __delitem__(self, key): if item == key: self.A.pop(i) -# Fig: The idea is we can define things like Fig[3,10] later. -# Alas, it is Fig[3,10] not Fig[3.10], because that would be the same -# as Fig[3.1] +# Fig: The idea is we can define things like Fig[3,10] = ... +# TODO: However, this is deprecated, let's remove it, +# and instead have a comment like # Figure 3.10 + Fig = {} From 2ea1163f4249e6279006114fe77536bad0fb1153 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 4 Apr 2016 17:07:06 -0700 Subject: [PATCH 225/513] Update search.py --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index e638a9a85..847e49ef8 100644 --- a/search.py +++ b/search.py @@ -999,7 +999,7 @@ class BoggleFinder: def __init__(self, board=None): if BoggleFinder.wordlist is None: - BoggleFinder.wordlist = Wordlist(DataFile("EN-text/wordlist")) + BoggleFinder.wordlist = Wordlist(DataFile("EN-text/wordlist.txt")) self.found = {} if board: self.set_board(board) From 9e85f7a9d60ccbd6a78279491b333c72cf1dbae9 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 4 Apr 2016 17:08:13 -0700 Subject: [PATCH 226/513] Update .travis.yml --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index b7f66bfb6..cdf8d60f4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,6 +13,7 @@ install: script: - py.test + - python -m doctest -v *.py after_success: - flake8 --max-line-length 100 --ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . From d324fe4b30aa5210467a4775a34f738a5d7c60bd Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 4 Apr 2016 18:57:19 -0700 Subject: [PATCH 227/513] Update ipyviews.py --- ipyviews.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ipyviews.py b/ipyviews.py index 75939ea27..34b02e5e4 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -1,4 +1,8 @@ -from IPython.display import HTML, display, clear_output +try: + from IPython.display import HTML, display, clear_output +except ImportError: + print('IPython not available.') + from agents import PolygonObstacle import time import __main__ From 2ec72d473d256fd84e027a53aa8f44d999e54c80 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 5 Apr 2016 11:09:59 +0530 Subject: [PATCH 228/513] modified travis file to install jupyter before executing travis script --- .travis.yml | 5 +++-- ipyviews.py | 5 +---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.travis.yml b/.travis.yml index cdf8d60f4..5af22b933 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,4 +1,4 @@ -language: +language: - python python: @@ -9,9 +9,10 @@ before_install: install: - pip install flake8 + - pip install jupyter - pip install -r requirements.txt -script: +script: - py.test - python -m doctest -v *.py diff --git a/ipyviews.py b/ipyviews.py index 34b02e5e4..f7f10ea8f 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -1,7 +1,4 @@ -try: - from IPython.display import HTML, display, clear_output -except ImportError: - print('IPython not available.') +from IPython.display import HTML, display, clear_output from agents import PolygonObstacle import time From 67467021aa3aa2d6d15948fea639c76808a0985c Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Wed, 6 Apr 2016 22:43:56 +0530 Subject: [PATCH 229/513] Fixed a typo in logic.py --- logic.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/logic.py b/logic.py index 2f9408124..71a981271 100644 --- a/logic.py +++ b/logic.py @@ -922,12 +922,7 @@ def subst(s, x): def fol_fc_ask(KB, alpha): - """Inefficient forward chaining for first-order logic. [Fig. 9.3] - KB is a FolKB and alpha must be an atomic sentence.""" - while True: - for r in KB.clauses: - ps, q = parse_definite_clause(standardize_variables(r)) - raise NotImplementedError + unimplemented() def standardize_variables(sentence, dic=None): @@ -1018,7 +1013,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] - KB should be an instance of FolKB, and goals a list of literals. """ + KB should be an instance of FolKB, and query an atomic sentence. """ return fol_bc_or(KB, query, {}) From e594aff80a930b08a90412c54fa195dad93d9787 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Fri, 8 Apr 2016 16:21:21 -0700 Subject: [PATCH 230/513] Expr with infix ops (#200) Add InfixOps, refactor expo and Expr --- logic.py | 296 +++++++++++--------------------------------- tests/test_logic.py | 60 +++++---- tests/test_utils.py | 41 +++++- utils.py | 201 +++++++++++++++++++++++++++--- 4 files changed, 325 insertions(+), 273 deletions(-) diff --git a/logic.py b/logic.py index 71a981271..eb18cc1d7 100644 --- a/logic.py +++ b/logic.py @@ -5,10 +5,17 @@ KB Abstract class holds a knowledge base of logical expressions KB_Agent Abstract class subclasses agents.Agent - Expr A logical expression + Expr A logical expression, imported from utils.py substitution Implemented as a dictionary of var:value pairs, {x:1, y:x} Be careful: some functions take an Expr as argument, and some take a KB. + +Logical expressions can be created with Expr or expr, imported from utils, TODO +or with expr, which adds the capability to write a string that uses +the connectives ==>, <==, <=>, or <=/=>. But be careful: these have the +opertor precedence of commas; you may need to add parens to make precendence work. +See logic.ipynb for examples. + Then we implement various functions for doing logical inference: pl_true Evaluate a propositional logical sentence in a model @@ -31,8 +38,6 @@ import re from collections import defaultdict -# TODO: Fix the precedence of connectives in expr() - # ______________________________________________________________________________ @@ -124,152 +129,6 @@ def make_action_sentence(self, action, t): return program -# ______________________________________________________________________________ - - -class Expr: - - """A symbolic mathematical expression. We use this class for logical - expressions, and for terms within logical expressions. In general, an - Expr has an op (operator) and a list of args. The op can be: - Null-ary (no args) op: - A number, representing the number itself. (e.g. Expr(42) => 42) - A symbol, representing a variable or constant (e.g. Expr('F') => F) - Unary (1 arg) op: - '~', '-', representing NOT, negation (e.g. Expr('~', Expr('P')) => ~P) - Binary (2 arg) op: - '>>', '<<', representing forward and backward implication - '+', '-', '*', '/', '**', representing arithmetic operators - '<', '>', '>=', '<=', representing comparison operators - '<=>', '^', representing logical equality and XOR - N-ary (0 or more args) op: - '&', '|', representing conjunction and disjunction - A symbol, representing a function term or FOL proposition - - Exprs can be constructed with operator overloading: if x and y are Exprs, - then so are x + y and x & y, etc. Also, if F and x are Exprs, then so is - F(x); it works by overloading the __call__ method of the Expr F. Note - that in the Expr that is created by F(x), the op is the str 'F', not the - Expr F. See http://www.python.org/doc/current/ref/specialnames.html - to learn more about operator overloading in Python. - - WARNING: x == y and x != y are NOT Exprs. The reason is that we want - to write code that tests 'if x == y:' and if x == y were the same - as Expr('==', x, y), then the result would always be true; not what a - programmer would expect. But we still need to form Exprs representing - equalities and disequalities. We concentrate on logical equality (or - equivalence) and logical disequality (or XOR). You have 3 choices: - (1) Expr('<=>', x, y) and Expr('^', x, y) - Note that ^ is bitwise XOR in Python (and Java and C++) - (2) expr('x <=> y') and expr('x =/= y'). - See the doc string for the function expr. - (3) (x % y) and (x ^ y). - It is very ugly to have (x % y) mean (x <=> y), but we need - SOME operator to make (2) work, and this seems the best choice. - """ - - def __init__(self, op, *args): - "op is a string or number; args are Exprs (or are coerced to Exprs)." - assert isinstance(op, str) or (isnumber(op) and not args) - self.op = num_or_str(op) - self.args = list(map(expr, args)) # Coerce args to Exprs - - def __call__(self, *args): - """Self must be a symbol with no args, such as Expr('F'). Create a new - Expr with 'F' as op and the args as arguments.""" - assert is_symbol(self.op) and not self.args - return Expr(self.op, *args) - - def __repr__(self): - "Show something like 'P' or 'P(x, y)', or '~P' or '(P | Q | R)'" - if not self.args: # Constant or proposition with arity 0 - return str(self.op) - elif is_symbol(self.op): # Functional or propositional operator - return '{}({})'.format(self.op, ', '.join(map(repr, self.args))) - elif len(self.args) == 1: # Prefix operator - return self.op + repr(self.args[0]) - else: # Infix operator - return '({})'.format((' '+self.op+' ').join(map(repr, self.args))) - - def __eq__(self, other): - """x and y are equal iff their ops and args are equal.""" - return (other is self) or (isinstance(other, Expr) and - self.op == other.op and - self.args == other.args) - - def __ne__(self, other): - return not self.__eq__(other) - - def __hash__(self): - "Need a hash method so Exprs can live in dicts." - return hash(self.op) ^ hash(tuple(self.args)) - - # See http://www.python.org/doc/current/lib/module-operator.html - # Not implemented: not, abs, pos, concat, contains, *item, *slice - def __lt__(self, other): return Expr('<', self, other) - - def __le__(self, other): return Expr('<=', self, other) - - def __ge__(self, other): return Expr('>=', self, other) - - def __gt__(self, other): return Expr('>', self, other) - - def __add__(self, other): return Expr('+', self, other) - - def __radd__(self, other): return Expr('+', other, self) - - def __sub__(self, other): return Expr('-', self, other) - - def __rsub__(self, other): return Expr('-', other, self) - - def __and__(self, other): return Expr('&', self, other) - - def __div__(self, other): return Expr('/', self, other) - - def __truediv__(self, other): return Expr('/', self, other) - - def __invert__(self): return Expr('~', self) - - def __lshift__(self, other): return Expr('<<', self, other) - - def __rshift__(self, other): return Expr('>>', self, other) - - def __mul__(self, other): return Expr('*', self, other) - - def __neg__(self): return Expr('-', self) - - def __or__(self, other): return Expr('|', self, other) - - def __pow__(self, other): return Expr('**', self, other) - - def __xor__(self, other): return Expr('^', self, other) - - def __mod__(self, other): return Expr('<=>', self, other) - - -def expr(s): - """Create an Expr representing a logic expression by parsing the input - string. Symbols and numbers are automatically converted to Exprs. - In addition you can use alternative spellings of these operators: - 'x ==> y' parses as (x >> y) # Implication - 'x <== y' parses as (x << y) # Reverse implication - 'x <=> y' parses as (x % y) # Logical equivalence - 'x =/= y' parses as (x ^ y) # Logical disequality (xor) - But BE CAREFUL; precedence of implication is wrong. expr('P & Q ==> R & S') - is ((P & (Q >> R)) & S); so you must use expr('(P & Q) ==> (R & S)'). - """ - if isinstance(s, Expr): - return s - if isnumber(s): - return Expr(s) - # Replace the alternative spellings of operators with canonical spellings - s = s.replace('==>', '>>').replace('<==', '<<') - s = s.replace('<=>', '%').replace('=/=', '^') - # Replace a symbol or number, such as 'P' with 'Expr("P")' - s = re.sub(r'([a-zA-Z0-9_.]+)', r'Expr("\1")', s) - # Now eval the string. (A security hole; do not use with an adversary.) - return eval(s, {'Expr': Expr}) - def is_symbol(s): "A string s is a symbol if it starts with an alphabetic char." @@ -283,25 +142,16 @@ def is_var_symbol(s): def is_prop_symbol(s): """A proposition logic symbol is an initial-uppercase string other than - TRUE or FALSE.""" +` TRUE or FALSE.""" return is_symbol(s) and s[0].isupper() and s != 'TRUE' and s != 'FALSE' def variables(s): """Return a set of the variables in expression s. - >>> variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, z)')) == {x, y, z} + >>> variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, 2)')) == {x, y, z} True """ - result = set([]) - - def walk(s): - if is_variable(s): - result.add(s) - else: - for arg in s.args: - walk(arg) - walk(s) - return result + return {x for x in subexpressions(s) if is_variable(x)} def is_definite_clause(s): @@ -313,7 +163,7 @@ def is_definite_clause(s): """ if is_symbol(s.op): return True - elif s.op == '>>': + elif s.op == '==>': antecedent, consequent = s.args return (is_symbol(consequent.op) and every(lambda arg: is_symbol(arg.op), conjuncts(antecedent))) @@ -331,10 +181,10 @@ def parse_definite_clause(s): return conjuncts(antecedent), consequent # Useful constant Exprs used in examples and code: -TRUE, FALSE = Expr('TRUE'), Expr('FALSE') -ZERO, ONE, TWO = 0, 1, 2 +TRUE, FALSE = Symbol('TRUE'), Symbol('FALSE') A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') + # ______________________________________________________________________________ @@ -374,13 +224,13 @@ def prop_symbols(x): return list(set(symbol for arg in x.args for symbol in prop_symbols(arg))) -def tt_true(alpha): - """Is the propositional sentence alpha a tautology? (alpha will be - coerced to an expr.) - >>> tt_true(expr("(P >> Q) <=> (~P | Q)")) +def tt_true(s): + """Is a propositional sentence a tautology? + >>> tt_true('P | ~P') True """ - return tt_entails(TRUE, expr(alpha)) + s = expr(s) + return tt_entails(TRUE, s) def pl_true(exp, model={}): @@ -420,9 +270,9 @@ def pl_true(exp, model={}): result = None return result p, q = args - if op == '>>': + if op == '==>': return pl_true(~p | q, model) - elif op == '<<': + elif op == '<==': return pl_true(p | ~q, model) pt = pl_true(p, model) if pt is None: @@ -432,7 +282,7 @@ def pl_true(exp, model={}): return None if op == '<=>': return pt == qt - elif op == '^': + elif op == '^': # xor or 'not equivalent' return pt != qt else: raise ValueError("illegal operator in logic expression" + str(exp)) @@ -443,11 +293,12 @@ def pl_true(exp, model={}): def to_cnf(s): - """Convert a propositional logical sentence s to conjunctive normal form. + """Convert a propositional logical sentence to conjunctive normal form. That is, to the form ((A | ~B | ...) & (B | C | ...) & ...) [p. 253] - >>> to_cnf("~(B|C)") + >>> to_cnf('~(B | C)') (~B & ~C) """ + s = expr(s) if isinstance(s, str): s = expr(s) s = eliminate_implications(s) # Steps 1, 2 from p. 253 @@ -457,17 +308,18 @@ def to_cnf(s): def eliminate_implications(s): "Change implications into equivalent form with only &, |, and ~ as logical operators." + s = expr(s) if not s.args or is_symbol(s.op): return s # Atoms are unchanged. args = list(map(eliminate_implications, s.args)) a, b = args[0], args[-1] - if s.op == '>>' or s.op == '==>': + if s.op == '==>': return (b | ~a) - elif s.op == '<<' or s.op == '<==': + elif s.op == '<==': return (a | ~b) elif s.op == '<=>': return (a | ~b) & (b | ~a) - elif s.op == '^' or s.op == '<=/=>': + elif s.op == '^': assert len(args) == 2 # TODO: relax this restriction return (a & ~b) | (~a & b) else: @@ -479,6 +331,7 @@ def move_not_inwards(s): """Rewrite sentence s by moving negation sign inward. >>> move_not_inwards(~(A | B)) (~A & ~B)""" + s = expr(s) if s.op == '~': def NOT(b): return move_not_inwards(~b) # noqa a = s.args[0] @@ -501,6 +354,7 @@ def distribute_and_over_or(s): >>> distribute_and_over_or((A & B) | C) ((A | C) & (B | C)) """ + s = expr(s) if s.op == '|': s = associate('|', s.args) if s.op != '|': @@ -539,7 +393,7 @@ def associate(op, args): else: return Expr(op, *args) -_op_identity = {'&': TRUE, '|': FALSE, '+': ZERO, '*': ONE} +_op_identity = {'&': TRUE, '|': FALSE, '+': 0, '*': 1} def dissociate(op, args): @@ -634,7 +488,7 @@ def clauses_with_premise(self, p): """Return a list of the clauses in KB that have p in their premise. This could be cached away for O(1) speed, but we'll recompute it.""" return [c for c in self.clauses - if c.op == '>>' and p in conjuncts(c.args[0])] + if c.op == '==>' and p in conjuncts(c.args[0])] def pl_fc_entails(KB, q): @@ -644,7 +498,7 @@ def pl_fc_entails(KB, q): True """ count = dict([(c, len(conjuncts(c.args[0]))) for c in KB.clauses - if c.op == '>>']) + if c.op == '==>']) inferred = defaultdict(bool) agenda = [s for s in KB.clauses if is_prop_symbol(s.op)] while agenda: @@ -664,7 +518,7 @@ def pl_fc_entails(KB, q): # Propositional Logic Forward Chaining example [Fig. 7.16] Fig[7, 15] = PropDefiniteKB() -for s in "P>>Q (L&M)>>P (B&L)>>M (A&P)>>L (A&B)>>L A B".split(): +for s in "P==>Q; (L&M)==>P; (B&L)==>M; (A&P)==>L; (A&B)==>L; A;B".split(';'): Fig[7, 15].tell(expr(s)) # ______________________________________________________________________________ @@ -869,7 +723,7 @@ def unify(x, y, s): def is_variable(x): "A variable is an Expr with no args and a lowercase symbol as the op." - return isinstance(x, Expr) and not x.args and is_var_symbol(x.op) + return isinstance(x, Expr) and not x.args and x.op[0].islower() def unify_var(var, x, s): @@ -922,7 +776,12 @@ def subst(s, x): def fol_fc_ask(KB, alpha): - unimplemented() + """Inefficient forward chaining for first-order logic. [Fig. 9.3] + KB is a FolKB and alpha must be an atomic sentence.""" + while True: + for r in KB.clauses: + ps, q = parse_definite_clause(standardize_variables(r)) + raise NotImplementedError def standardize_variables(sentence, dic=None): @@ -1013,7 +872,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] - KB should be an instance of FolKB, and query an atomic sentence. """ + KB should be an instance of FolKB, and goals a list of literals. """ return fol_bc_or(KB, query, {}) @@ -1049,9 +908,9 @@ def diff(y, x): ((x * 1) + (x * 1)) """ if y == x: - return ONE + return 1 elif not y.args: - return ZERO + return 0 else: u, op, v = y.args[0], y.op, y.args[-1] if op == '+': @@ -1082,56 +941,56 @@ def simp(x): args = list(map(simp, x.args)) u, op, v = args[0], x.op, args[-1] if op == '+': - if v == ZERO: + if v == 0: return u - if u == ZERO: + if u == 0: return v if u == v: - return TWO * u + return 2 * u if u == -v or v == -u: - return ZERO + return 0 elif op == '-' and len(args) == 1: if u.op == '-' and len(u.args) == 1: return u.args[0] # --y ==> y elif op == '-': - if v == ZERO: + if v == 0: return u - if u == ZERO: + if u == 0: return -v if u == v: - return ZERO + return 0 if u == -v or v == -u: - return ZERO + return 0 elif op == '*': - if u == ZERO or v == ZERO: - return ZERO - if u == ONE: + if u == 0 or v == 0: + return 0 + if u == 1: return v - if v == ONE: + if v == 1: return u if u == v: return u ** 2 elif op == '/': - if u == ZERO: - return ZERO - if v == ZERO: + if u == 0: + return 0 + if v == 0: return Expr('Undefined') if u == v: - return ONE + return 1 if u == -v or v == -u: - return ZERO + return 0 elif op == '**': - if u == ZERO: - return ZERO - if v == ZERO: - return ONE - if u == ONE: - return ONE - if v == ONE: + if u == 0: + return 0 + if v == 0: + return 1 + if u == 1: + return 1 + if v == 1: return u elif op == 'log': - if u == ONE: - return ZERO + if u == 1: + return 0 else: raise ValueError("Unknown op: " + op) # If we fall through to here, we can not simplify further @@ -1142,17 +1001,4 @@ def d(y, x): "Differentiate and then simplify." return simp(diff(y, x)) -# _________________________________________________________________________ - -# Utilities for doctest cases -# These functions print their arguments in a standard order -# to compensate for the random order in the standard representation - - - - - - -# ________________________________________________________________________ - diff --git a/tests/test_logic.py b/tests/test_logic.py index a3ff5396d..bc578a8ff 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -5,6 +5,8 @@ def test_expr(): assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' + assert (expr_handle_infix_ops('P & Q ==> R & ~S') + == "P & Q |InfixOp('==>', None)| R & ~S") def test_extend(): assert extend({x: 1}, y, 2) == {x: 1, y: 2} @@ -14,30 +16,33 @@ def test_PropKB(): assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 kb.tell(A & E) assert kb.ask(A) == kb.ask(E) == {} - kb.tell(E >> C) + kb.tell(E |implies| C) assert kb.ask(C) == {} kb.retract(E) assert kb.ask(E) is False assert kb.ask(C) is False + +def test_KB_wumpus(): # A simple KB that defines the relevant conditions of the Wumpus World as in Fig 7.4. # See Sec. 7.4.3 kb_wumpus = PropKB() # Creating the relevant expressions + # TODO: Let's just use P11, P12, ... = symbols('P11, P12, ...') P = {} B = {} - P[1,1] = Expr("P[1,1]") - P[1,2] = Expr("P[1,2]") - P[2,1] = Expr("P[2,1]") - P[2,2] = Expr("P[2,2]") - P[3,1] = Expr("P[3,1]") - B[1,1] = Expr("B[1,1]") - B[2,1] = Expr("B[2,1]") + P[1,1] = Symbol("P[1,1]") + P[1,2] = Symbol("P[1,2]") + P[2,1] = Symbol("P[2,1]") + P[2,2] = Symbol("P[2,2]") + P[3,1] = Symbol("P[3,1]") + B[1,1] = Symbol("B[1,1]") + B[2,1] = Symbol("B[2,1]") kb_wumpus.tell(~P[1,1]) - kb_wumpus.tell(B[1,1] % ((P[1,2] | P[2,1]))) - kb_wumpus.tell(B[2,1] % ((P[1,1] | P[2,2] | P[3,1]))) + kb_wumpus.tell(B[1,1] |equiv| ((P[1,2] | P[2,1]))) + kb_wumpus.tell(B[2,1] |equiv| ((P[1,1] | P[2,2] | P[3,1]))) kb_wumpus.tell(~B[1,1]) kb_wumpus.tell(B[2,1]) @@ -59,12 +64,15 @@ def test_PropKB(): # Statement: There is a pit in either [2,2] or [3,1]. assert kb_wumpus.ask(P[2,2] | P[3,1]) == {} -# TODO: resolve >> vs ==> -#def test_definite_clause(): -# assert not is_definite_clause(expr('~Farmer(Mac)')) -# assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) >> Hates(f, r)')) -# assert not is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) >> Hates(f, r)')) -# assert is_definite_clause(expr('(Farmer(f) | Rabbit(r)) >> Hates(f, r)')) + +def test_definite_clause(): + assert is_definite_clause(expr('A & B & C & D ==> E')) + assert is_definite_clause(expr('Farmer(Mac)')) + assert not is_definite_clause(expr('~Farmer(Mac)')) + assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) + assert not is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) ==> Hates(f, r)')) + assert not is_definite_clause(expr('(Farmer(f) | Rabbit(r)) ==> Hates(f, r)')) + def test_pl_true(): assert pl_true(P, {}) is None @@ -79,16 +87,16 @@ def test_pl_true(): def test_tt_true(): assert tt_true(P | ~P) assert tt_true('~~P <=> P') - assert not tt_true('(P | ~Q)&(~P | Q)') + assert not tt_true((P | ~Q) & (~P | Q)) assert not tt_true(P & ~P) assert not tt_true(P & Q) - assert tt_true('(P | ~Q)|(~P | Q)') + assert tt_true((P | ~Q) | (~P | Q)) assert tt_true('(A & B) ==> (A | B)') assert tt_true('((A & B) & C) <=> (A & (B & C))') assert tt_true('((A | B) | C) <=> (A | (B | C))') - assert tt_true('(A >> B) <=> (~B >> ~A)') - assert tt_true('(A >> B) <=> (~A | B)') - assert tt_true('(A <=> B) <=> ((A >> B) & (B >> A))') + assert tt_true('(A ==> B) <=> (~B ==> ~A)') + assert tt_true('(A ==> B) <=> (~A | B)') + assert tt_true('(A <=> B) <=> ((A ==> B) & (B ==> A))') assert tt_true('~(A & B) <=> (~A | ~B)') assert tt_true('~(A | B) <=> (~A & ~B)') assert tt_true('(A & (B | C)) <=> ((A & B) | (A & C))') @@ -116,7 +124,7 @@ def test_tt_entails(): assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) def test_eliminate_implications(): - assert repr(eliminate_implications(A >> (~B << C))) == '((~B | ~C) | ~A)' + assert repr(eliminate_implications('A ==> (~B <== C)')) == '((~B | ~C) | ~A)' assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' assert repr(eliminate_implications(A & B | C & ~D)) == '((A & B) | (C & ~D))' @@ -126,8 +134,10 @@ def test_dissociate(): assert dissociate('&', [A, B, C & D, P | Q]) == [A, B, C, D, P | Q] def test_associate(): - assert repr(associate('&', [(A&B),(B|C),(B&C)])) == '(A & B & (B | C) & B & C)' - assert repr(associate('|', [A|(B|(C|(A&B)))])) == '(A | B | C | (A & B))' + assert (repr(associate('&', [(A & B), (B | C), (B & C)])) + == '(A & B & (B | C) & B & C)') + assert (repr(associate('|', [A | (B | (C | (A & B)))])) + == '(A | B | C | (A & B))') def test_move_not_inwards(): assert repr(move_not_inwards(~(A | B))) == '(~A & ~B)' @@ -138,7 +148,7 @@ def test_to_cnf(): assert (repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' - assert repr(to_cnf("B <=> (P1|P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' + assert repr(to_cnf("B <=> (P1 | P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' assert repr(to_cnf("a | (b & c) | d")) == '((b | a | d) & (c | a | d))' assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' assert repr(to_cnf("A | (B | (C | (D & E)))")) == '((D | A | B | C) & (E | A | B | C))' diff --git a/tests/test_utils.py b/tests/test_utils.py index b6cf6d343..cc063847b 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -130,10 +130,45 @@ def test_sigmoid(): def test_step(): - assert step(1) == 1 + assert step(1) == step(0.5) == 1 assert step(0) == 1 - assert step(-1) == 0 - + assert step(-1) == step(-0.5) == 0 + + +def test_Expr(): + A, B, C = symbols('A, B, C') + assert symbols('A, B, C') == (Symbol('A'), Symbol('B'), Symbol('C')) + assert A.op == repr(A) == 'A' + assert arity(A) == 0 and A.args == () + + b = Expr('+', A, 1) + assert arity(b) == 2 and b.op == '+' and b.args == (A, 1) + + u = Expr('-', b) + assert arity(u) == 1 and u.op == '-' and u.args == (b,) + + assert (b ** u) == (b ** u) + assert (b ** u) != (u ** b) + + assert A + b * C ** 2 == A + (b * (C ** 2)) + + ex = C + 1 / (A % 1) + assert list(subexpressions(ex)) == [(C + (1 / (A % 1))), C, (1 / (A % 1)), 1, (A % 1), A, 1] + assert A in subexpressions(ex) + assert B not in subexpressions(ex) + + +def test_expr(): + P, Q, x, y, z, GP = symbols('P, Q, x, y, z, GP') + assert (expr(y + 2 * x) + == expr('y + 2 * x') + == Expr('+', y, Expr('*', 2, x))) + assert expr('P & Q ==> P') == Expr('==>', P & Q, P) + assert expr('P & Q <=> Q & P') == Expr('<=>', (P & Q), (Q & P)) + assert expr('P(x) | P(y) & Q(z)') == (P(x) | (P(y) & Q(z))) + # x is grandparent of z if x is parent of y and y is parent of z: + assert (expr('GP(x, z) <== P(x, y) & P(y, z)') + == Expr('<==', GP(x, z), P(x, y) & P(y, z))) if __name__ == '__main__': pytest.main() diff --git a/utils.py b/utils.py index 58d490bfc..9b7317847 100644 --- a/utils.py +++ b/utils.py @@ -1,19 +1,25 @@ """Provides some utilities widely used by other modules""" -# TODO: Priority queues may not belong here -- see treatment in search.py - -import operator -import random -import os.path import bisect +import collections import collections.abc +import functools +import operator +import os.path +import random +import re from grid import * # noqa # ______________________________________________________________________________ -# Functions on Sequences (mostly inspired by Common Lisp) +# Functions on Sequences and Iterables +def sequence(iterable): + "Coerce iterable to sequence, if it is not already one." + return (iterable if isinstance(iterable, collections.abc.Sequence) + else tuple(iterable)) + def removeall(item, seq): """Return a copy of seq (or string) with all occurences of item removed.""" if isinstance(seq, str): @@ -21,17 +27,14 @@ def removeall(item, seq): else: return [x for x in seq if x != item] - -def unique(seq): +def unique(seq): # TODO: replace with set """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) - def count(seq): """Count the number of items in sequence that are interpreted as true.""" return sum(bool(x) for x in seq) - def product(numbers): """Return the product of the numbers, e.g. product([2, 3, 10]) == 60""" result = 1 @@ -39,7 +42,6 @@ def product(numbers): result *= x return result - def first(iterable, default=None): "Return the first element of an iterable or the next element of a generator; or default." try: @@ -50,7 +52,7 @@ def first(iterable, default=None): return next(iterable, default) -def every(predicate, seq): +def every(predicate, seq): # TODO: replace with all """True if every element of seq satisfies predicate.""" return all(predicate(x) for x in seq) @@ -60,6 +62,9 @@ def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) +# ______________________________________________________________________________ +# argmin and argmax + identity = lambda x: x argmin = min @@ -79,10 +84,7 @@ def shuffled(iterable): random.shuffle(items) return items -def sequence(iterable): - "Coerce iterable to sequence, if it is not already one." - return (iterable if isinstance(iterable, collections.abc.Sequence) - else tuple(iterable)) + # ______________________________________________________________________________ # Statistical and mathematical functions @@ -243,7 +245,7 @@ def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 -try: # math.isclose was added in Python 3.5 +try: # math.isclose was added in Python 3.5; but we might be in 3.4 from math import isclose except ImportError: def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): @@ -337,10 +339,171 @@ def unimplemented(): "Use this as a stub for not-yet-implemented functions." raise NotImplementedError + +# ______________________________________________________________________________ +# Expressions + +# See https://docs.python.org/3/reference/expressions.html#operator-precedence +# See https://docs.python.org/3/reference/datamodel.html#special-method-names + +class Expr(object): + """A mathematical expression with an operator and 0 or more arguments. + op is a str like '+' or 'sin'; args are Expressions. + Expr('x') or Symbol('x') creates a symbol (a nullary Expr). + Expr('-', x) creates a unary; Expr('+', x, 1) creates a binary.""" + + def __init__(self, op, *args): + self.op = str(op) + self.args = args + + # Operator overloads + def __neg__(self): return Expr('-', self) + def __pos__(self): return Expr('+', self) + def __invert__(self): return Expr('~', self) + def __add__(self, other): return Expr('+', self, other) + def __sub__(self, other): return Expr('-', self, other) + def __mul__(self, other): return Expr('*', self, other) + def __pow__(self, other): return Expr('**', self, other) + def __mod__(self, other): return Expr('%', self, other) + def __and__(self, other): return Expr('&', self, other) + def __xor__(self, other): return Expr('^', self, other) + def __rshift__(self, other): return Expr('>>', self, other) + def __lshift__(self, other): return Expr('<<', self, other) + def __truediv__(self, other): return Expr('/', self, other) + def __floordiv__(self, other): return Expr('//', self, other) + def __matmul__(self, other): return Expr('@', self, other) + + # Reverse operator overloads + def __radd__(self, other): return Expr('+', other, self) + def __rsub__(self, other): return Expr('-', other, self) + def __rmul__(self, other): return Expr('*', other, self) + def __rdiv__(self, other): return Expr('/', other, self) + def __rpow__(self, other): return Expr('**', other, self) + def __rmod__(self, other): return Expr('%', other, self) + def __rand__(self, other): return Expr('&', other, self) + def __rxor__(self, other): return Expr('^', other, self) + def __ror__(self, other): return Expr('|', other, self) + def __rrshift__(self, other): return Expr('>>', other, self) + def __rlshift__(self, other): return Expr('<<', other, self) + def __rtruediv__(self, other): return Expr('/', other, self) + def __rfloordiv__(self, other): return Expr('//', other, self) + def __rmatmul__(self, other): return Expr('@', other, self) + + def __call__(self, *args): + "Call: if 'f' is a Symbol, then f(0) == Expr('f', 0)." + return Expr(self.op, *args) + + # Allow infix operators + def __or__(self, other): + "Allow 'P |implies| Q', where P, Q are Exprs and implies is an InfixOp." + if isinstance(other, InfixOp): + return InfixOp(other.op, lhs=self) + else: # Allow 'P | Q' also + return Expr('|', self, other) + + # Equality and repr + def __eq__(self, other): + "'x == y' evaluates to True or False; does not build an Expr." + return (isinstance(other, Expr) + and self.op == other.op + and self.args == other.args) + + def __hash__(self): return hash(self.op) ^ hash(self.args) + + def __repr__(self): + op = self.op + args = [str(arg) for arg in self.args] + if op.isidentifier(): # f(x) or f(x, y) + return '{}({})'.format(op, ', '.join(args)) if args else op + elif len(args) == 1: # -x or -(x + 1) + return op + args[0] + else: # (x - y) + opp = (' ' + op + ' ') + return '(' + opp.join(args) + ')' + +# An 'Expression' is either an Expr or a Number. +# Symbol is not an explicit type; it is any Expr with 0 args. + +Number = (int, float, complex) +Expression = (Expr, Number) + +def Symbol(name): + "A Symbol is just an Expr with no args." + return Expr(name) + +def symbols(names): + "Return a tuple of Symbols; names is a comma/whitespace delimited str." + return tuple(Symbol(name) for name in names.replace(',', ' ').split()) + +def subexpressions(x): + "Yield the subexpressions of an Expression (including x itself)." + yield x + if isinstance(x, Expr): + for arg in x.args: + yield from subexpressions(arg) + +def arity(expression): + "The number of sub-expressions in this expression." + if isinstance(expression, Expr): + return len(expression.args) + else: # expression is a number + return 0 + +# For operators that are not defined in Python, we allow new InfixOps: + +class InfixOp: + """Allow 'P |implies| Q, where P, Q are Exprs and implies is an InfixOp, + defined with implies = InfixOp('==>').""" + def __init__(self, op, lhs=None): + self.op = op + self.lhs = lhs + def __or__(self, other): + return Expr(self.op, self.lhs, other) + def __call__(self, lhs, rhs): + return Expr(self.op, lhs, rhs) + def __repr__(self): + return "InfixOp('{}', {})".format(self.op, self.lhs) + +infix_ops = (implies, rimplies, equiv) = [InfixOp(o) for o in ['==>', '<==', '<=>']] + +def expr(x): + """Shortcut to create an Expression. x is a str in which: + - identifiers are automatically defined as Symbols. + - '==>' is treated as an infix |implies|, as are all infix_ops + If x is already an Expression, it is returned unchanged. Example: + >>> expr('P & Q ==> Q') + ((P & Q) ==> Q) + """ + if isinstance(x, str): + return eval(expr_handle_infix_ops(x), + defaultkeydict(Symbol, InfixOp=InfixOp)) + else: + return x + +def expr_handle_infix_ops(x): + """Given a str, return a new str with '==>' replaced by |InfixOp('==>')|, etc. + >>> expr_handle_infix_ops('P ==> Q') + "P |InfixOp('==>', None)| Q" + """ + for op in infix_ops: + x = x.replace(op.op, '|' + str(op) + '|') + return x + +class defaultkeydict(collections.defaultdict): + """Like defaultdict, but the default_factory is a function of the key. + >>> d = defaultkeydict(len); d['four'] + 4 + """ + def __missing__(self, key): + self[key] = result = self.default_factory(key) + return result + + # ______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue -# TODO: Use queue.Queue +# TODO: Possibly use queue.Queue, queue.PriorityQueue +# TODO: Priority queues may not belong here -- see treatment in search.py class Queue: @@ -399,8 +562,6 @@ def pop(self): def __contains__(self, item): return item in self.A[self.start:] -# TODO: Use queue.PriorityQueue - class PriorityQueue(Queue): From ed417956d1f6c49a12b704363071a9e3dd78a1bd Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Sat, 9 Apr 2016 04:52:17 +0530 Subject: [PATCH 231/513] Implemented SAT_plan (#198) * Fixed a typo in logic.py * Added translate_to_SAT() * extract solution from model * added test cases * removed debug code --- logic.py | 90 +++++++++++++++++++++++++++++++++++++-------- tests/test_logic.py | 14 +++++++ 2 files changed, 89 insertions(+), 15 deletions(-) diff --git a/logic.py b/logic.py index eb18cc1d7..ef6f74237 100644 --- a/logic.py +++ b/logic.py @@ -678,7 +678,79 @@ def plan_route(current, goals, allowed): def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): - "[Fig. 7.22]" + """Converts a planning problem to Satisfaction problem by translating it to a cnf sentence. + TODO : Currently it fails if '_' character is used in transition. Change the expression names + [Fig. 7.22]""" + + #Functions used by SAT_plan + def translate_to_SAT(init, transition, goal, time): + action_sym = "State_{0}_{1}" + clauses = [] + states = [state for state in transition] + + #Symbol claiming state s at time t + state_sym = {} + for s in states: + for t in range(time+1): + state_sym[(s, t)] = Expr("In_{0}_at_{1}".format(s, t)) + + #Add initial state axiom + clauses.append(state_sym[init, 0]) + + #Add goal state axiom + clauses.append(state_sym[goal, time]) + + #All possible transitions + action_sym = {} + for s in states: + for action in transition[s]: + s_ = transition[s][action] + for t in range(time): + #Action 'action' taken from state 's' at time 't' to reach 's_' + action_sym[(s, action, t)] = Expr("Act_{0}_{1}_{2}".format(s, action, t)) + + # Change the state from s to s_ + clauses.append(action_sym[s, action, t] >> state_sym[s, t]) + clauses.append(action_sym[s, action, t] >> state_sym[s_, t + 1]) + + #Allow only one state at any time + for t in range(time+1): + #must be a state at any time + clauses.append(associate('|', [ state_sym[s, t] for s in states ])) + + for s in states: + for s_ in states[states.index(s)+1:]: + #for each pair of states s, s_ only one is possible at time t + clauses.append((~state_sym[s, t]) | (~state_sym[s_, t])) + + #Restrict to one transition per timestep + for t in range(time): + #list of possible transitions at time t + transitions_t = [tr for tr in action_sym if tr[2] == t] + + #make sure atleast one of the transition happens + clauses.append(associate('|', [ action_sym[tr] for tr in transitions_t ])) + + for tr in transitions_t: + for tr_ in transitions_t[transitions_t.index(tr) + 1 :]: + #there cannot be two transitions tr and tr_ at time t + clauses.append((~action_sym[tr]) | (~action_sym[tr_])) + + #Combine the clauses to form the cnf + return associate('&', clauses) + + def extract_solution(model): + true_syms = [ sym.__repr__() for sym in model if model[sym] ] + true_transitions = [ sym for sym in true_syms if sym.startswith('Act_')] + solution = [] + for sym in true_transitions: + # Extract time and action from 'Act_state_action_time' + solution.append((int(sym.split('_')[-1]), sym.split('_')[2])) + #Sort the actions based on time + solution.sort() + return [ action for time,action in solution ] + + #Body of SAT_plan algorithm for t in range(t_max): cnf = translate_to_SAT(init, transition, goal, t) model = SAT_solver(cnf) @@ -687,13 +759,6 @@ def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): return None -def translate_to_SAT(init, transition, goal, t): - unimplemented() - - -def extract_solution(model): - unimplemented() - # ______________________________________________________________________________ @@ -776,12 +841,7 @@ def subst(s, x): def fol_fc_ask(KB, alpha): - """Inefficient forward chaining for first-order logic. [Fig. 9.3] - KB is a FolKB and alpha must be an atomic sentence.""" - while True: - for r in KB.clauses: - ps, q = parse_definite_clause(standardize_variables(r)) - raise NotImplementedError + unimplemented() def standardize_variables(sentence, dic=None): @@ -872,7 +932,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] - KB should be an instance of FolKB, and goals a list of literals. """ + KB should be an instance of FolKB, and query an atomic sentence. """ return fol_bc_or(KB, query, {}) diff --git a/tests/test_logic.py b/tests/test_logic.py index bc578a8ff..2b0add9f7 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -191,6 +191,20 @@ def check_SAT(clauses, single_solution = {}): assert WalkSAT([A | B, ~A, ~(B | C), C | D, P | Q], 0.5, 100) is None assert WalkSAT([A | B, B & C, C | D, D & A, P, ~P], 0.5, 100) is None +def test_SAT_plan(): + transition = {'A':{'Left': 'A', 'Right': 'B'}, + 'B':{'Left': 'A', 'Right': 'C'}, + 'C':{'Left': 'B', 'Right': 'C'}} + assert SAT_plan('A', transition, 'C', 2) is None + assert SAT_plan('A', transition, 'B', 3) == ['Right'] + assert SAT_plan('C', transition, 'A', 3) == ['Left', 'Left'] + + transition = {(0, 0):{'Right': (0, 1), 'Down': (1, 0)}, + (0, 1):{'Left': (1, 0), 'Down': (1, 1)}, + (1, 0):{'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, + (1, 1):{'Left': (1, 0), 'Up': (0, 1)}} + assert SAT_plan((0, 0), transition, (1, 1), 2000) == ['Right', 'Down'] + if __name__ == '__main__': pytest.main() From 68a66007c2b6f25c340b14e800559ee39898d89a Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Sat, 9 Apr 2016 04:56:01 +0530 Subject: [PATCH 232/513] Added TicTacToe to canvas (#174) * added kernel test notebook * modified kernel.ipynb * Added canvas support * Added mouseclick to canvas * add TicTacToe to canvas * removed import statement in games.py * corrected HTML tags * Added comments and doctring * Added methods for drawing with normalized units * Added text support * Fixed int type during normalization --- canvas.js | 130 ++++++++++++++++ canvas.py | 122 +++++++++++++++ games.ipynb | 435 ++++++++++++++++++++++++++++++++++++++++++++++------ games.py | 7 - 4 files changed, 641 insertions(+), 53 deletions(-) create mode 100644 canvas.js create mode 100644 canvas.py diff --git a/canvas.js b/canvas.js new file mode 100644 index 000000000..b09c1b439 --- /dev/null +++ b/canvas.js @@ -0,0 +1,130 @@ +/* + JavaScript functions that are executed by running the corresponding methods of a Canvas object + Donot use these functions by making a js file. Instead use the python Canvas class. + See canvas.py for help on how to use the Canvas class to draw on the HTML Canvas +*/ + +//Manages the output of code executed in IPython kernel +function output_callback(out, block){ + console.log(out); + script = out.content.data['text/html']; + console.log(script); + script = script.substr(8, script.length - 17); + console.log(script); + eval(script) +} + +//Handles mouse click by calling mouse_click of Canvas object with the co-ordinates as arguments +function click_callback(element, event, varname){ + var rect = element.getBoundingClientRect(); + var x = event.clientX - rect.left; + var y = event.clientY - rect.top; + var kernel = IPython.notebook.kernel; + var exec_str = varname + ".mouse_click(" + String(x) + ", " + String(y) + ")"; + console.log(exec_str); + kernel.execute(exec_str,{'iopub': {'output': output_callback}}, {silent: false}); +} + +function rgbToHex(r,g,b){ + var hexValue=(r<<16) + (g<<8) + (b<<0); + var hexString=hexValue.toString(16); + hexString ='#' + Array(7-hexString.length).join('0') + hexString; //Add 0 padding + return hexString; +} + +function toRad(x){ + return x*Math.PI/180; +} + +//Canvas class to store variables +function Canvas(id){ + this.canvas = document.getElementById(id); + this.ctx = this.canvas.getContext("2d"); + this.WIDTH = this.canvas.width; + this.HEIGHT = this.canvas.height; + this.MOUSE = {x:0,y:0}; +} + +//Sets the fill color with which shapes are filled +Canvas.prototype.fill = function(r, g, b){ + this.ctx.fillStyle = rgbToHex(r,g,b); +} + +//Set the stroke color +Canvas.prototype.stroke = function(r, g, b){ + this.ctx.strokeStyle = rgbToHex(r,g,b); +} + +//Set width of the lines/strokes +Canvas.prototype.strokeWidth = function(w){ + this.ctx.lineWidth = w; +} + +//Draw a rectangle with top left at (x,y) with 'w' width and 'h' height +Canvas.prototype.rect = function(x, y, w, h){ + this.ctx.fillRect(x,y,w,h); +} + +//Draw a line with (x1, y1) and (x2, y2) as end points +Canvas.prototype.line = function(x1, y1, x2, y2){ + this.ctx.beginPath(); + this.ctx.moveTo(x1, y1); + this.ctx.lineTo(x2, y2); + this.ctx.stroke(); +} + +//Draw an arc with (x, y) as centre, 'r' as radius from angles start to stop +Canvas.prototype.arc = function(x, y, r, start, stop){ + this.ctx.beginPath(); + this.ctx.arc(x, y, r, toRad(start), toRad(stop)); + this.ctx.stroke(); +} + +//Clear the HTML canvas +Canvas.prototype.clear = function(){ + this.ctx.clearRect(0, 0, this.WIDTH, this.HEIGHT); +} + +//Change font, size and style +Canvas.prototype.font = function(font_str){ + this.ctx.font = font_str; +} + +//Draws "filled" text on the canvas +Canvas.prototype.fill_text = function(text, x, y){ + this.ctx.fillText(text, x, y); +} + +//Write text on the canvas +Canvas.prototype.stroke_text = function(text, x, y){ + this.ctx.strokeText(text, x, y); +} + + +//Test if the canvas functions are working +Canvas.prototype.test_run = function(){ + var dbg = false; + if(dbg) + alert("1"); + this.clear(); + if(dbg) + alert("2"); + this.fill(0, 200, 0); + if(dbg) + alert("3"); + this.rect(this.MOUSE.x, this.MOUSE.y, 100, 200); + if(dbg) + alert("4"); + this.stroke(0, 0, 50); + if(dbg) + alert("5"); + this.line(0, 0, 100, 100); + if(dbg) + alert("6"); + this.stroke(200, 200, 200); + if(dbg) + alert("7"); + this.arc(200, 100, 50, 0, 360); + if(dbg) + alert("8"); +} diff --git a/canvas.py b/canvas.py new file mode 100644 index 000000000..a58b67a0e --- /dev/null +++ b/canvas.py @@ -0,0 +1,122 @@ +from IPython.display import HTML, display, clear_output + +_canvas = """ + +
    + +
    + + +""" + +class Canvas: + """Inherit from this class to manage the HTML canvas element in jupyter notebooks. + To create an object of this class any_name_xyz = Canvas("any_name_xyz") + The first argument given must be the name of the object being create + IPython must be able to refernce the variable name that is being passed + """ + + def __init__(self, varname, id=None, width=800, height=600): + """""" + self.name = varname + self.id = id or varname + self.width = width + self.height = height + self.html = _canvas.format(self.id, self.width, self.height, self.name) + self.exec_list = [] + display(HTML(self.html)) + + def mouse_click(self, x, y): + "Override this method to handle mouse click at position (x, y)" + raise NotImplementedError + + def mouse_move(self, x, y): + raise NotImplementedError + + def exec(self, exec_str): + "Stores the command to be exectued to a list which is used later during update()" + if not isinstance(exec_str, str): + print("Invalid execution argument:",exec_str) + self.alert("Recieved invalid execution command format") + prefix = "{0}_canvas_object.".format(self.id) + self.exec_list.append(prefix + exec_str + ';') + + def fill(self, r, g, b): + "Changes the fill color to a color in rgb format" + self.exec("fill({0}, {1}, {2})".format(r, g, b)) + + def stroke(self, r, g, b): + "Changes the colors of line/strokes to rgb" + self.exec("stroke({0}, {1}, {2})".format(r, g, b)) + + def strokeWidth(self, w): + "Changes the width of lines/strokes to 'w' pixels" + self.exec("strokeWidth({0})".format(w)) + + def rect(self, x, y, w, h): + "Draw a rectangle with 'w' width, 'h' height and (x, y) as the top-left corner" + self.exec("rect({0}, {1}, {2}, {3})".format(x, y, w, h)) + + def rect_n(self, xn, yn, wn, hn): + "Similar to rect(), but the dimensions are normalized to fall between 0 and 1" + x = round(xn * self.width) + y = round(yn * self.height) + w = round(wn * self.width) + h = round(hn * self.height) + self.rect(x, y, w, h) + + def line(self, x1, y1, x2, y2): + "Draw a line from (x1, y1) to (x, y2)" + self.exec("line({0}, {1}, {2}, {3})".format(x1, y1, x2, y2)) + + def line_n(self, x1n, y1n, x2n, y2n): + "Similar to line(), but the dimensions are normalized to fall between 0 and 1" + x1 = round(x1n * self.width) + y1 = round(y1n * self.height) + x2 = round(x2n * self.width) + y2 = round(y2n * self.height) + self.line(x1, y1, x2, y2) + + def arc(self, x, y, r, start, stop): + "Draw an arc with (x, y) as centre, 'r' as radius from angles 'start' to 'stop'" + self.exec("arc({0}, {1}, {2}, {3}, {4})".format(x, y, r, start, stop)) + + def arc_n(self, xn ,yn, rn, start, stop): + """Similar to arc(), but the dimensions are normalized to fall between 0 and 1 + The normalizing factor for radius is selected between width and height by seeing which is smaller + """ + x = round(xn * self.width) + y = round(yn * self.height) + r = round(rn * min(self.width, self.height)) + self.arc(x, y, r, start, stop) + + def clear(self): + "Clear the HTML canvas" + self.exec("clear()") + + def font(self, font): + "Changes the font of text" + self.exec('font("{0}")'.format(font)) + + def text(self, txt, x, y, fill = True): + "Display a text at (x, y)" + if fill: + self.exec('fill_text("{0}", {1}, {2})'.format(txt, x, y)) + else: + self.exec('stroke_text("{0}", {1}, {2})'.format(txt, x, y)) + + def text_n(self, txt, xn, yn, fill = True): + "Similar to text(), but with normalized coordinates" + x = round(xn * self.width) + y = round(yn * self.height) + self.text(text, x, y, fill) + + def alert(self, message): + "Immediately display an alert" + display(HTML(''.format(message))) + + def update(self): + "Execute the JS code to execute the commands queued by exec()" + exec_code = "" + self.exec_list = [] + display(HTML(exec_code)) diff --git a/games.ipynb b/games.ipynb index d66c158a4..0139eb2f1 100644 --- a/games.ipynb +++ b/games.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -227,22 +227,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'a3'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(game52, 'A')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'a2'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(game52, 'A')" ] @@ -256,11 +278,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a1\n", + "b1\n", + "c1\n" + ] + } + ], "source": [ "print( alphabeta_player(game52, 'A') )\n", "print( alphabeta_player(game52, 'B') )\n", @@ -276,22 +308,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "minimax_decision('A', game52)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alphabeta_full_search('A', game52)" ] @@ -305,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -323,11 +377,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". . . \n", + ". . . \n", + ". . . \n" + ] + } + ], "source": [ "ttt.display(ttt.initial)" ] @@ -343,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -369,11 +433,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X O X \n", + "O . O \n", + "X . . \n" + ] + } + ], "source": [ "ttt.display(my_state)" ] @@ -387,22 +461,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(ttt, my_state)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 3)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(ttt, my_state)" ] @@ -416,11 +512,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alphabeta_player(ttt, my_state)" ] @@ -434,11 +541,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X X X \n", + "O . . \n", + "O . . \n" + ] + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "play_game(ttt, alphabeta_player, random_player)" ] @@ -454,11 +581,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n" + ] + } + ], "source": [ "for _ in range(10):\n", " print(play_game(ttt, alphabeta_player, alphabeta_player))" @@ -473,11 +647,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "O X O \n", + "X O . \n", + "O X X \n", + "-1\n", + "O X . \n", + "X O . \n", + "X . O \n", + "-1\n", + "X O X \n", + "X O X \n", + "O X O \n", + "0\n", + "O O X \n", + "X O X \n", + "X O . \n", + "-1\n", + "X O X \n", + "X O O \n", + "O X X \n", + "0\n", + "X O O \n", + "X O . \n", + "O X X \n", + "-1\n", + "X X O \n", + "O O O \n", + "X . X \n", + "-1\n", + "O X O \n", + "X O X \n", + "X O X \n", + "0\n", + "O X O \n", + "O X X \n", + "X O X \n", + "0\n", + "O X X \n", + "X O O \n", + "O X X \n", + "0\n" + ] + } + ], "source": [ "for _ in range(10):\n", " print(play_game(ttt, random_player, alphabeta_player))" @@ -492,13 +713,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "play_game(ttt, query_player, random_player)" + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "\n", + "
    \n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Inherit from Canvas to implement TicTacToe\n", + "from canvas import *\n", + "class Canvas_TicTacToe(Canvas):\n", + " def __init__(self, varname, id=None, width=800, height=600):\n", + " Canvas.__init__(self, varname, id=None, width=800, height=600)\n", + " self.state = ttt.initial\n", + " self.strokeWidth(5)\n", + " self.draw_board()\n", + " \n", + " def mouse_click(self, x, y):\n", + " self.argxy = (x, y)\n", + " x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1\n", + " prev_state = self.state\n", + " self.state = ttt.result(self.state, (x, y))\n", + " if not prev_state == self.state:\n", + " move = random_player(ttt, self.state)\n", + " self.state = ttt.result(self.state, move)\n", + " self.draw_board()\n", + "\n", + " def draw_board(self):\n", + " self.clear()\n", + " self.stroke(0, 0, 0)\n", + " offset = 1/20\n", + " self.line_n(0 + offset, 1/3, 1 - offset, 1/3)\n", + " self.line_n(0 + offset, 2/3, 1 - offset, 2/3)\n", + " self.line_n(1/3, 0 + offset, 1/3, 1 - offset)\n", + " self.line_n(2/3, 0 + offset, 2/3, 1 - offset)\n", + " board = self.state.board\n", + " for mark in board:\n", + " if board[mark] == 'X':\n", + " self.draw_x(mark)\n", + " elif board[mark] == 'O':\n", + " self.draw_o(mark)\n", + " self.update()\n", + " \n", + " def draw_x(self, position):\n", + " self.stroke(0, 255, 0)\n", + " x, y = [i-1 for i in position]\n", + " offset = 1/20\n", + " self.line_n(x/3 + offset, y/3 + offset, x/3 + 1/3 - offset, y/3 + 1/3 - offset)\n", + " self.line_n(x/3 + 1/3 - offset, y/3 + offset, x/3 + offset, y/3 + 1/3 - offset)\n", + "\n", + " def draw_o(self, position):\n", + " self.stroke(255, 0, 0)\n", + " x, y = [i-1 for i in position]\n", + " self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/7, 0, 360)\n", + "\n", + "rand_ttt = Canvas_TicTacToe(\"rand_ttt\", \"t3rand\", 400, 300)" ] }, { @@ -510,13 +817,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "play_game(ttt, query_player, alphabeta_player)" + "#play_game(ttt, query_player, alphabeta_player)" ] }, { @@ -528,46 +835,82 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B1\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "play_game(game52, alphabeta_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B1\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "play_game(game52, alphabeta_player, random_player)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "play_game(game52, query_player, alphabeta_player)" + "#play_game(game52, query_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "play_game(game52, alphabeta_player, query_player)" + "#play_game(game52, alphabeta_player, query_player)" ] }, { @@ -594,7 +937,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.4" + "version": "3.5.0" } }, "nbformat": 4, diff --git a/games.py b/games.py index 980a3fd43..4f5c6418a 100644 --- a/games.py +++ b/games.py @@ -135,7 +135,6 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): "Make a move by querying standard input." - # game.display(state) move_string = input('Your move? ') try: move = eval(move_string) @@ -157,17 +156,11 @@ def play_game(game, *players): """Play an n-person, move-alternating game.""" state = game.initial - print("Initial state:") - game.display(state) while True: for player in players: move = player(game, state) state = game.result(state, move) - print("State after %s's move:" % player.__name__) - game.display(state) if game.terminal_test(state): - print("\nGame's over!") - print("Final state:") game.display(state) return game.utility(state, game.to_move(game.initial)) From b9149df8a365bbb0e9a59bf08a82c3ae3c6fa4c0 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sat, 9 Apr 2016 02:58:22 -0700 Subject: [PATCH 233/513] Update logic.ipynb (#204) and update intro.ipynb --- intro.ipynb | 99 +++------ logic.ipynb | 591 +++++++++++++++++++++++++++++++++++++++------------- 2 files changed, 482 insertions(+), 208 deletions(-) diff --git a/intro.ipynb b/intro.ipynb index af23f6787..0f02870ab 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -4,29 +4,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# An Introduction To Using `aima-python` \n", - "*Author: Chirag Vartak* \n", - "*Date: 14th March 2016* \n", + "# An Introduction To `aima-python` \n", " \n", - "## About `aima-python` \n", - " \n", - " As I suspect you might already know, the repository [aima-python](https://github.com/aimacode/aima-python) implements in Python code, the algorithms in the textbook *Artificial Intelligence: A Modern Approach*. You can find these algorithms in the various modules of this repository. Typically, each module has the code for a single chapter in the book, but some modules may have code from more than two chapters in it. Most of the algorithms given in the figures of the book have been implemented. If you are looking for a particular algorithm or have trouble finding the module for the chapter you are interested in, [this index](https://github.com/aimacode/aima-python#index-of-code) might prove to be useful. The code in this repository takes care to implement the algorithms in the figures of the book *exactly as they are*. We have tried our best to write our code as close as we could to the pseudocodes in the textbook, and haven't done any optimizations to it that may hamper with code readability. The intention of this code is to be readable, so that you can relate it to the algorithms in the textbook. For algorithms that we thought really needed optimizations, we have written these seperately as different functions and stated so in comments. Also, before we proceed, I should let you know that if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository. \n", + "The [aima-python](https://github.com/aimacode/aima-python) repository implements, in Python code, the algorithms in the textbook *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. A typical module in the repository has the code for a single chapter in the book, but some modules combine several chapters. See [the index](https://github.com/aimacode/aima-python#index-of-code) if you can't find the algorithm you want. The code in this repository attempts to mirror the pseudocode in the textbook as closely as possible and to stress readability foremost; if you are looking for high-performance code with advanced features, there are other repositories for you. For each module, there are three files, for example:\n", + "\n", + "- [**`logic.py`**](https://github.com/aimacode/aima-python/blob/master/logic.py): Source code with data types and algorithms for fealing with logic; functions have docstrings explaining their use.\n", + "- [**`logic.ipynb`**](https://github.com/aimacode/aima-python/blob/master/logic.ipynb): A notebook like this one; gives more detailed examples and explanations of use.\n", + "- [**`tests/test_logic.py`**](https://github.com/aimacode/aima-python/blob/master/tests/test_logic.py): Test cases, used to verify the code is correct, and also useful to see examples of use.\n", + "\n", + "There is also an [aima-java](https://github.com/aimacode/aima-java) repository, if you prefer Java.\n", " \n", "## What version of Python?\n", " \n", - " The version of Python using which we have written and tested the code is Python 3.4. While running the code using Python 3.4 would be ideal, it should run fine on any Python 3.x. If you find that some function or module gives rise to errors on a different version of Python 3 which you are using, we would be glad if you could report it as an [Issue](https://github.com/aimacode/aima-python/issues). As far as Python 2 is concerned, the code simply will not work and produce too many errors. So, please *do not use the code in this repository with Python 2*. If, for some reason, you cannot obtain access to Python 3, we do have a [legacy branch](https://github.com/aimacode/aima-python/tree/aima3python2) that was developed a long time ago and was intended to work with Python 2. Not all modules have been implemented in this branch and its development and maintainence have been stopped. \n", + "The code is tested in Python [3.4](https://www.python.org/download/releases/3.4.3/) and [3.5](https://www.python.org/downloads/release/python-351/). If you try a different version of Python 3 and find a problem, please report it as an [Issue](https://github.com/aimacode/aima-python/issues). There is an incomplete [legacy branch](https://github.com/aimacode/aima-python/tree/aima3python2) for those who must run in Python 2. \n", " \n", - "## Installing Anaconda\n", - " \n", - " If you have Python installed on your computer directly from python.org, you should be able to get the code in this repository to run just fine. But what we prefer is that you get [Anaconda](https://www.continuum.io/downloads) installed on your computer. Anaconda is a completely free Python distribution and has recently gotten quite popular in the Python scientific computing community. Plus, it comes with additional tools like the powerful IPython interpreter, the Jupyter Notebook App and many essential software packages. After installing Anaconda, you will be good to go to use these IPython notebooks. Also, you can run code with multiple versions of Python using what they call [virtual environments](http://conda.pydata.org/docs/py2or3.html).\n", + "We recommend the [Anaconda](https://www.continuum.io/downloads) distribution of Python 3.5. It comes with additional tools like the powerful IPython interpreter, the Jupyter Notebook and many helpful packages for scientific computing. After installing Anaconda, you will be good to go to run all the code and all the IPython notebooks. \n", "\n", - "## Using these IPython notebooks \n", - " \n", - " An IPython notebook in this repository explains how to use a particular module and gives examples of its usage. An IPython notebook explains the module with the same name. For example, `games.ipynb` helps you with using the `games.py` module. A notebook has some content telling you more about the code in the module and some examples at the end which you can run in the notebook itself. \n", + "## IPython notebooks \n", " \n", - " You can use these IPython notebook in two ways: either you can view them as static HTML pages in your browser by clicking on their links in this repository on Gitub, or, you can download these notebooks and use them with a notebook app like Jupyter. (If you plan to use these notebooks with a notebook app, download the entire repository and then do so; a notebook might have some files it needs in the repo.) A notebook app allows you to run the code interactively in the browser, but if you just want to take a fleeting look at the notebook, viewing it as a static HTML page would be great too. \n", + "The IPython notebooks in this repository explain how to use the modules, and give examples of usage. \n", + "You can use them in two ways: \n", + "\n", + "1. View static HTML pages. (Just browse to the [repository](https://github.com/aimacode/aima-python) and click on a `.ipynb` file link.)\n", + "2. Run, modify, and re-run code, live. (Download the repository (by [zip file](https://github.com/aimacode/aima-python/archive/master.zip) or by `git` commands), start a Jupyter notebook server with the shell command \"`jupyter notebook`\" (issued from the directory where the files are), and click on the notebook you want to interact with.)\n", + "\n", " \n", - " If you don't know what IPython notebooks or the Jupyter Notebook App are or have never used them before, then I suggest [you read a bit](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) about them. Then, you might want to get your hands dirty and [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb). If you want to explore IPython notebooks some more before you get started with this repository, [this wiki page](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) has some truly amazing example notebooks. If you want to work with a specific version of Python, [virtual environments](http://conda.pydata.org/docs/py2or3.html) might be what you are looking for. To run the IPython interpreter or the Jupyter Notebook App with a specific version of Python, just create the particular virtual environment in the terminal and proceed as you normally would." + "You can [read about notebooks](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) and then [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb). " ] }, { @@ -35,101 +38,67 @@ "collapsed": true }, "source": [ - "## Helpful Tips" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Viewing the Source and Function Definitions\n", + "# Helpful Tips\n", "\n", - "The general work-flow of these notebooks includes importing implementations from corresponding python files and then illustrating the use of the imported Classes and Functions.\n", - "\n", - "Sometimes it might be really helpful to view the source of these implementation to gain a better understanding to their working. One can obviously do this by opening the python file but this can also be done inside IPy notebooks in a very easy way. The example below illustrates this." + "Most of these notebooks start by importing all the symbols in a module:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "from rl import PassiveTDAgent" + "from logic import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now to view the source of PassiveTDAgent we can use IPy magic funtion %psource. One can do this by adding a cell at any place using the Cell menu." + "From there, the notebook alternates explanations with examples of use. You can run the examples as they are, and you can modify the code cells (or add new cells) and run your own examples. If you have some really good examples to add, you can make a github pull request.\n", + "\n", + "If you want to see the source code of a function, you can open a browser or editor and see it in another window, or from within the notebook you can use the IPython magic funtion `%psource` (for \"print source\"):" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "%psource PassiveTDAgent" + "%psource WalkSAT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you are only interested in the definition / Class Constructor instead of the full source." + "Or see an abbreviated description of an object with a trainling question mark:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%pdef PassiveTDAgent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The docstring can be viewed by using the %pdoc magic function." - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "%pdoc PassiveTDAgent" + "WalkSAT?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can also use Object? to get both the definition and the docstring together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "PassiveTDAgent?" + "# Authors\n", + "\n", + "This notebook by [Chirag Vertak](https://github.com/chiragvartak) and [Peter Norvig](https://github.com/norvig)." ] } ], @@ -149,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.4" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/logic.ipynb b/logic.ipynb index 097cfc804..6693c50fa 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -6,46 +6,29 @@ "collapsed": true }, "source": [ - "# Explaining the logic.py module\n", - "*Author: Chirag Vartak*
    \n", - "*Date: 23rd March 2016*\n", - "\n", - "---" + "# Logic: `logic.py`; Chapters 6-8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## An Introduction\n", - "\n", - "Hello reader.
    \n", - "In this IPython notebook, I will help you a little so that you will become more comfortable with using the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module. The `logic.py` module implements the algorithms given in Chapter 6 (Logical Agents), Chapter 7 (First-Order Logic) and Chapter 8 (Inference in First-Order Logic) of the book *Artificial Intelligence: A Modern Approach*.\n", - "\n", - "Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository.\n", + "This notebook describes the [logic.py](https://github.com/aimacode/aima-python/blob/master/logic.py) module, which covers Chapters 6 (Logical Agents), 7 (First-Order Logic) and 8 (Inference in First-Order Logic) of *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. See the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) for instructions.\n", "\n", - "I am assuming that you have read at least Chapter 7 (Logical Agents). You really want to do this if you intend to make sense of anything I tell you in this notebook, or any code in the `logic.py` module, for that matter. If you haven't you should go back and read this chapter first, at least upto Sec. 7.5. As a side note, be sure to keep the `logic.py` module open and keep referring to it as you read this notebook. The docstrings of most classes and functions are well-written and will give you more insight and in some cases, even examples, of how to use that particular class or function.\n", + "We'll start by looking at `Expr`, the data type for logical sentences, and the convenience function `expr`. Then we'll cover `KB` and `ProbKB`, the classes for Knowledge Bases. Then, we will construct a knowledge base of a specific situation in the Wumpus World. We will next go through the `tt_entails` function and experiment with it a bit. The `pl_resolution` and `pl_fc_entails` functions will come next. \n", "\n", - "To briefly outline how I will proceed in this notebook, I will start by telling you more about the classes `KB` and `ProbKB`, the classes for the Knowledge Bases that we will be using. Next, we will begin with Propositional Logic; only after we are mostly done with it, we will be getting into First-Order Logic. In Propositional Logic, we will have a look at the class `Expr` and the `expr` function, and try to get more comfortable with using them to create and manipulate logical expressions. We will also play a little with other utility functions created to make working with statements easy. Then, we will construct a knowledge base of a specific situation in the Wumpus World. We will next go through the `tt_entails` function and experiment with it a bit. The `pl_resolution` and `pl_fc_entails` functions will come next.\n", - "\n", - "So let's get started." + "But the first step is to load the code:" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "## Knowledge Bases: `KB` and `PropKB`\n", - "\n", - "The class `KB` is just a template class which you have to inherit to create a knowledge base class that you plan to use. This class reminds you to implement all the methods mentioned here and will scream at you if you forget to. It is, what you might call in Java, an abstract class. The class `PropKB` has been derived from the class `KB` and all the methods have been implemented in here. Let's have a look at these classes in somewhat more detail.\n", - "\n", - "We see that the class `KB` has four methods, apart from `__init__`. A point to note here: the `ask` method simply calls the `ask_generator` method. Thus, this one has already been implemented and what you'll have to actually implement when you create your own knowledge base class (if you want to, though I doubt you'll ever need to; just use the ones we've created for you), will be the `ask_generator` function and not the `ask` function itself.\n", - "\n", - "The class `PropKB` now.\n", - "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base. Note that each one of these sentences will be a 'clause' i.e. a sentence which is made up of only literals and `or`s.\n", - "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method.\n", - "* `ask_generator(self, query)` : The `ask_generator` function is used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns an empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem a little bit weird to you. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is done to maintain consistency with the way things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true. Hence the dict, to return all these substitutions. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`.\n", - "* `retract(self, sentence)` : This function removes all the clauses of the sentence given, from the knowledge base. Like the `tell` function, you don't have to pass clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses and then remove those." + "from logic import *" ] }, { @@ -54,97 +37,119 @@ "collapsed": true }, "source": [ - "## Getting started with Propositional Logic" + "## Logical Sentences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### The `Expr` class\n", - "\n", - "The `Expr` class is the one that enables us to work with propositional logic. This class, combined with the `expr` function will enable us to work with propositional logic with much ease.\n", - "\n", - "An instance of the `Expr` class, an `Expr` object represents a symbolic mathematical expression. Truth be told, this class can handle not just Propositional Logic but also First-Order Logic. (As a matter of fact, you can also do arithmetic using this class but you would just be introducing unnecessary complication for a simple task). For the case of our Propositional Logic, an `Expr` object represents a propositional sentence. If you will have a look at its `__init__`, you will see that an `Expr` object just stores the operator and the arguments of a propositional sentence. This is important to note. The `Expr` class does not define the *logic* of Propositional Logic; nor will we be defining it ourselves. It just gives you a way to *represent* expressions. You won't be able to do any propositional math using `Expr`; you won't be be able assign a value of `True` to `P` and `False` to `Q` and then do a `P` ∧ `Q` to get `False`. No, you won't be able to do that. What you will be able to do is to create a representation of sentence and assign it to `P`. Something like,\n", - "\n", - "```python\n", - "sent = Expr(\"==>\", \"A & B\", \"C\")\n", - "```\n", - "\n", - "which is represents the sentence\n", - "\n", - "> (A ∧ B) → C\n", - "\n", - "That's not much, you say. We can create representations of sentences using strings, you continue. Well, we manipulate the `Expr` objects to convert a sentence to its CNF (`to_cnf`), check satisfiability of a sentence (`dpll_satisfiable`), use resolution to find out if a knowledge base entails a sentence (`pl_resolution`) and whatnot. Best of luck doing that with your string representations!\n", - "\n", - "So, the point to take away from the last two paragraphs: The `Expr` class just allows you to create good, easily manipulable representations of propositional sentences. It does a little more than that though. Before I get into that let us create a few expressions of our own to experiment with them later on." + "The `Expr` class is designed to represent any kind of mathematical expression. The simplest type of `Expr` is a symbol, which can be defined with the function `Symbol`:" ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "x" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Symbol('x')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can define multiple symbols at the same time with the function `symbols`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "from logic import *\n", - "\n", - "P = Expr(\"P\")\n", - "Q = Expr(\"Q\")\n", - "R1 = Expr(\"&\", \"A\", \"B\")\n", - "R2 = Expr(\"==>\", \"C | D\", \"E\")" + "(x, y, P, Q, f) = symbols('x, y, P, Q, f')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note here that you can create expressions that have no operators (like the literals `P` and `Q`), simple expressions of literals (like the sentence R1 which represents `A` ∧ `B`) and also expressions that have, as their arguments, complex sentences represented as strings. But, these strings that are allowed as arguments, can use only certain symbols in them. This is the list of symbols that you should use when you want to put complex sentences as arguments to the `Expr` constructor:\n", - "\n", - "| Operation | Propositional Symbol | Operator to use in Code |\n", - "|--------------------------|----------------------|-------------------------|\n", - "| Negation | ¬ | ~ |\n", - "| And | ∧ | & |\n", - "| Or | ∨ | | |\n", - "| Implies | → | >> or ==> |\n", - "| Biconditional | ↔ | % or <=> |\n", - "| **Some additional ones** | | |\n", - "| Inequality (Xor) | (Dunno) | =/= or ^ |\n", - "| Reverse Implication | ← | << or <== |\n", - "\n", - "Also, this is the precedence sequence with which the operators will be evaluated in code. The highest precedence operators are at the top:\n", - "\n", - " ~\n", - " % <=>\n", - " << <== >> ==>\n", - " &\n", - " ^\n", - " |\n", - " \n", - "Note that the `<=>` and the implication operators are quite at the top. So make sure to use parenthesis correctly when using them with others like `&`, `^` and `|`. You might note that the precedence of these operators is the same as that in Python language. This is not just a coincidence. More about this later.\n", - "\n", - "Getting back to the `Expr` class and the expressions that we have created, lets create a more complex expression from the ones we have already created:" + "We can combine `Expr`s with the regular Python infix and prefix operators. Here's how we would form the sentence for \"P and not Q\":" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(P & ~Q)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Cell: Creating complicated sentences\n", - "R3 = Expr(\"<=>\", R1, Q)\n", - "R4 = Expr(\"==>\", R2, P & ~Q)" + "P & ~Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So, these are the expressions that we've created now. To display these expressions in a nice, intuitive form, the `__repr__` method has been implemented accordingly. It called when we put the variable in the interpreter or when we use the `print` function. Let's try both:" + "This works because the `Expr` class overloads the `&` operator with this definition:\n", + "\n", + "```python\n", + "def __and__(self, other): return Expr('&', self, other)```\n", + " \n", + "and does similar overloads for the other operators. An `Expr` has two fields: `op` for the operator, which is always a string, and `args` for the arguments, which is a tuple of 0 or more expressions. Let's take a look at the fields for some `Expr` examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'&'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sentence = P & Q\n", + "\n", + "sentence.op" ] }, { @@ -157,7 +162,7 @@ { "data": { "text/plain": [ - "P" + "(P, Q)" ] }, "execution_count": 6, @@ -166,7 +171,7 @@ } ], "source": [ - "P" + "sentence.args" ] }, { @@ -179,7 +184,7 @@ { "data": { "text/plain": [ - "(A & B)" + "'P'" ] }, "execution_count": 7, @@ -188,7 +193,29 @@ } ], "source": [ - "R1" + "P.op" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P.args" ] }, { @@ -201,7 +228,7 @@ { "data": { "text/plain": [ - "(((C | D) ==> E) ==> (P & ~Q))" + "'P'" ] }, "execution_count": 9, @@ -210,7 +237,9 @@ } ], "source": [ - "R4" + "Pxy = P(x, y)\n", + "\n", + "Pxy.op" ] }, { @@ -221,28 +250,25 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "P\n", - "(A & B)\n", - "(((C | D) ==> E) ==> (P & ~Q))\n" - ] + "data": { + "text/plain": [ + "(x, y)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(P)\n", - "print(R1)\n", - "print(R4)" + "Pxy.args" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So, that's how it works. Now scroll above a little and have a look at the cell titled \"Creating complicated sentences\". Do you notice something amiss? Now note that, the third argument in the 2nd line is `P & ~Q`. Now, how is that done? It's a statement, for sure, but it's not in the form of a string. As a matter of fact, `P` and `Q` are both `Expr`s themselves.\n", - "\n", - "This is made possible because the `Expr` class overloads many operators. (It actually overloads mostly all the operators available in Python, but don't use them all; not, at least, for Propositional Logic.) Hence, you can do things like `P & ~Q` to *create `Expr`s by directly combining existing `Exprs`*. You might not immediately recognize the power and ease that this grants you, but I'll explain. Once, you have created some small, rudimentary expressions, you can use these overloaded operators to directly get your desired expressions; no need of using the `Expr` constructor each time. See how simple doing all that we did above becomes:" + "It is important to note that the `Expr` class does not define the *logic* of Propositional Logic; it just gives you a way to *represent* expressions. Think of an `Expr` as an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree). Each of the `args` in an `Expr` can be either a symbol, a number, or a nested `Expr`. We can nest these trees to any depth. An `Expr` can represent any kind of mathematical expression, not just logical sentences. For example:" ] }, { @@ -253,76 +279,355 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "P\n", - "(A & B)\n", - "(((C | D) >> E) >> (P & ~Q))\n" - ] + "data": { + "text/plain": [ + "(((3 * f(x, y)) + (P(y) / 2)) + 1)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Some simple, rudimentary sentences\n", - "P = Expr(\"P\")\n", - "Q = Expr(\"Q\")\n", - "A = Expr(\"A\")\n", - "B = Expr(\"B\")\n", - "C = Expr(\"C\")\n", - "D = Expr(\"D\")\n", - "E = Expr(\"E\")\n", + "3 * f(x, y) + P(y) / 2 + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operators for Constructing Logical Sentences\n", "\n", - "# Now for our complex expressions\n", - "R1 = A & B\n", - "R2 = (C | D) >> E\n", + "Here is a table of the operators that can be used to form sentences. Note that we have a problem: we want to use Python operators to make sentences, so that our programs (and our interactive sessions like the one here) will show simple code. But Python does not allow implication arrows as operators, so for now we will create them using functions (but Python will display them using arrows). Alternately, you can always use the more verbose `Expr` constructor forms:\n", "\n", - "# And the more complex expressions\n", - "R3 = R1 % Q\n", - "R4 = R2 >> (P & ~Q)\n", + "| Operation | Book | Python Input | Python Output | `Expr` Input\n", + "|--------------------------|----------------------|-------------------------|---|---|\n", + "| Negation | ¬ P | `~P` | `~P` | `Expr('~', P)`\n", + "| And | P ∧ Q | `P & Q` | `P & Q` | `Expr('&', P, Q)`\n", + "| Or | P ∨ Q | `P` | `Q`| `P` | `Q` | `Expr('`|`', P, Q)\n", + "| Inequality (Xor) | P ≠ Q | `P ^ Q` | `P ^ Q` | `Expr('^', P, Q)`\n", + "| Implication | P → Q | `implies(P, Q)` | `P ==> Q` | `Expr('==>', P, Q)`\n", + "| Reverse Implication | Q ← P | `rimplies(P, Q)` |`Q <== P` | `Expr('<==', Q, P)`\n", + "| Equivalence | P ↔ Q | `equiv(P, Q)` |`P ==> Q` | `Expr('==>', P, Q)`\n", "\n", - "# Let's print them and see if they are the same as before\n", - "print(P)\n", - "print(R1)\n", - "print(R4)" + "Here's an example of defining a sentence:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(~(P & Q) <=> (~P | ~Q))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "equiv(~(P & Q), (~P | ~Q))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Yes, yes they are. We cannot use `==>` and `<=>` when we use operator overloading, because those are not operators in Python. Instead, we have to use `>>` and `%` operators in their place. (Actually, `==>` and `<=>` are converted to `>>` and `%` internally.) Did you just cringe at using `%` for biconditionals? I am not too happy with that either. Ugly, I know, but for many reasons we *had to* implement it that way. But hey, it works like a charm.\n", + "## `expr`: a Shortcut for Constructing Sentences\n", "\n", - "Before we move on, I would like to point out something that might cause you some confusion. The `==` and `!=` operators for `Expr`s. They do not logically evaluate two expressions and then check if they are equal. So don't do something like\n", + "We can't write `(~(P & Q) <=> (~P | ~Q))` as a Python expression, because Python does not have the `<=>` operator. But we can do something almost as good:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(~(P & Q) <=> (~P | ~Q))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expr('~(P & Q) <=> (~P | ~Q)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `expr` takes a string as input, and parses it into an `Expr`. The string can contain arrow operators: `==>`, `<==`, or `<=>`. And `expr` automatically defines any symbols, so you don't need to pre-define them:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sqrt(((b ** 2) - ((4 * a) * c)))" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expr('sqrt(b ** 2 - 4 * a * c)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For now that's all you need to know about `expr`. Later we will explain the messy details of how it is implemented." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Propositional Knowledge Bases: `PropKB`\n", "\n", - "```python\n", - "A & (B | C) == (A & B) | (A & C)\n", - "```\n", + "The class `PropKB` can be used to represent a knowledge base of propositional logic sentences.\n", "\n", - "or even\n", + "We see that the class `KB` has four methods, apart from `__init__`. A point to note here: the `ask` method simply calls the `ask_generator` method. Thus, this one has already been implemented and what you'll have to actually implement when you create your own knowledge base class (if you want to, though I doubt you'll ever need to; just use the ones we've created for you), will be the `ask_generator` function and not the `ask` function itself.\n", + "\n", + "The class `PropKB` now.\n", + "* `__init__(self, sentence=None)` : The constructor `__init__` creates a single field `clauses` which will be a list of all the sentences of the knowledge base. Note that each one of these sentences will be a 'clause' i.e. a sentence which is made up of only literals and `or`s.\n", + "* `tell(self, sentence)` : When you want to add a sentence to the KB, you use the `tell` method. This method takes a sentence, converts it to its CNF, extracts all the clauses, and adds all these clauses to the `clauses` field. So, you need not worry about `tell`ing only clauses to the knowledge base. You can `tell` the knowledge base a sentence in any form that you wish; converting it to CNF and adding the resulting clauses will be handled by the `tell` method.\n", + "* `ask_generator(self, query)` : The `ask_generator` function is used by the `ask` function. It calls the `tt_entails` function, which in turn returns `True` if the knowledge base entails query and `False` otherwise. The `ask_generator` itself returns an empty dict `{}` if the knowledge base entails query and `None` otherwise. This might seem a little bit weird to you. After all, it makes more sense just to return a `True` or a `False` instead of the `{}` or `None` But this is done to maintain consistency with the way things are in First-Order Logic, where, an `ask_generator` function, is supposed to return all the substitutions that make the query true. Hence the dict, to return all these substitutions. I will be mostly be using the `ask` function which returns a `{}` or a `False`, but if you don't like this, you can always use the `ask_if_true` function which returns a `True` or a `False`.\n", + "* `retract(self, sentence)` : This function removes all the clauses of the sentence given, from the knowledge base. Like the `tell` function, you don't have to pass clauses to remove them from the knowledge base; any sentence will do fine. The function will take care of converting that sentence to clauses and then remove those." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TODO: More on KBs, plus what was promised in Intro Section" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: The Messy Details of the Implementation of `expr`\n", + "\n", + "How does `expr` parse a string into an `Expr`? It turns out there three tricks:\n", + "\n", + "1. We do a string substitution, replacing `\"==>\"` with `\"|InfixOp('==>', None)|\"`.\n", + "2. We `eval` the resulting string in an environment in which every identifier\n", + "is bound to a symbol with that identifier as the name.\n", + "3. A coordination between `Expr` and `InfixOp` creates the proper nested `Expr`.\n", "\n", - "```python\n", - "A & B == B & A\n", - "```\n", "\n", - "and expect it to return `True`. That's not how the `==` operator is intended to work. If you to know what it is supposed to do, have a look at the implementation of `__eq__`; that should tell you enough." + "That must sound very confusing, so we'll explain it in detail. Consider the sentence `\"P ==> Q\"`. If we try to evaluate that we get a `SyntaxError` because `==>` is not valid Python syntax. So we substitute it away, using the function `expr_handle_infix_ops` (from the `utils` module):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"P |InfixOp('==>', None)| Q\"" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expr_handle_infix_ops('P ==> Q')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### The `expr` function\n", - "\n" + "What does that mean? To Python, for any expression `op`, \"`P |op| Q`\" is the same as \"`((P | op) | Q)`\". So the first step is:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "InfixOp('==>', P)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first = (P | InfixOp('==>', None))\n", + "\n", + "first" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`InfixOp('==>', P)` means an infix operator whose operator string is `'==>'` and whose left-hand element is `P`. What happened here is that the `__or__` method in `Expr` says that if the object on the right is an `InfixOp`, then the result is an `InfixOp` whose `lhs` is the `Expr` on the left of the `\"|\"`.\n", + "\n", + "In the second step, we combine this with `Q`:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(P ==> Q)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first | Q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What happened here is that the `__or__` method for `InfixOp` says that when combined with anobject on the right, return a new `Expr` whose `op` and first `arg` comes from the `InfixOp` and whose second `arg` is the object on the right. This [trick](http://code.activestate.com/recipes/384122-infix-operators/) is due to [Ferdinand Jamitzky](http://code.activestate.com/recipes/users/98863/).\n", + "\n", + "Note that we can also use this notation in our own code, or in an interactive session, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((P & Q) ==> P)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P & Q |implies| P" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, this puts `implies` at the same precedence as `\"|\"`, which is not quite right. We get this:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(((P & Q) ==> P) | Q)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P & Q |implies| P | Q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which is probably not what we meant; when in doubt, put in extra parens:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((P & Q) ==> (P | Q))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P & Q |implies| (P | Q)" + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], - "source": [] + "source": [ + "# Authors\n", + "\n", + "This notebook by [Chirag Vertak](https://github.com/chiragvartak) and [Peter Norvig](https://github.com/norvig).\n", + "\n" + ] } ], "metadata": { @@ -341,7 +646,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.4" + "version": "3.5.1" } }, "nbformat": 4, From 728e1f97dda5d5a26fde6f7acb8b78b107eab2ff Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Sat, 9 Apr 2016 15:55:29 +0530 Subject: [PATCH 234/513] Fix implies operator in SAT_plan (#203) * temporary fix for eliminate_implications * Change from << to <== * Fixed implies operator in SAT_plan * changed Expr name to counter --- logic.py | 36 +++++++++++++++++------------------- tests/test_logic.py | 2 +- 2 files changed, 18 insertions(+), 20 deletions(-) diff --git a/logic.py b/logic.py index ef6f74237..5f208ed92 100644 --- a/logic.py +++ b/logic.py @@ -142,7 +142,7 @@ def is_var_symbol(s): def is_prop_symbol(s): """A proposition logic symbol is an initial-uppercase string other than -` TRUE or FALSE.""" + TRUE or FALSE.""" return is_symbol(s) and s[0].isupper() and s != 'TRUE' and s != 'FALSE' @@ -333,7 +333,8 @@ def move_not_inwards(s): (~A & ~B)""" s = expr(s) if s.op == '~': - def NOT(b): return move_not_inwards(~b) # noqa + def NOT(b): + return move_not_inwards(~b) a = s.args[0] if a.op == '~': return move_not_inwards(a.args[0]) # ~~A ==> A @@ -679,20 +680,18 @@ def plan_route(current, goals, allowed): def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): """Converts a planning problem to Satisfaction problem by translating it to a cnf sentence. - TODO : Currently it fails if '_' character is used in transition. Change the expression names [Fig. 7.22]""" #Functions used by SAT_plan def translate_to_SAT(init, transition, goal, time): - action_sym = "State_{0}_{1}" clauses = [] states = [state for state in transition] #Symbol claiming state s at time t - state_sym = {} + state_counter = itertools.count() for s in states: for t in range(time+1): - state_sym[(s, t)] = Expr("In_{0}_at_{1}".format(s, t)) + state_sym[(s, t)] = Expr("State_{}".format(next(state_counter))) #Add initial state axiom clauses.append(state_sym[init, 0]) @@ -701,17 +700,17 @@ def translate_to_SAT(init, transition, goal, time): clauses.append(state_sym[goal, time]) #All possible transitions - action_sym = {} + transition_counter = itertools.count() for s in states: for action in transition[s]: s_ = transition[s][action] for t in range(time): #Action 'action' taken from state 's' at time 't' to reach 's_' - action_sym[(s, action, t)] = Expr("Act_{0}_{1}_{2}".format(s, action, t)) + action_sym[(s, action, t)] = Expr("Transition_{}".format(next(transition_counter))) # Change the state from s to s_ - clauses.append(action_sym[s, action, t] >> state_sym[s, t]) - clauses.append(action_sym[s, action, t] >> state_sym[s_, t + 1]) + clauses.append(action_sym[s, action, t] |implies| state_sym[s, t]) + clauses.append(action_sym[s, action, t] |implies| state_sym[s_, t + 1]) #Allow only one state at any time for t in range(time+1): @@ -740,18 +739,17 @@ def translate_to_SAT(init, transition, goal, time): return associate('&', clauses) def extract_solution(model): - true_syms = [ sym.__repr__() for sym in model if model[sym] ] - true_transitions = [ sym for sym in true_syms if sym.startswith('Act_')] - solution = [] - for sym in true_transitions: - # Extract time and action from 'Act_state_action_time' - solution.append((int(sym.split('_')[-1]), sym.split('_')[2])) - #Sort the actions based on time - solution.sort() - return [ action for time,action in solution ] + true_transitions = [ t for t in action_sym if model[action_sym[t]]] + #Sort transitions based on time which is the 3rd element of the tuple + true_transitions.sort(key = lambda x: x[2]) + return [ action for s, action, time in true_transitions ] #Body of SAT_plan algorithm for t in range(t_max): + #dcitionaries to help extract the solution from model + state_sym = {} + action_sym = {} + cnf = translate_to_SAT(init, transition, goal, t) model = SAT_solver(cnf) if model is not False: diff --git a/tests/test_logic.py b/tests/test_logic.py index 2b0add9f7..d15795024 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -203,7 +203,7 @@ def test_SAT_plan(): (0, 1):{'Left': (1, 0), 'Down': (1, 1)}, (1, 0):{'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, (1, 1):{'Left': (1, 0), 'Up': (0, 1)}} - assert SAT_plan((0, 0), transition, (1, 1), 2000) == ['Right', 'Down'] + assert SAT_plan((0, 0), transition, (1, 1), 4) == ['Right', 'Down'] if __name__ == '__main__': From bf6e2beb68c636e1d900f157b8d938e7ffdd9342 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sat, 9 Apr 2016 15:59:52 +0530 Subject: [PATCH 235/513] Added Explicit Imports from utils (#202) * Made Log Usage Consistent in text.py * Added Explicit Imports from utils --- agents.py | 3 ++- csp.py | 3 ++- games.py | 2 +- learning.py | 6 +++++- logic.py | 6 +++++- mdp.py | 5 ++++- nlp.py | 2 -- planning.py | 1 - probability.py | 6 +++++- rl.py | 3 ++- search.py | 7 ++++++- tests/test_logic.py | 1 + tests/test_probability.py | 1 + tests/test_text.py | 4 +++- text.py | 7 ++++--- 15 files changed, 41 insertions(+), 16 deletions(-) diff --git a/agents.py b/agents.py index df853103b..6573dd9c7 100644 --- a/agents.py +++ b/agents.py @@ -35,7 +35,8 @@ # # Speed control in GUI does not have any effect -- fix it. -from utils import * # noqa +from utils import mean +from grid import distance2 import random import copy diff --git a/csp.py b/csp.py index 54b09a2f9..af99938e4 100644 --- a/csp.py +++ b/csp.py @@ -1,6 +1,6 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from utils import * # noqa +from utils import count, first, every, argmin_random_tie import search from collections import defaultdict @@ -8,6 +8,7 @@ import itertools import re +import random class CSP(search.Problem): diff --git a/games.py b/games.py index 4f5c6418a..8fc9e7457 100644 --- a/games.py +++ b/games.py @@ -3,7 +3,7 @@ import collections import random -from utils import * # noqa +from utils import argmax infinity = float('inf') GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') diff --git a/learning.py b/learning.py index eb319a524..8a9495994 100644 --- a/learning.py +++ b/learning.py @@ -1,6 +1,10 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" -from utils import * # noqa +from utils import ( + removeall, unique, product, argmax, argmax_random_tie, mean, + dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, + weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile, Fig +) import copy import heapq diff --git a/logic.py b/logic.py index 5f208ed92..ac7dbe1c5 100644 --- a/logic.py +++ b/logic.py @@ -31,11 +31,15 @@ diff, simp Symbolic differentiation and simplification """ -from utils import * # noqa +from utils import ( + removeall, unique, first, every, argmax, probability, num_or_str, + isnumber, issequence, Symbol, Expr, expr, subexpressions, Fig +) import agents import itertools import re +import random from collections import defaultdict # ______________________________________________________________________________ diff --git a/mdp.py b/mdp.py index 4d5ebe869..fefd8658a 100644 --- a/mdp.py +++ b/mdp.py @@ -6,7 +6,10 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" -from utils import * # noqa +from utils import argmax, vector_add, print_table, Fig +from grid import orientations, turn_right, turn_left + +import random class MDP: diff --git a/nlp.py b/nlp.py index b303e7ee4..02423e7dc 100644 --- a/nlp.py +++ b/nlp.py @@ -3,8 +3,6 @@ # (Written for the second edition of AIMA; expect some discrepanciecs # from the third edition until this gets reviewed.) -from utils import * # noqa - from collections import defaultdict # ______________________________________________________________________________ diff --git a/planning.py b/planning.py index c939b9808..52e4c0b36 100644 --- a/planning.py +++ b/planning.py @@ -3,7 +3,6 @@ # flake8: noqa -from utils import * import agents import math diff --git a/probability.py b/probability.py index 903ea7ee9..16f05197f 100644 --- a/probability.py +++ b/probability.py @@ -1,7 +1,11 @@ """Probability models. (Chapter 13-15) """ -from utils import * # noqa +from utils import ( + product, every, argmax, element_wise_product, matrix_multiplication, + vector_to_diagonal, vector_add, scalar_vector_product, inverse_matrix, + weighted_sample_with_replacement, rounder, isclose, probability, normalize +) from logic import extend import random diff --git a/rl.py b/rl.py index 44673b528..0cebd8f7d 100644 --- a/rl.py +++ b/rl.py @@ -1,9 +1,10 @@ """Reinforcement Learning (Chapter 21) """ -from utils import * # noqa import agents +import random + class PassiveADPAgent(agents.Agent): diff --git a/search.py b/search.py index 847e49ef8..7b5e0245d 100644 --- a/search.py +++ b/search.py @@ -4,7 +4,12 @@ then create problem instances and solve them with calls to the various search functions.""" -from utils import * # noqa +from utils import ( + is_in, argmin, argmax, argmax_random_tie, probability, + weighted_sample_with_replacement, memoize, print_table, DataFile, Stack, + FIFOQueue, PriorityQueue +) +from grid import distance import math import random diff --git a/tests/test_logic.py b/tests/test_logic.py index d15795024..62e3a23a2 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -1,5 +1,6 @@ import pytest from logic import * +from utils import InfixOp, expr_handle_infix_ops, Fig, count, implies, equiv def test_expr(): diff --git a/tests/test_probability.py b/tests/test_probability.py index c34fde77e..1183279cf 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -1,4 +1,5 @@ import pytest +import random from probability import * # noqa diff --git a/tests/test_text.py b/tests/test_text.py index b8bae0a1f..66dbd9703 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -1,7 +1,9 @@ import pytest import os +import random + from text import * # noqa -from utils import isclose +from utils import isclose, DataFile def test_unigram_text_model(): diff --git a/text.py b/text.py index ae38d7719..6763031b4 100644 --- a/text.py +++ b/text.py @@ -4,7 +4,7 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -from utils import * # noqa +from utils import argmin from learning import CountingProbDist import search @@ -12,6 +12,7 @@ from collections import defaultdict import heapq import re +import os class UnigramTextModel(CountingProbDist): @@ -154,8 +155,8 @@ def query(self, query_text, n=10): def score(self, word, docid): "Compute a score for this word on the document with this docid." # There are many options; here we take a very simple approach - return (math.log(1 + self.index[word][docid]) / - math.log(1 + self.documents[docid].nwords)) + return (log(1 + self.index[word][docid]) / + log(1 + self.documents[docid].nwords)) def total_score(self, words, docid): "Compute the sum of the scores of these words on the document with this docid." From bee6cdedb77ff0697ad5f14efbdb5a6560e1de80 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sat, 9 Apr 2016 03:35:53 -0700 Subject: [PATCH 236/513] Update logic.py from utils import implies --- logic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logic.py b/logic.py index ac7dbe1c5..62f4605ee 100644 --- a/logic.py +++ b/logic.py @@ -33,7 +33,7 @@ from utils import ( removeall, unique, first, every, argmax, probability, num_or_str, - isnumber, issequence, Symbol, Expr, expr, subexpressions, Fig + isnumber, issequence, Symbol, Expr, expr, subexpressions, implies, Fig ) import agents From 8db38020164207e09a0e7efcad0f460931d63c58 Mon Sep 17 00:00:00 2001 From: norvig Date: Sat, 9 Apr 2016 16:06:18 -0700 Subject: [PATCH 237/513] clean up Expr and InfixOp --- utils.py | 87 +++++++++++++++++++++++++------------------------------- 1 file changed, 39 insertions(+), 48 deletions(-) diff --git a/utils.py b/utils.py index 9b7317847..7a156a945 100644 --- a/utils.py +++ b/utils.py @@ -357,49 +357,45 @@ def __init__(self, op, *args): self.args = args # Operator overloads - def __neg__(self): return Expr('-', self) - def __pos__(self): return Expr('+', self) - def __invert__(self): return Expr('~', self) - def __add__(self, other): return Expr('+', self, other) - def __sub__(self, other): return Expr('-', self, other) - def __mul__(self, other): return Expr('*', self, other) - def __pow__(self, other): return Expr('**', self, other) - def __mod__(self, other): return Expr('%', self, other) - def __and__(self, other): return Expr('&', self, other) - def __xor__(self, other): return Expr('^', self, other) - def __rshift__(self, other): return Expr('>>', self, other) - def __lshift__(self, other): return Expr('<<', self, other) - def __truediv__(self, other): return Expr('/', self, other) - def __floordiv__(self, other): return Expr('//', self, other) - def __matmul__(self, other): return Expr('@', self, other) + def __neg__(self): return Expr('-', self) + def __pos__(self): return Expr('+', self) + def __invert__(self): return Expr('~', self) + def __add__(self, rhs): return Expr('+', self, rhs) + def __sub__(self, rhs): return Expr('-', self, rhs) + def __mul__(self, rhs): return Expr('*', self, rhs) + def __pow__(self, rhs): return Expr('**',self, rhs) + def __mod__(self, rhs): return Expr('%', self, rhs) + def __and__(self, rhs): return Expr('&', self, rhs) + def __xor__(self, rhs): return Expr('^', self, rhs) + def __rshift__(self, rhs): return Expr('>>', self, rhs) + def __lshift__(self, rhs): return Expr('<<', self, rhs) + def __truediv__(self, rhs): return Expr('/', self, rhs) + def __floordiv__(self, rhs): return Expr('//', self, rhs) + def __matmul__(self, rhs): return Expr('@', self, rhs) + def __or__(self, rhs): + if isinstance(rhs, Expression) : + return Expr('|', self, rhs) + else return NotImplemented # So that InfixOp can handle it # Reverse operator overloads - def __radd__(self, other): return Expr('+', other, self) - def __rsub__(self, other): return Expr('-', other, self) - def __rmul__(self, other): return Expr('*', other, self) - def __rdiv__(self, other): return Expr('/', other, self) - def __rpow__(self, other): return Expr('**', other, self) - def __rmod__(self, other): return Expr('%', other, self) - def __rand__(self, other): return Expr('&', other, self) - def __rxor__(self, other): return Expr('^', other, self) - def __ror__(self, other): return Expr('|', other, self) - def __rrshift__(self, other): return Expr('>>', other, self) - def __rlshift__(self, other): return Expr('<<', other, self) - def __rtruediv__(self, other): return Expr('/', other, self) - def __rfloordiv__(self, other): return Expr('//', other, self) - def __rmatmul__(self, other): return Expr('@', other, self) + def __radd__(self, lhs): return Expr('+', lhs, self) + def __rsub__(self, lhs): return Expr('-', lhs, self) + def __rmul__(self, lhs): return Expr('*', lhs, self) + def __rdiv__(self, lhs): return Expr('/', lhs, self) + def __rpow__(self, lhs): return Expr('**', lhs, self) + def __rmod__(self, lhs): return Expr('%', lhs, self) + def __rand__(self, lhs): return Expr('&', lhs, self) + def __rxor__(self, lhs): return Expr('^', lhs, self) + def __ror__(self, lhs): return Expr('|', lhs, self) + def __rrshift__(self, lhs): return Expr('>>', lhs, self) + def __rlshift__(self, lhs): return Expr('<<', lhs, self) + def __rtruediv__(self, lhs): return Expr('/', lhs, self) + def __rfloordiv__(self, lhs): return Expr('//', lhs, self) + def __rmatmul__(self, lhs): return Expr('@', lhs, self) def __call__(self, *args): "Call: if 'f' is a Symbol, then f(0) == Expr('f', 0)." return Expr(self.op, *args) - - # Allow infix operators - def __or__(self, other): - "Allow 'P |implies| Q', where P, Q are Exprs and implies is an InfixOp." - if isinstance(other, InfixOp): - return InfixOp(other.op, lhs=self) - else: # Allow 'P | Q' also - return Expr('|', self, other) # Equality and repr def __eq__(self, other): @@ -452,17 +448,12 @@ def arity(expression): # For operators that are not defined in Python, we allow new InfixOps: class InfixOp: - """Allow 'P |implies| Q, where P, Q are Exprs and implies is an InfixOp, - defined with implies = InfixOp('==>').""" - def __init__(self, op, lhs=None): - self.op = op - self.lhs = lhs - def __or__(self, other): - return Expr(self.op, self.lhs, other) - def __call__(self, lhs, rhs): - return Expr(self.op, lhs, rhs) - def __repr__(self): - return "InfixOp('{}', {})".format(self.op, self.lhs) + """Allow 'P |implies| Q, where P, Q are Exprs and implies is an InfixOp.""" + def __init__(self, op, lhs=None): self.op, self.lhs = op, lhs + def __call__(self, lhs, rhs): return Expr(self.op, lhs, rhs) + def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) + def __ror__(self, lhs): return InfixOp(self.op, lhs) + def __repr__(self): return "InfixOp('{}', {})".format(self.op, self.lhs) infix_ops = (implies, rimplies, equiv) = [InfixOp(o) for o in ['==>', '<==', '<=>']] From 240ab1aa9aeb5cbab47238c447f54b998d140783 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sat, 9 Apr 2016 16:11:31 -0700 Subject: [PATCH 238/513] cleanup Expr (#207) --- utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 7a156a945..d914c869a 100644 --- a/utils.py +++ b/utils.py @@ -372,10 +372,12 @@ def __lshift__(self, rhs): return Expr('<<', self, rhs) def __truediv__(self, rhs): return Expr('/', self, rhs) def __floordiv__(self, rhs): return Expr('//', self, rhs) def __matmul__(self, rhs): return Expr('@', self, rhs) + def __or__(self, rhs): if isinstance(rhs, Expression) : return Expr('|', self, rhs) - else return NotImplemented # So that InfixOp can handle it + else: + return NotImplemented # So that InfixOp can handle it # Reverse operator overloads def __radd__(self, lhs): return Expr('+', lhs, self) From df97b76d4a7368e5803c9549b29cdad69228c1b3 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sun, 10 Apr 2016 04:42:08 +0530 Subject: [PATCH 239/513] Minor cleanup (#206) * Used isclose from utils to maintain compatibilty for 3.4 * Removed redundant distancesquared function * Removed duplicate definition of clip in grid.py and used import from instead. * Fixed spelling typo --- grid.py | 13 ++----------- learning.py | 4 ++-- rl.py | 6 +++--- tests/test_grid.py | 11 ----------- utils.py | 3 +-- 5 files changed, 8 insertions(+), 29 deletions(-) diff --git a/grid.py b/grid.py index cac6a5b9e..0fb0efe9d 100644 --- a/grid.py +++ b/grid.py @@ -4,6 +4,7 @@ # __________________________________________________________________________ import math +from utils import clip orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)] @@ -25,19 +26,9 @@ def distance(a, b): return math.hypot((a[0] - b[0]), (a[1] - b[1])) -def distance_squared(a, b): - """The square of the distance between two (x, y) points.""" - return (a[0] - b[0])**2 + (a[1] - b[1])**2 - - def distance2(a, b): "The square of the distance between two (x, y) points." - return distance_squared(a, b) - - -def clip(x, lowest, highest): - """Return x clipped to the range [lowest..highest].""" - return max(lowest, min(x, highest)) + return (a[0] - b[0])**2 + (a[1] - b[1])**2 def vector_clip(vector, lowest, highest): diff --git a/learning.py b/learning.py index 8a9495994..3dda34c81 100644 --- a/learning.py +++ b/learning.py @@ -1,7 +1,7 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" from utils import ( - removeall, unique, product, argmax, argmax_random_tie, mean, + removeall, unique, product, argmax, argmax_random_tie, mean, isclose, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile, Fig ) @@ -826,7 +826,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): while True: errT, errV = cross_validation(learner, size, dataset, k) # Check for convergence provided err_val is not empty - if (err_val and math.isclose(err_val[-1], errV, rel_tol=1e-6)): + if (err_val and isclose(err_val[-1], errV, rel_tol=1e-6)): best_size = size return learner(dataset, best_size) diff --git a/rl.py b/rl.py index 0cebd8f7d..3cff46472 100644 --- a/rl.py +++ b/rl.py @@ -69,9 +69,9 @@ def take_single_action(mdp, s, a): ''' x = random.uniform(0, 1) cumulative_probability = 0.0 - for probabilty_state in mdp.T(s, a): - probabilty, state = probabilty_state - cumulative_probability += probabilty + for probability_state in mdp.T(s, a): + probability, state = probability_state + cumulative_probability += probability if x < cumulative_probability: break return state diff --git a/tests/test_grid.py b/tests/test_grid.py index d160ca6e9..b7da02121 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -10,17 +10,6 @@ def test_distance(): assert distance((1, 2), (5, 5)) == 5.0 -def test_distance_squared(): - assert distance_squared((1, 2), (5, 5)) == 25.0 - - -def test_clip(): - list_ = [clip(x, 0, 1) for x in [-1, 0.5, 10]] - res = [0, 0.5, 1] - - assert compare_list(list_, res) - - def test_vector_clip(): assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) diff --git a/utils.py b/utils.py index d914c869a..2a219b2fa 100644 --- a/utils.py +++ b/utils.py @@ -8,8 +8,7 @@ import os.path import random import re - -from grid import * # noqa +import math # ______________________________________________________________________________ # Functions on Sequences and Iterables From 64fa05b1868c79825ddee2d87cd19cb637609142 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sun, 10 Apr 2016 12:51:22 +0530 Subject: [PATCH 240/513] Removes obsolete Fig dictionary and modifies the codebase accordingly (#208) * removes obsolete Fig dictionary from utils.py * modified rl notebook according to the new Figure conventions * removes 'import Fig' and changed the names of Figures * fixes a small error (by me) in executing the cells in rl notebook --- learning.py | 10 +++++++--- logic.py | 21 +++++++++++++-------- mdp.py | 16 +++++++++------- rl.ipynb | 36 ++++++++++++++++++------------------ search.py | 11 ++++++----- tests/test_logic.py | 12 ++++++------ tests/test_mdp.py | 8 ++++---- utils.py | 38 ++++++++++++++++---------------------- 8 files changed, 79 insertions(+), 73 deletions(-) diff --git a/learning.py b/learning.py index 3dda34c81..071b721b7 100644 --- a/learning.py +++ b/learning.py @@ -3,7 +3,7 @@ from utils import ( removeall, unique, product, argmax, argmax_random_tie, mean, isclose, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, - weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile, Fig + weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile ) import copy @@ -886,7 +886,11 @@ def T(attrname, branches): for value, child in list(branches.items())) return DecisionFork(restaurant.attrnum(attrname), attrname, branches) -Fig[18, 2] = T('Patrons', +""" [Figure 18.2] +A decision tree for deciding whether to wait for a table at a hotel. +""" + +waiting_decision_tree = T('Patrons', {'None': 'No', 'Some': 'Yes', 'Full': T('WaitEstimate', {'>60': 'No', '0-10': 'Yes', @@ -910,7 +914,7 @@ def SyntheticRestaurant(n=20): "Generate a DataSet with n examples." def gen(): example = list(map(random.choice, restaurant.values)) - example[restaurant.target] = Fig[18, 2](example) + example[restaurant.target] = waiting_decision_tree(example) return example return RestaurantDataSet([gen() for i in range(n)]) diff --git a/logic.py b/logic.py index 62f4605ee..a7729d68d 100644 --- a/logic.py +++ b/logic.py @@ -33,7 +33,7 @@ from utils import ( removeall, unique, first, every, argmax, probability, num_or_str, - isnumber, issequence, Symbol, Expr, expr, subexpressions, implies, Fig + isnumber, issequence, Symbol, Expr, expr, subexpressions, implies ) import agents @@ -499,7 +499,7 @@ def clauses_with_premise(self, p): def pl_fc_entails(KB, q): """Use forward chaining to see if a PropDefiniteKB entails symbol q. [Fig. 7.15] - >>> pl_fc_entails(Fig[7,15], expr('Q')) + >>> pl_fc_entails(horn_clauses_KB, expr('Q')) True """ count = dict([(c, len(conjuncts(c.args[0]))) for c in KB.clauses @@ -518,13 +518,18 @@ def pl_fc_entails(KB, q): agenda.append(c.args[1]) return False -# Wumpus World example [Fig. 7.13] -Fig[7, 13] = expr("(B11 <=> (P12 | P21)) & ~B11") +""" [Figure 7.13] +Simple inference in a wumpus world example +""" +wumpus_world_inference = expr("(B11 <=> (P12 | P21)) & ~B11") + -# Propositional Logic Forward Chaining example [Fig. 7.16] -Fig[7, 15] = PropDefiniteKB() +""" [Figure 7.16] +Propositional Logic Forward Chaining example +""" +horn_clauses_KB = PropDefiniteKB() for s in "P==>Q; (L&M)==>P; (B&L)==>M; (A&P)==>L; (A&B)==>L; A;B".split(';'): - Fig[7, 15].tell(expr(s)) + horn_clauses_KB.tell(expr(s)) # ______________________________________________________________________________ # DPLL-Satisfiable [Fig. 7.17] @@ -690,7 +695,7 @@ def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): def translate_to_SAT(init, transition, goal, time): clauses = [] states = [state for state in transition] - + #Symbol claiming state s at time t state_counter = itertools.count() for s in states: diff --git a/mdp.py b/mdp.py index fefd8658a..d81f8d741 100644 --- a/mdp.py +++ b/mdp.py @@ -6,7 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" -from utils import argmax, vector_add, print_table, Fig +from utils import argmax, vector_add, print_table from grid import orientations, turn_right, turn_left import random @@ -97,8 +97,10 @@ def to_arrows(self, policy): dict([(s, chars[a]) for (s, a) in list(policy.items())])) # ______________________________________________________________________________ - -Fig[17, 1] = GridMDP([[-0.04, -0.04, -0.04, +1], +""" [Figure 17.1] +A 4x3 grid environment that presents the agent with a sequential decision problem. +""" +sequential_decision_environment = GridMDP([[-0.04, -0.04, -0.04, +1], [-0.04, None, -0.04, -1], [-0.04, -0.04, -0.04, -0.04]], terminals=[(3, 2), (3, 1)]) @@ -163,17 +165,17 @@ def policy_evaluation(pi, U, mdp, k=20): return U __doc__ += """ ->>> pi = best_policy(Fig[17,1], value_iteration(Fig[17,1], .01)) +>>> pi = best_policy(sequential_decision_environment, value_iteration(sequential_decision_environment, .01)) ->>> Fig[17,1].to_arrows(pi) +>>> sequential_decision_environment.to_arrows(pi) [['>', '>', '>', '.'], ['^', None, '^', '.'], ['^', '>', '^', '<']] ->>> print_table(Fig[17,1].to_arrows(pi)) +>>> print_table(sequential_decision_environment.to_arrows(pi)) > > > . ^ None ^ . ^ > ^ < ->>> print_table(Fig[17,1].to_arrows(policy_iteration(Fig[17,1]))) +>>> print_table(sequential_decision_environment.to_arrows(policy_iteration(sequential_decision_environment))) > > > . ^ None ^ . ^ > ^ < diff --git a/rl.ipynb b/rl.ipynb index cc0c0b59e..98b887f64 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -74,18 +74,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Agent Program can be obtained by creating the instance of the class by passing the appropriate parameters. Because of the __ call __ method the object that is created behaves like a callable and returns an appropriate action as most Agent Programs do. To instantiate the object we need a policy(pi) and a mdp whose utility of states will be estimated. Let us import a GridMDP object from the mdp module. **Fig[17, 1]** is similar to **Fig[21, 1]** but has some discounting as **gamma = 0.9**." + "The Agent Program can be obtained by creating the instance of the class by passing the appropriate parameters. Because of the __ call __ method the object that is created behaves like a callable and returns an appropriate action as most Agent Programs do. To instantiate the object we need a policy(pi) and a mdp whose utility of states will be estimated. Let us import a GridMDP object from the mdp module. **Figure 17.1 (sequential_decision_environment)** is similar to **Figure 21.1** but has some discounting as **gamma = 0.9**." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ - "from mdp import Fig" + "from mdp import sequential_decision_environment" ] }, { @@ -98,7 +98,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -107,14 +107,14 @@ } ], "source": [ - "Fig[17,1]" + "sequential_decision_environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Fig[17,1]** is a GridMDP object and is similar to the grid shown in **Fig 21.1**. The rewards in the terminal states are **+1** and **-1** and **-0.04** in rest of the states. Now we define a policy similar to **Fig 21.1** in the book." + "**Figure 17.1 (sequential_decision_environment)** is a GridMDP object and is similar to the grid shown in **Figure 21.1**. The rewards in the terminal states are **+1** and **-1** and **-0.04** in rest of the states. Now we define a policy similar to **Fig 21.1** in the book." ] }, { @@ -153,7 +153,7 @@ }, "outputs": [], "source": [ - "our_agent = PassiveTDAgent(policy, Fig[17,1], alpha=lambda n: 60./(59+n))" + "our_agent = PassiveTDAgent(policy, sequential_decision_environment, alpha=lambda n: 60./(59+n))" ] }, { @@ -197,7 +197,7 @@ } ], "source": [ - "print(value_iteration(Fig[17,1]))" + "print(value_iteration(sequential_decision_environment))" ] }, { @@ -218,13 +218,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "{(0, 1): 0.43655093803808254, (1, 2): 0.7111433090760988, (3, 2): 1, (0, 0): 0.3220542204171776, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.20098994088292488, (3, 1): 0.0, (2, 2): 0.8560074788087413, (2, 1): 0.6639270026362584, (0, 2): 0.5629080090683166}\n" + "{(0, 1): 0.40645681855595944, (1, 2): 0.7159329142704773, (3, 2): 1, (0, 0): 0.2886341019228155, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.20553303981983, (3, 1): -1, (2, 2): 0.8560486321875528, (2, 1): 0.606857283945162, (0, 2): 0.5612793239398001}\n" ] } ], "source": [ "for i in range(200):\n", - " run_single_trial(our_agent,Fig[17,1])\n", + " run_single_trial(our_agent,sequential_decision_environment)\n", "print(our_agent.U)" ] }, @@ -277,9 +277,9 @@ "outputs": [ { "data": { - "image/png": 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cL2v7dnOjZCoOmbiUILk4lJZaw7Jpk/2hznJINHfSmjXRVkWucG6lDRv8dNn2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\n1anGhRD5JZ/i0ABcDbyMZSQ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DiO2kw/U51K4d34bh3DQnN9f+f/x4O2egvHDYty/2HfJnn8VevvI4O8h0Cgfn\ngofpeoRYGXJybP1yVFfVkba5mJlpISDibVaqX99uvxePOnXsjEqn1uHcSKi80Urx1Bz80KyZBVcs\nJ+NVVU44JOO8jnTTrJldMiMVtnGKTtqHA+CtOdSrZ5dEiIfTT+H8dt9lLtHNSn5o2tSG1KZDddoJ\nh40bK/d+AumoaVPgk0+SXQqKRVo3K0UKh4pwQsEZqeTczrOoqHSHsxMOx47Z2Zjxdkgn2rnnAn/8\nY7JLUTkYDkSRpcHxYXiZmcGOZ6dZKRE7Z2eUkvvI2xku674VoxMOU6bYLSRTpeZQs2bw/rTVHcOB\nKDLWHBDckSfiRJc6dbxDYEODAQiGw549FgypEg7phOFAFFla1xycHbZzIlQiRqvUru0dApud7d3x\nO+Hg3CGO4VD5nHCI9Q58ROmA4YDEjswJFw45OeHDoaTExtcfPmxDWRkOlcsJh9AbMRERwwGAXYHU\nfaP5ioi25lCjRrDm4FwvPhEd4hQ95z4e2dnxnfBIVJ2ldTiEu9F7RUXqcwjt7HaalQ4etJ/iYn/K\nQ5FlZVmTUmXe8Y6oqkjrcAjtJE6EWJqVnJpDUZF1hvMM3cqVlWWd0bxsBpEXwyHBYu2QPnjQwqG6\n33IwFTnhwJoDkReHsiZYu3Y2PNWtvNFKxcUMh2TIyrLOaIYDkRfDIcG6dLEft0jh4IxWAtLzZiLJ\n5pz8yGYlIi82K1WCcH0O7tFKAGsOyeCEQ6y3ZyVKB2kdDpU1Oqi8PgeA4ZAMTjjEentWonSQ1uFQ\nWTWHnj29Nzlx9zkADIdkcMLhhBOSWw6iVJS2fQ7um/z4rW/f8K/vrjmwz6HyseZAFJmvNQcR6S4i\nK0RktYgMjDBPnogsEJGlIlLgZ3ncKrPmEA5rDsnHmgNRZL7VHEQkA8CbAK4C8AOAb0RknKoud83T\nAMD/AOimqptEpNK+prfcAhw4UFmv5uVcCdYpA8Oh8jn3EWfNgcgrYjiIyJMhTymAHQD+T1XXR7Hs\nTgDWqGphYHmjAdwEYLlrnt4AxqjqJgBQ1Z3RF71izjqrsl4psho1gP377bakDIfKd+iQ/U5mDZIo\nVZXVrFQXQB3XT10AHQFMEpFeUSy7OYCNrsebAs+5tQbQSES+FJF5InJX1CWvBjIyLBwaNmSfQzLs\n3ZvsEhClrog1B1XND/e8iDQC8AWAUeUsO5rrnGYBuBDAlQByAcwSkdmqujp0xvz8YHHy8vKQl5cX\nxeJTW0ZRPVIzAAAPPklEQVSGHb02aMCaQzIwkKm6KSgoQEFBQUKWJRrHtapFZIGqdihnns4A8lW1\ne+DxswBKVPVV1zwDAdRygkhE3gMwSVX/EbIsjaecqa52beuQ7tIF6NoV+N3vkl2i9FJSAmzbxns5\nUPUlIlDVuC7pGfNoJRG5HMCecmcE5gFoLSItRSQbwO0AxoXM8ymALiKSISK5AC4C8F2sZaqqjh2z\n3zVrsuaQDDVqMBiIIimrQ3pJmKcbAtgCIMzI/dJU9aiI9AcwGUAGgPdVdbmI9AtMH6aqK0RkEoDF\nAEoAvKuqaRcOOTls4iCi1BKxWUlEWoY8pQB2qep+n8sUrizVslnJuX9Dz57AlVcCjzyS3PIQUfVS\nkWalsjqkC+MuEcWkVi3eppKIUktcHdKVrTrXHDIygFWr7ESs+vWTXSIiqk4qUnNgOCSRiJ2Adfhw\nsktCRNVRpY5WosTKyEh2CYiIvBgOScZwIKJUxHBIssy0vWg6EaUyhkOSseZARKmI4ZBkDAciSkUM\nhyRjOBBRKmI4JBnDgYhSEcMhyRgORJSKGA5JxnAgolTEcEgyhgMRpSKGQ5IxHIgoFTEckozhQESp\niOGQZAwHIkpFDIckYzgQUSpiOCQZr61ERKmI4ZBkrDkQUSpiOCQZw4GIUhHDIckYDkSUihgOScZw\nIKJUxHBIMoYDEaUihkOScbQSEaUi7pqSaNo04Mwzk10KIiIvUdVkl6FcIqJVoZxERKlERKCqEs//\nslmJiIg8GA5EROThaziISHcRWSEiq0VkYBnzdRSRoyLS08/yEBFRdHwLBxHJAPAmgO4A2gHoJSJt\nI8z3KoBJAOJqGyMiosTys+bQCcAaVS1U1WIAowHcFGa+3wD4B4AdPpaFiIhi4Gc4NAew0fV4U+C5\n40SkOSww3g48xSFJREQpwM/zHKLZ0b8B4BlVVRERlNGslJ+ff/zvvLw85OXlVbR8RETVSkFBAQoK\nChKyLN/OcxCRzgDyVbV74PGzAEpU9VXXPOsQDIQTAPwE4AFVHReyLJ7nQEQUo4qc5+BnOGQCWAng\nSgCbAcwF0EtVl0eY/0MA41X1n2GmMRyIiGJUkXDwrVlJVY+KSH8AkwFkAHhfVZeLSL/A9GF+vTYR\nEVUML59BRFRN8fIZRESUUAwHIiLyYDgQEZEHw4GIiDwYDkRE5MFwICIiD4YDERF5MByIiMiD4UBE\nRB4MByIi8mA4EBGRB8OBiIg8GA5EROTBcCAiIg+GAxEReTAciIjIg+FAREQeDAciIvJgOBARkQfD\ngYiIPBgORETkwXAgIiIPhgMREXkwHIiIyIPhQEREHgwHIiLyYDgQEZEHw4GIiDx8DwcR6S4iK0Rk\ntYgMDDO9j4gsEpHFIjJDRM73u0xERFQ2UVX/Fi6SAWAlgKsA/ADgGwC9VHW5a55fAPhOVX8Uke4A\n8lW1c8hy1M9yEhFVRyICVZV4/tfvmkMnAGtUtVBViwGMBnCTewZVnaWqPwYezgFwis9lIiKicvgd\nDs0BbHQ93hR4LpL7AUzwtURERFSuTJ+XH3VbkIhcDuA+AJf4VxwiIoqG3+HwA4AWrsctYLWHUgKd\n0O8C6K6qe8ItKD8///jfeXl5yMvLS2Q5iYiqvIKCAhQUFCRkWX53SGfCOqSvBLAZwFx4O6RPBfBv\nAHeq6uwIy2GHNBFRjCrSIe1rzUFVj4pIfwCTAWQAeF9Vl4tIv8D0YQBeBNAQwNsiAgDFqtrJz3IR\nEVHZfK05JAprDkREsUvloaxERFQFMRyIiMiD4UBERB4MByIi8vD7PAciorgERi9SlBI9aIfhQEQp\ni6MUo+NHkLJZiYiIPBgORETkwXAgIiIPhgMREXkwHIiI4vTss89iyJAhvr/O+PHjcccdd/j+Om4M\nByKiOOzYsQMjR47EQw89BACYPXs2rr76ajRu3BhNmjTBbbfdhq1bt0a9rF69eqF58+Zo0KABunTp\ngrlz5x6ffsMNN2DZsmVYsmSJL+8lHIYDEVEcRowYgeuuuw45OTkAgKKiIjz00EPYsGEDNmzYgLp1\n6+Lee++Naln79+/HRRddhPnz52PPnj24++67cd111+HAgQPH5+nVqxeGDx/uy3sJh1dlJaKUFLii\naLKLEdGVV16J+++/H7179w47ff78+cjLy8PevXvjWn79+vVRUFCADh06AABmzpyJO++8E+vWrfPM\nG2ld8aqsRESVbMmSJWjTpk3E6V9//TXOPffcuJa9cOFCHDlyBK1atTr+3Nlnn43CwkLs378/rmXG\nimdIE1GVlagTg+OpoBQVFaFu3bphpy1evBivvPIKxo0bF/Ny9+7di7vuugv5+fmllu/8XVRUhDp1\n6sRe4BgxHIioykpmq1PDhg2xb98+z/Nr1qxBjx49MHToUFxyySUxLfPgwYO44YYbcPHFF2PgwIGl\npjmv1aBBg/gLHQM2KxERxeH888/HypUrSz23YcMGXH311XjxxRfRp0+fmJZ3+PBh3HzzzTj11FMx\nbNgwz/Tly5ejZcuWlVJrABgORERx6dGjB7766qvjj3/44QdcccUV6N+/Px588EHP/CNGjMDpp58e\ndlnFxcW49dZbkZubixEjRoSd56uvvkKPHj0SUvZoMByIiOLQt29fTJgwAYcOHQIAvPfee1i/fv3x\nvoK6deuiXr16x+ffuHEjunTpEnZZM2fOxOeff46pU6eiQYMGx/9/xowZx+cZPXo0+vXr5++bcuFQ\nViJKSak+lBUA/uM//gNNmjTB448/Xu683bp1w9ChQ8sc4RTJ+PHj8dFHH2H06NFhp/sxlJXhQEQp\nqSqEQ6rgeQ5ERFQpGA5EROTBcCAiIg+GAxEReTAciIjIg5fPIKKUJYm6eBLFzNdwEJHuAN4AkAHg\nPVV9Ncw8QwFcC+AnAPeo6gI/y0REVQOHsSaXb81KIpIB4E0A3QG0A9BLRNqGzNMDQCtVbQ3gQQBv\n+1We6qKgoCDZRUgZXBdBXBdBXBeJ4WefQycAa1S1UFWLAYwGcFPIPDcC+AsAqOocAA1EpKmPZary\nuOEHcV0EcV0EcV0khp/h0BzARtfjTYHnypvnFB/LREREUfAzHKJtMAztcWJDIxFRkvl2bSUR6Qwg\nX1W7Bx4/C6DE3SktIu8AKFDV0YHHKwB0VdVtIctiYBARxSHeayv5OVppHoDWItISwGYAtwPoFTLP\nOAD9AYwOhElRaDAA8b85IiKKj2/hoKpHRaQ/gMmwoazvq+pyEekXmD5MVSeISA8RWQPgAIB7/SoP\nERFFr0pcspuIiCpXSl8+Q0S6i8gKEVktIgPL/4+qTUQ+EJFtIrLE9VwjEZkqIqtEZIqINHBNezaw\nblaIyDXJKbU/RKSFiHwpIstEZKmIPBZ4Pu3Wh4jUFJE5IrIwsC7yA8+n3bpwiEiGiCwQkfGBx2m5\nLkSkUEQWB9bF3MBziVkXqpqSP7CmqDUAWgLIArAQQNtkl8vn93wpgA4Alrieew3AgMDfAwEMDvzd\nLrBOsgLraA2AGsl+DwlcF80AtA/8XQfASgBt03h95AZ+ZwKYDeCidF0Xgff4WwAfARgXeJyW6wLA\negCNQp5LyLpI5ZpDNCfRVSu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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -287,8 +287,8 @@ } ], "source": [ - "agent = PassiveTDAgent(policy, Fig[17,1], alpha=lambda n: 60./(59+n))\n", - "graph_utility_estimates(agent, Fig[17,1], 500, [(2,2)])" + "agent = PassiveTDAgent(policy, sequential_decision_environment, alpha=lambda n: 60./(59+n))\n", + "graph_utility_estimates(agent, sequential_decision_environment, 500, [(2,2)])" ] }, { @@ -307,9 +307,9 @@ "outputs": [ { "data": { - "image/png": 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0c2sHG0sb2FrZ6oVDyL0Q1LSriba12mL31d1lam6/UfVGGNF2RKFPHEhEZIjZ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\niIiIiIiIiIiIiKg8+3+ftEQRU4HjfQAAAABJRU5ErkJggg==\n", 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hLNenXLJwuqLp2KZEEHgy4iQvx17mg6Mf5OiNozPdJ+uOreMt39zCrSe3ev2eYeuHMaB3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\nyye9XXEwgA5mtgOJt5BfnWT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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -317,7 +317,7 @@ } ], "source": [ - "graph_utility_estimates(agent, Fig[17,1], 500, [(2,2), (3,2)])" + "graph_utility_estimates(agent, sequential_decision_environment, 500, [(2,2), (3,2)])" ] }, { @@ -346,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/search.py b/search.py index 7b5e0245d..5a98523ca 100644 --- a/search.py +++ b/search.py @@ -700,7 +700,7 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g -""" [Fig. 3.2] +""" [Figure 3.2] Simplified road map of Romania """ romania_map = UndirectedGraph(dict( @@ -726,7 +726,7 @@ def distance_to_node(n): Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) -""" [Fig. 4.9] +""" [Figure 4.9] Eight possible states of the vacumm world Each state is represented as * "State of the left room" "State of the right room" "Room in which the agent is present" @@ -750,9 +750,8 @@ def distance_to_node(n): State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) )) -""" [Fig. 4.23] +""" [Figure 4.23] One-dimensional state space Graph - """ one_dim_state_space = Graph(dict( State_1 = dict(Right = 'State_2'), @@ -770,7 +769,9 @@ def distance_to_node(n): State_5 = 4, State_6 = 3) -# Principal states and territories of Australia +""" [Figure 6.1] +Principal states and territories of Australia +""" australia_map = UndirectedGraph(dict( T=dict(), SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1), diff --git a/tests/test_logic.py b/tests/test_logic.py index 62e3a23a2..e156e45da 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -1,6 +1,6 @@ import pytest from logic import * -from utils import InfixOp, expr_handle_infix_ops, Fig, count, implies, equiv +from utils import InfixOp, expr_handle_infix_ops, count, implies, equiv def test_expr(): @@ -109,15 +109,15 @@ def test_dpll(): == {B: False, C: True, A: True, F: False, D: True, E: False}) assert dpll_satisfiable(A&~B) == {A: True, B: False} assert dpll_satisfiable(P&~P) == False - + def test_unify(): assert unify(x, x, {}) == {} assert unify(x, 3, {}) == {x: 3} def test_pl_fc_entails(): - assert pl_fc_entails(Fig[7,15], expr('Q')) - assert not pl_fc_entails(Fig[7,15], expr('SomethingSilly')) + assert pl_fc_entails(horn_clauses_KB, expr('Q')) + assert not pl_fc_entails(horn_clauses_KB, expr('SomethingSilly')) def test_tt_entails(): assert tt_entails(P & Q, Q) @@ -146,7 +146,7 @@ def test_move_not_inwards(): assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' def test_to_cnf(): - assert (repr(to_cnf(Fig[7, 13] & ~expr('~P12'))) == + assert (repr(to_cnf(wumpus_world_inference & ~expr('~P12'))) == "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' assert repr(to_cnf("B <=> (P1 | P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' @@ -203,7 +203,7 @@ def test_SAT_plan(): transition = {(0, 0):{'Right': (0, 1), 'Down': (1, 0)}, (0, 1):{'Left': (1, 0), 'Down': (1, 1)}, (1, 0):{'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, - (1, 1):{'Left': (1, 0), 'Up': (0, 1)}} + (1, 1):{'Left': (1, 0), 'Up': (0, 1)}} assert SAT_plan((0, 0), transition, (1, 1), 4) == ['Right', 'Down'] diff --git a/tests/test_mdp.py b/tests/test_mdp.py index 0f5bb656c..c4e6ed590 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -2,7 +2,7 @@ from mdp import * # noqa def test_value_iteration(): - assert value_iteration(Fig[17, 1], .01) == {(3, 2): 1.0, (3, 1): -1.0, + assert value_iteration(sequential_decision_environment, .01) == {(3, 2): 1.0, (3, 1): -1.0, (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, (0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537, (0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676, @@ -11,14 +11,14 @@ def test_value_iteration(): def test_policy_iteration(): - assert policy_iteration(Fig[17, 1]) == {(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0), + assert policy_iteration(sequential_decision_environment) == {(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0), (1, 0): (1, 0), (1, 2): (1, 0), (2, 0): (0, 1), (2, 1): (0, 1), (2, 2): (1, 0), (3, 0): (-1, 0), (3, 1): None, (3, 2): None} def test_best_policy(): - pi = best_policy(Fig[17, 1], value_iteration(Fig[17, 1], .01)) - assert Fig[17, 1].to_arrows(pi) == [['>', '>', '>', '.'], + pi = best_policy(sequential_decision_environment, value_iteration(sequential_decision_environment, .01)) + assert sequential_decision_environment.to_arrows(pi) == [['>', '>', '>', '.'], ['^', None, '^', '.'], ['^', '>', '^', '<']] diff --git a/utils.py b/utils.py index 2a219b2fa..80118ce1e 100644 --- a/utils.py +++ b/utils.py @@ -81,7 +81,7 @@ def shuffled(iterable): "Randomly shuffle a copy of iterable." items = list(iterable) random.shuffle(items) - return items + return items @@ -345,16 +345,16 @@ def unimplemented(): # See https://docs.python.org/3/reference/expressions.html#operator-precedence # See https://docs.python.org/3/reference/datamodel.html#special-method-names -class Expr(object): +class Expr(object): """A mathematical expression with an operator and 0 or more arguments. op is a str like '+' or 'sin'; args are Expressions. Expr('x') or Symbol('x') creates a symbol (a nullary Expr). Expr('-', x) creates a unary; Expr('+', x, 1) creates a binary.""" - - def __init__(self, op, *args): + + def __init__(self, op, *args): self.op = str(op) self.args = args - + # Operator overloads def __neg__(self): return Expr('-', self) def __pos__(self): return Expr('+', self) @@ -374,10 +374,10 @@ def __matmul__(self, rhs): return Expr('@', self, rhs) def __or__(self, rhs): if isinstance(rhs, Expression) : - return Expr('|', self, rhs) + return Expr('|', self, rhs) else: return NotImplemented # So that InfixOp can handle it - + # Reverse operator overloads def __radd__(self, lhs): return Expr('+', lhs, self) def __rsub__(self, lhs): return Expr('-', lhs, self) @@ -393,20 +393,20 @@ def __rlshift__(self, lhs): return Expr('<<', lhs, self) def __rtruediv__(self, lhs): return Expr('/', lhs, self) def __rfloordiv__(self, lhs): return Expr('//', lhs, self) def __rmatmul__(self, lhs): return Expr('@', lhs, self) - - def __call__(self, *args): + + def __call__(self, *args): "Call: if 'f' is a Symbol, then f(0) == Expr('f', 0)." return Expr(self.op, *args) # Equality and repr - def __eq__(self, other): + def __eq__(self, other): "'x == y' evaluates to True or False; does not build an Expr." - return (isinstance(other, Expr) - and self.op == other.op + return (isinstance(other, Expr) + and self.op == other.op and self.args == other.args) - + def __hash__(self): return hash(self.op) ^ hash(self.args) - + def __repr__(self): op = self.op args = [str(arg) for arg in self.args] @@ -450,7 +450,7 @@ def arity(expression): class InfixOp: """Allow 'P |implies| Q, where P, Q are Exprs and implies is an InfixOp.""" - def __init__(self, op, lhs=None): self.op, self.lhs = op, lhs + def __init__(self, op, lhs=None): self.op, self.lhs = op, lhs def __call__(self, lhs, rhs): return Expr(self.op, lhs, rhs) def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) def __ror__(self, lhs): return InfixOp(self.op, lhs) @@ -489,7 +489,7 @@ class defaultkeydict(collections.defaultdict): def __missing__(self, key): self[key] = result = self.default_factory(key) return result - + # ______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue @@ -591,9 +591,3 @@ def __delitem__(self, key): for i, (value, item) in enumerate(self.A): if item == key: self.A.pop(i) - -# Fig: The idea is we can define things like Fig[3,10] = ... -# TODO: However, this is deprecated, let's remove it, -# and instead have a comment like # Figure 3.10 - -Fig = {} From a6ca9ca30073d8729fd6fe28b861d7af0f870e23 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sun, 10 Apr 2016 16:55:31 +0530 Subject: [PATCH 241/513] Interactive TTT takes a player argument (#211) --- canvas.py | 2 +- games.ipynb | 247 +++++++++++++++++++++++++++++++++------------------- 2 files changed, 160 insertions(+), 89 deletions(-) diff --git a/canvas.py b/canvas.py index a58b67a0e..1f08a1ae0 100644 --- a/canvas.py +++ b/canvas.py @@ -66,7 +66,7 @@ def rect_n(self, xn, yn, wn, hn): self.rect(x, y, w, h) def line(self, x1, y1, x2, y2): - "Draw a line from (x1, y1) to (x, y2)" + "Draw a line from (x1, y1) to (x2, y2)" self.exec("line({0}, {1}, {2}, {3})".format(x1, y1, x2, y2)) def line_n(self, x1n, y1n, x2n, y2n): diff --git a/games.ipynb b/games.ipynb index 0139eb2f1..dab9055b1 100644 --- a/games.ipynb +++ b/games.ipynb @@ -235,7 +235,7 @@ { "data": { "text/plain": [ - "'a3'" + "'a1'" ] }, "execution_count": 4, @@ -257,7 +257,7 @@ { "data": { "text/plain": [ - "'a2'" + "'a3'" ] }, "execution_count": 5, @@ -551,8 +551,8 @@ "output_type": "stream", "text": [ "X X X \n", - "O . . \n", - "O . . \n" + ". . O \n", + ". . O \n" ] }, { @@ -656,46 +656,46 @@ "name": "stdout", "output_type": "stream", "text": [ + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", "O X O \n", - "X O . \n", - "O X X \n", + ". O X \n", + "X X O \n", + "-1\n", + "O O O \n", + ". X X \n", + ". X . \n", "-1\n", + "X X O \n", + "O O X \n", "O X . \n", - "X O . \n", - "X . O \n", "-1\n", + ". O . \n", + ". O X \n", "X O X \n", + "-1\n", "X O X \n", - "O X O \n", + "O O X \n", + "X X O \n", "0\n", + "O . . \n", + ". O X \n", + "X X O \n", + "-1\n", "O O X \n", - "X O X \n", "X O . \n", + "X X O \n", "-1\n", - "X O X \n", - "X O O \n", - "O X X \n", - "0\n", - "X O O \n", - "X O . \n", - "O X X \n", + ". X X \n", + "O O O \n", + ". . X \n", "-1\n", "X X O \n", "O O O \n", - "X . X \n", - "-1\n", - "O X O \n", - "X O X \n", - "X O X \n", - "0\n", - "O X O \n", - "O X X \n", - "X O X \n", - "0\n", - "O X X \n", - "X O O \n", - "O X X \n", - "0\n" + ". X X \n", + "-1\n" ] } ], @@ -713,56 +713,20 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
    \n", - "\n", - "
    \n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Inherit from Canvas to implement TicTacToe\n", "from canvas import *\n", - "class Canvas_TicTacToe(Canvas):\n", - " def __init__(self, varname, id=None, width=800, height=600):\n", - " Canvas.__init__(self, varname, id=None, width=800, height=600)\n", + "class Interactive_TicTacToe(Canvas):\n", + " def __init__(self, player, varname, id=None, width=300, height=300):\n", + " self.width = width\n", + " self.height = height\n", + " Canvas.__init__(self, varname, id=None, width=self.width, height=self.height)\n", + " self.player = player\n", " self.state = ttt.initial\n", " self.strokeWidth(5)\n", " self.draw_board()\n", @@ -773,7 +737,7 @@ " prev_state = self.state\n", " self.state = ttt.result(self.state, (x, y))\n", " if not prev_state == self.state:\n", - " move = random_player(ttt, self.state)\n", + " move = self.player(ttt, self.state)\n", " self.state = ttt.result(self.state, move)\n", " self.draw_board()\n", "\n", @@ -796,16 +760,114 @@ " def draw_x(self, position):\n", " self.stroke(0, 255, 0)\n", " x, y = [i-1 for i in position]\n", - " offset = 1/20\n", + " offset = 1/15\n", " self.line_n(x/3 + offset, y/3 + offset, x/3 + 1/3 - offset, y/3 + 1/3 - offset)\n", " self.line_n(x/3 + 1/3 - offset, y/3 + offset, x/3 + offset, y/3 + 1/3 - offset)\n", "\n", " def draw_o(self, position):\n", " self.stroke(255, 0, 0)\n", " x, y = [i-1 for i in position]\n", - " self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/7, 0, 360)\n", - "\n", - "rand_ttt = Canvas_TicTacToe(\"rand_ttt\", \"t3rand\", 400, 300)" + " self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/10, 0, 360)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "\n", + "
    \n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rand_ttt = Interactive_TicTacToe(random_player, \"rand_ttt\", \"t3rand\", 300, 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "\n", + "
    \n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alpha_ttt = Interactive_TicTacToe(alphabeta_player, \"alpha_ttt\", \"t3alpha\")" ] }, { @@ -817,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -835,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -853,7 +915,7 @@ "3" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -864,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -882,7 +944,7 @@ "3" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -893,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -904,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -919,6 +981,15 @@ "source": [ "Note that, here, if you are the first player, the `alphabeta_player` plays as MIN, and if you are the second player, the `alphabeta_player` plays as MAX. This happens because that's the way the game is defined in the class `Fig52Game`. Having a look at the code of this class should make it clear." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -937,7 +1008,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.5.1" } }, "nbformat": 4, From 078bfe679b88fed8653816bc65cf5433f99bf517 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Sun, 10 Apr 2016 19:27:18 +0800 Subject: [PATCH 242/513] Add tests for rules and lexicon in nlp.py (#212) --- tests/test_nlp.py | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 tests/test_nlp.py diff --git a/tests/test_nlp.py b/tests/test_nlp.py new file mode 100644 index 000000000..f5058d4a6 --- /dev/null +++ b/tests/test_nlp.py @@ -0,0 +1,10 @@ +import pytest +from nlp import * + +def test_rules(): + assert Rules(A = "B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} + + +def test_lexicon(): + assert Lexicon(Art = "the | a | an") == {'Art': ['the', 'a', 'an']} + \ No newline at end of file From 1085114668dc13d86dac8de70557cd4242ab9d20 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Sun, 10 Apr 2016 19:27:36 +0800 Subject: [PATCH 243/513] Add tests for parse_csv, weighted_mode and weighted_replicate (#210) --- tests/test_learning.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 tests/test_learning.py diff --git a/tests/test_learning.py b/tests/test_learning.py new file mode 100644 index 000000000..d4aecaaa0 --- /dev/null +++ b/tests/test_learning.py @@ -0,0 +1,14 @@ +import pytest +from learning import parse_csv, weighted_mode, weighted_replicate + +def test_parse_csv(): + assert parse_csv('1, 2, 3 \n 0, 2, na') == [[1, 2, 3], [0, 2, 'na']] + + +def test_weighted_mode(): + assert weighted_mode('abbaa', [1,2,3,1,2]) == 'b' + + +def test_weighted_replicate(): + assert weighted_replicate('ABC', [1,2,1], 4) == ['A', 'B', 'B', 'C'] + \ No newline at end of file From 2e12e83affe7a767d073632ecdd33bfa1017a1c5 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Sun, 10 Apr 2016 19:27:59 +0800 Subject: [PATCH 244/513] Add test for distance2 in grid.py (#209) --- tests/test_grid.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/test_grid.py b/tests/test_grid.py index b7da02121..2bfea35e0 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -10,6 +10,10 @@ def test_distance(): assert distance((1, 2), (5, 5)) == 5.0 +def test_distance2(): + assert distance2((1, 2), (5, 5)) == 25.0 + + def test_vector_clip(): assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) From 8b0888057568211e0b2f6b292b75129edc0ea14e Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 11 Apr 2016 00:14:19 +0530 Subject: [PATCH 245/513] Implemented Grid World (#214) --- ipyviews.py | 94 +++++++++++++++++++++++++++++++++++- js/gridworld.js | 126 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 219 insertions(+), 1 deletion(-) create mode 100644 js/gridworld.js diff --git a/ipyviews.py b/ipyviews.py index f7f10ea8f..1f33bc0aa 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -1,7 +1,9 @@ from IPython.display import HTML, display, clear_output - +from collections import defaultdict from agents import PolygonObstacle import time +import json +import copy import __main__ @@ -61,3 +63,93 @@ def show(self): clear_output() total_html = _CONTINUOUS_WORLD_HTML.format(self.width, self.height, self.object_name(), str(self.get_polygon_obstacles_coordinates()), _JS_CONTINUOUS_WORLD) display(HTML(total_html)) + + +# ______________________________________________________________________________ +# Grid environment + +_GRID_WORLD_HTML = ''' +
    + +
    + +
    +
    + +''' + +with open('js/gridworld.js', 'r') as js_file: + _JS_GRID_WORLD = js_file.read() + + +class GridWorldView: + """ View for grid world. Uses XYEnviornment in agents.py as model. + world: an instance of XYEnviornment. + block_size: size of individual blocks in pixes. + default_fill: color of blocks. A hex value or name should be passed. + """ + + def __init__(self, world, block_size=30, default_fill="white"): + self.time = time.time() + self.world = world + self.labels = defaultdict(str) # locations as keys + self.representation = {"default": {"type": "color", "source": default_fill}} + self.block_size = block_size + + def object_name(self): + globals_in_main = {x: getattr(__main__, x) for x in dir(__main__)} + for x in globals_in_main: + if isinstance(globals_in_main[x], type(self)): + if globals_in_main[x].time == self.time: + return x + + def set_label(self, coordinates, label): + """ Add lables to a particular block of grid. + coordinates: a tuple of (row, column). + rows and columns are 0 indexed. + """ + self.labels[coordinates] = label + + def set_representation(self, thing, repr_type, source): + """ Set the representation of different things in the + environment. + thing: a thing object. + repr_type : type of representation can be either "color" or "img" + source: Hex value in case of color. Image path in case of image. + """ + thing_class_name = thing.__class__.__name__ + if repr_type not in ("img", "color"): + raise ValueError('Invalid repr_type passed. Possible types are img/color') + self.representation[thing_class_name] = {"type": repr_type, "source": source} + + def handle_click(self, coordinates): + """ This method needs to be overidden. Make sure to include a + self.show() call at the end. """ + self.show() + + def map_to_render(self): + default_representation = {"val": "default", "tooltip": ""} + world_map = [[copy.deepcopy(default_representation) for _ in range(self.world.width)] + for _ in range(self.world.height)] + + for thing in self.world.things: + row, column = thing.location + thing_class_name = thing.__class__.__name__ + if thing_class_name not in self.representation: + raise KeyError('Representation not found for {}'.format(thing_class_name)) + world_map[row][column]["val"] = thing.__class__.__name__ + + for location, label in self.labels.items(): + row, column = location + world_map[row][column]["tooltip"] = label + + return json.dumps(world_map) + + def show(self): + clear_output() + total_html = _GRID_WORLD_HTML.format(self.object_name(), self.map_to_render(), + self.block_size, json.dumps(self.representation), _JS_GRID_WORLD) + display(HTML(total_html)) diff --git a/js/gridworld.js b/js/gridworld.js new file mode 100644 index 000000000..90b4c0e92 --- /dev/null +++ b/js/gridworld.js @@ -0,0 +1,126 @@ +var latest_output_area ="NONE"; // Jquery object for the DOM element of output area which was used most recently + +function handle_output(out, block){ + var output = out.content.data["text/html"]; + latest_output_area.html(output); +} + +function handle_click(canvas,coord) { + console.log(canvas,coord); + latest_output_area = $(canvas).parents('.output_subarea'); + $(canvas).parents('.output_subarea') + var world_object_name = canvas.dataset.world_name; + var command = world_object_name + ".handle_click(" + JSON.stringify(coord) + ")"; + console.log("Executing Command: " + command); + var kernel = IPython.notebook.kernel; + var callbacks = { 'iopub' : {'output' : handle_output}}; + kernel.execute(command,callbacks); +}; + + +function generateGridWorld(state,size,elements) +{ + // Declaring array to store image object + var $imgArray = new Object(), hasImg=false; + // Loading images LOOP + $.each(elements, function(i, val) { + // filtering for type img + if(val["type"]=="img") { + // setting image load + hasImg = true; + $imgArray[i] = $('').attr({height:size,width:size,src:val["source"]}).data({name:i,loaded:false}).load(function(){ + // Check for all image loaded + var execute=true; + $(this).data("loaded",true); + $.each($imgArray, function(i, val) { + if(!$(this).data("loaded")) { + execute=false; + // exit on unloaded image + return false; + } + }); + if (execute) { + // Converting loaded image to canvas covering block size. + $.each($imgArray, function(i, val) { + $imgArray[i] = $('').attr({width:size,height:size}).get(0); + $imgArray[i].getContext('2d').drawImage(val.get(0),0,0,size,size); + }); + // initialize the world + initializeWorld(); + } + }); + } + }); + + if(!hasImg) { + initializeWorld(); + } + + function initializeWorld(){ + var $parentDiv = $('div.map-grid-world'); + // remove object reference + $('div.map-grid-world').removeClass('map-grid-world'); + // get some info about the canvas + var row = state.length; + var column = state[0].length; + var canvas = $parentDiv.find('canvas').get(0); + var ctx = canvas.getContext('2d'); + canvas.width = size * column; + canvas.height = size * row; + + //Initialize previous positions + for(var i=0;i=0 && gx=0 && gy Date: Mon, 11 Apr 2016 00:16:18 +0530 Subject: [PATCH 246/513] Added TicTacToe to notebook (#213) * Error message of python errors * Added Canvas_TicTacToe class * Added TicTacToe to notebook * Added games.ipynb * moved js file to js folder --- canvas.py | 4 +- games.ipynb | 502 ++++---------------------------------- games.py | 77 +++++- canvas.js => js/canvas.js | 9 +- 4 files changed, 129 insertions(+), 463 deletions(-) rename canvas.js => js/canvas.js (95%) diff --git a/canvas.py b/canvas.py index 1f08a1ae0..6ba4a7f8b 100644 --- a/canvas.py +++ b/canvas.py @@ -1,7 +1,7 @@ from IPython.display import HTML, display, clear_output _canvas = """ - +
    @@ -109,7 +109,7 @@ def text_n(self, txt, xn, yn, fill = True): "Similar to text(), but with normalized coordinates" x = round(xn * self.width) y = round(yn * self.height) - self.text(text, x, y, fill) + self.text(txt, x, y, fill) def alert(self, message): "Immediately display an alert" diff --git a/games.ipynb b/games.ipynb index dab9055b1..20932daeb 100644 --- a/games.ipynb +++ b/games.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -208,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, @@ -227,44 +227,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(game52, 'A')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a3'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(game52, 'A')" ] @@ -278,21 +256,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a1\n", - "b1\n", - "c1\n" - ] - } - ], + "outputs": [], "source": [ "print( alphabeta_player(game52, 'A') )\n", "print( alphabeta_player(game52, 'B') )\n", @@ -308,44 +276,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "minimax_decision('A', game52)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'a1'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alphabeta_full_search('A', game52)" ] @@ -359,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false }, @@ -377,21 +323,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ". . . \n", - ". . . \n", - ". . . \n" - ] - } - ], + "outputs": [], "source": [ "ttt.display(ttt.initial)" ] @@ -407,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, @@ -433,21 +369,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X O X \n", - "O . O \n", - "X . . \n" - ] - } - ], + "outputs": [], "source": [ "ttt.display(my_state)" ] @@ -461,44 +387,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 2)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(ttt, my_state)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3, 3)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "random_player(ttt, my_state)" ] @@ -512,22 +416,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2, 2)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alphabeta_player(ttt, my_state)" ] @@ -541,33 +434,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X X X \n", - ". . O \n", - ". . O \n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "play_game(ttt, alphabeta_player, random_player)" + "outputs": [], + "source": [ + "bot_play = Canvas_TicTacToe('bot_play', 'random', 'alphabeta')" ] }, { @@ -581,58 +454,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "X X O \n", - "O O X \n", - "X O X \n", - "0\n" - ] - } - ], + "outputs": [], "source": [ "for _ in range(10):\n", " print(play_game(ttt, alphabeta_player, alphabeta_player))" @@ -647,58 +473,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X X O \n", - "O O X \n", - "X O X \n", - "0\n", - "O X O \n", - ". O X \n", - "X X O \n", - "-1\n", - "O O O \n", - ". X X \n", - ". X . \n", - "-1\n", - "X X O \n", - "O O X \n", - "O X . \n", - "-1\n", - ". O . \n", - ". O X \n", - "X O X \n", - "-1\n", - "X O X \n", - "O O X \n", - "X X O \n", - "0\n", - "O . . \n", - ". O X \n", - "X X O \n", - "-1\n", - "O O X \n", - "X O . \n", - "X X O \n", - "-1\n", - ". X X \n", - "O O O \n", - ". . X \n", - "-1\n", - "X X O \n", - "O O O \n", - ". X X \n", - "-1\n" - ] - } - ], + "outputs": [], "source": [ "for _ in range(10):\n", " print(play_game(ttt, random_player, alphabeta_player))" @@ -713,161 +492,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#Inherit from Canvas to implement TicTacToe\n", - "from canvas import *\n", - "class Interactive_TicTacToe(Canvas):\n", - " def __init__(self, player, varname, id=None, width=300, height=300):\n", - " self.width = width\n", - " self.height = height\n", - " Canvas.__init__(self, varname, id=None, width=self.width, height=self.height)\n", - " self.player = player\n", - " self.state = ttt.initial\n", - " self.strokeWidth(5)\n", - " self.draw_board()\n", - " \n", - " def mouse_click(self, x, y):\n", - " self.argxy = (x, y)\n", - " x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1\n", - " prev_state = self.state\n", - " self.state = ttt.result(self.state, (x, y))\n", - " if not prev_state == self.state:\n", - " move = self.player(ttt, self.state)\n", - " self.state = ttt.result(self.state, move)\n", - " self.draw_board()\n", - "\n", - " def draw_board(self):\n", - " self.clear()\n", - " self.stroke(0, 0, 0)\n", - " offset = 1/20\n", - " self.line_n(0 + offset, 1/3, 1 - offset, 1/3)\n", - " self.line_n(0 + offset, 2/3, 1 - offset, 2/3)\n", - " self.line_n(1/3, 0 + offset, 1/3, 1 - offset)\n", - " self.line_n(2/3, 0 + offset, 2/3, 1 - offset)\n", - " board = self.state.board\n", - " for mark in board:\n", - " if board[mark] == 'X':\n", - " self.draw_x(mark)\n", - " elif board[mark] == 'O':\n", - " self.draw_o(mark)\n", - " self.update()\n", - " \n", - " def draw_x(self, position):\n", - " self.stroke(0, 255, 0)\n", - " x, y = [i-1 for i in position]\n", - " offset = 1/15\n", - " self.line_n(x/3 + offset, y/3 + offset, x/3 + 1/3 - offset, y/3 + 1/3 - offset)\n", - " self.line_n(x/3 + 1/3 - offset, y/3 + offset, x/3 + offset, y/3 + 1/3 - offset)\n", - "\n", - " def draw_o(self, position):\n", - " self.stroke(255, 0, 0)\n", - " x, y = [i-1 for i in position]\n", - " self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/10, 0, 360)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
    \n", - "\n", - "
    \n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rand_ttt = Interactive_TicTacToe(random_player, \"rand_ttt\", \"t3rand\", 300, 300)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
    \n", - "\n", - "
    \n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha_ttt = Interactive_TicTacToe(alphabeta_player, \"alpha_ttt\", \"t3alpha\")" + "rand_play = Canvas_TicTacToe('rand_play', 'human', 'random')" ] }, { @@ -879,13 +510,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#play_game(ttt, query_player, alphabeta_player)" + "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" ] }, { @@ -897,65 +528,29 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "B1\n" - ] - }, - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, alphabeta_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "B1\n" - ] - }, - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "play_game(game52, alphabeta_player, random_player)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": false }, @@ -966,7 +561,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "collapsed": false }, @@ -981,15 +576,6 @@ "source": [ "Note that, here, if you are the first player, the `alphabeta_player` plays as MIN, and if you are the second player, the `alphabeta_player` plays as MAX. This happens because that's the way the game is defined in the class `Fig52Game`. Having a look at the code of this class should make it clear." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -1008,7 +594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.0" } }, "nbformat": 4, diff --git a/games.py b/games.py index 8fc9e7457..b03530a97 100644 --- a/games.py +++ b/games.py @@ -4,6 +4,7 @@ import random from utils import argmax +from canvas import Canvas infinity = float('inf') GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') @@ -305,7 +306,6 @@ def k_in_row(self, board, move, player, xxx_todo_changeme): class ConnectFour(TicTacToe): - """A TicTacToe-like game in which you can only make a move on the bottom row, or in a square directly above an occupied square. Traditionally played on a 7x6 board and requiring 4 in a row.""" @@ -316,3 +316,78 @@ def __init__(self, h=7, v=6, k=4): def actions(self, state): return [(x, y) for (x, y) in state.moves if y == 1 or (x, y-1) in state.board] + + +class Canvas_TicTacToe(Canvas): + """Play a 3x3 TicTacToe game on HTML canvas + TODO: Add restart button + """ + def __init__(self, varname, player_1='human', player_2='random', id=None, width=800, height=600): + valid_players = ('human', 'random', 'alphabeta') + if player_1 not in valid_players or player_2 not in valid_players: + raise TypeError("Players must be one of {}".format(valid_players)) + Canvas.__init__(self, varname, id, width, height) + self.ttt = TicTacToe() + self.state = self.ttt.initial + self.turn = 0 + self.strokeWidth(5) + self.players = (player_1, player_2) + self.draw_board() + self.font("Ariel 30px") + + def mouse_click(self, x, y): + player = self.players[self.turn] + if self.ttt.terminal_test(self.state): + return + + if player == 'human': + x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1 + if (x, y) not in self.ttt.actions(self.state): + #Invalid move + return + move = (x, y) + elif player == 'alphabeta': + move = alphabeta_player(self.ttt, self.state) + else: + move = random_player(self.ttt, self.state) + self.state = self.ttt.result(self.state, move) + self.turn ^= 1 + self.draw_board() + + def draw_board(self): + self.clear() + self.stroke(0, 0, 0) + offset = 1/20 + self.line_n(0 + offset, 1/3, 1 - offset, 1/3) + self.line_n(0 + offset, 2/3, 1 - offset, 2/3) + self.line_n(1/3, 0 + offset, 1/3, 1 - offset) + self.line_n(2/3, 0 + offset, 2/3, 1 - offset) + board = self.state.board + for mark in board: + if board[mark] == 'X': + self.draw_x(mark) + elif board[mark] == 'O': + self.draw_o(mark) + #End game message + if self.ttt.terminal_test(self.state): + utility = self.ttt.utility(self.state, self.ttt.to_move(self.ttt.initial)) + if utility == 0: + self.text_n('Game Draw!', 0.1, 0.1) + else: + self.text_n('Player {} wins!'.format(1 if utility>0 else 2), 0.1, 0.1) + else: #print which player's turn it is + self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1) + + self.update() + + def draw_x(self, position): + self.stroke(0, 255, 0) + x, y = [i-1 for i in position] + offset = 1/20 + self.line_n(x/3 + offset, y/3 + offset, x/3 + 1/3 - offset, y/3 + 1/3 - offset) + self.line_n(x/3 + 1/3 - offset, y/3 + offset, x/3 + offset, y/3 + 1/3 - offset) + + def draw_o(self, position): + self.stroke(255, 0, 0) + x, y = [i-1 for i in position] + self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/7, 0, 360) diff --git a/canvas.js b/js/canvas.js similarity index 95% rename from canvas.js rename to js/canvas.js index b09c1b439..d9d313d2e 100644 --- a/canvas.js +++ b/js/canvas.js @@ -4,13 +4,18 @@ See canvas.py for help on how to use the Canvas class to draw on the HTML Canvas */ + //Manages the output of code executed in IPython kernel function output_callback(out, block){ console.log(out); + //Handle error in python + if(out.msg_type == "error"){ + console.log("Error in python script!"); + console.log(out.content); + return ; + } script = out.content.data['text/html']; - console.log(script); script = script.substr(8, script.length - 17); - console.log(script); eval(script) } From 3fc27af613441e63e952a4eb56fa7ba56b5f6953 Mon Sep 17 00:00:00 2001 From: Yu Ting Date: Mon, 11 Apr 2016 02:48:56 +0800 Subject: [PATCH 247/513] Add tests in test_text.py (#215) --- tests/test_text.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/tests/test_text.py b/tests/test_text.py index 66dbd9703..df7103fd7 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -30,6 +30,11 @@ def test_shift_decoding(): assert msg == 'This is a secret message.' +def test_rot13_encoding(): + code = rot13('Hello, world!') + + assert code == 'Uryyb, jbeyq!' + def test_rot13_decoding(): flatland = DataFile("EN-text/flatland.txt").read() @@ -163,6 +168,27 @@ def verify_query(query, expected): Results(11.62, "aima-data/MAN/jar.txt"), ]) + +def test_words(): + assert words("``EGAD!'' Edgar cried.") == ['egad', 'edgar', 'cried'] + + +def test_canonicalize(): + assert canonicalize("``EGAD!'' Edgar cried.") == 'egad edgar cried' + + +def test_translate(): + text = 'orange apple lemon ' + func = lambda x: ('s ' + x) if x==' ' else x + + assert translate(text, func) == 'oranges apples lemons ' + + +def test_bigrams(): + assert bigrams('this') == ['th', 'hi', 'is'] + assert bigrams(['this', 'is', 'a', 'test']) == [['this', 'is'], ['is', 'a'], ['a', 'test']] + + # TODO: for .ipynb """ From 3451e8d4005528286805fd9c57bcba1855daaf9f Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Mon, 11 Apr 2016 13:48:09 -0400 Subject: [PATCH 248/513] Fix bug: was calling a generator as if it returned a value-or-None instead of an iterator --- logic.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/logic.py b/logic.py index a7729d68d..412cebc46 100644 --- a/logic.py +++ b/logic.py @@ -97,10 +97,9 @@ def ask_generator(self, query): def ask_if_true(self, query): "Return True if the KB entails query, else return False." - if self.ask_generator(query) == {}: + for _ in self.ask_generator(query): return True - else: - return False + return False def retract(self, sentence): "Remove the sentence's clauses from the KB." From 7d65328fe42d05b6fb605e1dccd59b5269cac568 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Mon, 11 Apr 2016 15:10:11 -0400 Subject: [PATCH 249/513] Correct mistaken comment. --- logic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logic.py b/logic.py index 412cebc46..0961ef2d7 100644 --- a/logic.py +++ b/logic.py @@ -91,7 +91,7 @@ def tell(self, sentence): self.clauses.extend(conjuncts(to_cnf(sentence))) def ask_generator(self, query): - "Return the empty substitution {} if KB entails query; else return None." + "Yield the empty substitution {} if KB entails query; else no results." if tt_entails(Expr('&', *self.clauses), query): yield {} From 956b396df0e2e0a5e92ec4b0b50b40ec8240e57a Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Mon, 11 Apr 2016 15:24:33 -0400 Subject: [PATCH 250/513] fix typo --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 2ee837edb..b2766d56f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -61,7 +61,7 @@ Patch Rules clearly under which circumstances the bug happens. Make sure the test fails without your patch. -- Follw the style guidelines described above. +- Follow the style guidelines described above. Running the Test-Suite ===================== From 49550b5d41e4f7d64c43aaba98e64ac553d384c9 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 11 Apr 2016 12:51:37 -0700 Subject: [PATCH 251/513] In Expr, function call only for Symbols --- utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 80118ce1e..74bd3928c 100644 --- a/utils.py +++ b/utils.py @@ -396,7 +396,10 @@ def __rmatmul__(self, lhs): return Expr('@', lhs, self) def __call__(self, *args): "Call: if 'f' is a Symbol, then f(0) == Expr('f', 0)." - return Expr(self.op, *args) + if self.args: + raise ValueError('can only do a call for a Symbol, not an Expr') + else: + return Expr(self.op, *args) # Equality and repr def __eq__(self, other): From 685a8f8ddb2b2c388d0155391fac1b74a9b8d1d4 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Tue, 12 Apr 2016 04:19:33 +0530 Subject: [PATCH 252/513] Implemented QLearningAgent (#217) * Implemented QLearningAgent * Added QLearningAgent in Index --- README.md | 2 +- rl.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 66 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 992b58f14..7d6d3b490 100644 --- a/README.md +++ b/README.md @@ -113,7 +113,7 @@ Here is a table of algorithms, the figure and page where they appear in the book | 19.12 | FOIL | | | 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | -| 21.8 | Q-Learning-Agent | | +| 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | | \* 21.2 | Naive-Communicating-Agent | | | 22.1 | HITS | | | | 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | diff --git a/rl.py b/rl.py index 3cff46472..8a2e57850 100644 --- a/rl.py +++ b/rl.py @@ -1,8 +1,10 @@ """Reinforcement Learning (Chapter 21) """ -import agents +from collections import defaultdict +from utils import argmax +import agents import random @@ -57,6 +59,68 @@ def update_state(self, percept): return percept +class QLearningAgent: + """ An exploratory Q-learning agent. It avoids having to learn the transition + model because the Q-value of a state can be related directly to those of + its neighbors. [Fig. 21.8] + """ + def __init__(self, mdp, Ne, Rplus, alpha=None): + + self.gamma = mdp.gamma + self.terminals = mdp.terminals + self.all_act = mdp.actlist + self.Ne = Ne # iteration limit in exploration function + self.Rplus = Rplus # large value to assign before iteration limit + self.Q = defaultdict(float) + self.Nsa = defaultdict(float) + self.s = None + self.a = None + self.r = None + + if alpha: + self.alpha = alpha + else: + self.alpha = lambda n: 1./(1+n) # udacity video + + def f(self, u, n): + """ Exploration function. Returns fixed Rplus untill + agent has visited state, action a Ne number of times. + Same as ADP agent in book.""" + if n < self.Ne: + return self.Rplus + else: + return u + + def actions_in_state(self, state): + """ Returns actions possible in given state. + Useful for max and argmax. """ + if state in self.terminals: + return [None] + else: + return self.all_act + + def __call__(self, percept): + s1, r1 = self.update_state(percept) + Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r + alpha, gamma, terminals, actions_in_state = self.alpha, self.gamma, self.terminals, self.actions_in_state + if s1 in terminals: + Q[(s1, None)] = r1 + if s is not None: + Nsa[(s, a)] += 1 + Q[(s, a)] += alpha(Nsa[(s, a)])*(r+gamma*max([Q[(s1, a1)] for a1 in actions_in_state(s1)])-Q[(s, a)]) + if s1 in terminals: + self.s = self.a = self.r = None + else: + self.s, self.r = s1, r1 + self.a = argmax(actions_in_state(s1), key=lambda a1: self.f(Q[(s1, a1)], Nsa[(s1, a1)])) + return self.a + + def update_state(self, percept): + ''' To be overriden in most cases. The default case + assumes the percept to be of type (state, reward)''' + return percept + + def run_single_trial(agent_program, mdp): ''' Execute trial for given agent_program and mdp. mdp should be an instance of subclass From 75617fa18b298fe64b453a6500f4b09966230f25 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 11 Apr 2016 15:57:27 -0700 Subject: [PATCH 253/513] replace TRUE, FALSE with True, False --- logic.py | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/logic.py b/logic.py index 0961ef2d7..aaf5adef5 100644 --- a/logic.py +++ b/logic.py @@ -144,9 +144,8 @@ def is_var_symbol(s): def is_prop_symbol(s): - """A proposition logic symbol is an initial-uppercase string other than - TRUE or FALSE.""" - return is_symbol(s) and s[0].isupper() and s != 'TRUE' and s != 'FALSE' + """A proposition logic symbol is an initial-uppercase string.""" + return is_symbol(s) and s[0].isupper() def variables(s): @@ -184,7 +183,6 @@ def parse_definite_clause(s): return conjuncts(antecedent), consequent # Useful constant Exprs used in examples and code: -TRUE, FALSE = Symbol('TRUE'), Symbol('FALSE') A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') @@ -233,7 +231,7 @@ def tt_true(s): True """ s = expr(s) - return tt_entails(TRUE, s) + return tt_entails(True, s) def pl_true(exp, model={}): @@ -241,12 +239,10 @@ def pl_true(exp, model={}): and False if it is false. If the model does not specify the value for every proposition, this may return None to indicate 'not obvious'; this may happen even when the expression is tautological.""" + if exp == True or exp == False: + return exp op, args = exp.op, exp.args - if exp == TRUE: - return True - elif exp == FALSE: - return False - elif is_prop_symbol(op): + if is_prop_symbol(op): return model.get(exp) elif op == '~': p = pl_true(args[0], model) @@ -364,7 +360,7 @@ def distribute_and_over_or(s): if s.op != '|': return distribute_and_over_or(s) if len(s.args) == 0: - return FALSE + return False if len(s.args) == 1: return distribute_and_over_or(s.args[0]) conj = first(arg for arg in s.args if arg.op == '&') @@ -397,7 +393,7 @@ def associate(op, args): else: return Expr(op, *args) -_op_identity = {'&': TRUE, '|': FALSE, '+': 0, '*': 1} +_op_identity = {'&': True, '|': False, '+': 0, '*': 1} def dissociate(op, args): @@ -447,7 +443,7 @@ def pl_resolution(KB, alpha): for i in range(n) for j in range(i+1, n)] for (ci, cj) in pairs: resolvents = pl_resolve(ci, cj) - if FALSE in resolvents: + if False in resolvents: return True new = new.union(set(resolvents)) if new.issubset(set(clauses)): From 386814f52dc51e9bc9f559f3c07ee13e17786a56 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 11 Apr 2016 16:40:55 -0700 Subject: [PATCH 254/513] implies changed to '==>' MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Following a suggestion by C.G.Vedant, changed |implies| to |’==>’| --- logic.py | 6 +++--- tests/test_logic.py | 10 +++++----- utils.py | 30 ++++++++++++++---------------- 3 files changed, 22 insertions(+), 24 deletions(-) diff --git a/logic.py b/logic.py index aaf5adef5..625e7a49b 100644 --- a/logic.py +++ b/logic.py @@ -33,7 +33,7 @@ from utils import ( removeall, unique, first, every, argmax, probability, num_or_str, - isnumber, issequence, Symbol, Expr, expr, subexpressions, implies + isnumber, issequence, Symbol, Expr, expr, subexpressions ) import agents @@ -713,8 +713,8 @@ def translate_to_SAT(init, transition, goal, time): action_sym[(s, action, t)] = Expr("Transition_{}".format(next(transition_counter))) # Change the state from s to s_ - clauses.append(action_sym[s, action, t] |implies| state_sym[s, t]) - clauses.append(action_sym[s, action, t] |implies| state_sym[s_, t + 1]) + clauses.append(action_sym[s, action, t] |'==>'| state_sym[s, t]) + clauses.append(action_sym[s, action, t] |'==>'| state_sym[s_, t + 1]) #Allow only one state at any time for t in range(time+1): diff --git a/tests/test_logic.py b/tests/test_logic.py index e156e45da..5d4bd4623 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -1,13 +1,13 @@ import pytest from logic import * -from utils import InfixOp, expr_handle_infix_ops, count, implies, equiv +from utils import expr_handle_infix_ops, count def test_expr(): assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' assert (expr_handle_infix_ops('P & Q ==> R & ~S') - == "P & Q |InfixOp('==>', None)| R & ~S") + == "P & Q |'==>'| R & ~S") def test_extend(): assert extend({x: 1}, y, 2) == {x: 1, y: 2} @@ -17,7 +17,7 @@ def test_PropKB(): assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 kb.tell(A & E) assert kb.ask(A) == kb.ask(E) == {} - kb.tell(E |implies| C) + kb.tell(E |'==>'| C) assert kb.ask(C) == {} kb.retract(E) assert kb.ask(E) is False @@ -42,8 +42,8 @@ def test_KB_wumpus(): B[2,1] = Symbol("B[2,1]") kb_wumpus.tell(~P[1,1]) - kb_wumpus.tell(B[1,1] |equiv| ((P[1,2] | P[2,1]))) - kb_wumpus.tell(B[2,1] |equiv| ((P[1,1] | P[2,2] | P[3,1]))) + kb_wumpus.tell(B[1,1] |'<=>'| ((P[1,2] | P[2,1]))) + kb_wumpus.tell(B[2,1] |'<=>'| ((P[1,1] | P[2,2] | P[3,1]))) kb_wumpus.tell(~B[1,1]) kb_wumpus.tell(B[2,1]) diff --git a/utils.py b/utils.py index 74bd3928c..51c89ea74 100644 --- a/utils.py +++ b/utils.py @@ -373,10 +373,11 @@ def __floordiv__(self, rhs): return Expr('//', self, rhs) def __matmul__(self, rhs): return Expr('@', self, rhs) def __or__(self, rhs): + "Allow both P | Q, and P |'==>'| Q." if isinstance(rhs, Expression) : return Expr('|', self, rhs) else: - return NotImplemented # So that InfixOp can handle it + return PartialExpr(rhs, self) # Reverse operator overloads def __radd__(self, lhs): return Expr('+', lhs, self) @@ -451,37 +452,34 @@ def arity(expression): # For operators that are not defined in Python, we allow new InfixOps: -class InfixOp: - """Allow 'P |implies| Q, where P, Q are Exprs and implies is an InfixOp.""" - def __init__(self, op, lhs=None): self.op, self.lhs = op, lhs - def __call__(self, lhs, rhs): return Expr(self.op, lhs, rhs) - def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) - def __ror__(self, lhs): return InfixOp(self.op, lhs) - def __repr__(self): return "InfixOp('{}', {})".format(self.op, self.lhs) - -infix_ops = (implies, rimplies, equiv) = [InfixOp(o) for o in ['==>', '<==', '<=>']] +class PartialExpr: + """Given 'P |'==>'| Q, first form PartialExpr('==>', P), then combine with Q.""" + def __init__(self, op, lhs): self.op, self.lhs = op, lhs + def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) + def __repr__(self): return "PartialExpr('{}', {})".format(self.op, self.lhs) def expr(x): """Shortcut to create an Expression. x is a str in which: - identifiers are automatically defined as Symbols. - - '==>' is treated as an infix |implies|, as are all infix_ops + - ==> is treated as an infix |'==>'|, as are <== and <=>. If x is already an Expression, it is returned unchanged. Example: >>> expr('P & Q ==> Q') ((P & Q) ==> Q) """ if isinstance(x, str): - return eval(expr_handle_infix_ops(x), - defaultkeydict(Symbol, InfixOp=InfixOp)) + return eval(expr_handle_infix_ops(x), defaultkeydict(Symbol)) else: return x +infix_ops = '==> <== <=>'.split() + def expr_handle_infix_ops(x): - """Given a str, return a new str with '==>' replaced by |InfixOp('==>')|, etc. + """Given a str, return a new str with ==> replaced by |'==>'|, etc. >>> expr_handle_infix_ops('P ==> Q') - "P |InfixOp('==>', None)| Q" + "P |'==>'| Q" """ for op in infix_ops: - x = x.replace(op.op, '|' + str(op) + '|') + x = x.replace(op, '|' + repr(op) + '|') return x class defaultkeydict(collections.defaultdict): From 997113bb67b022043b222444060354501662bdca Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 11 Apr 2016 21:16:40 -0700 Subject: [PATCH 255/513] Update logic.ipynb for |'==>'| --- logic.ipynb | 166 ++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 114 insertions(+), 52 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index 6693c50fa..e498dc7d6 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -28,6 +28,7 @@ }, "outputs": [], "source": [ + "from utils import *\n", "from logic import *" ] }, @@ -80,7 +81,7 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -91,7 +92,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can combine `Expr`s with the regular Python infix and prefix operators. Here's how we would form the sentence for \"P and not Q\":" + "We can combine `Expr`s with the regular Python infix and prefix operators. Here's how we would form the logical sentence \"P and not Q\":" ] }, { @@ -125,7 +126,7 @@ "```python\n", "def __and__(self, other): return Expr('&', self, other)```\n", " \n", - "and does similar overloads for the other operators. An `Expr` has two fields: `op` for the operator, which is always a string, and `args` for the arguments, which is a tuple of 0 or more expressions. Let's take a look at the fields for some `Expr` examples:" + "and does similar overloads for the other operators. An `Expr` has two fields: `op` for the operator, which is always a string, and `args` for the arguments, which is a tuple of 0 or more expressions. By \"expression,\" I mean either an instance of `Expr`, or a number. Let's take a look at the fields for some `Expr` examples:" ] }, { @@ -147,7 +148,7 @@ } ], "source": [ - "sentence = P & Q\n", + "sentence = P & ~Q\n", "\n", "sentence.op" ] @@ -162,7 +163,7 @@ { "data": { "text/plain": [ - "(P, Q)" + "(P, ~Q)" ] }, "execution_count": 6, @@ -268,7 +269,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is important to note that the `Expr` class does not define the *logic* of Propositional Logic; it just gives you a way to *represent* expressions. Think of an `Expr` as an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree). Each of the `args` in an `Expr` can be either a symbol, a number, or a nested `Expr`. We can nest these trees to any depth. An `Expr` can represent any kind of mathematical expression, not just logical sentences. For example:" + "It is important to note that the `Expr` class does not define the *logic* of Propositional Logic sentences; it just gives you a way to *represent* expressions. Think of an `Expr` as an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree). Each of the `args` in an `Expr` can be either a symbol, a number, or a nested `Expr`. We can nest these trees to any depth. Here is a deply nested `Expr`:" ] }, { @@ -299,19 +300,19 @@ "source": [ "## Operators for Constructing Logical Sentences\n", "\n", - "Here is a table of the operators that can be used to form sentences. Note that we have a problem: we want to use Python operators to make sentences, so that our programs (and our interactive sessions like the one here) will show simple code. But Python does not allow implication arrows as operators, so for now we will create them using functions (but Python will display them using arrows). Alternately, you can always use the more verbose `Expr` constructor forms:\n", + "Here is a table of the operators that can be used to form sentences. Note that we have a problem: we want to use Python operators to make sentences, so that our programs (and our interactive sessions like the one here) will show simple code. But Python does not allow implication arrows as operators, so for now we have to use a more verbose notation that Python does allow: `|'==>'|` instead of just `==>`. Alternately, you can always use the more verbose `Expr` constructor forms:\n", "\n", - "| Operation | Book | Python Input | Python Output | `Expr` Input\n", + "| Operation | Book | Python Infix Input | Python Output | Python `Expr` Input\n", "|--------------------------|----------------------|-------------------------|---|---|\n", "| Negation | ¬ P | `~P` | `~P` | `Expr('~', P)`\n", "| And | P ∧ Q | `P & Q` | `P & Q` | `Expr('&', P, Q)`\n", "| Or | P ∨ Q | `P` | `Q`| `P` | `Q` | `Expr('`|`', P, Q)\n", "| Inequality (Xor) | P ≠ Q | `P ^ Q` | `P ^ Q` | `Expr('^', P, Q)`\n", - "| Implication | P → Q | `implies(P, Q)` | `P ==> Q` | `Expr('==>', P, Q)`\n", - "| Reverse Implication | Q ← P | `rimplies(P, Q)` |`Q <== P` | `Expr('<==', Q, P)`\n", - "| Equivalence | P ↔ Q | `equiv(P, Q)` |`P ==> Q` | `Expr('==>', P, Q)`\n", + "| Implication | P → Q | `P` |`'==>'`| `Q` | `P ==> Q` | `Expr('==>', P, Q)`\n", + "| Reverse Implication | Q ← P | `Q` |`'<=='`| `P` |`Q <== P` | `Expr('<==', Q, P)`\n", + "| Equivalence | P ↔ Q | `P` |`'<=>'`| `Q` |`P ==> Q` | `Expr('==>', P, Q)`\n", "\n", - "Here's an example of defining a sentence:" + "Here's an example of defining a sentence with an implication arrow:" ] }, { @@ -324,7 +325,7 @@ { "data": { "text/plain": [ - "(~(P & Q) <=> (~P | ~Q))" + "(~(P & Q) ==> (~P | ~Q))" ] }, "execution_count": 12, @@ -333,7 +334,7 @@ } ], "source": [ - "equiv(~(P & Q), (~P | ~Q))" + "~(P & Q) |'==>'| (~P | ~Q)" ] }, { @@ -342,7 +343,7 @@ "source": [ "## `expr`: a Shortcut for Constructing Sentences\n", "\n", - "We can't write `(~(P & Q) <=> (~P | ~Q))` as a Python expression, because Python does not have the `<=>` operator. But we can do something almost as good:" + "If the `|'==>'|` notation looks ugly to you, you can use the function `expr` instead:" ] }, { @@ -355,7 +356,7 @@ { "data": { "text/plain": [ - "(~(P & Q) <=> (~P | ~Q))" + "(~(P & Q) ==> (~P | ~Q))" ] }, "execution_count": 13, @@ -364,14 +365,14 @@ } ], "source": [ - "expr('~(P & Q) <=> (~P | ~Q)')" + "expr('~(P & Q) ==> (~P | ~Q)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The function `expr` takes a string as input, and parses it into an `Expr`. The string can contain arrow operators: `==>`, `<==`, or `<=>`. And `expr` automatically defines any symbols, so you don't need to pre-define them:" + "`expr` takes a string as input, and parses it into an `Expr`. The string can contain arrow operators: `==>`, `<==`, or `<=>`, which are handled as if they were regular Python infix operators. And `expr` automatically defines any symbols, so you don't need to pre-define them:" ] }, { @@ -400,7 +401,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For now that's all you need to know about `expr`. Later we will explain the messy details of how it is implemented." + "For now that's all you need to know about `expr`. Later we will explain the messy details of how `expr` is implemented and how `|'==>'|` is handled." ] }, { @@ -424,24 +425,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# TODO: More on KBs, plus what was promised in Intro Section" + "# TODO: More on KBs, plus what was promised in Intro Section\n", + "\n", + "TODO: fill in here ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Appendix: The Messy Details of the Implementation of `expr`\n", + "## Appendix: The Implementation of `|'==>'|`\n", "\n", - "How does `expr` parse a string into an `Expr`? It turns out there three tricks:\n", - "\n", - "1. We do a string substitution, replacing `\"==>\"` with `\"|InfixOp('==>', None)|\"`.\n", - "2. We `eval` the resulting string in an environment in which every identifier\n", - "is bound to a symbol with that identifier as the name.\n", - "3. A coordination between `Expr` and `InfixOp` creates the proper nested `Expr`.\n", - "\n", - "\n", - "That must sound very confusing, so we'll explain it in detail. Consider the sentence `\"P ==> Q\"`. If we try to evaluate that we get a `SyntaxError` because `==>` is not valid Python syntax. So we substitute it away, using the function `expr_handle_infix_ops` (from the `utils` module):" + "Consider the `Expr` formed by this syntax:" ] }, { @@ -454,7 +449,7 @@ { "data": { "text/plain": [ - "\"P |InfixOp('==>', None)| Q\"" + "(P ==> ~Q)" ] }, "execution_count": 15, @@ -463,14 +458,14 @@ } ], "source": [ - "expr_handle_infix_ops('P ==> Q')" + "P |'==>'| ~Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What does that mean? To Python, for any expression `op`, \"`P |op| Q`\" is the same as \"`((P | op) | Q)`\". So the first step is:" + "What is the funny `|'==>'|` syntax? The trick is that \"`|`\" is just the regular Python or-operator, and so is exactly equivalent to this: " ] }, { @@ -483,7 +478,7 @@ { "data": { "text/plain": [ - "InfixOp('==>', P)" + "(P ==> ~Q)" ] }, "execution_count": 16, @@ -492,18 +487,14 @@ } ], "source": [ - "first = (P | InfixOp('==>', None))\n", - "\n", - "first" + "(P | '==>') | ~Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`InfixOp('==>', P)` means an infix operator whose operator string is `'==>'` and whose left-hand element is `P`. What happened here is that the `__or__` method in `Expr` says that if the object on the right is an `InfixOp`, then the result is an `InfixOp` whose `lhs` is the `Expr` on the left of the `\"|\"`.\n", - "\n", - "In the second step, we combine this with `Q`:" + "In other words, there are two applications of or-operators. Here's the first one:" ] }, { @@ -516,7 +507,7 @@ { "data": { "text/plain": [ - "(P ==> Q)" + "PartialExpr('==>', P)" ] }, "execution_count": 17, @@ -525,16 +516,16 @@ } ], "source": [ - "first | Q" + "P | '==>'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What happened here is that the `__or__` method for `InfixOp` says that when combined with anobject on the right, return a new `Expr` whose `op` and first `arg` comes from the `InfixOp` and whose second `arg` is the object on the right. This [trick](http://code.activestate.com/recipes/384122-infix-operators/) is due to [Ferdinand Jamitzky](http://code.activestate.com/recipes/users/98863/).\n", + "What is going on here is that the `__or__` method of `Expr` serves a dual purpose. If the right-hand-side is another `Expr` (or a number), then the result is an `Expr`, as in `(P | Q)`. But if the right-hand-side is a string, then the string is taken to be an operator, and we create a node in the abstract syntax tree corresponding to a partially-filled `Expr`, one where we know the left-hand-side is `P` and the operator is `==>`, but we don't yet know the right-hand-side.\n", "\n", - "Note that we can also use this notation in our own code, or in an interactive session, like this:" + "The `PartialExpr` class has an `__or__` method that says to create an `Expr` node with the right-hand-side filled in. Here we can see the combination of the `PartialExpr` with `Q` to create a complete `Expr`:" ] }, { @@ -547,7 +538,7 @@ { "data": { "text/plain": [ - "((P & Q) ==> P)" + "(P ==> ~Q)" ] }, "execution_count": 18, @@ -556,14 +547,26 @@ } ], "source": [ - "P & Q |implies| P" + "partial = PartialExpr('==>', P) \n", + "partial | ~Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Unfortunately, this puts `implies` at the same precedence as `\"|\"`, which is not quite right. We get this:" + "This [trick](http://code.activestate.com/recipes/384122-infix-operators/) is due to [Ferdinand Jamitzky](http://code.activestate.com/recipes/users/98863/), with a modification by [C. G. Vedant](https://github.com/Chipe1),\n", + "who suggested using a string inside the or-bars.\n", + "\n", + "## Appendix: The Implementation of `expr`\n", + "\n", + "How does `expr` parse a string into an `Expr`? It turns out there are two tricks (besides the Jamitzky/Vedant trick):\n", + "\n", + "1. We do a string substitution, replacing \"`==>`\" with \"`|'==>'|`\" (and likewise for other operators).\n", + "2. We `eval` the resulting string in an environment in which every identifier\n", + "is bound to a symbol with that identifier as the `op`.\n", + "\n", + "In other words," ] }, { @@ -576,7 +579,7 @@ { "data": { "text/plain": [ - "(((P & Q) ==> P) | Q)" + "(~(P & Q) ==> (~P | ~Q))" ] }, "execution_count": 19, @@ -585,14 +588,14 @@ } ], "source": [ - "P & Q |implies| P | Q" + "expr('~(P & Q) ==> (~P | ~Q)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "which is probably not what we meant; when in doubt, put in extra parens:" + "is equivalent to doing:" ] }, { @@ -605,7 +608,7 @@ { "data": { "text/plain": [ - "((P & Q) ==> (P | Q))" + "(~(P & Q) ==> (~P | ~Q))" ] }, "execution_count": 20, @@ -614,7 +617,66 @@ } ], "source": [ - "P & Q |implies| (P | Q)" + "P, Q = symbols('P, Q')\n", + "~(P & Q) |'==>'| (~P | ~Q)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to beware of: this puts `==>` at the same precedence level as `\"|\"`, which is not quite right. For example, we get this:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(((P & Q) ==> P) | Q)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P & Q |'==>'| P | Q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "which is probably not what we meant; when in doubt, put in extra parens:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((P & Q) ==> (P | Q))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(P & Q) |'==>'| (P | Q)" ] }, { From b3fa72514d81fa32e73923257eec6f057ab8b8c4 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 12 Apr 2016 18:45:08 +0530 Subject: [PATCH 256/513] fetches latest commits from aima-data submodule (#219) --- aima-data | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aima-data b/aima-data index 5b0526a5a..dec9000e8 160000 --- a/aima-data +++ b/aima-data @@ -1 +1 @@ -Subproject commit 5b0526a5a4d4312c3e65254c7e205a7ce327503b +Subproject commit dec9000e8c794c8055fa13522ba09b893c5f601f From 59e3abb885c68fd253ae77c6af1de943c615ab8c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Tue, 12 Apr 2016 18:46:22 +0530 Subject: [PATCH 257/513] Consistent Variable Naming (#220) * Change variable names from s_prime to s1, current_state to s1 * Sane default for update_state --- rl.py | 12 ++++++------ search.py | 29 +++++++++++++++-------------- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/rl.py b/rl.py index 8a2e57850..72bc35487 100644 --- a/rl.py +++ b/rl.py @@ -39,18 +39,18 @@ def __init__(self, pi, mdp, alpha=None): self.alpha = lambda n: 1./(1+n) # udacity video def __call__(self, percept): - s_prime, r_prime = self.update_state(percept) + s1, r1 = self.update_state(percept) pi, U, Ns, s, a, r = self.pi, self.U, self.Ns, self.s, self.a, self.r alpha, gamma, terminals = self.alpha, self.gamma, self.terminals - if not Ns[s_prime]: - U[s_prime] = r_prime + if not Ns[s1]: + U[s1] = r1 if s is not None: Ns[s] += 1 - U[s] += alpha(Ns[s]) * (r + gamma * U[s_prime] - U[s]) - if s_prime in terminals: + U[s] += alpha(Ns[s]) * (r + gamma * U[s1] - U[s]) + if s1 in terminals: self.s = self.a = self.r = None else: - self.s, self.a, self.r = s_prime, pi[s_prime], r_prime + self.s, self.a, self.r = s1, pi[s1], r1 return self.a def update_state(self, percept): diff --git a/search.py b/search.py index 5a98523ca..0b71317d5 100644 --- a/search.py +++ b/search.py @@ -437,34 +437,35 @@ def __init__(self, problem): self.result = {} def __call__(self, percept): - current_state = self.update_state(percept) - if self.problem.goal_test(current_state): + s1 = self.update_state(percept) + if self.problem.goal_test(s1): self.a = None else: - if current_state not in self.untried.keys(): - self.untried[current_state] = self.problem.actions( - current_state) + if s1 not in self.untried.keys(): + self.untried[s1] = self.problem.actions(s1) if self.s is not None: - if current_state != self.result[(self.s, self.a)]: - self.result[(self.s, self.a)] = current_state - unbacktracked[current_state].insert(0, self.s) - if len(self.untried[current_state]) == 0: - if len(self.unbacktracked[current_state]) == 0: + if s1 != self.result[(self.s, self.a)]: + self.result[(self.s, self.a)] = s1 + unbacktracked[s1].insert(0, self.s) + if len(self.untried[s1]) == 0: + if len(self.unbacktracked[s1]) == 0: self.a = None else: # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) # noqa - unbacktracked_pop = self.unbacktracked[current_state].pop(0) # noqa + unbacktracked_pop = self.unbacktracked[s1].pop(0) # noqa for (s, b) in self.result.keys(): if self.result[(s, b)] == unbacktracked_pop: self.a = b break else: - self.a = self.untried[current_state].pop(0) - self.s = current_state + self.a = self.untried[s1].pop(0) + self.s = s1 return self.a def update_state(self, percept): - raise NotImplementedError + ''' To be overriden in most cases. The default case + assumes th percept to be of type state''' + raise percept # ______________________________________________________________________________ From a62a3a9a4c6b59aadd3fa8833457ae33eb56f96c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Tue, 12 Apr 2016 18:47:31 +0530 Subject: [PATCH 258/513] Added QLearning to IPy Notebook (#221) --- rl.ipynb | 250 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 243 insertions(+), 7 deletions(-) diff --git a/rl.ipynb b/rl.ipynb index 98b887f64..103c32e9e 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -98,7 +98,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -218,7 +218,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{(0, 1): 0.40645681855595944, (1, 2): 0.7159329142704773, (3, 2): 1, (0, 0): 0.2886341019228155, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.20553303981983, (3, 1): -1, (2, 2): 0.8560486321875528, (2, 1): 0.606857283945162, (0, 2): 0.5612793239398001}\n" + "{(0, 1): 0.4496668011879283, (1, 2): 0.619085803445832, (3, 2): 1, (0, 0): 0.32062531035042224, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.235638474671875, (3, 1): -1, (2, 2): 0.7597530664991547, (2, 1): 0.4275522091676434, (0, 2): 0.5333144285450669}\n" ] } ], @@ -277,9 +277,9 @@ "outputs": [ { "data": { - "image/png": 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DiO2kw/U51K4d34bh3DQnN9f+f/x4O2egvHDYty/2HfJnn8VevvI4O8h0Cgfn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\negCNQp5LyLpI5ZpDNCfRVSu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LZOLFweMtB+cSt2XCgAF2PRIHUWyEQhzi3UolJel7aZSVmesnWQsx6Faqr7ff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\nrFq1Kq9pyq0khChKSkpK0HO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hLNenXLJwuqLp2KZEEHgy4iQvx17mg6Mf5OiNozPdJ+uOreMt39zCrSe3ev2eYeuHMaB3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\nw/8DGqwOkNBaudkAAAAASUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -320,6 +320,242 @@ "graph_utility_estimates(agent, sequential_decision_environment, 500, [(2,2), (3,2)])" ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Active Reinforcement Learning\n", + "\n", + "Unlike Passive Reinforcement Learning in Active Reinforcement Learning we are not bound by a policy pi and we need to select our actions. In other words the agent needs to learn an optimal policy. The fundamental tradeoff the agent needs to face is that of exploration vs. exploitation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### QLearning Agent\n", + "\n", + "The QLearningAgent class in the rl module implements the Agent Program described in **Fig 21.8** of the AIMA Book. In Q-Learning the agent learns an action-value function Q which gives the utility of taking a given action in a particular state. Q-Learning does not required a transition model and hence is a model free method. Let us look into the source before we see some usage examples." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource QLearningAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Agent Program can be obtained by creating the instance of the class by passing the appropriate parameters. Because of the __ call __ method the object that is created behaves like a callable and returns an appropriate action as most Agent Programs do. To instantiate the object we need a mdp similar to the PassiveTDAgent.\n", + "\n", + " Let us use the same GridMDP object we used above. **Figure 17.1 (sequential_decision_environment)** is similar to **Figure 21.1** but has some discounting as **gamma = 0.9**. The class also implements an exploration function **f** which returns fixed **Rplus** untill agent has visited state, action **Ne** number of times. This is the same as the one defined on page **842** of the book. The method **actions_in_state** returns actions possible in given state. It is useful when applying max and argmax operations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us create our object now. We also use the **same alpha** as given in the footnote of the book on **page 837**. We use **Rplus = 2** and **Ne = 5** as defined on page 843. **Fig 21.7** " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "q_agent = QLearningAgent(sequential_decision_environment, Ne=5, Rplus=2, \n", + " alpha=lambda n: 60./(59+n))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to try out the q_agent we make use of the **run_single_trial** function in rl.py (which was also used above). Let us use **200** iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for i in range(200):\n", + " run_single_trial(q_agent,sequential_decision_environment)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us see the Q Values. The keys are state-action pairs. Where differnt actions correspond according to:\n", + "\n", + "north = (0, 1)\n", + "south = (0,-1)\n", + "west = (-1, 0)\n", + "east = (1, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(float,\n", + " {((0, 0), (-1, 0)): -0.07323076923076924,\n", + " ((0, 0), (0, -1)): -0.0759999433406361,\n", + " ((0, 0), (0, 1)): 0.2244371077466747,\n", + " ((0, 0), (1, 0)): -0.07085714285714287,\n", + " ((0, 1), (-1, 0)): -0.04883916667786259,\n", + " ((0, 1), (0, -1)): -0.05252175603090532,\n", + " ((0, 1), (0, 1)): 0.3396752416362625,\n", + " ((0, 1), (1, 0)): -0.07323076923076924,\n", + " ((0, 2), (-1, 0)): -0.05158410382845185,\n", + " ((0, 2), (0, -1)): -0.04733337973118637,\n", + " ((0, 2), (0, 1)): -0.048398095611170026,\n", + " ((0, 2), (1, 0)): 0.4729172313717893,\n", + " ((1, 0), (-1, 0)): 0.14857758363326573,\n", + " ((1, 0), (0, -1)): -0.0759999433406361,\n", + " ((1, 0), (0, 1)): -0.07695450531425811,\n", + " ((1, 0), (1, 0)): -0.09719395035017139,\n", + " ((1, 2), (-1, 0)): 0.21593724199115555,\n", + " ((1, 2), (0, -1)): 0.26570820298073916,\n", + " ((1, 2), (0, 1)): 0.19612684250448048,\n", + " ((1, 2), (1, 0)): 0.6105607273543103,\n", + " ((2, 0), (-1, 0)): 0.06795076480003,\n", + " ((2, 0), (0, -1)): -0.11306695825372484,\n", + " ((2, 0), (0, 1)): -0.105596446586541,\n", + " ((2, 0), (1, 0)): -0.10409381636745853,\n", + " ((2, 1), (-1, 0)): -0.0383184014263534,\n", + " ((2, 1), (0, -1)): -0.7913059177862865,\n", + " ((2, 1), (0, 1)): -0.7672970392961057,\n", + " ((2, 1), (1, 0)): -0.8402721538112866,\n", + " ((2, 2), (-1, 0)): 0.2351847866756862,\n", + " ((2, 2), (0, -1)): 0.24909509983624728,\n", + " ((2, 2), (0, 1)): 0.25112211666264095,\n", + " ((2, 2), (1, 0)): 0.7743960998734626,\n", + " ((3, 0), (-1, 0)): -0.1037923159515085,\n", + " ((3, 0), (0, -1)): -0.07807333741195537,\n", + " ((3, 0), (0, 1)): -0.9374064176172849,\n", + " ((3, 0), (1, 0)): -0.07323076923076924,\n", + " ((3, 1), None): -1,\n", + " ((3, 2), None): 1})" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q_agent.Q" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Utility **U** of each state is related to **Q** by the following equation.\n", + "\n", + "**U (s) = max a Q(s, a)**\n", + "\n", + "Let us convert the Q Values above into U estimates.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "U = defaultdict(lambda: -1000.) # Very Large Negative Value for Comparison see below.\n", + "for state_action, value in q_agent.Q.items():\n", + " state, action = state_action\n", + " if U[state] < value:\n", + " U[state] = value" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(>,\n", + " {(0, 0): 0.2244371077466747,\n", + " (0, 1): 0.3396752416362625,\n", + " (0, 2): 0.4729172313717893,\n", + " (1, 0): 0.14857758363326573,\n", + " (1, 2): 0.6105607273543103,\n", + " (2, 0): 0.06795076480003,\n", + " (2, 1): -0.0383184014263534,\n", + " (2, 2): 0.7743960998734626,\n", + " (3, 0): -0.07323076923076924,\n", + " (3, 1): -1,\n", + " (3, 2): 1})" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "U" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us finally compare these estimates to value_iteration results." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(0, 1): 0.3984432178350045, (1, 2): 0.649585681261095, (3, 2): 1.0, (0, 0): 0.2962883154554812, (3, 0): 0.12987274656746342, (3, 1): -1.0, (2, 1): 0.48644001739269643, (2, 0): 0.3447542300124158, (2, 2): 0.7953620878466678, (1, 0): 0.25386699846479516, (0, 2): 0.5093943765842497}\n" + ] + } + ], + "source": [ + "print(value_iteration(sequential_decision_environment))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -346,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From 1ff5ae8eb4aeff63966f32564a062465b4aea1b3 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Thu, 14 Apr 2016 00:18:28 +0530 Subject: [PATCH 259/513] modifies Fig. to Figure all over the repository (#223) * modifies Fig. to Figure all over the repository * fixed a small type in logic.py --- agents.py | 10 +++++----- csp.py | 6 +++--- games.py | 12 ++++++------ learning.py | 12 ++++++------ logic.py | 20 ++++++++++---------- mdp.py | 4 ++-- nlp.py | 6 +++--- probability.py | 34 +++++++++++++++++----------------- rl.py | 6 +++--- search.py | 32 ++++++++++++++++---------------- tests/test_probability.py | 4 ++-- 11 files changed, 73 insertions(+), 73 deletions(-) diff --git a/agents.py b/agents.py index 6573dd9c7..7cd1146ef 100644 --- a/agents.py +++ b/agents.py @@ -118,7 +118,7 @@ def TableDrivenAgentProgram(table): """This agent selects an action based on the percept sequence. It is practical only for tiny domains. To customize it, provide as table a dictionary of all - {percept_sequence:action} pairs. [Fig. 2.7]""" + {percept_sequence:action} pairs. [Figure 2.7]""" percepts = [] def program(percept): @@ -136,7 +136,7 @@ def RandomAgentProgram(actions): def SimpleReflexAgentProgram(rules, interpret_input): - "This agent takes action based solely on the percept. [Fig. 2.10]" + "This agent takes action based solely on the percept. [Figure 2.10]" def program(percept): state = interpret_input(percept) rule = rule_match(state, rules) @@ -146,7 +146,7 @@ def program(percept): def ModelBasedReflexAgentProgram(rules, update_state): - "This agent takes action based on the percept and state. [Fig. 2.12]" + "This agent takes action based on the percept and state. [Figure 2.12]" def program(percept): program.state = update_state(program.state, program.action, percept) rule = rule_match(program.state, rules) @@ -173,7 +173,7 @@ def RandomVacuumAgent(): def TableDrivenVacuumAgent(): - "[Fig. 2.3]" + "[Figure 2.3]" table = {((loc_A, 'Clean'),): 'Right', ((loc_A, 'Dirty'),): 'Suck', ((loc_B, 'Clean'),): 'Left', @@ -189,7 +189,7 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): - "A reflex agent for the two-state vacuum environment. [Fig. 2.8]" + "A reflex agent for the two-state vacuum environment. [Figure 2.8]" def program(percept): location, status = percept if status == 'Dirty': diff --git a/csp.py b/csp.py index af99938e4..d20a12d32 100644 --- a/csp.py +++ b/csp.py @@ -157,7 +157,7 @@ def conflicted_vars(self, current): def AC3(csp, queue=None, removals=None): - """[Fig. 6.3]""" + """[Figure 6.3]""" if queue is None: queue = [(Xi, Xk) for Xi in csp.variables for Xk in csp.neighbors[Xi]] csp.support_pruning() @@ -251,7 +251,7 @@ def backtracking_search(csp, select_unassigned_variable=first_unassigned_variable, order_domain_values=unordered_domain_values, inference=no_inference): - """[Fig. 6.5] + """[Figure 6.5] """ def backtrack(assignment): @@ -306,7 +306,7 @@ def min_conflicts_value(csp, var, current): def tree_csp_solver(csp): - "[Fig. 6.11]" + "[Figure 6.11]" assignment = {} root = csp.variables[0] X, parent = topological_sort(csp.variables, root) diff --git a/games.py b/games.py index b03530a97..73b8a8312 100644 --- a/games.py +++ b/games.py @@ -15,7 +15,7 @@ def minimax_decision(state, game): """Given a state in a game, calculate the best move by searching - forward all the way to the terminal states. [Fig. 5.3]""" + forward all the way to the terminal states. [Figure 5.3]""" player = game.to_move(state) @@ -44,7 +44,7 @@ def min_value(state): def alphabeta_full_search(state, game): """Search game to determine best action; use alpha-beta pruning. - As in [Fig. 5.7], this version searches all the way to the leaves.""" + As in [Figure 5.7], this version searches all the way to the leaves.""" player = game.to_move(state) @@ -207,7 +207,7 @@ def __repr__(self): class Fig52Game(Game): - """The game represented in [Fig. 5.2]. Serves as a simple test case.""" + """The game represented in [Figure 5.2]. Serves as a simple test case.""" succs = dict(A=dict(a1='B', a2='C', a3='D'), B=dict(b1='B1', b2='B2', b3='B3'), @@ -334,12 +334,12 @@ def __init__(self, varname, player_1='human', player_2='random', id=None, width= self.players = (player_1, player_2) self.draw_board() self.font("Ariel 30px") - + def mouse_click(self, x, y): player = self.players[self.turn] if self.ttt.terminal_test(self.state): return - + if player == 'human': x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1 if (x, y) not in self.ttt.actions(self.state): @@ -379,7 +379,7 @@ def draw_board(self): self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1) self.update() - + def draw_x(self, position): self.stroke(0, 255, 0) x, y = [i-1 for i in position] diff --git a/learning.py b/learning.py index 071b721b7..b2e94c284 100644 --- a/learning.py +++ b/learning.py @@ -328,7 +328,7 @@ def __repr__(self): def DecisionTreeLearner(dataset): - "[Fig. 18.5]" + "[Figure 18.5]" target, values = dataset.target, dataset.values @@ -398,7 +398,7 @@ def information_content(values): def DecisionListLearner(dataset): - """[Fig. 18.11]""" + """[Figure 18.11]""" def decision_list_learning(examples): if not examples: @@ -511,7 +511,7 @@ def network(input_units, hidden_layer_sizes, output_units): def BackPropagationLearner(dataset, net, learning_rate, epoches): - "[Fig. 18.23] The back-propagation algorithm for multilayer network" + "[Figure 18.23] The back-propagation algorithm for multilayer network" # Initialise weights for layer in net: for node in layer: @@ -668,7 +668,7 @@ def predict(example): def AdaBoost(L, K): - """[Fig. 18.34]""" + """[Figure 18.34]""" def train(dataset): examples, target = dataset.examples, dataset.target N = len(examples) @@ -868,11 +868,11 @@ def score(learner, size): attrnames="sepal-len sepal-width petal-len petal-width class") # ______________________________________________________________________________ -# The Restaurant example from Fig. 18.2 +# The Restaurant example from [Figure 18.2] def RestaurantDataSet(examples=None): - "Build a DataSet of Restaurant waiting examples. [Fig. 18.3]" + "Build a DataSet of Restaurant waiting examples. [Figure 18.3]" return DataSet(name='restaurant', target='Wait', examples=examples, attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' + 'Raining Reservation Type WaitEstimate Wait') diff --git a/logic.py b/logic.py index 625e7a49b..1b73ed933 100644 --- a/logic.py +++ b/logic.py @@ -111,7 +111,7 @@ def retract(self, sentence): def KB_AgentProgram(KB): - """A generic logical knowledge-based agent program. [Fig. 7.1]""" + """A generic logical knowledge-based agent program. [Figure 7.1]""" steps = itertools.count() def program(percept): @@ -191,7 +191,7 @@ def parse_definite_clause(s): def tt_entails(kb, alpha): """Does kb entail the sentence alpha? Use truth tables. For propositional - kb's and sentences. [Fig. 7.10]. Note that the 'kb' should be an + kb's and sentences. [Figure 7.10]. Note that the 'kb' should be an Expr which is a conjunction of clauses. >>> tt_entails(expr('P & Q'), expr('Q')) True @@ -434,7 +434,7 @@ def disjuncts(s): def pl_resolution(KB, alpha): - "Propositional-logic resolution: say if alpha follows from KB. [Fig. 7.12]" + "Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]" clauses = KB.clauses + conjuncts(to_cnf(~alpha)) new = set() while True: @@ -493,7 +493,7 @@ def clauses_with_premise(self, p): def pl_fc_entails(KB, q): """Use forward chaining to see if a PropDefiniteKB entails symbol q. - [Fig. 7.15] + [Figure 7.15] >>> pl_fc_entails(horn_clauses_KB, expr('Q')) True """ @@ -527,7 +527,7 @@ def pl_fc_entails(KB, q): horn_clauses_KB.tell(expr(s)) # ______________________________________________________________________________ -# DPLL-Satisfiable [Fig. 7.17] +# DPLL-Satisfiable [Figure 7.17] def dpll_satisfiable(s): @@ -633,7 +633,7 @@ def inspect_literal(literal): return literal, True # ______________________________________________________________________________ -# Walk-SAT [Fig. 7.18] +# Walk-SAT [Figure 7.18] def WalkSAT(clauses, p=0.5, max_flips=10000): @@ -670,7 +670,7 @@ def sat_count(sym): class HybridWumpusAgent(agents.Agent): - "An agent for the wumpus world that does logical inference. [Fig. 7.20]""" + "An agent for the wumpus world that does logical inference. [Figure 7.20]""" def __init__(self): unimplemented() @@ -684,7 +684,7 @@ def plan_route(current, goals, allowed): def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): """Converts a planning problem to Satisfaction problem by translating it to a cnf sentence. - [Fig. 7.22]""" + [Figure 7.22]""" #Functions used by SAT_plan def translate_to_SAT(init, transition, goal, time): @@ -767,7 +767,7 @@ def extract_solution(model): def unify(x, y, s): """Unify expressions x,y with substitution s; return a substitution that would make x,y equal, or None if x,y can not unify. x and y can be - variables (e.g. Expr('x')), constants, lists, or Exprs. [Fig. 9.1]""" + variables (e.g. Expr('x')), constants, lists, or Exprs. [Figure 9.1]""" if s is None: return None elif x == y: @@ -933,7 +933,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): - """A simple backward-chaining algorithm for first-order logic. [Fig. 9.6] + """A simple backward-chaining algorithm for first-order logic. [Figure 9.6] KB should be an instance of FolKB, and query an atomic sentence. """ return fol_bc_or(KB, query, {}) diff --git a/mdp.py b/mdp.py index d81f8d741..83f6009d3 100644 --- a/mdp.py +++ b/mdp.py @@ -109,7 +109,7 @@ def to_arrows(self, policy): def value_iteration(mdp, epsilon=0.001): - "Solving an MDP by value iteration. [Fig. 17.4]" + "Solving an MDP by value iteration. [Figure 17.4]" U1 = dict([(s, 0) for s in mdp.states]) R, T, gamma = mdp.R, mdp.T, mdp.gamma while True: @@ -140,7 +140,7 @@ def expected_utility(a, s, U, mdp): def policy_iteration(mdp): - "Solve an MDP by policy iteration [Fig. 17.7]" + "Solve an MDP by policy iteration [Figure 17.7]" U = dict([(s, 0) for s in mdp.states]) pi = dict([(s, random.choice(mdp.actions(s))) for s in mdp.states]) while True: diff --git a/nlp.py b/nlp.py index 02423e7dc..77a22931a 100644 --- a/nlp.py +++ b/nlp.py @@ -53,14 +53,14 @@ def __repr__(self): return '' % self.name E0 = Grammar('E0', - Rules( # Grammar for E_0 [Fig. 22.4] + Rules( # Grammar for E_0 [Figure 22.4] S='NP VP | S Conjunction S', NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', # noqa VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb', PP='Preposition NP', RelClause='That VP'), - Lexicon( # Lexicon for E_0 [Fig. 22.3] + Lexicon( # Lexicon for E_0 [Figure 22.3] Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", # noqa Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", # noqa Adjective="right | left | east | south | back | smelly", @@ -116,7 +116,7 @@ def rewrite(tokens, into): class Chart: - """Class for parsing sentences using a chart data structure. [Fig 22.7] + """Class for parsing sentences using a chart data structure. [Figure 22.7] >>> chart = Chart(E0); >>> len(chart.parses('the stench is in 2 2')) 1 diff --git a/probability.py b/probability.py index 16f05197f..5a26fac32 100644 --- a/probability.py +++ b/probability.py @@ -16,7 +16,7 @@ def DTAgentProgram(belief_state): - "A decision-theoretic agent. [Fig. 13.1]" + "A decision-theoretic agent. [Figure 13.1]" def program(percept): belief_state.observe(program.action, percept) program.action = argmax(belief_state.actions(), @@ -272,7 +272,7 @@ def sample(self, event): def __repr__(self): return repr((self.variable, ' '.join(self.parents))) -# Burglary example [Fig. 14.2] +# Burglary example [Figure 14.2] T, F = True, False @@ -290,7 +290,7 @@ def __repr__(self): def enumeration_ask(X, e, bn): """Return the conditional probability distribution of variable X - given evidence e, from BayesNet bn. [Fig. 14.9] + given evidence e, from BayesNet bn. [Figure 14.9] >>> enumeration_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary ... ).show_approx() 'False: 0.716, True: 0.284'""" @@ -320,7 +320,7 @@ def enumerate_all(variables, e, bn): def elimination_ask(X, e, bn): - """Compute bn's P(X|e) by variable elimination. [Fig. 14.11] + """Compute bn's P(X|e) by variable elimination. [Figure 14.11] >>> elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary ... ).show_approx() 'False: 0.716, True: 0.284'""" @@ -409,7 +409,7 @@ def all_events(variables, bn, e): # ______________________________________________________________________________ -# Fig. 14.12a: sprinkler network +# [Figure 14.12a]: sprinkler network sprinkler = BayesNet([ ('Cloudy', '', 0.5), @@ -423,7 +423,7 @@ def all_events(variables, bn, e): def prior_sample(bn): """Randomly sample from bn's full joint distribution. The result - is a {variable: value} dict. [Fig. 14.13]""" + is a {variable: value} dict. [Figure 14.13]""" event = {} for node in bn.nodes: event[node.variable] = node.sample(event) @@ -434,7 +434,7 @@ def prior_sample(bn): def rejection_sampling(X, e, bn, N): """Estimate the probability distribution of variable X given - evidence e in BayesNet bn, using N samples. [Fig. 14.14] + evidence e in BayesNet bn, using N samples. [Figure 14.14] Raises a ZeroDivisionError if all the N samples are rejected, i.e., inconsistent with e. >>> random.seed(47) @@ -443,9 +443,9 @@ def rejection_sampling(X, e, bn, N): 'False: 0.7, True: 0.3' """ counts = dict((x, 0) - for x in bn.variable_values(X)) # bold N in Fig. 14.14 + for x in bn.variable_values(X)) # bold N in [Figure 14.14] for j in range(N): - sample = prior_sample(bn) # boldface x in Fig. 14.14 + sample = prior_sample(bn) # boldface x in [Figure 14.14] if consistent_with(sample, e): counts[sample[X]] += 1 return ProbDist(X, counts) @@ -461,7 +461,7 @@ def consistent_with(event, evidence): def likelihood_weighting(X, e, bn, N): """Estimate the probability distribution of variable X given - evidence e in BayesNet bn. [Fig. 14.15] + evidence e in BayesNet bn. [Figure 14.15] >>> random.seed(1017) >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T), ... burglary, 10000).show_approx() @@ -469,7 +469,7 @@ def likelihood_weighting(X, e, bn, N): """ W = dict((x, 0) for x in bn.variable_values(X)) for j in range(N): - sample, weight = weighted_sample(bn, e) # boldface x, w in Fig. 14.15 + sample, weight = weighted_sample(bn, e) # boldface x, w in [Figure 14.15] W[sample[X]] += weight return ProbDist(X, W) @@ -479,7 +479,7 @@ def weighted_sample(bn, e): return the event and its weight, the likelihood that the event accords to the evidence.""" w = 1 - event = dict(e) # boldface x in Fig. 14.15 + event = dict(e) # boldface x in [Figure 14.15] for node in bn.nodes: Xi = node.variable if Xi in e: @@ -492,11 +492,11 @@ def weighted_sample(bn, e): def gibbs_ask(X, e, bn, N): - """[Fig. 14.16]""" + """[Figure 14.16]""" assert X not in e, "Query variable must be distinct from evidence" - counts = {x: 0 for x in bn.variable_values(X)} # bold N in Fig. 14.16 + counts = {x: 0 for x in bn.variable_values(X)} # bold N in [Figure 14.16] Z = [var for var in bn.variables if var not in e] - state = dict(e) # boldface x in Fig. 14.16 + state = dict(e) # boldface x in [Figure 14.16] for Zi in Z: state[Zi] = random.choice(bn.variable_values(Zi)) for j in range(N): @@ -560,7 +560,7 @@ def backward(HMM, b, ev): def forward_backward(HMM, ev, prior): - """[Fig. 15.4] + """[Figure 15.4] Forward-Backward algorithm for smoothing. Computes posterior probabilities of a sequence of states given a sequence of observations.""" t = len(ev) @@ -588,7 +588,7 @@ def forward_backward(HMM, ev, prior): def fixed_lag_smoothing(e_t, HMM, d, ev, t): - """[Fig. 15.6] + """[Figure 15.6] Smoothing algorithm with a fixed time lag of 'd' steps. Online algorithm that outputs the new smoothed estimate if observation for new time step is given.""" diff --git a/rl.py b/rl.py index 72bc35487..eed070ac3 100644 --- a/rl.py +++ b/rl.py @@ -11,7 +11,7 @@ class PassiveADPAgent(agents.Agent): """Passive (non-learning) agent that uses adaptive dynamic programming - on a given MDP and policy. [Fig. 21.2]""" + on a given MDP and policy. [Figure 21.2]""" NotImplemented @@ -19,7 +19,7 @@ class PassiveTDAgent: """The abstract class for a Passive (non-learning) agent that uses temporal differences to learn utility estimates. Override update_state method to convert percept to state and reward. The mdp being probided - should be an instance of a subclass of the MDP Class.[Fig. 21.4] + should be an instance of a subclass of the MDP Class.[Figure 21.4] """ def __init__(self, pi, mdp, alpha=None): @@ -62,7 +62,7 @@ def update_state(self, percept): class QLearningAgent: """ An exploratory Q-learning agent. It avoids having to learn the transition model because the Q-value of a state can be related directly to those of - its neighbors. [Fig. 21.8] + its neighbors. [Figure 21.8] """ def __init__(self, mdp, Ne, Rplus, alpha=None): diff --git a/search.py b/search.py index 0b71317d5..9d5798256 100644 --- a/search.py +++ b/search.py @@ -105,7 +105,7 @@ def expand(self, problem): for action in problem.actions(self.state)] def child_node(self, problem, action): - "[Fig. 3.10]" + "[Figure 3.10]" next = problem.result(self.state, action) return Node(next, self, action, problem.path_cost(self.path_cost, self.state, @@ -139,7 +139,7 @@ def __hash__(self): class SimpleProblemSolvingAgentProgram: - """Abstract framework for a problem-solving agent. [Fig. 3.1]""" + """Abstract framework for a problem-solving agent. [Figure 3.1]""" def __init__(self, initial_state=None): self.state = initial_state @@ -174,7 +174,7 @@ def search(self, problem): def tree_search(problem, frontier): """Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. - Don't worry about repeated paths to a state. [Fig. 3.7]""" + Don't worry about repeated paths to a state. [Figure 3.7]""" frontier.append(Node(problem.initial)) while frontier: node = frontier.pop() @@ -187,7 +187,7 @@ def tree_search(problem, frontier): def graph_search(problem, frontier): """Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. - If two paths reach a state, only use the first one. [Fig. 3.7]""" + If two paths reach a state, only use the first one. [Figure 3.7]""" frontier.append(Node(problem.initial)) explored = set() while frontier: @@ -217,7 +217,7 @@ def depth_first_graph_search(problem): def breadth_first_search(problem): - "[Fig. 3.11]" + "[Figure 3.11]" node = Node(problem.initial) if problem.goal_test(node.state): return node @@ -267,12 +267,12 @@ def best_first_graph_search(problem, f): def uniform_cost_search(problem): - "[Fig. 3.14]" + "[Figure 3.14]" return best_first_graph_search(problem, lambda node: node.path_cost) def depth_limited_search(problem, limit=50): - "[Fig. 3.17]" + "[Figure 3.17]" def recursive_dls(node, problem, limit): if problem.goal_test(node.state): return node @@ -293,7 +293,7 @@ def recursive_dls(node, problem, limit): def iterative_deepening_search(problem): - "[Fig. 3.18]" + "[Figure 3.18]" for depth in range(sys.maxsize): result = depth_limited_search(problem, depth) if result != 'cutoff': @@ -318,7 +318,7 @@ def astar_search(problem, h=None): def recursive_best_first_search(problem, h=None): - "[Fig. 3.26]" + "[Figure 3.26]" h = memoize(h or problem.h, 'h') def RBFS(problem, node, flimit): @@ -351,7 +351,7 @@ def RBFS(problem, node, flimit): def hill_climbing(problem): """From the initial node, keep choosing the neighbor with highest value, - stopping when no neighbor is better. [Fig. 4.2]""" + stopping when no neighbor is better. [Figure 4.2]""" current = Node(problem.initial) while True: neighbors = current.expand(problem) @@ -371,7 +371,7 @@ def exp_schedule(k=20, lam=0.005, limit=100): def simulated_annealing(problem, schedule=exp_schedule()): - "[Fig. 4.5]" + "[Figure 4.5]" current = Node(problem.initial) for t in range(sys.maxsize): T = schedule(t) @@ -394,7 +394,7 @@ def and_or_graph_search(problem): The agent must be able to handle all possible states of the AND node(as it may end up in any of them) returns a conditional plan to reach goal state, or failure if the former is not possible""" - "[Fig. 4.11]" + "[Figure 4.11]" # functions used by and_or_search def or_search(state, problem, path): @@ -426,7 +426,7 @@ class OnlineDFSAgent: """The abstract class for an OnlineDFSAgent. Override update_state method to convert percept to state. While initilizing the subclass a problem needs to be provided which is an instance of a subclass - of the Problem Class. [Fig. 4.21] """ + of the Problem Class. [Figure 4.21] """ def __init__(self, problem): self.problem = problem @@ -509,11 +509,11 @@ def goal_test(self, state): class LRTAStarAgent: - """ [Fig. 4.24] + """ [Figure 4.24] Abstract class for LRTA*-Agent. A problem needs to be provided which is an instanace of a subclass of Problem Class. - Takes a OnlineSearchProblem [Fig. 4.23] as a problem + Takes a OnlineSearchProblem [Figure 4.23] as a problem """ def __init__(self, problem): @@ -578,7 +578,7 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): - "[Fig. 4.8]" + "[Figure 4.8]" for i in range(ngen): new_population = [] for i in len(population): diff --git a/tests/test_probability.py b/tests/test_probability.py index 1183279cf..8bcec5e58 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -137,7 +137,7 @@ def test_fixed_lag_smoothing(): d = 1 assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] - + def test_particle_filtering(): N = 10 @@ -164,7 +164,7 @@ def test_particle_filtering(): >>> P['rain'] #doctest:+ELLIPSIS 0.2... -# A Joint Probability Distribution is dealt with like this (Fig. 13.3): # noqa +# A Joint Probability Distribution is dealt with like this [Figure 13.3]: # noqa >>> P = JointProbDist(['Toothache', 'Cavity', 'Catch']) >>> T, F = True, False >>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008 From dda64bda4a22535c93e90cb2e9edd92769fc7f9b Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Thu, 14 Apr 2016 00:18:40 +0530 Subject: [PATCH 260/513] adds table for Figures of implemented data structures (#224) * adds table for Figures of implemented data structures * fixed a small typo --- README.md | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7d6d3b490..1fdf18446 100644 --- a/README.md +++ b/README.md @@ -17,12 +17,12 @@ When complete, this project will have Python code for all the pseudocode algorit -# Index of Code # +# Index of Code -Here is a table of algorithms, the figure and page where they appear in the book, and the file where they appear in the code. Unfortuately, this chart was made for the old second edition; and has only been partially upfdated to third edition, and not at all to fourth edition. We could use help fixing up the table, based on the figures in [algorithms.pdf](https://github.com/aimacode/aima-pseudocode/blob/master/algorithms.pdf). Empty implementations are a good place for contributors to look for an issue. +Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. -| **Fig** | **Name (in 3rd edition)** | **Name (in code)** | **File** +| **Figure** | **Name (in 3rd edition)** | **Name (in repository)** | **File** |:--------|:-------------------|:---------|:-----------| | 2.1 | Environment | `Environment` | [`agents.py`](../master/agents.py) | | 2.1 | Agent | `Agent` | [`agents.py`](../master/agents.py) | @@ -123,6 +123,22 @@ Here is a table of algorithms, the figure and page where they appear in the book | 25.9 | Monte-Carlo-Localization| | +# Index of data structures + +Here is a table of the implemented data structures, the figure, name of the implementation in the reposiroty, and the file where they are implemented. + +| **Figure** | **Name (in repository)** | **File** | +|:-----------|:-------------------------|:---------| +| 3.2 | romania_map | [`search.py`](../master/search.py) | +| 4.9 | vacumm_world | [`search.py`](../master/search.py) | +| 4.23 | one_dim_state_space | [`search.py`](../master/search.py) | +| 6.1 | australia_map | [`search.py`](../master/search.py) | +| 7.13 | wumpus_world_inference | [`logic.py`](../master/login.py) | +| 7.16 | horn_clauses_KB | [`logic.py`](../master/logic.py) | +| 17.1 | sequential_decision_environment | [`mdp.py`](../master/mdp.py) | +| 18.2 | waiting_decision_tree | [`learning.py`](../master/learning.py) | + + # Acknowledgements Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Thanks to all! From 5882de387f88e769da8548bd7e73f58c68e7d0b4 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Thu, 14 Apr 2016 00:19:15 +0530 Subject: [PATCH 261/513] Fix mistakes (#225) * fixes an error in search.py * modifies the method name 'exec' which is a built-in name --- canvas.py | 24 ++++++++++++------------ search.py | 2 +- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/canvas.py b/canvas.py index 6ba4a7f8b..8133babfd 100644 --- a/canvas.py +++ b/canvas.py @@ -33,7 +33,7 @@ def mouse_click(self, x, y): def mouse_move(self, x, y): raise NotImplementedError - def exec(self, exec_str): + def execute(self, exec_str): "Stores the command to be exectued to a list which is used later during update()" if not isinstance(exec_str, str): print("Invalid execution argument:",exec_str) @@ -43,19 +43,19 @@ def exec(self, exec_str): def fill(self, r, g, b): "Changes the fill color to a color in rgb format" - self.exec("fill({0}, {1}, {2})".format(r, g, b)) + self.execute("fill({0}, {1}, {2})".format(r, g, b)) def stroke(self, r, g, b): "Changes the colors of line/strokes to rgb" - self.exec("stroke({0}, {1}, {2})".format(r, g, b)) + self.execute("stroke({0}, {1}, {2})".format(r, g, b)) def strokeWidth(self, w): "Changes the width of lines/strokes to 'w' pixels" - self.exec("strokeWidth({0})".format(w)) + self.execute("strokeWidth({0})".format(w)) def rect(self, x, y, w, h): "Draw a rectangle with 'w' width, 'h' height and (x, y) as the top-left corner" - self.exec("rect({0}, {1}, {2}, {3})".format(x, y, w, h)) + self.execute("rect({0}, {1}, {2}, {3})".format(x, y, w, h)) def rect_n(self, xn, yn, wn, hn): "Similar to rect(), but the dimensions are normalized to fall between 0 and 1" @@ -67,7 +67,7 @@ def rect_n(self, xn, yn, wn, hn): def line(self, x1, y1, x2, y2): "Draw a line from (x1, y1) to (x2, y2)" - self.exec("line({0}, {1}, {2}, {3})".format(x1, y1, x2, y2)) + self.execute("line({0}, {1}, {2}, {3})".format(x1, y1, x2, y2)) def line_n(self, x1n, y1n, x2n, y2n): "Similar to line(), but the dimensions are normalized to fall between 0 and 1" @@ -79,7 +79,7 @@ def line_n(self, x1n, y1n, x2n, y2n): def arc(self, x, y, r, start, stop): "Draw an arc with (x, y) as centre, 'r' as radius from angles 'start' to 'stop'" - self.exec("arc({0}, {1}, {2}, {3}, {4})".format(x, y, r, start, stop)) + self.execute("arc({0}, {1}, {2}, {3}, {4})".format(x, y, r, start, stop)) def arc_n(self, xn ,yn, rn, start, stop): """Similar to arc(), but the dimensions are normalized to fall between 0 and 1 @@ -92,18 +92,18 @@ def arc_n(self, xn ,yn, rn, start, stop): def clear(self): "Clear the HTML canvas" - self.exec("clear()") + self.execute("clear()") def font(self, font): "Changes the font of text" - self.exec('font("{0}")'.format(font)) + self.execute('font("{0}")'.format(font)) def text(self, txt, x, y, fill = True): "Display a text at (x, y)" if fill: - self.exec('fill_text("{0}", {1}, {2})'.format(txt, x, y)) + self.execute('fill_text("{0}", {1}, {2})'.format(txt, x, y)) else: - self.exec('stroke_text("{0}", {1}, {2})'.format(txt, x, y)) + self.execute('stroke_text("{0}", {1}, {2})'.format(txt, x, y)) def text_n(self, txt, xn, yn, fill = True): "Similar to text(), but with normalized coordinates" @@ -116,7 +116,7 @@ def alert(self, message): display(HTML(''.format(message))) def update(self): - "Execute the JS code to execute the commands queued by exec()" + "Execute the JS code to execute the commands queued by execute()" exec_code = "" self.exec_list = [] display(HTML(exec_code)) diff --git a/search.py b/search.py index 9d5798256..595ef965b 100644 --- a/search.py +++ b/search.py @@ -465,7 +465,7 @@ def __call__(self, percept): def update_state(self, percept): ''' To be overriden in most cases. The default case assumes th percept to be of type state''' - raise percept + return percept # ______________________________________________________________________________ From f129c5caef927591c2ae4ea57872f0757b11455c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 14 Apr 2016 00:20:31 +0530 Subject: [PATCH 262/513] Additional Changes for Python3 Porting (#222) * Replaced mean with standard lib function introduced in 3.4 * #TODO Replaced every with all * Proper names for variables introduced in 2to3 conversion. --- agents.py | 2 +- csp.py | 11 +++++------ games.py | 4 ++-- learning.py | 4 +++- logic.py | 4 ++-- probability.py | 6 +++--- tests/test_logic.py | 4 ++-- utils.py | 12 ------------ 8 files changed, 18 insertions(+), 29 deletions(-) diff --git a/agents.py b/agents.py index 7cd1146ef..3e2440e00 100644 --- a/agents.py +++ b/agents.py @@ -35,8 +35,8 @@ # # Speed control in GUI does not have any effect -- fix it. -from utils import mean from grid import distance2 +from statistics import mean import random import copy diff --git a/csp.py b/csp.py index d20a12d32..a125b8981 100644 --- a/csp.py +++ b/csp.py @@ -1,6 +1,6 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from utils import count, first, every, argmin_random_tie +from utils import count, first, argmin_random_tie import search from collections import defaultdict @@ -98,16 +98,16 @@ def actions(self, state): return [(var, val) for val in self.domains[var] if self.nconflicts(var, val, assignment) == 0] - def result(self, state, xxx_todo_changeme): + def result(self, state, action): "Perform an action and return the new state." - (var, val) = xxx_todo_changeme + (var, val) = action return state + ((var, val),) def goal_test(self, state): "The goal is to assign all variables, with all constraints satisfied." assignment = dict(state) return (len(assignment) == len(self.variables) and - every(lambda variables: self.nconflicts(variables, assignment[variables], assignment) == 0, self.variables)) + all(self.nconflicts(variables, assignment[variables], assignment) == 0 for variables in self.variables)) # These are for constraint propagation @@ -177,8 +177,7 @@ def revise(csp, Xi, Xj, removals): revised = False for x in csp.curr_domains[Xi][:]: # If Xi=x conflicts with Xj=y for every possible y, eliminate Xi=x - if every(lambda y: not csp.constraints(Xi, x, Xj, y), - csp.curr_domains[Xj]): + if all(not csp.constraints(Xi, x, Xj, y) for y in csp.curr_domains[Xj]): csp.prune(Xi, x, removals) revised = True return revised diff --git a/games.py b/games.py index 73b8a8312..32f65a9dd 100644 --- a/games.py +++ b/games.py @@ -289,9 +289,9 @@ def compute_utility(self, board, move, player): else: return 0 - def k_in_row(self, board, move, player, xxx_todo_changeme): + def k_in_row(self, board, move, player, delta_x_y): "Return true if there is a line through move on board for player." - (delta_x, delta_y) = xxx_todo_changeme + (delta_x, delta_y) = delta_x_y x, y = move n = 0 # n is number of moves in row while board.get((x, y)) == player: diff --git a/learning.py b/learning.py index b2e94c284..30cc2c759 100644 --- a/learning.py +++ b/learning.py @@ -1,7 +1,7 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" from utils import ( - removeall, unique, product, argmax, argmax_random_tie, mean, isclose, + removeall, unique, product, argmax, argmax_random_tie, isclose, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile ) @@ -10,6 +10,8 @@ import heapq import math import random + +from statistics import mean from collections import defaultdict # ______________________________________________________________________________ diff --git a/logic.py b/logic.py index 1b73ed933..ede813427 100644 --- a/logic.py +++ b/logic.py @@ -32,7 +32,7 @@ """ from utils import ( - removeall, unique, first, every, argmax, probability, num_or_str, + removeall, unique, first, argmax, probability, num_or_str, isnumber, issequence, Symbol, Expr, expr, subexpressions ) import agents @@ -168,7 +168,7 @@ def is_definite_clause(s): elif s.op == '==>': antecedent, consequent = s.args return (is_symbol(consequent.op) and - every(lambda arg: is_symbol(arg.op), conjuncts(antecedent))) + all(is_symbol(arg.op) for arg in conjuncts(antecedent))) else: return False diff --git a/probability.py b/probability.py index 5a26fac32..634a4854f 100644 --- a/probability.py +++ b/probability.py @@ -2,7 +2,7 @@ """ from utils import ( - product, every, argmax, element_wise_product, matrix_multiplication, + product, argmax, element_wise_product, matrix_multiplication, vector_to_diagonal, vector_add, scalar_vector_product, inverse_matrix, weighted_sample_with_replacement, rounder, isclose, probability, normalize ) @@ -176,7 +176,7 @@ def add(self, node_spec): net, and its variable must not.""" node = BayesNode(*node_spec) assert node.variable not in self.variables - assert every(lambda parent: parent in self.variables, node.parents) + assert all((parent in self.variables) for parent in node.parents) self.nodes.append(node) self.variables.append(node.variable) for parent in node.parents: @@ -242,7 +242,7 @@ def __init__(self, X, parents, cpt): assert isinstance(cpt, dict) for vs, p in list(cpt.items()): assert isinstance(vs, tuple) and len(vs) == len(parents) - assert every(lambda v: isinstance(v, bool), vs) + assert all(isinstance(v, bool) for v in vs) assert 0 <= p <= 1 self.variable = X diff --git a/tests/test_logic.py b/tests/test_logic.py index 5d4bd4623..de2764b2c 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -179,9 +179,9 @@ def check_SAT(clauses, single_solution = {}): # Sometimes WalkSat may run out of flips before finding a solution soln = WalkSAT(clauses) if soln: - assert every(lambda x: pl_true(x, soln), clauses) + assert all(pl_true(x, soln) for x in clauses) if single_solution: #Cross check the solution if only one exists - assert every(lambda x: pl_true(x, single_solution), clauses) + assert all(pl_true(x, single_solution) for x in clauses) assert soln == single_solution # Test WalkSat for problems with solution check_SAT([A & B, A & C]) diff --git a/utils.py b/utils.py index 51c89ea74..746f5e809 100644 --- a/utils.py +++ b/utils.py @@ -50,13 +50,6 @@ def first(iterable, default=None): except TypeError: return next(iterable, default) - -def every(predicate, seq): # TODO: replace with all - """True if every element of seq satisfies predicate.""" - - return all(predicate(x) for x in seq) - - def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) @@ -106,11 +99,6 @@ def histogram(values, mode=0, bin_function=None): else: return sorted(bins.items()) -def mean(numbers): - "The mean or average of numbers." - numbers = sequence(numbers) - return sum(numbers) / len(numbers) - def dotproduct(X, Y): """Return the sum of the element-wise product of vectors X and Y.""" From 5f471e0c676a96a1dccd6097a5d42af82d99da0a Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Wed, 13 Apr 2016 14:52:52 -0400 Subject: [PATCH 263/513] Change name of argument from 2to3 conversion --- nlp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nlp.py b/nlp.py index 77a22931a..1235f107b 100644 --- a/nlp.py +++ b/nlp.py @@ -168,9 +168,9 @@ def scanner(self, j, word): if Bb and self.grammar.isa(word, Bb[0]): self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]]) - def predictor(self, xxx_todo_changeme): + def predictor(self, edge): "Add to chart any rules for B that could help extend this edge." - (i, j, A, alpha, Bb) = xxx_todo_changeme + (i, j, A, alpha, Bb) = edge B = Bb[0] if B in self.grammar.rules: for rhs in self.grammar.rewrites_for(B): From 710331b0d17b969b402a3e500324066787163fb7 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Fri, 15 Apr 2016 20:32:17 -0400 Subject: [PATCH 264/513] Add instructions to fetch aima-data. --- CONTRIBUTING.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b2766d56f..9cf485e54 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -75,6 +75,12 @@ Clone this repository:: git clone https://github.com/aimacode/aima-python.git +Fetch the aima-data submodule:: + + cd aima-python + git submodule init + git submodule update + Then you can run the testsuite with:: py.test From a9bc2267244796c40ebb93ce2727b56a10f71ecb Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 11:36:45 -0400 Subject: [PATCH 265/513] Delete unused (and incorrect) accessor method. --- probability.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/probability.py b/probability.py index 634a4854f..590e01922 100644 --- a/probability.py +++ b/probability.py @@ -533,9 +533,6 @@ def __init__(self, transition_model, sensor_model, prior= [0.5, 0.5]): self.sensor_model = sensor_model self.prior = prior - def transition_model(self): - return self.transition_model - def sensor_dist(self, ev): if ev is True: return self.sensor_model[0] From aa1fc91166d14a44ec809faeaf1a52efe18b94c7 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 12:01:41 -0400 Subject: [PATCH 266/513] Delete redundant (and buggy) weighted_sample_with_replacement; add a little testing for it. --- probability.py | 20 +------------------- tests/test_probability.py | 5 ++++- 2 files changed, 5 insertions(+), 20 deletions(-) diff --git a/probability.py b/probability.py index 590e01922..62eee5b1d 100644 --- a/probability.py +++ b/probability.py @@ -650,23 +650,5 @@ def particle_filtering(e, N, HMM): w[i] = float("{0:.4f}".format(w[i])) # STEP 2 - s = weighted_sample_with_replacement(N, s, w) + s = weighted_sample_with_replacement(s, w, N) return s - - -def weighted_sample_with_replacement(N, s, w): - """ - Performs Weighted sampling over the paricles given weights of each particle. - We keep on picking random states unitll we fill N number states in new distribution - """ - s_wtd = [] - cnt = 0 - while (cnt <= N): - # Generate a random number from 0 to N-1 - i = random.randint(0, N-1) - if (probability(w[i])): - s_wtd.append(s[i]) - cnt += 1 - return s_wtd - - diff --git a/tests/test_probability.py b/tests/test_probability.py index 8bcec5e58..19f00a6c6 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -146,8 +146,11 @@ def test_particle_filtering(): umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) + s = particle_filtering(umbrella_evidence, N, umbrellaHMM) + assert len(s) == N + assert all(state in 'AB' for state in s) + # XXX 'A' and 'B' are really arbitrary names, but I'm letting it stand for now - assert particle_filtering(umbrella_evidence, N, umbrellaHMM) # The following should probably go in .ipynb: From af0351ee88d896940e9ddea42986532538052245 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 12:07:08 -0400 Subject: [PATCH 267/513] Remove unused import. --- probability.py | 2 +- tests/test_probability.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/probability.py b/probability.py index 62eee5b1d..ea5ee1dc3 100644 --- a/probability.py +++ b/probability.py @@ -4,7 +4,7 @@ from utils import ( product, argmax, element_wise_product, matrix_multiplication, vector_to_diagonal, vector_add, scalar_vector_product, inverse_matrix, - weighted_sample_with_replacement, rounder, isclose, probability, normalize + weighted_sample_with_replacement, isclose, probability, normalize ) from logic import extend diff --git a/tests/test_probability.py b/tests/test_probability.py index 19f00a6c6..5aa472bc8 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -1,6 +1,7 @@ import pytest import random from probability import * # noqa +from utils import rounder def tests(): From 8f586685fd970818761232b20f880398d7cf5c1f Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 12:19:20 -0400 Subject: [PATCH 268/513] Style: delete excess parentheses. In some of these cases you need to read ahead to see that the paren is not introducing a tuple, for example. --- csp.py | 2 +- games.py | 6 +++--- logic.py | 4 ++-- mdp.py | 2 +- probability.py | 12 ++++++------ search.py | 10 +++++----- utils.py | 10 +++++----- 7 files changed, 23 insertions(+), 23 deletions(-) diff --git a/csp.py b/csp.py index a125b8981..fc8efaaec 100644 --- a/csp.py +++ b/csp.py @@ -640,7 +640,7 @@ def zebra_constraint(A, a, B, b, recurse=0): if A == 'Coffee' and B == 'Green': return same if A == 'Green' and B == 'Ivory': - return (a - 1) == b + return a - 1 == b if recurse == 0: return zebra_constraint(B, b, A, a, 1) if ((A in Colors and B in Colors) or diff --git a/games.py b/games.py index 32f65a9dd..0c42d7592 100644 --- a/games.py +++ b/games.py @@ -232,7 +232,7 @@ def terminal_test(self, state): return state not in ('A', 'B', 'C', 'D') def to_move(self, state): - return ('MIN' if state in 'BCD' else 'MAX') + return 'MIN' if state in 'BCD' else 'MAX' class TicTacToe(Game): @@ -266,7 +266,7 @@ def result(self, state, move): def utility(self, state, player): "Return the value to player; 1 for win, -1 for loss, 0 otherwise." - return (state.utility if player == 'X' else -state.utility) + return state.utility if player == 'X' else -state.utility def terminal_test(self, state): "A state is terminal if it is won or there are no empty squares." @@ -285,7 +285,7 @@ def compute_utility(self, board, move, player): self.k_in_row(board, move, player, (1, 0)) or self.k_in_row(board, move, player, (1, -1)) or self.k_in_row(board, move, player, (1, 1))): - return (+1 if player == 'X' else -1) + return +1 if player == 'X' else -1 else: return 0 diff --git a/logic.py b/logic.py index ede813427..338e7fca2 100644 --- a/logic.py +++ b/logic.py @@ -313,9 +313,9 @@ def eliminate_implications(s): args = list(map(eliminate_implications, s.args)) a, b = args[0], args[-1] if s.op == '==>': - return (b | ~a) + return b | ~a elif s.op == '<==': - return (a | ~b) + return a | ~b elif s.op == '<=>': return (a | ~b) & (b | ~a) elif s.op == '^': diff --git a/mdp.py b/mdp.py index 83f6009d3..0e7e5fd19 100644 --- a/mdp.py +++ b/mdp.py @@ -82,7 +82,7 @@ def T(self, state, action): def go(self, state, direction): "Return the state that results from going in this direction." state1 = vector_add(state, direction) - return (state1 if state1 in self.states else state) + return state1 if state1 in self.states else state def to_grid(self, mapping): """Convert a mapping from (x, y) to v into a [[..., v, ...]] grid.""" diff --git a/probability.py b/probability.py index ea5ee1dc3..8a8ba382e 100644 --- a/probability.py +++ b/probability.py @@ -260,7 +260,7 @@ def p(self, value, event): 0.375""" assert isinstance(value, bool) ptrue = self.cpt[event_values(event, self.parents)] - return (ptrue if value else 1 - ptrue) + return ptrue if value else 1 - ptrue def sample(self, event): """Sample from the distribution for this variable conditioned @@ -545,15 +545,15 @@ def forward(HMM, fv, ev): scalar_vector_product(fv[1], HMM.transition_model[1])) sensor_dist = HMM.sensor_dist(ev) - return(normalize(element_wise_product(sensor_dist, prediction))) + return normalize(element_wise_product(sensor_dist, prediction)) def backward(HMM, b, ev): sensor_dist = HMM.sensor_dist(ev) prediction = element_wise_product(sensor_dist, b) - return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), - scalar_vector_product(prediction[1], HMM.transition_model[1])))) + return normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]), + scalar_vector_product(prediction[1], HMM.transition_model[1]))) def forward_backward(HMM, ev, prior): @@ -579,7 +579,7 @@ def forward_backward(HMM, ev, prior): sv = sv[::-1] - return(sv) + return sv # _________________________________________________________________________ @@ -608,7 +608,7 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): if t > d: # always returns a 1x2 matrix - return([normalize(i) for i in matrix_multiplication([f], B)][0]) + return [normalize(i) for i in matrix_multiplication([f], B)][0] else: return None diff --git a/search.py b/search.py index 595ef965b..acf4f2b57 100644 --- a/search.py +++ b/search.py @@ -286,7 +286,7 @@ def recursive_dls(node, problem, limit): cutoff_occurred = True elif result is not None: return result - return ('cutoff' if cutoff_occurred else None) + return 'cutoff' if cutoff_occurred else None # Body of depth_limited_search: return recursive_dls(Node(problem.initial), problem, limit) @@ -526,7 +526,7 @@ def __init__(self, problem): def __call__(self, s1): # as of now s1 is a state rather than a percept if self.problem.goal_test(s1): self.a = None - return(self.a) + return self.a else: if s1 not in self.H: self.H[s1] = self.problem.h(s1) @@ -553,14 +553,14 @@ def LRTA_cost(self, s, a, s1, H): """ print(s, a, s1) if s1 is None: - return(self.problem.h(s)) + return self.problem.h(s) else: # sometimes we need to get H[s1] which we haven't yet added to H # to replace this try, except: we can initialize H with values from problem.h try: - return(self.problem.c(s, a, s1) + self.H[s1]) + return self.problem.c(s, a, s1) + self.H[s1] except: - return(self.problem.c(s, a, s1) + self.problem.h(s1)) + return self.problem.c(s, a, s1) + self.problem.h(s1) # ______________________________________________________________________________ # Genetic Algorithm diff --git a/utils.py b/utils.py index 746f5e809..3e95e233c 100644 --- a/utils.py +++ b/utils.py @@ -130,13 +130,13 @@ def _mat_mult(X_M, Y_M): for j in range(len(Y_M[0])): for k in range(len(Y_M)): result[i][j] += X_M[i][k] * Y_M[k][j] - return(result) + return result result = X_M for Y in Y_M: result = _mat_mult(result, Y) - return(result) + return result def vector_to_diagonal(v): """Converts a vector to a diagonal matrix with vector elements @@ -157,7 +157,7 @@ def scalar_vector_product(X, Y): return [X*y for y in Y] def scalar_matrix_product(X, Y): - return([scalar_vector_product(X, y) for y in Y]) + return [scalar_vector_product(X, y) for y in Y] def inverse_matrix(X): """Inverse a given square matrix of size 2x2""" @@ -167,7 +167,7 @@ def inverse_matrix(X): assert det != 0 inv_mat = scalar_matrix_product(1.0/det, [[X[1][1], -X[0][1]], [-X[1][0], X[0][0]]]) - return(inv_mat) + return inv_mat def probability(p): @@ -215,7 +215,7 @@ def num_or_str(x): def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) - return([(n / total) for n in numbers]) + return [(n / total) for n in numbers] def clip(x, lowest, highest): From 7776583aadd1af11de1c57bd7b2e9738dc072cba Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 23:02:33 -0400 Subject: [PATCH 269/513] Remove unnecessary list coercions. --- csp.py | 2 +- learning.py | 7 +++---- mdp.py | 2 +- nlp.py | 4 ++-- probability.py | 10 +++++----- search.py | 2 +- 6 files changed, 13 insertions(+), 14 deletions(-) diff --git a/csp.py b/csp.py index fc8efaaec..7d1d485a4 100644 --- a/csp.py +++ b/csp.py @@ -658,7 +658,7 @@ def solve_zebra(algorithm=min_conflicts, **args): ans = algorithm(z, **args) for h in range(1, 6): print('House', h, end=' ') - for (var, val) in list(ans.items()): + for (var, val) in ans.items(): if val == h: print(var, end=' ') print() diff --git a/learning.py b/learning.py index 30cc2c759..4f53e495d 100644 --- a/learning.py +++ b/learning.py @@ -209,8 +209,7 @@ def __getitem__(self, item): def top(self, n): "Return (count, obs) tuples for the n most frequent observations." - return heapq.nlargest( - n, [(v, k) for (k, v) in list(self.dictionary.items())]) + return heapq.nlargest(n, [(v, k) for (k, v) in self.dictionary.items()]) def sample(self): "Return a random sample from the distribution." @@ -301,7 +300,7 @@ def add(self, val, subtree): def display(self, indent=0): name = self.attrname print('Test', name) - for (val, subtree) in list(self.branches.items()): + for (val, subtree) in self.branches.items(): print(' ' * 4 * indent, name, '=', val, '==>', end=' ') subtree.display(indent + 1) @@ -885,7 +884,7 @@ def RestaurantDataSet(examples=None): def T(attrname, branches): branches = dict((value, (child if isinstance(child, DecisionFork) else DecisionLeaf(child))) - for value, child in list(branches.items())) + for value, child in branches.items()) return DecisionFork(restaurant.attrnum(attrname), attrname, branches) """ [Figure 18.2] diff --git a/mdp.py b/mdp.py index 0e7e5fd19..08b97a034 100644 --- a/mdp.py +++ b/mdp.py @@ -94,7 +94,7 @@ def to_arrows(self, policy): chars = { (1, 0): '>', (0, 1): '^', (-1, 0): '<', (0, -1): 'v', None: '.'} return self.to_grid( - dict([(s, chars[a]) for (s, a) in list(policy.items())])) + dict([(s, chars[a]) for (s, a) in policy.items()])) # ______________________________________________________________________________ """ [Figure 17.1] diff --git a/nlp.py b/nlp.py index 1235f107b..8f2d1888f 100644 --- a/nlp.py +++ b/nlp.py @@ -14,7 +14,7 @@ def Rules(**rules): >>> Rules(A = "B C | D E") {'A': [['B', 'C'], ['D', 'E']]} """ - for (lhs, rhs) in list(rules.items()): + for (lhs, rhs) in rules.items(): rules[lhs] = [alt.strip().split() for alt in rhs.split('|')] return rules @@ -24,7 +24,7 @@ def Lexicon(**rules): >>> Lexicon(Art = "the | a | an") {'Art': ['the', 'a', 'an']} """ - for (lhs, rhs) in list(rules.items()): + for (lhs, rhs) in rules.items(): rules[lhs] = [word.strip() for word in rhs.split('|')] return rules diff --git a/probability.py b/probability.py index 8a8ba382e..d2386dbf4 100644 --- a/probability.py +++ b/probability.py @@ -46,7 +46,7 @@ def __init__(self, varname='?', freqs=None): self.varname = varname self.values = [] if freqs: - for (v, p) in list(freqs.items()): + for (v, p) in freqs.items(): self[v] = p self.normalize() @@ -237,10 +237,10 @@ def __init__(self, X, parents, cpt): elif isinstance(cpt, dict): # one parent, 1-tuple if cpt and isinstance(list(cpt.keys())[0], bool): - cpt = dict(((v,), p) for v, p in list(cpt.items())) + cpt = dict(((v,), p) for v, p in cpt.items()) assert isinstance(cpt, dict) - for vs, p in list(cpt.items()): + for vs, p in cpt.items(): assert isinstance(vs, tuple) and len(vs) == len(parents) assert all(isinstance(v, bool) for v in vs) assert 0 <= p <= 1 @@ -390,7 +390,7 @@ def normalize(self): "Return my probabilities; must be down to one variable." assert len(self.variables) == 1 return ProbDist(self.variables[0], - dict((k, v) for ((k,), v) in list(self.cpt.items()))) + dict((k, v) for ((k,), v) in self.cpt.items())) def p(self, e): "Look up my value tabulated for e." @@ -454,7 +454,7 @@ def rejection_sampling(X, e, bn, N): def consistent_with(event, evidence): "Is event consistent with the given evidence?" return all(evidence.get(k, v) == v - for k, v in list(event.items())) + for k, v in event.items()) # _________________________________________________________________________ diff --git a/search.py b/search.py index acf4f2b57..39a0f9855 100644 --- a/search.py +++ b/search.py @@ -639,7 +639,7 @@ def __init__(self, dict=None, directed=True): def make_undirected(self): "Make a digraph into an undirected graph by adding symmetric edges." for a in list(self.dict.keys()): - for (b, distance) in list(self.dict[a].items()): + for (b, distance) in self.dict[a].items(): self.connect1(b, a, distance) def connect(self, A, B, distance=1): From 3c2639ab183b99edc1214c1f206c5ea541681c6a Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 23:20:15 -0400 Subject: [PATCH 270/513] Use dict comprehensions now that we can. --- csp.py | 12 ++++++------ learning.py | 12 ++++++------ logic.py | 7 ++++--- mdp.py | 11 ++++++----- probability.py | 24 +++++++++++------------- 5 files changed, 33 insertions(+), 33 deletions(-) diff --git a/csp.py b/csp.py index 7d1d485a4..3ed98f5c5 100644 --- a/csp.py +++ b/csp.py @@ -115,7 +115,7 @@ def support_pruning(self): """Make sure we can prune values from domains. (We want to pay for this only if we use it.)""" if self.curr_domains is None: - self.curr_domains = dict((v, list(self.domains[v])) for v in self.variables) + self.curr_domains = {v: list(self.domains[v]) for v in self.variables} def suppose(self, var, value): "Start accumulating inferences from assuming var=value." @@ -137,8 +137,8 @@ def choices(self, var): def infer_assignment(self): "Return the partial assignment implied by the current inferences." self.support_pruning() - return dict((v, self.curr_domains[v][0]) - for v in self.variables if 1 == len(self.curr_domains[v])) + return {v: self.curr_domains[v][0] + for v in self.variables if 1 == len(self.curr_domains[v])} def restore(self, removals): "Undo a supposition and all inferences from it." @@ -512,7 +512,7 @@ def flatten(seqs): return sum(seqs, []) _ROWS = flatten([list(map(flatten, list(zip(*brow)))) for brow in _BGRID]) _COLS = list(zip(*_ROWS)) -_NEIGHBORS = dict([(v, set()) for v in flatten(_ROWS)]) +_NEIGHBORS = {v: set() for v in flatten(_ROWS)} for unit in map(set, _BOXES + _ROWS + _COLS): for v in unit: _NEIGHBORS[v].update(unit - set([v])) @@ -567,8 +567,8 @@ def __init__(self, grid): the digits 1-9 denote a filled cell, '.' or '0' an empty one; other characters are ignored.""" squares = iter(re.findall(r'\d|\.', grid)) - domains = dict((var, ([ch] if ch in '123456789' else '123456789')) - for var, ch in zip(flatten(self.rows), squares)) + domains = {var: [ch] if ch in '123456789' else '123456789' + for var, ch in zip(flatten(self.rows), squares)} for _ in squares: raise ValueError("Not a Sudoku grid", grid) # Too many squares CSP.__init__(self, None, domains, self.neighbors, different_values_constraint) diff --git a/learning.py b/learning.py index 4f53e495d..aef12a325 100644 --- a/learning.py +++ b/learning.py @@ -241,9 +241,9 @@ def NaiveBayesLearner(dataset): targetvals = dataset.values[dataset.target] target_dist = CountingProbDist(targetvals) - attr_dists = dict(((gv, attr), CountingProbDist(dataset.values[attr])) - for gv in targetvals - for attr in dataset.inputs) + attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr]) + for gv in targetvals + for attr in dataset.inputs} for example in dataset.examples: targetval = example[dataset.target] target_dist.add(targetval) @@ -882,9 +882,9 @@ def RestaurantDataSet(examples=None): def T(attrname, branches): - branches = dict((value, (child if isinstance(child, DecisionFork) - else DecisionLeaf(child))) - for value, child in branches.items()) + branches = {value: (child if isinstance(child, DecisionFork) + else DecisionLeaf(child)) + for value, child in branches.items()} return DecisionFork(restaurant.attrnum(attrname), attrname, branches) """ [Figure 18.2] diff --git a/logic.py b/logic.py index 338e7fca2..fd73407fd 100644 --- a/logic.py +++ b/logic.py @@ -497,8 +497,9 @@ def pl_fc_entails(KB, q): >>> pl_fc_entails(horn_clauses_KB, expr('Q')) True """ - count = dict([(c, len(conjuncts(c.args[0]))) for c in KB.clauses - if c.op == '==>']) + count = {c: len(conjuncts(c.args[0])) + for c in KB.clauses + if c.op == '==>'} inferred = defaultdict(bool) agenda = [s for s in KB.clauses if is_prop_symbol(s.op)] while agenda: @@ -642,7 +643,7 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): # set of all symbols in all clauses symbols = set(sym for clause in clauses for sym in prop_symbols(clause)) # model is a random assignment of true/false to the symbols in clauses - model = dict([(s, random.choice([True, False])) for s in symbols]) + model = {s: random.choice([True, False]) for s in symbols} for i in range(max_flips): satisfied, unsatisfied = [], [] for clause in clauses: diff --git a/mdp.py b/mdp.py index 08b97a034..ab1cd88c7 100644 --- a/mdp.py +++ b/mdp.py @@ -93,13 +93,14 @@ def to_grid(self, mapping): def to_arrows(self, policy): chars = { (1, 0): '>', (0, 1): '^', (-1, 0): '<', (0, -1): 'v', None: '.'} - return self.to_grid( - dict([(s, chars[a]) for (s, a) in policy.items()])) + return self.to_grid({s: chars[a] for (s, a) in policy.items()}) # ______________________________________________________________________________ + """ [Figure 17.1] A 4x3 grid environment that presents the agent with a sequential decision problem. """ + sequential_decision_environment = GridMDP([[-0.04, -0.04, -0.04, +1], [-0.04, None, -0.04, -1], [-0.04, -0.04, -0.04, -0.04]], @@ -110,7 +111,7 @@ def to_arrows(self, policy): def value_iteration(mdp, epsilon=0.001): "Solving an MDP by value iteration. [Figure 17.4]" - U1 = dict([(s, 0) for s in mdp.states]) + U1 = {s: 0 for s in mdp.states} R, T, gamma = mdp.R, mdp.T, mdp.gamma while True: U = U1.copy() @@ -141,8 +142,8 @@ def expected_utility(a, s, U, mdp): def policy_iteration(mdp): "Solve an MDP by policy iteration [Figure 17.7]" - U = dict([(s, 0) for s in mdp.states]) - pi = dict([(s, random.choice(mdp.actions(s))) for s in mdp.states]) + U = {s: 0 for s in mdp.states} + pi = {s: random.choice(mdp.actions(s)) for s in mdp.states} while True: U = policy_evaluation(pi, U, mdp) unchanged = True diff --git a/probability.py b/probability.py index d2386dbf4..c2f2e9adf 100644 --- a/probability.py +++ b/probability.py @@ -237,7 +237,7 @@ def __init__(self, X, parents, cpt): elif isinstance(cpt, dict): # one parent, 1-tuple if cpt and isinstance(list(cpt.keys())[0], bool): - cpt = dict(((v,), p) for v, p in cpt.items()) + cpt = {(v,): p for v, p in cpt.items()} assert isinstance(cpt, dict) for vs, p in cpt.items(): @@ -344,8 +344,8 @@ def make_factor(var, e, bn): is the pointwise product of these factors for bn's variables.""" node = bn.variable_node(var) variables = [X for X in [var] + node.parents if X not in e] - cpt = dict((event_values(e1, variables), node.p(e1[var], e1)) - for e1 in all_events(variables, bn, e)) + cpt = {event_values(e1, variables): node.p(e1[var], e1) + for e1 in all_events(variables, bn, e)} return Factor(variables, cpt) @@ -373,24 +373,23 @@ def __init__(self, variables, cpt): def pointwise_product(self, other, bn): "Multiply two factors, combining their variables." variables = list(set(self.variables) | set(other.variables)) - cpt = dict((event_values(e, variables), self.p(e) * other.p(e)) - for e in all_events(variables, bn, {})) + cpt = {event_values(e, variables): self.p(e) * other.p(e) + for e in all_events(variables, bn, {})} return Factor(variables, cpt) def sum_out(self, var, bn): "Make a factor eliminating var by summing over its values." variables = [X for X in self.variables if X != var] - cpt = dict((event_values(e, variables), - sum(self.p(extend(e, var, val)) - for val in bn.variable_values(var))) - for e in all_events(variables, bn, {})) + cpt = {event_values(e, variables): sum(self.p(extend(e, var, val)) + for val in bn.variable_values(var)) + for e in all_events(variables, bn, {})} return Factor(variables, cpt) def normalize(self): "Return my probabilities; must be down to one variable." assert len(self.variables) == 1 return ProbDist(self.variables[0], - dict((k, v) for ((k,), v) in self.cpt.items())) + {k: v for ((k,), v) in self.cpt.items()}) def p(self, e): "Look up my value tabulated for e." @@ -442,8 +441,7 @@ def rejection_sampling(X, e, bn, N): ... burglary, 10000).show_approx() 'False: 0.7, True: 0.3' """ - counts = dict((x, 0) - for x in bn.variable_values(X)) # bold N in [Figure 14.14] + counts = {x: 0 for x in bn.variable_values(X)} # bold N in [Figure 14.14] for j in range(N): sample = prior_sample(bn) # boldface x in [Figure 14.14] if consistent_with(sample, e): @@ -467,7 +465,7 @@ def likelihood_weighting(X, e, bn, N): ... burglary, 10000).show_approx() 'False: 0.702, True: 0.298' """ - W = dict((x, 0) for x in bn.variable_values(X)) + W = {x: 0 for x in bn.variable_values(X)} for j in range(N): sample, weight = weighted_sample(bn, e) # boldface x, w in [Figure 14.15] W[sample[X]] += weight From 677274b4e7f3f0ee0339a9f1a8f57defd55bcca8 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 23:36:42 -0400 Subject: [PATCH 271/513] Fix bug: used Py2 syntax for except. --- agents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.py b/agents.py index 3e2440e00..6de58b830 100644 --- a/agents.py +++ b/agents.py @@ -314,7 +314,7 @@ def delete_thing(self, thing): """Remove a thing from the environment.""" try: self.things.remove(thing) - except(ValueError, e): + except ValueError as e: print(e) print(" in Environment delete_thing") print(" Thing to be removed: {} at {}" .format(thing, thing.location)) From 083f8b2e763d5027715630f8a863b9530540990f Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 23:45:26 -0400 Subject: [PATCH 272/513] Remove unimplemented(): it's not worth it without *-imports. --- csp.py | 6 +++--- learning.py | 4 ++-- logic.py | 6 +++--- utils.py | 5 ----- 4 files changed, 8 insertions(+), 13 deletions(-) diff --git a/csp.py b/csp.py index 3ed98f5c5..d80ec6336 100644 --- a/csp.py +++ b/csp.py @@ -1,6 +1,6 @@ """CSP (Constraint Satisfaction Problems) problems and solvers. (Chapter 6).""" -from utils import count, first, argmin_random_tie +from utils import argmin_random_tie, count, first import search from collections import defaultdict @@ -320,11 +320,11 @@ def tree_csp_solver(csp): def topological_sort(xs, x): - unimplemented() + raise NotImplementedError def make_arc_consistent(Xj, Xk, csp): - unimplemented() + raise NotImplementedError # ______________________________________________________________________________ # Map-Coloring Problems diff --git a/learning.py b/learning.py index aef12a325..b3dc29987 100644 --- a/learning.py +++ b/learning.py @@ -412,11 +412,11 @@ def decision_list_learning(examples): def find_examples(examples): """Find a set of examples that all have the same outcome under some test. Return a tuple of the test, outcome, and examples.""" - unimplemented() + raise NotImplementedError def passes(example, test): "Does the example pass the test?" - unimplemented() + raise NotImplementedError def predict(example): "Predict the outcome for the first passing test." diff --git a/logic.py b/logic.py index fd73407fd..7f2ab1a97 100644 --- a/logic.py +++ b/logic.py @@ -674,11 +674,11 @@ class HybridWumpusAgent(agents.Agent): "An agent for the wumpus world that does logical inference. [Figure 7.20]""" def __init__(self): - unimplemented() + raise NotImplementedError def plan_route(current, goals, allowed): - unimplemented() + raise NotImplementedError # ______________________________________________________________________________ @@ -844,7 +844,7 @@ def subst(s, x): def fol_fc_ask(KB, alpha): - unimplemented() + raise NotImplementedError def standardize_variables(sentence, dic=None): diff --git a/utils.py b/utils.py index 3e95e233c..102051f54 100644 --- a/utils.py +++ b/utils.py @@ -322,11 +322,6 @@ def DataFile(name, mode='r'): return AIMAFile(['aima-data', name], mode) -def unimplemented(): - "Use this as a stub for not-yet-implemented functions." - raise NotImplementedError - - # ______________________________________________________________________________ # Expressions From 28b485b4813a8e66a00a5d0f1a1cf43791e95d33 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sat, 16 Apr 2016 23:53:37 -0400 Subject: [PATCH 273/513] Remove unused imports. --- logic.py | 3 +-- utils.py | 1 - 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/logic.py b/logic.py index 7f2ab1a97..5bb526180 100644 --- a/logic.py +++ b/logic.py @@ -32,13 +32,12 @@ """ from utils import ( - removeall, unique, first, argmax, probability, num_or_str, + removeall, unique, first, argmax, probability, isnumber, issequence, Symbol, Expr, expr, subexpressions ) import agents import itertools -import re import random from collections import defaultdict diff --git a/utils.py b/utils.py index 102051f54..15a2cb3c3 100644 --- a/utils.py +++ b/utils.py @@ -7,7 +7,6 @@ import operator import os.path import random -import re import math # ______________________________________________________________________________ From ac49792d36e6710897dd0cfe88ead23688b087d0 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:02:04 -0400 Subject: [PATCH 274/513] Fix bugs in diff() and simp(). Add a smoke test. --- logic.py | 4 ++-- tests/test_logic.py | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/logic.py b/logic.py index 5bb526180..09494b04d 100644 --- a/logic.py +++ b/logic.py @@ -977,7 +977,7 @@ def diff(y, x): u, op, v = y.args[0], y.op, y.args[-1] if op == '+': return diff(u, x) + diff(v, x) - elif op == '-' and len(args) == 1: + elif op == '-' and len(y.args) == 1: return -diff(u, x) elif op == '-': return diff(u, x) - diff(v, x) @@ -998,7 +998,7 @@ def diff(y, x): def simp(x): "Simplify the expression x." - if not x.args: + if isnumber(x) or not x.args: return x args = list(map(simp, x.args)) u, op, v = args[0], x.op, args[-1] diff --git a/tests/test_logic.py b/tests/test_logic.py index de2764b2c..4cca74b51 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -173,6 +173,9 @@ def test_ask(query, kb=None): assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' +def test_d(): + assert d(x*x - x, x) == 2*x - 1 + def test_WalkSAT(): def check_SAT(clauses, single_solution = {}): # Make sure the solution is correct if it is returned by WalkSat From 2ab564b8e53dfa8b801fd7f5642ccbaa8523e790 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:04:16 -0400 Subject: [PATCH 275/513] Fix typos in comments. --- README.md | 2 +- rl.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1fdf18446..1e9589da4 100644 --- a/README.md +++ b/README.md @@ -125,7 +125,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t # Index of data structures -Here is a table of the implemented data structures, the figure, name of the implementation in the reposiroty, and the file where they are implemented. +Here is a table of the implemented data structures, the figure, name of the implementation in the repository, and the file where they are implemented. | **Figure** | **Name (in repository)** | **File** | |:-----------|:-------------------------|:---------| diff --git a/rl.py b/rl.py index eed070ac3..bca05aa9e 100644 --- a/rl.py +++ b/rl.py @@ -18,7 +18,7 @@ class PassiveADPAgent(agents.Agent): class PassiveTDAgent: """The abstract class for a Passive (non-learning) agent that uses temporal differences to learn utility estimates. Override update_state - method to convert percept to state and reward. The mdp being probided + method to convert percept to state and reward. The mdp being provided should be an instance of a subclass of the MDP Class.[Figure 21.4] """ From 19ca12f80f66a4749739e58ae8279fadfb2425cf Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:10:08 -0400 Subject: [PATCH 276/513] Formatting: remove tabs. --- tests/test_grid.py | 2 +- tests/test_learning.py | 7 +++---- tests/test_nlp.py | 5 ++--- 3 files changed, 6 insertions(+), 8 deletions(-) diff --git a/tests/test_grid.py b/tests/test_grid.py index 2bfea35e0..9a3994669 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -11,7 +11,7 @@ def test_distance(): def test_distance2(): - assert distance2((1, 2), (5, 5)) == 25.0 + assert distance2((1, 2), (5, 5)) == 25.0 def test_vector_clip(): diff --git a/tests/test_learning.py b/tests/test_learning.py index d4aecaaa0..882e00a1d 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -2,13 +2,12 @@ from learning import parse_csv, weighted_mode, weighted_replicate def test_parse_csv(): - assert parse_csv('1, 2, 3 \n 0, 2, na') == [[1, 2, 3], [0, 2, 'na']] + assert parse_csv('1, 2, 3 \n 0, 2, na') == [[1, 2, 3], [0, 2, 'na']] def test_weighted_mode(): - assert weighted_mode('abbaa', [1,2,3,1,2]) == 'b' + assert weighted_mode('abbaa', [1,2,3,1,2]) == 'b' def test_weighted_replicate(): - assert weighted_replicate('ABC', [1,2,1], 4) == ['A', 'B', 'B', 'C'] - \ No newline at end of file + assert weighted_replicate('ABC', [1,2,1], 4) == ['A', 'B', 'B', 'C'] diff --git a/tests/test_nlp.py b/tests/test_nlp.py index f5058d4a6..87d11965e 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -2,9 +2,8 @@ from nlp import * def test_rules(): - assert Rules(A = "B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} + assert Rules(A = "B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} def test_lexicon(): - assert Lexicon(Art = "the | a | an") == {'Art': ['the', 'a', 'an']} - \ No newline at end of file + assert Lexicon(Art = "the | a | an") == {'Art': ['the', 'a', 'an']} From b9c24331871edbc633413c68f2aef9b03169c579 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:26:58 -0400 Subject: [PATCH 277/513] Fix some pyflakes complaints. --- learning.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/learning.py b/learning.py index b3dc29987..e8cb8baa0 100644 --- a/learning.py +++ b/learning.py @@ -11,7 +11,10 @@ import math import random -from statistics import mean +# XXX statistics.mode is not quite the same as the old utils.mode: +# it insists on there being a unique most-frequent value. Code using mode +# needs to be revisited, or we need to restore utils.mode. +from statistics import mean, mode from collections import defaultdict # ______________________________________________________________________________ @@ -391,7 +394,7 @@ def split_by(attr, examples): def information_content(values): "Number of bits to represent the probability distribution in values." probabilities = normalize(removeall(0, values)) - return sum(-p * log2(p) for p in probabilities) + return sum(-p * math.log2(p) for p in probabilities) # ______________________________________________________________________________ @@ -439,7 +442,6 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3], epoches: Number of passes over the dataset """ - examples = dataset.examples i_units = len(dataset.inputs) o_units = 1 # As of now, dataset.target gives only one index. @@ -586,7 +588,6 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): def PerceptronLearner(dataset, learning_rate=0.01, epoches=100): """Logistic Regression, NO hidden layer""" - examples = dataset.examples i_units = len(dataset.inputs) o_units = 1 # As of now, dataset.target gives only one index. hidden_layer_sizes = [] From 24587245ca4bcb882aa5eee9930014ca03f41dc2 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:28:27 -0400 Subject: [PATCH 278/513] Fix: there is no utils.caller anymore. --- nlp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nlp.py b/nlp.py index 8f2d1888f..a935e1a0a 100644 --- a/nlp.py +++ b/nlp.py @@ -156,7 +156,7 @@ def add_edge(self, edge): if edge not in self.chart[end]: self.chart[end].append(edge) if self.trace: - print('%10s: added %s' % (caller(2), edge)) + print('Chart: added %s' % (edge,)) if not expects: self.extender(edge) else: From c4565b9b9b3ca93993d2afc0cb3b728d4536c750 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:34:39 -0400 Subject: [PATCH 279/513] Missing imports. --- search.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/search.py b/search.py index 39a0f9855..8b309f967 100644 --- a/search.py +++ b/search.py @@ -7,10 +7,11 @@ from utils import ( is_in, argmin, argmax, argmax_random_tie, probability, weighted_sample_with_replacement, memoize, print_table, DataFile, Stack, - FIFOQueue, PriorityQueue + FIFOQueue, PriorityQueue, name ) from grid import distance +from collections import defaultdict import math import random import sys From a828fe0dd17025cd251cacdfd0a8e5a1aac80474 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:39:34 -0400 Subject: [PATCH 280/513] Typo in comment. --- search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/search.py b/search.py index 8b309f967..f2b67486e 100644 --- a/search.py +++ b/search.py @@ -425,9 +425,9 @@ def and_search(states, problem, path): class OnlineDFSAgent: """The abstract class for an OnlineDFSAgent. Override update_state - method to convert percept to state. While initilizing the subclass + method to convert percept to state. While initializing the subclass a problem needs to be provided which is an instance of a subclass - of the Problem Class. [Figure 4.21] """ + of the Problem class. [Figure 4.21] """ def __init__(self, problem): self.problem = problem From 08b6aea338ab2572a38b232086db1e3ee566cab7 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 00:40:39 -0400 Subject: [PATCH 281/513] Typo in comment. --- search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/search.py b/search.py index f2b67486e..ab6a5f9e6 100644 --- a/search.py +++ b/search.py @@ -464,8 +464,8 @@ def __call__(self, percept): return self.a def update_state(self, percept): - ''' To be overriden in most cases. The default case - assumes th percept to be of type state''' + '''To be overriden in most cases. The default case + assumes the percept to be of type state.''' return percept # ______________________________________________________________________________ From 194a2f192414f13a037d42f5b9f4bebede7e04d0 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Sun, 17 Apr 2016 01:57:34 -0400 Subject: [PATCH 282/513] Style: address pep8 warnings. --- csp.py | 6 +++--- games.py | 10 ++++----- ipyviews.py | 5 +++-- learning.py | 1 - logic.py | 58 ++++++++++++++++++++++++-------------------------- mdp.py | 6 +++--- nlp.py | 3 --- probability.py | 4 ++-- rl.py | 13 +++++------ search.py | 29 ++++++++++++------------- utils.py | 2 +- 11 files changed, 66 insertions(+), 71 deletions(-) diff --git a/csp.py b/csp.py index d80ec6336..421115484 100644 --- a/csp.py +++ b/csp.py @@ -106,8 +106,9 @@ def result(self, state, action): def goal_test(self, state): "The goal is to assign all variables, with all constraints satisfied." assignment = dict(state) - return (len(assignment) == len(self.variables) and - all(self.nconflicts(variables, assignment[variables], assignment) == 0 for variables in self.variables)) + return (len(assignment) == len(self.variables) + and all(self.nconflicts(variables, assignment[variables], assignment) == 0 + for variables in self.variables)) # These are for constraint propagation @@ -663,4 +664,3 @@ def solve_zebra(algorithm=min_conflicts, **args): print(var, end=' ') print() return ans['Zebra'], ans['Water'], z.nassigns, ans - diff --git a/games.py b/games.py index 0c42d7592..431ba5a14 100644 --- a/games.py +++ b/games.py @@ -232,7 +232,7 @@ def terminal_test(self, state): return state not in ('A', 'B', 'C', 'D') def to_move(self, state): - return 'MIN' if state in 'BCD' else 'MAX' + return 'MIN' if state in 'BCD' else 'MAX' class TicTacToe(Game): @@ -266,7 +266,7 @@ def result(self, state, move): def utility(self, state, player): "Return the value to player; 1 for win, -1 for loss, 0 otherwise." - return state.utility if player == 'X' else -state.utility + return state.utility if player == 'X' else -state.utility def terminal_test(self, state): "A state is terminal if it is won or there are no empty squares." @@ -343,7 +343,7 @@ def mouse_click(self, x, y): if player == 'human': x, y = int(3*x/self.width) + 1, int(3*y/self.height) + 1 if (x, y) not in self.ttt.actions(self.state): - #Invalid move + # Invalid move return move = (x, y) elif player == 'alphabeta': @@ -368,14 +368,14 @@ def draw_board(self): self.draw_x(mark) elif board[mark] == 'O': self.draw_o(mark) - #End game message if self.ttt.terminal_test(self.state): + # End game message utility = self.ttt.utility(self.state, self.ttt.to_move(self.ttt.initial)) if utility == 0: self.text_n('Game Draw!', 0.1, 0.1) else: self.text_n('Player {} wins!'.format(1 if utility>0 else 2), 0.1, 0.1) - else: #print which player's turn it is + else: # Print which player's turn it is self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1) self.update() diff --git a/ipyviews.py b/ipyviews.py index 1f33bc0aa..7cb28850b 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -133,7 +133,7 @@ def handle_click(self, coordinates): def map_to_render(self): default_representation = {"val": "default", "tooltip": ""} world_map = [[copy.deepcopy(default_representation) for _ in range(self.world.width)] - for _ in range(self.world.height)] + for _ in range(self.world.height)] for thing in self.world.things: row, column = thing.location @@ -150,6 +150,7 @@ def map_to_render(self): def show(self): clear_output() - total_html = _GRID_WORLD_HTML.format(self.object_name(), self.map_to_render(), + total_html = _GRID_WORLD_HTML.format( + self.object_name(), self.map_to_render(), self.block_size, json.dumps(self.representation), _JS_GRID_WORLD) display(HTML(total_html)) diff --git a/learning.py b/learning.py index e8cb8baa0..8ff115a1f 100644 --- a/learning.py +++ b/learning.py @@ -290,7 +290,6 @@ def __init__(self, attr, attrname=None, branches=None): self.attrname = attrname or attr self.branches = branches or {} - def __call__(self, example): "Given an example, classify it using the attribute and the branches." attrvalue = example[self.attr] diff --git a/logic.py b/logic.py index 09494b04d..c64b64431 100644 --- a/logic.py +++ b/logic.py @@ -238,7 +238,7 @@ def pl_true(exp, model={}): and False if it is false. If the model does not specify the value for every proposition, this may return None to indicate 'not obvious'; this may happen even when the expression is tautological.""" - if exp == True or exp == False: + if exp in (True, False): return exp op, args = exp.op, exp.args if is_prop_symbol(op): @@ -639,7 +639,7 @@ def inspect_literal(literal): def WalkSAT(clauses, p=0.5, max_flips=10000): """Checks for satisfiability of all clauses by randomly flipping values of variables """ - # set of all symbols in all clauses + # Set of all symbols in all clauses symbols = set(sym for clause in clauses for sym in prop_symbols(clause)) # model is a random assignment of true/false to the symbols in clauses model = {s: random.choice([True, False]) for s in symbols} @@ -655,14 +655,14 @@ def WalkSAT(clauses, p=0.5, max_flips=10000): else: # Flip the symbol in clause that maximizes number of sat. clauses def sat_count(sym): - #returns the the number of clauses satisfied after flipping the symbol + # Return the the number of clauses satisfied after flipping the symbol. model[sym] = not model[sym] count = len([clause for clause in clauses if pl_true(clause, model)]) model[sym] = not model[sym] return count sym = argmax(prop_symbols(clause), key=sat_count) model[sym] = not model[sym] - #If no solution is found within the flip limit, we return failure + # If no solution is found within the flip limit, we return failure return None # ______________________________________________________________________________ @@ -686,71 +686,71 @@ def SAT_plan(init, transition, goal, t_max, SAT_solver=dpll_satisfiable): """Converts a planning problem to Satisfaction problem by translating it to a cnf sentence. [Figure 7.22]""" - #Functions used by SAT_plan + # Functions used by SAT_plan def translate_to_SAT(init, transition, goal, time): clauses = [] states = [state for state in transition] - #Symbol claiming state s at time t + # Symbol claiming state s at time t state_counter = itertools.count() for s in states: for t in range(time+1): - state_sym[(s, t)] = Expr("State_{}".format(next(state_counter))) + state_sym[s, t] = Expr("State_{}".format(next(state_counter))) - #Add initial state axiom + # Add initial state axiom clauses.append(state_sym[init, 0]) - #Add goal state axiom + # Add goal state axiom clauses.append(state_sym[goal, time]) - #All possible transitions + # All possible transitions transition_counter = itertools.count() for s in states: for action in transition[s]: s_ = transition[s][action] for t in range(time): - #Action 'action' taken from state 's' at time 't' to reach 's_' - action_sym[(s, action, t)] = Expr("Transition_{}".format(next(transition_counter))) + # Action 'action' taken from state 's' at time 't' to reach 's_' + action_sym[s, action, t] = Expr("Transition_{}".format(next(transition_counter))) # Change the state from s to s_ clauses.append(action_sym[s, action, t] |'==>'| state_sym[s, t]) clauses.append(action_sym[s, action, t] |'==>'| state_sym[s_, t + 1]) - #Allow only one state at any time + # Allow only one state at any time for t in range(time+1): - #must be a state at any time - clauses.append(associate('|', [ state_sym[s, t] for s in states ])) + # must be a state at any time + clauses.append(associate('|', [state_sym[s, t] for s in states])) for s in states: for s_ in states[states.index(s)+1:]: - #for each pair of states s, s_ only one is possible at time t + # for each pair of states s, s_ only one is possible at time t clauses.append((~state_sym[s, t]) | (~state_sym[s_, t])) - #Restrict to one transition per timestep + # Restrict to one transition per timestep for t in range(time): - #list of possible transitions at time t + # list of possible transitions at time t transitions_t = [tr for tr in action_sym if tr[2] == t] - #make sure atleast one of the transition happens - clauses.append(associate('|', [ action_sym[tr] for tr in transitions_t ])) + # make sure at least one of the transitions happens + clauses.append(associate('|', [action_sym[tr] for tr in transitions_t])) for tr in transitions_t: for tr_ in transitions_t[transitions_t.index(tr) + 1 :]: - #there cannot be two transitions tr and tr_ at time t - clauses.append((~action_sym[tr]) | (~action_sym[tr_])) + # there cannot be two transitions tr and tr_ at time t + clauses.append(~action_sym[tr] | ~action_sym[tr_]) - #Combine the clauses to form the cnf + # Combine the clauses to form the cnf return associate('&', clauses) def extract_solution(model): - true_transitions = [ t for t in action_sym if model[action_sym[t]]] - #Sort transitions based on time which is the 3rd element of the tuple + true_transitions = [t for t in action_sym if model[action_sym[t]]] + # Sort transitions based on time, which is the 3rd element of the tuple true_transitions.sort(key = lambda x: x[2]) - return [ action for s, action, time in true_transitions ] + return [action for s, action, time in true_transitions] - #Body of SAT_plan algorithm + # Body of SAT_plan algorithm for t in range(t_max): - #dcitionaries to help extract the solution from model + # dictionaries to help extract the solution from model state_sym = {} action_sym = {} @@ -1062,5 +1062,3 @@ def simp(x): def d(y, x): "Differentiate and then simplify." return simp(diff(y, x)) - - diff --git a/mdp.py b/mdp.py index ab1cd88c7..8b0714da9 100644 --- a/mdp.py +++ b/mdp.py @@ -102,9 +102,9 @@ def to_arrows(self, policy): """ sequential_decision_environment = GridMDP([[-0.04, -0.04, -0.04, +1], - [-0.04, None, -0.04, -1], - [-0.04, -0.04, -0.04, -0.04]], - terminals=[(3, 2), (3, 1)]) + [-0.04, None, -0.04, -1], + [-0.04, -0.04, -0.04, -0.04]], + terminals=[(3, 2), (3, 1)]) # ______________________________________________________________________________ diff --git a/nlp.py b/nlp.py index a935e1a0a..097e384fb 100644 --- a/nlp.py +++ b/nlp.py @@ -182,6 +182,3 @@ def extender(self, edge): for (i, j, A, alpha, B1b) in self.chart[j]: if B1b and B == B1b[0]: self.add_edge([i, k, A, alpha + [edge], B1b[1:]]) - - - diff --git a/probability.py b/probability.py index c2f2e9adf..98add0b2b 100644 --- a/probability.py +++ b/probability.py @@ -526,7 +526,7 @@ class HiddenMarkovModel: """ A Hidden markov model which takes Transition model and Sensor model as inputs""" - def __init__(self, transition_model, sensor_model, prior= [0.5, 0.5]): + def __init__(self, transition_model, sensor_model, prior=[0.5, 0.5]): self.transition_model = transition_model self.sensor_model = sensor_model self.prior = prior @@ -598,7 +598,7 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): O_t = vector_to_diagonal(HMM.sensor_dist(e_t)) if t > d: f = forward(HMM, f, e_t) - O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t- d])) + O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t - d])) B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t) else: B = matrix_multiplication(B, T_model, O_t) diff --git a/rl.py b/rl.py index bca05aa9e..079456284 100644 --- a/rl.py +++ b/rl.py @@ -54,7 +54,7 @@ def __call__(self, percept): return self.a def update_state(self, percept): - ''' To be overriden in most cases. The default case + ''' To be overridden in most cases. The default case assumes th percept to be of type (state, reward)''' return percept @@ -104,19 +104,20 @@ def __call__(self, percept): Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r alpha, gamma, terminals, actions_in_state = self.alpha, self.gamma, self.terminals, self.actions_in_state if s1 in terminals: - Q[(s1, None)] = r1 + Q[s1, None] = r1 if s is not None: - Nsa[(s, a)] += 1 - Q[(s, a)] += alpha(Nsa[(s, a)])*(r+gamma*max([Q[(s1, a1)] for a1 in actions_in_state(s1)])-Q[(s, a)]) + Nsa[s, a] += 1 + Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1] for a1 in actions_in_state(s1)) + - Q[s, a]) if s1 in terminals: self.s = self.a = self.r = None else: self.s, self.r = s1, r1 - self.a = argmax(actions_in_state(s1), key=lambda a1: self.f(Q[(s1, a1)], Nsa[(s1, a1)])) + self.a = argmax(actions_in_state(s1), key=lambda a1: self.f(Q[s1, a1], Nsa[s1, a1])) return self.a def update_state(self, percept): - ''' To be overriden in most cases. The default case + ''' To be overridden in most cases. The default case assumes the percept to be of type (state, reward)''' return percept diff --git a/search.py b/search.py index ab6a5f9e6..5253fca9a 100644 --- a/search.py +++ b/search.py @@ -51,8 +51,9 @@ def result(self, state, action): def goal_test(self, state): """Return True if the state is a goal. The default method compares the - state to self.goal or checks for state in self.goal if it is a list, as specified in the constructor. Override this - method if checking against a single self.goal is not enough.""" + state to self.goal or checks for state in self.goal if it is a + list, as specified in the constructor. Override this method if + checking against a single self.goal is not enough.""" if isinstance(self.goal, list): return is_in(state, self.goal) else: @@ -411,7 +412,7 @@ def or_search(state, problem, path): def and_search(states, problem, path): "returns plan in form of dictionary where we take action plan[s] if we reach state s" # noqa - plan = dict() + plan = {} for s in states: plan[s] = or_search(s, problem, path) if plan[s] is None: @@ -464,7 +465,7 @@ def __call__(self, percept): return self.a def update_state(self, percept): - '''To be overriden in most cases. The default case + '''To be overridden in most cases. The default case assumes the percept to be of type state.''' return percept @@ -535,12 +536,12 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept # self.result[(self.s, self.a)] = s1 # no need as we are using problem.output # minimum cost for action b in problem.actions(s) - self.H[self.s] = min([self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) - for b in self.problem.actions(self.s)]) + self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) + for b in self.problem.actions(self.s)) # costs for action b in problem.actions(s1) costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H) - for b in self.problem.actions(s1)] + for b in self.problem.actions(s1)] # an action b in problem.actions(s1) that minimizes costs self.a = list(self.problem.actions(s1))[costs.index(min(costs))] @@ -780,8 +781,8 @@ def distance_to_node(n): NT=dict(WA=1, Q=1), NSW=dict(Q=1, V=1))) australia_map.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30), - Q=(145, 20), NSW=(145, 32), T=(145, 42), - V=(145, 37)) + Q=(145, 20), NSW=(145, 32), T=(145, 42), + V=(145, 37)) class GraphProblem(Problem): @@ -813,8 +814,10 @@ def h(self, node): class GraphProblemStochastic(GraphProblem): """ - A version of Graph Problem where an action can lead to undeterministic output i.e. multiple possible states - Define the graph as dict(A = dict(Action = [[, , ...],], ...), ...) + A version of GraphProblem where an action can lead to + nondeterministic output i.e. multiple possible states + + Define the graph as dict(A = dict(Action = [[, , ...], ], ...), ...) A the dictionary format is different, make sure the graph is created as a directed graph """ @@ -1153,7 +1156,3 @@ def compare_graph_searchers(): GraphProblem('Q', 'WA', australia_map)], header=['Searcher', 'romania_map(Arad, Bucharest)', 'romania_map(Oradea, Neamt)', 'australia_map']) - -# ______________________________________________________________________________ - - diff --git a/utils.py b/utils.py index 15a2cb3c3..09da13c61 100644 --- a/utils.py +++ b/utils.py @@ -191,7 +191,7 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] -def rounder(numbers, d = 4): +def rounder(numbers, d=4): "Round a single number, or sequence of numbers, to d decimal places." if isinstance(numbers, (int, float)): return round(numbers, d) From 6525e23b3cecbcfde9b2bd9781ed1de092c9281c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 18 Apr 2016 13:00:21 +0530 Subject: [PATCH 283/513] Removed unnecessary list coercions around map (#228) --- agents.py | 2 +- csp.py | 4 ++-- learning.py | 4 ++-- logic.py | 6 ++---- search.py | 2 +- utils.py | 6 +++--- 6 files changed, 11 insertions(+), 13 deletions(-) diff --git a/agents.py b/agents.py index 6de58b830..274630d91 100644 --- a/agents.py +++ b/agents.py @@ -848,7 +848,7 @@ def score(env): env.add_thing(agent) env.run(steps) return agent.performance - return mean(list(map(score, envs))) + return mean(map(score, envs)) # _________________________________________________________________________ diff --git a/csp.py b/csp.py index 421115484..d696a787c 100644 --- a/csp.py +++ b/csp.py @@ -510,7 +510,7 @@ def flatten(seqs): return sum(seqs, []) _CELL = itertools.count().__next__ _BGRID = [[[[_CELL() for x in _R3] for y in _R3] for bx in _R3] for by in _R3] _BOXES = flatten([list(map(flatten, brow)) for brow in _BGRID]) -_ROWS = flatten([list(map(flatten, list(zip(*brow)))) for brow in _BGRID]) +_ROWS = flatten([list(map(flatten, zip(*brow))) for brow in _BGRID]) _COLS = list(zip(*_ROWS)) _NEIGHBORS = {v: set() for v in flatten(_ROWS)} @@ -583,7 +583,7 @@ def abut(lines1, lines2): return list( map(' | '.join, list(zip(lines1, lines2)))) print('\n------+-------+------\n'.join( '\n'.join(reduce( - abut, list(map(show_box, brow)))) for brow in self.bgrid)) + abut, map(show_box, brow))) for brow in self.bgrid)) # ______________________________________________________________________________ # The Zebra Puzzle diff --git a/learning.py b/learning.py index 8ff115a1f..ca953ae0a 100644 --- a/learning.py +++ b/learning.py @@ -101,14 +101,14 @@ def setproblem(self, target, inputs=None, exclude=()): to not use in inputs. Attributes can be -n .. n, or an attrname. Also computes the list of possible values, if that wasn't done yet.""" self.target = self.attrnum(target) - exclude = list(map(self.attrnum, exclude)) + exclude = map(self.attrnum, exclude) if inputs: self.inputs = removeall(self.target, inputs) else: self.inputs = [a for a in self.attrs if a != self.target and a not in exclude] if not self.values: - self.values = list(map(unique, list(zip(*self.examples)))) + self.values = list(map(unique, zip(*self.examples))) self.check_me() def check_me(self): diff --git a/logic.py b/logic.py index c64b64431..a4346a5ed 100644 --- a/logic.py +++ b/logic.py @@ -903,7 +903,7 @@ def fetch_rules_for_goal(self, goal): test_kb = FolKB( - list(map(expr, ['Farmer(Mac)', + map(expr, ['Farmer(Mac)', 'Rabbit(Pete)', 'Mother(MrsMac, Mac)', 'Mother(MrsRabbit, Pete)', @@ -916,10 +916,9 @@ def fetch_rules_for_goal(self, goal): # '(Human(h) & Mother(m, h)) ==> Human(m)' '(Mother(m, h) & Human(h)) ==> Human(m)' ])) -) crime_kb = FolKB( - list(map(expr, + map(expr, ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', # noqa 'Owns(Nono, M1)', 'Missile(M1)', @@ -929,7 +928,6 @@ def fetch_rules_for_goal(self, goal): 'American(West)', 'Enemy(Nono, America)' ])) -) def fol_bc_ask(KB, query): diff --git a/search.py b/search.py index 5253fca9a..ddb62ef3d 100644 --- a/search.py +++ b/search.py @@ -584,7 +584,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): for i in range(ngen): new_population = [] for i in len(population): - fitnesses = list(map(fitness_fn, population)) + fitnesses = map(fitness_fn, population) p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2) child = p1.mate(p2) if random.uniform(0, 1) < pmut: diff --git a/utils.py b/utils.py index 09da13c61..81b01748a 100644 --- a/utils.py +++ b/utils.py @@ -86,7 +86,7 @@ def histogram(values, mode=0, bin_function=None): Sorted by increasing value, or if mode=1, by decreasing count. If bin_function is given, map it over values first.""" if bin_function: - values = list(map(bin_function, values)) + values = map(bin_function, values) bins = {} for val in values: @@ -299,8 +299,8 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): for row in table] sizes = list( - map(lambda seq: max(list(map(len, seq))), - list(zip(*[list(map(str, row)) for row in table])))) + map(lambda seq: max(map(len, seq)), + list(zip(*[map(str, row) for row in table])))) for row in table: print(sep.join(getattr( From 8bcc7b0d9481209cf2508ca6844eb97af6888f39 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Tue, 19 Apr 2016 05:37:46 +0530 Subject: [PATCH 284/513] Notebook for MDPs (#229) * Intro and representation of MDPs in code * Added Image for MDP --- images/mdp-a.png | Bin 0 -> 31989 bytes mdp.ipynb | 234 ++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 232 insertions(+), 2 deletions(-) create mode 100644 images/mdp-a.png diff --git a/images/mdp-a.png 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"markdown", + "metadata": {}, + "source": [ + "# Markov decision processes (MDPs)\n", + "\n", + "This IPy notebook acts as supporting material for topics covered in **Chapter 17 Making Complex Decisions** of the book* Artificial Intelligence: A Modern Approach*. We makes use of the implementations in mdp.py module. This notebook also includes a brief summary of the main topics as a review. Let us import everything from the mdp module to get started." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mdp import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review\n", + "Before we start playing with the actual implementations let us review a couple of things about MDPs.\n", + "\n", + "- A stochastic process has the **Markov property** if the conditional probability distribution of future states of the process (conditional on both past and present states) depends only upon the present state, not on the sequence of events that preceded it.\n", + "\n", + " -- Source: [Wikipedia](https://en.wikipedia.org/wiki/Markov_property)\n", + "\n", + "Often it is possible to model many different phenomena as a Markov process by being flexible with our definition of state.\n", + " \n", + "\n", + "- MDPs help us deal with fully-observable and non-deterministic/stochastic environments. For dealing with partially-observable and stochastic cases we make use of generalization of MDPs named POMDPs (partially observable Markov decision process).\n", + "\n", + "Our overall goal to solve a MDP is to come up with a policy which guides us to select the best action in each state so as to maximize the expected sum of future rewards." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MDP\n", + "\n", + "To begin with let us look at the implementation of MDP class defined in mdp.py The docstring tells us what all is required to define a MDP namely - set of states,actions, initial state, transition model, and a reward function. Each of these are implemented as methods. Do not close the popup so that you can follow along the description of code below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource MDP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **_ _init_ _** method takes in the following parameters:\n", + "\n", + "- init: the initial state.\n", + "- actlist: List of actions possible in each state.\n", + "- terminals: List of terminal states where only possible action is exit\n", + "- gamma: Discounting factor. This makes sure that delayed rewards have less value compared to immediate ones.\n", + "\n", + "**R** method returns the reward for each state by using the self.reward dict.\n", + "\n", + "**T** method is not implemented and is somewhat different from the text. Here we return (probability, s') pairs where s' belongs to list of possible state by taking action a in state s.\n", + "\n", + "**actions** method returns list of actions possible in each state. By default it returns all actions for states other than terminal states.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us implement the simple MDP in the image below. States A, B have actions X, Y available in them. Their probabilities are shown just above the arrows. We start with using MDP as base class for our CustomMDP. Obviously we need to make a few changes to suit our case. We make use of a transition matrix as our transitions are not very simple.\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Transition Matrix as nested dict. State -> Actions in state -> States by each action -> Probabilty\n", + "t = {\n", + " \"A\": {\n", + " \"X\": {\"A\":0.3, \"B\":0.7},\n", + " \"Y\": {\"A\":1.0}\n", + " },\n", + " \"B\": {\n", + " \"X\": {\"End\":0.8, \"B\":0.2},\n", + " \"Y\": {\"A\":1.0}\n", + " },\n", + " \"End\": {}\n", + "}\n", + "\n", + "init = \"A\"\n", + "\n", + "terminals = [\"End\"]\n", + "\n", + "rewards = {\n", + " \"A\": 5,\n", + " \"B\": -10,\n", + " \"End\": 100\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class CustomMDP(MDP):\n", + "\n", + " def __init__(self, transition_matrix, rewards, terminals, init, gamma=.9):\n", + " # All possible actions.\n", + " actlist = []\n", + " for state in transition_matrix.keys():\n", + " actlist.extend(transition_matrix.keys())\n", + " actlist = list(set(actlist))\n", + "\n", + " MDP.__init__(self, init, actlist, terminals=terminals, gamma=gamma)\n", + " self.t = transition_matrix\n", + " self.reward = rewards\n", + " for state in self.t:\n", + " self.states.add(state)\n", + "\n", + " def T(self, state, action):\n", + " return [(new_state, prob) for new_state, prob in self.t[state][action].items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we instantize the class with the parameters for our MDP in the picture." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import mdp" + "our_mdp = CustomMDP(t, rewards, terminals, init, gamma=.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this we have sucessfully represented our MDP. Later we will look at ways to solve this MDP." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grid MDP\n", + "\n", + "Now we look at a concrete implementation that makes use of the MDP as base class. The GridMDP class in the mdp module is used to represent a grid world MDP like the one shown in in **Fig 17.1** of the AIMA Book. The code should be easy to understand if you have gone through the CustomMDP example.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource GridMDP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **_ _init_ _** method takes **grid** as an extra parameter compared to the MDP class. The grid is a nested list of rewards in states.\n", + "\n", + "**go** method returns the state by going in particular direction by using vector_add.\n", + "\n", + "**T** method is not implemented and is somewhat different from the text. Here we return (probability, s') pairs where s' belongs to list of possible state by taking action a in state s.\n", + "\n", + "**actions** method returns list of actions possible in each state. By default it returns all actions for states other than terminal states.\n", + "\n", + "**to_arrows** are used for representing the policy in a grid like format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create a GridMDP like the one in **Fig 17.1** as follows: \n", + "\n", + " GridMDP([[-0.04, -0.04, -0.04, +1],\n", + " [-0.04, None, -0.04, -1],\n", + " [-0.04, -0.04, -0.04, -0.04]],\n", + " terminals=[(3, 2), (3, 1)])\n", + " \n", + "In fact the **sequential_decision_environment** in mdp module has been instantized using the exact same code." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequential_decision_environment" ] }, { From 5089669a767e95b64340fecdc7d5dd2c30b942d2 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Wed, 27 Apr 2016 01:40:39 -0400 Subject: [PATCH 285/513] Start of CYK parser. The grammar still needs to be updated accordingly. --- nlp.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/nlp.py b/nlp.py index 097e384fb..83686170f 100644 --- a/nlp.py +++ b/nlp.py @@ -182,3 +182,27 @@ def extender(self, edge): for (i, j, A, alpha, B1b) in self.chart[j]: if B1b and B == B1b[0]: self.add_edge([i, k, A, alpha + [edge], B1b[1:]]) + + +# ______________________________________________________________________________ +# CYK Parsing + +def CYK_parse(words, grammar): + "[Figure 23.5]" + # We use 0-based indexing instead of the book's 1-based. + N = len(words) + P = defaultdict(float) + # Insert lexical rules for each word. + for (i, word) in enumerate(words): + for (X, p) in grammar.categories[word]: # XXX grammar.categories needs changing, above + P[X, i, 1] = p + # Combine first and second parts of right-hand sides of rules, + # from short to long. + for length in range(2, N+1): + for start in range(N-length+1): + for len1 in range(1, length): # N.B. the book incorrectly has N instead of length + len2 = length - len1 + for (X, Y, Z, p) in grammar.cnf_rules(): # XXX grammar needs this method + P[X, start, length] = max(P[X, start, length], + P[Y, start, len1] * P[Z, start+len1, len2] * p) + return P From a70ff5192756e6aa7c26c4288986a33bda4bdc3a Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 28 Apr 2016 09:42:27 -0700 Subject: [PATCH 286/513] README: GSoC applications over. --- README.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 1e9589da4..2915931f1 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,20 @@ # ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* We're loooking for one student sponsored by Google Summer of Code ([GSoC](https://summerofcode.withgoogle.com/)) to work on this project; if you want to be that student, make some good [contributions](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) here by looking through the [Issues](https://github.com/aimacode/aima-python/issues) and resolving some), and submit an [application](https://summerofcode.withgoogle.com/terms/student). (However, be warned that we've had over 150 students express interest, so competition will be tough.) And we're always [looking for solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) who are not affiliated with GSoC. A big thank you to everyone who has contributed! +Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. ## Python 3.4 -This code is in Python 3.4. (Of course, the current version, Python 3.5, also works.) You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.4 (Python 3.5, also works, but Python 2.x does not). You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project When complete, this project will have Python code for all the pseudocode algorithms in the book. For each major topic, such as `logic`, we will have the following three files in the main branch: - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. -- `logic.ipynb`: A Jupyter notebook that explains and gives examples of how to use the code. +- `logic.ipynb`: A Jupyter (IPython) notebook that explains and gives examples of how to use the code. - `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). - - # Index of Code Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. @@ -141,4 +139,4 @@ Here is a table of the implemented data structures, the figure, name of the impl # Acknowledgements -Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Thanks to all! +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors! From ab868f5f224f93ba0e3a3e0543cbbc423869f7c6 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 28 Apr 2016 09:43:39 -0700 Subject: [PATCH 287/513] README: Fix link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2915931f1..aa2347734 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. +Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. ## Python 3.4 From 464ca5df146316f1f0ceb52a4fa186cdfc222567 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 23 May 2016 04:17:44 +0530 Subject: [PATCH 288/513] Update Depth Limited Search from 2nd to 3rd ed --- search.py | 4 ++-- tests/test_search.py | 4 +--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/search.py b/search.py index ddb62ef3d..1124a66c2 100644 --- a/search.py +++ b/search.py @@ -278,12 +278,12 @@ def depth_limited_search(problem, limit=50): def recursive_dls(node, problem, limit): if problem.goal_test(node.state): return node - elif node.depth == limit: + elif limit == 0: return 'cutoff' else: cutoff_occurred = False for child in node.expand(problem): - result = recursive_dls(child, problem, limit) + result = recursive_dls(child, problem, limit-1) if result == 'cutoff': cutoff_occurred = True elif result is not None: diff --git a/tests/test_search.py b/tests/test_search.py index e97406777..1af525c15 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -27,9 +27,7 @@ def test_iterative_deepening_search(): assert iterative_deepening_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_depth_limited_search(): - # output flickers between 49 and 50 - # assert len(depth_limited_search(romania_problem).solution()) == 50 - pass + assert len(depth_limited_search(romania_problem).solution()) == 50 def test_astar_search(): assert astar_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] From 2f84be4bb4c1a9e47d1b2f1bb3d0bf34b38debef Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Sun, 22 May 2016 10:58:39 +0530 Subject: [PATCH 289/513] planning.py (#232) * Added Action class for PDDL * Added tests for Action class * Changed doc string * Added PDLL class * Added Air-Cargo-problem * Tested Air Cargo Problem --- planning.py | 137 ++++++++++++++++++++++++++++++++++++++--- tests/test_planning.py | 34 ++++++++++ 2 files changed, 163 insertions(+), 8 deletions(-) create mode 100644 tests/test_planning.py diff --git a/planning.py b/planning.py index 52e4c0b36..9e52c839e 100644 --- a/planning.py +++ b/planning.py @@ -1,13 +1,134 @@ """Planning (Chapters 10-11) """ -# flake8: noqa +from utils import Expr, expr, first +from logic import FolKB -import agents +class PDLL: + """ + PDLL used to deine a search problem + It stores states in a knowledge base consisting of first order logic statements + The conjunction of these logical statements completely define a state + """ -import math -import random -import sys -import time -import bisect -import string + def __init__(self, initial_state, actions, goal_test): + self.kb = FolKB(initial_state) + self.actions = actions + self.goal_test_func = goal_test + + def goal_test(self): + return self.goal_test_func(self.kb) + + def act(self, action): + """ + Performs the action given as argument + Note that action is an Expr like expr('Remove(Glass, Table)') or expr('Eat(Sandwich)') + """ + action_name = action.op + args = action.args + list_action = first(a for a in self.actions if a.name == action_name) + if list_action is None: + raise Exception("Action '{}' not found".format(action_name)) + if not list_action.check_precond(self.kb, args): + raise Exception("Action '{}' pre-conditions not satisfied".format(action)) + list_action(self.kb, args) + +class Action: + """ + Defines an action schema using preconditions and effects + Use this to describe actions in PDDL + action is an Expr where variables are given as arguments(args) + Precondition and effect are both lists with positive and negated literals + Example: + precond_pos = [expr("Human(person)"), expr("Hungry(Person)")] + precond_neg = [expr("Eaten(food)")] + effect_add = [expr("Eaten(food)")] + effect_rem = [expr("Hungry(person)")] + eat = Action(expr("Eat(person, food)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + """ + + def __init__(self,action , precond, effect): + self.name = action.op + self.args = action.args + self.precond_pos = precond[0] + self.precond_neg = precond[1] + self.effect_add = effect[0] + self.effect_rem = effect[1] + + def __call__(self, kb, args): + return self.act(kb, args) + + def substitute(self, e, args): + """Replaces variables in expression with their respective Propostional symbol""" + new_args = [args[i] for x in e.args for i in range(len(self.args)) if self.args[i]==x] + return Expr(e.op, *new_args) + + def check_precond(self, kb, args): + """Checks if the precondition is satisfied in the current state""" + #check for positive clauses + for clause in self.precond_pos: + if self.substitute(clause, args) not in kb.clauses: + return False + #check for negative clauses + for clause in self.precond_neg: + if self.substitute(clause, args) in kb.clauses: + return False + return True + + def act(self, kb, args): + """Executes the action on the state's kb""" + #check if the preconditions are satisfied + if not self.check_precond(kb, args): + raise Exception("Action pre-conditions not satisfied") + #remove negative literals + for clause in self.effect_rem: + kb.retract(self.substitute(clause, args)) + #add positive literals + for clause in self.effect_add: + kb.tell(self.substitute(clause, args)) + + +def air_cargo(): + init = [expr('At(C1, SFO)'), + expr('At(C2, JFK)'), + expr('At(P1, SFO)'), + expr('At(P2, JFK)'), + expr('Cargo(C1)'), + expr('Cargo(C2)'), + expr('Plane(P1)'), + expr('Plane(P2)'), + expr('Airport(JFK)'), + expr('Airport(SFO)')] + + def goal_test(kb): + required = [expr('At(C1 , JFK)'), expr('At(C2 ,SFO)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ## Actions + # Load + precond_pos = [expr("At(c, a)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), expr("Airport(a)")] + precond_neg = [] + effect_add = [expr("In(c, p)")] + effect_rem = [expr("At(c, a)")] + load = Action(expr("Load(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + # Unload + precond_pos = [expr("In(c, p)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), expr("Airport(a)")] + precond_neg = [] + effect_add = [expr("At(c, a)")] + effect_rem = [expr("In(c, p)")] + unload = Action(expr("Unload(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + # Load + # Used used 'f' instead of 'from' because 'from' is a python keyword and expr uses eval() function + precond_pos = [expr("At(p, f)"), expr("Plane(p)"), expr("Airport(f)"), expr("Airport(to)")] + precond_neg = [] + effect_add = [expr("At(p, to)")] + effect_rem = [expr("At(p, f)")] + fly = Action(expr("Fly(p, f, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [load, unload, fly], goal_test) + diff --git a/tests/test_planning.py b/tests/test_planning.py new file mode 100644 index 000000000..aed4812ea --- /dev/null +++ b/tests/test_planning.py @@ -0,0 +1,34 @@ +from planning import * +from utils import expr +from logic import FolKB + +def test_action(): + precond = [[expr("P(x)"), expr("Q(y, z)")] + ,[expr("Q(x)")]] + effect = [[expr("Q(x)")] + , [expr("P(x)")]] + a=Action(expr("A(x,y,z)"),precond, effect) + args = [expr("A"), expr("B"), expr("C")] + assert a.substitute(expr("P(x, z, y)"), args) == expr("P(A, C, B)") + test_kb = FolKB([expr("P(A)"), expr("Q(B, C)"), expr("R(D)")]) + assert a.check_precond(test_kb, args) + a.act(test_kb, args) + assert test_kb.ask(expr("P(A)")) is False + assert test_kb.ask(expr("Q(A)")) is not False + assert test_kb.ask(expr("Q(B, C)")) is not False + assert not a.check_precond(test_kb, args) + +def test_air_cargo(): + p = air_cargo() + assert p.goal_test() is False + solution =[expr("Load(C1 , P1, SFO)"), + expr("Fly(P1, SFO, JFK)"), + expr("Unload(C1, P1, JFK)"), + expr("Load(C2, P2, JFK)"), + expr("Fly(P2, JFK, SFO)"), + expr("Unload (C2, P2, SFO)")] + + for action in solution: + p.act(action) + + assert p.goal_test() From da8b17e1247392b8a42f8d488837a810231f03ea Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 22 May 2016 20:04:44 +0530 Subject: [PATCH 290/513] removes pseudo-codes which are deleted from 3rd edition --- README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/README.md b/README.md index aa2347734..b5cc93563 100644 --- a/README.md +++ b/README.md @@ -81,12 +81,9 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | -| \* 12.6 | House-Building-Problem | | -| \* 12.22 | Continuous-POP-Agent | | | 11.10 | Doubles-tennis | | | 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`](../master/probability.py) | | 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | -| \* 13.4 | Enumerate-Joint-Ask | `enumerate_joint_ask` | [`probability.py`](../master/probability.py) | | 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`](../master/probability.py) | | 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | | 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | @@ -112,12 +109,9 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | -| \* 21.2 | Naive-Communicating-Agent | | | 22.1 | HITS | | | | 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | | 23.5 | CYK-Parse | | | -| \* 23.1 | Viterbi-Segmentation | `viterbi_segment` | [`text.py`](../master/text.py) | -| \* 24.21 | Align | | | 25.9 | Monte-Carlo-Localization| | From 887bd6c8cbc6999f501af6abb8948863dae0dd1c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Tue, 24 May 2016 14:34:55 +0530 Subject: [PATCH 291/513] Better Tests for depth_limited_search to make build pass --- tests/test_search.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tests/test_search.py b/tests/test_search.py index 1af525c15..e4eb8436f 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -27,7 +27,11 @@ def test_iterative_deepening_search(): assert iterative_deepening_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_depth_limited_search(): - assert len(depth_limited_search(romania_problem).solution()) == 50 + solution_3 = depth_limited_search(romania_problem, 3).solution() + assert solution_3[-1] == 'Bucharest' + assert depth_limited_search(romania_problem, 2) == 'cutoff' + solution_50 = depth_limited_search(romania_problem).solution() + assert solution_50[-1] == 'Bucharest' def test_astar_search(): assert astar_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] From 319ff5200efc4a89581621a7028470b8fde5ccab Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Fri, 27 May 2016 16:57:17 +0530 Subject: [PATCH 292/513] Added Index Notebook for Binder --- index.ipynb | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 index.ipynb diff --git a/index.ipynb b/index.ipynb new file mode 100644 index 000000000..59dd6177b --- /dev/null +++ b/index.ipynb @@ -0,0 +1,66 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AIMA Python Binder Index\n", + "\n", + "Welcome to the AIMA Python Code Repository. You should be seeing this index notebook if you clicked on the **Launch Binder** button on the [repository](https://github.com/aimacode/aima-python). If you are viewing this notebook directly on Github we suggest that you use the **Launch Binder** button instead. Binder allows you to experiment with all the code in the browser itself without the need of installing anything on your local machine. Below is the list of notebooks that should assist you in navigating the different notebooks available. \n", + "\n", + "If you are completely new to AIMA Python or Jupyter Notebooks we suggest that you start with the Introduction Notebook.\n", + "\n", + "# List of Notebooks\n", + "\n", + "1. [**Introduction**](./intro.ipynb)\n", + "\n", + "2. [**Agents**](./agents.ipynb)\n", + "\n", + "3. [**Search**](./search.ipynb)\n", + "\n", + "4. [**Games**](./games.ipynb)\n", + "\n", + "5. [**Constraint Satisfaction Problems**](./csp.ipynb)\n", + "\n", + "6. [**Logic**](./logic.ipynb)\n", + "\n", + "7. [**Planning**](./planning.ipynb)\n", + "\n", + "8. [**Probability**](./probability.ipynb)\n", + "\n", + "9. [**Markov Decision Processes**](./mdp.ipynb)\n", + "\n", + "10. [**Learning**](./learning.ipynb)\n", + "\n", + "11. [**Reinforcement Learning**](./rl.ipynb)\n", + "\n", + "12. [**Statistical Language Processing Tools**](./text.ipynb)\n", + "\n", + "13. [**Natural Language Processing**](./nlp.ipynb)\n", + "\n", + "Besides the notebooks it is also possible to make direct modifications to the Python/JS code. To view/modify the complete set of files [click here](.) to view the Directory structure." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 3bd230aa52bab551e90f8987f447d3b00a466753 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Fri, 27 May 2016 17:00:03 +0530 Subject: [PATCH 293/513] Added Info and Binder Badge --- README.md | 2 +- intro.ipynb | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index b5cc93563..1156b38a7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) +# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/aimacode/aima-python) Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. diff --git a/intro.ipynb b/intro.ipynb index 0f02870ab..a4850ebc2 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -23,13 +23,14 @@ "## IPython notebooks \n", " \n", "The IPython notebooks in this repository explain how to use the modules, and give examples of usage. \n", - "You can use them in two ways: \n", + "You can use them in three ways: \n", "\n", "1. View static HTML pages. (Just browse to the [repository](https://github.com/aimacode/aima-python) and click on a `.ipynb` file link.)\n", "2. Run, modify, and re-run code, live. (Download the repository (by [zip file](https://github.com/aimacode/aima-python/archive/master.zip) or by `git` commands), start a Jupyter notebook server with the shell command \"`jupyter notebook`\" (issued from the directory where the files are), and click on the notebook you want to interact with.)\n", + "3. Binder - Click on the binder badge on the [repository](https://github.com/aimacode/aima-python) main page to opens the notebooks in an executable environment, online. This method does not require any extra installation. The code can be executed and modified from the browser itself.\n", "\n", " \n", - "You can [read about notebooks](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) and then [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb). " + "You can [read about notebooks](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) and then [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb)." ] }, { @@ -118,7 +119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From b73ef7f04e92de0d485b9da9925e8038accba085 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 27 May 2016 11:02:53 +0530 Subject: [PATCH 294/513] Re writes badges in html to open links in new tab --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1156b38a7..d9e1e9d4e 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)`aima-python`[![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/aimacode/aima-python) +# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)aima-python     Build StatusBinder Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. From 7ce9a2ae67ecf687a337a1fd69eedc8f68c8f762 Mon Sep 17 00:00:00 2001 From: go-bears Date: Fri, 27 May 2016 00:08:38 -0700 Subject: [PATCH 295/513] text edits to search.ipynb (#236) * some fixes for typos and spacing and some edits for clarity * some fixes for typos and spacing and some edits for clarity * format and edit intro --- search.ipynb | 65 +++++++++++++++++++++++++++++++++------------------- 1 file changed, 41 insertions(+), 24 deletions(-) diff --git a/search.ipynb b/search.ipynb index 80c9743c5..41a9a01da 100644 --- a/search.ipynb +++ b/search.ipynb @@ -14,11 +14,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Introduction\n", + "Introduction\n", + "============\n", + "\n", "Hello!\n", - " In this IPython notebook, we study different kinds of search techniques used in [ search.py ]( https://github.com/aimacode/aima-python/blob/master/search.py ) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", + "In this IPython notebook, we'll study different kinds of search techniques used in [ search.py ]( https://github.com/aimacode/aima-python/blob/master/search.py ) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", + "\n", + "The code in this IPython notebook, and the entire `aima-python` repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the `intro` IPython notebook. \n", "\n", - " The code in this IPython notebook, and the entire aima-python repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the 'intro' IPython notebook. Now that you have all that sorted out, lets get started!" + "Now that you have all that sorted out, let's get started!" ] }, { @@ -32,14 +36,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Also called `blind search`, in such search strategies with all the information we have about any state all we can do is generate its successors and check whether it's a `goal state` or not. THAT'S IT. NOTHING MORE(Well ....not really. See the `value` method defined in the following section).\n", + "Uninformed Search strategies are called `blind search`. In such search strategies, the only information we have about any state is generated by checking if a piece of data, or any of its successors, matches our `goal state` or not. THAT'S IT. NOTHING MORE. (Well ....not really. See the `value` method defined in the following section).\n", "\n", "First let's formulate the problem we intend to solve. So let's import everything from our module." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -55,12 +59,12 @@ "The first thing we observe is '`from utils import *`'. This means that everything in utils.py is imported for use in this module. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n", " \n", "The `Problem` class is an abstract class on which we define our problems(*duh*).\n", - "Again, if you are confused about what `abstract class` means have a look at the 'Intro' notebook.\n", + "Again, if you are confused about what `abstract class` means have a look at the `Intro` notebook.\n", "The `Problem` class has six methods.\n", - "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. In this and all of the below methods `self` refers to the object itself- the object whose method is called. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins his task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", + "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. In this and all of the below methods `self` refers to the object itself--the object whose method is called. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins his task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", "* `actions(self, state)` : This method returns all the possible actions our agent can make in state `state`.\n", "* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n", - "* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else , ofcourse, `False` is returned.\n", + "* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else, of course, `False` is returned.\n", "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n", "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test." ] @@ -69,7 +73,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now using the above abstract class as a parent there is another named called `GraphProblem` in our module. It creates a graph problem from an instance of the `Graph` class. To create a graph, simply do `graph = Graph(dict(...))`. The dictionary must contain nodes of the graph as keys, so make sure they are `hashable`. If you don't know what that means just use strings or numbers. Each node in the dictionary should correspond to another dictionary which contain the adjacent nodes as keys and the edge length as its value. The `Graph` class creates a directed(edges allow only one way traffic) by default.If you want to make an undirected graph, use `UndirectedGraph` instead, but make sure to mention any edge in only one of its nodes." + "Now the above abstract class acts as a parent class, and there is another named called `GraphProblem` in our module. It creates a graph problem from an instance of the `Graph` class. To create a graph, simply type `graph = Graph(dict(...))`. The dictionary must contain nodes of the graph as keys, so make sure they are `hashable`. If you don't know what that means just use strings or numbers. Each node contains the adjacent nodes as keys and the edge length as its value. Each dictionary then should correspond to another dictionary in the graph. The `Graph` class creates a directed(edges allow only one way traffic) by default. If you want to make an undirected graph, use `UndirectedGraph` instead, but make sure to mention any edge in only one of its nodes." ] }, { @@ -81,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -105,7 +109,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Suppose we are in a museum showcasing statues of various animals. To navigate through the museum there are paths between some statues and the entrance. We define the entrance and the statues as nodes in our graph with the path connecting them as edges. The cost/weight of an edge specifies its length. So `Start = dict(Dog = 3, Cat = 9, Mouse = 4)` means that there are paths from `Start` to `Dog`, `Cat` and `Mouse` with path costs 3, 9 and 4 respectively. See the image below to better understand the graph." + "Imagine we are in a museum showcasing statues of various animals. To navigate through the museum there are paths between some statues and the entrance. We define the entrance and the statues as nodes in our graph with the path connecting them as edges. The cost/weight of an edge specifies is its length. So `Start = dict(Dog = 3, Cat = 9, Mouse = 4)` means that there are paths from `Start` to `Dog`, `Cat` and `Mouse` with path costs 3, 9 and 4 respectively. \n", + "\n", + "Here's an image below to better understand our graph." ] }, { @@ -127,12 +133,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In breadth first search the `shallowest` unexpanded node is chosen for expansion. That means that all nodes of a given depth must be expanded before any node of the next depth level. It accomplishes this by using a `FIFO` meaning 'First In First Out' queue. Any thing thats gets in the queue first also gets out first just like the checkout queue in a supermarket. To use the algorithm, first we need to define our problem. Say we want to find the statue of `Monkey` and we start from the entrance which is the `Start` state. We define our problem using the `GraphProblem` class." + "In Breadth First Search, the `shallowest` unexpanded node is chosen for expansion. That means that all nodes of a given depth must be expanded before any node of the next depth level. This search strategy accomplishes this by using a `FIFO` meaning 'First In First Out' queue. Anything that gets in the queue first also gets out first just like the checkout queue in a supermarket. To use the algorithm, first we need to define our problem. Say we want to find the statue of `Monkey` and we start from the entrance which is the `Start` state. We'll define our problem using the `GraphProblem` class." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -150,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -161,7 +167,7 @@ "['Cat', 'Monkey']" ] }, - "execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +181,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We get the output as `['Cat', 'Monkey']`. That is because first the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order when we are explanding the `Start` node. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that in breadth first search the goal test is done when it is being added to the queue." + "We get the output as `['Cat', 'Monkey']`. That is because first the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order when we are expanding the `Start` node. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that during a breadth first search, the goal test is done when the node is being added to the queue." ] }, { @@ -189,12 +195,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In uniform cost search instead of expanding the shallowest node we expand the node with the lowest path cost(cost to reach upto that node from the start). Instead of a `FIFO` queue we use something called a `priority queue` which selects the element with the highest `priority` of all elements in the queue. For our problem lower path cost means higher priority. Whenever we need to enqueue a node already in the queue we update its path cost if the newer path is better. This is a very important step and it means that the path cost to a node may keep getting better until it is selected for expansion. This is the reason that we do a goal check only when a node is selected for expanion." + "In Uniform-cost Search, we expand the node with the lowest path cost (the cost to reach that node from the start) instead of expanding the shallowest node. Rather than a `FIFO` queue, we use something called a `priority queue` which selects the element with the highest `priority` of all elements in the queue. For our problem, the shortest path between animals has the higher priority; the shortest path has the lowest path cost. Whenever we need to enqueue a node already in the queue, we will update its path cost if the newer path is better. This is a very important step, and it means that the path cost to a node may keep getting better until it is selected for expansion. This is the reason that we do a goal check only when a node is selected for expanion." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -205,7 +211,7 @@ "['Dog', 'Bear', 'Monkey']" ] }, - "execution_count": 5, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -219,12 +225,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We got `['Dog', 'Bear', 'Monkey']` instead of `['Cat', 'Monkey']` because the path cost is lower! We can also see the path cost with the path_cost attribute. Lets compare the path cost of the Breadth first search solution and Uniform cost search solution" + "We got the path`['Dog', 'Bear', 'Monkey']` instead of `['Cat', 'Monkey']`. Why? The path cost is lower! We can also see the path cost with the path_cost attribute. Let's compare the path cost of the Breadth first search solution and Uniform cost search solution" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -235,7 +241,7 @@ "(18, 17)" ] }, - "execution_count": 6, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -248,8 +254,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We were right! The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former has only two roads compared to three roads in `ucs_node`." + "We were right! \n", + "\n", + "The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former passes through two roads compared to the three roads in the `ucs_node` solution." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -268,7 +285,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.4.3" } }, "nbformat": 4, From 3a0bc75119d0f8d79b10be9b98e7ffdc54dfe9e8 Mon Sep 17 00:00:00 2001 From: reachtarunhere Date: Fri, 27 May 2016 19:44:47 +0530 Subject: [PATCH 296/513] Removed refrence to depreacated utils * imports --- search.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/search.ipynb b/search.ipynb index 41a9a01da..0fa3575b9 100644 --- a/search.ipynb +++ b/search.ipynb @@ -56,7 +56,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The first thing we observe is '`from utils import *`'. This means that everything in utils.py is imported for use in this module. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n", + "The search and other modules of the repository make use of several imports from the utils module. We will point the useful ones out if they are required to follow the material below. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n", " \n", "The `Problem` class is an abstract class on which we define our problems(*duh*).\n", "Again, if you are confused about what `abstract class` means have a look at the `Intro` notebook.\n", @@ -285,7 +285,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.1" } }, "nbformat": 4, From 1b4e2d7d9808a2aba388232f6ccf86fae3d462db Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sat, 28 May 2016 20:05:24 -0700 Subject: [PATCH 297/513] Add version of search.ipynb for 4th edition. --- Search-4e.ipynb | 2198 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2198 insertions(+) create mode 100644 Search-4e.ipynb diff --git a/Search-4e.ipynb b/Search-4e.ipynb new file mode 100644 index 000000000..f93abe76b --- /dev/null +++ b/Search-4e.ipynb @@ -0,0 +1,2198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "*Note: This is not yet ready, but shows the direction I'm leaning in for Fourth Edition Search.*\n", + "\n", + "# State-Space Search\n", + "\n", + "This notebook describes several state-space search algorithms, and how they can be used to solve a variety of problems. We start with a simple algorithm and a simple domain: finding a route from city to city. Later we will explore other algorithms and domains.\n", + "\n", + "## The Route-Finding Domain\n", + "\n", + "Like all state-space search problems, in a route-finding problem you will be given:\n", + "- A start state (for example, `'A'` for the city Arad).\n", + "- A goal state (for example, `'B'` for the city Bucharest).\n", + "- Actions that can change state (for example, driving from `'A'` to `'S'`).\n", + "\n", + "You will be asked to find:\n", + "- A path from the start state, through intermediate states, to the goal state.\n", + "\n", + "We'll use this map:\n", + "\n", + "\n", + "\n", + "A state-space search problem can be represented by a *graph*, where the vertexes of the graph are the states of the problem (in this case, cities) and the edges of the graph are the actions (in this case, driving along a road).\n", + "\n", + "We'll represent a city by its single initial letter. \n", + "We'll represent the graph of connections as a `dict` that maps each city to a list of the neighboring cities (connected by a road). For now we don't explicitly represent the actions, nor the distances\n", + "between cities." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "romania = {\n", + " 'A': ['Z', 'T', 'S'],\n", + " 'B': ['F', 'P', 'G', 'U'],\n", + " 'C': ['D', 'R', 'P'],\n", + " 'D': ['M', 'C'],\n", + " 'E': ['H'],\n", + " 'F': ['S', 'B'],\n", + " 'G': ['B'],\n", + " 'H': ['U', 'E'],\n", + " 'I': ['N', 'V'],\n", + " 'L': ['T', 'M'],\n", + " 'M': ['L', 'D'],\n", + " 'N': ['I'],\n", + " 'O': ['Z', 'S'],\n", + " 'P': ['R', 'C', 'B'],\n", + " 'R': ['S', 'C', 'P'],\n", + " 'S': ['A', 'O', 'F', 'R'],\n", + " 'T': ['A', 'L'],\n", + " 'U': ['B', 'V', 'H'],\n", + " 'V': ['U', 'I'],\n", + " 'Z': ['O', 'A']}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Suppose we want to get from `A` to `B`. Where can we go from the start state, `A`?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Z', 'T', 'S']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "romania['A']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We see that from `A` we can get to any of the three cities `['Z', 'T', 'S']`. Which should we choose? *We don't know.* That's the whole point of *search*: we don't know which immediate action is best, so we'll have to explore, until we find a *path* that leads to the goal. \n", + "\n", + "How do we explore? We'll start with a simple algorithm that will get us from `A` to `B`. We'll keep a *frontier*—a collection of not-yet-explored states—and expand the frontier outward until it reaches the goal. To be more precise:\n", + "\n", + "- Initially, the only state in the frontier is the start state, `'A'`.\n", + "- Until we reach the goal, or run out of states in the frontier to explore, do the following:\n", + " - Remove the first state from the frontier. Call it `s`.\n", + " - If `s` is the goal, we're done. Return the path to `s`.\n", + " - Otherwise, consider all the neighboring states of `s`. For each one:\n", + " - If we have not previously explored the state, add it to the end of the frontier.\n", + " - Also keep track of the previous state that led to this new neighboring state; we'll need this to reconstruct the path to the goal, and to keep us from re-visiting previously explored states.\n", + " \n", + "# A Simple Search Algorithm: `breadth_first`\n", + " \n", + "The function `breadth_first` implements this strategy:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "from collections import deque # Doubly-ended queue: pop from left, append to right.\n", + "\n", + "def breadth_first(start, goal, neighbors):\n", + " \"Find a shortest sequence of states from start to the goal.\"\n", + " frontier = deque([start]) # A queue of states\n", + " previous = {start: None} # start has no previous state; other states will\n", + " while frontier:\n", + " s = frontier.popleft()\n", + " if s == goal:\n", + " return path(previous, s)\n", + " for s2 in neighbors[s]:\n", + " if s2 not in previous:\n", + " frontier.append(s2)\n", + " previous[s2] = s\n", + " \n", + "def path(previous, s): \n", + " \"Return a list of states that lead to state s, according to the previous dict.\"\n", + " return [] if (s is None) else path(previous, previous[s]) + [s]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "A couple of things to note: \n", + "\n", + "1. We always add new states to the end of the frontier queue. That means that all the states that are adjacent to the start state will come first in the queue, then all the states that are two steps away, then three steps, etc.\n", + "That's what we mean by *breadth-first* search.\n", + "2. We recover the path to an `end` state by following the trail of `previous[end]` pointers, all the way back to `start`.\n", + "The dict `previous` is a map of `{state: previous_state}`. \n", + "3. When we finally get an `s` that is the goal state, we know we have found a shortest path, because any other state in the queue must correspond to a path that is as long or longer.\n", + "3. Note that `previous` contains all the states that are currently in `frontier` as well as all the states that were in `frontier` in the past.\n", + "4. If no path to the goal is found, then `breadth_first` returns `None`. If a path is found, it returns the sequence of states on the path.\n", + "\n", + "Some examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['A', 'S', 'F', 'B']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('A', 'B', romania)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['L', 'T', 'A', 'S', 'F', 'B', 'U', 'V', 'I', 'N']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('L', 'N', romania)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['N', 'I', 'V', 'U', 'B', 'F', 'S', 'A', 'T', 'L']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('N', 'L', romania)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['E']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('E', 'E', romania)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Now let's try a different kind of problem that can be solved with the same search function.\n", + "\n", + "## Word Ladders Problem\n", + "\n", + "A *word ladder* problem is this: given a start word and a goal word, find the shortest way to transform the start word into the goal word by changing one letter at a time, such that each change results in a word. For example starting with `green` we can reach `grass` in 7 steps:\n", + "\n", + "`green` → `greed` → `treed` → `trees` → `tress` → `cress` → `crass` → `grass`\n", + "\n", + "We will need a dictionary of words. I'll make a local copy of the list of 5-letter words from the [Stanford GraphBase](http://www-cs-faculty.stanford.edu/~uno/sgb.html) project (the `!` indicates that these are shell commands, not Python):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "! [ -e sgb-words.txt ] || curl -O http://www-cs-faculty.stanford.edu/~uno/sgb-words.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "which\r\n", + "there\r\n", + "their\r\n", + "about\r\n", + "would\r\n", + "these\r\n", + "other\r\n", + "words\r\n", + "could\r\n", + "write\r\n" + ] + } + ], + "source": [ + "! head sgb-words.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "We can assign `WORDS` to be the set of all the words in this file:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5757" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "WORDS = set(open('sgb-words.txt').read().split())\n", + "len(WORDS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "And define `neighboring_words` to return the set of all words that are a one-letter change away from a given `word`:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def neighboring_words(word):\n", + " \"All words that are one letter away from this word.\"\n", + " neighbors = {word[:i] + c + word[i+1:]\n", + " for i in range(len(word))\n", + " for c in 'abcdefghijklmnopqrstuvwxyz'\n", + " if c != word[i]}\n", + " return neighbors & WORDS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'cello', 'hallo', 'hells', 'hullo', 'jello'}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neighboring_words('hello')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'would'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neighboring_words('world')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Now we can create `word_neighbors` as a dict of `{word: {neighboring_word, ...}}`: " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "word_neighbors = {word: neighboring_words(word)\n", + " for word in WORDS}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Now the `breadth_first` function can be used to solve a word ladder problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['green', 'greed', 'treed', 'trees', 'treys', 'greys', 'grays', 'grass']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('green', 'grass', word_neighbors)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['smart',\n", + " 'start',\n", + " 'stars',\n", + " 'sears',\n", + " 'bears',\n", + " 'beans',\n", + " 'brans',\n", + " 'brand',\n", + " 'braid',\n", + " 'brain']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('smart', 'brain', word_neighbors)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['frown',\n", + " 'flown',\n", + " 'flows',\n", + " 'slows',\n", + " 'stows',\n", + " 'stoas',\n", + " 'stoae',\n", + " 'stole',\n", + " 'stile',\n", + " 'smile']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first('frown', 'smile', word_neighbors)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# More General Search Algorithms\n", + "\n", + "Now we'll embelish the `breadth_first` algorithm to make a family of search algorithms with more capabilities:\n", + "\n", + "1. We distinguish between an *action* and the *result* of an action.\n", + "3. We allow different measures of the cost of a solution (not just the number of steps in the sequence).\n", + "4. We search through the state space in an order that is more likely to lead to an optimal solution quickly.\n", + "\n", + "Here's how we do these things:\n", + "\n", + "1. Instead of having a graph of neighboring states, we instead have an object of type *Problem*. A Problem\n", + "has one method, `Problem.actions(state)` to return a collection of the actions that are allowed in a state,\n", + "and another method, `Problem.result(state, action)` that says what happens when you take an action.\n", + "2. We keep a set, `explored` of states that have already been explored. We also have a class, `Frontier`, that makes it efficient to ask if a state is on the frontier.\n", + "3. Each action has a cost associated with it (in fact, the cost can vary with both the state and the action).\n", + "4. The `Frontier` class acts as a priority queue, allowing the \"best\" state to be explored next.\n", + "We represent a sequence of actions and resulting states as a linked list of `Node` objects.\n", + "\n", + "The algorithm `breadth_first_search` is basically the same as `breadth_first`, but using our new conventions:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def breadth_first_search(problem):\n", + " \"Search for goal; paths with least number of steps first.\"\n", + " if problem.is_goal(problem.initial): \n", + " return Node(problem.initial)\n", + " frontier = FrontierQ(Node(problem.initial), LIFO=False)\n", + " explored = set()\n", + " while frontier:\n", + " node = frontier.pop()\n", + " explored.add(node.state)\n", + " for action in problem.actions(node.state):\n", + " child = node.child(problem, action)\n", + " if child.state not in explored and child.state not in frontier:\n", + " if problem.is_goal(child.state):\n", + " return child\n", + " frontier.add(child)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next is `uniform_cost_search`, in which each step can have a different cost, and we still consider first one os the states with minimum cost so far." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def uniform_cost_search(problem, costfn=lambda node: node.path_cost):\n", + " frontier = FrontierPQ(Node(problem.initial), costfn)\n", + " explored = set()\n", + " while frontier:\n", + " node = frontier.pop()\n", + " if problem.is_goal(node.state):\n", + " return node\n", + " explored.add(node.state)\n", + " for action in problem.actions(node.state):\n", + " child = node.child(problem, action)\n", + " if child.state not in explored and child not in frontier:\n", + " frontier.add(child)\n", + " elif child in frontier and frontier.cost[child] < child.path_cost:\n", + " frontier.replace(child)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, `astar_search` in which the cost includes an estimate of the distance to the goal as well as the distance travelled so far." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def astar_search(problem, heuristic):\n", + " costfn = lambda node: node.path_cost + heuristic(node.state)\n", + " return uniform_cost_search(problem, costfn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Search Tree Nodes\n", + "\n", + "The solution to a search problem is now a linked list of `Node`s, where each `Node`\n", + "includes a `state` and the `path_cost` of getting to the state. In addition, for every `Node` except for the first (root) `Node`, there is a previous `Node` (indicating the state that lead to this `Node`) and an `action` (indicating the action taken to get here)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class Node(object):\n", + " \"\"\"A node in a search tree. A search tree is spanning tree over states.\n", + " A Node contains a state, the previous node in the tree, the action that\n", + " takes us from the previous state to this state, and the path cost to get to \n", + " this state. If a state is arrived at by two paths, then there are two nodes \n", + " with the same state.\"\"\"\n", + "\n", + " def __init__(self, state, previous=None, action=None, step_cost=1):\n", + " \"Create a search tree Node, derived from a previous Node by an action.\"\n", + " self.state = state\n", + " self.previous = previous\n", + " self.action = action\n", + " self.path_cost = 0 if previous is None else (previous.path_cost + step_cost)\n", + "\n", + " def __repr__(self): return \"\".format(self.state, self.path_cost)\n", + " \n", + " def __lt__(self, other): return self.path_cost < other.path_cost\n", + " \n", + " def child(self, problem, action):\n", + " \"The Node you get by taking an action from this Node.\"\n", + " result = problem.result(self.state, action)\n", + " return Node(result, self, action, \n", + " problem.step_cost(self.state, action, result)) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Frontiers\n", + "\n", + "A frontier is a collection of Nodes that acts like both a Queue and a Set. A frontier, `f`, supports these operations:\n", + "\n", + "* `f.add(node)`: Add a node to the Frontier.\n", + "\n", + "* `f.pop()`: Remove and return the \"best\" node from the frontier.\n", + "\n", + "* `f.replace(node)`: add this node and remove a previous node with the same state.\n", + "\n", + "* `state in f`: Test if some node in the frontier has arrived at state.\n", + "\n", + "* `f[state]`: returns the node corresponding to this state in frontier.\n", + "\n", + "* `len(f)`: The number of Nodes in the frontier. When the frontier is empty, `f` is *false*.\n", + "\n", + "We provide two kinds of frontiers: One for \"regular\" queues, either first-in-first-out (for breadth-first search) or last-in-first-out (for depth-first search), and one for priority queues, where you can specify what cost function on nodes you are trying to minimize." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "from collections import OrderedDict\n", + "import heapq\n", + "\n", + "class FrontierQ(OrderedDict):\n", + " \"A Frontier that supports FIFO or LIFO Queue ordering.\"\n", + " \n", + " def __init__(self, initial, LIFO=False):\n", + " \"\"\"Initialize Frontier with an initial Node.\n", + " If LIFO is True, pop from the end first; otherwise from front first.\"\"\"\n", + " self.LIFO = LIFO\n", + " self.add(initial)\n", + " \n", + " def add(self, node):\n", + " \"Add a node to the frontier.\"\n", + " self[node.state] = node\n", + " \n", + " def pop(self):\n", + " \"Remove and return the next Node in the frontier.\"\n", + " (state, node) = self.popitem(self.LIFO)\n", + " return node\n", + " \n", + " def replace(self, node):\n", + " \"Make this node replace the nold node with the same state.\"\n", + " del self[node.state]\n", + " self.add(node)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class FrontierPQ:\n", + " \"A Frontier ordered by a cost function; a Priority Queue.\"\n", + " \n", + " def __init__(self, initial, costfn=lambda node: node.path_cost):\n", + " \"Initialize Frontier with an initial Node, and specify a cost function.\"\n", + " self.heap = []\n", + " self.states = {}\n", + " self.costfn = costfn\n", + " self.add(initial)\n", + " \n", + " def add(self, node):\n", + " \"Add node to the frontier.\"\n", + " cost = self.costfn(node)\n", + " heapq.heappush(self.heap, (cost, node))\n", + " self.states[node.state] = node\n", + " \n", + " def pop(self):\n", + " \"Remove and return the Node with minimum cost.\"\n", + " (cost, node) = heapq.heappop(self.heap)\n", + " self.states.pop(node.state, None) # remove state\n", + " return node\n", + " \n", + " def replace(self, node):\n", + " \"Make this node replace a previous node with the same state.\"\n", + " if node.state not in self:\n", + " raise ValueError('{} not there to replace'.format(node.state))\n", + " for (i, (cost, old_node)) in enumerate(self.heap):\n", + " if old_node.state == node.state:\n", + " self.heap[i] = (self.costfn(node), node)\n", + " heapq._siftdown(self.heap, 0, i)\n", + " return\n", + "\n", + " def __contains__(self, state): return state in self.states\n", + " \n", + " def __len__(self): return len(self.heap)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Search Problems\n", + "\n", + "`Problem` is the abstract class for all search problems. You can define your own class of problems as a subclass of `Problem`. You will need to override the `actions` and `result` method to describe how your problem works. You will also have to either override `is_goal` or pass a collection of goal states to the initialization method. If actions have different costs, you should override the `step_cost` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class Problem(object):\n", + " \"\"\"The abstract class for a search problem.\"\"\"\n", + "\n", + " def __init__(self, initial=None, goals=(), **additional_keywords):\n", + " \"\"\"Provide an initial state and optional goal states.\n", + " A subclass can have additional keyword arguments.\"\"\"\n", + " self.initial = initial # The initial state of the problem.\n", + " self.goals = goals # A collection of possibe goal states.\n", + " self.__dict__.update(**additional_keywords)\n", + "\n", + " def actions(self, state):\n", + " \"Return a list of actions executable in this state.\"\n", + " raise NotImplementedError # Override this!\n", + "\n", + " def result(self, state, action):\n", + " \"The state that results from executing this action in this state.\"\n", + " raise NotImplementedError # Override this!\n", + "\n", + " def is_goal(self, state):\n", + " \"True if the state is a goal.\" \n", + " return state in self.goals # Optionally override this!\n", + "\n", + " def step_cost(self, state, action, result=None):\n", + " \"The cost of taking this action from this state.\"\n", + " return 1 # Override this if actions have different costs " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def action_sequence(node):\n", + " \"The sequence of actions to get to this node.\"\n", + " actions = []\n", + " while node.previous:\n", + " actions.append(node.action)\n", + " node = node.previous\n", + " return actions[::-1]\n", + "\n", + "def state_sequence(node):\n", + " \"The sequence of states to get to this node.\"\n", + " states = [node.state]\n", + " while node.previous:\n", + " node = node.previous\n", + " states.append(node.state)\n", + " return states[::-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Two Location Vacuum World" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "dirt = '*'\n", + "clean = ' '\n", + "\n", + "class TwoLocationVacuumProblem(Problem):\n", + " \"\"\"A Vacuum in a world with two locations, and dirt.\n", + " Each state is a tuple of (location, dirt_in_W, dirt_in_E).\"\"\"\n", + "\n", + " def actions(self, state): return ('W', 'E', 'Suck')\n", + " \n", + " def is_goal(self, state): return dirt not in state\n", + " \n", + " def result(self, state, action):\n", + " \"The state that results from executing this action in this state.\" \n", + " (loc, dirtW, dirtE) = state\n", + " if action == 'W': return ('W', dirtW, dirtE)\n", + " elif action == 'E': return ('E', dirtW, dirtE)\n", + " elif action == 'Suck' and loc == 'W': return (loc, clean, dirtE)\n", + " elif action == 'Suck' and loc == 'E': return (loc, dirtW, clean) \n", + " else: raise ValueError('unknown action: ' + action)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "problem = TwoLocationVacuumProblem(initial=('W', dirt, dirt))\n", + "result = uniform_cost_search(problem)\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Suck', 'E', 'Suck']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "action_sequence(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('W', '*', '*'), ('W', ' ', '*'), ('E', ' ', '*'), ('E', ' ', ' ')]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "state_sequence(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Suck']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "problem = TwoLocationVacuumProblem(initial=('E', clean, dirt))\n", + "result = uniform_cost_search(problem)\n", + "action_sequence(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Water Pouring Problem\n", + "\n", + "Here is another problem domain, to show you how to define one. The idea is that we have a number of water jugs and a water tap and the goal is to measure out a specific amount of water (in, say, ounces or liters). You can completely fill or empty a jug, but because the jugs don't have markings on them, you can't partially fill them with a specific amount. You can, however, pour one jug into another, stopping when the seconfd is full or the first is empty." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class PourProblem(Problem):\n", + " \"\"\"Problem about pouring water between jugs to achieve some water level.\n", + " Each state is a tuples of levels. In the initialization, provide a tuple of \n", + " capacities, e.g. PourProblem(capacities=(8, 16, 32), initial=(2, 4, 3), goals={7}), \n", + " which means three jugs of capacity 8, 16, 32, currently filled with 2, 4, 3 units of \n", + " water, respectively, and the goal is to get a level of 7 in any one of the jugs.\"\"\"\n", + " \n", + " def actions(self, state):\n", + " \"\"\"The actions executable in this state.\"\"\"\n", + " jugs = range(len(state))\n", + " return ([('Fill', i) for i in jugs if state[i] != self.capacities[i]] +\n", + " [('Dump', i) for i in jugs if state[i] != 0] +\n", + " [('Pour', i, j) for i in jugs for j in jugs if i != j])\n", + "\n", + " def result(self, state, action):\n", + " \"\"\"The state that results from executing this action in this state.\"\"\"\n", + " result = list(state)\n", + " act, i, j = action[0], action[1], action[-1]\n", + " if act == 'Fill': # Fill i to capacity\n", + " result[i] = self.capacities[i]\n", + " elif act == 'Dump': # Empty i\n", + " result[i] = 0\n", + " elif act == 'Pour':\n", + " a, b = state[i], state[j]\n", + " result[i], result[j] = ((0, a + b) \n", + " if (a + b <= self.capacities[j]) else\n", + " (a + b - self.capacities[j], self.capacities[j]))\n", + " else:\n", + " raise ValueError('unknown action', action)\n", + " return tuple(result)\n", + "\n", + " def is_goal(self, state):\n", + " \"\"\"True if any of the jugs has a level equal to one of the goal levels.\"\"\"\n", + " return any(level in self.goals for level in state)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 13)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p7 = PourProblem(initial=(2, 0), capacities=(5, 13), goals={7})\n", + "p7.result((2, 0), ('Fill', 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Pour', 0, 1), ('Fill', 0), ('Pour', 0, 1)]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = uniform_cost_search(p7)\n", + "action_sequence(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Visualization Output" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def showpath(searcher, problem):\n", + " \"Show what happens when searcvher solves problem.\"\n", + " problem = Instrumented(problem)\n", + " print('\\n{}:'.format(searcher.__name__))\n", + " result = searcher(problem)\n", + " if result:\n", + " actions = action_sequence(result)\n", + " state = problem.initial\n", + " path_cost = 0\n", + " for steps, action in enumerate(actions, 1):\n", + " path_cost += problem.step_cost(state, action, 0)\n", + " result = problem.result(state, action)\n", + " print(' {} =={}==> {}; cost {} after {} steps'\n", + " .format(state, action, result, path_cost, steps,\n", + " '; GOAL!' if problem.is_goal(result) else ''))\n", + " state = result\n", + " msg = 'GOAL FOUND' if result else 'no solution'\n", + " print('{} after {} results and {} goal checks'\n", + " .format(msg, problem._counter['result'], problem._counter['is_goal']))\n", + " \n", + "from collections import Counter\n", + "\n", + "class Instrumented:\n", + " \"Instrument an object to count all the attribute accesses in _counter.\"\n", + " def __init__(self, obj):\n", + " self._object = obj\n", + " self._counter = Counter()\n", + " def __getattr__(self, attr):\n", + " self._counter[attr] += 1\n", + " return getattr(self._object, attr) " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "uniform_cost_search:\n", + " (2, 0) ==('Pour', 0, 1)==> (0, 2); cost 1 after 1 steps\n", + " (0, 2) ==('Fill', 0)==> (5, 2); cost 2 after 2 steps\n", + " (5, 2) ==('Pour', 0, 1)==> (0, 7); cost 3 after 3 steps\n", + "GOAL FOUND after 83 results and 22 goal checks\n" + ] + } + ], + "source": [ + "showpath(uniform_cost_search, p7)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "uniform_cost_search:\n", + " (0, 0) ==('Fill', 0)==> (7, 0); cost 1 after 1 steps\n", + " (7, 0) ==('Pour', 0, 1)==> (0, 7); cost 2 after 2 steps\n", + " (0, 7) ==('Fill', 0)==> (7, 7); cost 3 after 3 steps\n", + " (7, 7) ==('Pour', 0, 1)==> (1, 13); cost 4 after 4 steps\n", + " (1, 13) ==('Dump', 1)==> (1, 0); cost 5 after 5 steps\n", + " (1, 0) ==('Pour', 0, 1)==> (0, 1); cost 6 after 6 steps\n", + " (0, 1) ==('Fill', 0)==> (7, 1); cost 7 after 7 steps\n", + " (7, 1) ==('Pour', 0, 1)==> (0, 8); cost 8 after 8 steps\n", + " (0, 8) ==('Fill', 0)==> (7, 8); cost 9 after 9 steps\n", + " (7, 8) ==('Pour', 0, 1)==> (2, 13); cost 10 after 10 steps\n", + "GOAL FOUND after 110 results and 32 goal checks\n" + ] + } + ], + "source": [ + "p = PourProblem(initial=(0, 0), capacities=(7, 13), goals={2})\n", + "showpath(uniform_cost_search, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class GreenPourProblem(PourProblem): \n", + " def step_cost(self, state, action, result=None):\n", + " \"The cost is the amount of water used in a fill.\"\n", + " if action[0] == 'Fill':\n", + " i = action[1]\n", + " return self.capacities[i] - state[i]\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "uniform_cost_search:\n", + " (0, 0) ==('Fill', 0)==> (7, 0); cost 7 after 1 steps\n", + " (7, 0) ==('Pour', 0, 1)==> (0, 7); cost 7 after 2 steps\n", + " (0, 7) ==('Fill', 0)==> (7, 7); cost 14 after 3 steps\n", + " (7, 7) ==('Pour', 0, 1)==> (1, 13); cost 14 after 4 steps\n", + " (1, 13) ==('Dump', 1)==> (1, 0); cost 14 after 5 steps\n", + " (1, 0) ==('Pour', 0, 1)==> (0, 1); cost 14 after 6 steps\n", + " (0, 1) ==('Fill', 0)==> (7, 1); cost 21 after 7 steps\n", + " (7, 1) ==('Pour', 0, 1)==> (0, 8); cost 21 after 8 steps\n", + " (0, 8) ==('Fill', 0)==> (7, 8); cost 28 after 9 steps\n", + " (7, 8) ==('Pour', 0, 1)==> (2, 13); cost 28 after 10 steps\n", + "GOAL FOUND after 184 results and 48 goal checks\n" + ] + } + ], + "source": [ + "p = GreenPourProblem(initial=(0, 0), capacities=(7, 13), goals={2})\n", + "showpath(uniform_cost_search, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def compare_searchers(problem, searchers=None):\n", + " \"Apply each of the search algorithms to the problem, and show results\"\n", + " if searchers is None: \n", + " searchers = (breadth_first_search, uniform_cost_search)\n", + " for searcher in searchers:\n", + " showpath(searcher, problem)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "breadth_first_search:\n", + " (0, 0) ==('Fill', 0)==> (7, 0); cost 7 after 1 steps\n", + " (7, 0) ==('Pour', 0, 1)==> (0, 7); cost 7 after 2 steps\n", + " (0, 7) ==('Fill', 0)==> (7, 7); cost 14 after 3 steps\n", + " (7, 7) ==('Pour', 0, 1)==> (1, 13); cost 14 after 4 steps\n", + " (1, 13) ==('Dump', 1)==> (1, 0); cost 14 after 5 steps\n", + " (1, 0) ==('Pour', 0, 1)==> (0, 1); cost 14 after 6 steps\n", + " (0, 1) ==('Fill', 0)==> (7, 1); cost 21 after 7 steps\n", + " (7, 1) ==('Pour', 0, 1)==> (0, 8); cost 21 after 8 steps\n", + " (0, 8) ==('Fill', 0)==> (7, 8); cost 28 after 9 steps\n", + " (7, 8) ==('Pour', 0, 1)==> (2, 13); cost 28 after 10 steps\n", + "GOAL FOUND after 100 results and 31 goal checks\n", + "\n", + "uniform_cost_search:\n", + " (0, 0) ==('Fill', 0)==> (7, 0); cost 7 after 1 steps\n", + " (7, 0) ==('Pour', 0, 1)==> (0, 7); cost 7 after 2 steps\n", + " (0, 7) ==('Fill', 0)==> (7, 7); cost 14 after 3 steps\n", + " (7, 7) ==('Pour', 0, 1)==> (1, 13); cost 14 after 4 steps\n", + " (1, 13) ==('Dump', 1)==> (1, 0); cost 14 after 5 steps\n", + " (1, 0) ==('Pour', 0, 1)==> (0, 1); cost 14 after 6 steps\n", + " (0, 1) ==('Fill', 0)==> (7, 1); cost 21 after 7 steps\n", + " (7, 1) ==('Pour', 0, 1)==> (0, 8); cost 21 after 8 steps\n", + " (0, 8) ==('Fill', 0)==> (7, 8); cost 28 after 9 steps\n", + " (7, 8) ==('Pour', 0, 1)==> (2, 13); cost 28 after 10 steps\n", + "GOAL FOUND after 184 results and 48 goal checks\n" + ] + } + ], + "source": [ + "compare_searchers(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Grid\n", + "\n", + "An environment where you can move in any of 4 directions, unless there is an obstacle there.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{(0, 0): [(0, 1), (1, 0)],\n", + " (0, 1): [(0, 2), (0, 0), (1, 1)],\n", + " (0, 2): [(0, 3), (0, 1), (1, 2)],\n", + " (0, 3): [(0, 2), (1, 3)],\n", + " (0, 4): [(0, 3), (1, 4)],\n", + " (1, 0): [(1, 1), (2, 0), (0, 0)],\n", + " (1, 1): [(1, 2), (1, 0), (0, 1)],\n", + " (1, 2): [(1, 3), (1, 1), (2, 2), (0, 2)],\n", + " (1, 3): [(1, 4), (1, 2), (2, 3), (0, 3)],\n", + " (1, 4): [(1, 3), (2, 4)],\n", + " (2, 0): [(3, 0), (1, 0)],\n", + " (2, 1): [(2, 2), (2, 0), (3, 1), (1, 1)],\n", + " (2, 2): [(2, 3), (3, 2), (1, 2)],\n", + " (2, 3): [(2, 4), (2, 2), (3, 3), (1, 3)],\n", + " (2, 4): [(2, 3), (3, 4), (1, 4)],\n", + " (3, 0): [(3, 1), (4, 0), (2, 0)],\n", + " (3, 1): [(3, 2), (3, 0), (4, 1)],\n", + " (3, 2): [(3, 3), (3, 1), (4, 2), (2, 2)],\n", + " (3, 3): [(3, 4), (3, 2), (4, 3), (2, 3)],\n", + " (3, 4): [(3, 3), (4, 4), (2, 4)],\n", + " (4, 0): [(4, 1), (3, 0)],\n", + " (4, 1): [(4, 2), (4, 0), (3, 1)],\n", + " (4, 2): [(4, 3), (4, 1), (3, 2)],\n", + " (4, 3): [(4, 4), (4, 2), (3, 3)],\n", + " (4, 4): [(4, 3), (3, 4)]}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "N, S, E, W = DIRECTIONS = [(0, 1), (0, -1), (1, 0), (-1, 0)]\n", + "\n", + "def Grid(width, height, obstacles=0.1):\n", + " \"\"\"A 2-D grid, width x height, with obstacles that are either a collection of points,\n", + " or a fraction between 0 and 1 indicating the density of obstacles, chosen at random.\"\"\"\n", + " grid = {(x, y) for x in range(width) for y in range(height)}\n", + " if isinstance(obstacles, (float, int)):\n", + " obstacles = random.sample(grid, int(width * height * obstacles))\n", + " def neighbors(x, y):\n", + " for (dx, dy) in DIRECTIONS:\n", + " (nx, ny) = (x + dx, y + dy)\n", + " if (nx, ny) not in obstacles and 0 <= nx < width and 0 <= ny < height:\n", + " yield (nx, ny)\n", + " return {(x, y): list(neighbors(x, y))\n", + " for x in range(width) for y in range(height)}\n", + "\n", + "Grid(5, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class GridProblem(Problem):\n", + " \"Create with a call like GridProblem(grid=Grid(10, 10), initial=(0, 0), goal=(9, 9))\"\n", + " def actions(self, state): return DIRECTIONS\n", + " def result(self, state, action):\n", + " #print('ask for result of', state, action)\n", + " (x, y) = state\n", + " (dx, dy) = action\n", + " r = (x + dx, y + dy)\n", + " return r if r in self.grid[state] else state" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "uniform_cost_search:\n", + "no solution after 8 results and 2 goal checks\n" + ] + } + ], + "source": [ + "gp = GridProblem(grid=Grid(5, 5, 0.3), initial=(0, 0), goals={(4, 4)})\n", + "showpath(uniform_cost_search, gp)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Finding a hard PourProblem\n", + "\n", + "What solvable two-jug PourProblem requires the most steps? We can define the hardness as the number of steps, and then iterate over all PourProblems with capacities up to size M, keeping the hardest one." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def hardness(problem):\n", + " L = breadth_first_search(problem)\n", + " #print('hardness', problem.initial, problem.capacities, problem.goals, L)\n", + " return len(action_sequence(L)) if (L is not None) else 0" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hardness(p7)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Pour', 0, 1), ('Fill', 0), ('Pour', 0, 1)]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "action_sequence(breadth_first_search(p7))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((0, 0), (7, 9), {8})" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = 9 # Maximum capacity to consider\n", + "\n", + "phard = max((PourProblem(initial=(a, b), capacities=(A, B), goals={goal})\n", + " for A in range(C+1) for B in range(C+1)\n", + " for a in range(A) for b in range(B)\n", + " for goal in range(max(A, B))),\n", + " key=hardness)\n", + "\n", + "phard.initial, phard.capacities, phard.goals" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "breadth_first_search:\n", + " (0, 0) ==('Fill', 1)==> (0, 9); cost 1 after 1 steps\n", + " (0, 9) ==('Pour', 1, 0)==> (7, 2); cost 2 after 2 steps\n", + " (7, 2) ==('Dump', 0)==> (0, 2); cost 3 after 3 steps\n", + " (0, 2) ==('Pour', 1, 0)==> (2, 0); cost 4 after 4 steps\n", + " (2, 0) ==('Fill', 1)==> (2, 9); cost 5 after 5 steps\n", + " (2, 9) ==('Pour', 1, 0)==> (7, 4); cost 6 after 6 steps\n", + " (7, 4) ==('Dump', 0)==> (0, 4); cost 7 after 7 steps\n", + " (0, 4) ==('Pour', 1, 0)==> (4, 0); cost 8 after 8 steps\n", + " (4, 0) ==('Fill', 1)==> (4, 9); cost 9 after 9 steps\n", + " (4, 9) ==('Pour', 1, 0)==> (7, 6); cost 10 after 10 steps\n", + " (7, 6) ==('Dump', 0)==> (0, 6); cost 11 after 11 steps\n", + " (0, 6) ==('Pour', 1, 0)==> (6, 0); cost 12 after 12 steps\n", + " (6, 0) ==('Fill', 1)==> (6, 9); cost 13 after 13 steps\n", + " (6, 9) ==('Pour', 1, 0)==> (7, 8); cost 14 after 14 steps\n", + "GOAL FOUND after 150 results and 44 goal checks\n" + ] + } + ], + "source": [ + "showpath(breadth_first_search, PourProblem(initial=(0, 0), capacities=(7, 9), goals={8}))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "uniform_cost_search:\n", + " (0, 0) ==('Fill', 1)==> (0, 9); cost 1 after 1 steps\n", + " (0, 9) ==('Pour', 1, 0)==> (7, 2); cost 2 after 2 steps\n", + " (7, 2) ==('Dump', 0)==> (0, 2); cost 3 after 3 steps\n", + " (0, 2) ==('Pour', 1, 0)==> (2, 0); cost 4 after 4 steps\n", + " (2, 0) ==('Fill', 1)==> (2, 9); cost 5 after 5 steps\n", + " (2, 9) ==('Pour', 1, 0)==> (7, 4); cost 6 after 6 steps\n", + " (7, 4) ==('Dump', 0)==> (0, 4); cost 7 after 7 steps\n", + " (0, 4) ==('Pour', 1, 0)==> (4, 0); cost 8 after 8 steps\n", + " (4, 0) ==('Fill', 1)==> (4, 9); cost 9 after 9 steps\n", + " (4, 9) ==('Pour', 1, 0)==> (7, 6); cost 10 after 10 steps\n", + " (7, 6) ==('Dump', 0)==> (0, 6); cost 11 after 11 steps\n", + " (0, 6) ==('Pour', 1, 0)==> (6, 0); cost 12 after 12 steps\n", + " (6, 0) ==('Fill', 1)==> (6, 9); cost 13 after 13 steps\n", + " (6, 9) ==('Pour', 1, 0)==> (7, 8); cost 14 after 14 steps\n", + "GOAL FOUND after 159 results and 45 goal checks\n" + ] + } + ], + "source": [ + "showpath(uniform_cost_search, phard)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class GridProblem(Problem):\n", + " \"\"\"A Grid.\"\"\"\n", + "\n", + " def actions(self, state): return ['N', 'S', 'E', 'W'] \n", + " \n", + " def result(self, state, action):\n", + " \"\"\"The state that results from executing this action in this state.\"\"\" \n", + " (W, H) = self.size\n", + " if action == 'N' and state > W: return state - W\n", + " if action == 'S' and state + W < W * W: return state + W\n", + " if action == 'E' and (state + 1) % W !=0: return state + 1\n", + " if action == 'W' and state % W != 0: return state - 1\n", + " return state" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "breadth_first_search:\n", + " 0 ==S==> 10; cost 1 after 1 steps\n", + " 10 ==S==> 20; cost 2 after 2 steps\n", + " 20 ==S==> 30; cost 3 after 3 steps\n", + " 30 ==S==> 40; cost 4 after 4 steps\n", + " 40 ==E==> 41; cost 5 after 5 steps\n", + " 41 ==E==> 42; cost 6 after 6 steps\n", + " 42 ==E==> 43; cost 7 after 7 steps\n", + " 43 ==E==> 44; cost 8 after 8 steps\n", + "GOAL FOUND after 135 results and 49 goal checks\n", + "\n", + "uniform_cost_search:\n", + " 0 ==S==> 10; cost 1 after 1 steps\n", + " 10 ==S==> 20; cost 2 after 2 steps\n", + " 20 ==E==> 21; cost 3 after 3 steps\n", + " 21 ==E==> 22; cost 4 after 4 steps\n", + " 22 ==E==> 23; cost 5 after 5 steps\n", + " 23 ==S==> 33; cost 6 after 6 steps\n", + " 33 ==S==> 43; cost 7 after 7 steps\n", + " 43 ==E==> 44; cost 8 after 8 steps\n", + "GOAL FOUND after 1036 results and 266 goal checks\n" + ] + } + ], + "source": [ + "compare_searchers(GridProblem(initial=0, goals={44}, size=(10, 10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'test_frontier ok'" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def test_frontier():\n", + " \n", + " #### Breadth-first search with FIFO Q\n", + " f = FrontierQ(Node(1), LIFO=False)\n", + " assert 1 in f and len(f) == 1\n", + " f.add(Node(2))\n", + " f.add(Node(3))\n", + " assert 1 in f and 2 in f and 3 in f and len(f) == 3\n", + " assert f.pop().state == 1\n", + " assert 1 not in f and 2 in f and 3 in f and len(f) == 2\n", + " assert f\n", + " assert f.pop().state == 2\n", + " assert f.pop().state == 3\n", + " assert not f\n", + " \n", + " #### Depth-first search with LIFO Q\n", + " f = FrontierQ(Node('a'), LIFO=True)\n", + " for s in 'bcdef': f.add(Node(s))\n", + " assert len(f) == 6 and 'a' in f and 'c' in f and 'f' in f\n", + " for s in 'fedcba': assert f.pop().state == s\n", + " assert not f\n", + "\n", + " #### Best-first search with Priority Q\n", + " f = FrontierPQ(Node(''), lambda node: len(node.state))\n", + " assert '' in f and len(f) == 1 and f\n", + " for s in ['book', 'boo', 'bookie', 'bookies', 'cook', 'look', 'b']:\n", + " assert s not in f\n", + " f.add(Node(s))\n", + " assert s in f\n", + " assert f.pop().state == ''\n", + " assert f.pop().state == 'b'\n", + " assert f.pop().state == 'boo'\n", + " assert {f.pop().state for _ in '123'} == {'book', 'cook', 'look'}\n", + " assert f.pop().state == 'bookie'\n", + " \n", + " #### Romania: Two paths to Bucharest; cheapest one found first\n", + " S = Node('S')\n", + " SF = Node('F', S, 'S->F', 99)\n", + " SFB = Node('B', SF, 'F->B', 211)\n", + " SR = Node('R', S, 'S->R', 80)\n", + " SRP = Node('P', SR, 'R->P', 97)\n", + " SRPB = Node('B', SRP, 'P->B', 101)\n", + " f = FrontierPQ(S)\n", + " f.add(SF); f.add(SR), f.add(SRP), f.add(SRPB); f.add(SFB)\n", + " def cs(n): return (n.path_cost, n.state) # cs: cost and state\n", + " assert cs(f.pop()) == (0, 'S')\n", + " assert cs(f.pop()) == (80, 'R')\n", + " assert cs(f.pop()) == (99, 'F')\n", + " assert cs(f.pop()) == (177, 'P')\n", + " assert cs(f.pop()) == (278, 'B')\n", + " return 'test_frontier ok'\n", + "\n", + "test_frontier()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "p = plt.plot([i**2 for i in range(10)])\n", + "plt.savefig('destination_path.eps', format='eps', dpi=1200)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'itertools' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m 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\u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m tb.add_cell(i, j, 2./ncols, 2./nrows, text='{:0.2f}'.format(0.1234), \n\u001b[1;32m 19\u001b[0m loc='center', facecolor=random.choice(colors), edgecolor='grey') # facecolors=\n", + "\u001b[0;31mNameError\u001b[0m: name 'itertools' is not defined" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#### import itertools\n", + "import random\n", + "# http://stackoverflow.com/questions/10194482/custom-matplotlib-plot-chess-board-like-table-with-colored-cells\n", + "\n", + "from matplotlib.table import Table\n", + "\n", + "def main():\n", + " grid_table(8, 8)\n", + " plt.axis('scaled')\n", + " plt.show()\n", + "\n", + "def grid_table(nrows, ncols):\n", + " fig, ax = plt.subplots()\n", + " ax.set_axis_off()\n", + " colors = ['white', 'lightgrey', 'dimgrey']\n", + " tb = Table(ax, bbox=[0,0,2,2])\n", + " for i,j in itertools.product(range(ncols), range(nrows)):\n", + " tb.add_cell(i, j, 2./ncols, 2./nrows, text='{:0.2f}'.format(0.1234), \n", + " loc='center', facecolor=random.choice(colors), edgecolor='grey') # facecolors=\n", + " ax.add_table(tb)\n", + " #ax.plot([0, .3], [.2, .2])\n", + " #ax.add_line(plt.Line2D([0.3, 0.5], [0.7, 0.7], linewidth=2, color='blue'))\n", + " return fig\n", + "\n", + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class defaultkeydict(collections.defaultdict):\n", + " \"\"\"Like defaultdict, but the default_factory is a function of the key.\n", + " >>> d = defaultkeydict(abs); d[-42]\n", + " 42\n", + " \"\"\"\n", + " def __missing__(self, key):\n", + " self[key] = self.default_factory(key)\n", + " return self[key]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From bc45dd1a176e6c191641d63cffea3122873abce1 Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 29 May 2016 16:53:22 +0530 Subject: [PATCH 298/513] uncommented itertools to show the chessboard and renamed the notebook --- Search-4e.ipynb => search-4e.ipynb | 120 +++++++++++------------------ 1 file changed, 46 insertions(+), 74 deletions(-) rename Search-4e.ipynb => search-4e.ipynb (78%) diff --git a/Search-4e.ipynb b/search-4e.ipynb similarity index 78% rename from Search-4e.ipynb rename to search-4e.ipynb index f93abe76b..4ef222b75 100644 --- a/Search-4e.ipynb +++ b/search-4e.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "button": false, "collapsed": false, @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "button": false, "collapsed": true, @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "button": false, "collapsed": false, @@ -222,7 +222,7 @@ "['A', 'S', 'F', 'B']" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "button": false, "collapsed": false, @@ -250,7 +250,7 @@ "['L', 'T', 'A', 'S', 'F', 'B', 'U', 'V', 'I', 'N']" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -261,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "button": false, "collapsed": false, @@ -278,7 +278,7 @@ "['N', 'I', 'V', 'U', 'B', 'F', 'S', 'A', 'T', 'L']" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -289,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -300,7 +300,7 @@ "['E']" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "button": false, "collapsed": false, @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "button": false, "collapsed": false, @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "button": false, "collapsed": false, @@ -415,7 +415,7 @@ "5757" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -441,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "button": false, "collapsed": false, @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "button": false, "collapsed": false, @@ -488,7 +488,7 @@ "{'cello', 'hallo', 'hells', 'hullo', 'jello'}" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -510,7 +510,7 @@ "{'would'}" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "button": false, "collapsed": false, @@ -567,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "button": false, "collapsed": false, @@ -581,10 +581,10 @@ { "data": { "text/plain": [ - "['green', 'greed', 'treed', 'trees', 'treys', 'greys', 'grays', 'grass']" + "['green', 'greed', 'treed', 'trees', 'tress', 'cress', 'crass', 'grass']" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "button": false, "collapsed": false, @@ -621,7 +621,7 @@ " 'brain']" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -650,11 +650,11 @@ " 'flown',\n", " 'flows',\n", " 'slows',\n", - " 'stows',\n", - " 'stoas',\n", - " 'stoae',\n", - " 'stole',\n", - " 'stile',\n", + " 'slots',\n", + " 'slits',\n", + " 'spits',\n", + " 'spite',\n", + " 'smite',\n", " 'smile']" ] }, @@ -1598,22 +1598,22 @@ "{(0, 0): [(0, 1), (1, 0)],\n", " (0, 1): [(0, 2), (0, 0), (1, 1)],\n", " (0, 2): [(0, 3), (0, 1), (1, 2)],\n", - " (0, 3): [(0, 2), (1, 3)],\n", + " (0, 3): [(0, 4), (0, 2)],\n", " (0, 4): [(0, 3), (1, 4)],\n", " (1, 0): [(1, 1), (2, 0), (0, 0)],\n", - " (1, 1): [(1, 2), (1, 0), (0, 1)],\n", - " (1, 2): [(1, 3), (1, 1), (2, 2), (0, 2)],\n", - " (1, 3): [(1, 4), (1, 2), (2, 3), (0, 3)],\n", - " (1, 4): [(1, 3), (2, 4)],\n", - " (2, 0): [(3, 0), (1, 0)],\n", + " (1, 1): [(1, 2), (1, 0), (2, 1), (0, 1)],\n", + " (1, 2): [(1, 1), (2, 2), (0, 2)],\n", + " (1, 3): [(1, 4), (1, 2), (0, 3)],\n", + " (1, 4): [(2, 4), (0, 4)],\n", + " (2, 0): [(2, 1), (3, 0), (1, 0)],\n", " (2, 1): [(2, 2), (2, 0), (3, 1), (1, 1)],\n", - " (2, 2): [(2, 3), (3, 2), (1, 2)],\n", - " (2, 3): [(2, 4), (2, 2), (3, 3), (1, 3)],\n", - " (2, 4): [(2, 3), (3, 4), (1, 4)],\n", + " (2, 2): [(2, 1), (3, 2), (1, 2)],\n", + " (2, 3): [(2, 4), (2, 2), (3, 3)],\n", + " (2, 4): [(3, 4), (1, 4)],\n", " (3, 0): [(3, 1), (4, 0), (2, 0)],\n", - " (3, 1): [(3, 2), (3, 0), (4, 1)],\n", + " (3, 1): [(3, 2), (3, 0), (4, 1), (2, 1)],\n", " (3, 2): [(3, 3), (3, 1), (4, 2), (2, 2)],\n", - " (3, 3): [(3, 4), (3, 2), (4, 3), (2, 3)],\n", + " (3, 3): [(3, 4), (3, 2), (4, 3)],\n", " (3, 4): [(3, 3), (4, 4), (2, 4)],\n", " (4, 0): [(4, 1), (3, 0)],\n", " (4, 1): [(4, 2), (4, 0), (3, 1)],\n", @@ -1681,7 +1681,7 @@ "text": [ "\n", "uniform_cost_search:\n", - "no solution after 8 results and 2 goal checks\n" + "no solution after 12 results and 3 goal checks\n" ] } ], @@ -1888,21 +1888,6 @@ "showpath(uniform_cost_search, phard)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "button": false, - "collapsed": true, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 50, @@ -2075,7 +2060,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2103,24 +2088,11 @@ } }, "outputs": [ - { - "ename": "NameError", - "evalue": "name 'itertools' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m tb.add_cell(i, j, 2./ncols, 2./nrows, text='{:0.2f}'.format(0.1234), \n\u001b[1;32m 19\u001b[0m loc='center', facecolor=random.choice(colors), edgecolor='grey') # facecolors=\n", - "\u001b[0;31mNameError\u001b[0m: name 'itertools' is not defined" - ] - }, { "data": { - "image/png": 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ZWTU0NKiyslJVVVX6+OOP13PstM3MzOiLL77Q559/rh9++OGV87/88ou+/vpr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\n63K5dPDgQQUCAS0vL+v27ds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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2128,7 +2100,7 @@ } ], "source": [ - "#### import itertools\n", + "import itertools\n", "import random\n", "# http://stackoverflow.com/questions/10194482/custom-matplotlib-plot-chess-board-like-table-with-colored-cells\n", "\n", From 644ffbc6425cd52c435e1e6f9d950936829d138a Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 29 May 2016 16:54:14 +0530 Subject: [PATCH 299/513] adds 4th edition search notebook --- index.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/index.ipynb b/index.ipynb index 59dd6177b..2ae5742bb 100644 --- a/index.ipynb +++ b/index.ipynb @@ -18,6 +18,8 @@ "\n", "3. [**Search**](./search.ipynb)\n", "\n", + "4. [**Search - 4th edition**](./search-4e.ipynb)\n", + "\n", "4. [**Games**](./games.ipynb)\n", "\n", "5. [**Constraint Satisfaction Problems**](./csp.ipynb)\n", @@ -58,7 +60,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.1" } }, "nbformat": 4, From 5a4b8bd49231a1274e3456253649dc52b40e7a8f Mon Sep 17 00:00:00 2001 From: Jonathon Belotti Date: Tue, 31 May 2016 00:25:34 +1000 Subject: [PATCH 300/513] Updating README's Index of Code to reflect actual implementation status of algorithms (#237) --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index d9e1e9d4e..88098c25c 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)aima-python Build StatusBinder -Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're loooking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. +Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're looking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. ## Python 3.4 @@ -48,7 +48,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | | 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | | 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.24 | LRTA\*-Agent | | | +| 4.24 | LRTA\*-Agent | `LRTAStarAgent` | [`search.py`](../master/search.py) | | 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | | 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | | 6 | CSP | `CSP` | [`csp.py`](../master/csp.py) | @@ -66,7 +66,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | | 7.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | | 7.20 | Hybrid-Wumpus-Agent | | | -| 7.22 | SATPlan | | +| 7.22 | SATPlan | `SAT_plan` | [`logic.py`](../master/logic.py) | | 9 | Subst | `subst` | [`logic.py`](../master/logic.py) | | 9.1 | Unify | `unify` | [`logic.py`](../master/logic.py) | | 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | @@ -89,7 +89,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | | 14.14 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | | 14.15 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | -| 14.16 | Gibbs-Ask | | +| 14.16 | Gibbs-Ask | `gibbs_ask` | [`probability.py`](../master/probability.py) | | 15.4 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | | 15.6 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | | 15.17 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | @@ -99,19 +99,19 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 17.7 | POMDP-Value-Iteration | | | | 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | | 18.8 | Cross-Validation | `cross_validation` | [`learning.py`](../master/learning.py) | -| 18.11 | Decision-List-Learning | | -| 18.24 | Back-Prop-Learning | | +| 18.11 | Decision-List-Learning | `DecisionListLearner` | [`learning.py`](../master/learning.py) | +| 18.24 | Back-Prop-Learning | `BackPropagationLearner` | [`learning.py`](../master/learning.py) | | 18.34 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | | 19.2 | Current-Best-Learning | | | 19.3 | Version-Space-Learning | | | 19.8 | Minimal-Consistent-Det | | | 19.12 | FOIL | | -| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | +| 21.2 | Passive-ADP-Agent | | | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | | 22.1 | HITS | | | | 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | -| 23.5 | CYK-Parse | | | +| 23.5 | CYK-Parse | `CYK_parse` | [`nlp.py`](../master/nlp.py) | | 25.9 | Monte-Carlo-Localization| | From e2645fb77abcafd6b787ca369a28d882fafc2290 Mon Sep 17 00:00:00 2001 From: reachtarunhere Date: Wed, 1 Jun 2016 10:01:11 +0530 Subject: [PATCH 301/513] Added Example and Applet for Value Iteration --- mdp.ipynb | 190 ++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 179 insertions(+), 11 deletions(-) diff --git a/mdp.ipynb b/mdp.ipynb index 629027758..bd05bf894 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 172, "metadata": { "collapsed": true }, @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 173, "metadata": { "collapsed": false }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 174, "metadata": { "collapsed": true }, @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 175, "metadata": { "collapsed": false }, @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 176, "metadata": { "collapsed": false }, @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 177, "metadata": { "collapsed": true }, @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 178, "metadata": { "collapsed": false }, @@ -229,10 +229,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -241,14 +241,182 @@ "sequential_decision_environment" ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Value Iteration\n", + "\n", + "Now that we have looked how to represent MDPs. Let's aim at solving them. Our ultimate goal is to obtain an optimal policy. We start with looking at Value Iteration and a visualisation that should help us understanding it better.\n", + "\n", + "We start by calculating Value/Utility for each of the states. The Value of each state is the expected sum of discounted future rewards given we start in that state and follow a particular policy pi.The algorithm Value Iteration (**Fig. 17.4** in the book) relies on finding solutions of the Bellman's Equation. The intuition Value Iteration works is because values propagate. This point will we more clear after we encounter the visualisation. For more information you can refer to **Section 17.2** of the book. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource value_iteration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It takes as inputs two parameters an MDP to solve and epsilon the maximum error allowed in the utility of any state. It returns a dictionary containing utilities where the keys are the states and values represent utilities. Let us solve the **sequencial_decision_enviornment** GridMDP.\n" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 180, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{(0, 0): 0.2962883154554812,\n", + " (0, 1): 0.3984432178350045,\n", + " (0, 2): 0.5093943765842497,\n", + " (1, 0): 0.25386699846479516,\n", + " (1, 2): 0.649585681261095,\n", + " (2, 0): 0.3447542300124158,\n", + " (2, 1): 0.48644001739269643,\n", + " (2, 2): 0.7953620878466678,\n", + " (3, 0): 0.12987274656746342,\n", + " (3, 1): -1.0,\n", + " (3, 2): 1.0}" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "value_iteration(sequential_decision_environment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate that values propagate out of states let us create a simple visualisation. We will be using a modified version of the value_iteration function which will store U over time. We will also remove the parameter epsilon and instead add the number of iterations we want." + ] + }, + { + "cell_type": "code", + "execution_count": 181, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "def value_iteration_instru(mdp, iterations=20):\n", + " U_over_time = []\n", + " U1 = {s: 0 for s in mdp.states}\n", + " R, T, gamma = mdp.R, mdp.T, mdp.gamma\n", + " for _ in range(iterations):\n", + " U = U1.copy()\n", + " for s in mdp.states:\n", + " U1[s] = R(s) + gamma * max([sum([p * U[s1] for (p, s1) in T(s, a)])\n", + " for a in mdp.actions(s)])\n", + " U_over_time.append(U)\n", + " return U_over_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we define a function to create the visualisation from the utilities returned by **value_iteration_instru**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "columns = 4\n", + "rows = 3\n", + "U_over_time = value_iteration_instru(sequential_decision_environment)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_grid(iteration):\n", + " data = U_over_time[iteration]\n", + " grid = []\n", + " for row in range(rows):\n", + " current_row = []\n", + " for column in range(columns):\n", + " try:\n", + " current_row.append(data[(column, row)])\n", + " except KeyError:\n", + " current_row.append(0)\n", + " grid.append(current_row)\n", + " grid.reverse() # output like book\n", + " fig = plt.matshow(grid, cmap=plt.cm.bwr);\n", + " plt.axis('off')\n", + " fig.axes.get_xaxis().set_visible(False)\n", + " fig.axes.get_yaxis().set_visible(False) " + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAADtCAYAAAAr+2lCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAzZJREFUeJzt2rENwzAMAEExyP4r0wsE6Qwbj7uSalg9WGh29wAUfZ5e\nAOAuAgdkCRyQJXBAlsABWQIHZH3/Pc4cf0iA19s982vuggOyBA7IEjggS+CALIEDsgQOyBI4IEvg\ngCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOy\nBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4\nIEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAs\ngQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQO\nyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL\n4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIED\nsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgS\nOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CA\nLIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IE\nDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjgg\nS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyB\nA7IEDsgSOCBL4IAsgQOyBA7IEjggS+CALIEDsgQOyBI4IEvggCyBA7IEDsgSOCBL4IAsgQOyBA7I\nEjggS+CALIEDsgQOyJrdfXoHgFu44IAsgQOyBA7IEjggS+CALIEDsi6WyArVfE1QKgAAAABJRU5E\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "iteration_slider = widgets.IntSlider(min=0, max=15, step=1, value=0)\n", + "w=widgets.interactive(plot_grid,iteration=iteration_slider)\n", + "display(w)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Move the slider above to observe how the utility changes across iterations." + ] } ], "metadata": { From 2c4f28a83ca4205ecc6db640a522e7aac6e0d35c Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 1 Jun 2016 21:43:36 +0530 Subject: [PATCH 302/513] updates aima-data submodule to add sgb-words to data --- aima-data | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aima-data b/aima-data index dec9000e8..0e76ea3ef 160000 --- a/aima-data +++ b/aima-data @@ -1 +1 @@ -Subproject commit dec9000e8c794c8055fa13522ba09b893c5f601f +Subproject commit 0e76ea3ef2a15a4dfa383f188ceec12d9d16d0a8 From dcdeb256c93111fd975bf70e45e12b8cb14bbcea Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 1 Jun 2016 22:05:42 +0530 Subject: [PATCH 303/513] reverts last commit which is breaking the build because of the change in submodule --- aima-data | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aima-data b/aima-data index 0e76ea3ef..dec9000e8 160000 --- a/aima-data +++ b/aima-data @@ -1 +1 @@ -Subproject commit 0e76ea3ef2a15a4dfa383f188ceec12d9d16d0a8 +Subproject commit dec9000e8c794c8055fa13522ba09b893c5f601f From 0852e9efbe915f7516068889aa0bb8a62a9e5c8d Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 7 Jun 2016 12:07:58 +0530 Subject: [PATCH 304/513] updates submodule - adds sgb-words to aima-data --- aima-data | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aima-data b/aima-data index dec9000e8..1ad2ae2d3 160000 --- a/aima-data +++ b/aima-data @@ -1 +1 @@ -Subproject commit dec9000e8c794c8055fa13522ba09b893c5f601f +Subproject commit 1ad2ae2d378f658d8f0ff8f4d2202b66b675397f From ad73cdb7731b14d5491c19757ed0affb3ddcdab7 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 7 Jun 2016 21:33:59 +0530 Subject: [PATCH 305/513] cleans games notebook and interactive TTT, notebooks imports like 'from a import b,c,d' --- games.ipynb | 740 ++++++++++++++++++++++++++++++++++++++-------------- games.py | 12 +- mdp.ipynb | 37 +-- 3 files changed, 574 insertions(+), 215 deletions(-) diff --git a/games.ipynb b/games.ipynb index 20932daeb..e51a0a2bc 100644 --- a/games.ipynb +++ b/games.ipynb @@ -4,180 +4,184 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Explaining the games.py module\n", - "*Author: Chirag Vartak*
    \n", - "*Date: 12th March 2016*" + "# Games or Adversarial search\n", + "\n", + "This notebook serves as supporting material for topics covered in **Chapter 5 - Adversarial Search** in the book *Artificial Intelligence: A Modern Approach.* This notebook uses implementations from [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. Let's import required classes, methods, global variables etc., from games module." ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ - "## An Introduction" + "from games import (GameState, Game, Fig52Game, TicTacToe, query_player, random_player, \n", + " alphabeta_player, play_game, minimax_decision, alphabeta_full_search,\n", + " alphabeta_search, Canvas_TicTacToe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " Hello all! \n", - " In this IPython notebook, I plan to help you a little so that you will be able to use the [games.py](https://github.com/aimacode/aima-python/blob/master/games.py) module. You might already know that the `games.py` module implements the algorithms in Chapter 5 (Adversarial Search) of the book *Artificial Intelligence: A Modern Approach*. \n", - " \n", - " Before we begin, if you are unsure of how to use the [aima-python](https://github.com/aimacode/aima-python) repository or are not familiar with IPython notebooks you should read the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) first. Also, if you are more comfortable with Java than you are with Python we also have the [aima-java](https://github.com/aimacode/aima-java) repository.\n", - " \n", - " What we will do to learn to use the code in this module is simply dive in! I feel this is the correct approach as I assume you must have already read Chapter 5 of AIMA. If you haven't, you might want to go back and do that first. If you are tired (or just lazy), at least read the chapter upto Sec. 5.3 because this module covers the algorithms only till that section anyway. So, I will start by explaining what the class `Game` is and then we will immediately start implementing the `TicTacToe` game. After we define the rules of the `TicTacToe` game, we will create AI players who use different search strategies, namely Minimax Search and Alpha-Beta Search. We will make these players play among themselves, and later on we ourselves will play against these AI players (Yay!). \n", + "## `GameState` namedtuple\n", " \n", - "The reason I chose the `TicTacToe` game for demonstration of this module should be obvious to you. Everyone knows it and has played it, it is analyzed in quite some detail in AIMA, and most importantly, it has comparatively few states (fewer than 362,880) so that we can explore the search tree completely. \n", - " \n", - " So let's begin." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Implementing TicTacToe" + " `GameState` is a [namedtuple](https://docs.python.org/3.5/library/collections.html#collections.namedtuple) which represents the current state of a game. Let it be Tic-Tac-Toe or any other game." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "collapsed": true + }, "source": [ - "To use the code in `games.py` let's import everything from it:" + "## `Game` class\n", + " \n", + "Let's have a look at the class `Game` in our module. We see that it has functions, namely `actions`, `result`, `utility`, `terminal_test`, `to_move` and `display`. \n", + "\n", + "We see that these functions have not actually been implemented. This class is actually just a template class; we are supposed to create the class for our game, `TicTacToe` by inheriting this `Game` class and implement all the methods mentioned in `Game`. Do not close the popup so that you can follow along the description of code below." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from games import *" + "%psource Game" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that, here, as the module `games.py` does a `from utils import *`, all the names (global variables, functions etc.) available in `utils.py` are directly available to us now." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### The class `Game` \n", - " \n", - "Let's have a look at the class `Game` in our module. We see that it has six functions, namely `actions`, `result`, `utility`, `terminal_test`, `to_move` and `display`. We see that these functions have not actually been implemented. This class is actually just a template class; we are supposed to create the class for our game, `TicTacToe` by inheriting this `Game` class and implement all the methods mentioned in `Game`. If you forget to implement any one of those, a `NotImplementedError` will be raised. So, in this sense, the `Game` class is what you might call an abstract class in Java: it implements nothing, just tells you all that you are supposed to implement and screams at you if forget to implement what it asks. \n", - " \n", - " Now let's get into some details of all these methods in our `Game` class. You have to implement these methods when you create the new class that would represent your game.\n", + " Now let's get into details of all the methods in our `Game` class. You have to implement these methods when you create new classes that would represent your game.\n", " \n", - "* `__init__(self, )` : When you create a class inherited from the `Game` class (class `TicTacToe` in our case), you'll have to create an object of this inherited class to initialize the game. This initialization might require some additional information which would be passed to `__init__` as variables. For the case of our `TicTacToe` game, this additional information would be the number of rows `h`, number of columns `v` and how many consecutive X's or O's are needed in a row, column or diagonal for a win `k`. Also, the initial game state has to be defined here in `__init__`.\n", - "* `actions(self, state)` : Given a game state, this method should generates all the legal actions possible from this state, as a list or a generator. Returning a generator rather than a list has the advantage that it saves space and you can still operate on it as a list.\n", + "* `actions(self, state)` : Given a game state, this method generates all the legal actions possible from this state, as a list or a generator. Returning a generator rather than a list has the advantage that it saves space and you can still operate on it as a list.\n", + "\n", + "\n", "* `result(self, state, move)` : Given a game state and a move, this method returns the game state that you get by making that move on this game state.\n", + "\n", + "\n", "* `utility(self, state, player)` : Given a terminal game state and a player, this method returns the utility for that player in the given terminal game state. While implementing this method assume that the game state is a terminal game state. The logic in this module is such that this method will be called only on terminal game states.\n", + "\n", + "\n", "* `terminal_test(self, state)` : Given a game state, this method should return `True` if this game state is a terminal state, and `False` otherwise.\n", - "* `to_move(self, state)` : Given a game state, this method returns the player who is to play next. This information is typically stored in the game state, so all this method does is extract this information and return it." + "\n", + "\n", + "* `to_move(self, state)` : Given a game state, this method returns the player who is to play next. This information is typically stored in the game state, so all this method does is extract this information and return it.\n", + "\n", + "\n", + "* `display(self, state)` : This method prints/displays current state of the game." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Deciding the game state representation\n", - " \n", - " Now, before we start implementing our `TicTacToe` game, we need to decide how we will be representing our game state. Typically, a game state will give you all the current information about the game at any point in time. Yes, all of it. When you are given a game state, you should be able to tell whose turn it is next, how the game will look like on a real-life board (if it has one) etc. A game state need not include the history of the game. If you can play the game further given a game state, you game state representation is acceptable. While we might like to include all kinds of information in our game state, we wouldn't want to put too much information into it. Modifying this game state to generate a new one would be a real pain then.\n", - " \n", - " Now, as for our `TicTacToe` game state, would storing only the positions of all the X's and O's be sufficient to represent all the game information at that point in time? Well, does it tell us whose turn it is next? Looking at the 'X's and O's on the board and counting them should tell us that. But that would mean extra computing. To avoid this, we will also store whose move it is next in the game state. \n", - " \n", - " Think about what we've done here. We have reduced extra computation by storing additional information in a game state. Now, this information might not be absolutely essential to tell us about the state of the game, but it does save us additional computation time. We'll do more of this later on. \n", + "## `TicTacToe` class\n", " \n", - " The `TicTacToe` game defines its game state as:" + " Take a look at the class `TicTacToe`. All the methods mentioned in the class `Game` have been implemented here." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves')" + "%psource TicTacToe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The game state is called, quite appropriately, `GameState`, and it has 4 variables, namely, `to_move`, `utility`, `board` and `moves`. \n", - " \n", - " I'll describe these variables in some more detail:\n", - " \n", - "* `to_move` : It represents whose turn it is to move next. This will be a string of a single character, either 'X' or 'O'.\n", - "* `utility` : It stores the utility of the game state. Storing this utility is a good idea, because, when you do a Minimax Search or an Alphabeta Search, you generate many recursive calls, which travel all the way down to the terminal states. When these recursive calls go back up to the original callee, we have calculated utilities for many game states. We store these utilities in their respective `GameState`s to avoid calculating them all over again.\n", - "* `board` : A dict that stores all the positions of X's and O's on the board\n", - "* `moves` : It stores the list of legal moves possible from the current position. Note here, that storing the moves as a list, as it is done here, increases the space complexity of Minimax Search from `O(m)` to `O(bm)`. Refer to Sec. 5.2.1 of the book." + " The class `TicTacToe` has been inherited from the class `Game`. As mentioned earlier, you really want to do this. Catching bugs and errors becomes a whole lot easier.\n", + "\n", + "Additional methods in TicTacToe:\n", + "\n", + "* `__init__(self, h=3, v=3, k=3)` : When you create a class inherited from the `Game` class (class `TicTacToe` in our case), you'll have to create an object of this inherited class to initialize the game. This initialization might require some additional information which would be passed to `__init__` as variables. For the case of our `TicTacToe` game, this additional information would be the number of rows `h`, number of columns `v` and how many consecutive X's or O's are needed in a row, column or diagonal for a win `k`. Also, the initial game state has to be defined here in `__init__`.\n", + "\n", + "\n", + "* `compute_utility(self, board, move, player)` : A method to calculate the utility of TicTacToe game. If 'X' wins with this move, this method returns 1; if 'O' wins return -1; else return 0.\n", + "\n", + "\n", + "* `k_in_row(self, board, move, player, delta_x_y)` : This method returns `True` if there is a line formed on TicTacToe board with the latest move else `False.`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Representing a move \n", + "## GameState in TicTacToe game\n", + "\n", + " Now, before we start implementing our `TicTacToe` game, we need to decide how we will be representing our game state. Typically, a game state will give you all the current information about the game at any point in time. When you are given a game state, you should be able to tell whose turn it is next, how the game will look like on a real-life board (if it has one) etc. A game state need not include the history of the game. If you can play the game further given a game state, you game state representation is acceptable. While we might like to include all kinds of information in our game state, we wouldn't want to put too much information into it. Modifying this game state to generate a new one would be a real pain then.\n", " \n", - " Now that we have decided how our game state will be represented, it's time to decide how our move will be represented. My advice on this: keep it simple. Becomes easy to use this move to modify a current game state to generate a new one. \n", + " Now, as for our `TicTacToe` game state, would storing only the positions of all the X's and O's be sufficient to represent all the game information at that point in time? Well, does it tell us whose turn it is next? Looking at the 'X's and O's on the board and counting them should tell us that. But that would mean extra computing. To avoid this, we will also store whose move it is next in the game state. \n", " \n", - " For our `TicTacToe` game, we'll just represent a move by a tuple, where the first and the second elements of the tuple will represent the row and column, respectively, where the next X or O is to be made. Whether to make an X or an O will be decided by the `to_move` variable in the `GameState`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### The class `TicTacToe` \n", + " Think about what we've done here. We have reduced extra computation by storing additional information in a game state. Now, this information might not be absolutely essential to tell us about the state of the game, but it does save us additional computation time. We'll do more of this later on. \n", + " \n", + " The `TicTacToe` game defines its game state as:\n", " \n", - " Take a look at the class `TicTacToe`. All the methods mentioned in the class `Game` have been implemented here. Some points to note in this class might be: \n", - " \n", - "* The class `TicTacToe` has been inherited from the class `Game`. As I mentioned earlier, you really want to do this. Catching bugs and errors becomes a whole lot easier.\n", - "* A `display` function has been implemented. This function prints the given game state on the console. This might come in handy for debugging and is great when we play ourselves against AIs that we will be creating.\n", - "* Additional functions `compute_utility` and `k_in_a_row` are created, which are used by other functions. Well, no one said that you can't do this." + " `GameState = namedtuple('GameState', 'to_move, utility, board, moves')`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Creating players to play the games " + "The game state is called, quite appropriately, `GameState`, and it has 4 variables, namely, `to_move`, `utility`, `board` and `moves`. \n", + " \n", + " I'll describe these variables in some more detail:\n", + " \n", + "* `to_move` : It represents whose turn it is to move next. This will be a string of a single character, either 'X' or 'O'.\n", + "\n", + "\n", + "* `utility` : It stores the utility of the game state. Storing this utility is a good idea, because, when you do a Minimax Search or an Alphabeta Search, you generate many recursive calls, which travel all the way down to the terminal states. When these recursive calls go back up to the original callee, we have calculated utilities for many game states. We store these utilities in their respective `GameState`s to avoid calculating them all over again.\n", + "\n", + "\n", + "* `board` : A dict that stores all the positions of X's and O's on the board\n", + "\n", + "\n", + "* `moves` : It stores the list of legal moves possible from the current position. Note here, that storing the moves as a list, as it is done here, increases the space complexity of Minimax Search from `O(m)` to `O(bm)`. Refer to Sec. 5.2.1 of the book." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## `random_player` and `alphabeta_player` \n", + "## Representing a move in TicTacToe game\n", " \n", - " So, we have finished implementation of the `TicTacToe` class. What this class does is that, it just defines the rules of the game. We need more to create an AI that can actually play the game. This is where `random_player` and `alphabeta_player` come in. \n", + " Now that we have decided how our game state will be represented, it's time to decide how our move will be represented. Becomes easy to use this move to modify a current game state to generate a new one.\n", " \n", - " The `random_player` is a function that plays random moves in the game. That's it. There isn't much more to this guy. \n", - " \n", - " The `alphabeta_player`, on the other hand, calls the `alphabeta_full_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely." + " For our `TicTacToe` game, we'll just represent a move by a tuple, where the first and the second elements of the tuple will represent the row and column, respectively, where the next move is to be made. Whether to make an 'X' or an 'O' will be decided by the `to_move` in the `GameState` namedtuple." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## `query_player` and `play_game` \n", - " \n", - " The `query_player` function allows you, a human opponent, to play the game. This function requires a `display` method to be implemented in your game class, so that successive game states can be displayed on the terminal, making it easier for you to visualize the game and play accordingly. \n", + "## Players to play games\n", + "\n", + " So, we have finished implementation of the `TicTacToe` class. What this class does is that, it just defines the rules of the game. We need more to create an AI that can actually play the game. This is where `random_player` and `alphabeta_player` come in. \n", + "\n", + "### query_player\n", + " The `query_player` function allows you, a human opponent, to play the game. This function requires a `display` method to be implemented in your game class, so that successive game states can be displayed on the terminal, making it easier for you to visualize the game and play accordingly. \n", + "\n", + "### random_player\n", + " The `random_player` is a function that plays random moves in the game. That's it. There isn't much more to this guy. \n", + "\n", + "### alphabeta_player\n", + " The `alphabeta_player`, on the other hand, calls the `alphabeta_full_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely.\n", " \n", + "### play_game\n", " The `play_game` function will be the one that will actually be used to play the game. You pass as arguments to it, an instance of the game you want to play and the players you want in this game. Use it to play AI vs AI, AI vs human, or even human vs human matches!" ] }, @@ -185,11 +189,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Examples \n", - " \n", - " I will show some code examples below that you can run. The games' classes which I will use are `TicTacToe` and the `Fig52Game`. The `Fig52Game` is already implemented (actually both are) in the module. This is that small game in Fig 5.2 of the book. \n", - " \n", - " Have fun executing and modifying these examples!" + "## Let's play some games\n", + "### Game52" ] }, { @@ -203,18 +204,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start by experimenting with the `Fig52Game` first. For that we'll first create an instance of this game:" + "Let's start by experimenting with the `Fig52Game` first. For that we'll create an instance of the subclass Fig52Game inherited from the class Game:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from games import *\n", "game52 = Fig52Game()" ] }, @@ -222,90 +222,206 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we try out our `random_player`. Given a game state it will give us a random move every time:" + "First we try out our `random_player(game, state)`. Given a game state it will give us a random move every time:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a1\n", + "a3\n" + ] + } + ], "source": [ - "random_player(game52, 'A')" + "print(random_player(game52, 'A'))\n", + "print(random_player(game52, 'A'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `alphabeta_player(game, state)` will always give us the best move possible:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a1\n", + "b1\n", + "c1\n" + ] + } + ], "source": [ - "random_player(game52, 'A')" + "print( alphabeta_player(game52, 'A') )\n", + "print( alphabeta_player(game52, 'B') )\n", + "print( alphabeta_player(game52, 'C') )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `alphabeta_player` will always give us the best move:" + "What the `alphabeta_player` does is, it simply calls the method `alphabeta_full_search`. They both are essentially the same. In the module, both `alphabeta_full_search` and `minimax_decision` have been implemented. They both do the same job and return the same thing, which is, the best move in the current state. It's just that `alphabeta_full_search` is more efficient w.r.t time because it prunes the search tree and hence, explores lesser number of states." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print( alphabeta_player(game52, 'A') )\n", - "print( alphabeta_player(game52, 'B') )\n", - "print( alphabeta_player(game52, 'C') )" + "minimax_decision('A', game52)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'a1'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphabeta_full_search('A', game52)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What the `alphabeta_player` does is, it simply calls the method `alphabeta_full_search`. They both are essentially the same. In the module, both `alphabeta_full_search` and `minimax_decision` have been implemented. They both do the same job and return the same thing, which is, the best move in the current state. It's just that `alphabeta_full_search` is more efficient w.r.t time because it prunes the search tree and hence, explores lesser number of states." + "Demonstrating the play_game function on the game52:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B1\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "minimax_decision('A', game52)" + "play_game(game52, alphabeta_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B2\n" + ] + }, + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "play_game(game52, alphabeta_player, random_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "alphabeta_full_search('A', game52)" + "#play_game(game52, query_player, alphabeta_player)\n", + "#play_game(game52, alphabeta_player, query_player)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's play `TicTacToe`. First we initialize the game:" + "Note that, here, if you are the first player, the alphabeta_player plays as MIN, and if you are the second player, the alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TicTacToe game\n", + "Now let's play `TicTacToe`. First we initialize the game by creating an instance of the subclass TicTacToe inherited from the class Game:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -323,11 +439,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". . . \n", + ". . . \n", + ". . . \n" + ] + } + ], "source": [ "ttt.display(ttt.initial)" ] @@ -343,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -364,16 +490,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So, how does this game state look like?" + "So, how does this game state looks like?" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X O X \n", + "O . O \n", + "X . . \n" + ] + } + ], "source": [ "ttt.display(my_state)" ] @@ -382,27 +518,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `random_player` will behave how he is supposed to:" + "The `random_player` will behave how he is supposed to i.e. *pseudo-randomly*:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 3)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(ttt, my_state)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "random_player(ttt, my_state)" ] @@ -416,11 +574,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 2)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alphabeta_player(ttt, my_state)" ] @@ -434,31 +603,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "O X O \n", + "O . X \n", + "O X X \n", + "-1\n" + ] + } + ], "source": [ - "bot_play = Canvas_TicTacToe('bot_play', 'random', 'alphabeta')" + "print(play_game(ttt, random_player, alphabeta_player))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output is +1, hence `alphabeta_player` wins. \n", + "The output is -1, hence `random_player` loses implies `alphabeta_player` wins. \n", " \n", " Since, an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n", + "X X O \n", + "O O X \n", + "X O X \n", + "0\n" + ] + } + ], "source": [ "for _ in range(10):\n", " print(play_game(ttt, alphabeta_player, alphabeta_player))" @@ -468,16 +695,63 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A `random_player` should never win against an `alphabeta_player`." + "A `random_player` should never win against an `alphabeta_player`. Let's test that." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X . . \n", + "O O O \n", + ". X X \n", + "-1\n", + "O O O \n", + "X X O \n", + "X X . \n", + "-1\n", + "O X . \n", + ". O X \n", + "X . O \n", + "-1\n", + "O . . \n", + ". O X \n", + "X X O \n", + "-1\n", + "X O X \n", + "X O O \n", + ". O X \n", + "-1\n", + "O . X \n", + "X O . \n", + ". X O \n", + "-1\n", + "O O X \n", + "X O X \n", + "X O . \n", + "-1\n", + "O O O \n", + "O X X \n", + "X . X \n", + "-1\n", + "X X O \n", + "O O X \n", + "O X . \n", + "-1\n", + "X . X \n", + "O O O \n", + ". X . \n", + "-1\n" + ] + } + ], "source": [ "for _ in range(10):\n", " print(play_game(ttt, random_player, alphabeta_player))" @@ -487,94 +761,178 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's play a game ourselves against a `random_player`:" + "## Canvas_TicTacToe(Canvas)\n", + "\n", + "This subclass is used to play TicTacToe game interactively in Jupyter notebooks. TicTacToe class is called while initializing this subclass.\n", + "\n", + "Let's have match between `random_player` and `alphabeta_player`. Click on the board to call players to make a move." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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    \n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" + "rand_play = Canvas_TicTacToe('rand_play', 'human', 'random')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Demonstrating the `play_game` function on the `game52`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "play_game(game52, alphabeta_player, alphabeta_player)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "play_game(game52, alphabeta_player, random_player)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#play_game(game52, query_player, alphabeta_player)" + "Yay! We win. But we cannot win against an `alphabeta_player`, however hard we try." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "#play_game(game52, alphabeta_player, query_player)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "\n", + "
    \n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "Note that, here, if you are the first player, the `alphabeta_player` plays as MIN, and if you are the second player, the `alphabeta_player` plays as MAX. This happens because that's the way the game is defined in the class `Fig52Game`. Having a look at the code of this class should make it clear." + "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" ] } ], @@ -594,7 +952,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.0" + "version": "3.5.1" } }, "nbformat": 4, diff --git a/games.py b/games.py index 431ba5a14..2fb78ecd3 100644 --- a/games.py +++ b/games.py @@ -1,13 +1,13 @@ """Games, or Adversarial Search (Chapter 5)""" -import collections +from collections import namedtuple import random from utils import argmax from canvas import Canvas infinity = float('inf') -GameState = collections.namedtuple('GameState', 'to_move, utility, board, moves') +GameState = namedtuple('GameState', 'to_move, utility, board, moves') # ______________________________________________________________________________ # Minimax Search @@ -280,7 +280,7 @@ def display(self, state): print() def compute_utility(self, board, move, player): - "If X wins with this move, return 1; if O return -1; else return 0." + "If 'X' wins with this move, return 1; if 'O' wins return -1; else return 0." if (self.k_in_row(board, move, player, (0, 1)) or self.k_in_row(board, move, player, (1, 0)) or self.k_in_row(board, move, player, (1, -1)) or @@ -322,7 +322,7 @@ class Canvas_TicTacToe(Canvas): """Play a 3x3 TicTacToe game on HTML canvas TODO: Add restart button """ - def __init__(self, varname, player_1='human', player_2='random', id=None, width=800, height=600): + def __init__(self, varname, player_1='human', player_2='random', id=None, width=300, height=300): valid_players = ('human', 'random', 'alphabeta') if player_1 not in valid_players or player_2 not in valid_players: raise TypeError("Players must be one of {}".format(valid_players)) @@ -383,11 +383,11 @@ def draw_board(self): def draw_x(self, position): self.stroke(0, 255, 0) x, y = [i-1 for i in position] - offset = 1/20 + offset = 1/15 self.line_n(x/3 + offset, y/3 + offset, x/3 + 1/3 - offset, y/3 + 1/3 - offset) self.line_n(x/3 + 1/3 - offset, y/3 + offset, x/3 + offset, y/3 + 1/3 - offset) def draw_o(self, position): self.stroke(255, 0, 0) x, y = [i-1 for i in position] - self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/7, 0, 360) + self.arc_n(x/3 + 1/6, y/3 + 1/6, 1/9, 0, 360) diff --git a/mdp.ipynb b/mdp.ipynb index bd05bf894..a69e07be2 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -11,13 +11,13 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "from mdp import *" + "from mdp import MDP, GridMDP, sequential_decision_environment, value_iteration" ] }, { @@ -25,6 +25,7 @@ "metadata": {}, "source": [ "## Review\n", + "\n", "Before we start playing with the actual implementations let us review a couple of things about MDPs.\n", "\n", "- A stochastic process has the **Markov property** if the conditional probability distribution of future states of the process (conditional on both past and present states) depends only upon the present state, not on the sequence of events that preceded it.\n", @@ -50,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -87,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -119,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -153,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -181,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -221,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -229,10 +230,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 178, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -274,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -295,7 +296,7 @@ " (3, 2): 1.0}" ] }, - "execution_count": 180, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -313,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -341,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -355,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -384,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 13, "metadata": { "collapsed": false, "scrolled": true @@ -394,7 +395,7 @@ "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAADtCAYAAAAr+2lCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, From d0e53a843cf5319e19a0e538b84fa5b7fc2dc8af Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 9 Jun 2016 00:36:53 +0530 Subject: [PATCH 306/513] Review & Graph Coloring --- csp.ipynb | 116 ++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 113 insertions(+), 3 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 0d2aa513a..aebed67d5 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -1,14 +1,120 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Constraint Satisfaction Problems (CSPs)\n", + "\n", + "This IPy notebook acts as supporting material for topics covered in **Chapter 6 Constraint Satisfaction Problems** of the book* Artificial Intelligence: A Modern Approach*. We make use of the implementations in **csp.py** module. Even though this notebook includes a brief summary of the main topics familiarity with the material present in the book is expected. We will look at some visualizations and solve some of the CSP problems described in the book. Let us import everything from the csp module to get started." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from csp import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review\n", + "\n", + "CSPs are a special kind of search problems. Here we don't treat the space as a black box but the state has a particular form and we use that to our advantage to tweak our algorithms to be more suited to the problems. A CSP State is defined by a set of variables which can take values from corresponding domains. These variables can take only certain values in their domains to satisfy the constraints. A set of assignments which satisfies all constraints passes the goal test. Let us start by exploring the CSP class which we will use to model our CSPs. You can keep the popup open and read the main page to get a better idea of the code.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource CSP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The __ _ _init_ _ __ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as dict where key specify the variables and value specify the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list its value which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph Coloring\n", + "\n", + "We use the graph coloring problem as our running example for demonstrating the different algorithms in the **csp module**. The idea of map coloring problem is that the adjacent nodes (those connected by edges) should not have the same color throughout the graph. The graph can be colored using a fixed number of colors. Here each node is a variable and the values are the colors that can be assigned to them. Given that the domain will be the same for all our nodes we use a custom dict defined by the **UniversalDict** class. The **UniversalDict** Class takes in a parameter which it returns as value for all the keys of the dict. It is very similar to **defaultdict** in Python except that it does not support item assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "collapsed": false }, + "outputs": [ + { + "data": { + "text/plain": [ + "['R', 'G', 'B']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = UniversalDict(['R','G','B'])\n", + "s[5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this what we need is that the neighbors must not have the same color. This is defined in the function **different_values_constraint** of the module." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource different_values_constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows to take input in the form of strings and return a Dict of the form compatible with the **CSP Class**." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "import csp" + "%pdoc parse_neighbors" ] }, { @@ -37,7 +143,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" + }, + "widgets": { + "state": {}, + "version": "1.1.1" } }, "nbformat": 4, From fe0a6edffd1dc57160c9bc3bac729a55a267c26c Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 9 Jun 2016 00:42:07 +0530 Subject: [PATCH 307/513] Helper Functions & Backtracking Search --- csp.ipynb | 261 ++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 255 insertions(+), 6 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index aebed67d5..079557641 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -71,7 +71,7 @@ "['R', 'G', 'B']" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -117,14 +117,263 @@ "%pdoc parse_neighbors" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables our the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **australia**, **usa** and **france** are three CSPs that have been created using **MapColoringCSP**. **australia** corresponds to ** Figure 6.1 ** in the book." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource MapColoringCSP" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " ,\n", + " )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "australia, usa, france" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper Functions\n", + "\n", + "We will now implement few helper functions that will help us visualize the Coloring Problem. We will make some modifications to the existing Classes and Functions for additional book keeping. To begin with we modify the **assign** and **unassign** methods in the **CSP** to add a copy of the assignment to the **assingment_history**. We call this new class **InstruCSP**. This would allow us to see how the assignment evolves over time." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import copy\n", + "class InstruCSP(CSP):\n", + " \n", + " def __init__(self, variables, domains, neighbors, constraints):\n", + " super().__init__(variables, domains, neighbors, constraints)\n", + " self.assingment_history = []\n", + " \n", + " def assign(self, var, val, assignment):\n", + " super().assign(var,val, assignment)\n", + " self.assingment_history.append(copy.deepcopy(assignment))\n", + " \n", + " def unassign(self, var, assignment):\n", + " super().unassign(var,assignment)\n", + " self.assingment_history.append(copy.deepcopy(assignment)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we modify the **MapColoringCSP** function to use the **InstruCSP**. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def ModMapColoringCSP(colors, neighbors):\n", + " if isinstance(neighbors, str):\n", + " neighbors = parse_neighbors(neighbors)\n", + " return InstruCSP(list(neighbors.keys()), UniversalDict(colors), neighbors,\n", + " different_values_constraint)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now use the france graph for plotting purposes. The **parse_neighbors** function is used for parsing them." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "neighbors = parse_neighbors(\"\"\"AL: LO FC; AQ: MP LI PC; AU: LI CE BO RA LR MP; BO: CE IF CA FC RA\n", + " AU; BR: NB PL; CA: IF PI LO FC BO; CE: PL NB NH IF BO AU LI PC; FC: BO\n", + " CA LO AL RA; IF: NH PI CA BO CE; LI: PC CE AU MP AQ; LO: CA AL FC; LR:\n", + " MP AU RA PA; MP: AQ LI AU LR; NB: NH CE PL BR; NH: PI IF CE NB; NO:\n", + " PI; PA: LR RA; PC: PL CE LI AQ; PI: NH NO CA IF; PL: BR NB CE PC; RA:\n", + " AU BO FC PA LR\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to create an InstruCSP instance for our problem." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "coloring_problem1 = ModMapColoringCSP('RGBY', neighbors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Backtracking Search\n", + "\n", + "For solving a CSP the main issue with Naive search algorithms is that they can continue expanding obviously wrong paths. In backtracking search, we check constraints as we go. Backtracking is just the above idea combined with the fact that we are dealing with one variable at a time. Backtracking Search is implemented in the repository as the function **backtracking_search**. This is the same as **Figure 6.5** in the book. The function takes as input a CSP and few other optional parameters which can be used to further speed it up. The function returns the correct assignment if it satisfies the goal. We will discuss these later. Let us solve our **coloring_problem1** with **backtracking_search**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "result = backtracking_search(coloring_problem1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'AL': 'R',\n", + " 'AQ': 'B',\n", + " 'AU': 'G',\n", + " 'BO': 'B',\n", + " 'BR': 'Y',\n", + " 'CA': 'R',\n", + " 'CE': 'R',\n", + " 'FC': 'Y',\n", + " 'IF': 'G',\n", + " 'LI': 'Y',\n", + " 'LO': 'G',\n", + " 'LR': 'Y',\n", + " 'MP': 'R',\n", + " 'NB': 'G',\n", + " 'NH': 'B',\n", + " 'NO': 'R',\n", + " 'PA': 'G',\n", + " 'PC': 'G',\n", + " 'PI': 'Y',\n", + " 'PL': 'B',\n", + " 'RA': 'R'}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result # A dictonary of assingments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also check the number of assingments made." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "37" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coloring_problem1.nassigns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us check the total number of assingments and unassingments which is the lentgh ofour assingment history." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "53" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(coloring_problem1.assingment_history)" + ] } ], "metadata": { From 7404dc3b73d5112059e40c79b7870c204f20f001 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 9 Jun 2016 00:59:51 +0530 Subject: [PATCH 308/513] Visualization Applet for Graph Coloring --- csp.ipynb | 366 ++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 289 insertions(+), 77 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 079557641..aeae6a27c 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -71,7 +71,7 @@ "['R', 'G', 'B']" ] }, - "execution_count": 3, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": { "collapsed": true }, @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -145,12 +145,12 @@ { "data": { "text/plain": [ - "(,\n", - " ,\n", - " )" + "(,\n", + " ,\n", + " )" ] }, - "execution_count": 7, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 28, "metadata": { "collapsed": true }, @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": true }, @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": true }, @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": true }, @@ -277,42 +277,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'AL': 'R',\n", - " 'AQ': 'B',\n", - " 'AU': 'G',\n", - " 'BO': 'B',\n", - " 'BR': 'Y',\n", - " 'CA': 'R',\n", - " 'CE': 'R',\n", - " 'FC': 'Y',\n", - " 'IF': 'G',\n", - " 'LI': 'Y',\n", - " 'LO': 'G',\n", - " 'LR': 'Y',\n", - " 'MP': 'R',\n", - " 'NB': 'G',\n", - " 'NH': 'B',\n", - " 'NO': 'R',\n", - " 'PA': 'G',\n", - " 'PC': 'G',\n", - " 'PI': 'Y',\n", - " 'PL': 'B',\n", - " 'RA': 'R'}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result # A dictonary of assingments." ] @@ -326,22 +295,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "37" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "coloring_problem1.nassigns" ] @@ -355,25 +313,146 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "53" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(coloring_problem1.assingment_history)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization\n", + "\n", + "Next, we define some functions to create the visualisation from the assingment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which return such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. This will be more clear with the example below. If this sounds confusing do not worry this is not the part of the core material and our only goal is to help you visualize how the process works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def make_update_step_function(graph, instru_csp):\n", + " \n", + " def draw_graph(graph):\n", + " # create networkx graph\n", + " G=nx.Graph(graph)\n", + " # draw graph\n", + " pos = nx.spring_layout(G,k=0.15)\n", + " return (G, pos)\n", + " \n", + " G, pos = draw_graph(graph)\n", + " \n", + " def update_step(iteration):\n", + " # here iteration is the index of the assingment_history we want to visualize.\n", + " current = instru_csp.assingment_history[iteration]\n", + " # We convert the particular assingment to a default dict so that the color for nodes which \n", + " # have not been assigned defaults to black.\n", + " current = defaultdict(lambda: 'Black', current)\n", + "\n", + " # Now we use colors in the list and default to black otherwise.\n", + " colors = [current[node] for node in G.node.keys()]\n", + " # Finally drawing the nodes.\n", + " nx.draw(G, pos, node_color=colors, node_size=500)\n", + "\n", + " labels = {label:label for label in G.node}\n", + " # Labels shifted by offset so as to not overlap nodes.\n", + " label_pos = {key:[value[0], value[1]+0.03] for key, value in pos.items()}\n", + " nx.draw_networkx_labels(G, label_pos, labels, font_size=20)\n", + "\n", + " # show graph\n", + " plt.show()\n", + "\n", + " return update_step # <-- this is a function\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally let us plot our problem. We first use the function above to obtain a step function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "step_func = make_update_step_function(neighbors, coloring_problem1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we set the canvas size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "matplotlib.rcParams['figure.figsize'] = (18.0, 18.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assingment_history)-1, step=1, value=0)\n", + "w=widgets.interactive(step_func,iteration=iteration_slider)\n", + "display(w)" + ] } ], "metadata": { @@ -395,7 +474,140 @@ "version": "3.4.3" }, "widgets": { - "state": {}, + "state": { + "11c257bf5afd4dc085bc8625f2f5b064": { + "views": [] + }, + "2a5d565045c54a1aa994bd1f4d20598e": { + "views": [] + }, + "2d85cab81ee44791a94c94dfc338dbb2": { + "views": [] + }, + "3274aeea4b0b48c48ad4fc3292e5a9db": { + "views": [] + }, + "32f7a26654264000801324cd162b3736": { + "views": [] + }, + "3bdbe39cfebe45bd8f567a9eea384ae0": { + "views": [] + }, + "51076ed152d44022b5198b97fb41d079": { + "views": [] + }, + "57e00a3004bd4f6daa3594ad1235203a": { + "views": [] + }, + "6309ade1ff624145b66134ff05478ed7": { + "views": [] + }, + "641e3e122b7b401da4ffe4cfa8ef491e": { + "views": [] + }, + "7139845f3d75490382a04b4edf9d52f1": { + "views": [] + }, + "72798785fb3840f0bf54ce8e43da385a": { + "views": [] + }, + "73c1ce651784464fbf4a4b77d01d13f6": { + "views": [] + }, + "74c14cd38b594a73a6690ceec29fa82b": { + "views": [] + }, + "757fdae1fc99468890645b38a1ade51a": { + "views": [] + }, + "7c47d1ae17fe42c3bfca9a8643b5b5e7": { + "views": [] + }, + "7cadfb57eb9e4ca69f38edd7a0871003": { + "views": [] + }, + "8155171d610a4a4193e4b85d8c33a645": { + "views": [] + }, + "81bf3789a7d6487d8c97bbbaa2fb10e5": { + "views": [] + }, + "90e417c92a4f45408065bccdeceade40": { + "views": [] + }, + "93fdec2526be4434868b9a5ae72d6a68": { + "views": [] + }, + "99e6ce2b4591444caa39228ad478622d": { + "views": [] + }, + "9b3ce605a2fe43ea9efd9a2523934a78": { + "views": [] + }, + "9ce18c6af15846cbbd1dbccb0d7f7acd": { + "views": [] + }, + "a21b1344ddc64c928a5ecc9f9448e3a6": { + "views": [] + }, + "a3be52e2f5fe497ba0a38089b3408dcd": { + "views": [] + }, + "a63d0abd1b014dcfb76c3602b7daa3bf": { + "views": [] + }, + "a833f96aaaa8423cb23fcdcd9d50b7ef": { + "views": [] + }, + "ad62d3f676ee4dcc9d2c13bccd4c9ba5": { + "views": [] + }, + "ae8536dbdab94589bff99f542a84cbd2": { + "views": [] + }, + "b1679e217ef64dfeb70f20a73aecf9ce": { + "views": [] + }, + "b4e2bb7ccec84be2bcf4e66d8c7fe531": { + "views": [] + }, + "bad5f393034f467b88d9948b3658bb79": { + "views": [] + }, + "c0e8d394e38a4c3eaf5e780baefa26b8": { + "views": [] + }, + "c3dbc0a876044adea6a983d887a182fa": { + "views": [] + }, + "c425298ee6e0473fac87abb3f93b96f9": { + "views": [] + }, + "cfcb6ce7a19f4581a4b5d7865cded856": { + "views": [] + }, + "dff08c132aee450087e607174f2e47c5": { + "views": [] + }, + "e8827da62e204484ac7b783629e684a8": { + "views": [] + }, + "eba28e17a6bf45d69ee25e86ed55e313": { + "views": [] + }, + "f2119b193f2b45e095b41a7db2b3eadf": { + "views": [] + }, + "f2bb5a744c004774a9d480e58684c045": { + "views": [] + }, + "f45d16e50aa345e2b80ad07c98f9b66a": { + "views": [] + }, + "f4719605f006430fa9a1a2f3b5961a43": { + "views": [] + } + }, "version": "1.1.1" } }, From f00a6580c9ce76bcd6e814e53d6f27076b14a11f Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 9 Jun 2016 01:00:29 +0530 Subject: [PATCH 309/513] Added networkx in requirements.txt. It is now only used for notebook dependencies. --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index e69de29bb..c4a6dd78f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -0,0 +1 @@ +networkx==1.11 \ No newline at end of file From 45f4432f665b546e8e5b311f68891f44f2d154a7 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sat, 11 Jun 2016 05:05:36 +0530 Subject: [PATCH 310/513] Removed France Refrence & More general function for converting to instru --- csp.ipynb | 286 ++++++++++++++++++++++++++++++++---------------------- 1 file changed, 172 insertions(+), 114 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index aeae6a27c..90c143587 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -33,9 +33,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -71,7 +71,7 @@ "['R', 'G', 'B']" ] }, - "execution_count": 23, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -145,12 +145,12 @@ { "data": { "text/plain": [ - "(,\n", - " ,\n", - " )" + "(,\n", + " ,\n", + " )" ] }, - "execution_count": 27, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -196,63 +196,89 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we modify the **MapColoringCSP** function to use the **InstruCSP**. " + "Next, we define **make_instru** which takes an instance of **CSP** and returns a **InstruCSP** instance. " ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "def ModMapColoringCSP(colors, neighbors):\n", - " if isinstance(neighbors, str):\n", - " neighbors = parse_neighbors(neighbors)\n", - " return InstruCSP(list(neighbors.keys()), UniversalDict(colors), neighbors,\n", - " different_values_constraint)" + "def make_instru(csp):\n", + " return InstruCSP(csp.variables, csp.domains, csp.neighbors,\n", + " csp.constraints)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will now use the france graph for plotting purposes. The **parse_neighbors** function is used for parsing them." + "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes are they are connected to." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "neighbors = parse_neighbors(\"\"\"AL: LO FC; AQ: MP LI PC; AU: LI CE BO RA LR MP; BO: CE IF CA FC RA\n", - " AU; BR: NB PL; CA: IF PI LO FC BO; CE: PL NB NH IF BO AU LI PC; FC: BO\n", - " CA LO AL RA; IF: NH PI CA BO CE; LI: PC CE AU MP AQ; LO: CA AL FC; LR:\n", - " MP AU RA PA; MP: AQ LI AU LR; NB: NH CE PL BR; NH: PI IF CE NB; NO:\n", - " PI; PA: LR RA; PC: PL CE LI AQ; PI: NH NO CA IF; PL: BR NB CE PC; RA:\n", - " AU BO FC PA LR\"\"\")" + "neighbors = {\n", + " 0: [6, 11, 15, 18, 4, 11, 6, 15, 18, 4], \n", + " 1: [12, 12, 14, 14], \n", + " 2: [17, 6, 11, 6, 11, 10, 17, 14, 10, 14], \n", + " 3: [20, 8, 19, 12, 20, 19, 8, 12], \n", + " 4: [11, 0, 18, 5, 18, 5, 11, 0], \n", + " 5: [4, 4], \n", + " 6: [8, 15, 0, 11, 2, 14, 8, 11, 15, 2, 0, 14], \n", + " 7: [13, 16, 13, 16], \n", + " 8: [19, 15, 6, 14, 12, 3, 6, 15, 19, 12, 3, 14], \n", + " 9: [20, 15, 19, 16, 15, 19, 20, 16], \n", + " 10: [17, 11, 2, 11, 17, 2], \n", + " 11: [6, 0, 4, 10, 2, 6, 2, 0, 10, 4], \n", + " 12: [8, 3, 8, 14, 1, 3, 1, 14], \n", + " 13: [7, 15, 18, 15, 16, 7, 18, 16], \n", + " 14: [8, 6, 2, 12, 1, 8, 6, 2, 1, 12], \n", + " 15: [8, 6, 16, 13, 18, 0, 6, 8, 19, 9, 0, 19, 13, 18, 9, 16], \n", + " 16: [7, 15, 13, 9, 7, 13, 15, 9], \n", + " 17: [10, 2, 2, 10], \n", + " 18: [15, 0, 13, 4, 0, 15, 13, 4], \n", + " 19: [20, 8, 15, 9, 15, 8, 3, 20, 3, 9], \n", + " 20: [3, 19, 9, 19, 3, 9]\n", + "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we are ready to create an InstruCSP instance for our problem." + "Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "coloring_problem1 = ModMapColoringCSP('RGBY', neighbors)" + "coloring_problem = MapColoringCSP('RGBY', neighbors)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "coloring_problem1 = make_instru(coloring_problem)" ] }, { @@ -266,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -277,11 +303,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'R',\n", + " 1: 'R',\n", + " 2: 'R',\n", + " 3: 'R',\n", + " 4: 'G',\n", + " 5: 'R',\n", + " 6: 'G',\n", + " 7: 'R',\n", + " 8: 'B',\n", + " 9: 'R',\n", + " 10: 'G',\n", + " 11: 'B',\n", + " 12: 'G',\n", + " 13: 'G',\n", + " 14: 'Y',\n", + " 15: 'Y',\n", + " 16: 'B',\n", + " 17: 'B',\n", + " 18: 'B',\n", + " 19: 'G',\n", + " 20: 'B'}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "result # A dictonary of assingments." ] @@ -295,11 +352,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "coloring_problem1.nassigns" ] @@ -313,11 +381,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "len(coloring_problem1.assingment_history)" ] @@ -333,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -354,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -404,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -422,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -440,11 +519,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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m4IMPzl577ZW33norm2++eY455pj89Kc/zTbbbJOnnnoqbdu2TZKy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qXKafw/xmvAgAFyc3MzOjYAPBSLxaLVq1dr1KhRatu2rT766COVLVvW6FgObe/evRoyZIji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\n5NixYxo+fLguX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display\n", @@ -475,136 +565,104 @@ }, "widgets": { "state": { - "11c257bf5afd4dc085bc8625f2f5b064": { - "views": [] - }, - "2a5d565045c54a1aa994bd1f4d20598e": { - "views": [] - }, - "2d85cab81ee44791a94c94dfc338dbb2": { - "views": [] - }, - "3274aeea4b0b48c48ad4fc3292e5a9db": { - "views": [] - }, - "32f7a26654264000801324cd162b3736": { - "views": [] - }, - "3bdbe39cfebe45bd8f567a9eea384ae0": { - "views": [] - }, - "51076ed152d44022b5198b97fb41d079": { - "views": [] - }, - "57e00a3004bd4f6daa3594ad1235203a": { - "views": [] - }, - "6309ade1ff624145b66134ff05478ed7": { - "views": [] - }, - "641e3e122b7b401da4ffe4cfa8ef491e": { - "views": [] - }, - "7139845f3d75490382a04b4edf9d52f1": { + "00eea433b80142c8b14748c4bf9d8d04": { "views": [] }, - "72798785fb3840f0bf54ce8e43da385a": { + "0412648e99a94ab19d5b6e8c4eda8a85": { 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"f4719605f006430fa9a1a2f3b5961a43": { + "fe6fe229f7d2411b9c191b513d756177": { "views": [] } }, From 728f1b462851044807278b8554b82104317e361d Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sun, 12 Jun 2016 11:43:00 +0530 Subject: [PATCH 311/513] Display Values in Visualization & Changed to Step Function Pattern --- mdp.ipynb | 621 +++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 593 insertions(+), 28 deletions(-) diff --git a/mdp.ipynb b/mdp.ipynb index a69e07be2..41bbb4269 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -230,7 +230,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -309,6 +309,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Visualization for Value Iteration\n", + "\n", "To illustrate that values propagate out of states let us create a simple visualisation. We will be using a modified version of the value_iteration function which will store U over time. We will also remove the parameter epsilon and instead add the number of iterations we want." ] }, @@ -343,6 +345,50 @@ { "cell_type": "code", "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from collections import defaultdict\n", + "\n", + "def make_plot_grid_step_function(columns, row, U_over_time):\n", + " '''ipywidgets interactive function supports\n", + " single parameter as input. This function\n", + " creates and return such a function by taking\n", + " in input other parameters\n", + " '''\n", + " def plot_grid_step(iteration):\n", + " data = U_over_time[iteration]\n", + " data = defaultdict(lambda: 0, data)\n", + " grid = []\n", + " for row in range(rows):\n", + " current_row = []\n", + " for column in range(columns):\n", + " current_row.append(data[(column, row)])\n", + " grid.append(current_row)\n", + " grid.reverse() # output like book\n", + " fig = plt.matshow(grid, cmap=plt.cm.bwr)\n", + "\n", + " plt.axis('off')\n", + " fig.axes.get_xaxis().set_visible(False)\n", + " fig.axes.get_yaxis().set_visible(False)\n", + "\n", + " for col in range(len(grid)):\n", + " for row in range(len(grid[0])):\n", + " magic = grid[col][row]\n", + " fig.axes.text(row, col, \"{0:.2f}\".format(magic), va='center', ha='center')\n", + "\n", + " plt.show()\n", + " \n", + " return plot_grid_step" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": { "collapsed": true }, @@ -356,36 +402,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_grid(iteration):\n", - " data = U_over_time[iteration]\n", - " grid = []\n", - " for row in range(rows):\n", - " current_row = []\n", - " for column in range(columns):\n", - " try:\n", - " current_row.append(data[(column, row)])\n", - " except KeyError:\n", - " current_row.append(0)\n", - " grid.append(current_row)\n", - " grid.reverse() # output like book\n", - " fig = plt.matshow(grid, cmap=plt.cm.bwr);\n", - " plt.axis('off')\n", - " fig.axes.get_xaxis().set_visible(False)\n", - " fig.axes.get_yaxis().set_visible(False) " + "plot_grid_step = make_plot_grid_step_function(columns, rows, U_over_time)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true @@ -393,9 +421,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -406,8 +434,8 @@ "import ipywidgets as widgets\n", "from IPython.display import display\n", "\n", - "iteration_slider = widgets.IntSlider(min=0, max=15, step=1, value=0)\n", - "w=widgets.interactive(plot_grid,iteration=iteration_slider)\n", + "iteration_slider = widgets.IntSlider(min=1, max=15, step=1, value=0)\n", + "w=widgets.interactive(plot_grid_step,iteration=iteration_slider)\n", "display(w)\n", " " ] @@ -416,7 +444,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Move the slider above to observe how the utility changes across iterations." + "Move the slider above to observe how the utility changes across iterations. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click." ] } ], @@ -436,7 +464,544 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" + }, + "widgets": { + "state": { + "00d75b759a1647a69706c9cf5b0e8a98": { + "views": [] + }, + "019d2fd6c4b34bbf94ebb66ebb593689": { + "views": [] + }, + "01caaec7f6054144b22cac9e1f78d164": { + "views": [] + }, + "032a46b26c964232a6aaacdfe220bdd6": { + "views": [] + }, + "05384c38e94147459de2a2844c3fb2e2": { + "views": [] + }, + "060bca32714b4cb89b1211b966903789": { + "views": [] + }, + "06a7db67ab4849559d36ff59a5ff8bef": { + "views": [] + }, + "074a3d5a4b014d7ba946ea15cc9545d3": { + "views": [] + }, + "07b99c25d7d64da1a1f5e6b2c58d7716": { + "views": [] + }, + "07bf2f9854be4024b4859b495fb4eb4f": { + "views": [] + }, + "08450b23514b491fb8d194b9777e3f90": { + "views": [] + }, + 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+0530 Subject: [PATCH 312/513] NQueens Applet --- csp.ipynb | 331 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 293 insertions(+), 38 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 90c143587..7fb378957 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -145,9 +145,9 @@ { "data": { "text/plain": [ - "(,\n", - " ,\n", - " )" + "(,\n", + " ,\n", + " )" ] }, "execution_count": 7, @@ -526,9 +526,9 @@ "outputs": [ { "data": { - "image/png": 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m4IMPzl577ZW33norm2++eY455pj89Kc/zTbbbJOnnnoqbdu2TZKy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qXKafw/xmvAgAFyc3MzOjYAPBSLxaLVq1dr1KhRatu2rT766COVLVvW6FgObe/evRoyZIji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\nIhSAeWvXrl35256elkbQJ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ss87KfffdlwceeCD9+vXLpZdemgULFpQ7WuGZCAWoP2eEAsA/9e7d\nO4888ki5YwD/Ys0118wFF1yQI444Ij/4wQ+y6aabpn379nnwwQdz33335dBDD82BBx5Y7pgkef31\n1zN27NiMHTs2t912W7p27Zra2tr88pe/zJAhQ1JTU1PuiC3Cm2++mXnz5qV3797ljvKtrLjiirn2\n2mtz77335qijjsoZZ5yRESNGZJNNNil3tMIyEQpQf4pQAPin3r1756abbip3DOBzhg4dmh133DEX\nXHBBnn766c++vskmm2TnnXdOZaVNTuUwe/bs3H333Z895OjNN9/MpptumqFDh2b48OEtpshrbh56\n6KH079+/xZ2Nut5662X8+PG5+uqrs99++2XFFVfM8OHDs8oqq5Q7WuFUV1crQgHqyW+NAPBPtsZD\n8/PSSy+lf//+ufLKK3PuuefmzTffzEcffZS//e1veemll7L++uv7AGMhKZVKefzxx3Paaadl6NCh\n6datW0466aQstthiufDCC/POO+/kqquuyt57760EbYCWsC3+q1RUVGSHHXbIU089ldra2my88cbZ\ne++98/rrr5c7WqG0a9cudXV1qaurK3cUgBZHEQoA/6QIhebnhBNOyLRp03LKKadkn332Sbdu3dKx\nY8fU1tbm6quvzrx583LYYYeVO2ZhTZs2LVdccUV++tOfZqmllsrWW2+dF154IQceeGBee+213HPP\nPTn++OMzcODAtGnTptxxC6ElF6Gfat++fQ477LBMmTIlSy65ZFZdddUcf/zxmT59ermjFUJFRUWq\nqqqcEwpQD4pQAPinxRdfPPPmzfOHGjQjDz30UJJkww03/MJrq666ahZffPG8/PLL+eCDDxZysmKa\nO3duxo0bl1//+tfp379/VlhhhYwePToDBw7M3XffnRdffDHnnHNOttlmm3Tu3LnccQvp063xRbDY\nYovlD3/4QyZPnpyXX345K664Ys4555zMmzev3NFaPEUoQP0oQgHgnyoqKkyFQjPTvn37JP+YTPy8\nuXPnfvbBxafX8d2USqVMmTIlZ511Vrbaaqt07do1Rx99dCorKzNy5Mi8++67uf7663PggQdmhRVW\nKHfcwnvnnXcyffr0LLfccuWO0qiWXnrpXHLJJfnb3/6Wa665Jj/4wQ9yww03pFQqlTtai+WcUID6\nUYQCwL9QhELzsskmm6RUKuWUU07J3Llz/+213/72t5k/f34GDhyYRRZZpEwJW54PP/ww1157bfbb\nb78sv/zy2WijjfLwww9n1113zQsvvJCJEyfmpJNOyvrrr5927dqVO26rMnny5Ky55pot7kFJ39Ya\na6yRW2+9NWeccUaOO+64DBkyJBMnTix3rBbJRChA/XhqPAD8C0UoNL0bbrgh119/fZLkrbfeSpKM\nHz8+e+65Z5JkySWXzIgRI5IkxxxzTG644Ybcfvvt6devX374wx+mQ4cOue+++zJx4sTU1NTkT3/6\nU3neSAsxf/78TJo06bOnuz/++ONZd911U1tbm0MPPTQrr7xyYYu3lqZI2+K/SkVFRTbffPMMHTo0\no0aNynbbbZf11lsvp5xySpZffvlyx2sxTIQC1I8iFAD+hSIUmt4jjzySSy655LP/rqioyNSpUzN1\n6tQkybLLLvtZEdqlS5dMmjQpf/jDH3LjjTfm4osvTl1dXXr06JG99torRx99dFZcccWyvI/m7OWX\nX/6s+LzjjjvSq1ev1NbW5sQTT8z666+f6urqckfkSzz88MPZfvvtyx1joWjTpk323nvv7LTTTjnj\njDMycODA7L777jn22GPTpUuXcsdr9qqrq02EAtRDRcnBLADwmQsuuCDjx4/PhRdeWO4oAN/ajBkz\nctddd2XMmDEZO3Zs3n///Wy22WYZOnRoNttss/Ts2bPcEfkWll9++dxyyy1ZaaWVyh1loXv77bdz\n4okn5n//939z9NFH55BDDlHYf42BAwfmzDPPzNprr13uKAAtijNCAeBfmAgFWoIFCxZk8uTJGTZs\nWDbeeOP06NEjp512Wnr06JErrrgib731Vi6//PLsscceStAW4v3338+7776bvn37ljtKWXTv3j1n\nn3127rnnntx3333p169fLr/88ixYsKDc0ZolE6EA9WNrPAD8C0Uo0Fy99dZbGTt2bMaOHZtbb701\niy22WGpra/OLX/wiG264YTp27FjuiDTA5MmTs/rqq6eysnXPqvTr1y/XX3997r777hx11FE544wz\nMmLEiGy00UbljtasVFVVOSMUoB4UoQDwLz4tQkulkoeHAGX1ySef5N577/3srM9XXnklm2yySYYO\nHZqTTjopyy67bLkj0ogefvjhrLnmmuWO0WxssMEGuf/++zN69OjsvffeWXnllTN8+PCsvPLK5Y7W\nLJgIBaif1v1xIwB8TqdOndKuXbt88MEH5Y4CtDKlUilPPfVURo4cmc033zzdunXLb37zm9TU1OTc\nc8/NtGnTcvXVV2ffffdVghaQIvSLKioq8uMf/zhPP/10Ntlkk2y44YbZd9998+abb5Y7WtmZCAWo\nH0UoAHzOl22Pv+yyy1JZWZnKykoPUgIazXvvvZerrroqe++9d5Zeeulsvvnmeeqpp7LPPvvk5Zdf\nzvjx43PCCSdknXXWSdu2NnMV2UMPPZT+/fuXO0azVFVVlcMPPzzPPvtsOnfunFVWWSUnnHBCZsyY\nUe5oZWMiFKB+FKEA8DmfL0JfffXVHHLIIenUqZPt8kCDzJs3L/fcc0+OP/74DBw4MMstt1wuu+yy\nrL766rntttvy0ksv5fzzz8/222+fxRdfvNxxWUg+/vjjvP76663yafHfxeKLL54RI0bkoYceynPP\nPZcVV1wx559/fubPn1/uaI3qmmuuyaGHHpoNNtggnTt3TmVlZXbfffd/u+bzE6ELFizIBRdckCFD\nhmSJJZZITU1N+vTpk5122inPP//8wn4LAM2Wj5UB4HM+X4TuueeeWXLJJbPddtvltNNOK2MyoCV6\n4YUXPjvnc9y4cenTp0+GDh2a4cOHZ5111klVVVW5I1JmjzzySFZddVVTv9/Ssssum8svvzwPPvhg\njjrqqIwcOTLDhw/PlltuWYgPLE866aQ89thj6dixY3r16pVnnnnmC9f860TozJkz86Mf/Sh33nln\n1lhjjfz0pz9NdXV1Xn/99dxzzz2ZMmVKVlhhhYX9NgCaJT9pAeBz/rUI/dOf/pRx48Zl3Lhxuf32\n28ucDGgJPv7449x5550ZM2ZMxo4dm5kzZ2bo0KHZcccdc/7556dbt27ljkgzY1t8/QwYMCB33HFH\n/va3v+Xoo4/O6aefnhEjRmTAgAHljtYgI0eOTK9evdKnT5/cdddd2Wijjb5wzb9OhO67774ZN25c\nzj///Oyzzz5fuLaurq7JMwO0FIpQAPic3r1757bbbsvTTz+dX/3qV/n5z3+e9dZbTxEKfKm6uro8\n9NBDn019PvLIIxk0aFBqa2tz7bXX5gc/+EEhptRoOg8//PCXll18s4qKimy55Zapra3NRRddlB/9\n6EfZcMMNc8opp7TYh4oNGTLkG6/5dCJ08uTJufLKK7Pzzjt/aQmaJG3atGnsiAAtliIUAD6nd+/e\neeWVV7Lbbrtl2WWXzcknn1zuSNBqvPjii5k0aVIeeeSRfPjhh+nQoUP69euXAQMGNKutw6+99tpn\nxedtt92W//iP/0htbW2OPfbYbLDBBqmpqSl3RFqQhx9+OEcccUS5Y7Robdu2zc9+9rPsvPPOOf30\n09O/f//sueeeOfbYYwt53u6nE6GXX355KioqstNOO+Xjjz/OjTfemNdeey1dunTJxhtvnD59+pQ7\nKkCz0jx+kwSAZqR379557LHHMmPGjNx3333O74MmVldXlyuuuCLDhg3L1KlT07Zt28yYMSOlUilJ\nUlNTkzZt2qRdu3Y59NBDc/DBB6dLly4LNeOsWbNy9913f7bd/e23386mm26a2tranH766enVq9dC\nzUNxzJw5My+99FK+//3vlztKIXTs2DG//e1vs+++++aEE07ISiutlF/+8pc56KCDCvXzvLq6Ou+9\n914efPDBJMlLL72UvfbaK++///6/XXfAAQfkzDPPNJUO8E+eGg8An/PGG2/kww8/zBFHHJGBAweW\nOw4U2jPPPJPVV189BxxwQJ566qnMnj0706dP/6wETf5RQk6fPj3vv/9+hg0blj59+uS6665r0lyl\nUimPPfZYRowYkc022yzdu3fPKaecki5dumTUqFF5++238z//8z/Za6+9lKA0yKOPPpqVV1457dq1\nK3eUQunRo0fOO++8jBs3LnfeeWf69euXK6+8MgsWLCh3tEZRVVWVOXPm5J133kmpVMovfvGLbLzx\nxnnmmWcyffr03HbbbVlhhRVyzv9j777Dqi7/PoC/D3spiIgpomiCGwfmCFBEAs1IgRypvxTcihNx\nizkTNVOsNPdWxG1qgucgQ8oBDkzF3CPFjSAg6zx/FD3lRDjn3Ge8X9f1u/Ji3N83zyMCb+7PfS9d\nipkzZ4qOS0SkNliEEhER/UthYSEGDhwIfX19BAcH/+d1/y5miKjsfvnlF7i4uOD8+fN4/vx5id4n\nNzcXGRkZ6N27N0JCQhT6eXn//n1s2rQJffr0QdWqVeHv74/r168jODgYd+7cQXx8PKZMmYKPPvqI\nZ+6RwqSkpKBZs2aiY2it+vXrY9++fVizZg2+/fZbtGrVCnFxcaJjlZmJiQlyc3P/KXbr1auHrVu3\nwtHREWZmZmjXrh2ioqIgkUiwcOFCFBQUCE5MRKQeWIQSERH9S1ZWFv744w8UFhaievXq0NPT++d/\nM2bMAAD0798fenp6GDNmjOC0RJorPj4eAQEByM7OLtUOrezsbCxbtgyTJ08udYa8vDzExsZi4sSJ\naNasGZycnLB9+3a0atUKiYmJuHz5Mn744Qd07twZ5cuXL/VziN4mOTmZRagKeHh44Pjx4xg1ahT6\n9OmDzz//HBcuXBAdq9SKd4RaWVlBIpHA19f3lfF3Z2dn1KxZE5mZmRr9sRIRKRLPCCUiIvoXY2Nj\n9O/fH9HR0XBycvrPjbMpKSk4deoU3N3dUadOHbRu3VpcUCINlpGRAX9/f2RnZ5dpnezsbCxatAgd\nO3aEu7v7O99eLpfj0qVL/1xyFB8fj3r16sHb2xsRERFo2bIlx5NJ5VJSUjBs2DDRMXSCnp4eevbs\nCX9/f3z//fdo06YNAgIC8PXXX+ODDz4QHe+9FO8IrVOnDk6cOAErK6vXvl3xRVE5OTmqjEdEpLZY\nhBIREf2LiYkJli9fjuDgYDg5OWHEiBH/vG769Ok4deoU+vTpg6CgIIEpiTTbyJEjSzwK/y45OTno\n3r07rl+/DiMjo1de/+TJE0ilUkRHRyM6OhqFhYXw9vbGV199hXXr1qn80iWif8vNzcUff/yBhg0b\nio6iU0xMTDB27FgEBQVh9uzZaNCgAUaOHImQkBCYm5uLjlcixTtCO3XqhA0bNuDcuXOvvE1eXh7+\n+OMPAPjPL3aJiHQZR+OJiIhew97eHrdu3Xrl5TwnlKhsHjx4gK1btyI3N1dha2ZmZmL37t0AgIKC\nAiQlJeHrr79G69atUb16daxatQr169fHgQMHcPPmTaxatQrdunVjCUrCpaamwsnJCSYmJqKj6CRr\na2t8++23OHnyJC5cuAAnJyesXLkShYWFoqO9U/GO0ICAAFStWhWRkZE4ceLEf95mxowZyMjIgKen\nJ2xtbQUlJSJSL9wRSkRE9Br29vY4derUKy9/+fwtIno/q1evhp6eYn8Xn5WVhXHjxiEyMhIymQw1\natSAt7c3Zs2aBVdXV5ZMpLaSk5Ph4uIiOobOq1mzJrZs2YLjx48jNDQUixYtwrx589CxY0chX/f3\n7Nnzzy937t27BwBISkpCYGAgAMDGxgYdO3bEixcvYGZmhrVr18LX1xfu7u7w9/eHnZ0djh07hsTE\nRHzwwQdYtmyZyj8GIiJ1JZFzawsREdErEhISMGHCBBw9elR0FCKt4urqiqSkJIWvK5FIsGrVKnTo\n0AFVqlRR+PpEyjBw4EA0btyYZ4SqEblcjn379mH8+PGoWrUq5s+fr/LLrKZPn/7PBY2v4+DggPXr\n1yM0NPSff09TU1Mxc+ZMxMXFISMjAx988AE+++wzTJkyRePOPyUiUiYWoURERK9x/fp1tGnTBjdv\n3hQdhUirlC9fHpmZmUpZNzY2lrdvk0Zp3rw5lixZwsv31FBBQQFWrlyJ6dOnw8vLC7NmzUKNGjVE\nx/pHcnIyBg4ciOTkZNFRiIg0Cs8IJSIieg07Ozvcu3dPI84JI9IU+fn5yMrKUtr6t2/fVtraRIqW\nl5eH8+fPo3HjxqKj0GsYGBhg8ODBuHTpEmrVqoVmzZph3LhxePr0qehoAP66LEmRZy0TEekKFqFE\nRESvYWhoCBsbG9y9e1d0FCKN9fz5c1y/fh3Hjx/H/v37sW7dOqU+r6ioSKnrEynS77//jlq1asHM\nzEx0FHqLcuXKYfr06UhNTcWTJ0/g5OSERYsWIS8vT2guExMTvHjxQmgGIiJNxMuSiIiI3qD45vhq\n1aqJjkKkFrKzs/HgwQM8ePAA9+/ff+1///3noqIi2NraolKlSrC1tYWNjQ309PSUstNaIpHAxsZG\n4esSKUtKSgqPctAgVatWxYoVKzBy5EiMHz8eS5YswTfffIOuXbsKuVCJO0KJiEqHRSgREdEbFBeh\nPLuNtFVOTs4bi83XvaygoOA/xWalSpX++XPdunVfeZm5ufkrBcHZs2dx5swZhX8s2dnZHDEmjcIb\n4zVTw4YNsX//fshkMowdOxYLFy7EggUL4ObmptIc3BFKRFQ6LEKJiIjeoLgIJdIUubm5r5SYb9u9\nmZeX90p5WfxfJyenV15nYWFR5p1P7du3x/nz55Gfn6+gj/ov1apVQ7ly5RS6JpEypaSk4MsvvxQd\ng0rJ09MTJ0+exObNm9GrVy80a9YMc+fORZ06dVTyfO4IJSIqHRahREREb2Bvb89b40movLy8txaZ\nL5edubm5rxSaxUVm7dq1X3lduXLlVD7SOWjQICxdulShRaiZmRlGjhypsPWIlK2goACpqalo0qSJ\n6ChUBnp6eujduze++OILREREwM3NDd26dcO0adNga2ur1GdzRygRUemwCCUiInoDe3t7HD16VHQM\n0iJ5eXl4+PBhic7XvH//PnJycmBjY/PaXZu1atV6pey0tLQUclbd+3ByckLz5s1x9OhRhV1upKen\nh759+ypkLSJVuHDhAncxaxETExOMGzcO/fr1w8yZM1G/fn2MHj0ao0ePVtplWIaGhigoKEBhYSH0\n9YklM+8AACAASURBVPWV8gwiIm3EIpSIiOgNOBpP75Kfn//aYvNNuzefP38OGxub1+7a/Oijj14p\nO62srNS+2CyNNWvWwNnZGdnZ2WVey9zcHIsWLYKlpaUCkhGpRkpKCs8H1UIVK1bEokWLMHz4cEyc\nOBFOTk6YMWMG+vTpo/CyUiKRwNjYGC9evFBa2UpEpI0kcrlcLjoEERGROrl16xa2bNmCgwcPIj4+\nHuXLl4e+vj5q1qwJd3d3dO7cGW3atNHKgkrXFRQU4OHDhyUeR8/MzETFihXfOI7+8susrKygp6cn\n+sNUC8uWLUNISEiZylBTU1O0adMGBw8e5OcjaZSRI0fC3t4eY8eOFR2FlOjYsWMYO3YsMjIyMG/e\nPPj4+Cj03yorKytcu3YNFSpUUNiaRETajkUoERHR3y5fvoyhQ4ciISEBcrn8tWdvSSQSmJubw9ra\nGvPnz0fXrl1ZwKixgoICPHr0qMS3omdkZLxSbL6u0Cz+b4UKFVhslsHMmTMxd+7cUpWhEokEH330\nEY4cOQJTU1MlpCNSHnd3d0yfPh2enp6io5CSyeVy7NmzB+PHj0f16tUxf/78Mp0N++DBAxw7dgwn\nT55EeHg4AgIC0KBBAzRv3hwtWrTg7ngiondgEUpERDpPLpcjIiICEydOxIsXL0p8bqGZmRnatGmD\nTZs2wdraWskpCQAKCwtfKTbftmszIyMDFSpUeGehWfznChUq8Kw1FZs1axbCwsJgYGBQ4guUTE1N\nUa9ePVSsWBEHDx7k/89IoxQWFsLKygq3bt2ClZWV6DikIvn5+VixYgVmzJgBHx8fzJo1C/b29iV+\n/4SEBMyePRtHjhyBsbExnj9/jsLCQgB/nRdqamqKvLw8fP7555g0aRIaN26srA+FiEijsQglIiKd\nJpfLMWLECKxevbpUu9KMjIxgZ2eH3377Tek3xGqjwsJCPH78+J2FZvGfnz59CktLy3cWmsX/tba2\nZkmmxnJzc9GsWTOMGjUKx44dw5YtW2BgYIDMzMxX3tbU1BRyuRwtW7ZEeHg4XFxc4O3tjdatW2P2\n7NkC0hOVzsWLF9GpUydcuXJFdBQS4NmzZ5g3bx6WLl2KgQMHYsKECW/dxZmRkYEhQ4Zgz549Jfo+\nRU9PD8bGxhgyZAjmzJkDY2NjRcYnItJ4LEKJiEinzZs3D9OnTy/TOYWGhoZwdHTE6dOnYWhoqMB0\nmqeoqAhPnjwp8a3oT548Qfny5Ut0vmZxsWlgwLsetcXEiRPxxx9/ICoqChKJBJmZmdi3bx+OHj2K\n48ePIzMzE0ZGRqhfvz7c3d3x6aefombNmv+8/4MHD9C8eXMsWrQIfn5+Aj8SopLbvHkzdu3ahaio\nKNFRSKDbt28jLCwM+/fvx5QpUzBo0CAYGRn9521u3rwJV1dXPHjw4LXH9byNqakpHB0dERcXx53H\nRET/wiKUiIh01vnz59G8eXPk5OSUeS0zMzOMGTMGM2fOVEAy9VFUVISnT5+W+Fb0x48fo1y5ciU6\nX7NSpUqwsbFhsamjTpw4AV9fX5w5cwaVK1cu9TonT57Ep59+iri4ONSrV0+BCYmUIyQkBJUqVcKE\nCRNERyE1cPbsWYwbNw5XrlzBN998g4CAAEgkEjx8+BCNGzdGenr6PyPw78vIyAh16tTB8ePHYWJi\nouDkRESaiUUoERHprNatW+PYsWNQ1JdCExMTpKWloXr16gpZTxnkcvl/is13jaM/fPgQFhYWJTpf\ns7jY1PVdsfRuubm5cHFxwdSpU9GjR48yr7dmzRqEh4fj+PHjKF++vAISEilPu3btMHHiRHh7e4uO\nQmokJiYGoaGhMDMzw4IFCzBnzhzExMQgLy+vTOuamppi0KBB+O677xSUlIhIs7EIJSIinXThwgW4\nuLgoZDdoMWNjY4waNQpz585V2JrvIpfLkZGRUaLzNYuLTTMzsxKdr1lcbL48qkdUVpMmTcLFixex\nY8cOSCQShaw5dOhQ/Pnnn9i5cyf09PQUsiaRohUVFcHa2hqXL1+GjY2N6DikZgoLC7Fx40aEhITg\n6dOnpd4J+jJTU1MkJiaiWbNmClmPiEiTsQglIiKdNHbsWCxevBgFBQUKXdfa2hqPHj0q9fvL5XI8\ne/asROdrFr/M1NS0ROdrFhebvDiBRDpx4gQ+++wznDlzBh988IHC1s3Ly0O7du3QsWNHTJkyRWHr\nEinS5cuX0b59e9y4cUN0FFJjjRo1wrlz5xS2nkQigZ+fH3bs2KGwNYmINBUP5SIiIp0UGxur8BIU\nALKzs/Hnn3+iatWqAP4qNjMzM0t0vmbx64yNjV9baNrb28PFxeU/r7OxseG5X6QxXrx4gcDAQCxa\ntEihJSjw11l4UVFRaNGiBVxcXNCxY0eFrk+kCCkpKdyVR2917tw5XL16VaFryuVyHDhwAI8ePULF\nihUVujYRkaZhEUpERDrp4sWLSlm3sLAQvr6+APBPuWlgYPDaHZp2dnZo0qTJK69jsUnaaubMmXB0\ndFTIuaCvU7VqVURGRsLf3x9Hjx5F7dq1lfIcotJiEUrvcuTIERQVFSl8XSMjI/z222/o1KmTwtcm\nItIkLEKJiEgnKfJs0H/T19dH+/bt0a1bt392bpqZmSnlWUSa5OTJk1ixYgXOnDmjsHNBX8fV1RXT\npk2Dv78/fv31V5ibmyvtWUTvKzk5GaNGjRIdg9RYfHw8cnNzFb7u8+fPceLECRahRKTzeJI8ERHp\nJGVdpmJoaIjmzZujefPmqFGjBktQIvz/SPzChQsVPhL/OkOGDIGLiwv69esHHodP6kIulyMlJQUu\nLi6io5Aau379ulLWLSwsxJUrV5SyNhGRJmERSkREOqlSpUpKWVcikcDBwUEpaxNpqlmzZqFWrVro\n2bOnSp4nkUiwdOlSXL58GQsXLlTJM4ne5ebNmzA2NlbJLwNIcynzlzfKGLknItI0LEKJiEgnKWtH\nTnZ2NpydnZWyNpEmSklJwfLly7Fs2TKljsS/zMTEBDt37sSCBQsgk8lU9lyiN+H5oFQSlStXVsq6\nEonkn4sciYh0GYtQIiLSSf7+/rCwsFD4uk2aNOFlR0R/y8vLQ9++ffHtt9+iSpUqKn9+9erVsWnT\nJvTq1Qs3b95U+fOJ/i05OZlj8fRObdq0gZGRkcLXtbCwQIsWLRS+LhGRpmERSkREOqlHjx4KHz+z\nsLDA+PHjFbomkSabNWsWHBwc0KtXL2EZPD09MXbsWPj7+yvtkjSikuCOUCoJV1dXpRSh+fn5aNWq\nlcLXJSLSNBI5T5AnIiIdNXXqVCxcuBDZ2dkKWa969eq4fPkyDA0NFbIekSZLSUlBhw4dcPr0aeHj\nmHK5HD179oSJiQlWr16t0hF9IuCvv4MffPABTp48CXt7e9FxSI3J5XLY29vjzp07Cl3X1dUViYmJ\nCl2TiEgTcUcoERHprKlTp6Jq1aoKKUVMTU0RFRXFEpQIf43EBwYGYsGCBcJLUOCvs/FWrlyJ5ORk\nLF26VHQc0kF//vkn5HI5qlWrJjoKqTmJRIKJEyfC3NxcYWuam5tjypQpCluPiEiTsQglIiKdZWRk\nhH379qFcuXJlWkcikSAkJIRnbxH9bc6cObC3t8f//vc/0VH+YW5ujl27dmH69Ok4evSo6DikY4rH\n4rkbmUpi8ODBqFmzJvT0yv7jupGREdq1a4cOHTooIBkRkeZjEUpERDqtbt26SEhIgLW1danO5DI1\nNYWPjw+ioqJw7949JSQk0iynT5/Gjz/+iOXLl6td6fPhhx9i7dq16NatG/7880/RcUiH8HxQeh/6\n+vpYsWJFmdeRSCSwtLTE6tWrFZCKiEg7sAglIiKd5+zsjLS0NHTo0AFmZmYlKm/Mzc1RpUoVREdH\n4+DBg+jduzc8PT1x//59FSQmUk/5+fno27cv5s+frxYj8a/TsWNHDBkyBF27dkVeXp7oOKQjeGM8\nvY/bt28jKCgIX375JSwsLEr1SyUDAwNYW1sjMTERlSpVUkJKIiLNxCKUiIgIgI2NDfbs2YNDhw7B\n19cXenp6MDExgZmZGQwNDWFsbIzy5cvD2NgYjo6OiIiIwJUrV+Dm5gYAmDJlCrp27Yr27dvj4cOH\ngj8aIjHmzJkDOzs7fPXVV6KjvNWkSZNQqVIljBo1SnQU0hHcEUollZaWBjc3NwQFBWHjxo04fvw4\n6tWrBzMzsxKvYW5ujtatW+PMmTNwcnJSYloiIs3DW+OJiIheIpfLYWdnh4iICDx58gTPnj2DoaEh\nateuDRcXF1SuXPmN7zd58mQcOHAAMpkM1tbWKk5OJM6ZM2fg5eWF06dPw87OTnScd3r27BlatGiB\n8ePHIzAwUHQc0mLp6emoW7cuHj9+rHbHRZB6SU5OxmeffYY5c+b859+lgoIC/PDDDwgPD0dWVhZy\ncnJQUFDwn/eVSCQwMjJCtWrVMG3aNPTu3Zt/34iIXoNFKBER0UvS0tLwySef4MaNG+/9Q4RcLse4\nceMgk8lw+PBhVKhQQUkpidRHfn4+WrRogREjRmhUqXjhwgW0bdsWBw4cQPPmzUXHIS118OBBLFiw\nAFKpVHQUUmMymQw9evTA8uXL0aVLl9e+TVFREeLi4pCYmIj4+Hjcu3cPenp6qFatGgwMDAAAu3fv\nZgFKRPQWBqIDEBERqRupVIr27duX6gcJiUSCefPmYfTo0fDx8UFMTAwsLS2VkJJIfcydOxdVqlRB\n3759RUd5L/Xq1cNPP/2EgIAAnDhxAra2tqIjkRZKSUnh+aD0Vjt37sTgwYOxbds2eHh4vPHt9PT0\n0K5dO7Rr1+6V1xX/EpeIiN6OZ4QSERG9RCqVwtPTs9TvL5FI8N1336FFixbo2LEjMjMzFZiOSL2c\nPXsWERERanlLfEn4+fmhd+/e6N69+yujpkSKwPNB6W1WrlyJ4OBgHDp06K0l6Ls4OTlBIpHg4sWL\nigtHRKSFWIQSERH9S1FREY4cOYL27duXaR2JRIKIiAg4Ozvj008/RVZWloISEqmP4lviw8PDUa1a\nNdFxSm3GjBkwNjbG+PHjRUchLcQilF5HLpcjPDwcs2fPRlxcHJo2bVqm9SQSCXx8fHDo0CEFJSQi\n0k4sQomIiP7l9OnTsLW1RdWqVcu8lp6eHn788UfUqVMHvr6+yM7OVkBCIvURHh4OW1tbjToX9HX0\n9fWxefNm7N69G1u2bBEdh7TIo0eP8PjxY9SuXVt0FFIjRUVFCA0NxYYNG5CYmAhHR0eFrOvj44Po\n6GiFrEVEpK1YhBIREf1LWcfiX6anp4fly5ejevXq+Pzzz5GTk6OwtYlESk1NxeLFi7FixQqNHIl/\nmbW1NXbu3IkRI0bg7NmzouOQljh16hSaNGkCPT3+2EV/KSgoQFBQEJKSkhAfHw87OzuFrd2+fXsk\nJiYiNzdXYWsSEWkbfkUmIiL6F5lMVuax+Jfp6elh9erVqFy5Mvz8/PgDCmm84pH4uXPnwt7eXnQc\nhWncuDEiIiLg5+eHx48fi45DWoBj8fRvOTk5CAgIQHp6OmJiYmBtba3Q9a2srNCwYUMkJiYqdF0i\nIm3CIpSIiOhveXl5OHr0aJkuK3gTfX19rFu3DpaWlvjiiy/w4sULhT+DSFXmz58PGxsbBAUFiY6i\ncF9++SU6d+6MXr16obCwUHQc0nDJycm8MZ4AABkZGejQoQPMzc2xZ88emJubK+U5PCeUiOjtWIQS\nERH97dixY3B0dFT4Do1iBgYG2LhxI4yNjdG9e3fk5+cr5TlEynTu3Dl89913WjMS/zrz5s1Dbm4u\npk2bJjoKaTjuCCUASE9Ph4eHB5ydnbFx40YYGRkp7Vk8J5SI6O1YhBIREf1NGWPxLzM0NMSWLVtQ\nVFSEL7/8kmUoaZSCggIEBgZizpw5qF69uug4SmNgYIDIyEhs2LABu3fvFh2HNFRGRgbu3r2LOnXq\niI5CAl27dg2urq7o0qULIiIilH5e7EcffYRbt27h7t27Sn0OEZGmYhFKRET0N6lUqvQiFACMjIwQ\nFRWFnJwc9O7dGwUFBUp/JpEizJ8/H1ZWVujfv7/oKEpna2uL7du3Y+DAgbh48aLoOKSBTp06hcaN\nG0NfX190FBIkNTUV7u7uGDNmDKZNm6aSXfT6+vpo3749d4USEb0Bi1AiIiIAz58/R0pKCtzc3FTy\nPGNjY+zYsQNPnz5Fnz59eBYhqb3ff/8dCxcuxMqVK7V2JP5lH330EebOnYsuXbrg2bNnouOQhuFY\nvG5LSkqCl5cXFixYgKFDh6r02TwnlIjozViEEhERAUhMTESzZs2UdnnB65iYmGD37t24d+8e+vXr\nh6KiIpU9m+h9FI/Ez549GzVq1BAdR6WCgoLQrl079OnTh5+j9F5YhOquAwcOoEuXLli/fj169Oih\n8uf7+PggJiaG/2YREb0Gi1AiIiL8NRbv6emp8ueamppi7969uH79OgYOHMgfWkgtffvtt7C0tMSA\nAQNERxFi8eLFSE9PxzfffCM6CmkQFqG6adOmTQgMDMTevXvh4+MjJIO9vT0qVaqEU6dOCXk+EZE6\nYxFKREQE1VyU9Cbm5ub4+eefkZaWhmHDhkEulwvJQfQ658+fx4IFC7T6lvh3MTIywvbt2/Hjjz/i\n4MGDouOQBsjKysKNGzdQv3590VFIhSIiIjBhwgTIZDK0atVKaBZvb2+OxxMRvQaLUCIi0nmPHz/G\npUuX0LJlS2EZLCwscODAAZw+fRojRoxgGUpqoXgkfubMmXBwcBAdR6iqVasiMjISffv2xZUrV0TH\nITV35swZNGjQAIaGhqKjkArI5XKEhYXh+++/R0JCAho0aCA6Es8JJSJ6AxahRESk844cOYKPP/4Y\nRkZGQnOUK1cOv/zyC44dO4aQkBCWoSTcwoULYWFhgYEDB4qOohbc3NwQFhYGPz8/PH/+XHQcUmMc\ni9cdhYWFGDp0KPbv34/ExES1+aVR27ZtkZKSwoveiIhewiKUiIh0nsix+JdZWlri0KFDiIuLw4QJ\nE1iGkjAXLlzA/PnzsWrVKujp8VvGYkOHDkWzZs3Qv39/fn7SGyUnJ8PFxUV0DFKyvLw89OzZExcv\nXkRsbCxsbW1FR/qHmZkZWrVqhSNHjoiOQkSkVvhdLRER6TypVKo2RSgAVKhQAdHR0Th06BCmTp3K\nsoVUrrCwEIGBgZgxY4ba7G5SFxKJBEuXLsWlS5fw3XffiY5Daoo7QrVfVlYWfH19kZeXh4MHD6J8\n+fKiI72C54QSEb2KRSgREem0O3fu4P79+2jcuLHoKP9RsWJFxMTEYPfu3ZgxY4boOKRjFi5cCDMz\nMwwaNEh0FLVkamqKnTt3Yv78+YiNjRUdh9RMTk4OLl++jIYNG4qOQkry6NEjeHl5oVq1aoiKioKJ\niYnoSK/Fc0KJiF7FIpSIiHRabGwsPDw8oK+vLzrKKypVqgSpVIqtW7dizpw5ouOQjrh48SLCw8M5\nEv8ONWrUwKZNm9CzZ0/cvHlTdBxSI2fPnkXdunVhbGwsOgopwe3bt+Hu7o62bdti5cqVMDAwEB3p\njRo1aoTs7Gxe8EZE9C/87paIiHSauo3Fv6xy5cqQyWRYt24d5s2bJzoOabnikfjp06ejZs2aouOo\nPU9PT4SEhCAgIAC5ubmi45Ca4Fi89kpLS4ObmxuCgoIQHh4OiUQiOtJbSSQSeHt7Izo6WnQUIiK1\nwSKUiIh0llwuh1Qqhaenp+gob1WlShXIZDIsX76cZxKSUi1atAgmJiYYMmSI6CgaIyQkBB9++CGG\nDh3K83wJAItQbZWcnAwPDw9MmzYNY8eOFR2nxHhOKBHRf7EIJSIinXXlyhUUFhaiTp06oqO8k52d\nHWQyGZYsWYIlS5aIjkNaKC0tDd988w1H4t+TRCLBqlWrcOLECSxbtkx0HFIDvDFe+8hkMnTs2BFL\nly5FYGCg6Djv5ZNPPsGRI0eQn58vOgoRkVpQ3wNNiIiIlKx4LF7dR9uKVa9eHTKZDB4eHjA0NMTg\nwYNFRyItUVhYiKCgIHz99deoVauW6Dgax9zcHLt27YKrqysaN26Mjz/+WHQkEuTFixe4ePEinJ2d\nRUchBdm5cycGDx6Mbdu2wcPDQ3Sc91apUiXUrl0bv/76K9q0aSM6DhGRcPx1PxER6SxNGIt/mYOD\nA2QyGebMmYOVK1eKjkNaYvHixTAwMMDQoUNFR9FYtWvXxpo1a9CtWzfcvXtXdBwS5Pfff8eHH34I\nU1NT0VFIAVauXIng4GAcOnRII0vQYj4+PjwnlIjobyxCiYhIJxUVFSE2NlatL0p6k1q1akEqlWL6\n9OlYt26d6Dik4S5duoQ5c+Zg9erVHIkvo08//RSDBg3CF198gby8PNFxSACeD6od5HI5wsPDMXv2\nbMTFxaFp06aiI5UJzwklIvp//G6XiIh0UmpqKipUqAB7e3vRUUrF0dERhw8fxqRJk7Bp0ybRcUhD\nFY/Eh4WF4cMPPxQdRytMnjwZNjY2GD16tOgoJADPB9V8RUVFCA0NxYYNG5CYmAhHR0fRkcqsdevW\nuHTpEh4+fCg6ChGRcCxCiYhIJ2niWPzL6tSpg5iYGISGhiIyMlJ0HNJAS5YsgZ6eHoKDg0VH0Rp6\nenpYv349pFIp1q5dKzoOqRh3hGq2goICBAUFISkpCfHx8bCzsxMdSSGMjIzg4eGBmJgY0VGIiIRj\nEUpERDpJJpNp5Fj8y+rXr49Dhw5h1KhR2LFjh+g4pEH++OMPzJo1iyPxSmBpaYldu3Zh3LhxOHny\npOg4pCL5+fk4d+4cmjRpIjoKlUJOTg4CAgKQnp6OmJgYWFtbi46kUDwnlIjoL/yul4iIdE5+fj4S\nEhLQrl070VEUolGjRjh48CCGDh2KPXv2iI5DGqCoqAhBQUGYOnUqateuLTqOVqpXrx6WLVuGgIAA\nPHjwQHQcUoELFy6gevXqsLCwEB2F3lNGRgY6dOgAc3Nz7NmzB+bm5qIjKZy3tzeio6Mhl8tFRyEi\nEopFKBER6ZwTJ06gZs2asLGxER1FYZo0aYIDBw5g4MCB2L9/v+g4pOaWLFkCABg+fLjgJNrN398f\nvXr1Qo8ePVBQUCA6DikZx+I1U3p6Ojw8PODs7IyNGzfCyMhIdCSlqF27NkxMTHDu3DnRUYiIhGIR\nSkREOkdbxuJf5uLigr179yIwMJC3w9IbXb58GTNnzuRIvIrMnDkThoaGmDBhgugopGQsQjXPtWvX\n4OrqCj8/P0RERGj9v4k+Pj78/oCIdJ52/0tPRET0GlKpVCuLUABo2bIldu/ejf/97384fPiw6Dik\nZopH4qdMmaIVNyFrAn19fWzevBk7d+7E1q1bRcchJeKN8ZolNTUV7u7uGDNmDMLCwiCRSERHUjqe\nE0pEBEjkPCSEiIh0SHZ2NmxtbXH37l2UK1dOdBylSUhIgL+/P6KiouDh4SE6DqmJJUuWIDIyEnFx\ncdDX1xcdR6ecOXMGXl5ekEqlcHZ2Fh2HFKywsBCWlpa4ffs2rKysRMehd0hKSoKfnx8WL16MHj16\niI6jMs+ePYOdnR3S09NhZmYmOg4RkRDcEUpERDolKSkJjRs31uoSFADc3d0RFRWFbt26ISEhQXQc\nUgNXrlzB9OnTsXr1apagAjRu3BiLFy+Gv78/njx5IjoOKdilS5fwwQcfsATVAAcOHECXLl2wfv16\nnSpBAaB8+fJo2rQp4uPjRUchIhKGRSgREekUbR6Lf5mHhwc2b96MgIAAJCUliY5DAhWPxE+aNAlO\nTk6i4+isnj17wtfXF7169UJhYaHoOKRAHIvXDJs2bUJgYCD27t0LHx8f0XGE4DmhRKTrWIQSEZFO\nkUql8PT0FB1DZby8vLBhwwZ06dIFx48fFx2HBPnxxx+Rn5+PkSNHio6i8+bNm4fs7Gx8/fXXoqOQ\nAvGiJPUXERGBCRMmQCaToVWrVqLjCMNzQolI17EIJSIinfH06VNcuHABrVu3Fh1FpXx8fLBmzRr4\n+voiOTlZdBxSsatXr+Lrr7/GmjVrOBKvBgwNDbFt2zasW7cOu3fvFh2HFIRFqPqSy+UICwvD999/\nj4SEBDRo0EB0JKGaNm2K+/fv49atW6KjEBEJwSKUiIh0RlxcHFq3bg1jY2PRUVSuU6dOWL58OTp1\n6oTTp0+LjkMqUlRUhH79+mHixImoU6eO6Dj0N1tbW2zfvh0DBw7ExYsXRcehMioqKsKpU6dYhKqh\nwsJCDBs2DPv370diYiIcHBxERxJOX18fXl5e3BVKRDqLRSgREekMXRuLf1nnzp3xww8/oGPHjkhN\nTRUdh1Rg2bJlyM3NxahRo0RHoZe0aNEC33zzDfz8/PDs2TPRcagMrly5ggoVKqBixYqio9C/5OXl\noWfPnrhw4QJiY2Nha2srOpLa4DmhRKTLWIQSEZHOkMlkOnNR0psEBARg0aJF8PHxwfnz50XHISW6\ndu0awsLCOBKvxvr164e2bduib9++KCoqEh2HSolj8eonKysLvr6+yMvLw8GDB1G+fHnRkdSKt7c3\nDh8+zEvbiEgnsQglIiKdcO/ePdy5c4c/rALo3r075s2bh08++QRpaWmi45ASFN8SP378eNStW1d0\nHHqLxYsX4+7du5g7d67oKFRKLELVy6NHj+Dl5YVq1aohKioKJiYmoiOpnapVq8LOzg4nT54UHYWI\nSOVYhBIRkU6QyWRo27Ytd8b9rXfv3pg9eza8vLxw+fJl0XFIwX766Sfk5ORgzJgxoqPQOxgbG2PH\njh344Ycf8Msvv4iOQ6WQnJwMFxcX0TEIwO3bt+Hu7o62bdti5cqVMDAwEB1JbXE8noh0FYtQIiLS\nCRyLf1Xfvn0xbdo0tG/fHlevXhUdhxTk+vXrHInXMFWrVkVkZCT69OnDz0UNI5fLuSNUTaSlpcHN\nzQ1BQUEIDw+HRCIRHUmtsQglIl3FIpSIiHSCVCplEfoa/fv3x4QJE+Dp6Ynr16+LjkNlJJfLxvAp\nRwAAIABJREFU0a9fP4SGhqJevXqi49B7cHNzw9SpU+Hn54fnz5+LjkMldOPGDZiamqJy5cqio+i0\n5ORkeHh4YNq0aRg7dqzoOBrB3d0dZ8+exdOnT0VHISJSKRahRESk9a5evYrc3FwWQ28wZMgQhISE\nwNPTE7du3RIdh8pg+fLlyMrK4ki8hho2bBiaNGmCAQMGQC6Xi45DJcCxePFiY2PRsWNHLF26FIGB\ngaLjaAwTExO4urpCJpOJjkJEpFIsQomISOvJZDJ4enpyTO4thg8fjuHDh8PT0xN37twRHYdK4fr1\n65gyZQrWrFnDc/E0lEQiwbJly5CWloZFixaJjkMlwLF4sXbu3Inu3btj27Zt6NKli+g4Gofj8USk\ni1iEEhGR1uNYfMmMHj0aAwYMgKenJ+7evSs6Dr0HuVyO/v37IyQkBPXr1xcdh8rA1NQUO3fuRHh4\nOGJjY0XHoXdgESrOypUrERwcjEOHDsHDw0N0HI1UXIRyBzoR6RIWoUREpNXkcvk/O0Lp3caNG4ev\nvvoK7du3R3p6uug4VEIrVqzAs2fPeDaelqhRowY2btyInj178rgKNSaXyzkaL4BcLkd4eDhmz56N\nuLg4NG3aVHQkjVWvXj0UFhbi0qVLoqMQEakMi1AiItJqv//+OywsLODg4CA6isaYPHkyunXrBi8v\nLzx8+FB0HHqHGzduYPLkyRyJ1zJeXl4YM2YM/P39kZubKzoOvcadO3cgkUhQtWpV0VF0RlFREUJD\nQ7FhwwYkJibC0dFRdCSNJpFI4O3tjejoaNFRiIhUhkUoERFpNY7Fl860adPw+eefw8vLC48fPxYd\nh95ALpdjwIABGDNmDBo0aCA6DinY2LFjUatWLQwbNoyjq2qoeCye50+rRkFBAYKCgpCUlIT4+HjY\n2dmJjqQVeE4oEekaFqFERKTVpFIpx+JLQSKRYNasWfD29sYnn3yCJ0+eiI5Er7Fy5Uo8fvwYoaGh\noqOQEkgkEqxatQrHjx/HTz/9JDoOvYTng6pOTk4OAgICkJ6ejpiYGFhbW4uOpDW8vLwQHx+PFy9e\niI5CRKQSLEKJiEhrFRQUID4+nkVoKUkkEoSHh6NNmzbw8fFBRkaG6Ej0Lzdv3sSkSZOwdu1ajsRr\nMQsLC+zatQvTpk3Dr7/+KjoO/QvPB1WNjIwMdOjQAebm5tizZw/Mzc1FR9Iq1tbWqF+/Po4ePSo6\nChGRSrAIJSIirZWcnIzq1avD1tZWdBSNJZFIsHDhQrRs2RIdO3ZEZmam6EiE/x+JHzVqFBo2bCg6\nDilZ7dq1sXr1anTt2hV3794VHYf+xh2hypeeng4PDw84Oztj48aNMDIyEh1JK/GcUCLSJSxCiYhI\na/G2eMWQSCSIiIiAs7MzPv30U2RlZYmOpPNWr16NR48eYfz48aKjkIp06tQJAwcORNeuXZGXlyc6\njs67d+8ecnNzUaNGDdFRtNa1a9fg6uoKPz8/REREQE+PP7oqC88JJSJdwq8mRESktXhRkuJIJBL8\n+OOPqFOnDnx9fZGdnS06ks66desWJkyYwFviddCUKVNQsWJFjBkzRnQUnceLkpQrNTUV7u7uGDNm\nDMLCwvh/ZyVr2bIlrl+/jvT0dNFRiIiUjkUoERFppdzcXBw7dgxt2rQRHUVr6OnpYfny5ahRowY+\n//xz5OTkiI6kc4pH4keOHIlGjRqJjkMqpqenh/Xr1yMmJgbr1q0THUencSxeeZKSkuDl5YUFCxZg\n6NChouPoBAMDA3h6enI8noh0AotQIiLSSr/++isaNGgAS0tL0VG0ip6eHlatWoUPPvgAXbp0QW5u\nruhIOmXNmjW4f/8+R+J1mKWlJXbt2oXQ0FAkJyeLjqOzWIQqx4EDB9ClSxesX78ePXr0EB1Hp/j4\n+LAIJSKdwCKUiIi0EsfilUdfXx9r165FhQoVEBAQgBcvXoiOpBNu376N8ePHY+3atTA0NBQdhwSq\nX78+li5dioCAADx48EB0HJ3EG+MVb9OmTQgMDMTevXvh4+MjOo7OKb4wqaioSHQUIiKlYhFKRERa\nSSqV8qIkJTIwMMCGDRtgamqKbt268fIWJZPL5Rg4cCCGDx8OZ2dn0XFIDQQEBODLL79Ejx49UFBQ\nIDqOTnn48CGePn2KWrVqiY6iNSIiIjBhwgTIZDK0atVKdByd5ODgACsrK5w5c0Z0FCIipWIRSkRE\nWufZs2dITU3Fxx9/LDqKVjM0NMTmzZsBAF9++SXy8/MFJ9Je69atw927dzFx4kTRUUiNzJo1CwYG\nBvx7oWKnTp1C06ZNeYu5AsjlcoSFheH7779HQkICGjRoIDqSTuPt8USkC/jVm4iItE58fDxatmwJ\nU1NT0VG0npGREbZt24bc3Fz07t2bO9OU4M6dOxg3bhxH4ukV+vr62Lx5M3bs2IHIyEjRcXQGx+IV\no7CwEMOGDcP+/fuRmJgIBwcH0ZF0Hs8JJSJdwCKUiIi0DsfiVcvY2Bg7duzA06dP0adPHxQWFoqO\npDWKR+KHDRuGxo0bi45DaqhixYrYuXMngoODkZqaKjqOTuBFSWWXl5eHnj174sKFC4iNjYWtra3o\nSASgbdu2OHHiBLKyskRHISJSGhahRESkdWQyGS9KUjETExPs3r0b6enp6NevHy9bUJD169fjzp07\nHH2mt2rSpAkWLVoEPz8/PHnyRHQcrccitGyysrLg6+uLvLw8HDx4EOXLlxcdif5mYWGBjz76CEeO\nHBEdhYhIaViEEhGRVrl//z5u3LiB5s2bi46ic0xNTbF3715cv34dAwcOZBlaRnfu3EFoaCjWrl0L\nIyMj0XFIzfXq1QufffYZevfuzc89JXr69CnS09Ph5OQkOopGevToEby8vFCtWjVERUXBxMREdCR6\nCc8JJSJtxyKUiIi0SmxsLNzd3WFgYCA6ik4yMzPDzz//jLS0NAwbNgxyuVx0JI0kl8sxaNAgDB06\nFE2aNBEdhzTE/Pnz8fz5c3z99deio2itU6dOoXHjxtDX1xcdRePcvn0b7u7uaNu2LVauXMmv02qK\nRSgRaTsWoUREpFU4Fi+ehYUFDhw4gNOnT2PEiBEsQ0th48aNuHXrFiZNmiQ6CmkQQ0NDREZGYu3a\ntdizZ4/oOFqJY/Glk5aWBjc3NwQFBSE8PBwSiUR0JHoDZ2dnZGRk4Nq1a6KjEBEpBYtQIiLSKlKp\nlEWoGihXrhx++eUXHDt2DCEhISxD38Off/6JkJAQjsRTqVSuXBnbt2/HgAEDkJaWJjqO1mER+v6S\nk5Ph4eGBadOmYezYsaLj0Dvo6enB29ubt8cTkdZiEUpERFrjxo0bePbsGRo0aCA6CgGwtLREdHQ0\n4uLiMH78eJahJVA8Ej948GA0bdpUdBzSUC1atMCcOXPg5+eHzMxM0XG0SnJyMlxcXETH0BixsbHo\n2LEjli1bhsDAQNFxqIQ4Hk9E2oxFKBERaQ2ZTAZPT0/o6fHLm7qwsrJCTEwMoqOjMWXKFJah77Bp\n0ybcuHEDU6ZMER2FNFz//v3h7u6Ovn378vNOQTIzM3Hr1i3Uq1dPdBSNsHPnTnTv3h1RUVHo3Lmz\n6Dj0Hj755BPIZDLk5+eLjkJEpHD8SZGIiLQGx+LVk7W1NQ4fPoy9e/di+vTpouOorbt372LMmDEc\niSeFiYiIwJ9//om5c+eKjqIVzpw5g4YNG/KSnxJYuXIlgoODcejQIbRt21Z0HHpPlStXRs2aNXH8\n+HHRUYiIFI5FKBERaQW5XA6pVApPT0/RUeg1bGxsIJVKsW3bNsyePVt0HLUjl8sxePBgDBo0iOcP\nksIYGxtj+/btWLJkCcdcFYBj8e8ml8sRHh6O2bNnIy4ujkd8aDCOxxORtmIRSkREWuHixYswNjZG\nrVq1REehN7C1tYVUKsX69esxb9480XHUypYtW3D16lWOxJPC2dnZITIyEl999RWuXr0qOo5G40VJ\nbyeXyxEaGooNGzYgMTERjo6OoiNRGbAIJSJtxSKUiIi0QvFYvEQiER2F3qJKlSqQyWRYsWIFvvvu\nO9Fx1MK9e/cwevRorFmzBsbGxqLjkBZyd3fHlClT4O/vj+zsbNFxNBaL0DcrKChAYGAgkpKSEB8f\nDzs7O9GRqIw+/vhjXLhwAY8ePRIdhYhIoViEEhGRVuBYvOaws7ODTCbDkiVLsGTJEtFxhCoeie/f\nvz+aN28uOg5pseDgYDg7O2PAgAG8PKkUsrOzceXKFTRs2FB0FLWTk5ODgIAApKenIyYmBtbW1qIj\nkQIYGxujTZs2kEqloqMQESkUi1AiItJ4hYWFiIuLYxGqQezt7SGTyfDtt99i2bJlouMIs3XrVly+\nfBlhYWGio5CWk0gk+Omnn3DhwgUsXrxYdByNc/bsWdSrV48Xmb0kIyMDHTp0gLm5Ofbs2QNzc3PR\nkUiBOB5PRNqIVx4SEZHGO3XqFKpUqYIqVaqIjkLvwcHBATKZDB4eHjAwMED//v1FR1Kp9PR0jBo1\nCvv37+dIPKmEqakpdu7ciVatWqFJkybw8PAQHUljcCz+Venp6ejQoQPc3NywePFi6Olxj4228fHx\nQXh4OORyOY8eIiKtwa9WRESk8TgWr7lq1aoFqVSK6dOnY926daLjqIxcLseQIUPQr18/jsSTSjk4\nOGDjxo3o2bMnbt26JTqOxmAR+l/Xrl2Dm5sb/Pz8EBERwRJUSzk6OsLQ0BDnz58XHYWISGH4FYuI\niDSeTCZD+/btRcegUnJ0dMThw4cxadIkbNq0SXQclYiMjERaWhqmTZsmOgrpIC8vL4waNQoBAQHI\nzc0VHUcjJCcnw8XFRXQMtZCamgp3d3eMHj0aYWFh3CmoxSQSCby9vREdHS06ChGRwrAIJSIijfbi\nxQskJSVxxFPD1alTBzExMQgNDUVkZKSwHHfu3EFQUBDs7OxgYmKCmjVrYvTo0Xj69KnCnpGeno6R\nI0fylngSKjQ0FA4ODggODublSe/w4sULpKWloVGjRqKjCJeUlAQvLy8sWLAAQ4cOFR2HVIDnhBKR\ntmERSkREGu23335D3bp1YWVlJToKlVH9+vURHR2NUaNGYceOHSp//tWrV9GsWTOsW7cOrVq1wpgx\nY/Dhhx9i8eLF+Pjjj/HkyZMyP0Mul2Po0KEIDAxEixYtFJCaqHQkEglWr16N3377DcuXLxcdR62d\nO3cOtWvXhqmpqegoQh04cABdunTB+vXr0aNHD9FxSEXat2+PpKQk5OTkiI5CRKQQvCyJiIg0Gsfi\ntUvDhg1x8OBBdOjQAQYGBujcubPKnj1kyBA8fPgQS5Ys+c9Op5CQEHz33XeYPHkyfvzxxzI9Iyoq\nChcuXNCZIwBIvVlYWGDXrl1wdXWFs7MzWrduLTqSWuJYPLBp0yaEhIRg7969aNWqleg4pEKWlpZw\ndnZGQkICvL29RcchIioz7gglIiKNJpVKWYRqmSZNmmD//v0YOHAgfv75Z5U88+rVq4iJiYGDg8Mr\n457Tp0+Hubk5NmzYUKYdMffv38eIESOwZs0amJiYlDUykUI4Ojpi9erV6NatG+7duyc6jlrS9YuS\nIiIiMGHCBEilUpagOornhBKRNmERSkREGisrKwunT5+Gq6ur6CikYC4uLti3bx+CgoLwyy+/KP15\nsbGxAPDa3S4WFhZwdXVFdnY2fvvtt1I/Y9iwYejTpw9atmxZ6jWIlOGzzz5D//790bVrV+Tl5YmO\no3Z0tQiVy+UICwvD999/j4SEBDRo0EB0JBKE54QSkTZhEUpERBorISEBzZs3h5mZmegopAQtWrTA\nnj178NVXX+Hw4cNKfVZaWhokEgmcnJxe+3pHR0cAwKVLl0q1flRUFM6dO4fp06eXOiORMk2dOhUV\nKlRASEiI6ChqJT8/H7///juaNGkiOopKFRYWYtiwYdi/fz8SExPh4OAgOhIJ1Lx5c/z555+4c+eO\n6ChERGXGIpSIiDQWx+K1X+vWrbFjxw707NkTR44cUdpzMjIyAPx1FtrrFL+8NLfHP3jwAMOHD+dI\nPKk1PT09bNiwAYcOHcK6detEx1Eb58+fR40aNWBubi46isrk5eWhZ8+euHDhAmJjY2Frays6Egmm\nr68PLy8vjscTkVZgEUpERBpLKpXC09NTdAxSMnd3d2zbtg3dunVDQkKC6DjvLTg4GP/73/94th6p\nPUtLS+zatQtjx45FcnKy6DhqQdfG4rOysuDr64u8vDwcPHgQ5cuXFx2J1ATPCSUibcEilIiINNKj\nR49w9epVtGjRQnQUUgEPDw9s3rwZAQEBSEpKUvj6xTs+i3eGvqz45VZWVu+17vbt23HmzBnMmDGj\nbAGJVKRBgwZYunQpAgIC8ODBA9FxhNOlG+MfPXoELy8v2NvbIyoqijvY6T98fHwQExODwsJC0VGI\niMqERSgREWmk2NhYuLm5wdDQUHQUUhEvLy9s2LABXbp0wfHjxxW6dp06dSCXy994Bugff/wBAG88\nQ/R1Hj58+M9IvKmpqUJyEqnCF198gR49eqBHjx4oKCgQHUcoXdkRevv2bbi7u8PDwwMrVqyAgYGB\n6EikZqpVq4bKlSsjJSVFdBQiojJhEUpERBqJY/G6ycfHB2vWrIGvr69CR3fbtWsHAK8d+8vKysLR\no0dhZmb2XuPtwcHB6NWrF1q3bq2wnESqMnv2bOjr62PixImiowhTWFiIs2fPav1FSWlpaXBzc0NQ\nUBDmzp0LiUQiOhKpKd4eT0TagEUoERFpJJlMxouSdFSnTp2wYsUKdOrUCadPn1bImrVq1YK3tzeu\nX7+O77///j+vCwsLw/Pnz/HVV1+VeGfnjh07cOrUKcycOVMh+YhUTV9fH1u2bMH27dsRGRkpOo4Q\naWlpqFKlyhsvUdMGycnJ8PDwwLRp0zB27FjRcUjN8ZxQItIGErlcLhcdgoiI6H3cvn0bTZo0wf37\n96Gnx9/p6aodO3YgODgY0dHRaNSoUZnXu3r1KlxdXXH//n18/vnnqFevHn777TccOXIEdevWxdGj\nR1GhQoV3rvPw4UM0atQI27dvh6ura5lzEYl06tQpeHt7QyaTKeTzTJNs2LAB+/fvx9atW0VHUYrY\n2Fh0794dK1asQOfOnUXHIQ2QnZ2NypUr486dO7xIi4g0Fn96JCIijSOVStGuXTuWoDouICAAixYt\ngo+PD86fP1/m9WrVqoWTJ0+ib9++OH78OBYuXIhr165h9OjR+PXXX0tUggLAiBEj0LNnT5agpBWa\nNm2K7777Dn5+fnjy5InoOCqlzeeD7ty5E927d0dUVBRLUCoxMzMztG7dGjKZTHQUIqJS4ynYRESk\ncTgWT8W6d++OgoICfPLJJ5BKpahbt26Z1rOzs8OqVatK/f67du3CyZMnFTayT6QOevfujRMnTqB3\n797Yt2+fzvwSKiUlBVOnThUdQ+FWrlyJsLAwHDp0CE2bNhUdhzRM8TmhXbp0ER2FiKhUOBpPREQa\nRS6Xw97eHrGxsXB0dBQdh9TEunXrMHnyZKF/Lx49eoRGjRph27ZtcHNzE5KBSFny8/Ph5eWFtm3b\nYsaMGaLjKF1RURGsrKxw/fp1WFtbi46jEHK5HPPmzcOyZcsQHR3Nr6FUKqmpqejcuTOuXLnCi7WI\nSCNxRygREWmUS5cuQSKRoHbt2qKjkBrp06cPCgoK0L59exw5cgS1atVSeYYRI0age/fuLEFJKxka\nGmLbtm1o3rw5XFxctH6c+vLly6hYsaJWlaChoaH45ZdfkJiYCDs7O9GRSEM1bNgQL168wJUrV/i9\nGBFpJBahRESkUYrH4rkLgV7Wr18/5Ofnw9PTE0eOHIGDg4PKnr17924cP34cZ86cUdkziVStcuXK\n2L59O3x9fVG3bl3UqVNHdCSl0abzQQsKCtC/f39cunQJ8fHxWlPukhgSiQTe3t44dOgQi1Ai0ki6\nccAPERFpDalUCk9PT9ExSE0NHjwYY8eOhaenJ27duqWSZz5+/BhDhw7F6tWrYWZmppJnEonSsmVL\nzJ49G35+fsjMzBQdR2mSk5Ph4uIiOkaZ5eTkICAgAOnp6YiJiWEJSgpRfE4oEZEmYhFKREQao6io\nCLGxsbwoid4qODgYw4cPh6enJ+7cuaP0540cORLdunWDu7u70p9FpA4GDBgANzc39O3bF9p63YA2\n7AjNyMhAhw4dYG5ujj179sDc3Fx0JNISXl5eiIuLQ15enugoRETvjUUoERFpjDNnzqBSpUo824ze\nafTo0RgwYAA8PT1x9+5dpT1n7969+PXXXzF79mylPYNIHS1ZsgR37tzB3LlzRUdROLlcrvFFaHp6\nOjw8PODs7IyNGzfCyMhIdCTSIjY2NnBycsKvv/4qOgoR0XtjEUpERBqDY/H0PsaNG4c+ffqgffv2\nSE9PV/j6jx8/xpAhQ7B69WrutCKdY2xsjO3bt2PJkiVaNyJ7/fp1WFhYwNbWVnSUUrl27Rrc3Nzg\n5+eHiIgI6OnxRz5SPI7HE5Gm4ldFIiLSGMUXJRGV1KRJk9C9e3d4eXnh4cOHCl171KhRCAgIQJs2\nbRS6LpGmqFatGrZu3YqvvvoKV69eFR1HYZKTkzV2N2hqairc3d0xevRohIWF8WJBUhoWoUSkqViE\nEhGRRsjLy0NiYiI8PDxERyENExYWhs6dO8PLywuPHz9WyJr79u3D0aNH8c033yhkPSJN1aZNG0ye\nPBn+/v7Izs4WHUchNHUsPikpCV5eXliwYAGGDh0qOg5puVatWuHKlSu4f/++6ChERO+FRSgREWmE\n48ePo3bt2qhYsaLoKKRhJBIJZs6cCR8fH3zyySd48uTJK2+Tm5uLTZs2IbB7dzg7OMDKzAzlTExQ\nw8YGndu1w9w5c3D79m0AwJMnTzgST/Qvw4cPR6NGjTBgwACtuDxJE4vQAwcOoEuXLli/fj169Ogh\nOg7pAENDQ3h4eODw4cOioxARvReJXBu+WyEiIq03Y8YMZGZmYv78+aKjkIaSy+UICQlBYmIiYmJi\nYGlpifz8fMz/5hssWrAATeRy+GdlwQXAhwD0ATwAkAIg1tgYkRIJvDw9UWRqiipVqmDJkiVCPx4i\ndZKdnQ1XV1f06dMHo0aNEh2n1ORyOWxtbXHmzBlUrVpVdJwS2bRpE0JCQrB79260atVKdBzSIUuX\nLsVvv/2GdevWiY5CRFRi3BFKREQaQSqV8nxQKhOJRIJvv/0WrVq1QocOHXDy5Em0aNAACeHhSMzM\nRHRWFgYD+AiANQBLALUBdAOw9MUL3MjNRYNDh3Bw5040athQ5IdCpHb+j737Dq/xbvwH/j7Z0wgy\nCCJDiGiTqPEQI0IipWhjxKalRamq0RaxV1DFgyqKGk0fexOcGCERmmUksVcRImSPk+Tcvz/6zflJ\nY2Sck/uck/frulxXcnKfz/0+fR6SvM9nmJiYYN++fVi8eDHOnDkjdpxy+/vvv6GrqwsbGxuxo5TK\nqlWr8MMPP0AqlbIEpUrn6+uLEydOaMVMcCKqOliEEhGR2svKykJUVBQ8PT3FjkIaTiKRYOXKlbC1\ntYVXmzb48vZtHM3ORuNSPNccwKzCQlwUBAR99x2WLlyo6rhEGsXOzg7btm3DgAED8OjRI7HjlEvR\nsnh1P2RIEATMnDkTq1evRlhYGJo1ayZ2JKqC7O3tYWpqiitXrogdhYio1FiEEhGR2rtw4QLc3d1h\nZmYmdhTSAsnJyQiXSrGxsBBjBAFlrTuaAziXnY1fFizAn8HBqohIpLG6du2Kb7/9Fv7+/sjNzRU7\nTplFRUWhRYsWYsd4p8LCQnz99dc4cuQIzp8/Dzs7O7EjURVWNCuUiEhTsAglIiK1x2XxpCyCIGDs\n8OEYnJWF/hUYpx6AXdnZmPDVV0hKSlJWPCKtMHXqVDRs2BDjxo3TuCWz6n5Qkkwmw6BBg5CQkIDT\np0/D0tJS7EhUxfn6+iIkJETsGEREpcYilIiI1J5UKkXnzp3FjkFa4PTp07hy7hzmyGQVHqsFgM9z\nczFj0qSKByPSIhKJBJs2bUJERATWr18vdpwyUeciNDMzE5988gny8vJw7NgxVKtWTexIRPDy8kJk\nZCSysrLEjkJEVCosQomISK29evUKN2/e5CEQpBSrFy/GpKwsGClpvO/y87Fn7168evVKSSMSaQdz\nc3Ps27cPgYGBiIiIEDtOqTx9+hQymQwNGjQQO0oJKSkp6NKlC+rXr49du3bByEhZ/4oRVYy5uTk8\nPDxw9uxZsaMQEZUKi1AiIlJrZ86cQdu2bWFgYCB2FNJwqampOHX2LAYpccw6AHx1dLB7924ljkqk\nHRo3boxNmzahb9++GrGFhLoelPT333+jffv26NSpEzZs2AA9PT2xIxEVw31CiUiTsAglIiK1xmXx\npCzR0dH4wMgIyj5yq312Ni6dOaPkUYm0Q48ePTBy5Ej07dsXMiVsSaFK6rgs/saNG/D09MTnn3+O\nxYsXq11JSwRwn1Ai0iwsQomISK2FhobyoCRSiri4OLip4BRrdwBxf/2l9HGJtMXMmTNRo0YNTFLz\n/XSjo6PV6sT4qKgodOrUCbNmzcLkyZPFjkP0Vu7u7khJScHDhw/FjkJE9F4sQomISG09efIESUlJ\ncHNzEzsKaYH09HRYqGBGmgWAtIwMpY9LpC10dHSwbds2hISEYOvWrWLHeauoqCi1mRF6+vRp+Pn5\nYd26dRgxYoTYcYjeSUdHB127duWsUCLSCCxCiYhIbYWGhqJTp07Q1dUVOwppAT09PeTrKP9HHxkA\nPf5/lOidatSogX379mHSpEmIjo4WO04JycnJSE9Ph729vdhRsHfvXvTv3x+7du1Cr169xI5DVCo+\nPj7cJ5SINAKLUCIiUltcFk/KZG9vj5umpkof9yaApKdP0bZtW4wcORLLly/H8ePH8fDhQwiCoPT7\nEWmqZs2aYe3atfjss8/w4sULseMUExMTA3d3d9H34Ny4cSPGjRuHkJAQdOzYUdQsRGVxow1bAAAg\nAElEQVTh4+MDqVSKgoICsaMQEb0TjxwkIiK1JAgCpFIppk6dKnYU0hItWrTAD3I5BADKrDqidHXx\nxbffokfPnoiPj0d8fDyOHj2K+Ph4pKeno2nTpnBxcVH8adq0KRo1asSZzlQl9e3bF3/99RcCAgJw\n/PhxtTkBPSoqStT9QQVBwJIlS7Bu3TqcPXsWTk5OomUhKg8bGxvUr18fly9fxn/+8x+x4xARvZVE\n4FQFIiJSQ7dv30aHDh3w+PFj0WfokHaQy+VwqlsXfzx7htbKGhOAk6kp/pBK0bp1yVFTU1ORkJCg\nKEiL/iQnJ6Nx48YlClJHR0fo6+srKR2ReiooKICfnx/c3d2xZMkSseMA+Keg/fTTTzFw4MBKv7cg\nCJgyZQqOHz+OkJAQ1KtXr9IzECnD1KlTYWJigtmzZ4sdhYjorViEEhGRWlq/fj3CwsKwbds2saOQ\nFvlm3Dg8+eUX7JbLlTLeUQCBTk7468aNMhX2mZmZSExMLFaOJiQk4NGjR3BwcChWjrq4uKBx48Yw\nMjJSSmYidZCSkoKPPvoIQUFB6NevX4XG2rNnD86ePYvY2FjExcUhIyMDgwcPfuPBTA8ePECjRo1K\nPC4IAiQSCQICAvDHH39UKE9ZFBQUYOTIkbh58yYOHz4MCwuLSrs3kbKdOnUKgYGBiIiIEDsKEdFb\nqcdaFCIion+RSqXw8/MTOwZpievXr2PGjBm4dOkSZEZGOJedjQ4VHDMHwEQTEywOCirzrGUzMzN8\n9NFH+Oijj4qPmZODmzdvKsrR3bt3Iz4+Hnfv3kWDBg2KlaMuLi5o0qQJTFWw7ymRqtWqVQt79+6F\nj48PXFxc4OrqWu6x5s+fjytXrsDMzAy2trZITEx873Pc3NzQu3dvAP/8vVu+fDmmTZuG5s2blztH\nWeXk5CAgIAAymQwnT57k32XSeJ6enrh+/TpevXqFmjVrih2HiOiNOCOUiIjUjlwuh5WVFaKiotCg\nQQOx45AGu3//PmbNmoVjx45h6tSp+Prrr3Hy5El8N2AAIrOzUauc4woAxhkY4GW3bgg+cECZkd9I\nJpPh9u3bipmjRUXpzZs3YW1tXaIgbdq0KapXr67yXEQVtW3bNsydOxeXL19GjRo1yjXG2bNnYWtr\nCwcHB5w9exZeXl7vnRE6fPhwbNq0CcA/B/PNmjULYWFhFXotZZGWloaePXuiXr162LJlCwwMDCrt\n3kSq9PHHH+Pzzz9Hnz59xI5CRPRGnBFKRERq59q1a6hRowZLUCq3Z8+eYf78+fjjjz/w9ddf49at\nW4pisGfPnrgwahR8N2xASDnKUAHADH19nKtXD+e2bFF29DcyMDBQlJyvKygowL179xTl6JkzZ7B2\n7VokJCSgZs2aJQ5qcnFxQa1a5a1/iZRvyJAhuHz5MgYNGoRDhw5BR0enzGNU9HT16OhoeHh4VGiM\nsnj27Bm6desGT09PrFy5slyvmUhd+fr6IiQkhEUoEaktFqFERKR2pFIpvL29xY5BGig1NRXLli3D\nL7/8giFDhiAhIQGWlpYlrlv888+YrqcHj19+wcbsbHQt5fhPAHxlYoKkhg0Revas6Ev/9PT04OTk\nBCcnJ/Ts2VPxuFwux8OHDxUF6aVLl7BlyxbEx8fD0NCwRDnq4uICKysrHkxGovjpp5/g7e2NOXPm\nYM6cOZVyzydPnmD9+vVISUnB3r17FcvkVe3evXvw8fHBkCFDEBgYyL9zpHV8fHzw008/KfbdJSJS\nNyxCiYhI7UilUgwdOlTsGKRBsrOzsXr1aixbtgw9evRAdHQ0GjZs+NbrJRIJFi5bhk4+Phg5aBA+\nzMnB2KwsdAWg+4brbwH4VV8fv+vpYeyECZg+e7ZaL2XV0dGBnZ0d7Ozsiu21KwgCnjx5oihIr127\nhp07dyI+Ph6FhYUlytGmTZuifv36/GWWVEpfXx87d+5Ey5Yt0aJFi2KlvqqcPHkSJ0+eBPDPGwdR\nUVGQSqX4/fffUb9+fZXc8+rVq/Dz88O0adMwduxYldyDSGxNmjQBANy4cUPxMRGROmERSkREaiU/\nPx9hYWHYvHmz2FFIA+Tn5+O3337DvHnz8J///Adnz55F06ZNS/18Hx8fxN+/j+A//sC0JUvQ7+FD\nuBkbw6GgAEJ+Ph7L5Ug0MECeri6Gf/45Ir/5Bvb29ip8RaolkUhQr1491KtXD126dCn2teTk5GKn\n2B8+fBgJCQnIyMgosf+oi4sL7OzsoKv7ptqYqOysra2xa9cu9OzZE2FhYXB2dlbJfUxMTDBz5kz0\n7t0b9vb2SE9Ph5OTE9q2bYvTp0+jS5cuiI2NhbGxsVLvGx4ejk8//RQrV65EQECAUscmUicSiUSx\nPJ5FKBGpIx6WREREaiUiIgJjxoxBbGys2FFIjcnlcvz555+YOXMm7O3tsXDhwhInsJfHy5cvER0d\njYcPH+LatWs4ceIEDhw4AHt7+yo7K/LVq1fFDmgq+jg5ORnOzs4lClIHBwfo6+uLHZs01Pr167Fi\nxQpERkbC3Ny8zM9/32FJ/xYWFoapU6fi/Pnz8PT0xKVLl7BixQqMHz++PPHf6OjRoxg+fDi2bdsG\nX19fpY1LpK52796NTZs24ejRo2JHISIqgTNCiYhIrUilUnTu3FnsGKSmBEHAkSNHMH36dBgbG2PD\nhg3w8vJS2vgWFhaKmZLXrl1DSEgIHBwclDa+JqpZsybatm2Ltm3bFns8IyMDiYmJinK0aA/Sx48f\nw8HBocRJ9o0bN4ahoaFIr4I0xZdffonLly9jxIgR2LVrl8rfgIiKioKHhwd0dXUxcuRIREZG4ty5\nc0orQnfs2IFJkybh4MGDaNOmjVLGJFJ33t7e+Pzzz5GbmwsjIyOx4xARFcMilIiI1EpoaCgmTZok\ndgxSQ+fOncO0adOQmpqKBQsWoGfPniotSaysrJCUlKSy8TWdubk5WrZsiZYtWxZ7PCcnBzdu3FAU\npEV7kN67dw8NGzYscZJ9kyZNYGJiItKrIHW0evVqdOjQAUFBQfjhhx9Ueq/o6GjFqfN16tQBAGRl\nZSll7FWrVmHp0qWQSqVo1qyZUsYk0gQ1a9ZEs2bNcOHCBR5+SURqh0UoERGpjZycHFy6dAkdOnQQ\nOwqpkZiYGEybNg2JiYmYO3cuBg4cWCl7U9aqVQvp6enIz8/nUu8yMDY2hpubG9zc3Io9LpPJcOvW\nLcXS+sOHD2PJkiW4desWbGxsShSkTZs2RbVq1UR6FSQmQ0ND7NmzB61atYKHhwd8fHxUdq/o6GhM\nnDgRwD9bswCo8D7AgiBg1qxZ+PPPPxEWFgY7O7uKxiTSOEX7hLIIJSJ1wyKUiIjUxoULF/DBBx+U\na1840j43b97EzJkzcfbsWUyfPh0HDhyo1JPadXR0ULt2bTx//hz16tWrtPtqKwMDAzRr1qzEzLiC\nggLcu3dPsQdpaGgoVq9ejcTERNSsWfONJ9nXqlVLpFdBlcXW1hbBwcHo168fLl68iEaNGill3JiY\nGLi5uUEikSArKwt3795Fs2bNIJVKsWLFCkgkEgwePLjc4xcWFmL8+PGIjIzE+fPnYWlpqZTcRJrG\n19cXo0ePxpIlS8SOQkRUDA9LIiIitTFt2jTo6upi3rx5YkchEf3999+YM2cO9u3bh++++w4TJkyA\nqampKFnc3NywadMmeHh4iHL/qkwul+Phw4fFTrIvWm5vZGT0xoLUysqqyh5qpa1WrlyJzZs3Izw8\n/K1bKBw4cAD79+8HACQlJSEkJAT29vZo3749AKB27dpYunQpAMDLywu3bt1C27ZtoaOjg9OnT6N5\n8+YIDQ2FRCLB/Pnz8eOPP5Yrq0wmw9ChQ/Hs2TMcOHCAM5qpSisoKECdOnUQHx8PGxsbseMQESmw\nCCUiIrXRunVrBAUFoVOnTmJHIRG8ePECixcvxubNmzFq1ChMnToVFhYWomby9fXFt99+Cz8/P1Fz\n0P8nCAKePHlSohy9fv06BEEoUY66uLjA1taWBamGEgQBQ4YMAQBs27btjf87zpkzB3Pnzn3rGHZ2\ndrhz5w4AYPPmzdi3bx+uXbuGp0+fIj8/H7a2tmjbti2+/vprtGvXrlw5MzMz4e/vDxMTEwQHB/OA\nGCIAffr0Qc+ePTF06FCxoxARKbAIJSIitZCamor69esjOTmZv0BWMRkZGfj555+xatUq9OvXD4GB\ngWoze2To0KHo3Lkzhg8fLnYUeg9BEJCcnFyiII2Pj0dWVpaiFH19L1I7Ozvo6OiIHZ3eIzs7G+3a\ntcPw4cMxYcIEpY37+eefo3Xr1vjqq68qNE5KSgq6d+8OV1dXrFu3Dnp63H2MCAA2bNiAM2fOYMeO\nHWJHISJS4HdpIiJSC+fOnUObNm1YglYhubm5WLduHRYvXowuXbogMjISDg4OYscqxsrKCs+ePRM7\nBpWCRCKBpaUlLC0tS8wqf/nypaIUTUhIQGhoKOLj45GSkgJnZ+cSBzU5ODiwzFIjJiYm2Lt3L9q0\naQM3NzfFKe8VFR0djTFjxlRojL///hs+Pj7o2bMnFi1axJnHRK/x8fHB9OnTIZfL+aYTEakN/oRH\nRERqQSqV8mTRKqKgoABbt27FnDlz8OGHH+LEiRP44IMPxI71RtbW1vj777/FjkEVZGFhgXbt2pVY\n9pyeno7ExERFSbpp0ybEx8fjyZMncHR0LFGQOjk5wdDQUKRXUbU1atQI27Ztw4ABA3Dp0iXY2tpW\naLzc3FzcvHkTzZs3L/cYN27cgK+vL8aNG4fJkydXKA+RNmrYsCEsLCwQExODFi1aiB2HiAgAi1Ai\nIlITUqkUmzZtEjsGqZAgCNizZw8CAwNhaWmJ4OBgtG3bVuxY72RlZYWoqCixY5CKVKtWDa1atUKr\nVq2KPZ6dnY0bN24oCtI///wT8fHxuH//Pho2bFjioCZnZ+e3HuRDyuPj44NvvvkG/v7+OHfuXIVK\n6atXr6Jx48blXoUQFRWFHj16YOHChRgxYkS5cxBpO19fX5w4cYJFKBGpDe4RSkREonv27BmaNGmC\nFy9eQFdXV+w4pGSCIODkyZOYNm0a5HI5Fi5cCF9fX41YQnry5EksXrwYUqlU7CikBvLy8nD79u0S\nJ9nfvn0bNjY2JQrSJk2a8ORwJRMEAX379kXNmjWxYcOGco/z66+/IjIyslxvwJ0+fRr9+/fHhg0b\n0KtXr3JnIKoKjh49iiVLluDMmTNiRyEiAsAZoUREpAZCQ0PRsWNHlqBa6OLFi5g2bRoeP36M+fPn\nw9/fX6P2CeMeofQ6Q0NDNGvWDM2aNSv2eEFBAe7evasoRk+dOoX//ve/SExMhIWFxRtPsrewsBDp\nVWg2iUSCzZs3o02bNli/fj2+/PLLco0THR0NDw+PMj9v7969GD16NHbt2qW0vUqJtFnHjh3Rv39/\nZGRkwNzcXOw4REQsQomISHyhoaHo3Lmz2DFIia5du4YZM2YgKioKs2bNwvDhwzXy8BkWoVQaenp6\naNy4MRo3bozevXsrHpfL5Xjw4IGiIA0PD8dvv/2G+Ph4mJiYlChHXVxcYGlpqRGzpcVkbm6Offv2\nwdPTEx988AHatGlT5jGio6MxfPjwMj1n48aNmDlzJkJCQuDu7l7mexJVRaampmjVqhVOnz6Nnj17\nih2HiIhL44mISHz29vY4dOhQiVlWpHnu3buHWbNmISQkBN9//z3Gjh1b7j341EFhYSGMjIyQk5Oj\nkUUuqSdBEPD48WNFQVq0F+n169chkUhKlKMuLi6oV68eC9J/OXjwIL7++mtcvnwZ1tbWpX6eTCZD\njRo1kJycDFNT0/deLwgClixZgnXr1uHEiRNwcnKqSGyiKmfJkiV4+PAhVq9eLXYUIiLOCCUiInHd\nu3cP2dnZcHFxETsKVUBSUhLmz5+P4OBgjB8/Hrdu3dKKvRF1dXVhYWGB5ORk2NjYiB2HtIREIoGt\nrS1sbW3h4+OjeFwQBDx//rxYOXrw4EHEx8cr/p38d0HasGFDjdpuQpl69uyJqKgo9OvXD1KpFPr6\n+qV6Xnx8PBo1alTqEnTKlCk4fvw4zp8/j3r16lU0NlGV4+vriz59+ogdg4gIAItQIiISWdGyeM50\n0kypqalYsmQJfv31VwwbNgyJiYmoU6eO2LGUqmh5PItQUjWJRAIrKytYWVnBy8ur2NdSUlKQkJCg\nKEhPnTqF+Ph4vHz5Ek2aNClRkNrb21eJWcyzZs1CVFQUJk2ahFWrVpXqOaXdH7SgoACjRo3CjRs3\ncO7cOe7rSlROH3zwATIyMnD37l3Y29uX+PqGDRvw22+/4fr16xAEAU2bNsXIkSPx5Zdf8udDIlI6\n7f/piIiI1JpUKoW3t7fYMaiMsrOzsWrVKvz000/o1asXYmNjUb9+fbFjqYS1tTWSkpLEjkFVXK1a\nteDp6QlPT89ij6enpyMxMVGxzH7jxo2Ij4/H06dP4ejoWOIkeycnJxgYGIj0KpRPR0cH27dvR8uW\nLbFt2zYMGTJE8bWUlBTs3r0bZ86cQVRUFDIzM6Gnp4fCwkK4urri8uXLaNmy5RvHzcnJQUBAAGQy\nGU6ePFmq2aNE9GYSiQQ+Pj4ICQnBmDFjin1t0KBBCA4OhpWVFQYOHAgTExOcPHkSY8aMQUREBLZs\n2SJOaCLSWtwjlIiIRCMIAmxsbBAREYFGjRqJHYdKQSaT4bfffsO8efPg6emJefPmwdnZWexYKjVk\nyBB06dIFw4YNEzsKUallZ2fjxo0bioK06M+DBw9gZ2dX4iR7Z2dnmJiYiB273K5duwYvLy+EhITA\n2toaEydOxMGDB6Gjo4Ps7OwS1+vq6sLQ0BD169fH0qVL8cknnyi+lpaWhp49e6JevXrYsmWLVhXH\nRGLZsWMHdu3ahf379yse27dvH/z9/eHg4IBLly6hZs2aAP6Zjf3ZZ5/hyJEj2LNnT7FD6IiIKopF\nKBERieb69ev45JNPcPfuXbGj0HsUFhYiODgYs2bNgqOjIxYuXIgWLVqIHatSTJ48GZaWlpg6darY\nUYgqLC8vD7du3SpWjiYkJOD27duoW7duiZPsmzZtCnNzc7Fjl8rOnTsxduxY5ObmIi8vDwUFBaV6\nnomJCbp164bffvsNeXl56NatGzw9PbFy5coqu/8qkbI9f/4cjRs3RnJysmI/32HDhmH79u1Ys2YN\nRo8eXez6uLg4uLu7o3Pnzjh16pQYkYlIS3FpPBERiYbL4tWfIAg4fPgwpk2bBjMzM/z222/o1KmT\n2LEqlZWVFZfGk9YwNDSEq6srXF1diz1eUFCAO3fuKMrRU6dOYdWqVUhMTETt2rVLFKQuLi6K2Vvq\nIiEhAWlpaaUuQItkZ2fjyJEjcHd3h0QiwfDhwxEYGMi9CYmUyNLSEg4ODrh48SLat28PAIrvrW9a\nFVS0l2hYWBgKCgqqxJ7HRFQ5+K8JERGJRiqVIiAgQOwY9BZnz57Fjz/+iIyMDCxYsACffPJJlSwG\nrKysEBcXJ3YMIpXS09ODs7MznJ2d8emnnyoeLywsxIMHDxQFaXh4uGIfUjMzszeeZF+nTp1K/7di\nw4YNWLJkSZlL0CJ5eXm4f/8+6tatix9//LFK/ltHpGpF+4QWFaG1a9cGANy7d6/EtUWrhQoKCnD3\n7l00bty48oISkVbj0ngiIhJFQUEB6tSpg8TERFhZWYkdh14THR2NadOm4ebNm5g7dy4GDBgAXV1d\nsWOJJiQkBMuWLcPJkyfFjkKkNgRBwN9//61YWv/6UnsdHZ0S5aiLiwvq1q2rkoLx/v37aNas2Rv3\nAi0rExMTTJ06FbNmzVJCMiJ63ZkzZzBlyhRcvnwZAPDHH39g8ODBcHR0RGRkZLE9Qv39/XHo0CFI\nJBKEh4ejdevWYkYnIi3CIpSIiERx6dIlfPHFF7h69arYUej/3LhxA4GBgTh//jxmzJiBkSNH8pAQ\nALGxsRg6dCiuXLkidhQitScIAp49e1aiHI2Pj0dubu4bC9IGDRpUaC9OHx8fhIaGorCwUCmvwdjY\nGImJiWjQoIFSxiOif8hkMtSpUwd37txB7dq1IZfL0aNHD4SEhMDS0hK9evWCkZERTp06haSkJJiZ\nmeHRo0e4ePEiWrZsKXZ8ItISXBpPRESikEql6Ny5s9gxCMCjR48wZ84cHDhwAJMmTcLmzZthamoq\ndiy1YWVlhWfPnokdg0gjSCQSWFtbw9raGl5eXsW+lpKSUqwgPXHiBOLj45GamgpnZ+cSBWmjRo3e\nuy/ggwcPEBYWprQSFPhnO4A1a9YgKChIaWMSEWBgYICOHTvi1KlTCAgIgI6ODg4dOoTly5dj+/bt\n2Lp1K4yMjODl5YW9e/fC398fwD/7ixIRKQtnhBIRkSi6du2K8ePHo2fPnmJHqbKSk5OxaNEi/P77\n7/jqq68wZcoUtTv8RB0UFBTA2NgYOTk5PKyBSAXS0tKQmJhY4iT7p0+fwsnJqVg52rRpUzg5OSlm\nq8+ePRuLFi2CTCZTaqYaNWrg5cuX3CuUSMlWr16NqKgobN68+Z3X5eXloXr16qhevTrfjCQipeJP\n80REVOlyc3Nx8eJF7N69W+woVVJ6ejqWL1+O1atXIyAgANevX4e1tbXYsdSWnp4eatasiRcvXvC/\nE5EKVK9eHa1bty6xB2BWVhZu3LihKEd37NiB+Ph4PHjwAI0aNYKLiwsuXbqk9BIU+KeEefToEZfH\nEymZr68vFi1aBEEQ3vlGQ3BwMGQyGQYOHFiJ6YioKmARSkRElS4iIgIuLi6oXr262FGqlNzcXPzy\nyy9YvHgxfHx8cOnSJdjb24sdSyMULY9nEUpUeUxNTeHh4QEPD49ij+fl5eHmzZuIj4/HkSNHVHJv\nfX19xMTEsAglUjJHR0cYGhri+vXrcHV1RUZGBszNzYtdExsbiylTpqBWrVr4/vvvRUpKRNqKRSgR\nEVW60NBQeHt7ix2jyigoKMDvv/+OOXPmwN3dHadOnULz5s3FjqVRuE8okfowNDRE8+bN0bx5cwwe\nPFgl9ygsLMSrV69UMjZRVSaRSODr64uQkBC4urqia9euMDY2hqurK8zNzZGQkIAjR47A1NQUhw4d\n4huQRKR05T+ekYiIqJykUimL0Eogl8uxa9cuuLq6Yvv27fjf//6HAwcOsAQtBxahROpJVXt4ymQy\nHDp0CBs3bsSRI0cQExODZ8+eQS6Xq+R+RFWJj48PQkJCAAB9+/ZFZmYmduzYgZ9//hlXr17F6NGj\ncf36dXh6eoqclIi0EQ9LIiKiSpWeno66desiOTkZxsbGYsfRSoIg4MSJE5g2bRokEgkWLlyIrl27\n8tCPCvjuu+9Qt25dTJ48WewoRPQaW1tbPH78WOnjGhkZoU+fPjAwMMCTJ08Uf9LS0mBpaYm6desq\n/tjY2JT4vHbt2tDR4ZwTojdJS0uDra0tnj17BhMTE7HjEFEVw6XxRERUqcLCwtCqVSuWoCoSERGB\nH3/8EUlJSZg/fz78/f1ZgCoBZ4QSqaePPvpIJUVoYWEh1qxZg2rVqhV7XCaTISkpSVGMPn36FE+e\nPMH58+eLfZ6eng5ra+u3FqVFH9eqVYv/RlOVU716dbi5uSEsLAy+vr5ixyGiKoZFKBERVSoui1eN\nq1evYvr06YiNjcXs2bMxdOhQ6Onx27yyWFtb4/r162LHIKJ/6d27N6RSKTIzM5U6rqOjY4kSFAAM\nDAzQoEGD9x6ilJubi6SkJEUxWvTn7NmzxT7PyspSFKbvmmFqYWHBwpS0StE+oSxCiaiy8TckIiKq\nVFKpFOvWrRM7hta4e/cuZs6ciVOnTuGHH37Azp07YWRkJHYsrWNlZYWkpCSxYxDRv/Tr1w/jxo1T\n6phmZmaYOnVqhcYwMjKCnZ0d7Ozs3nldbm5usbK06OPExMRin+fk5CgK0nfNMK1RowYLU9IIPj4+\nGDFihNgxiKgK4h6hRERUaZKTk+Hk5IQXL15wtmIFPX36FPPnz8f//vc/jB8/Ht999x3Mzc3FjqW1\nYmJiMHz4cMTFxYkdhYj+ZcaMGVi2bBny8vKUMp6lpSXu37+vVlu4ZGdn4+nTp28sTV//PC8v751F\nadHn1atXZ2FKoiosLISVlRViYmJQv359seMQURXC30KJiKjSnD59Gu3bt2cJWgGvXr1CUFAQNmzY\ngOHDhyMxMRG1a9cWO5bW4x6hROqpsLAQurq6yM/PV8p4JiYmCA4OVqsSFPgnl4ODAxwcHN55XVZW\nlqIgfb0ovXLlSrHPCwoK3lmUFn1sbm7OwpRUQldXF126dMHJkyfx+eefix2HiKoQ/iZKRESVRiqV\nonPnzmLH0EhZWVlYtWoVli9fjt69eyM2NpYzKCpRnTp1kJKSoihdiEh8Dx48wODBg2FgYICTJ0+i\nd+/eyMjIKPd4hoaGGD9+vEZ/nzI1NYWjoyMcHR3feV1mZuYbZ5TGxMQUewxAqWaYckUClYevry+O\nHz/OIpSIKhWXxhMRUaVxcnLCnj178MEHH4gdRWPIZDJs2LABCxYsQPv27TFv3jw0btxY7FhVUu3a\ntREfHw9LS0uxoxBVeX/++Se++eYbTJkyBZMmTYKOjg6io6Ph7e2NrKysMs8QNTIygkQiwYkTJ+Dp\n6ami1JonIyPjnUvxiz7W0dEp1QxTU1NTsV8SqZG///4bH374IZ4/f843GYmo0nBGKBERVYqHDx8i\nLS0Nrq6uYkfRCIWFhfjjjz8wa9YsODs74/Dhw/Dw8BA7VpVWtDyeRSiReDIyMjB+/HhERETg2LFj\naNGiheJrHh4eSEhIwNChQxEeHo6srKz3jmdsbAwjIyNs27YN+vr68Pf3R2hoKJo1a6bKl6ExzM3N\n4ezsDGdn57deIwgC0tPTSxSljx49QmRkZLHHDAwMSjXD1MTEpBJfJYnF1tYWNjY2+Ouvv9C6dWux\n4xBRFcEilIiIKoVUKoWXlxd0dHTEjqLWBEHAwYMHMWPGDFSrVg1btmxBhw4dxM+YANIAACAASURB\nVI5F+P9FaPPmzcWOQlQlRUZGYtCgQfDy8kJ0dPQbZxdaW1sjJCQEp06dQlBQEMLCwmBsbIyMjAzI\n5XLo6OjA1NQUhYWFqF69Or777juMGjUK1atXBwD89NNP8PPzw4ULF7j9SClJJBJUr14d1atXR5Mm\nTd56nSAISEtLKzGj9P79+wgPDy9WpBoZGb13hqmNjY3a7eVKZefr64sTJ06wCCWiSsMilIiIKkVo\naCi8vb3FjqHWTp8+jWnTpiE7OxuLFi1C9+7deUiFGrG2tkZSUpLYMYiqnMLCQixevBirVq3C2rVr\n4e/v/87rJRIJunbtiq5du+LVq1eIjo7GhAkT4OrqiubNm8PBwQEtWrSAg4NDiTfnBg8ejKSkJHTr\n1g3nz59HzZo1VfnSqhSJRIIaNWqgRo0acHFxeet1giDg1atXJWaY3rlzB2FhYcWKVFNT01LNMDU0\nNKzEV0pl4evrizlz5mDw4MGIj49HdnY2jIyM4OLiAnt7e/4cRERKxz1CiYhI5QRBQL169RAWFvbe\nE2+ror/++gvTpk3D3bt3MXfuXAQEBHDmrBqaOHEibG1tMWnSJLGjEFUZDx8+xJAhQ6Crq4utW7fC\n1ta2XOO0a9cOQUFBpd7/c9KkSbh06RJOnDjBWYdqShAEvHz58o17lr7+cVJSEszMzN47w9Ta2pqF\naSWLi4vDkiVL8Mcff8DY2Bj6+vqKrxUUFEAQBPTq1QuTJ08utg0GEVFFsAglIiKVS0hIQLdu3XD/\n/n2+s/+axMREBAYGIjw8HIGBgfjiiy+K/RJA6mXx4sV4+fIllixZInYUoiph165dGDduHL777jtM\nnjy5QoepNGnSBPv373/n0u3XyeVyDBkyBFlZWdi9ezf09LiQTlPJ5fJihenbDn5KSkpC9erV3zvD\n1Nramt+rK+jly5cYNWoUjh8/jry8PBQWFr71Wh0dHRgZGaFjx47YsmUL9+kmogpjEUpERCq3Zs0a\nREVFYdOmTWJHUQsPHz7EnDlzcPDgQUyePBnjx4/nwRAaYPPmzThz5gx+//13saMQabXMzEx88803\nCAsLwx9//IGWLVtWeMzatWsjISEBderUKfVzZDIZevTogUaNGmHdunV8I0/LyeVyvHjx4q1FadHH\nz58/R40aNd47w9TKyoqF6RtERUWha9euyMrKgkwmK/XzDAwMYGxsjKNHj6Jt27YqTEhE2o5vbRIR\nkcpJpdL37ulWFSQnJ2PhwoXYunUrRo8ejVu3bqFGjRpix6JSKjosiYhU5/Llyxg4cCA6dOiAmJgY\nmJmZVXjMwsJCpKamlnm/TwMDA+zZswdeXl6YM2cOZs+eXeEspL50dHRgaWkJS0tLuLm5vfW6wsJC\nRWH6elEaFxeHY8eOKT5PTk6GhYXFe2eYWlpaVpkZxzExMejUqRMyMzPL/FyZTAaZTIauXbtCKpWi\nTZs2KkhIRFUBZ4QSEZFKFRYWok6dOrh27Rrq1q0rdhxRpKen46effsLq1asxcOBATJ8+HdbW1mLH\nojKKiorCyJEjERMTI3YUIq1TWFiIJUuWYMWKFVi9ejX69u2rtLFTUlLg5OSEly9fluv5z549Q7t2\n7TBlyhR89dVXSstF2q2wsBDPnz9/7wzTFy9eoHbt2u+dYWppaVmh7SHElpmZCUdHR6W8oWhhYYE7\nd+7wzWQiKpeq8dYTERGJJjY2FtbW1lWyBM3JycHatWuxZMkSdOvWDX/99RcaNWokdiwqJ84IJVKN\nR48eYciQIQD+OTyufv36Sh2/qGgqLysrK4SEhKB9+/awsrJC7969lZiOtJWuri5sbGxgY2PzzusK\nCgoUhenrRenly5eLff7y5UvUqVPnvTNM69Spo5YHLn777bdIS0tTylhZWVkYM2YMgoODlTIeEVUt\nLEKJiEilpFIpvL29xY5RqQoKCrB582bMnTsXH330EUJDQ9GsWTOxY1EFWVpaIjk5GXK5XC1/ySTS\nRHv27MHYsWMxYcIEfP/99yqZ8ZaSklKhIhQAHBwccOjQIfj5+aFWrVpo3769ktJRVaenp6coMd8l\nPz8fz549KzGj9OLFi8U+T01NhaWl5VuL0qLHateuXWnfy54+fYodO3YgNzdXKePl5eVh//79uHfv\nHt9gJqIyYxFKREQqJZVKMXr0aLFjVAq5XI5du3YhMDAQtra22L17N1q3bi12LFISAwMDVKtWDSkp\nKWU6cIWISsrMzMS3336LM2fO4NChQ2jVqpXK7vXixQvUqlWrwuO0aNECO3bsQJ8+fSCVSuHq6qqE\ndESlo6+vD1tbW9ja2r7zOplMpihMX59ReuHChWIlalpammLFzrtmmdaqVavCB4X98ssvFXr+m8jl\ncqxatQo///yz0scmIu3GIpSIiFRGJpMhPDwcf/75p9hRVEoQBBw/fhzTp0+Hrq4u1qxZgy5duvCE\nYS1UtDyeRShR+f31118YOHAg2rVrh5iYGJibm6v0fhVdGv+6rl27YsWKFfj4449x/vx5NGjQQCnj\nEimLgYEB6tev/94tJvLy8pCUlFRi/9Jz584V+zwzM1NRmL5rH1MLC4u3/tyzc+dOpc0GLSKTybBn\nzx4WoURUZixCiYiohD179uDs2bOIjY1FXFwcMjIyMHjwYGzdurXEtQUFBVizZg3i4uIQExOD+Ph4\n5OfnY+PGjXB0dESTJk3KfFKvJrlw4QJ+/PFHJCcnY/78+fjss89YgGqxoiKUM8GIyk4ul2PZsmVY\ntmwZ/vvf/6J///6Vct+UlBSlzAgtMmDAACQlJaFbt244f/48LCwslDY2UWUxNDREw4YN0bBhw3de\nl5ubi6SkpBIHPZ0+fbrY59nZ2YqC9PWi1NLSEnfu3FHJa0hKSkJWVhZMTU1VMj4RaScWoUREVML8\n+fNx5coVmJmZwdbWFomJiW+9NisrCxMnToREIoGVlRVsbGzw6NEjAP8si+/cuXNlxa5UV65cwfTp\n03HlyhXMnj0bQ4YMgZ4ev61qOx6YRFQ+jx8/xtChQ5Gfn4/Lly+/t3xRJmXOCC0yceJEPHnyBD16\n9MCpU6dgYmKi1PGJ1IWRkRHs7OxgZ2f3zutycnLw9OnTEjNMIyMjIQiCyrLdvXsXzZs3V8n4RKSd\nuNM/ERGVsGLFCty8eRNpaWlYu3btO3+ANTExwbFjxxQ/8I4YMULxtdDQUK07KOnOnTsYNGgQfHx8\n0KVLF9y8eRMjRoxgCVpFsAglKrt9+/bBw8MDXl5eOH36dKWWoIByDkt6k6CgIDg4OCAgIAAFBQVK\nH59IkxgbG8Pe3h7t2rVD3759MWHCBAQFBWHx4sUqe6NAIpEgPz9fJWMTkfZiEUpERCV07NgRDg4O\npbpWX18fvr6+sLKyKvZ4bm4uYmJi4OnpqYqIle7JkycYM2YMWrdujSZNmuDWrVuYMGECDA0NxY5G\nlYhFKFHpZWVl4csvv8TkyZNx4MABzJgxQyWnwr+Psg5L+jcdHR1s2rQJMpkMY8aMUdmsNyJNZm5u\nrrKyUi6Xw8zMTCVjE5H2YhFKREQqcevWLbRo0ULjlwu+fPkS33//PZo3bw4zMzPcuHEDgYGBKj/c\ng9QTi1Ci0omOjkaLFi0Ub4q1adNGtCyqWBpfRF9fH7t370ZsbCxmzZqlknsQaTIbGxuVvQEik8lK\n/cY9EVERFqFERKQSCQkJGr0sPjMzEwsWLEDjxo2RmpqKuLg4LF26VCWzikhzWFtbswgleoeiA5G6\ndeuGWbNmYevWrahWrZqomZR9WNK/mZmZ4ciRIwgODsYvv/yisvsQaSKJRIIPP/xQJWM3adJElFnm\nRKTZuKEZERGpREJCAgIDA8WOUWZ5eXlYv349Fi5ciE6dOiEiIgJOTk5ixyI1YWVlhaSkJLFjEKml\nJ0+eYNiwYcjJycGlS5fee7hKZVHljNAilpaWCAkJQfv27WFlZYXPPvtMpfcj0iSjR4/GlStXkJmZ\nqbQxTU1NMXr0aKWNR0RVB2eEEhGRSjx//hwtW7YUO0apFRYWYuvWrWjSpAmOHTuGo0ePIjg4mCUo\nFcOl8URvduDAAXh4eMDT0xNnzpxRmxJULpcjNTUVFhYWKr+Xvb09Dh8+jNGjR+Ps2bMqvx+Rpujb\nty90dJRbPQiCgCFDhih1TCKqGliEEhGRSjg6OsLAwEDsGO8lCAL279+PDz/8EOvXr8fWrVtx9OhR\nuLu7ix2N1JClpSWSk5Mhl8vFjkKkFrKzszF69GhMnDgRe/fuxaxZs6Cnpz6LzlJTU2FmZlZpmdzd\n3REcHIy+ffvi6tWrlXJPInVnZGSElStXwtTUVCnjmZqaIigoiAclEVG5sAglIiKlEwQBTZs2FTvG\ne4WGhqJNmzaYNWsWgoKCEBYWhvbt24sdi9SYoaEhTE1N8erVK7GjEIkuNjYWLVq0QGZmJmJiYtC2\nbVuxI5VQGcvi/83b2xurVq3Cxx9/jAcPHlTqvYnU1bBhw9CmTRsYGhpWaBwDAwM0b94cY8eOVVIy\nIqpqWIQSEZHSqXsRevnyZXTt2hVffvklvv32W8TExKB79+6QSCRiRyMNwOXxVNXJ5XIsX74cPj4+\nmDFjBrZv347q1auLHeuNVH1Q0tsEBARg8uTJ6NatG1JSUir9/kTqRiKRYN++fWjcuHG5y1ADAwPY\n2dnh6NGjSl9qT0RVB//1ICIipUpPTwcA1K9fX+QkJSUkJMDf3x+ffvop+vTpg4SEBAwYMIA/TFOZ\nsAilquzp06fw8/PD7t27ERkZiUGDBokd6Z3EmBFaZMKECejVqxd69OiB7OxsUTIQqRNzc3OEh4fD\nx8cHJiYmZXquqakpOnbsiMjISNSsWVNFCYmoKlCfDXyIiEhtHDhwAPv37wcAxQnZ4eHhGDFiBACg\ndu3aWLp0qeL6oKAgJCYmAvhnuTkAbNmyBRcuXAAAeHp64osvvqi0/P/24MEDzJ49G0eOHMGUKVOw\nfft2GBsbi5aHNBuLUKqqDh06hFGjRuGrr75CYGCgWu0F+jYpKSmiFaEAsGjRIgwfPhz9+/fHvn37\nNOK/GZEqmZmZ4eDBg9i0aRNGjhwJExMTZGVlvfV6Q0NDmJub4+eff8agQYO4eoeIKozfiYmIqITY\n2Fhs3bpV8blEIsG9e/dw7949AICdnV2xIvT48eM4d+4cgH+WxUskEkRERCAiIkLxfDGK0OfPn2PB\nggXYvn07xo4di1u3bqnt8k3SHNbW1oo3CIiqguzsbEyePBnHjh3D7t274enpKXakUnvx4oUoS+OL\nSCQSbNy4Eb169cJXX32FjRs3ssghAnD37l2MHDkSn332GX7//XdERkbiwYMHip8jbW1tUb9+faSm\npiIuLg66urpiRyYiLSERBEEQOwQREWkHQRDQoEEDSKVSNG7cWLQcaWlpWLZsGdauXYtBgwZh+vTp\nsLKyEi0PaZcFCxYgMzMTixYtEjsKkcpduXIFAwYMwIcffoi1a9eiRo0aYkcqkx9++AHVq1fHjz/+\nKGqOrKwseHl5wcfHB/Pnzxc1C5HYXr16BUdHR0RFRcHOzk7xuCAIKCwshK6uLiQSCfLy8mBtbY34\n+HjY2NiIF5iItAo3RSMiIqW5desWBEGAk5OTKPfPycnB0qVL4eTkhEePHiEqKgqrVq1iCUpKxaXx\nVBXI5XKsWLEC3t7e+OGHH7Bjxw6NK0EB8Q5L+jdTU1McOXIEu3btwurVq8WOQySqlStXolevXsVK\nUOCfGdR6enqKWdOGhobo0aMH9u7dK0JKItJWXBpPRERKExoaCm9v70pf9pefn4/Nmzdj7ty5aNWq\nFc6cOQMXF5dKzUBVB4tQ0nZJSUkYPnw4UlNTcfHiRTg4OIgdqdzEPCzp3+rUqYPjx4/D09MTVlZW\n6Nu3r9iRiCpdWloaVq9ejYsXL5bq+r59+2L58uX4+uuvVZyMiKoKzgglIiKlkUql8Pb2rrT7yeVy\n/Pnnn3BxccHOnTuxd+9e7N27lyUoqRSLUNJmR44cgbu7O1q2bImwsDCNLkEB8Q9L+rdGjRrhyJEj\n+Prrr3HmzBmx4xBVutWrV+Pjjz+Go6Njqa738fFBXFwc9+YmIqXhjFAiIlIKuVyO06dP4+eff1b5\nvQRBwLFjxzB9+nTo6+tj3bp1lVrAUtXGIpS0UU5ODqZOnYqDBw9i586daN++vdiRlELsw5LexM3N\nDf/73//Qr18/nDx5Eh9++KHYkYgqRUZGBlauXImwsLBSP8fIyAjdu3fH3r17MXbsWBWmI6KqgjNC\niYhIKa5cuYJatWrB1tZWpfc5f/48OnTogMmTJ2PmzJmIjIxkCUqVysrKCs+fPwfPmyRtcfXqVbRq\n1QrJycmIi4vTmhIUUK+l8a/z8vLC6tWr0b17d9y/f1/sOESVYu3atejSpQucnZ3L9Ly+ffti165d\nKkpFRFUNi1AiIlIKVS+Lj42NRffu3TF48GCMHDkSV69exaefflrp+5ESGRkZwcjICKmpqWJHIaoQ\nQRCwatUqdO7cGZMnT0ZwcLBGHoj0NnK5HK9evYKFhYXYUd6oX79++P777+Hr64sXL16IHYdIpbKy\nsrB8+XJMnz69zM/19fVFbGwsl8cTkVKwCCUiIqWQSqXo3Lmz0se9ffs2BgwYAD8/P3Tr1g03btzA\nsGHDoKurq/R7EZWWtbU1fyEjjfbs2TN0794d27dvR0REBIYNG6Z1byylpaXB1NQU+vr6Ykd5q/Hj\nx8Pf3x/du3dHVlaW2HGIVObXX39Fhw4d0KxZszI/18jICB9//DFPjycipWARSkREFZafn4/z58/D\ny8tLaWM+fvwYo0ePRps2beDq6opbt25h/PjxMDQ0VNo9iMqL+4SSJjt69Cjc3d3h7u6OCxculPrQ\nEk2jbgclvc2CBQvg4uKCfv36IT8/X+w4REqXk5ODpUuXYsaMGeUeg8vjiUhZWIQSEVGFXbp0CY6O\njko5kCIlJQVTp07FBx98gGrVquHGjRuYPn06zMzMlJCUSDlYhJImys3NxYQJEzBmzBgEBwdjwYIF\naj1bsqLU8aCkN5FIJFi/fj0AYNSoUdx/mLTOhg0b0KZNmwodDObr64uYmBh+7yWiCuOp8UREVCqC\nIODMmTMIDQ3FuXPn8PjxYwiCgDp16kBXVxcNGzZEQUEB9PTK960lMzMTK1aswIoVK9CnTx9cuXIF\n9erVU/KrIFIOFqGkaa5du4aBAwfC2dkZsbGxqFmzptiRVE5dD0p6E319fezcuRPe3t6YNm0aFi1a\nJHYkIqXIzc1FUFAQDh48WKFxjI2NFcvjx4wZo6R0RFQVsQglIqJ3ksvlWL9+PebNm4f09HTk5OSg\nsLBQ8fW7d+8C+OcHVEtLS0ycOBFTp04t9RL2vLw8/Prrr1i0aBG8vLxw8eJFrV2mSdqDRShpCkEQ\nsGbNGsyZMwdBQUEYMWKE1u0F+jYpKSkaMSO0iKmpKQ4fPgxPT0/Y2Njgm2++ETsSUYVt2rQJ7u7u\naNGiRYXH6tu3L/773/+yCCWiCmERSkREb3X//n307dsXCQkJ7z3EIScnBzk5OVi8eDE2b96MvXv3\nws3N7a3XFxYWYtu2bZg9ezZcXV1x/PjxCi2ZIqpMVlZWuHTpktgxiN7p+fPn+OKLL5CUlITw8HA4\nOTmJHalSadKM0CK1a9dGSEgIPD09YW1tjX79+okdiajcZDIZFi9erLS9Pbt164YRI0bg+fPnsLS0\nVMqYRFT1cI9QIiJ6o+vXr8PDwwMxMTFlOsk2Ozsb9+7dg6enJ06fPl3i64IgYO/evWjevDk2bdqE\n7du34/DhwyxBSaNwRiipu5CQELi7u8PV1RUXLlyociUooDmHJf1bw4YNceTIEYwbNw6hoaFixyEq\nt99//x0uLi5o3bq1UsYzNjaGn58fT48nogphEUpERCUkJSWhQ4cOePXqVbFl8GWRlZWFTz75BFev\nXlU8durUKbRu3Rrz5s3DTz/9hLNnz8LT01NZsYkqDYtQUld5eXmYOHEiRo0ahe3bt2PRokUwMDAQ\nO5YoNOWwpDf54IMPsHPnTgQEBCA2NlbsOERllp+fj4ULFyIwMFCp4/L0eCKqKBahRERUjCAIGDp0\nKDIyMio8VnZ2Nvr06YPw8HB4e3tjzJgx+O677xAVFQU/P78qs08daR9ra2skJSWJHYOomPj4eLRu\n3RoPHz5EbGwsvLy8xI4kKk1cGv+6Tp06Ye3atejevTvu3bsndhyiMtm+fTvs7e3Rrl07pY7r5+eH\nqKgoPH/+XKnjElHVwSKUiIiKOXz4MMLDw5Gfn1/hsQRBwJ07d+Dn54f+/fsjPj4eAQEB0NHhtx/S\nbFZWVnj+/DkEQRA7ChEEQcAvv/yCjh07Yty4cdi9ezcsLCzEjiU6TTss6U369OmD6dOnw9fXF8nJ\nyWLHISqVgoICLFy4EDNnzlT62EXL4/ft26f0sYmoauBvokREVMyCBQvKtCfo+xQWFsLY2BgjR46E\nvr6+0sYlEpOxsTEMDAyQlpYmdhSq4pKTk9G7d29s3LgR58+fx8iRIznb/v9o+ozQImPHjkW/fv3Q\nvXt3ZGZmih2H6L3+/PNP1K1bFx07dlTJ+FweT0QVwSKUiIgUHj58iLi4OKWPm52djXPnzil9XCIx\ncZ9QEtvJkyfh5uaGJk2aICIiAs7OzmJHUiuaeljSm8ybNw/NmzdHnz59lLJig0hVCgsLMX/+fJXM\nBi3i5+eHv/76i7OkiahcWIQSEZHCxYsXVTJrMycnB+Hh4Uofl0hMLEJJLHl5eZg8eTJGjBiBrVu3\nIigoqMoeiPQ2giAgJSVFa7YIkEgk+PXXX6Gvr48vvviC23KQ2tq1axcsLCzQuXNnld3D2NgY3bp1\n4/J4IioXFqFERKRw+fJllSy7Kygo4IxQ0josQkkMiYmJaNOmDe7cuYO4uDh4e3uLHUktpaWlwcTE\nRKsKYj09Pfzvf//D7du38cMPP4gdh6gEuVyumA2q6i06uDyeiMqLRSgRESk8ffpUZbNMuHyJtA2L\nUKpMgiDg119/Rfv27TFmzBjs3btX4w8CUiVtOCjpTUxMTHDo0CEcPHgQK1asEDsOUTH79u2DiYkJ\nfH19VX4vPz8/XL58GS9evFD5vYhIu+iJHYCIiNSHnp7qvi3wpHjSNtbW1ixCqVK8ePECI0eOxMOH\nDxEWFoYmTZqIHUntactBSW9Sq1YthISEoF27drC2tkZAQIDYkYggl8sxd+5cLFiwoFIObCsqXPft\n24dRo0ap/H5EpD34WykRESk4ODiorAx1cHBQybhEYrGyskJSUpLYMUjLSaVSuLm5wcnJCRERESxB\nS0mbDkp6kwYNGuDo0aOYMGECTp06JXYcIhw6dAi6urro3r17pd2Ty+OJqDxYhBIRkULLli1hYmKi\n9HGNjY3Rvn17pY9LJCYujSdVkslkmDp1KoYNG4bNmzdj6dKlMDQ0FDuWxnjx4oVWLo1/XfPmzbFr\n1y4MHDgQ0dHRYsehKkwQBMydOxeBgYGVMhu0yMcff4zIyEgujyeiMmERSkRECm3atIFMJlPJ2F5e\nXioZl0gsLEJJVW7cuIH//Oc/uHHjBmJjY9G1a1exI2kcbV4a/7oOHTpg3bp16NGjB+7cuSN2HKqi\njh07hvz8fPTq1atS7/v68ngiotJiEUpERAo1atRA7969lb6fZ7169eDi4qLUMYnExiKUlE0QBGzY\nsAGenp4YNWoU9u/fXyXKPFXQ1sOS3uSzzz7DzJkz0a1bNzx//lzsOFTFvD4bVIz94Lk8nojKikUo\nEREVM336dBgYGChtPAMDA2RnZ6Nz586IiIhQ2rhEYisqQgVBEDsKaYGUlBT4+/tjzZo1OHfuHEaP\nHl2pS0y1TVWZEVpk9OjRGDBgAD7++GNkZmaKHYeqkJMnTyI9PR3+/v6i3J/L44morFiEEhFRMWlp\naTAwMFDKoUkGBgbo0aMHHjx4gEGDBqF///745JNPEBcXp4SkROIyNTWFrq4uMjIyxI5CGu706dNw\nc3ODnZ0dIiMj0bRpU7EjaTxtPyzpTebMmQMPDw/4+/urbJsbotcVzQadMWOGKLNBgX++F/v4+GD/\n/v2i3J+INA+LUCIiAgAUFBRg9uzZ6NOnDzZv3gwPD48KHcyhp6cHa2trbNiwAXp6evjiiy9w8+ZN\ndO3aFd26dUNAQABu3rypxFdAVPm4PJ4qQiaT4YcffsDgwYOxceNGLF++nAciKUlVOCzp3yQSCdau\nXQsjIyN8/vnnkMvlYkciLXfmzBkkJyejf//+oubg8ngiKgsWoUREhLt376JDhw6IiIhAdHQ0Pvvs\nM0ilUri5uZXrFHljY2PUr18fFy9ehIWFheJxIyMjfPPNN7h16xY+/PBDtGvXDl988QUePHigzJdD\nVGmsra2RlJQkdgzSQDdv3kTbtm1x/fp1xMbGwtfXV+xIWqWqLY0voqenh+DgYNy7dw/ff/+92HFI\ny82dOxfTp0+Hrq6uqDm6d++OixcvIiUlRdQcRKQZWIQSEVVhgiBg27ZtaN26Nfr164djx47BxsYG\nAGBmZoawsDBMnToVxsbGpVoqr6OjAxMTEwwZMgRXr15VjPVvZmZm+PHHH3Hr1i3Y2NjAw8MD33zz\nDQsl0jicEUplJQgCfvvtN7Rr1w4jRozAwYMHUadOHbFjaZ2qdFjSv5mYmODQoUM4evQoli9fLnYc\n+n/s3XdUlOf6NeA9oCCIjSBg1MSGPaHZsGFDQEBEAcVuYgdRY4s9CvaoiBV7jbEiKoKgCCgogg52\nBYxJTBR7QZAizPfH+cmXxIY6M8+Ufa111jpH4J09OQSHPff9PhoqPj4et2/fRu/evUVHQdmyZeHg\n4MD1eCIqERahRERa6unTp+jTpw/mz5+PY8eOYcyYMW/c36l06dKYOXMmUlNTMWjQIBgYGKB8+fIo\nU6ZM8efo6ekV/1n37t0RFxeHkJAQlC1b9oMZKlasiMDAQFy7dg26urpo2KQUpwAAIABJREFU1KgR\nJk+ejMePH8v9+RIpAotQ+hhPnjyBt7c3li1bhtjYWPj6+vJAJAWQyWRaXYQCgLGxMSIjIxEUFIQd\nO3aIjkMaKCAgAJMnT5bLPeXlgevxRFRSLEKJiLTQqVOnYGVlBWNjY6SkpMDS0vK9n1+3bl2sXbsW\njx49QmRkJBYtWgQHBwdYWlpi7ty5CAsLw71797Bnzx40adLko/OYmppi6dKlSE1NxaNHj1C3bl0E\nBgbyEBpSeSxCqaTi4uJgaWmJqlWr4uzZs2jUqJHoSBorKysLZcqU0fr7rVavXh0RERH44YcfEBUV\nJToOaZDTp08jPT0d/fr1Ex2lmIuLC06fPs0304nog1iEEhFpkYKCAkyfPh1eXl5YsWIFVqxYAQMD\ngxJ/vYGBAezs7ODn5wcPDw+0aNEC48aNQ7t27VC+fPnPzle9enWsXbsWp0+fxrVr12BhYYGlS5ci\nNzf3s69NpAgsQulDCgoKMGXKFPj4+CAkJARBQUH/mqon+dPGg5LepVGjRti3bx/69u2Lc+fOiY5D\nGuL1NKienp7oKMWMjIzQqVMnrscT0QexCCUi0hI3b95EmzZtkJKSAqlUCldX18+6niLXOS0sLLBj\nxw5ER0cjLi4OFhYWWLt2LQoKChT2mESfgkUovU9GRgZatWqFCxcuQCqVwtnZWXQkraCtByW9S+vW\nrbF27Vq4ubkhIyNDdBxSc8nJybh06RIGDhwoOsobvL29uR5PRB/EIpSISMPJZDJs2bIFLVq0QO/e\nvREeHg5zc3O5XVuRvvnmGxw4cAB79+7Fnj170KBBA+zYsQOFhYUKfVyikmIRSm8jk8mwefNm2NnZ\noV+/fjh8+DDMzMxEx9Ia2n5/0Lfp1q0bfvrpJzg5OfFnFn2WgIAATJo0SSVvPeHi4oLExESuxxPR\ne7EIJSLSYE+ePIGPjw8WLVqEmJgY+Pv7v3Eg0qdS5gEfzZs3R3R0NNatW4eVK1fCysoKBw4cUHgR\nS/QhLELpv548eYJevXph8eLFiImJwahRo3ggkpJxIvTthg4din79+qFLly68Bzd9EqlUipSUFAwe\nPFh0lLd6vR4fFhYmOgoRqTAWoUREGio+Ph5WVlYwNTVFcnIyvvnmG7k/hrKLyPbt2yMhIQHz58/H\nTz/9VFyQshAlUczNzZGZmcnvQQLw75+7Z8+eVcjPXfqwR48esQh9hxkzZqBJkybo3r078vPzRcch\nNRMYGIiJEyeq9H2OeXo8EX0Ii1AiIg1TUFCAqVOnolevXli9ejWCg4M/6kCkkhI14SSRSODi4oLz\n589j/Pjx8PPzKy5IiZTNyMgIEokEL168EB2FBCooKMC0adPQs2dPrFq1CsuXL1fIz10qGR6W9G4S\niQSrVq2CkZERBg4ciKKiItGRSE1cunQJiYmJGDp0qOgo7+Xq6oqEhAQ8efJEdBQiUlEsQomINMjr\ngzlSU1MhlUrRpUsXhT6eyCk4HR0deHt748qVKxgwYAB69+4NFxcXSKVSYZlIO3E9Xru9Poju3Llz\nkEqlcHFxER1J63E1/v10dXXxyy+/4Pbt2xg/fjwn2qlEAgMD8cMPP8DQ0FB0lPcyMjJCx44duR5P\nRO/EIpSISAPIZDJs2rQJdnZ26N+/v1IO5lCVe96VKlUKgwYNQlpaGpydndGlSxd4e3vj+vXroqOR\nlmARqp1kMhm2bt2KFi1awMfHR64H0dHn4WFJH2ZgYICDBw8iKioKixcvFh2HVNy1a9dw4sQJjBgx\nQnSUEuF6PBG9D4tQIiI19+TJE/Ts2RNLly7FiRMn4Ofnp7SSUpWmSPT19eHn54eMjAzY2NigTZs2\nGDRoEH7//XfR0UjDsQjVPk+fPkXv3r2xYMECHD9+HKNHj5bbQXT0+TgRWjKVKlVCZGQkli9fjm3b\ntomOQypszpw5GDt2LIyMjERHKRFXV1ecPHmS6/FE9FZ8xUZEpMZiY2NhaWmJL7/8EmfPnkXjxo2V\n9tiqMhH6X2XLlsWPP/6I9PR0VKtWDba2tvDz88Pdu3dFRyMNxSJUu5w6dQpWVlYwNjZGSkoKvv32\nW9GR6D94WFLJVatWDRERERg/fjwiIyNFxyEVlJaWhqNHj8LX11d0lBIrV64c1+OJ6J1YhBIRqaH8\n/HxMnjwZffr0wdq1axEUFCTkBE9Vmgj9r4oVKyIgIADXrl2Dnp4eGjdujEmTJuHRo0eio5GGYRGq\nHV69eoUZM2bA09MTy5cvx8qVK3kgkoriYUkfp2HDhggNDUW/fv2QnJwsOg6pmLlz52LUqFEoX768\n6CgfhevxRPQuLEKJiNRMWloaWrVqhcuXL0MqlcLJyUlIDlWdCP0vU1NTLFmyBBcuXMDTp09Rr149\nzJ49G1lZWaKjkYYwNzdnEarhbt26hbZt2yIpKQlSqRRubm6iI9E7yGQyFqGfoGXLltiwYQO6du2K\n9PR00XFIRfz22284fPgw/P39RUf5aG5ubjh58iSePn0qOgoRqRgWoUREakImk2HDhg1o1aoVBg0a\nhIMHD8LU1FR4JnVRrVo1hISEICkpCenp6ahTpw4WL16Mly9fio5Gas7MzAyZmZmiY5CCbN++Hc2a\nNYOXlxciIiJQpUoV0ZHoPV68eAE9PT0hWxLqrmvXrggICICTkxN/phEAYN68eRg5ciQqVqwoOspH\nK1euHDp06MD1eCJ6A4tQIiI18PjxY3h5eSE4OBixsbEYOXKk8IlM0Y//qWrXro1t27bh+PHjSEhI\ngIWFBdasWYP8/HzR0UhNcTVeMz179gx9+vTB3LlzER0djbFjx/JAJDXAg5I+z+DBgzFw4EA4Ozvj\n+fPnouOQQH/88Qf279+PMWPGiI7yybgeT0Rvw1dzREQqLiYmBpaWlvjqq69w9uxZNGrUSHSkYuo0\nEfpfjRs3xv79+xEaGorQ0FDUr18f27ZtQ2FhoehopGZYhGqexMREWFlZoXz58khJSYGVlZXoSFRC\nPCjp802bNg12dnbw8PBAXl6e6DgkyPz58zFs2DAYGxuLjvLJ3NzcEB8fz/V4IvoXFqFERCoqPz8f\nkyZNQr9+/bBhwwYsWbIE+vr6omMVU9eJ0P9q2rQpjh49ik2bNiEkJATffvst9u/fr9YlLykXi1DN\n8erVK/z000/o3r07goKCsHr1ahgaGoqORR+B9wf9fBKJBMuXL0fFihUxYMAAFBUViY5ESnb79m3s\n2rULY8eOFR3ls5QvXx7t27fHwYMHRUchIhXCIpSISAXduHEDdnZ2uH79OlJTU9G5c2fRkd5Kk8pC\ne3t7nDx5EosWLUJgYGBxQapJz5EUo1y5cigsLER2drboKPQZfv/9d9jb2yMhIQHnz5+Hu7u76Ej0\nCbgaLx+6urrYsWMH7t69ix9++IF/F2qZhQsX4vvvv0flypVFR/lsXI8nov9iEUpEpEJkMhnWrVuH\n1q1bY8iQIThw4IDKvgjVlInQf5JIJOjSpQtSUlIwadIkjBkzprggJXoXiUTCqVA198svv6BZs2bo\n3r07jh49ii+//FJ0JPpEjx494kSonJQpUwZhYWE4fvw4Fi5cKDoOKcndu3exY8cOjB8/XnQUuXBz\nc0NcXByePXsmOgoRqYhSogMQEdH/PHr0CEOGDMGtW7cQHx+PBg0aiI70QZo6IaKjowMvLy94eHhg\n+/bt6N+/P+rXr4/AwEDY2tqKjkcq6HURWqtWLdFR6CM8f/4cvr6+SE5ORmRkJGxsbERHos/EiVD5\nqlixIiIjI9GqVSuYm5tjwIABoiORgi1atAgDBgyAmZmZ6ChyUaFCBbRr1w4HDx5Ev379RMchIhXA\niVAiIhVw7NgxWFpaolatWjhz5oxalKCaOBH6X6VKlcLAgQNx/fp1uLq6ws3NDZ6enrh69aroaKRi\nzM3NkZmZKToGfYTTp0/DysoKhoaGOHfuHEtQDcHDkuSvatWqiIiIwKRJkxARESE6DinQvXv3sHnz\nZkyYMEF0FLny9vbmejwRFWMRSkQkUF5eHiZMmICBAwdi8+bN+Pnnn1XqQKQP0dSJ0P/S19eHr68v\nMjIy0KxZM7Rr1w4DBgzArVu3REcjFcHVePVRWFiIgIAAdOvWDYsXL0ZISAjKli0rOhbJCQ9LUowG\nDRogNDQU/fv3R1JSkug4pCCLFy9G7969Ne72IFyPJ6J/YhFKRCTI9evXYWdnh/T0dKSmpqJTp06i\nI30UbZgI/S9DQ0NMnDgR6enpqFGjBpo0aYKRI0fizp07oqORYCxC1cMff/yBdu3aITY2FufPn4eH\nh4foSCRnXI1XHDs7O2zatAndunVDWlqa6DgkZw8fPsSGDRswadIk0VHkrkKFCrC3t8ehQ4dERyEi\nFcAilIhIyWQyGUJCQtCmTRsMHz4coaGhavtLm7ZMhP5XhQoVMGvWLNy4cQNly5bFN998gwkTJuDh\nw4eio5EgLEJV36+//oqmTZuia9euiI6ORtWqVUVHIgXgYUmK5erqijlz5sDR0RF3794VHYfkaOnS\npfDy8kL16tVFR1EInh5PRK+xCCUiUqKHDx/Cw8MDa9euxcmTJzF06FC1naxU19zyZGJigkWLFuHi\nxYvIzs5G/fr1MWvWLDx//lx0NFIyFqGqKysrCwMHDsTMmTMRERGBCRMmQEeHL4E1FSdCFe+7777D\n4MGD4ezszFVjDfH48WOsWbMGP/74o+goCtO1a1fExsbyNRoRsQglIlKW6OhoWFlZoV69ejh9+jTq\n168vOtJn09aJ0P+qWrUqVq1ahbNnz+K3336DhYUFFi1ahJycHNHRSElYhKqmpKQkWFtbo3Tp0jh/\n/jxsbW1FRyIFkslknAhVkilTpqB169bo1q0b8vLyRMehz7Rs2TJ069YNNWrUEB1FYSpUqIC2bdty\nPZ6IWIQSESlaXl4exo0bh++++w5bt27FggULoKenJzrWZ+NE6Jtq1aqFLVu24MSJE0hKSoKFhQVW\nrVqF/Px80dFIwViEqpbCwkLMmTMHXbt2xYIFC7Bu3ToeiKQFsrOzoaurCwMDA9FRNJ5EIsGyZctg\nYmKCfv36obCwUHQk+kTPnj3DypUrMWXKFNFRFI7r8UQEsAglIlKoq1evonnz5rh16xZSU1PRoUMH\n0ZHkihOhb9ewYUPs3bsXBw8exKFDh1C/fn1s2bKFvyhqMBahquP27dvo0KEDjh07hnPnzqFHjx6i\nI5GScC1euXR1dbFt2zbcv38fY8aM4WsCNbV8+XK4uLigdu3aoqMoXNeuXRETE8P1eCItxyKUiEgB\nZDIZVq1aBXt7e/j5+WHfvn0at6rHidAPs7W1RUREBLZs2YL169fjm2++wd69e1FUVCQ6GslZhQoV\nkJ+fz9shCLZnzx7Y2trC2dkZx44dQ7Vq1URHIiXiWrzylSlTBmFhYYiPj8f8+fNFx6GPlJWVhWXL\nlmnFNCgAVKxYkevxRMQilIhI3h48eAB3d3ds3LgRCQkJGDx4sMaWhpz+KJk2bdogPj4eS5Yswbx5\n89C0aVNERETwn58GkUgknAoV6MWLF/juu+8wZcoUhIeH48cff4Surq7oWKRknAgVo0KFCoiIiMDa\ntWuxadMm0XHoI6xcuRIODg6oV6+e6ChKw/V4ImIRSkQkR0ePHoWVlRUaNWqExMRE1K1bV3QkhdHU\ncldRJBIJnJyckJKSgilTpmDcuHFo27Yt4uPjRUcjOWERKkZycjKsra0hkUgglUrRtGlT0ZFIkEeP\nHrEIFeTLL79EZGQkJk+ejPDwcNFxqASys7OxdOlSTJ06VXQUpXJ3d+d6PJGWYxFKRCQHubm5GDt2\nLIYMGYLt27dj3rx5GnEg0odwovHjSSQS9OjRA5cuXcKQIUMwcODA4oKU1BuLUOUqLCzE/Pnz4erq\nirlz52LDhg0wMjISHYsEevjwIVfjBapXrx7CwsIwcOBAnDlzRnQc+oA1a9bA3t4ejRo1Eh1FqSpW\nrIg2bdrg8OHDoqMQkSAsQomIPtOVK1fQvHlz/PXXX0hNTUX79u1FR1IKToR+Hl1dXfTv3x/Xr1+H\nu7s73N3d0aNHD1y5ckV0NPpELEKV56+//kKnTp0QERGBlJQUeHl5iY5EKoCr8eI1b94cmzdvRrdu\n3XD9+nXRcegdcnJy8PPPP2PatGmiowjB9Xgi7cYilIjoE8lkMqxYsQLt2rXD6NGjsXv3bhgbG4uO\npVScCP18enp6GDFiBDIyMmBnZ4cOHTqgf//++O2330RHo4/EIlQ59u3bB1tbWzg4OCAmJgbVq1cX\nHYlUBA9LUg0uLi6YP38+nJ2dcefOHdFx6C3WrVuHFi1a4NtvvxUdRQh3d3ccP34cWVlZoqMQkQAs\nQomIPsH9+/fh6uqKLVu2IDExEd99953WTUhq2/NVNAMDA4wfPx7p6emoU6cOmjVrhhEjRuDvv/8W\nHY1KiEWoYr148QKDBw/GpEmTcOjQIUyZMoUHItG/cCJUdQwcOBDDhg2Dk5MTnj59KjoO/UNubi4W\nLlyI6dOni44iTKVKldC6dWuuxxNpKRahREQfKSIiAlZWVrC0tERiYiIsLCxERxKGE6HyV758ecyY\nMQM3btxA+fLl8e2332L8+PF4+PCh6Gj0ASxCFefcuXOwsbFBYWEhpFIpmjVrJjoSqSAelqRaJk2a\nhHbt2qFbt27Izc0VHYf+z8aNG2FjYwMbGxvRUYTiejyR9mIRSkRUQrm5ufD398ewYcOwc+dOzJ07\nF6VLlxYdSxhOhCrWF198gQULFuDy5cvIzc1FvXr1MGPGDDx79kx0NHoHc3NzZGZmio6hUYqKirBw\n4UI4OzsjICAAmzZtQrly5UTHIhXFw5JUi0QiQVBQEMzMzNC3b18UFhaKjqT18vLyMH/+fK2eBn3t\n9Xr8ixcvREchIiVjEUpEVAKXLl1C06ZNkZmZiQsXLsDe3l50JJXAiVDFq1KlClasWIFz587h9u3b\nsLCwwIIFC5CTkyM6Gv0HJ0Ll6++//4aDgwMOHTqE5ORk9OzZU3QkUnFcjVc9Ojo62Lp1Kx4/fgx/\nf3++bhBsy5YtaNiwIafqARgbG6Nly5ZcjyfSQixCiYjeQyaTITg4GB06dMC4ceOwa9cuVKpUSXQs\nlcCJUOWqUaMGNm3ahLi4OJw7dw516tTBihUrkJeXJzoa/R8WofITGhoKGxsbtG/fHrGxsfj6669F\nRyIVJ5PJeFiSitLX10doaCgSEhIwd+5c0XG0VkFBAebOnYsZM2aIjqIyuB5PpJ1YhBIRvcO9e/fQ\npUsX7NixA6dPn8bAgQNZ/v0HJzuUr0GDBti9ezfCw8MRERGBevXqYdOmTXj16pXoaFqvYsWKyM3N\n5b3wPkN2djaGDRuG8ePHIywsDNOmTeOBSFQiOTk5kEgkMDQ0FB2F3qJChQqIiIjAhg0bsGHDBtFx\ntNK2bdtQp04dtGzZUnQUldGtWzccO3aM6/FEWoZFKBHRW4SHh8PKygq2trY4deoU6tSpIzqSymEp\nLJa1tTXCw8OxY8cObN68GY0bN8bu3btRVFQkOprWkkgkMDU15VToJ5JKpbC1tcXLly8hlUrRokUL\n0ZFIjfCgJNVXpUoVREZGYtq0aTh06JDoOFrl1atXnAZ9i9fr8eHh4aKjEJESsQglIvqHly9fws/P\nDyNHjsSuXbsQGBio1QcifQgnQsVr1aoVYmNjERwcjEWLFsHW1hbh4eH8/0YQrsd/vKKiIvz8889w\ndHTEzJkzsXXrVpQvX150LFIzPChJPdStWxdhYWH47rvvkJiYKDqO1ti5cyeqVauGtm3bio6icrge\nT6R9WIQSEf2fixcvokmTJnj48CEuXLjAF4sfwIlQ1SGRSNC5c2ecPXsWM2bMwKRJk9C6dWvExsaK\njqZ1WIR+nDt37sDR0REHDhzA2bNn4ePjIzoSqSkelKQ+mjVrhm3btqF79+64du2a6Dgar7CwEIGB\ngTwp/h26deuG6OhoZGdni45CRErCIpSItF5RURGCgoLQsWNHTJo0CTt37kTFihVFx1ILnDpULRKJ\nBB4eHrhw4QJGjBiB77//Hp07d0ZycrLoaFrD3NycRWgJhYWFwcbGpri0r1GjhuhIpMZ4UJJ6cXJy\nwsKFC+Hk5IS///5bdByNtnv3bpiYmKBDhw6io6gkY2Nj2NnZcT2eSIuUEh2AiEiku3fvYtCgQXj2\n7BnOnDmD2rVri46kNjgRqrp0dXXRt29f9OzZE5s2bYKHhweaNm2KgIAANG7cWHQ8jWZmZobMzEzR\nMVRaTk4Oxo0bh6NHj2L//v08uIPkghOh6qd///7IzMyEk5MT4uPjUalSJdGRNE5RURECAwOxZMkS\nvm57j9fr8d7e3qKjEJEScCKUiLTWoUOHYGNjg2bNmiE+Pp4lKGmc0qVLY+jQoUhPT0fbtm3RsWNH\n9O3bFxkZGaKjaSyuxr9famoqmjRpgqysLEilUpagJDc8LEk9TZgwAR07doS7uztevnwpOo7G2b9/\nP4yMjNC5c2fRUVRat27dEBUVxfV4Ii3BIpSItE5OTg5GjhwJf39/7NmzB7Nnz+aBSJ+Iq/HqwcDA\nAGPHjkVGRgbq16+PFi1aYOjQobh9+7boaBqHRejbFRUVYcmSJXBwcMCUKVOwfft2VKhQQXQs0iA8\nLEk9SSQSLFmyBFWrVkWfPn1QWFgoOpLGKCoqQkBAAKZPn85p0A/44osv0KJFC67HE2kJFqFEpFVe\nTyM9ffoUUqkUrVu3Fh1JbfFFtfopV64cpk2bhrS0NHzxxRewsrLC2LFjcf/+fdHRNAaL0DfdvXsX\nzs7O2Lt3L5KSktC3b1/RkUgDcTVefeno6GDz5s14/vw5/Pz8+CarnBw8eBC6urpwcXERHUUt8PR4\nIu3BIpSItMJ/p5F++eUXHogkB/xlRT0ZGxtj3rx5uHLlCgoLC9GgQQNMmzYNT58+FR1N7bEI/bdD\nhw7B2toaLVq0QHx8PGrVqiU6EmkoHpak3vT19bF//34kJSUhICBAdBy1J5PJEBAQgBkzZvCN6xLi\nejyR9mARSkQa786dO3BycsLevXtx9uxZTiPJCV9Yqz9zc3MEBwfj/PnzuHv3LiwsLDBv3jz+EvAZ\nWIT+z8uXL+Hr6wt/f3/s3bsXs2bNQqlSPKOTFIcToeqvfPnyOHLkCLZs2YJ169aJjqPWjhw5glev\nXqFr166io6gNExMTNG/eHEeOHBEdhYgUjEUoEWm0sLAw2NjYoFWrVoiPj0fNmjVFR9IonAjVDF9/\n/TU2bNiAkydPIjU1FXXq1EFwcDDy8vJER1M7lSpVQnZ2tlb/s7t48SKaNGmCx48f8xYkpDScCNUM\n5ubmiIyMxIwZMxAWFiY6jlqSyWSYPXs2pk2bBh0d/rr/MbgeT6Qd+JORiDRSTk4Ohg8fjrFjx2L/\n/v2YOXMmp5HkjBOhmqd+/frYtWsXIiIiEBUVhbp162LDhg149eqV6GhqQ0dHB6amplo5FVpUVISg\noCB07NgRkyZN4i1ISKk4Eao5LCwscOjQIQwZMgQJCQmi46idqKgovHjxAj169BAdRe14eHjg6NGj\nyMnJER2FiBSIRSgRaRypVAobGxtkZ2dDKpWiZcuWoiNpLE6EaiYrKyscPnwYO3fuxPbt29GoUSP8\n+uuvKCoqEh1NLWjjenxmZia6dOmCX3/9FWfOnEH//v35ZgkpTU5ODmQyGQwNDUVHITlp0qQJtm3b\nhu7du+PKlSui46gNToN+HhMTEzRr1ozr8UQajj8diUhjFBUV4eeff0bnzp0xY8YMbNu2DRUqVBAd\nS2Ox5NB8LVu2RExMDFauXIklS5bA2toahw4dYgH+AdpWhIaHh8Pa2hpNmzbFyZMnUbt2bdGRSMu8\nXovn30uaxdHREYsXL4azszNu374tOo5aOHHiBB4+fAhvb2/RUdQW1+OJNB/3RIlII/z9998YMGAA\ncnNzkZycjBo1aoiOpBVYiGk+iUSCTp06oWPHjjh48CCmTJmCuXPnYs6cOejQoYPoeCpJW4rQly9f\nYuLEiTh48CB2796NNm3aiI5EWopr8Zqrb9++yMzMhJOTE06dOoVKlSqJjqTSZs+ejalTp0JXV1d0\nFLXl4eGBCRMmICcnh1PmRBqKE6FEpPZCQ0NhY2MDe3t7xMbGsgRVEk7eaBeJRAJ3d3ekpqZi1KhR\nGDZsGDp16oSkpCTR0VSONhShly5dQrNmzXD//n2kpqayBCWheFCSZhs/fjycnJzQtWtXvHz5UnQc\nlRUXF4e//voLvXv3Fh1FrVWuXBlNmzZFRESE6ChEpCAsQolIbWVnZ2Po0KEYP348Dhw4gOnTp/NA\nJCXjRKj20dXVRe/evXH16lX07NkTnp6e6Nq1Ky5evCg6msrQ5CJUJpMhODgYHTp0wLhx4/Drr79y\nQouE40So5lu0aBG++uor+Pj48AC/dwgICMCUKVP4WlgOuB5PpNlYhBKRWjp37hxsbGyQl5cHqVQK\nOzs70ZG0DidCtVvp0qUxZMgQpKeno0OHDujcuTN8fHyQlpYmOppwmlqE3r9/H66urti+fTtOnz6N\ngQMH8ucAqQQWoZpPR0cHmzZtQk5ODnx9fflG7H8kJibi5s2b6Nevn+goGsHDwwORkZGcQCbSUCxC\niUitFBUVYeHChXB2dsasWbOwZcsWlC9fXnQsrcVfRKhMmTIYM2YMMjIy0LhxY7Rq1QqDBw/Gn3/+\nKTqaMJpYhEZERMDKygpWVlZISEhAnTp1REciKsbVeO2gp6eHffv2ISUlBbNmzRIdR6UEBARg8uTJ\nKF26tOgoGsHU1BRNmjThejyRhmIRSkRq46+//kKnTp1w+PBhJCcno1evXqIjaTVOgtE/GRkZYerU\nqUhLS4OpqSmsra0xevRojSsES8Lc3ByZmZk4fvw4PDw8UKVKFZQpUwZVq1aFk5MTIiMjRUcssdzc\nXIwePRrDhg3Dzp07MWfOHP6iTSqHE6Hao1y5cjhy5Ai2b9+ONWvC9XFMAAAgAElEQVTWiI6jEs6e\nPYsrV65gwIABoqNoFK7HE2kuFqFEpBb27dsHW1tbdOzYESdOnMDXX38tOhKBE6H0pkqVKmHu3Lm4\nevUqAKBhw4aYMmUKnjx5IjiZ8piZmeHWrVtwcHDA+fPn4e7ujvHjx8PV1RUPHz5EbGys6Iglcvny\nZTRr1gx37txBamoq7O3tRUcieitOhGoXMzMzHD16FLNnz0ZoaKjoOMIFBARg0qRJ0NfXFx1Fo3h4\neCAiIoLr8UQaiHdSJiKV9uLFC4wZMwaxsbE4ePAgmjdvLjoS/R9OhNL7mJmZYdmyZRg3bhxmz56N\nunXrYsyYMRg9ejSMjIxEx1Ooffv2IS8vDwMHDsS6deveOLiisLBQULKSkclkWLlyJWbNmoUFCxZg\n0KBB/PedVBonQrVP7dq1cejQITg7O8PExARt2rQRHUkIqVSK8+fPc3JRAUxNTWFra4vIyEh4eHiI\njkNEcsSJUCJSWcnJybCxsUFhYSGkUilLUBXEiVD6kK+++grr169HQkICLl++jDp16iAoKAi5ubmi\noylEfn4+ZsyYAV1dXcycOfOtp/fq6uoKSFYyDx48QNeuXbFlyxYkJibiu+++YwlKKo9FqHaytbXF\njh074OnpicuXL4uOI0RAQAAmTpyIMmXKiI6ikbgeT6SZWIQSkcopLCzEvHnz4OLigsDAQGzatAnl\nypUTHYv+g+UIfYy6deti586dOHr0KGJiYlC3bl2sX78eBQUFoqPJVXR0NB48eABjY2Pcv38f4eHh\nWLhwIYKDg3HmzBnR8d7r6NGjsLKyQuPGjZGQkAALCwvRkYhKhKvx2svBwQFLly5Fly5dtO6QvosX\nL+L06dMYMmSI6Cgaq3v37jhy5AjX44k0DFfjiUil3L59G/369YNMJkNKSgq++uor0ZHoPTgRSh/L\n0tISBw8exJkzZzB16lQsWLAAs2fPRs+ePaGjo/7vzyYnJ0MikcDIyAg9e/bEH3/8UfymgUwmQ9u2\nbbF3716Vml7Ly8vDjz/+iL1792L79u1o37696EhEH4UTodqtd+/eyMzMhJOTE06dOgVjY2PRkZQi\nMDAQ48aNg6GhoegoGsvU1BQ2NjZcjyfSMOr/GwcRaYw9e/bA1tYWjo6OiImJYQmq4jgRSp+jRYsW\nOH78OEJCQhAcHAwrKyscPHhQ7cv1+/fvQyaT4ffff0dhYSESEhKQlZWFixcvwtHREfHx8fD29hYd\ns9jVq1fRvHlz/Pnnn0hNTWUJSmrn5cuXePXqFcqWLSs6Cgn0ww8/wMXFBa6ursjJyREdR+GuXr2K\nuLg4DB8+XHQUjcf1eCLNwyKUiITLysrCd999hylTpiA8PByTJ09W6Xvo0f+n7qUVidehQwckJiZi\nzpw5mD59enFBqq6KiooA/O8+oL1794adnR0MDQ3RqFEj7N+/H9WqVUNcXBySkpKE5pTJZFi9ejXs\n7e3h5+eHvXv3crWY1NKjR49gYmLCN+cICxYsQO3atdGrVy+8evVKdByFmjNnDsaOHavxhw+qAq7H\nE2keFqFEJNTZs2dhY2MDiUQCqVSKpk2bio5EJcRfOkleJBIJ3NzcIJVKMXbsWIwYMQIdOnTA6dOn\nRUf7aBUrVgQAVKtW7Y0DoQwMDODo6Ajgfz/7RHn48CG6deuG9evX49SpUxg8eDD/fSa1xbV4ek1H\nRwcbNmxAXl4ehg8frrFv1t64cQNRUVHw9fUVHUUrmJmZwdraGkePHhUdhYjkhEUoEQlRWFiIOXPm\nwM3NDfPmzcOGDRv4rrYa0tRfMkgMHR0d9OrVC1evXkWfPn3Qq1cvuLm54cKFC6KjlVi9evUAABUq\nVMC9e/fe+HilSpUAQNhkSXR0NCwtLVG/fn2cPn26OC+RuuJBSfRPenp62LdvHy5cuICZM2eKjqMQ\nc+fOhb+/Pw8SVSKuxxNpFhahRKR0f/75J9q3b49jx44hJSUFnp6eoiPRJ+AEGSlKqVKl8P333yMt\nLQ0ODg5wcnJCr169cOPGDdHRPqhjx46QSCS4d+/eW4vQy5cvAwBq1qyp1Fx5eXkYP348Bg0ahK1b\nt2LBggXQ09NTagYiReBEKP2XkZERwsPDsXPnTqxevVp0HLm6efMmwsPDMWrUKNFRtEr37t0RHh7+\nxqYHEaknFqFEpFS7du1CkyZN0KVLFxw7dgzVq1cXHYk+AydCSZH09fXh7++PjIwMWFpaonXr1vj+\n++/xxx9/iI72Tl999RXc3Nxw//59XL169V8fi4qKwtGjR1GpUiU4OTkpLdP169fRokUL3Lx5Excu\nXEDHjh2V9thEisaJUHobU1NTHD16FIGBgdi/f7/oOHIzb948+Pr6Ft+GhZTD3NwcVlZWXI8n0hAs\nQolIKbKysjBgwABMnz4dR44cwY8//sgDkdQcJ0JJWcqWLYvJkycjPT0dVapUgY2NDfz9/ZGZmSk6\n2lutXLkSVatWxb179+Dg4ICJEyfC09MTLi4uKFWqFNavX6+UlUaZTIaQkBC0adMGI0aMwP79+1kY\nkcbhRCi9S61atXD48GEMHz4ccXFxouN8tt9//x2hoaEYPXq06ChaievxRJqDRSgRKdyZM2dgZWUF\nPT09nD9/Hk2aNBEdieSEE6GkTBUrVkRgYCCuXbsGXV1dNGrUCJMnT8bjx49FR/uXqlWrIiUlBRKJ\nBBkZGQgODkZ8fDzc3d2RkJCAbt26KTzDo0eP0L17d6xZswYnT57E0KFD+eYFaSQWofQ+1tbW+OWX\nX+Dl5YVLly7J/fr79u2Dv78/2rZtiwoVKkBHRwf9+/d/5+e/ePECU6dORYMGDWBgYABjY2M4OTkh\nJibmg481f/58DBs2DMbGxvJ8ClRCPXr04Ho8kYZgEUpEClNYWIiAgAC4u7tj4cKFWLduHQ9E0iAs\nVUgUU1NTLF26FKmpqXj06BHq1q2LwMBAZGVliY5WzNTUFKampkhISEBubi7u37+PvXv3KuWNoOPH\nj8PKygq1a9fGmTNnUL9+fYU/JpEoXI2nD+nUqROCg4PRpUsXud9aJTAwECtXrsSFCxdQrVq19742\nevr0KZo3b4558+ahdOnSGDFiBDw9PSGVStGpUyds2rTpnV97+/Zt7N69Gz/88INc81PJmZub49tv\nv0VUVJToKET0mViEEpFC/P7772jXrh1iY2Nx7tw59OjRQ3QkUgBOhJJI1atXx9q1a3HmzBlcu3YN\nFhYWWLp0qcpMa5ibmyt1fT8/Px8TJ07EgAEDsHHjRvz888/Q19dX2uMTicCJUCqJXr16Yfz48XBy\ncsKjR4/kdt2goCCkpaXh2bNnWLVq1XtfF82cORPXrl2Dp6cnUlNTsWTJEqxduxZXrlxB9erVMWrU\nKNy5c+etX7tw4UIMHjyY3+uCcT2eSDOwCCUiudu5cyeaNWuGrl27Ijo6GtWqVRMdiRSAE6GkKurU\nqYMdO3YgOjoa8fHxsLCwwNq1a1FQUCA0l5mZ2VtPjleEGzduwM7ODjdu3EBqaiocHByU8rhEonEi\nlEpq9OjR6Nq1K1xdXZGdnS2Xa9rb26N27dol+twDBw5AIpFg1qxZ0NH5/7+Gm5iY4IcffsDLly+x\ncePGN77uzp072LFjB8aNGyeXzPTpevTogcOHDyMvL090FCL6DCxCiUhunj9/jn79+uGnn35CREQE\nJkyY8K8XeqR5OBFKquSbb75BaGgo9u3bh71796JBgwbYsWMHCgsLheRRRhEqk8mwfv16tG7dGkOG\nDMGBAwc4MURahROh9DHmz5+PunXromfPnnj16pVSH/v1hkCtWrXe+FitWrUgk8lw/PjxNz62aNEi\nDBgwAGZmZgrPSO9XpUoVfPPNN1yPJ1JzbCiISC4SExNhZWUFQ0NDnD9/Hra2tqIjkYJxIpRUVbNm\nzRAVFYX169dj1apVsLS0RGhoqNKLe0UXoY8fP4anpydWrFiBuLg4DB8+nP9ektZhEUofQyKRYP36\n9SgsLMSwYcOU+vfC6+/TW7duvfGx3377DcD/pvv/KTMzE1u2bMGECRMUH5BKxNvbm+vxRGqORSgR\nfZZXr15h1qxZ8PDwwOLFixESEoKyZcuKjkVKwolQUmXt2rXDqVOnsGDBAsyePRvNmzdHVFSU0r5v\nFVmEnjhxApaWlvj666+RlJSEhg0bKuRxiFRZbm4u8vPzeRAjfZTSpUtjz549uHTpEqZNm6a0x3Vx\ncYFMJsPMmTNRVFRU/OcPHjzA0qVLAQBPnjz519csXrwYffr0wZdffqm0nPR+XI8nUn+lRAcgIvV1\n69Yt9O3bFwYGBpBKpXyRpmU4eUbqQCKRwMXFBc7Ozti7dy/8/f1hbm6OOXPmoFWrVgp9bDMzM0il\nUrleMz8/HzNmzMC2bduwceNGODo6yvX6ROrk0aNHMDEx4d9H9NGMjIwQHh6OVq1aoUqVKvDz81P4\nY86ePRtRUVHYu3cvrl27ho4dOyI7OxthYWGoVq0a/vzzz3/dUurBgwfYsGEDLl68qPBsVHJVqlRB\n48aNER0dDVdXV9FxiOgTcCKUiD7Jjh070KxZM3Tv3h1RUVEsQbUUJ0JJXejo6MDb2xuXL1/GgAED\n0KdPH7i4uMi9qPwneU+EpqWloWXLlrhy5QpSU1NZgpLW40FJ9DkqV66Mo0ePYt68eUpZdTY3N0dy\ncjJ8fX3x4sULrF69GkeOHIGPj0/x45uamhZ//tKlS+Ht7c1DR1UQT48nUm8sQonoozx79gx9+vRB\nYGAgoqKiMG7cOB6IpKU4gUPqqFSpUhg0aBBu3LgBZ2dnuLi4wNvbG9evX5f7Y8mrCJXJZNi4cSNa\ntWqFQYMG4eDBg6hcubIcEhKpN94flD5XzZo1ER4eDl9fX8TGxir88SpXrozg4GD89ttvyM3NxV9/\n/YWgoCD88ccfAP53j2vgf/eADgkJwY8//qjwTPTxevTogYMHD3I9nkhNsb0gkoN9+/bB398fbdu2\nRYUKFaCjo4P+/fu/92sSExPRpUsXfPHFFzA0NISlpSWWLVv2r3sGqZqEhARYWVmhfPnyOHfuHKyt\nrUVHIsE4EUrqSl9fH35+fkhPT4etrS3atm2LQYMG4ffff5fbY+jp6eH27duIjo7GqVOn8Pjx44++\nxpMnT+Dt7Y2goCDExsbC19eXb0IQ/R8WoSQPVlZW2LVrF7y9vXHhwgUhGbZs2QKJRILevXsDAIKC\nguDh4YEaNWoIyUPv9+WXXxavxxOR+mERSiQHgYGBWLlyJS5cuIBq1ap98JfUsLAw2Nvb49SpU+je\nvTtGjRqFgoICjB07Fj4+PkpKXXKvXr3CzJkz0aNHDwQFBWH16tUwNDQUHYsEYxlDmqBs2bKYNGkS\n0tPTUb16ddja2sLPzw937979pOtlZGRg1KhRMDU1hZWVFZ49ewYvLy+4urqiSpUqMDU1xZgxY4pP\nCH6fuLg4WFpaomrVqjh79iwaNWr0SZmINBVX40le2rdvjxUrVsDFxUWub4j9k0wmQ3Z29ht/vm3b\nNmzbtg2tWrWCu7s7nj59ilWrVmHy5MkKyUHywfV4IvXFw5KI5CAoKAjVqlVD7dq1ERcXh/bt27/z\nc7OysjBkyBCUKlUKcXFxxVOVAQEBaN++Pfbu3Yvdu3fD29tbWfHf67fffkOfPn1Qrlw5SKVSVKlS\nRXQkUiGcCCVNUaFCBcyePRujRo3C/Pnz0bhxYwwePBgTJ04sUdHy/Plz+Pn5Yc+ePSgsLERBQUHx\nx549e1b83x88eIBVq1YhJCQEvXv3xrJly9448bqgoAA//fQTNm3ahA0bNsDZ2Vl+T5RIg3AilOTJ\n29sb9+7dg6OjIxISEkr0vRUWFoYDBw4AADIzMwH8b+tr0KBBAAATExMsWrQIAJCTkwMzMzM4ODig\ndu3a0NHRQUJCAk6fPo1GjRph9+7dAIDly5fD1dUVtWvXVsTTJDnp0aMHfvrpJ+Tl5UFfX190HCL6\nCJwIJZIDe3v7Er9Y2bNnDx4+fAgfH59/rZbr6ekhMDAQMpkMq1evVlTUEpPJZNi2bRuaN28Ob29v\nREZGsgSlf+FEKGmiypUrY/Hixbh48SKeP3+OevXqYfbs2Xj+/Pk7v+bixYuoU6cO9uzZg9zc3H+V\noG9TUFCA3Nxc/PLLL6hTpw6uXr1a/LGMjAy0bt0aqampkEqlLEGJ3oMToSRvo0aNQvfu3eHi4vLW\n6c3/Sk1NxdatW7F161ZERUVBIpHg1q1bxX+2f//+4s/V19eHj48Prl+/jpCQEKxevRovX77EvHnz\nkJycDHNzczx//hzBwcGYMmWKIp8myUHVqlXRsGFDHDt2THQUIvpILEKJlOzEiROQSCRvPe23bdu2\nMDQ0RGJi4gd/kVakp0+fonfv3pg3bx6io6MxduxYHohEb8WJUNJUVatWxerVq5GUlISMjAxYWFhg\n8eLFePny5b8+79KlS2jTpg0ePHiA3Nzcj3qM3Nxc3L9/Hy1btsTVq1exefNm2NnZoW/fvjh8+DDM\nzMzk+ZSINA4nQkkR5s6di4YNG8LLy+uDr8dnzpyJwsLCd/7n5s2bxZ9bqlQprFu3DteuXUNWVhay\nsrJw/vx5TJo0CWXKlAEArFy5Ep07d0bdunUV+hxJPrgeT6Se2GwQKdmNGzcA4K0vcHR1dVGzZk28\nevWqRPePU4STJ0/CysoKxsbGSElJgZWVlZAcpPo4EUraoHbt2ti6dStiYmKQmJgICwsLrF69Gvn5\n+cjOzkbnzp3fOy36ITKZDM+fP0eTJk2wcOFCxMTEYNSoUfz3i6gEWISSIkgkEqxduxYSiQRDhgxR\n2pu+L168QFBQEKZOnaqUx6PP9/r0+Pz8fNFRiOgjsAglUrLX94qrUKHCWz/++s+fPn2qtEzA/1Y1\np0+fDm9vb6xYsQIrV67kgUj0QZwIJW3RqFEj7Nu3DwcOHEBYWBjq168PV1fXf93/81PJZDIUFBTA\n3t4e33zzjRzSEmkHrsaTopQuXRq7d+/G9evXlbamvmbNGrRr1w4NGzZUyuPR56tWrRoaNGjA9Xgi\nNcMilIhw8+ZNtGnTBsnJyZBKpXB1dRUdidQAJ9ZIGzVp0gSRkZFYtGgR4uLi3liV/1SvXr3C5s2b\nce/ePblcj0gbcCKUFKls2bI4fPgwQkNDERwcrNDHysnJwc8//4xp06Yp9HFI/rgeT6R+WIQSKdnr\nic93TRG9/vOKFSsqPItMJsOWLVvQokUL+Pj44MiRIzA3N1f445Lm4EQoaSupVAo9PT25X3ft2rVy\nvyaRpuJEKCmaiYkJIiMjsXDhwuJT3RVh7dq1aNmyJbcC1JCnpyfX44nUDItQIiWrV68eACAtLe2N\njxUWFuLWrVsoVaoUatWqpdAcT548gY+PDxYtWoTjx49j9OjRPBCJPgonQkmb7dy5E3l5eXK9Zm5u\nLnbs2CHXaxJpqry8POTm5qJ8+fKio5CGq1GjBo4cOQI/Pz/ExMTI/fq5ublYtGgRpk+fLvdrk+JV\nq1YN9erVw/Hjx0VHIaISYutBpGQdOnSATCZDZGTkGx+Li4tDTk4OWrVqhdKlSyssQ3x8PKysrFC5\ncmUkJyfj22+/VdhjkWbjRChpo9zcXPz5558KufZvv/32wVOKiej/T4PyTTlShm+//Ra7d+9Gr169\nkJqaKtdrb9iwAba2trC2tpbrdUl5uB5PpF5YhBIpmaenJ0xMTPDrr7/i3LlzxX+el5eHadOmQSKR\nYMSIEQp57IKCAkydOhU9e/bE6tWrsXz5chgYGCjksUjz8ZdP0la3bt1S2M9OPT09hZWsRJqEa/Gk\nbO3atcOqVavg4uKCW7duyeWaeXl5mD9/PqdB1ZynpyfCwsL4RiaRmiglOgCRJggLC8OBAwcAAJmZ\nmQCAxMREDBo0CMD/7i+0aNEiAEC5cuWwbt06eHl5oV27dujVqxeMjY1x8OBBpKWlwcvLC15eXnLP\nmJGRgd69e8PExASpqakwMzOT+2OQ9uFEKGmj/Px8hb0RoKOjw/uMEZUAD0oiETw9PXHv3j04Ojoi\nISEBlStX/qzrbd68GY0bN0bTpk3llJBEqF69OurWrYvjx4/DyclJdBwi+gAWoURykJqaiq1btxb/\nb4lEglu3bhW/W1yjRo3iIhQA3N3dERcXhzlz5mD//v3Izc1FnTp1sHTpUowaNUqu2WQyGTZv3oyJ\nEydixowZ8PPz4yQfyQW/j0hblS1bFoWFhQq5dmFhIQwNDRVybSJNwolQEsXX1xd3796Fi4sLYmJi\nYGRk9EnXKSgowLx587Bz5045JyQRvL29sWfPHhahRGpAIuM4D5HGevLkCYYNG4Zr165h586daNy4\nsehIpEESExMxfvx4JCYmio5CpFSvXr1C2bJlFTK5WaZMGWRnZ/PwOqIPWLNmDaRSKUJCQkRHIS0k\nk8kwePBg/P333zh06NAb9/YvLCyEVCpFSkoKUlNT8eLFCxgZGcHa2hpNmjSBtbU1Nm/ejJ07dyI6\nOlrQsyB5un37NqytrXH37l2FnvVARJ+PE6FEGio2Nhb9+/eHh4cHtm7dijJlyoiORBqI76WRNipV\nqhTq1q2Ly5cvy/3aDRs2ZAlKVAJcjSeRJBIJQkJC4OHhge+//x6bN2+Gjo4OsrKysHz5cgQFBSE3\nNxevXr3Cy5cvi7/O0NAQOjo6MDQ0REFBAX755ReBz4LkqXr16rCwsEBMTAwcHR1FxyGi9+ArbSIN\nk5+fj8mTJ6N3794ICQnBsmXLWIKSQnA1nrTZ0KFD5f6z1cDAAEOHDpXrNYk0FVfjSbRSpUph165d\nSE9Px+TJk3Hs2DHUrl0bgYGBePDgAbKysv5VggJATk4OXrx4gfv37+PZs2cYOHAgTpw4IegZkLzx\n9Hgi9cAilEiDpKWloVWrVrh06RJSU1Ph7OwsOhJpOE6EkjaSSqWIjIxEbm6uXK/78uVLHDlyBBcu\nXJDrdYk0ESdCSRUYGhri8OHD2LRpE7p06YIHDx68UX6+S1FREe7duwdXV1cEBwcrOCkpg6enJw4c\nOMDT44lUHItQIg0gk8mwYcMGtGrVCgMHDsShQ4dgamoqOhZpOE6EkrY5f/483N3d4eLiAgcHBwQG\nBqJs2bJyuXbZsmWxcOFCtG3bFk5OTujevTtSU1Plcm0iTcSJUFIVu3btQlZW1ieXXzk5OZg8eTLW\nr18v52SkbF999RXq1KmDmJgY0VGI6D1YhBKpucePH8PLywvBwcGIjY2Fr68vCypSGk6EkjY4d+4c\nunbtCjc3N3To0AE3b97EmDFj8OOPP8LCwuKzD0UoXbo0GjZsiHHjxmHcuHG4efMmWrduDWdnZ3h4\neEAqlcrpmRBpDk6Ekiq4du0axo8f/9kbAjk5OfD390d6erqckpEoXI8nUn0sQonUWExMDCwtLfHV\nV18hKSkJjRo1Eh2JtAgLd9J0KSkpcHNzQ9euXdGpUydkZGRg9OjRMDAwAADo6uri6NGjqFKlyieX\noaVLl0a1atUQERFRfEiSoaEhfvjhB9y8eRP29vZwcXFBt27dWIgS/QOLUBJNJpOhV69ecrtNSl5e\nHnr37i2Xa5E4XI8nUn0sQonUUH5+PiZNmoR+/fph/fr1WLJkCQ9EIiE4EUqaKDk5Ga6urujWrRsc\nHR1x8+ZN+Pv7Fxeg/2RqaoqUlBTY2Nh89Jp82bJl0axZM6SkpLx1xdfQ0BBjxozBzZs30b59e7i4\nuMDd3R3nz5//5OdGpCm4Gk+inTlzBjdv3pTba6GioiJcvXoV586dk8v1SIyvv/4atWvX5iFYRCqM\nRSiRmrlx4wbs7Oxw7do1pKamwtHRUXQk0lKcCCVNc/bsWbi4uMDDwwPOzs7IyMiAn5/fB99oqly5\nMhITE7FgwQKUK1cORkZG7/18IyMjlC9fHosXL8bJkydhbGz83s83MDDA6NGjcfPmTXTs2LF4SpW/\nLJO2ys/PR05ODipUqCA6CmmxpUuXIicnR67XzM3NRVBQkFyvScrH9Xgi1cYilEgJioqKkJGRgfPn\nz+PixYt4/vz5R19DJpNh3bp1aNWqFQYPHoywsDBUrlxZAWmJSo4ToaQJkpKS0KVLF/To0QMuLi7I\nyMiAr6/vR03a6+jowNfXFw8ePMCaNWvQoUMHGBsbo1SpUtDX1wcAVKxYEZ06dcLatWtx//59DBs2\n7KPeUDAwMIC/vz9u3rwJBwcHuLu7w83NDSkpKR/9nInU2ePHj2FsbMw35EioEydOyP11UFFREY4f\nPy7Xa5LycT2eSLWxCCVSkJycHGzcuBG2trYwNDSElZUV2rdvj9atW8PExARVq1bFyJEjcePGjQ9e\n69GjR+jRowdWrlyJ+Ph4jBgxgi/+STh+D5K6O3PmDJydneHp6Qk3NzdkZGRg5MiRn3WrEX19ffTp\n0wfHjx/Ho0eP8OTJE/z1119o1qwZDh06hOjoaPj4+BSXo5+iTJkyGDVqFDIyMuDo6Ihu3brBxcUF\nZ8+e/eRrEqkT3h+URHv06NEnDTaUxMOHDxV2bVKOGjVqoGbNmoiNjRUdhYjegkUokZzJZDJs3LgR\nZmZmGD16NM6fP4+8vDxkZ2fj+fPnyMrKQkFBAe7cuYP169fD2toabm5uePDgwVuvd+zYMVhaWqJm\nzZpISkpCw4YNlfyMiN6NE6Gkjk6fPg0nJyd4e3vD3d0dGRkZGDFixGeVk+9iZGQEExMTVK9eHXfu\n3JHrtcuUKQM/Pz9kZGQUT7R26dIFSUlJcn0cIlXDIpREu3PnjsLuz6+vr4/MzEyFXJuUh+vxRKqL\nRSiRHL148QIODg7w9/fHixcv8OLFi/d+fkFBAV6+fImoqCjUqVMHMTExxR/Ly8vDhAkTMGDAAGzc\nuBGLFy9WyC/pRJ+KE6GkbhITE+Ho6IhevXrBw8MD6enpGD58uFJ+tlatWhV///23Qq5dpkwZ+Pr6\nIiMjA66urvD09ISzszPOnDmjkMcjEo0HJZFoinwjWCKRoEGI3f4AACAASURBVKioSGHXJ+Xw9PRE\naGgo7t+/j/Xr16N79+6wsLCAoaEhKlasiDZt2mDjxo3v/F5KTExEly5d8MUXX8DQ0BCWlpZYtmwZ\nvzeI5IBFKJGcZGdno02bNkhISEB2dvZHfW1+fj6eP38ONzc3REdH4/r167Czs0NaWhouXLiAzp07\nKyg10efhRCipg4SEBHTu3Bk+Pj7o0aMH0tPTMWzYMKW+ufTll1/KfSL0v/T19TFy5EhkZGTA3d0d\n3t7ecHJywunTpxX6uETKxolQEq1y5crIz89XyLXz8vJ4DoAGqFmzJmrUqIG5c+di6NChOHv2LFq0\naIGxY8fC09MTV65cweDBg9GzZ883vjYsLAz29vY4deoUunfvjlGjRqGgoABjx46Fj4+PgGdDpFlY\nhBLJyaBBg3D9+nXk5uZ+8jVycnLg6uoKOzs7DBs2DAcOHOALfVJZnAglVXfq1Ck4ODigT58+8PLy\nQnp6OoYOHQo9PT2lZ1FGEfqavr4+hg8fjvT0dHh4eKBXr15wdHREYmKiUh6fSNE4EUqimZubK+zv\nEolEgqioKK7HawAvLy/cvHkThw4dwl9//YVt27Zhzpw5WL9+Pa5fv47q1atj3759CA0NLf6arKws\nDBkyBKVKlUJcXBzWrVuHBQsWIDU1FXZ2dti7dy92794t8FkRqT8WoURycPjwYYSHh39WCfpafn4+\natasiaFDh7JoIpXHiVBSRSdPnkSnTp3Qr18/9OzZE2lpaRgyZIiQAvQ1Ra7Gv4u+vj6GDRuG9PR0\n9OjRAz4+PujcuTMSEhKUmoNI3jgRSqJJJBK0aNFCIde2sLDArl270KBBAzRq1AijRo1CaGgoHj9+\nrJDHI8Xx8vJCUlISHB0d3/iYqakphg8fDplM9q9Dlfbs2YOHDx/Cx8cH1tbWxX+up6eHwMBAyGQy\nrF69WhnxiTQWi1CizySTyeDn54ecnBy5XTMtLe1f9wslUkUs6knVxMfHo2PHjujfvz98fHyQlpaG\nwYMHCy1AX1PmROh/6enpYejQoUhPT4eXlxf69OkDBwcHnDp1Skgeos/FIpRUwffffy/3v1+MjIyw\nbNkyHDhwAA8fPsSWLVtQvXp1hISE4P+xd99xNfb/H8BfV3vJrJtC47QncttESCpb9t57y7qp7CSZ\nqWQkVDbJ6q5kZKSiqRTlRqQkmhrn98f3Vw/u26xzus54Px+P+487+ZyXVee8zvt9XZqamrCwsMDy\n5ctx5coVfPr0iaePTXhPS0sLrVu3RmRk5Dd/XFpaGgAgJSVV87GIiAgwDPPN8rRHjx5QUFBAVFQU\nysvL+ROaEDFARSghdXTr1i3k5eXx9MyioiK4ubnx9ExC+IEmQokgiIyMhJWVFSZNmoSxY8ciLS0N\nU6dOrXmBIQiqi1A2/83IyMhg+vTpSEtLw8iRIzF+/Hj07t0bt27dYi0TIbVBq/GETfn5+Vi3bh1m\nz57N8+8zqqqq6NWrFwBAUlIS7du3h6OjI65evYrc3Fzs2rULysrK2LZtG1q0aIGuXbti7dq1iIiI\n4MlmGuG97909vrKyEn5+fmAYBjY2NjUfT01NBQDo6en95+dISkpCS0sLFRUVePbsGf9CEyLiqAgl\npI4CAgJ+++ZIvyI8PJxvF2EnhBdoIpSw7caNG+jVqxemTJmC8ePHIzU1FVOmTBGoArRagwYNAEAg\nJnhkZGQwbdo0pKWlYcyYMZg4cSKsrKxw8+ZNtqMR8ktoIpSw4f3791i7di10dXXx+vVrREdH4+rV\nq5CXl+fJ+fLy8ggICPju8ysZGRl069atpvh89+4d1q9fj6qqKqxevRoqKiro3bs3Nm3ahLt379LE\noIBwcHDAuXPnUFFR8dXHV6xYgaSkJNjZ2aFv3741Hy8oKAAANGzY8JvnVX/8w4cPfEpMiOijIpSQ\nOrp9+zZfJnzk5OSQmJjI83MJ4SWaCCVsuHHjBnr27Ilp06Zh4sSJePLkCSZPniyQBWg1hmFYuU7o\nj0hLS2Pq1KlITU3FuHHjMHnyZPTq1eu7K3yECAqaCCX16csCNDs7Gw8ePICvry+0tbXRrVs3zJkz\nBwoKCnV6DAUFBSxevBgdOnT45Z8jLy//VfH56tUrLFmyBO/fv8ecOXPQrFkz2NnZwd3dHXFxcaiq\nqqpTRlI72traaNWq1VdvNu7evRs7duyAkZERjh49ymI6QsQTFaGE1BG/1hK4XC6Sk5P5cjYhvEAT\noaQ+cblcREREwNLSEtOnT8fkyZPx5MkTTJo0SaAL0C+xeZ3QH5GWlsaUKVPw5MkTTJw4EVOnTkXP\nnj2/unkDIYKEJkJJfcjLy8Nff/0FXV1dvHnzBg8fPqwpQL/k5uaGUaNGQVFRsVaPo6CggAkTJmDj\nxo11yqusrPxV8ZmRkYEpU6bg2bNnGDNmDFRUVDBs2DDs27cPKSkp9GZ2PfpyPX7v3r1YtGgRTExM\nEB4ejkaNGn31udUTn9WTof9W/fF//zxCyK+jIpSQOuLX2klVVRVd64cIPHoSTfiNy+UiPDwclpaW\nmDlzJqZOnYqUlBRMnDjxq5sLCANBLUKrSUtLY9KkSTUTttOmTYOlpSUiIiLo3zoRGOXl5SgqKvru\n2ighdZWXl4c1a9ZAT08POTk5iImJwYEDB6ClpfXNz2cYBr6+vnB1dYWCggIkJSV/6XEkJSWhqKiI\nHTt2wNPTk+dvMDdr1uyr4jMhIQFDhw5FbGws+vfvDzU1NYwdOxYHDx7E8+fPefrY5GsODg44e/Ys\n3N3dsWDBApiZmSE8PByqqqr/+Vx9fX0A/7t57r9VVlbi+fPnkJKS+k8hTwj5dVSEElJHsrKyfDlX\nUlKSZ9ccIoQfaCKU8BOXy0VYWBh69OiBWbNmYfr06UhOTsaECROErgCtJmir8d8jJSVVc8mBqVOn\nYsaMGbC0tER4eDgVooR179+/R+PGjSEhQS9jCG99WYDm5uYiJiYGPj4+0NTU/OnPZRgGc+fORWJi\nIkaMGAE5OTkoKSn957kSwzBo0KAB5OTkMHr0aCQlJWHmzJn18pzqy+IzMzMTUVFRsLKyQnh4OLp0\n6QItLS1MnToVx48fF+g37YSRtrY2pKSksHz5crRr1w4RERHfnWq3srICl8vF1atX//NjkZGRKC4u\nRteuXYVmG4YQQUTPIAipIw6Hw7ezjY2N+XY2IbxApQjhNS6Xi7///hvdu3fHnDlzMHPmTCQnJ2P8\n+PFCW4BWE/SJ0H+TkpLChAkTkJKSgunTp2PWrFno0aMHwsLC6N8+YQ2txRNey83NxerVq78qQL29\nvX+pAP03LS0tnDhxAq9fv4a3tzfmzp2Lzp07Q1paGhYWFpg3bx68vb2RnZ0Nf39/aGho8P4X9BtZ\nvyw+L1++jHbt2uHs2bMwNTWFoaEh5s6dizNnziAvL4+1nKJgw4YNyM7OhoqKCv7++280btz4u587\nfPhwNGvWDIGBgYiJian5eFlZGf766y8wDIPZs2fXR2xCRBbDpWeyhNTJvHnz4OnpyfMXhdLS0igq\nKqJ3+4jAio+Px7hx4xAfH892FCICqgtQZ2dn5OXlYe3atRg1atQvrxgKg5MnT+LkyZM4ffo021Fq\npaKiAoGBgdiwYQNUVFTg5OSEPn360HQ4qVc3b97EmjVrcOvWLbajECGXm5sLd3d3+Pj4wMHBAatW\nreJbMamlpYWwsDChWWeurKxEfHw8wsPDER4ejtu3b0NLSwtWVlawsrJCjx49oKyszHZMoeDn54fJ\nkydDSkqqZir03xPtmpqamDhxYs3/X7hwAQ4ODpCVlcWoUaPQpEkTXLx4EWlpaXBwcEBgYGB9/zII\nESnCPVpBiAAYO3Ysjhw5gqKiIp6eq6uri6KiIroQNhFo9F4aqSsul4vQ0FA4OzsjPz8fa9euxciR\nI0WqAK0mLKvx3yMlJYVx48Zh9OjRCAwMxPz589G0aVM4OTmhb9++VIiSekEToaSuvixAR4wYgbi4\nOLRu3ZqvjykpKYnKykq+PgYvSUpKom3btmjbti2WLl2K8vJyPHz4EOHh4fDw8MCoUaNgYmJSU4x2\n6dIFCgoKbMcWSJmZmWAYBpWVlSgvL//mTbEsLS2/KkIHDRqEyMhIbNq0CWfPnkVpaSl0dHTg4eGB\n+fPn12d8QkQSTYQSUkdcLhd6enpIT0/n2Zny8vLo0qULYmNjMXr0aMybNw+GhoY8O58QXkhISMCY\nMWOQkJDAdhQihLhcLq5duwYXFxcUFBRg7dq1GDFihEgWoNWeP3+Onj17Iisri+0oPFFZWYmgoCBs\n2LABjRs3hpOTE6ytrakQJXzl4+OD6OhoHDhwgO0oRMi8e/cO7u7uOHDgAEaMGIFVq1bxvQCtpq+v\njwsXLsDAwKBeHo/fSktLcffu3ZqJ0cePH6N9+/Y1xWiHDh0gIyPDdkyBs2XLFvzzzz/w9PRkOwoh\nYo2uEUpIHTEMA09PT569CyolJYVOnTohNDQUiYmJaNasGXr16gVra2tcunQJVVVVPHkcQniB3ksj\nv4vL5eLKlSvo3LkzlixZgoULFyIhIQGjR48W6RIUAFq0aIHs7GyR+TouKSmJMWPGIDExEfPnz8fi\nxYvRpUsXXL16lb42EL7Jy8tD06ZN2Y5BhMi7d++wYsUK6Ovr4+PHj4iLi8P+/fvrrQQFhG8i9Gfk\n5OTQq1cvbNiwAXfu3EF2djZWrFiBT58+YeHChWjWrBlsbGywbds2PHz4UKR+7XVRffd4+v0ghF1U\nhBLCA3379sXw4cN5cpd3eXl5HDt2DAzDQE1NDS4uLsjKysL48ePh4uICPT097Ny5EwUFBTxITkjt\n0dQX+R1cLheXL19Gp06dsGzZMixevBgJCQkidx3QH5GTk0ODBg1E7qYTkpKSGD16NBISErBo0SIs\nXboUnTt3xpUrV6gQJTxHq/HkV+Xk5MDR0REGBgYoLCzE48eP4enpWa8FaDVRK0L/rUGDBujfvz/c\n3NwQExODzMxMzJw5E//88w8mTpyIZs2aYfDgwdi9ezcSExPF9nuDjo4OWrRoQdc4JoRlVIQSwiPe\n3t4wNzevUxmqoKCAkJAQqKmpffVxWVlZjB8/Hg8ePMCxY8fw4MEDaGlpYe7cuXjy5EldoxNSa+L6\nRJb8Oi6Xi5CQEHTs2BGOjo5YunQpEhISRPY6oD8j7NcJ/RFJSUmMHDkSCQkJWLJkCZYtW4aOHTvi\n8uXL9LWC8AxNhJKf+bIALSoqwqNHj7Bv3z60atWKtUyiXoT+W5MmTTBkyBDs2bMHSUlJSElJqfn+\nMGjQIDRv3hyjRo2Cj48P0tPTxep7hIODA06dOsV2DELEGhWhhPCInJwcwsPD0bdv399ek5eVlUWT\nJk0QGhqK7t27f/fzGIZBp06dcOLECSQmJqJp06bo2bMn+vXrh5CQEJFZtyTCgSZCyY9wuVxcunQJ\nHTp0wMqVK+Ho6Ij4+HiMGDHiP3dLFSdqamp4/fo12zH4SkJCAiNGjEBCQgKWL18OR0dHdOjQASEh\nIWL1YpfwB02Eku/JycnB8uXLYWBggOLiYsTHx7NegFYTtyL035o3b47Ro0fjwIEDyMjIwP3799Gv\nXz/cunULlpaW0NDQwKRJk3D06FG8fPmS7bh85eDggDNnzoj13wdC2Ca+r0QI4QN5eXlcuHABR44c\nQePGjaGkpPTDz5eRkYGsrCyGDh2KjIwMdOnS5ZcfS01NDevXr0dWVhbGjh0LJycnWpsn9Y5KDfJv\nXC4XwcHB+PPPP7F69WqsXLkSjx8/xvDhw8W6AK0mDkVoNQkJCTg4OCA+Ph4rVqzAqlWr0KFDBwQH\nB9PXDlJrVISSf3v79i2WLVsGAwMDlJaWIj4+Hnv37kXLli3ZjlZD3IvQf9PU1MTkyZPh7++Ply9f\nIjQ0FB07dkRwcDDatGkDPT09zJo1CydPnkROTg7bcXlKV1cXzZs3x+3bt9mOQojYolckhPCBg4MD\n3rx5g4MHD6JHjx5o0KBBzbXhFBUVAQCtW7fGokWLkJaWhhMnTqBRo0a1eixZWVlMmDAB0dHR8Pf3\nx/3796GlpYV58+bR2jzhK5oIJV/icrm4ePEi2rdvj7/++gurV6/Go0ePMGzYMCpAvyDKq/HfIyEh\ngeHDh+PRo0dYuXIl/vrrL7Rv3x4XL16kQpT8NlqNJ9WqC1BDQ0OUlZUhPj4ee/bsEagCtBoVod/H\nMAz09fUxe/ZsnDp1Cjk5OTh16hT09fXh7+8PXV1dmJmZYdGiRbh48SI+fPjAduQ6o/V4QtjFcOkZ\nKCF8x+Vy8fbtWxQUFEBaWhqLFi3CuHHjMGLECL483uvXr+Hl5QUfHx+0adMGCxYsgI2NDZURhKeS\nk5MxbNgwpKSksB2FsKi6AHVxcUFVVRWcnJwwaNAg+nrzHfv378ejR4/g7e3NdhTWVFVV4fz583Bx\ncYGkpCScnJwwcOBAenOF/JLGjRsjIyMDTZo0YTsKYcmbN2/g5uaGw4cPY9y4cVixYgXU1dXZjvVD\n3bp1w5YtW354CSzybRUVFYiJiUF4eDjCw8Nx7949GBoawsrKClZWVujatWvNoImwSEtLg6WlJV6+\nfCmW10snhG30KoWQesAwDJo3bw59fX1oa2ujTZs2SEhI4NvjVa/NZ2ZmYsyYMVi7di309fWxa9cu\nWpsnPEOlhXjjcrk4f/48LCws4OzsjHXr1iE2NhZDhgyhEvQHxGk1/nskJCQwdOhQxMXFYe3atXB2\ndoaFhQXOnz9PE6LkhyoqKvDp06dab9EQ4fbmzRssWbIERkZGqKioQGJiInbv3i3wJShAE6F1ISUl\nhY4dO2LVqlUIDQ1Fbm4u3NzcICsriw0bNuCPP/5Ajx494OzsjJs3b6KsrIztyD+lp6cHVVVV3Llz\nh+0ohIgleqVCCAtMTU35WoRWk5OTw4QJE/Dw4UP4+fnh7t270NLSwvz585Gamsr3xyeij0oL8VNV\nVYVz586hXbt2cHFxgZOTE2JjYzF48GAqQH+BOK7Gf4+EhASGDBmC2NhYODk5wcXFBW3btsW5c+fo\n5n/km96/f4/GjRvT1xox82UBWllZicTEROzatQtqampsR/tlVITyjqysLCwtLeHi4oJbt27h7du3\nWLNmDUpKSrB06VI0a9YM1tbW2Lp1Kx48eICKigq2I3/Tl+vxhYWFePbsGTIyMvDx40eWkxEi+uhZ\nBCEsqK8itBrDMOjSpQsCAwORkJCARo0aoUePHrCxscHly5fpBSepFZoIFS9VVVU4e/Ys2rVrhw0b\nNsDFxQWxsbEYNGgQ/V34DTQR+l8Mw2DQoEGIjY3F+vXrsWHDBrRt2xZnz56l70/kK3SjJPGSnZ2N\nxYsXw8jICFVVVUJZgFajIpR/FBUV0a9fP7i6uiI6OhovXrzA3LlzkZ2djalTp6JZs2YYOHAgdu7c\nifj4eIH5vmJsbAxfX1+0bNkSTZo0gbm5Odq0aYNmzZqhRYsWGDlyJKKiomjogBA+oGuEEsKCiooK\nKCsrIycn56d3lueX0tJSBAUFYffu3fj48SPmz5+PSZMmQVlZmZU8RPg8efIEgwYNouliEVc9Abp+\n/XpISUnB2dkZ9vb2VH7WUkVFBeTl5VFcXAxpaWm24wgkLpeLS5cuwdnZGeXl5XBycqJLLhAAwK1b\nt7Bq1Sq627KIy87OhqurK44ePYqJEyfC0dERLVq0YDtWndjY2GDhwoXo378/21HEztu3b3Hjxg2E\nh4cjIiIC+fn56NWrV801RnV1dev1Oc3Tp08xduxYJCUlobi4+LufxzAMFBQU0Lp1awQEBMDc3Lze\nMhIi6ugZJSEskJKSgoGBAZKTk1nLICcnh4kTJ9aszUdFRUFTU5PW5skvoyJMtFVVVeH06dNo06YN\ntmzZgk2bNuHhw4cYMGAA/dnXgZSUFFRUVPD27Vu2owgshmEwYMAAPHz4EJs3b8aWLVtgbm6O06dP\nC8wkD2EHTYSKtuzsbCxatAjGxsaQkJBAUlISPDw8hL4EBWgilE1//PEHRo4cCW9vb6SlpSE2Nhb2\n9va4d+8eevfujVatWmHChAk4cuQIXrx4wdcsPj4+MDc3R0xMzA9LUOB/bwoWFRXhyZMn6Ny5M7Zs\n2ULToYTwCBWhhLCkvtfjv+fLtfn4+Hg0bNgQPXr0QP/+/XHlyhV60Ul+iJ6QiZ6qqiqcOnUK5ubm\ncHV1xZYtWxAdHU1ToDxE1wn9NQzDwN7eHtHR0di6dSu2bdsGc3NznDx5kr43iam8vDw0bdqU7RiE\nx16/fo2FCxfWFKDJycnYsWOHSBSg1SQkJOjrloD4d/F548YNdOvWDVevXkX79u2ho6ODGTNmIDAw\nkKdvWu7YsQOLFy9GSUnJb/1d4HK5KCkpwcaNG7FixQqe5SFEnFERSghLBKUI/VLLli2xceNGZGVl\nYdSoUVizZg0MDAxq1ucJ+RKVYqKlqqoKJ0+ehJmZGdzc3ODq6ooHDx7Azs6O/qx5jK4T+nsYhoGd\nnR3u378PV1dXuLu7w8zMDEFBQTRhJWZoIlS0vHr1CgsWLICJiQmkpKRqCtDmzZuzHY3naCJUMDEM\n85/i8/z58zAxMUFgYCAMDAxgYmKCBQsW4Pz588jPz6/V44SGhmLt2rU/nQL9keLiYuzbtw/Hjx+v\n9RmEkP+hIpQQlghiEVqtem0+JiYGhw8fxp07d6CpqYkFCxYgLS2N7XhEgNBEqPCrrKxEUFAQTE1N\n4e7uDjc3N9y/fx+2trZUgPIJFaG1wzAMbG1tce/ePbi5ucHDwwNmZmYIDAykgkFMUBEqGl69eoX5\n8+fD1NQUMjIySElJgbu7u0gWoNWoCBUODMN8VXzm5ubiyJEjaNmyJby8vKChoYH27dvD0dERV69e\nRWFh4U/P/PjxI8aMGVOnErRacXExZs+ejTdv3tT5LELEGRWhhLBEkIvQagzDoGvXrggKCkJ8fDwa\nNGiA7t27w9bWltbmCZVkQq6yshKBgYEwNTWFh4cH3N3dce/ePfTv35/+bPmMVuPrhmEY9O/fH3fv\n3sWOHTuwa9cumJqaIiAggIoGEUer8cLtywJUVlYWKSkp2L59O/744w+2o/EdFaHCSVJS8qviMzc3\nFzt37oSSkhK2bt2K5s2bo1u3bli3bh1u3LiB0tLS/5yxc+fOXypMf1VpaSmcnZ15dh4h4oiKUEJY\n0qJFC1RUVAjNDTNatmyJTZs2ISsrCyNGjKhZm9+zZw+tzYsxmggVPpWVlQgICICJiQl27doFDw8P\n3L17FzY2NlSA1hOaCOUNhmHQr18/REVFYefOndizZw9MTExw4sQJKhxEFE2ECqeXL19i3rx5MDU1\nhZycnFgVoNWoCBUNMjIyXxWfOTk5cHZ2RkVFBVauXAkVFRX06dMHmzdvxr1791BaWordu3d/syCt\nrfLycvj7+6OoqIhnZxIibqgIJYQlDMMIxVTov8nJyWHSpEmIiYnBoUOHcPv2bWhqamLhwoV4+vQp\n2/FIPaLSTLhUVlbixIkTMDExwZ49e7Br1y5ERUWhX79+9GdZz6gI5S2GYWBtbY07d+5g9+7d2Ldv\nH4yNjXH8+HEqHkQMTYQKl3/++Qdz586FmZkZFBQU8OTJE7i5uYlVAVqNilDRpKCg8FXx+fLlSyxa\ntAi5ubmYNWsWmjZtioKCAp4/rpSUFEJDQ3l+LiHigopQQlgkjEVoNYZh0K1bt5q1eSUlJXTt2hW2\ntra4evUqrc2LCZoIFXyVlZU4fvw4jI2NsW/fPuzevRt37tyBtbU1FaAsUVdXpyKUDxiGQd++fXH7\n9m3s3bsX+/fvh5GREY4dO4aKigq24xEeoIlQ4VBdgLZp0waKiop48uQJtm3bBlVVVbajsYaKUPHQ\nsGFD2NvbY8eOHXj06BHWrFnDl+dahYWFuH//Ps/PJURcUBFKCIuEuQj90pdr8w4ODli1ahUMDQ2x\nd+9efPr0ie14hE+oRBNsFRUVOHbsGIyMjLB//37s3bsXt2/fRt++fenPjmVqamp0jVA+YhgGffr0\nwa1bt+Dp6Qlvb28YGRnB39+fClEhR0WoYPvnn38wZ84cmJubQ0lJiQrQL1ARKp5SU1NRXl7O83Or\nqqoQHR3N83MJERdUhBLCIlEpQqvJy8tj8uTJiI2Nha+vL27evElr8yKOJkIFT0VFBfz9/WFkZARv\nb294enri1q1b6NOnDxWgAqJJkyYoKSnhyR1kyfcxDIPevXvj5s2b8PLywoEDB2BoaAg/Pz8qRIVQ\nRUUFPn78iEaNGrEdhfzLixcvMHv2bJibm0NZWRmpqalwdXWFiooK29EEBhWh4omXN0n6N7pGKCG1\nR0UoISwyMTFBcnKyyD0xYhgG3bt3x8mTJ/Ho0SMoKiqia9eusLOzw7Vr12htXkRQqSZYKioqcPTo\nURgaGuLAgQPw8vLCzZs30bt3b/qzEjAMw6BFixbIzs5mO4pYYBgGVlZWiIyMhI+PDw4dOgQDAwMc\nOXKEClEhkp+fj0aNGkFSUpLtKOT/VRegbdu2RcOGDZGamoqtW7dSAfoNVISKJ0VFRb6dLS8vz7ez\nCRF1VIQSwiJlZWWoqKjg2bNnbEfhm1atWmHz5s3IysrC8OHDsWLFClqbJ4SHKioq4OfnB0NDQxw8\neBA+Pj6IjIyElZUVFaACTF1dndbj6xnDMOjVqxciIyPh6+sLPz8/GBgY4PDhw3xZXSS8RTdKEhxZ\nWVmYNWsW2rZti0aNGlEB+guoCBVPbdu2haysLM/PlZCQgIWFBc/PJURcUBFKCMtEbT3+e6rX5uPi\n4uDr64vIyEhoampi0aJFSE9PZzseqSVajWdPRUUFjhw5UlPkHDhwAJGRkejVqxcVoEKA7hzPrp49\neyIiIgIHDx6Ev78/DAwMcOjQISpEBRhdH5R9WVlZmDlzJtq1a4cmTZogNTUVW7ZsoT+XX0BFqHhq\n3749X4pQRUVFdOzYkefnEiIuqAglhGXiUoRWq16bP0WlLQAAIABJREFUP3XqFB49egQFBQV06dIF\n9vb2tDYvZKhsY0d5eTkOHz4MfX19+Pn5wdfXFzdu3EDPnj3ZjkZ+AxWhgsHS0hLh4eE4fPgwjh8/\nDn19fRw8eJAKUQFERSh7MjMzMWPGDLRr1w5NmzZFamoqNm/eTH8ev4GKUPHUqVMnSEjwvnKpqKiA\ntbU1z88lRFxQEUoIy0xNTZGYmMh2DFZ8uTY/dOhQrFixAkZGRti3bx+tzQsJmgitP+Xl5Th06BD0\n9fVx7NgxHD58GBEREVSACilajRcsPXr0QFhYGPz8/BAQEAA9PT34+vri8+fPbEcj/49W4+tfdQFq\nYWEBFRUVpKWlUQFaS1SEiidpaWnMnj2bp1OhUlJSGDFiBJSVlXl2JiHihopQQlgmbhOh3yIvL48p\nU6YgLi4OPj4+iIiIgKamJhYvXkxr8wKMJkLrR3l5OQ4ePAh9fX2cOHECfn5+CAsLQ48ePdiORuqA\nJkIFU/fu3fH333/D398fQUFB0NPTw4EDB6gQFQA0EVp/nj9/junTp8PCwgKqqqpIS0vDpk2bqIiu\nAypCxdeyZct4emMjWVlZrF+/nmfnESKOqAglhGX6+vp48eIFSkpK2I7COoZh0KNHD5w+fRpxcXGQ\nk5ND586dYW9vj+vXr9P0oQCiPxP++fz5M3x9faGnp4fAwEAcPXoUf//9N7p37852NMIDVIQKtm7d\nuiE0NBTHjx/HqVOnoKenB29vbypEWUQTofz3/PlzTJs2De3bt0fz5s3x9OlTbNy4kX7feYCKUPHV\npEkTHDlyBAoKCnU+S1FREe7u7mjdujUPkhEivqgIJYRl0tLS0NHRQUpKCttRBErr1q2xZcsWvHjx\nAkOGDMHy5cthZGQET09PFBYWsh2PgCZC+eXz58/w8fGBnp4eTp48iWPHjiE0NBTdunVjOxrhIXV1\ndSpChUDXrl1x/fp1nDhxAmfPnoWuri68vLxQVlbGdjSxQxOh/PPs2bOaArRFixZ4+vQpNmzYgCZN\nmrAdTWRQESreBg0ahGnTptXpDAUFBYwdOxYzZszgUSpCxBcVoYQIAFqP/z55eXlMnToVjx49gre3\nN8LDw6GhoYHFixcjIyOD7XhijyZCeefz58/w9vaGnp4ezpw5gxMnTuD69evo2rUr29EIH7Ro0QKv\nXr2if0NCokuXLrh27RoCAwNx4cIF6OrqYv/+/VSI1iMqQnnv2bNnmDp1Kv7880+oqalRAcpHVISK\ntzdv3iAkJASDBg2CvLz8bw8TyMvLY+7cufDy8qJBBEJ4gIpQQgSAiYkJFaE/8eXafGxsLGRlZdGp\nUycMGDAAoaGhVCawgJ6I8UZZWRm8vLygq6uLc+fOISAgANeuXUOXLl3Yjkb4qEGDBpCSkkJBQQHb\nUchv6Ny5M65cuYJTp04hODgYurq68PT0pEK0HtBqPO9kZGRgypQp6NChA1q2bIn09HSsX7+eClA+\noiJUfH348AE2NjaYMGECzp8/j7t370JPTw9KSko/fS6tpKSEVq1aITQ0FNu2baPn3oTwCBWhhAgA\nmgj9PRoaGti6dSuysrIwaNAgLFu2DMbGxrQ2zwIqoGuvrKwM+/fvh66uLi5cuICgoCBcvXoVnTt3\nZjsaqSd0nVDh1bFjR1y+fBmnT59GSEgIdHR0sG/fPpSWlrIdTWTRRGjdVRegHTt2RKtWrfD06VO4\nuLigcePGbEcTeVSEiqfi4mLY29vD0tISa9euBQCYm5sjJSUFwcHBsLOzQ8OGDSEnJwdlZWUoKytD\nXl4eDRo0QJ8+fRAUFITMzEzaDiKEx6gIJUQAUBFaOwoKCpg2bRoePXqE/fv3IywsDBoaGliyZAmt\nzdcDele6dsrKyuDp6QldXV0EBwfj1KlTuHLlCjp16sR2NFLP1NXV8erVK7ZjkDro0KEDQkJCcObM\nGVy5cgU6OjrYu3cvFaJ8QBOhtZeeno7JkyejY8eOaN26NRWgLKAiVPyUl5fDwcEBWlpa8PDw+Op5\nM8Mw6NmzJ4KDg/Hhwwc8e/YMN27cQHh4OFJTU1FQUIDQ0FDY2tpCQoIqG0J4jf5VESIAWrdujaKi\nIuTl5bEdRSgxDANLS0ucOXMGsbGxkJGRobX5ekK/t7+utLQU+/btg46ODkJCQnD69GlcvnwZHTt2\nZDsaYQlNhIqODh064NKlSzh37hyuXbsGDoeDPXv2UCHKI5WVlfjw4QMVd78pPT0dkyZNQqdOnaCp\nqYn09HQ4OzvT7yMLqAgVL1VVVZg0aRIkJSVx6NChn5aZLVq0QNu2bWFhYYFWrVrRsAEhfEZFKCEC\ngGEYuk4oj/x7bX7p0qUwNjbG/v37aW2ex+hJ2q8pLS3F3r17oaOjgytXruDMmTMICQlBhw4d2I5G\nWEZFqOj5888/ERwcjIsXLyI0NBQcDge7d+9GSUkJ29GEWn5+Pho2bAgpKSm2owiFLwtQLS0tpKen\nw8nJCY0aNWI7mtiiIlR8cLlcLFiwAC9fvkRQUBCkpaXZjkQI+RcqQgkRELQez1vVa/OPHz+Gp6cn\nQkNDoaGhgaVLl+LZs2dsxxMZNBH6faWlpdizZw90dHRw7do1nDt3DpcuXaIClNSg1XjRZWFhgYsX\nL+LixYsICwsDh8PBzp07qRCtJVqL/zVPnz7FxIkT0alTJ2hra1MBKkCoCBUfzs7OiIqKwsWLFyEv\nL892HELIN1ARSoiAoCKUP6qvwXP27FnExsZCSkoKHTt2xMCBA/H3339Tkfeb3r9/D19fXwwdOhTd\nu3dHdnY2GjVqhO7du+PQoUP0+wmgpKQEu3fvBofDQWhoKM6fP4/g4GD8+eefbEcjAoYmQkWfhYUF\nLly4gJCQEERGRoLD4cDDwwPFxcVsRxMqdKOkH0tLS8OECRPQpUsX6OjoICMjA+vWraMCVIBQESoe\ndu/ejYCAAFy9ehUNGzZkOw4h5DuoCCVEQFARyn8aGhpwdXVFVlYWBgwYgMWLF8PExAReXl4oKipi\nO55QOHXqFGbMmIEHDx6gXbt2UFBQwPDhw5GUlIRp06Zh5MiRbEdkTUlJCXbt2gUdHR2EhYXVTIO1\nb9+e7WhEQFERKj7atm2Lc+fO4fLly7h16xY4HA527NhBhegvoonQb0tNTcX48ePRtWtX6OrqIj09\nHWvXrqUCRgBRESr6/P39sX37doSGhkJVVZXtOISQH6AilBABYWpqiqSkJFRVVbEdReQpKChg+vTp\niI+Px759+3D9+nW0bt2a1uZ/gb6+PoKDg/Hy5Uvs2bMHysrK8PX1xZMnT9CqVSucOXMG586dYztm\nvSopKcHOnTvB4XAQERGB4OBgXLhwARYWFmxHIwJOXV2dilAx06ZNG5w9exZXr17FnTt3wOFw4O7u\nTm/G/QRNhH6tugDt1q0b9PX1qQAVAlSEirbg4GAsX74cV69ehYaGBttxCCE/QUUoIQKicePGUFZW\nxosXL9iOIja+XJuPiYmBpKQkOnbsiEGDBiEsLIzWvL+hZ8+esLOzq/n/6t8jVVVVzJo1C1wuFzdu\n3GApXf0qLi6Gh4cHOBwOIiMjERISgvPnz6Ndu3ZsRyNConnz5njz5g29ASaGzM3NcebMGVy7dg13\n794Fh8PB9u3bxb4Q5XK5CAoKgpWVFVq2bAkFBYWa66t+/vyZ7XisS01Nxbhx49CtWzcYGBggIyMD\nf/31FxWgQoCKUNF18+ZNTJ06FRcvXoSRkRHbcQghv4CKUEIECK3Hs0dTUxPbtm1DVlYW7O3tsWjR\nIlqb/4l/3zW++q6Yon5X3+LiYuzYsQMcDge3bt3C5cuXce7cObRt25btaETIyMrKomHDhnj37h3b\nUQhLzMzMcPr0aVy/fh33798Hh8OBm5ub2H7fmT59OkaPHo3ExETY2tpi0aJFsLCwQHJyMgICAnDi\nxAm2I7LiyZMnGDt2LLp37w4jIyNkZGRgzZo1UFZWZjsa+UVUhIqmuLg4DB8+HAEBAXQzTEKECBWh\nhAgQKkLZ9+Xa/N69e3Ht2jVoaGhg2bJleP78OdvxBE71RGhlZSX8/PzAMAxsbGxYTsUfRUVFcHd3\nB4fDwZ07d3D16lWcPXsWbdq0YTsaEWJ0nVAC/K8QPXXqFEJDQxEdHQ1tbW1s27YNhYWFbEerNy9e\nvMChQ4fQvHlzpKSkwMfHB5s3b8bJkydhbW0NAFi3bh3LKetXdQHao0cPGBsbIz09HatXr6YCVAhR\nESp60tLSYGdnBy8vL/Tu3ZvtOISQ30BFKCEChIpQwcEwDHr16oVz584hOjoaDMPgzz//xODBg2lt\n/v99ORG6YsUKJCUlwc7ODn379mUxFe8VFRVh+/bt4HA4uHv3Lq5du4YzZ87A3Nyc7WhEBKirq+PV\nq1dsxyACwtTUFCdPnkRYWBhiYmLA4XDg6uoqFoVo9WR0x44d/3NjJBkZGcjLy4vN9HRKSgrGjBmD\nHj16wMTEBBkZGVSACjkqQkXLy5cvYW1tjQ0bNmDo0KFsxyGE/CYqQgkRIFSECiYtLS24ubkhKysL\ntra2WLhwIUxNTeHt7S2264vVuFwudu/ejR07dsDIyAhHjx5lOxLPFBUVwc3NDRwOB/fv30doaChO\nnz4NMzMztqMREUIToeRbTExMEBQUhPDwcMTFxUFbWxtbtmzBp0+f2I7GN8bGxmjevDkePHiAvLy8\nr34sIyMDJSUlIvdG278lJydj9OjRsLS0hJmZGTIyMrBq1So0aNCA7WikjqgIFR25ubmwtrbG3Llz\nMXXqVLbjEEJqgYpQQgRI9YXv6YYAgklRUREzZsxAQkICdu/eXXNnSHFdm2cYBsXFxTXXUw0PD0ej\nRo3YjlVnhYWF2LZtG7S1tREdHY3Q0FCcOnUKpqambEcjIoiKUPIjxsbGCAwMxI0bN5CQkAAOh4PN\nmzfj48ePbEfjOTk5OVy4cAGKioowMjLCzJkzsXr1aowYMQJJSUno2rUrvLy82I7JF9UFaM+ePWFu\nbo6MjAysXLmSClARQkWoaPj06RNsbW0xcOBALF++nO04hJBaoiKUEAEiJycHTU1NPHnyhO0o5AcY\nhoGVldU31+bDw8PFZm3ex8cHhYWFMDMzQ3h4OFRVVdmOVCeFhYVwdXUFh8NBTEwMwsLCcPLkSSpA\nCV/Rajz5FUZGRjhx4gQiIyORlJQEHR0dbNq0SeQKUTMzM0yePBmlpaXw9fWFq6srzpw5AwkJCYwb\nNw7NmjVjOyJPJSUlYdSoUejVqxfatGlDBagIoyJU+JWWlmLw4MFo06YNtmzZwnYcQkgdUBFKiICh\n9Xjh8uXafP/+/TF//nyYmprCx8dHpNfmXV1d4eTkBCkpKURERAj1i9NPnz5h69at0NbWRlxcHMLD\nwxEUFAQTExO2oxExQBOh5HcYGhri+PHjuHnzJlJSUsDhcLBx40YUFBSwHa3OKisrYWVlhTVr1mDG\njBnIyMhAUVERoqOjUVlZidmzZ2PlypVsx+SJpKQkjBw5ElZWVmjXrh0yMjKwYsUKKkBFGBWhwq2i\nogJjxoxB06ZNsX///q+uk08IET5UhBIiYKgIFU6KioqYOXMmEhMTsWvXLly+fBkaGhpYvnw5MjMz\n2Y7HUxs2bMCqVavQpk0bKCsro3HjxmxHqpVPnz5hy5Yt4HA4ePz4MW7cuIHAwEAYGxuzHY2IESpC\nSW0YGBjg2LFjuH37NlJTU6Gjo4MNGzYIdSHq7++Pu3fvYtiwYXBzc4OmpmbNpkzDhg2hrq4Od3d3\nof6empiYWFOAWlhYICMjA46OjlBSUmI7GuEzCQkJVFVVsR2D1AKXy8XMmTNRWFgIf39/SEpKsh2J\nEFJHVIQSImCoCBVuDMOgd+/eOH/+PB48eAAul4v27dtjyJAhiIiIEPq1eT8/v5pJ0A4dOqC0tBQu\nLi5f/efn58d2zB/6+PEjNm/eDA6Hg8TERERGRiIgIABGRkZsRyNiiIpQUhf6+vrw9/fHnTt38PTp\nU3A4HKxfvx4fPnxgO9pvi4mJAcMw6Nmz51cfz83NhYqKCjp06ICqqirExcWxE7AOEhMTMWLECPTu\n3Rvt27enAlQM0USocOJyuXB0dERycjLOnj0LWVlZtiMRQniAilBCBAwVoaJDW1sb27dvR1ZWFmxs\nbDBv3jyYmZnBx8cHxcXFbMerlczMTDAMg8rKShw4cADFxcVYv379V/8JahH68eNHbNq0CRwOB8nJ\nybh58yaOHz8OQ0NDtqMRMaaqqor8/Hy6SR6pEz09PRw9ehR3795FRkYGdHR04OLiIlSFqIyMDLhc\nLt69e/fVx3Nzc9GsWbOaj8vIyLARr1YSEhLg4OCAPn36oEOHDnj27BmWL19OBagYoiJUOLm6uuLK\nlSsICQmhf7eEiBAqQgkRMFpaWnj//r1QvXghP/bl2vzOnTsREhKC1q1bw9HRUehW/JycnFBZWYnK\nykq8ffsWTZs2rfn/6v/Cw8PZjvmVgoICbNy4ERwOB0+ePMHt27dx7NgxGBgYsB2NEEhKSkJVVRVv\n3rxhOwoRAbq6uvDz88Pdu3fx/Plz6OjowMnJCfn5+WxH+6nevXsD+N+N+L6cks7Ly0NlZSXu3LkD\nOTk5dOnSha2Ivyw+Ph7Dhw9H37590bFjR2RkZGDZsmVQVFRkOxphCRWhwsfHxwc+Pj64fv06mjRp\nwnYcQggPURFKiICRkJCAsbExEhMT2Y5CeKx6bf7ChQt48OABqqqq0L59ewwdOlRo1+YFOXNBQQE2\nbNgAHR0dpKWl4c6dO/D394e+vj7b0Qj5Cq3HE17T1dXFkSNHcO/ePfzzzz/Q1dXFunXrBLoQtbW1\nxZAhQ/D27VsYGhpi0qRJWLlyJdatW4eHDx8C+N90liBfl7q6ALW2tkbnzp2pACU1qAgVLqdOnYKL\niwuuX78ONTU1tuMQQniMilBCBBCtx4u+6rX5zMxMWFtbY+7cuTAzM6tZNxcGgnrHzA8fPmD9+vXQ\n0dFBeno67ty5g6NHj0JPT4/taIR8k7q6Ol69esV2DCKCdHR0cOjQIdy/fx+vXr2Cjo4O1q5di/fv\n37Md7ZtOnz4NT09PmJqa4vz589ixYwfS09Ohra2N69evY968eWxH/KbHjx9j2LBh6NevH7p06YJn\nz55h6dKlVICSGlSECo/qrzWXL1+Gjo4O23EIIXxARSghAsjU1JQmQsWEkpISZs2ahaSkJHh4eCA4\nOBgaGhpYsWIFsrKy2I73U4I0Efrhwwe4uLhAR0cHz549Q1RUFPz8/KgAJQKPJkIJv3E4HBw8eBDR\n0dHIzs6Grq4u/vrrL4ErRBmGwcyZM3H79m18+PABnz9/xty5czF16tSa1XlB8ujRIwwdOhQ2Njbo\n2rUrMjIysGTJEigoKLAdjQgYKkKFw7179zB27FicOXMG5ubmbMchhPAJFaGECCCaCBU/DMOgT58+\nuHjxIu7fv4+Kigq0a9cOQ4cOxY0bNwSqcKwmKBOhHz58gLOzM3R0dJCZmYl79+7hyJEj0NXVZTsa\nIb+EilBSX7S1teHr64uHDx/i7du30NXVxZo1a5CXl8d2tO+qvlmSIKkuQPv374/u3btTAUp+iopQ\nwZeYmIhBgwbBz88P3bp1YzsOIYSPqAglRABVF6GCWH4R/tPW1oa7uzuysrLQt29fzJkzB+bm5gK5\nNs/m39H8/Hw4OTlBR0cHL168wP3793H48GFaYyJCh1bjSX3T0tLCgQMHEBMTg3fv3kFPTw+rV69G\nbm4u29H+Iy8vD02bNmU7BgAgLi4OQ4YMga2tLXr06IGMjAwsXryYClDyU1SECrbnz5/DxsYGHh4e\nsLW1ZTsOIYTPqAglRACpqKhAVlaWXhiLOSUlJcyePRtJSUnYsWOHwK3NszUR+v79e6xbtw66urp4\n+fIl7t+/j0OHDoHD4bCSh5C6oolQwhZNTU34+PggJiYGeXl50NfXx6pVqwSqEBWEidC4uDgMHjwY\ndnZ26NmzJzIyMrBo0SIqQMkvoyJUcL158wZ9+/bFqlWrMGbMGLbjEELqARWhhAgoWo8n1b5cm793\n7x7Ky8vRrl07DBs2DJGRkaxOZdbnY79//x5r166Frq4uXr9+jQcPHuDgwYNUgBKhR0UoYZumpia8\nvb0RGxuL/Px86OnpYeXKlXj37h3b0VidCP2yAO3VqxcyMjKwcOFCyMvLs5KHCC8qQgXThw8fYGNj\ngwkTJmDu3LlsxyGE1BMqQgkRUCYmJlSEkv/gcDjYsWMHsrKy0Lt3b8yaNQtt2rSBr69vva/N19dE\naF5eHtasWQNdXV28efMGDx8+hK+vL7S1tevl8QnhNypCiaDQ0NCAl5cXHj16hI8fP0JfXx+Ojo7I\nyclhLRMbE6GxsbEYNGgQ7O3tYWVlRQUoqTMqQgVPcXEx7O3tYWlpibVr17IdhxBSj6gIJURA0UQo\n+RElJSXMmTMHycnJ2L59Oy5cuAANDQ2sXLkSL168qLcc/JwIzc3NxerVq6Gnp4d3794hJiYGBw4c\ngJaWFt8ekxA2NG7cGGVlZSgqKmI7CiEAgNatW8PT0xOPHz9GUVERDAwMsHz58novRKuqqpCfn48m\nTZrUy+PFxMRg4MCBGDBgAPr06YP09HQsWLCAClBSZ1SECpby8nI4ODhAS0sLHh4eAnMDUEJI/aAi\nlBABRUUo+RUMw6Bv374IDg7GvXv38PnzZ7Rt25Zva/OlpaW4fv06Nm/ejPHjx6OoqAgODg7Ytm0b\nIiIi8Pnz5zo/Rm5uLlatWgV9fX3k5eUhJiYGPj4+0NTUrPsvgBABxDAMTYUSgdSqVSvs27cP8fHx\nKCkpgYGBAZYtW4a3b9/Wy+MXFBRAUVER0tLSfH2chw8fYsCAARg4cCD69u2LjIwMzJ8/nwpQwjNU\nhAqOqqoqTJo0CZKSkjh06BAkJKgSIUTc0L96QgSUsbExUlNTUV5eznYUIiS+tzZ/8OBBlJSU1Ons\nt2/fYvHixVBRUYGDgwOcnZ1x6dIlVFZW4vTp01i7di0GDx4MVVVVrFq1Cvn5+b/9GO/evcPKlSuh\nr6+P/Px8xMbGwtvbmwpQIhaoCCWCrGXLlti7dy/i4+NRVlYGQ0NDLF26FG/evOHr4/J7Lb66AB08\neDD69etXU4DKycnx7TGJeKIiVDBwuVwsWLAAL1++RFBQEN/fZCGECCYqQgkRUAoKCmjZsiWePn3K\ndhQiZKrX5pOSkuDm5obz589DQ0MDq1atqtXafGBgIHR1deHp6YnCwkJ8/PjxPwX958+f8fHjRxQU\nFMDDwwMcDgeXLl36pfPfvXuHFStWwMDAAAUFBYiLi4OXlxc0NDR+OyshwkpdXR2vXr1iOwYhP9Sy\nZUvs2bMHCQkJKC8vh5GREZYsWcLTQpTL5eLly5eIiIhASEgIZGRk8OHDB56dDwDR0dGwt7fH4MGD\nYWNjg/T0dMybN48KUMI3VIQKBmdnZ0RFReHixYs08U2IGKMilBABRuvxpC4kJCRgbW2N4OBgREVF\nobS0FG3btsXw4cNx8+bNn67Nc7lcLFu2DFOnTsWnT59+ee29rKwM+fn5GDlyJLZs2fLdz8vJyYGj\noyMMDAzw6dMnxMXFYf/+/WjduvVv/ToJEQU0EUqEibq6Onbv3o3ExERUVlbCyMgIixcvRnZ2dq3P\njI2NxdixY9GwYUPo6upiyJAhWLNmDZ4+fQpVVVWoqalhzZo1ePnyZa0f48GDB7Czs8PQoUPRv39/\npKenY+7cuVSAEr6jIpR9u3btQkBAAK5evYqGDRuyHYcQwiIqQgkRYFSEEl7R0dGBh4cHMjMz0atX\nL8yYMQNt27bFoUOHvrs2v379euzfv7/Wd6MvLi7Gxo0bsXfv3q8+npOTg+XLl8PAwABFRUV49OgR\nPD09qQAlYo2KUCKM1NTUsGvXLiQmJoLL5cLY2BiLFi36rUI0Ozsb1tbW6N69O4KCgvDp0yeUlpai\noKAAxcXFqKioQHl5ObKzs+Hu7g5dXV0sX74cZWVlv/wY1QXosGHDYGdnRwUoqXdUhLLr6NGjcHd3\nR2hoKFRVVdmOQwhhGRWhhAgwKkIJrzVo0ABz585FcnIytm3bhrNnz9aszf/zzz81nxcdHQ1XV9da\nl6DViouL4ejoiCdPnuDt27dYtmwZDAwMUFJSgvj4eOzbtw+tWrWq6y+LEKFHq/FEmKmpqWHnzp1I\nSkoCwzAwNjbGggULfvp3OiwsDPr6+rhx4waKi4t/WhSVlZWhtLQUnp6eMDIy+ur71rfcv38ftra2\nGDZsGOzt7ZGeno45c+ZAVlb2t3+NhNQFFaHsuXjxIhwdHXHt2jW67BIhBAAVoYQINCpCCb9Ur81f\nunQJd+7cQUlJCdq0aQMHBwfcuHEDo0aNqvMNlqqVlZXB0tIShoaGKCsrQ3x8PPbu3YuWLVvy5HxC\nRAFNhBJR0KJFC3h4eCA5ORlSUlIwNTXF/Pnzv1mIhoWFYeDAgfj06dNv3xiyuLgYWVlZ+PPPP795\n9r1799C/f384ODhgwIABSE9Px+zZs6kAJayhIpQdkZGRmDZtGoKDg2FoaMh2HEKIgGC4P7tIHCGE\nNZWVlWjQoAFycnKgpKTEdhwi4j59+oSjR49iy5YtyM7ORlVVFc/OlpKSwoULF2Bra8uzMwkRJU+f\nPoWNjQ0yMjLYjkIIz7x9+xZubm44dOgQxowZg5UrV6Jly5Z4/fo19PX1UVhYWKfzpaSkYGJigocP\nH0JSUhL37t2Di4sLkpKSsHr1akyePJnKTyIQCgoK0KpVK3z8+JHtKGIjNjYWNjY2CAgIQO/evdmO\nQwgRIDQRSogAk5SUhKGhIZKSktiOQsRA9dq8iYkJT0tQAKiqqkJAQABPzyRElFRPhNL700SU/PHH\nH9i+fTtSUlIgLy8PMzMzzJ07Fw4ODigtLa3z+RUVFXj69CkWLVoEGxsbjBw5EoMHD8bTp08xa9Ys\nKkGJwKCJ0PqVlpYGOzs7eHt7UwlKCPkPKkLBJv3JAAAgAElEQVQJEXC0Hk/qE5fLRVRUFM/Praqq\nQkREBM/PJURUKCoqQlZWFvn5+WxHIYTn/vjjD7i5ueHJkyf4+PEjoqKiUFFRwZOzi4qKsG/fPtjb\n2yMtLQ0zZ86kApQIHCpC688///wDa2trbNq0CUOGDGE7DiFEAFERSoiAoyKU1KfXr1//9rXaflVO\nTk6d1yAJEWV0nVAi6lRVVfH582dISPD2JYiioiKUlZWpACUCi4rQ+pGbmwtra2vMmzcPU6ZMYTsO\nIURAURFKiICjIpTUpzdv3vDthaSsrCzevXvHl7MJEQVUhBJRx+VyERISwvPLrxQWFuL48eM8PZMQ\nXqIilP8+ffqE/v37Y/DgwVi2bBnbcQghAoyKUEIEnImJCRISEui6caRe8PvvGf09JuT71NXVv3kH\nbEJERWZmJt++D8TFxfHlXEJ4QUJCAlwul54H8UlpaSkGDx6Mdu3aYfPmzWzHIYQIOCpCCRFwLVq0\nQFVVFd6+fct2FCIGmjdvjs+fP/Pl7LKyMqioqPDlbEJEAU2EElGXlpYGaWlpvpydl5fHt+9fhNQV\nwzCQkJCgqVA+qKiowJgxY9C0aVN4enqCYRi2IxFCBBwVoYQIOIZhaD2e1Bt1dXVISUnx5WwVFRU0\naNCAL2cTIgqoCCWijp9FpYSEBBWhRKDRejzvcblczJw5E4WFhfD394ekpCTbkQghQoCKUEKEABWh\npL68f/8eampqPD9XQkICPXv25Pm5hIgSWo0nok5RUZFvZ3O5XMjJyfHtfELqiopQ3uJyuXB0dERy\ncjLOnj1LN0sjhPwyKkIJEQJUhBJ+Ki8vx6VLlzB8+HBoa2ujefPmkJeX5+ljyMrKYuHChTw9kxBR\nQxOhRNSZmJigtLSUL2erqanxbaOBEF6gIpS3XF1dceXKFYSEhEBJSYntOIQQIUJFKCFCgIpQwg8J\nCQlYunQpWrVqhc2bN8Pa2hpZWVkIDw+Hqqoqzx6HYRh8/vwZPj4+NO1GyA9QEUpEnaqqKt8Ki06d\nOvHlXEJ4hYpQ3vHx8YGPjw+uX7+OJk2asB2HECJkqAglRAiYmJggJSWFnjyROsvNzcWePXtgYWEB\nW1tbyMnJITIyElFRUZgxYwYaNWoECQkJBAYG8mwqVE5ODrdu3UKzZs1gZmaG1atX48OHDzw5mxBR\n0rx5c+Tk5NDXeiLSxo8fDxkZGZ6eqaSkhKlTp/L0TEJ4jYpQ3jh16hRcXFxw/fp1vlzOiRAi+qgI\nJUQINGjQAKqqqsjIyGA7ChFC5eXlCA4OxrBhw6Cjo4N79+5h69atyMzMxKZNm6Cvr/+fn9OpUycs\nWbKkztdzU1BQwKZNm9C5c2ds3boVjx8/Rk5ODvT09LBjxw6+rUgSIoxkZGTQuHFj5OTksB2FEL5Z\nsGABJCR4+xKkqKgIUVFRKCgo4Om5hPASFaF1d/36dcybNw+XL1+Gjo4O23EIIUKKilBChAStx5Pf\nFR8fjyVLlqBly5ZwdXVF//79kZWVhePHj6Nv374/vbPmhg0bMGXKFCgoKNTq8RUUFODo6IjFixfX\nfKxly5bw9fVFREQEIiMjoa+vj6NHj9ILA0L+H63HE1GXn58PRUVFnpWhCgoK8PLyQmZmJnR0dLB5\n82YUFhby5GxCeImK0Lq5e/cuxo4dizNnzsDc3JztOIQQIUZFKCFCwtTUFImJiWzHIAIuNzcXu3fv\nRrt27WBvbw8FBQXcvn0bt2/fxrRp09CwYcNfPothGOzatQteXl5QUlKCtLT0L/08GRkZNGzYEMeO\nHYOTk9M3P8fY2BgXLlzA8ePH4e3tjXbt2uHKlSvgcrm/nI8QUURFKBFVJSUlWLFiBfr374+tW7dC\nTU2tzmWovLw87O3tMWPGDPj5+eHWrVtISEgAh8OBm5sbiouLeZSekLqjIrT2EhMTMXjwYPj5+aFb\nt25sxyGECDkqQgkREjQRSr6nvLwcFy5cwJAhQ6Cjo4Po6Gi4ubkhMzMTGzduhK6ubq3PZhgG48eP\nR1paGqZNmwZFRUUoKyv/Z5pUSkoKysrKaNCgAebNm4f09HQMGTLkp+d369YNt2/fxvr167FkyRJY\nWVnhwYMHtc5LiLBTV1enm4oRkRMZGQlzc3NkZWUhPj4e06ZNQ2RkJBo3blzrMlReXh5mZmbw8/Or\n+ZiBgQECAgIQFhaG+/fvg8PhYNeuXXQZFiIQJCQkqAithWfPnsHGxgY7d+6Era0t23EIISKA4dL4\nDSFCISkpCUOHDkVqairbUYiAePz4MY4cOYITJ05AT08PkyZNgoODA5SVlfn2mMXFxYiIiEB0dDQe\nPnyI4uJiKCoqokOHDujQoQN69uwJWVnZWp1dUVGBI0eOwNnZGZ07d8bmzZvrVOISIoycnZ1RVVWF\n9evXsx2FkDorKCiAo6MjQkJC4OnpiYEDB37148+fP4ednR2ysrJ+a3pTQUEB/fv3h7+//w9v7Pfo\n0SM4OTkhJiYGq1atwrRp02r9PYqQutLU1ERERAS0tLTYjiI03rx5g27dumHJkiWYM2cO23EIISKC\nilBChMT/sXefUVFd/9fA9wCCFMUeFTvSm4C9ISpYsICF2BVF7JXYC1bEChYkYscKKihWiA0LCBYE\nBhWwxCgW1FipAvO8+C3z/JOYBGWGO2V/1sqLmJlzN1kCM3u+59zPnz9DX18fb968kdrdvEnxZGVl\nYf/+/di1axfevn2L4cOHY9iwYUp1YHxOTg42bNiAtWvXon///li4cCFq1qwpdCyiMhEcHIyEhARs\n27ZN6ChEpRIZGYkJEyage/fuWLVq1T8ezVJYWIgVK1bAz88PIpEI2dnZ/7imnp4edHV1ERwc/LdS\n9d/cuHEDPj4+SElJwfz58+Hh4VHi416IpMXQ0BBRUVFK9ZpNlt69ewcHBwf069cPCxYsEDoOESkR\nbo0nUhDlypWDkZER7ty5I3QUKmMFBQU4evQoXF1dYWxsjFu3bmHdunV49OgRlixZonQvqHV0dDB7\n9mzcu3cPOjo6sLCwwMKFC/HhwwehoxHJHLfGk6LLysrCgAED4O3tjT179mDLli3/ej61hoYGFixY\ngKysLKxbtw4ODg6oVKnSH0ewqKuro3bt2nB1dUVYWBiePXv2TSUoADRt2hQnT55EaGgoDh8+DBMT\nE+zcuROFhYWl+lqJvgXPCC25nJwc9OjRA46Ojpg/f77QcYhIybAIJVIglpaWPCdURUgkEiQmJmLK\nlCmoU6cO/P390bt3bzx58gQhISHo2LGj1O64K6+qVq2KNWvW4NatW3j8+DGMjY2xYcMGFBQUCB2N\nSGZ4syRSVBKJBHv27IGVlRXq16+P5ORkdOjQocTP19XVhZeXFy5evIi3b98iISEBhoaGyM7ORmZm\nJiIiItCtW7dS/e5r1aoVoqOjsWvXLuzevRtmZmbYu3cvyykqEyxCS6agoAD9+vVDo0aNsG7dOohE\nIqEjEZGSUe530URKhjdMUn5ZWVnw9/dHkyZN4ObmhkqVKiEuLg4xMTHw8PBAhQoVhI5Y5urXr4/d\nu3cjOjoaUVFRf9wMo7i4WOhoRFLHIpQU0ePHj9GtWzesXbsWp06dwsqVK0t9jI+Ojg40NDRkcqZn\n+/btceHCBWzZsgVBQUGwsrJCWFgYf6+QTLEI/W/FxcUYMWIENDQ0sH37dqX/0J+IhMGfLEQKhEWo\nciooKEB4eDh69eoFY2Nj3L59GwEBAXj48CEWL14MQ0NDoSPKBWtra5w8eRI7duxAQEAAmjZtil9+\n+UXoWERSVb16dbx//x75+flCRyH6T8XFxdi4cSPs7e3h4OCA69evw97eXipry/o2BiKRCB07dsSV\nK1fg7++PNWvWoEmTJoiIiJD5tUk1sQj9dxKJBJMmTUJmZiZCQ0N5ji8RyQyLUCIFwiJUeUgkEty6\ndQuTJ0+GgYEBNmzYgD59+uDJkyfYvXs3HB0d+Sn4P+jQoQOuXbuGefPmYcKECXBycsLNmzeFjkUk\nFWpqaqhZsyaeP38udBSif3Xnzh20bdsWYWFhuHr1KubMmSP14qIstsSKRCJ06dIF8fHx8PX1xdKl\nS2Fvb48TJ06wECWpYhH673x8fBAXF4fIyEjeGJaIZIrvsokUSN26dZGbm4vXr18LHYW+04sXL7B2\n7VpYW1ujb9++qFKlCuLj43Hx4kWMGDFCJbe+fw+RSIS+ffsiNTUVffv2Rc+ePTFo0CA8fPhQ6GhE\npcbt8STPCgoKsHTpUjg4OGDIkCGIiYmBiYmJ1K9T1iWkSCRCjx49cPPmTSxYsABz5sxBy5YtERUV\nxUKUpIJF6D9bv349QkNDcebMmX+9uRoRkTSwCCVSICKRiDdMUkD5+fk4cuQIevbsCVNTU4jFYmza\ntAkPHjzAokWL0KhRI6EjKqxy5cph7NixSE9Ph7m5OZo3b47JkycjKytL6GhE341FKMmr69evo2nT\nprh27Rpu3ryJ8ePHy3T3ghA3SRGJRHBzc0NSUhK8vb0xdepUtGvXDufPny/zLKRcWIR+XUhICNau\nXYvo6GjUqFFD6DhEpAJYhBIpGG6PVwwSiQQ3b97EpEmTUKdOHQQGBqJfv354+vQpdu7cCQcHB259\nlyI9PT3Mnz8fd+/ehZqaGszNzbFkyRJ8+vRJ6GhE38zAwACZmZlCxyD6Q05ODn766Sf07NkTs2fP\nxokTJ1CvXj2ZXlPoKUw1NTW4u7tDLBZj7NixGDNmDBwdHXH58mVBc5HiYhH6d5GRkZg5cyaioqJQ\nv359oeMQkYrgu3AiBcMiVL69ePECa9asgZWVFfr374/q1avj+vXrOH/+PIYPHw49PT2hIyq16tWr\nIyAgAAkJCUhLS4OxsTGCgoLw+fNnoaMRlRgnQkmenD9/HlZWVnj+/DlSUlIwaNCgMpvUFGIi9K/U\n1dUxZMgQ3L17F8OGDcOwYcPg7OyMa9euCR2NFAyL0D+LiYmBp6cnjh8/DjMzM6HjEJEKYRFKpGBY\nhMqf/Px8HD58GD169ICZmRnu3LmDzZs34/79+1i4cCEaNGggdESV06hRI+zbtw8nT57E0aNHYW5u\njkOHDgk+YURUEixCSR68e/cOnp6eGDFiBDZs2IB9+/ahevXqZXZ9eft5raGhAQ8PD6SlpaFfv35w\nd3eHi4sLbty4IXQ0UhAsQv+/W7duoX///jhw4ACaNWsmdBwiUjEsQokUwJEjRzB58mS0b98eLi4u\niI+Px9ChQ7/6WA8PD6ipqf3rP05OTmX8FSgfiUSC69evY8KECTAwMEBQUBB+/PFHPH36FDt27ED7\n9u259V0O2NraIioqCkFBQfDz80OLFi1w4cIFoWMR/StujSehRUREwMLCAlpaWhCLxXBxcSnzDBKJ\nRC4mQv9KU1MTXl5eyMjIQPfu3dG7d2+4uroiKSlJ6Ggk51iE/k96ejpcXFywZcsWdOrUSeg4RKSC\nNIQOQET/bdmyZUhOToaenh7q1q2LO3fuIDs7+6uPdXNzQ8OGDb/630JCQvDo0SN0795dlnGV2vPn\nz7F3717s2rUL+fn5GD58OG7evMlzjeRc586dcf36dYSFhcHT0xPGxsbw8/ODjY2N0NGI/oYToSSU\nFy9eYOLEiUhJScHBgwfRrl07QfPIYxH6hZaWFiZMmICRI0diy5Yt6Nq1K9q2bYtFixbBwsJC6Hgk\nh1iEAk+ePIGzszOWL18ONzc3oeMQkYoSSeRt3wkR/U1MTAzq1KkDQ0NDxMTEoEOHDujQocM3Tba9\nf/8etWvXRnFxMTIzM1GlShUZJlYueXl5iIyMxK5duxAXF4e+fftixIgRaNOmjVy/SaOvKygoQHBw\nMJYtWwZnZ2csWbKExxeQXHn37h3q1auHDx8+CB2FVIREIsGuXbswa9YseHp6YuHChShfvrygmZKT\nkzF48GCFOQ4oOzsbmzdvxpo1a9CpUyf4+PjAxMRE6FgkR7p3744JEyYIMmEtD16/fo127dph1KhR\n+Omnn4SOQ0QqjPs2iRSAg4MDDA0N//Rn7969+6Y1QkJCkJubi759+7IELQGJRIKEhASMHz8eBgYG\nCA4OxqBBg/D06VNs27YNbdu2ZQmqoDQ1NTFx4kRkZGSgYcOGsLe3x/Tp0/H69WuhoxEBAPT19VFY\nWIiPHz8KHYVUwMOHD+Hs7IxNmzYhOjoavr6+gpegXyjS71ldXV3MmDED9+/fh6WlJdq2bYvhw4fj\nwYMHQkcjOaHKE6EfP35Et27d4OrqyhKUiATHIpRIAYlEIrx9+/abnrN161aIRCJ4eXnJKJVyyMzM\nxMqVK2Fubo7BgwfDwMAAiYmJOHv2LIYMGQJdXV2hI5KUVKhQAYsXL0Zqairy8/NhamoKX19f5OTk\nCB2NVJxIJIKBgQG3x5NMFRUVwd/fH82bN4eTkxPi4+PRpEkToWP9QVE3rVWoUAFz587F/fv30ahR\nI7Ro0QKenp54/Pix0NFIYKpahObl5aF3796ws7ODr6+v0HGIiFiEEimqb5kIvXbtGsRiMUxMTNC+\nfXsZplJMeXl5CA0NRbdu3WBpaYn79+9j27ZtSE9Px7x581CvXj2hI5IM1axZE4GBgYiLi0NSUhKM\njY2xdetWFBYWCh2NVBjPCSVZEovFaNOmDY4dO4a4uDjMnDkTGhryd+sARZoI/St9fX34+PggIyMD\nNWvWhJ2dHcaNG4enT58KHY0EoopFaGFhIQYOHIhq1aph8+bNCv09TUTKg0UokYL6+PEj8vPzS/TY\nLVu2QCQSYfTo0TJOpTgkEgmuXbuGsWPHwsDAANu3b8eQIUOQmZmJrVu38vxPFWRkZITQ0FBERETg\nwIEDsLS0REREhMJOJZFiYxFKspCfn49FixbB0dERI0eOxPnz52FkZCR0rK9Slp+9lStXxrJly5CW\nloaKFSvC2toakydPxvPnz4WORmVM1YpQiUQCLy8v5OTkYM+ePVBXVxc6EhERABahRAqrQoUKuHfv\n3n8+7sOHDzh06BA0NTUxfPjwMkgm3zIzM+Hn5wczMzMMGzYM9erVw+3btxEdHY3BgwdDR0dH6Igk\nsGbNmuHcuXMICAjAokWL0Lp1a1y+fFnoWKRiDAwMkJmZKXQMUiLXrl2DnZ0dEhMTcfv2bXh5eUFN\nTb7fCijTB5LVqlXDypUrcffuXWhoaMDCwgLe3t7IysoSOhqVEVUqQiUSCWbMmIG7d+8iPDwcWlpa\nQkciIvqDfL/6IaJ/VKlSpRLdSXXPnj3IyclR6Zsk5ebm4sCBA+jSpQusrKzw6NEj7NixA2lpaZg7\ndy7q1q0rdESSMyKRCF27dkViYiImTJiAoUOHolevXhCLxUJHIxXBiVCSlk+fPmHq1Klwc3ODj48P\njh49CgMDA6Fj/SdlmQj9qx9++AHr1q2DWCxGQUEBzMzMMHv2bLx580boaCRjqlSE+vn5ISoqCidP\nnuT5+kQkd1iEEimokhahX26SNGbMmDJIJT8kEgni4uIwZswYGBgYYNeuXRgxYgQyMzOxZcsWtG7d\nWqkmTUg21NTUMGTIEKSlpcHR0REdO3bEyJEj8eTJE6GjkZJjEUrSEB0dDSsrK7x9+xZisRju7u4K\n87tPIpEoTNbvUbt2bWzcuBGJiYl49+4djI2NsWDBgm++GSYpDlUpQrds2YKtW7ciKipKZYcwiEi+\nsQglUlCVK1f+zyI0ISEBycnJMDExQbt27coombCePn2KFStWwNTUFB4eHmjYsCGSk5MRFRWFgQMH\nQltbW+iIpIC0tLQwbdo0ZGRkoFatWmjSpAlmzpzJN6wkM7xrPJXG77//jhEjRsDLywtBQUHYvXs3\nqlatKnSsb6bMRegX9erVw88//4wbN27g2bNnMDIywpIlS/Dhwweho5GUqUIRGhYWhiVLliA6Ohq1\na9cWOg4R0VexCCVSUCWZCP1ykyQvL68ySiWMnJwc7N+/H87OzrC2tsbjx4+xe/du3L17F7Nnz0ad\nOnWEjkhKQl9fH8uXL0dKSgrev38PY2NjrF69Grm5uUJHIyVTu3ZtnhFK30wikeDQoUOwtLRExYoV\nkZKSgq5duwod67so69b4f9KwYUNs374dcXFxuH//Pho3bowVK1bg06dPQkcjKVH2IjQqKgqTJk3C\n6dOn0bhxY6HjEBH9IxahRArg2LFj8PDwgIeHB/z8/AAAKSkpePHiBQYNGoQZM2b87TkfP35EaGgo\ntLS0MGzYsLKOLHMSiQRXr16Fl5cX6tSpg5CQEIwcORKZmZn4+eef0bJlS5WYJCFh1K5dG1u2bMHl\ny5cRFxcHY2Nj7Ny5U6nf4FDZqlWrFp49e6ZyZRB9v2fPnqFPnz5YuHAhDh8+jA0bNqBChQpCxyoV\nVfw9bmRkhJCQEMTExCApKQmGhoZYs2YNcnJyhI5GpaTMRWhcXByGDBmC8PBwWFtbCx2HiOhfsQgl\nUgC3b99GSEgIQkJCEB0dDZFIhEePHqGwsBChoaEIDw//23P27duH3Nxc9OnTR6nO5/ntt9+wfPly\nmJiYwNPTE4aGhkhJScGZM2cwYMAAbn2nMmVqaorw8HCEhYVhx44dsLGxwfHjx1leUanp6OhAR0cH\nv//+u9BRSM5JJBJs27YNNjY2sLKywu3bt9G6dWuhY5Waqv8cNTMzw8GDB3H27FnExcWhcePG2LBh\nA/Ly8oSORt9JWYtQsVgMV1dXhISEoE2bNkLHISL6TyxCiRSAj48PioqK/vaPp6cnNm3ahAcPHvzt\nOWPHjkVRURH27t0rQGLpysnJwd69e+Hk5ARbW1s8ffoUe/bswZ07dzBr1iyFuPstKbdWrVrh0qVL\n8PPzw5w5c9C+fXvExsYKHYsUnIGBAbfH07+6f/8+OnXqhODgYJw7dw5LliyBlpaW0LGkRhUnQv/K\nysoKR44cwcmTJ3H27FkYGRkhKCgI+fn5Qkejb6SMRejDhw/RtWtXBAQEoFu3bkLHISIqERahRArM\nysqqRHeOV0QSiQRXrlyBp6cnDAwMsH//fowePRqZmZkICgpCixYt+AaJ5IpIJEKPHj2QlJSEUaNG\nYcCAAXBzc8O9e/eEjkYKineOp39SWFiINWvWoGXLlujRowfi4uKUbjuqqk+E/pWtrS0iIyMRHh6O\nyMhImJiYYNu2bfj8+bPQ0aiElK0IffHiBZydnTF37lwMHDhQ6DhERCXGIpRIgSljEfr48WMsW7YM\nRkZG8PLygrGxMVJTU3Hq1Cm4u7ujfPnyQkck+lfq6uoYMWIE0tLS0Lp1a7Rr1w5eXl4stOibsQil\nr0lKSkKrVq1w+vRpxMfHY/r06VBXVxc6lkzwA8+/a9asGU6fPo39+/fj4MGDMDU1xe7du1FYWCh0\nNPoPylSEvnv3Dl26dMHw4cMxfvx4oeMQEX0TFqFECuxLEaroUxPZ2dnYs2cPOnXqBDs7Ozx//hwH\nDhxAamoqZs6cidq1awsdkeibaWtrY8aMGUhPT0flypVhZWWFuXPn4t27d0JHIwXBrfH0f+Xl5WH+\n/PlwcnLC2LFjcfbsWRgaGgodS2YU/bWNrLVu3Rpnz57Fjh07sH37dlhYWGD//v1KU7QpI2UpQnNy\nctCjRw84Ojpi/vz5QschIvpmLEKJFFi1atWgra2Np0+fCh3lm0kkEly6dAmjRo1CnTp1cPDgQYwd\nOxaZmZkIDAxEs2bNOAlCSqFy5cpYuXIlbt++jZcvX8LY2Bjr1q3j+W70nzgRSl9cvXoVtra2uHPn\nDm7fvo1Ro0Yp/e9IiUSi9F+jNDg4OCAmJgaBgYHYuHEjrK2tcejQIRQXFwsdjf5CGYrQgoIC9OvX\nD40aNcK6dev4PUpEColFKJGCs7S0VKjt8b/++iuWLFmCxo0bY9y4cTAzM8OdO3dw8uRJ9O/fn1vf\nSWnVrVsX27dvx/nz53Hx4kWYmJhgz549Cv+miGSHRSh9/PgRkyZNQv/+/bFs2TKEh4er1C4Jliwl\nIxKJ0LlzZ8TGxmLt2rVYtWoVbG1tcfToUU7WyhFFL0KLi4sxYsQIaGhoYPv27VBTY5VARIqJP72I\nFJwinBP66dMn7N69Gx07dkTTpk2RlZWF0NBQiMVi/PTTT6hVq5bQEYnKjKWlJSIjI7Fnzx4EBQXB\nzs4OZ86c4ZtV+hsDAwMWoSrs9OnTsLS0RHZ2NsRiMfr27St0pDLFn4nfTiQSoWvXrkhISMCyZcuw\nePFiNG3aFCdPnuT/TzmgyEWoRCLBpEmTkJmZidDQUJQrV07oSERE341FKJGCk9citLi4GDExMfDw\n8EDdunVx+PBhTJgwAZmZmdi0aROaNm3KSQ9Sae3atcPVq1exePFiTJs2DZ06dcL169eFjkVypHbt\n2jwjVAW9fv0aQ4cOxYQJE7Bt2zbs2LEDVapUETqWIPg64fuIRCL07NkTN2/exLx58zB79my0atUK\n0dHRLEQFpMhFqI+PD+Li4hAZGQltbW2h4xARlQqLUCIFJ29F6KNHj7B48WI0btwYEydOhKWlJe7e\nvYvjx4+jb9++0NLSEjoikdwQiURwdXVFSkoKBg4cCFdXV7i7uyMjI0PoaCQHfvjhB7x69Yp3g1YR\nEokEBw8ehJWVFapXr46UlBQ4OTkJHUswLOxKT01NDX369EFSUhKmTZuGKVOmoH379rhw4YLQ0VSS\nohahAQEBCA0NxZkzZ6Cvry90HCKiUmMRSqTgzM3NkZ6ejs+fPwuW4dOnT9i1axccHR3RvHlzvHnz\nBocPH0ZycjK8vb1Rs2ZNwbIRKQINDQ2MHj0aGRkZsLW1RatWrTB+/Hi8ePFC6GgkoHLlyqFq1arI\nysoSOgrJ2NOnT9GrVy8sW7YMR48exbp166Crqyt0LMFxIlQ61NTU8OOPP0IsFsPLywujR49Gx44d\ncfXqVaGjqRQ1NTWFu4lVSEgI1q1bh+joaNSoUUPoOEREUsEilEiBJSUlwcfHBxKJBJUrV4ampiZ0\ndHRgamqKkSNH4vTp0zJ7wVVcXIyLF26vUuAAACAASURBVC9ixIgRqFOnDsLDwzFp0iQ8ffoUGzZs\ngJ2dHd/AEH0jHR0dzJkzB2lpadDW1oaFhQV8fHzw4cMHoaORQAwMDLg9XokVFxfj559/hq2tLZo1\na4Zbt26hRYsWQseSC5wIlT51dXUMHToU9+7dw5AhQzBkyBB06dIF8fHxQkdTCYo2ERoZGYmZM2ci\nKioK9evXFzoOEZHUsAglUkAJCQlo0qQJWrduDX9/f+Tn5yM7OxufP39Gbm4u0tLSsHPnTri7u6N2\n7doICQmR2huKhw8fwsfHB4aGhpg8eTKsra2RlpaGyMhI9OnTh1vfiaSgatWqWLt2LW7duoVHjx7B\n2NgYGzduREFBgdDRqIzxzvHKKz09HY6Ojti9ezcuXryIhQsXQlNTU+hYcoUfqMqGhoYGRo4cibS0\nNPTp0wf9+/dHjx49cPPmTaGjKTVFKkIvXrwIT09PnDhxAmZmZkLHISKSKhahRAqkqKgIM2bMQIcO\nHZCUlIScnJx/fUH16dMnvHz5EuPHj0enTp3w5s2b77rux48fsXPnTjg4OKBFixZ49+4dwsPDkZSU\nhOnTp+OHH3743i+JiP5F/fr1ERISgqioKJw+fRpmZmY4cOCAwm2to+/HIlT5fP78GX5+fmjdujX6\n9OmDK1euwMLCQuhYcocTobKnqamJMWPGICMjA127dkWvXr3g5uaG5ORkoaMpJUUpQm/evAl3d3cc\nPHgQTZs2FToOEZHUsQglUhBFRUXo168fNm/ejNzc3G96bnZ2Nq5cuQJ7e/sSnzVXXFyM8+fPY9iw\nYahbty6OHj2KqVOnIjMzE+vXr4etrS0nNYjKiI2NDU6dOoVt27bB398fzZo1w9mzZ4WORWWAW+OV\nS2JiIlq0aIHz58/j+vXrmDJlCtTV1YWOJZckEglfZ5QRLS0tTJw4Effv30f79u3h7OwMd3d33Llz\nR+hoSkURitC0tDT06NEDwcHB6Nixo9BxiIhkgkUokYKYNm0aoqOjkZOT813P//z5M549e4aOHTv+\n642VHjx4gIULF6Jhw4aYNm0a7OzskJ6ejmPHjsHNzY3b9ogE5OjoiPj4eMyZMwfjx4+Hs7Mzbt26\nJXQskiFOhCqH3NxczJkzB127dsWUKVMQFRWFhg0bCh1L7rEILVva2tqYNm0aHjx4gKZNm6JDhw4Y\nPHgw0tPThY6mFOS9CH3y5AmcnZ3h6+sLV1dXoeMQEckMi1AiBXDlyhVs27btu0vQLz5//oxHjx7B\n19f3T3/+4cMHbN++He3bt0erVq3w4cMHHDt2DElJSZg6dSrvEkkkR0QiEfr164fU1FS4ubnBxcUF\ngwYNwsOHD4WORjLAIlTxXbp0CTY2Nnjw4AGSk5MxfPhwFnwlwK3xwtHV1cXMmTPx4MEDmJubo02b\nNhgxYgR/z5SSPBehr1+/hrOzMyZPngwPDw+h4xARyRSLUCI5J5FI4OHh8c3b4f9JTk4O/Pz88OzZ\nM5w7dw5Dhw5FvXr1cOLECXh7e+Pp06cICAhAkyZNpHI9IpKNcuXKYdy4ccjIyICZmRmaN2+OKVOm\n4NWrV0JHIyliEaq4Pnz4gHHjxmHQoEFYtWoVwsLCeKb2N2JhLKwKFSpg3rx5yMjIQIMGDdC8eXOM\nHj0ajx8/FjqaQpLXIvTjx4/o1q0b3Nzc4O3tLXQcIiKZYxFKJOdiY2Px/Plzqa5ZWFgIMzMz/PTT\nT2jatCkyMjIQERGB3r17c+s7kYLR09PDggUL/jjLzczMDEuXLsWnT58ETkbSwDNCFdPx48dhYWGB\noqIiiMVibjP9DpwIlR+VKlXCokWLkJ6ejho1asDOzg7jx4/nz6ZvJI9FaF5eHnr37g17e3ssX75c\n6DhERGWCRSiRnAsODi71lvi/KiwshKamJhITEzFlyhRUr15dqusTUdmrUaMG1q9fj/j4eNy9exfG\nxsYICgr61zOBSf5VrVoVHz9+RF5entBRqASysrIwcOBATJs2DSEhIQgODkalSpWEjqWwOBEqX6pU\nqYLly5fj3r170NPTg5WVFaZMmYIXL14IHU0hyFsRWlhYiIEDB6J69eoIDAzk9xsRqQwWoURy7urV\nqzKZinj//j3evXsn9XWJSFiGhobYv38/Tpw4gfDwcFhYWODQoUOcrlJQampqqFWrltR3BpB0SSQS\n7N27F1ZWVqhTpw6Sk5Ph6OgodCyFxp9Z8qt69epYtWoV7ty5A5FIBHNzc8yYMYNHs/wHeSpCJRIJ\nvLy8kJOTgz179kBdXV3oSEREZYZFKJEcKyoqktk5TDo6OkhKSpLJ2kQkPDs7O/zyyy8IDAzEihUr\n0KJFC1y8eFHoWPQduD1evv32229wcXHB6tWrcfLkSaxevRo6OjpCx1IKnFCTbzVr1kRAQABSUlKQ\nm5sLU1NTzJ07F2/evBE6mlySlyJUIpFgxowZuHv3LsLDw3ksFhGpHBahRHJM1lshP3z4INP1iUh4\nTk5OuHHjBqZNm4aRI0eie/fuSE5OFjoWfQPeMEk+FRcXIzAwEPb29mjTpg1u3LiBpk2bCh1LaXAi\nVHEYGBhg06ZNSExMxJs3b2BiYgIfHx/uPPoLeSlC/fz8EBUVhZMnT0JXV1foOEREZY5FKJEc09DQ\nQHFxsUzXJyLlp6amhoEDB+LevXvo1q0bnJ2dMWzYMPz6669CR6MSYBEqf+7du4f27dvjwIEDuHz5\nMubNm4dy5coJHUupSCQSToQqmHr16mHLli1ISEjAkydPYGRkhGXLlqn8B++zZs1C586dMW7cOBw7\ndgxVqlSBjY0N5s+fj5cvX5Zpli1btmDr1q2IiopClSpVyvTaRETygkUokRzT0tKCvr6+TNYuLCyE\noaGhTNYmIvmkqamJSZMmIT09HQ0bNoS9vT2mT5/ObYxyjlvj5cfnz5+xfPlytGvXDgMHDsSlS5dg\namoqdCylxSJUMTVq1Ag7duxAbGws0tLS0LhxY6xcuRLZ2dlCRxNEQEAAcnJy0KRJEzRs2BBDhw5F\n+fLl4evrCysrK9y/f79McoSFhWHJkiWIjo5G7dq1y+SaRETyiEUokZyzsbGRybpFRUVo3LixTNYm\nIvlWsWJFLF68GKmpqcjLy4OJiQlWrFiBnJwcoaPRV3AiVD582fp+9epV3Lx5ExMmTICaGl9Kywq3\nxis+IyMj7NmzBzExMbh16xYMDQ2xbt065ObmCh2tTH38+BGxsbGYOnUqjIyMsH79esTHx2Pu3Ll4\n/fo1/Pz8ZJ4hKioKkyZNwunTp/n6n4hUHl+9Ecm5/v37S/38HpFIhI4dO/INHJGKq1mzJjZv3ozY\n2FgkJibC2NgYW7duRWFhodDR6P9gESqsnJwczJgxAy4uLpgxYwZOnjyJevXqCR1LJXAiVDmYmZkh\nNDQUv/zyC65cuYLGjRtj48aNMj8LX158uRnRX88IdXd3BwCZT/zHxcVhyJAhCA8Ph7W1tUyvRUSk\nCNiCEMm5oUOHSv2cUF1dXcyYMUOqaxKR4jI2NkZYWBjCw8Oxf/9+WFpaIiIighNZcoJFqHAuXLgA\na2trPH36FCkpKRgyZAjLuTLCnz/Kx8rKCuHh4Th+/Diio6NhZGSEn3/+GQUFBUJHKxN/LUIjIyMh\nEong6Ogos2umpKTA1dUVISEhaNOmjcyuQ0SkSFiEEsm5ChUqYPLkydDR0ZHKempqajAyMoKDg4NU\n1iMi5dG8eXOcP38e/v7+8PHxQZs2bXDlyhWhY6m8L2eEshgqO+/evcPo0aMxbNgw+Pv748CBA6hR\no4bQsVQOS2flZGdnh+PHj+PIkSM4evQojI2NsX37dnz+/FnoaDJ15MgRPHjwANOnT0e7du2wZMkS\neHp6Ytq0aTK53sOHD9G1a1cEBASgW7duMrkGEZEiYhFKpAAWL16MH374QSpvCMqXL4+wsDC+uSCi\nrxKJROjWrRsSExMxbtw4DBkyBL169UJqaqrQ0VRWhQoVAPzvnDmSvaNHj8LS0hIaGhoQi8Xo2bOn\n0JFUEot/5de8eXOcOXMG+/btw/79+2FmZoaQkBClPZ7l0KFD+PXXX7F+/XrExsaiZcuWGDBgAMqV\nKyf1az1//hxOTk6YP38+Bg4cKPX1iYgUGYtQIgWgpaWFU6dOoWLFiqVaR1tbG9u2beMh6UT0n9TV\n1TF06FDcu3cPHTp0gKOjI0aOHIknT54IHU3liEQibo8vAy9fvoS7uztmzpyJffv2ISgoCPr6+kLH\nUmn80FY1tGnTBufOncO2bduwdetWWFpa4sCBA3/aRq4Mjh07htatW+PFixcIDw9HVlYWnJycsG/f\nPqle5+3bt+jSpQs8PDwwbtw4qa5NRKQMWIQSKQhTU1NcuXIFVatWhZaW1jc9VyQSQVtbG8HBwfxU\nmIi+Sfny5TF9+nSkp6ejZs2aaNKkCWbNmoW3b98KHU2lfNkeT9InkUiwe/duWFtbw9DQEElJSTw+\nRg5wIlT1dOjQAZcuXcLGjRuxfv162NjY4PDhw1I/K18oX84IrV69Onr37o3o6GhoaGjA29tbatfI\nzs5Gjx490KlTJ8ybN09q6xIRKRMWoUQKxNLSEhkZGejVqxd0dHRKdNd3PT09mJub48aNGxgyZEgZ\npCQiZVSpUiX4+voiOTkZb9++hbGxMVavXo3c3Fyho6kEToTKxq+//oquXbti/fr1OHPmDFasWAFt\nbW2hYxH+V4RyIlT1iEQiODk5IS4uDqtWrYKfnx/s7Oxw7NgxhS/H/3qzpHr16sHc3ByvXr3Cy5cv\nS71+QUEB+vXrByMjI6xdu5bfP0RE/4BFKJGCqVy5MsLCwnDp0iUMGDAAWlpa0NPTQ4UKFaCjowM9\nPT1UrFgRGhoaaN26Nfbt24ekpCSYm5sLHZ2IlICBgQGCg4Nx6dIlxMbGwsTEBDt37lS6LYzyhkWo\ndBUVFWH9+vVo2rQpOnbsiPj4eNja2godi/6CRY7qEolE6N69O65fv47Fixdj4cKFaNasGU6dOqWw\nhehfi1AAePbsGUQiEfT09Eq1dlFREYYPHw5NTU1s27atRMMSRESqSkPoAET0fezt7bFv3z6EhITg\n3r17SElJwadPn6CpqQljY2PY2NhwqoWIZMbMzAwRERGIjY3FrFmzsHbtWvj5+cHFxYXlhQwYGBjg\n4cOHQsdQCqmpqRg1ahS0tLQQGxsLY2NjoSPRVyhq2UXSJRKJ0Lt3b/Ts2RPh4eGYMWMGli5diiVL\nlqBz585y//smIyMDP/zwAypWrPinIlQikWD+/PnIyspCly5doKur+93XkEgkmDRpEp4/f44zZ85A\nQ4Nv8YmI/g1/ShIpOHV1dVhYWMDCwkLoKESkglq3bo1Lly7hxIkTmDVrFlatWoWVK1eiVatWQkdT\nKrVr18aVK1eEjqHQCgoKsGLFCmzatAlLly6Fl5cXp6bknLyXXFR21NTU0K9fP7i5uSEsLAwTJ07E\nDz/8gKVLl8r1mb6nTp3CnDlz0LZtW+jr6+PZs2cYNWoUYmJi8PDhQzRo0ABBQUGlusbChQsRHx+P\nCxcuoHz58lJKTkSkvPjqj4iIiEpFJBKhZ8+eSE5OhoeHB9zd3dGnTx/cu3dP6GhKg1vjSyc+Ph72\n9va4ceMGEhMTMXbsWJagco4TofQ16urqGDhwIFJTU+Hp6YlRo0ahU6dOiI2NFTraV3Xu3Bmenp54\n/fo1zp8/j9evXyMiIgI1atT449ztBg0afPf6AQEBCAsLw+nTp1GxYkXpBSciUmJ8BUhERERSoa6u\nDg8PD6Snp6Nly5Zo164dxowZwwJPCliEfp/s7GxMnz4drq6umDdvHiIjI1GnTh2hY1EJcSKU/omG\nhgaGDRuGu3fvYtCgQRg0aBC6du2KhIQEoaP9iYWFBTZs2IBbt24hNjYWRkZG+P333xEbG4vZs2eX\n6mzQkJAQrFu3Dr/88gtq1KghxdRERMqNRSgRERFJlba2NmbOnIm0tDTo6+vDysoK8+bNw/v374WO\nprBq166N58+fo7i4WOgoCuPs2bOwsrLCq1evkJKSggEDBrBYUyCcCKWSKFeuHEaNGoX09HS4urqi\nb9++6NmzJxITE4WO9jdfu1nS94qMjMTMmTMRFRWFevXqSWVNIiJVwSKUiIiIZKJKlSpYtWoVbt++\njefPn8PY2Bj+/v7Iz88XOprCKV++PPT09PDmzRuho8i9t2/fYuTIkRg1ahQCAwOxZ88eVKtWTehY\n9B1YXFNJaWpqYuzYscjIyICzszNcXFzQp08fpKSkCB3tD9IqQi9evAhPT0+cOHECZmZmUkhGRKRa\nWIQSERGRTNWtWxc7duzAuXPncP78eZiYmGDv3r2cbvxG3B7/344cOQILCwvo6upCLBajW7duQkei\n78SJUPoe5cuXx6RJk/DgwQO0bdsWTk5O+PHHH3H37l2ho0mlCL158ybc3d1x8OBBNG3aVErJiIhU\nC4tQIiIiKhOWlpY4fvw4QkJCEBgYCDs7O5w5c4aFRwkZGBggMzNT6Bhy6fnz5+jTpw/mz5+PQ4cO\nYePGjahQoYLQsagUJBIJJ0Lpu2lra2P69Om4f/8+7Ozs4ODggKFDhyIjI0OwTKUtQtPS0tCjRw8E\nBwejY8eOUkxGRKRaWIQSERFRmWrfvj1iY2Ph4+ODqVOnolOnTrh+/brQseQeJ0L/TiKRYPv27bCx\nsYG5uTkSExPRpk0boWORlLAIpdLS09PDrFmzcP/+fZiYmKB169YYOXIkHj16VOZZSlOEPnnyBM7O\nzvD19YWrq6uUkxERqRYWoURERFTmRCIR3NzcIBaLMWDAALi6usLd3V3QaR15xyL0zx48eIDOnTsj\nKCgIv/zyC5YtW4by5csLHYukhJPiJE0VK1bE/PnzkZGRgbp166JZs2YYM2YMfvvttzLL8L1F6OvX\nr+Hs7IzJkyfDw8NDBsmIiFQLi1AiIiISjIaGBry8vJCeno4mTZqgVatWmDBhAl6+fCl0NLnDrfH/\nU1RUhLVr16JFixbo1q0brl27BhsbG6FjkQxwIpSkrVKlSli8eDHS0tJQtWpV2NraYuLEiWXys/V7\nitAPHz6gW7ducHNzg7e3t4ySERGpFhahREREJDhdXV3MnTsX9+7dg5aWFszNzeHj44OPHz8KHU1u\ncCIUSElJQatWrXDy5EnEx8fjp59+goaGhtCxSAY4EUqyVLVqVfj6+uLu3bsoX748rKysMG3aNLx4\n8UJm11RTU/ummwTm5eXB1dUV9vb2WL58ucxyERGpGhahREREJDeqVauGdevW4ebNm3j48CGMjIyw\nceNGFBQUCB1NcKpchObn52PBggXo1KkTvLy8cO7cORgaGgodi2SME6EkazVq1MCaNWuQmpqK4uJi\nmJubY+bMmXj9+rXUr/UtE6GFhYUYMGAAqlevjsDAQH4vEBFJEYtQIiIikjsNGjTAnj17cObMGZw6\ndQpmZmY4ePDgN03TKBtVLUJjY2Nha2sLsViM27dvw9PTk6WACuBEKJWlWrVqYf369UhOTsanT59g\nYmKCefPm4ffffy/Vuo8fP8bq1avh4uICMzMzZGdno3bt2ujQoQN8fHxw+/btvz2nuLgYo0ePRm5u\nLvbs2QN1dfVSZSAioj8TSfgqg4iIiOTc+fPnMWvWLBQXF2PlypXo3Lmz0JHKXGFhIbS1tZGTk4Ny\n5coJHUfmPn36hLlz5+Lw4cPYsGED+vbtywJUhURGRmLbtm2IjIwUOgqpoMePH2PZsmWIiIjAxIkT\nMXXqVFSqVKnEz79z5w4mTpyIuLg4SCQS5Ofn/+0x6urq0NLSQqNGjeDv74/OnTtDIpHgp59+Qlxc\nHH755Rfo6upK88siIiJwIpSIiIgUQMeOHZGQkIBZs2Zh7NixcHZ2xq1bt4SOJTNHjhzB5MmT0b59\ne+jr60NNTQ0jR45E9erVS3QjKU9PT6ipqUFNTQ0PHz4sg8TSdebMGVhaWuLjx48Qi8Xo168fS1AV\nw1kNElL9+vWxdetWxMfH49dff4WRkRGWL1/+n+dWSyQS+Pr6omnTprh48SLy8vK+WoIC/7vxW05O\nDsRiMXr37o3hw4dj8eLFiI6OxokTJ1iCEhHJCItQIiIiUggikQju7u64e/cuXF1d4eLigsGDBytk\n0fdfli1bhsDAQCQlJaFOnTp/lIAl2R5//Phx7NixAxUqVFC48vDNmzcYNmwYxo0bh+DgYOzcuRNV\nqlQROhYJQCKRKNzfX1I+hoaG2LVrF65evYo7d+6gcePGWLVqFbKzs//22OLiYgwfPhzLly9Hbm7u\nN5X5OTk5OHDgAFasWIGIiAj+3CMikiEWoURERKRQypUrh/HjxyMjIwMmJiZo1qwZpkyZglevXgkd\nTWoCAgKQnp6O9+/fY/PmzX+8oTYwMEBmZuY/Pu/169fw8vLCgAEDYGdnV1ZxS00ikSA0NBSWlpao\nUqUKUlJS4OzsLHQsEhiLUJIXxsbG2LdvH86fP48bN26gcePG8Pf3R25u7h+PmT17No4cOYKcnJzv\nusbnz58BAKNHj+ZENBGRDLEIJSIiIoWkp6eHhQsX4u7duyguLoapqSmWLl2KT58+CR2t1BwcHL56\nV/T/mggdPXo0RCIRAgMDZRlPqjIzM+Hq6oolS5YgIiICAQEB0NPTEzoWCYxFEMkjCwsLhIWFISoq\nCpcuXULjxo2xadMmXLp0CZs2bfruEvSLgoICXL9+HVu3bpVSYiIi+isWoURERKTQatSogY0bNyIh\nIQF37tyBsbExfv755z+ma5TJvxWhu3btQmRkJIKDg1G5cuUyTvbtiouLsWXLFjRp0gS2tra4desW\nWrZsKXQskiOcCCV5ZW1tjYiICERGRuL06dPo1KnTn6ZDSyM7OxvTp0/Hhw8fpLIeERH9GYtQIiIi\nUgqGhoY4cOAAjh8/jsOHD8PCwgKHDx9Wqsmyf9oa//jxY0ydOhVDhw5Fjx49BEj2bTIyMtCxY0fs\n2LEDFy5cwKJFi6ClpSV0LJIjyvR9S8rL3t4e8+bNQ7ly5aS+dkhIiNTXJCIiFqFERESkZOzt7XH2\n7FkEBgbC19cXLVu2xMWLF4WOJRVfmwiVSCQYPnw4KlSogPXr1wuUrGQKCwuxatUqtGrVCq6uroiN\njYWlpaXQsUhOcSKUFEFQUBDy8vKkumZ2djY2bNgg1TWJiOh/NIQOQERERCQLTk5O6NSpEw4ePIiR\nI0fC1NQUfn5+sLa2Fjrad/taEbpu3TpcvnwZp06dgr6+vkDJ/tvt27cxatQoVKlSBdevX0fDhg2F\njkRyjBOhpCguX74sk7+vjx49Ql5eHsqXLy/1tYmIVBknQomIiEhpqampYdCgQbh79y66du0KJycn\nDB8+HI8fPxY62nf5axGakZGB+fPnw8PDA126dBEw2T/Ly8vD3Llz4ezsjIkTJyI6OpolKJUIJ0JJ\n3uXn53/1uBJp0NHRgVgslsnaRESqjEUoERERKT0tLS1MnjwZGRkZqF+/Puzs7ODt7Y03b94IHe2b\nVK1aFTk5OX/clOPOnTvIz8/Hjh07oKam9qd/YmJiAACNGzeGmpoaIiMjyzzv5cuXYWNjg4yMDCQn\nJ8PDw4PlFpUIJ0JJEXz69Anq6uoyWVskEuHdu3cyWZuISJVxazwRERGpjIoVK2LJkiUYN24clixZ\nAhMTE3h7e2PKlCnQ0dEROt5/EolEqFWrFp49ewZDQ0M0aNAAnp6eX33siRMn8PLlS7i7u6NixYpo\n0KBBmeX88OED5syZg6NHj2LTpk1wc3Mrs2uTcpBIJCzNSe6pq6vLtLRXU+PcEhGRtLEIJSIiIpVT\nq1YtBAUFYdq0aZg3bx6MjY2xaNEijBgxAhoa8v3y6Mv2eENDQ9jY2CA4OPirj3N0dMTLly/h6+uL\nRo0alVm+kydPYty4cXB2doZYLEblypXL7NqkXFiEkrzT19eX2UTo58+fy/QDLCIiVcGPmIiIiEhl\nGRsb49ChQzhy5Aj27t0LKysrHD16VPBtuceOHYOHhwc8PDzg5+cHAIiNjYWHhweePXuGVatWCZrv\na169eoXBgwdj8uTJ2LlzJ7Zt28YSlL6b0N+DRCUhEolgbm4us/V5njIRkfTJ98gDERERURlo0aIF\nLly4gNOnT2P27NlYvXo1Vq5cibZt2wqS5/bt2wgJCfnj30UiER49eoRHjx5BIpHg48ePJVqnLCbq\nJBIJ9u/fD29vbwwZMgQpKSkKccwAyT9OhJIicHV1RWpqKvLy8qS6roODA78HiIhkQCThx61ERERE\nfygqKsK+ffuwYMECNGnSBL6+vrCwsBA61h9WrVqFrKwsrFmzRugoePLkCcaOHYsnT55g+/btaNas\nmdCRSEmEhobiyJEjCAsLEzoK0b96+fIlGjRoINUiVE9PD5GRkXB0dJTamkRE9D/cGk9ERET0f6ir\nq2PYsGFIS0uDg4MDHB0dMWrUKDx9+lToaAAAAwMDZGZmCpqhuLgYmzdvhq2tLVq2bIkbN26wBCWp\n4zQcKYJq1arB2tpaauuJRCI0bNgQHTp0kNqaRET0/7EIJSIiIvqK8uXLY/r06UhPT0eNGjVgbW2N\nWbNm4e3bt4Lm+nKzJKF8KYj37t2LS5cuYcGCBdDU1BQsDyknblojRZCUlIRWrVqhXLlyUjsTuXz5\n8ggLC+MHAUREMsIilIiIiOhfVKpUCStWrEBKSgp+//13GBsbY82aNVI/D66khCpCP3/+DF9fX7Rp\n0wbu7u64fPmyTG8SQsQiiORVTk4OZs6cCScnJ4wdOxaXL1/GyZMnS30+so6ODjZu3AhTU1MpJSUi\nor9iEUpERERUAgYGBti6dSsuXbqEK1euwNjYGLt27UJRUVGZ5vhShJblxNzNmzfRrFkzXL58GTdv\n3sSkSZOgrq5eZtcn1cOJUJJXUVFRsLS0RGZmJsRiMUaOHAmRSIRWrVrh1KlT0NPT+66fj9ra2liz\nZg1GjRolg9RERPQFi1AiIiKikITTUQAAIABJREFUb2BmZoajR4/iwIED2LZtG2xsbHDixIkyK24q\nVKgAdXV1vH//XubX+jL11L17d3h7e+PUqVOoX7++zK9LJJFIOBFKciUrKwuDBw/GuHHjEBQUhH37\n9qFGjRp/eoyDgwPEYjGaN28OXV3dEq2rq6uL+vXrIyYmBuPGjZNFdCIi+j9YhBIRERF9hzZt2uDy\n5cvw9fXFzJkz0aFDB1y7dq1Mrl0W2+MvXrwIGxsb/Pbbb0hJScHQoUNZTFGZ4t83kgcSiQTbt2+H\npaUl6tSpA7FYjC5duvzj4+vXr4+rV68iNDQU7dq1g6amJipWrAgtLS2oq6v/6d9NTU0RGBiItLQ0\n3nCOiKiMaAgdgIiIiEhRiUQi9OrVC927d0dISAj69++P5s2bw9fXFyYmJjK77pciVBZndL5//x4z\nZ87EqVOnEBgYiF69ekn9GkT/hVvjSR6kpaVhzJgxyMnJQXR0NJo0aVKi54lEIri4uMDFxQXv3r1D\nYmIiUlJSkJ2dDU1NTZiamsLe3h41a9aU8VdARER/xSKUiIiIqJQ0NDQwcuRIDBgwABs3bkSbNm3Q\nt29f+Pj4oHbt2lK/noGBATIzM6W+bmRkJMaPH48ePXpALBZDX19f6tcgKilOhJJQ8vPz4efnh02b\nNmHhwoUYP378d5+LXKlSJTg6OsLR0VHKKYmI6HtwazwRERGRlOjo6GDWrFlIT09HxYoVYWVlhfnz\n50v9PE9pb41/+fIlfvzxR3h7e2Pfvn34+eefWYKSoDgRSkK5fPkymjRpgsTERNy6dYs3hyMiUjIs\nQomIiIikrEqVKli9ejUSExORmZkJY2NjBAQEID8/v9RrFxUVQU1NDVevXkVERATOnTuHV69efdda\nEokEISEhsLa2RoMGDZCcnAwHB4dSZySSBk6EUll6+/YtRo8ejYEDB8LX1xdHjx5F3bp1hY5FRERS\nxiKUiIiISEbq1auHnTt34uzZszh79ixMTU2xd+9eFBcXf9M6xcXFOHv2LJydnaGrq4uAgABERUVh\nxIgR6Nu3L+rUqYMaNWpgzpw5ePr0aYnWfPz4Mbp16wZ/f3+cPn0aK1euhLa29vd8mURSx4lQKisS\niQQHDx6EhYUFtLS0kJqaCjc3N6FjERGRjLAIJSIiIpIxKysrnDhxArt27cKmTZtgZ2eHqKioEpU9\naWlpsLW1hZubG3755Rfk5+cjLy8PhYWF+PDhA96/f4+CggK8evUK/v7+MDIywty5c1FQUPDV9YqK\nirBhwwbY29vDwcEBCQkJsLOzk/aXTFRqnAglWfv111/h4uKC5cuXIzw8HJs2beKxIERESo5FKBER\nEVEZcXBwQFxcHBYuXIjJkyejc+fOuH79+j8+fu/evbC1tYVYLManT5/+c/0vJen69ethbW2N58+f\n/+m/37lzB23btsWhQ4dw9epVzJkzB+XKlSv110UkbZwIJVkqLCzEmjVr0LRpU7Rv3x63bt1Cy5Yt\nhY5FRERlgEUoERERURkSiUTo06cPxGIx3N3d0bt3b/z444+4f//+nx4XEhICLy8v5ObmfvNW+pyc\nHDx48ADNmzdHVlYWCgoKsGTJErRv3x7Dhg1DTEwMTExMpPllEUmVRCLhRCjJxI0bN9C8eXNERUUh\nPj4es2fP5gdCREQqhEUoERERkQDKlSuHMWPGICMjA9bW1mjZsiUmTJiAly9fIjU1FWPHjkVubu53\nr19YWIiXL1+iS5cusLOzQ0JCAhITEzFu3DioqfElIMk/FqEkTR8/fsTUqVPRo0cPTJ8+HdHR0TA0\nNBQ6FhERlTG+CiYiIiISkK6uLubNm4d79+5BU1MTZmZm6NChA/Ly8kq99ufPn3H79m20bt0ax48f\n5x2QSWFwazxJ0/Hjx2FhYYH3798jNTUVQ4YMYdFORKSiWIQSERERyYFq1arB398fK1euxLt376Ra\nBEVERHzz9noiobGootJ69uwZ+vXrB29vb+zatQs7d+5E1apVhY5FREQCYhFKREREJEd27dqFwsJC\nqa6Zn5+PU6dOSXVNIlniRCiVRnFxMYKCgmBjYwMzMzMkJyejY8eOQsciIiI5oCF0ACIiIiL6n9zc\nXCQkJEh93Y8fP+LQoUPo2bOn1NcmkhVOhNL3EIvFGDNmDADg4sWLsLCwEDgRERHJE06EEhEREcmJ\npKQk6OjoyGTta9euyWRdIlngRCh9q9zcXMybNw+Ojo4YNmwYLl++zBKUiIj+hhOhRERERHLi/v37\nMjvL88mTJzJZl0hWOBFKJXXu3DmMHTsWtra2SE5ORq1atYSOREREcopFKBEREZGcKCgokNkknLTP\nHSWSJU6EUkm8fv0a3t7eiImJwaZNm9CjRw+hIxERkZzj1ngiIiIiOaGnpwc1Ndm8PCtfvrxM1iWS\nBYlEwolQ+kcSiQQhISGwtLRE1apVIRaLWYISEVGJcCKUiIiISE5YWVnJbGu8iYmJTNYlkhUWofQ1\n9+/fx9ixY/H777/j5MmTsLe3FzoSEREpEE6EEhEREckJY2NjFBUVSX1ddXV1tG/fXurrEskKt8bT\nXxUUFMDX1xctW7ZE9+7dkZCQwBKUiIi+GYtQIiIiIjmhrq6O/v37Q11dXerrDh06VKprEskaJ0Lp\ni7i4ONjb2+Pq1au4ceMGpk+fDg0Nbm4kIqJvxyKUiIiISI54e3tDS0tLqmt+KUL37dvHmyaRQuBE\nKAHA+/fvMX78ePTt2xcLFizAiRMn0KBBA6FjERGRAmMRSkRERCRHbGxs4OrqKrWbG2lrayMmJgZr\n167Fli1bYGJiguDgYOTn50tlfSJZ4USo6pJIJDhy5AjMzc1RVFSE1NRUuLu78+8EERGVGotQIiIi\nIjmzefNmVKxYsdRv+nV0dDBx4kQ0a9YMXbp0waVLl7Br1y5ERETA0NAQ/v7+yM7OllJqIunhRKjq\nevLkCXr37o0FCxYgNDQUW7ZsQeXKlYWORURESoJFKBEREZGc0dfXx8WLF6Gvr//dZaiOjg66du2K\nFStW/OnP27Vrh9OnT+PYsWO4evUqGjVqhOXLl+Pdu3fSiE4kNZz+Uy1FRUVYv349bG1t0axZMyQm\nJqJt27ZCxyIiIiXDIpSIiIhIDpmZmSEhIQFGRkbQ0dH5pudqa2vDy8sLYWFh/3jjJXt7exw+fBgX\nLlxAeno6DA0NMXfuXGRlZUkjPlGpcCJUtSQmJqJly5Y4evT/sXffUVpW9/q476FJsSN2o1LUSBHr\noILMqFiixhKDvUWNsRsjiTWx92M0sddj78auSBRQQcRKE0XAGKMiokZEpL+/P84x35NfLKgzPMMz\n17WWawm87Od+0bV4557P3vv+DB06NKecckqdn5UMAIkiFACgwerUqVNGjx6dfv36pXXr1mnTps3X\nvraqqipt2rTJmmuumSeffDJ//OMf5+v2+bXXXjs33nhjXnzxxXzyySdZa621cvTRR+edd96py7cC\n30mlUjER2gh8/vnn6devX7bZZpscdthheeqpp7LGGmsUHQuAElOEAgA0YM2bN8+pp56ayZMn55JL\nLslWW22VZZZZJknSpEmTNGvWLMsuu2w6dOiQJ598MmPHjs3GG2/8nZ+z+uqr54orrsjo0aPTvHnz\nrLPOOjnooIMyfvz4un5LMF8UoeX2+OOPp0uXLnn//fczatSoHHDAAf6bA1DvFKEAAAuBNm3a5MAD\nD0z//v3z4YcfZs6cOZk+fXpmzZqVwYMHZ/bs2amurv7BRcKKK66YCy+8MG+++WZWWmml9OjRI3vu\nuWdGjRpVR+8Evp2t8eX1wQcfZI899sjhhx+eq666KrfcckuWXXbZomMB0EgoQgEAFkJNmzbNIoss\nkqqqqqy55pqZOXNm3nrrrTpbv23btjnttNMyceLEdO/ePVtttVV23HHHDB8+vM6eAd/EdGC5zJs3\nL9dee226du2aVVddNaNGjcpWW21VdCwAGhlFKADAQq6qqio1NTUZOHBgna+9+OKL57e//W0mTpyY\nPn36ZNddd82WW26ZgQMHmtqj3vh/q1zGjh2bmpqaXHPNNRkwYEDOPffc73wJHADUBUUoAEAJ1NbW\nZtCgQfW2fqtWrXLEEUdk/Pjx2WuvvfKrX/0qm266aR5++GGlFfXCROjCb+bMmTn11FPTq1ev9O3b\nN0OHDs0666xTdCwAGjFFKABACdTW1i6QKc0WLVrkgAMOyGuvvZZjjjkmJ598crp3754777wzc+fO\nrddn03go1xd+gwcPzjrrrJMRI0bk1VdfzRFHHJGmTZsWHQuARk4RCgBQAh07dsy8efMyYcKEBfK8\npk2bpm/fvnnllVdy1lln5eKLL87aa6+dG264IbNmzVogGSg3E6ELp48//jgHHXRQ9t5775x77rn5\ny1/+kpVXXrnoWACQRBEKAFAKVVVV9b49/uueu/3222fo0KG58sorc9ttt6VTp0659NJL88UXXyzQ\nLJSHidCFT6VSyW233ZbOnTunVatWGTNmTHbaaaeiYwHAv1GEAgCUxJfb44vwZRE7YMCA3HXXXRkw\nYEDat2+f8847L1OnTi0kEwuvSqViInQh8tZbb2XbbbfNueeem/vvvz9//vOfs/jiixcdCwD+gyIU\nAKAkvrw5vuhpuurq6jzwwAN54oknMmLEiLRv3z6///3v89FHHxWai4WLIrThmz17di644IJsuOGG\nqa2tzUsvvZTq6uqiYwHA11KEAgCURPv27dOsWbO8+eabRUdJknTt2jW33XZbhg0blvfffz+dOnXK\ncccdl/fee6/oaDRwRZf5fLsXXnghG264YQYMGJDnn38+v/vd79K8efOiYwHAN1KEAgCUxJfb04va\nHv91OnbsmGuuuSYjRozInDlz0qVLlxx66KF56623io5GA2YitGH67LPPcvTRR2eHHXZIv3790r9/\n/3To0KHoWAAwXxShAAAl8uX2+IZolVVWycUXX5zXX389Sy21VDbYYIPsu+++GTt2bNHRaGBMhDZM\nDz74YDp37pzPPvssY8aMyV577aWwBmChoggFACiRL2+Ob8hF0rLLLpuzzz47EyZMyJp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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -543,6 +543,205 @@ "w=widgets.interactive(step_func,iteration=iteration_slider)\n", "display(w)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NQueens Visualization\n", + "\n", + "Just like the Graph Coloring Problem we will start with defining a few helper functions to help us visualize the assignments as they evolve over time. The **make_plot_board_step_function** behaves similar to the **make_update_step_function** introduced earlier. It initializes a chess board in the form of a 2D grid with alternating 0s and 1s. This is used by **plot_board_step** function which draws the board using matplotlib and adds queens to it. This function also calls the **label_queen_conflicts** which modifies the grid placing 3 in positions in a position where there is a conflict." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def label_queen_conflicts(assingment,grid):\n", + " ''' Mark grid with queens that are under conflict. '''\n", + " for col, row in assingment.items(): # check each queen for conflict\n", + " row_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " if temp_row == row and temp_col != col}\n", + " up_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " if temp_row+temp_col == row+col and temp_col != col}\n", + " down_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " if temp_row-temp_col == row-col and temp_col != col}\n", + " \n", + " # Now marking the grid.\n", + " for col, row in row_conflicts.items():\n", + " grid[col][row] = 3\n", + " for col, row in up_conflicts.items():\n", + " grid[col][row] = 3\n", + " for col, row in down_conflicts.items():\n", + " grid[col][row] = 3\n", + "\n", + " return grid\n", + "\n", + "def make_plot_board_step_function(instru_csp):\n", + " '''ipywidgets interactive function supports\n", + " single parameter as input. This function\n", + " creates and return such a function by taking\n", + " in input other parameters.\n", + " '''\n", + " n = len(instru_csp.variables)\n", + " \n", + " \n", + " def plot_board_step(iteration):\n", + " ''' Add Queens to the Board.'''\n", + " data = instru_csp.assingment_history[iteration]\n", + " \n", + " grid = [[(col+row+1)%2 for col in range(n)] for row in range(n)]\n", + " grid = label_queen_conflicts(data, grid) # Update grid with conflict labels.\n", + " \n", + " # color map of fixed colors\n", + " cmap = matplotlib.colors.ListedColormap(['white','lightsteelblue','red'])\n", + " bounds=[0,1,2,3] # 0 for white 1 for black 2 onwards for conflict labels (red).\n", + " norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n", + " \n", + " fig = plt.imshow(grid, interpolation='nearest', cmap = cmap,norm=norm)\n", + "\n", + " plt.axis('off')\n", + " fig.axes.get_xaxis().set_visible(False)\n", + " fig.axes.get_yaxis().set_visible(False)\n", + "\n", + " # Place the Queens Unicode Symbol\n", + " for col, row in data.items():\n", + " fig.axes.text(row, col, u\"\\u265B\", va='center', ha='center', family='Dejavu Sans', fontsize=32)\n", + " plt.show()\n", + " \n", + " return plot_board_step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us visualize a solution obtained via backtracking. We use of the previosuly defined **make_instru** function for keeping a history of steps." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "eight_queens_csp = NQueensCSP(8)\n", + "backtracking_instru_queen = make_instru(eight_queens_csp)\n", + "result = backtracking_search(backtracking_instru_queen)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "backtrack_queen_step = make_plot_board_step_function(backtracking_instru_queen) # Step Function for Widgets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n", + "matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n", + "\n", + "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assingment_history)-1, step=0, value=0)\n", + "w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n", + "display(w)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us finally repeat the above steps for **min_conflicts** solution." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "twelve_queens_csp = NQueensCSP(12)\n", + "conflicts_instru_queen = make_instru(eight_queens_csp)\n", + "result = min_conflicts(conflicts_instru_queen)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "conflicts_step = make_plot_board_step_function(conflicts_instru_queen)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The visualization has same features as the above. But here it also highlights the conflicts by labeling the conflicted queens with a red background." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "dabc8b03ade64950a473b7a1fb33c332": { "views": [] }, - "e3ff076587bd4a4ba3a23c0b5c572aa9": { + "de894237d8154203a17df8fe3fac10b6": { "views": [] }, - "e63cba8e2d5c4f1f836c06f1f41d3abf": { + "e4f69c894d1742549ea3b5d1c576d780": { "views": [] }, - "e8560481bb6d44d89d2e5ab87ee17031": { + "eaa04091ba7e49d4a62c3d6e6845ca3f": { "views": [] }, - "fdef4a83ecb74016983173951aa82e1f": { + "fb4ee56210f24757b93f94f392de1a9f": { "views": [] }, - "fe6fe229f7d2411b9c191b513d756177": { + "fcd462cccda040a68f002169df257f3a": { "views": [] } }, From 9c11d9f53b7051d723d50433ecd072276f0a1830 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 13 Jun 2016 04:47:24 +0530 Subject: [PATCH 313/513] Same Size for Both Boards --- csp.ipynb | 187 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 165 insertions(+), 22 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 7fb378957..fdb8fd399 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -145,9 +145,9 @@ { "data": { "text/plain": [ - "(,\n", - " ,\n", - " )" + "(,\n", + " ,\n", + " )" ] }, "execution_count": 7, @@ -526,9 +526,9 @@ "outputs": [ { "data": { - "image/png": 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ss87KfffdlwceeCD9+vXLpZdemgULFpQ7WuGZCAWoP2eEAsA/9e7d\nO4888ki5YwD/Ys0118wFF1yQI444Ij/4wQ+y6aabpn379nnwwQdz33335dBDD82BBx5Y7pgkef31\n1zN27NiMHTs2t912W7p27Zra2tr88pe/zJAhQ1JTU1PuiC3Cm2++mXnz5qV3797ljvKtrLjiirn2\n2mtz77335qijjsoZZ5yRESNGZJNNNil3tMIyEQpQf4pQAPin3r1756abbip3DOBzhg4dmh133DEX\nXHBBnn766c++vskmm2TnnXdOZaVNTuUwe/bs3H333Z895OjNN9/MpptumqFDh2b48OEtpshrbh56\n6KH079+/xZ2Nut5662X8+PG5+uqrs99++2XFFVfM8OHDs8oqq5Q7WuFUV1crQgHqyW+NAPBPtsZD\n8/PSSy+lf//+ufLKK3PuuefmzTffzEcffZS//e1veemll7L++uv7AGMhKZVKefzxx3Paaadl6NCh\n6datW0466aQstthiufDCC/POO+/kqquuyt57760EbYCWsC3+q1RUVGSHHXbIU089ldra2my88cbZ\ne++98/rrr5c7WqG0a9cudXV1qaurK3cUgBZHEQoA/6QIhebnhBNOyLRp03LKKadkn332Sbdu3dKx\nY8fU1tbm6quvzrx583LYYYeVO2ZhTZs2LVdccUV++tOfZqmllsrWW2+dF154IQceeGBee+213HPP\nPTn++OMzcODAtGnTptxxC6ElF6Gfat++fQ477LBMmTIlSy65ZFZdddUcf/zxmT59ermjFUJFRUWq\nqqqcEwpQD4pQAPinxRdfPPPmzfOHGjQjDz30UJJkww03/MJrq666ahZffPG8/PLL+eCDDxZysmKa\nO3duxo0bl1//+tfp379/VlhhhYwePToDBw7M3XffnRdffDHnnHNOttlmm3Tu3LnccQvp063xRbDY\nYovlD3/4QyZPnpyXX345K664Ys4555zMmzev3NFaPEUoQP0oQgHgnyoqKkyFQjPTvn37JP+YTPy8\nuXPnfvbBxafX8d2USqVMmTIlZ511Vrbaaqt07do1Rx99dCorKzNy5Mi8++67uf7663PggQdmhRVW\nKHfcwnvnnXcyffr0LLfccuWO0qiWXnrpXHLJJfnb3/6Wa665Jj/4wQ9yww03pFQqlTtai+WcUID6\nUYQCwL9QhELzsskmm6RUKuWUU07J3Llz/+213/72t5k/f34GDhyYRRZZpEwJW54PP/ww1157bfbb\nb78sv/zy2WijjfLwww9n1113zQsvvJCJEyfmpJNOyvrrr5927dqVO26rMnny5Ky55pot7kFJ39Ya\na6yRW2+9NWeccUaOO+64DBkyJBMnTix3rBbJRChA/XhqPAD8C0UoNL0bbrgh119/fZLkrbfeSpKM\nHz8+e+65Z5JkySWXzIgRI5IkxxxzTG644Ybcfvvt6devX374wx+mQ4cOue+++zJx4sTU1NTkT3/6\nU3neSAsxf/78TJo06bOnuz/++ONZd911U1tbm0MPPTQrr7xyYYu3lqZI2+K/SkVFRTbffPMMHTo0\no0aNynbbbZf11lsvp5xySpZffvlyx2sxTIQC1I8iFAD+hSIUmt4jjzySSy655LP/rqioyNSpUzN1\n6tQkybLLLvtZEdqlS5dMmjQpf/jDH3LjjTfm4osvTl1dXXr06JG99torRx99dFZcccWyvI/m7OWX\nX/6s+LzjjjvSq1ev1NbW5sQTT8z666+f6urqckfkSzz88MPZfvvtyx1joWjTpk323nvv7LTTTjnj\njDMycODA7L777jn22GPTpUuXcsdr9qqrq02EAtRDRcnBLADwmQsuuCDjx4/PhRdeWO4oAN/ajBkz\nctddd2XMmDEZO3Zs3n///Wy22WYZOnRoNttss/Ts2bPcEfkWll9++dxyyy1ZaaWVyh1loXv77bdz\n4okn5n//939z9NFH55BDDlHYf42BAwfmzDPPzNprr13uKAAtijNCAeBfmAgFWoIFCxZk8uTJGTZs\nWDbeeOP06NEjp512Wnr06JErrrgib731Vi6//PLsscceStAW4v3338+7776bvn37ljtKWXTv3j1n\nn3127rnnntx3333p169fLr/88ixYsKDc0ZolE6EA9WNrPAD8C0Uo0Fy99dZbGTt2bMaOHZtbb701\niy22WGpra/OLX/wiG264YTp27FjuiDTA5MmTs/rqq6eysnXPqvTr1y/XX3997r777hx11FE544wz\nMmLEiGy00UbljtasVFVVOSMUoB4UoQDwLz4tQkulkoeHAGX1ySef5N577/3srM9XXnklm2yySYYO\nHZqTTjopyy67bLkj0ogefvjhrLnmmuWO0WxssMEGuf/++zN69OjsvffeWXnllTN8+PCsvPLK5Y7W\nLJgIBaif1v1xIwB8TqdOndKuXbt88MEH5Y4CtDKlUilPPfVURo4cmc033zzdunXLb37zm9TU1OTc\nc8/NtGnTcvXVV2ffffdVghaQIvSLKioq8uMf/zhPP/10Ntlkk2y44YbZd9998+abb5Y7WtmZCAWo\nH0UoAHzOl22Pv+yyy1JZWZnKykoPUgIazXvvvZerrroqe++9d5Zeeulsvvnmeeqpp7LPPvvk5Zdf\nzvjx43PCCSdknXXWSdu2NnMV2UMPPZT+/fuXO0azVFVVlcMPPzzPPvtsOnfunFVWWSUnnHBCZsyY\nUe5oZWMiFKB+FKEA8DmfL0JfffXVHHLIIenUqZPt8kCDzJs3L/fcc0+OP/74DBw4MMstt1wuu+yy\nrL766rntttvy0ksv5fzzz8/222+fxRdfvNxxWUg+/vjjvP76663yafHfxeKLL54RI0bkoYceynPP\nPZcVV1wx559/fubPn1/uaI3qmmuuyaGHHpoNNtggnTt3TmVlZXbfffd/u+bzE6ELFizIBRdckCFD\nhmSJJZZITU1N+vTpk5122inPP//8wn4LAM2Wj5UB4HM+X4TuueeeWXLJJbPddtvltNNOK2MyoCV6\n4YUXPjvnc9y4cenTp0+GDh2a4cOHZ5111klVVVW5I1JmjzzySFZddVVTv9/Ssssum8svvzwPPvhg\njjrqqIwcOTLDhw/PlltuWYgPLE866aQ89thj6dixY3r16pVnnnnmC9f860TozJkz86Mf/Sh33nln\n1lhjjfz0pz9NdXV1Xn/99dxzzz2ZMmVKVlhhhYX9NgCaJT9pAeBz/rUI/dOf/pRx48Zl3Lhxuf32\n28ucDGgJPv7449x5550ZM2ZMxo4dm5kzZ2bo0KHZcccdc/7556dbt27ljkgzY1t8/QwYMCB33HFH\n/va3v+Xoo4/O6aefnhEjRmTAgAHljtYgI0eOTK9evdKnT5/cdddd2Wijjb5wzb9OhO67774ZN25c\nzj///Oyzzz5fuLaurq7JMwO0FIpQAPic3r1757bbbsvTTz+dX/3qV/n5z3+e9dZbTxEKfKm6uro8\n9NBDn019PvLIIxk0aFBqa2tz7bXX5gc/+EEhptRoOg8//PCXll18s4qKimy55Zapra3NRRddlB/9\n6EfZcMMNc8opp7TYh4oNGTLkG6/5dCJ08uTJufLKK7Pzzjt/aQmaJG3atGnsiAAtliIUAD6nd+/e\neeWVV7Lbbrtl2WWXzcknn1zuSNBqvPjii5k0aVIeeeSRfPjhh+nQoUP69euXAQMGNKutw6+99tpn\nxedtt92W//iP/0htbW2OPfbYbLDBBqmpqSl3RFqQhx9+OEcccUS5Y7Robdu2zc9+9rPsvPPOOf30\n09O/f//sueeeOfbYYwt53u6nE6GXX355KioqstNOO+Xjjz/OjTfemNdeey1dunTJxhtvnD59+pQ7\nKkCz0jx+kwSAZqR379557LHHMmPGjNx3333O74MmVldXlyuuuCLDhg3L1KlT07Zt28yYMSOlUilJ\nUlNTkzZt2qRdu3Y59NBDc/DBB6dLly4LNeOsWbNy9913f7bd/e23386mm26a2tranH766enVq9dC\nzUNxzJw5My+99FK+//3vlztKIXTs2DG//e1vs+++++aEE07ISiutlF/+8pc56KCDCvXzvLq6Ou+9\n914efPDBJMlLL72UvfbaK++///6/XXfAAQfkzDPPNJUO8E+eGg8An/PGG2/kww8/zBFHHJGBAweW\nOw4U2jPPPJPVV189BxxwQJ566qnMnj0706dP/6wETf5RQk6fPj3vv/9+hg0blj59+uS6665r0lyl\nUimPPfZYRowYkc022yzdu3fPKaecki5dumTUqFF5++238z//8z/Za6+9lKA0yKOPPpqVV1457dq1\nK3eUQunRo0fOO++8jBs3LnfeeWf69euXK6+8MgsWLCh3tEZRVVWVOXPm5J133kmpVMovfvGLbLzx\nxnnmmWcyffr03HbbbVlhhRVyzv9j777Dqi7/PoC/D3spiIgpomiCGwfmCFBEAs1IgRypvxTcihNx\nizkTNVOsNPdWxG1qgucgQ8oBDkzF3CPFjSAg6zx/FD3lRDjn3Ge8X9f1u/Ji3N83zyMCb+7PfS9d\nipkzZ4qOS0SkNliEEhER/UthYSEGDhwIfX19BAcH/+d1/y5miKjsfvnlF7i4uOD8+fN4/vx5id4n\nNzcXGRkZ6N27N0JCQhT6eXn//n1s2rQJffr0QdWqVeHv74/r168jODgYd+7cQXx8PKZMmYKPPvqI\nZ+6RwqSkpKBZs2aiY2it+vXrY9++fVizZg2+/fZbtGrVCnFxcaJjlZmJiQlyc3P/KXbr1auHrVu3\nwtHREWZmZmjXrh2ioqIgkUiwcOFCFBQUCE5MRKQeWIQSERH9S1ZWFv744w8UFhaievXq0NPT++d/\nM2bMAAD0798fenp6GDNmjOC0RJorPj4eAQEByM7OLtUOrezsbCxbtgyTJ08udYa8vDzExsZi4sSJ\naNasGZycnLB9+3a0atUKiYmJuHz5Mn744Qd07twZ5cuXL/VziN4mOTmZRagKeHh44Pjx4xg1ahT6\n9OmDzz//HBcuXBAdq9SKd4RaWVlBIpHA19f3lfF3Z2dn1KxZE5mZmRr9sRIRKRLPCCUiIvoXY2Nj\n9O/fH9HR0XBycvrPjbMpKSk4deoU3N3dUadOHbRu3VpcUCINlpGRAX9/f2RnZ5dpnezsbCxatAgd\nO3aEu7v7O99eLpfj0qVL/1xyFB8fj3r16sHb2xsRERFo2bIlx5NJ5VJSUjBs2DDRMXSCnp4eevbs\nCX9/f3z//fdo06YNAgIC8PXXX+ODDz4QHe+9FO8IrVOnDk6cOAErK6vXvl3xRVE5OTmqjEdEpLZY\nhBIREf2LiYkJli9fjuDgYDg5OWHEiBH/vG769Ok4deoU+vTpg6CgIIEpiTTbyJEjSzwK/y45OTno\n3r07rl+/DiMjo1de/+TJE0ilUkRHRyM6OhqFhYXw9vbGV199hXXr1qn80iWif8vNzcUff/yBhg0b\nio6iU0xMTDB27FgEBQVh9uzZaNCgAUaOHImQkBCYm5uLjlcixTtCO3XqhA0bNuDcuXOvvE1eXh7+\n+OMPAPjPL3aJiHQZR+OJiIhew97eHrdu3Xrl5TwnlKhsHjx4gK1btyI3N1dha2ZmZmL37t0AgIKC\nAiQlJeHrr79G69atUb16daxatQr169fHgQMHcPPmTaxatQrdunVjCUrCpaamwsnJCSYmJqKj6CRr\na2t8++23OHnyJC5cuAAnJyesXLkShYWFoqO9U/GO0ICAAFStWhWRkZE4ceLEf95mxowZyMjIgKen\nJ2xtbQUlJSJSL9wRSkRE9Br29vY4derUKy9/+fwtIno/q1evhp6eYn8Xn5WVhXHjxiEyMhIymQw1\natSAt7c3Zs2aBVdXV5ZMpLaSk5Ph4uIiOobOq1mzJrZs2YLjx48jNDQUixYtwrx589CxY0chX/f3\n7Nnzzy937t27BwBISkpCYGAgAMDGxgYdO3bEixcvYGZmhrVr18LX1xfu7u7w9/eHnZ0djh07hsTE\nRHzwwQdYtmyZyj8GIiJ1JZFzawsREdErEhISMGHCBBw9elR0FCKt4urqiqSkJIWvK5FIsGrVKnTo\n0AFVqlRR+PpEyjBw4EA0btyYZ4SqEblcjn379mH8+PGoWrUq5s+fr/LLrKZPn/7PBY2v4+DggPXr\n1yM0NPSff09TU1Mxc+ZMxMXFISMjAx988AE+++wzTJkyRePOPyUiUiYWoURERK9x/fp1tGnTBjdv\n3hQdhUirlC9fHpmZmUpZNzY2lrdvk0Zp3rw5lixZwsv31FBBQQFWrlyJ6dOnw8vLC7NmzUKNGjVE\nx/pHcnIyBg4ciOTkZNFRiIg0Cs8IJSIieg07Ozvcu3dPI84JI9IU+fn5yMrKUtr6t2/fVtraRIqW\nl5eH8+fPo3HjxqKj0GsYGBhg8ODBuHTpEmrVqoVmzZph3LhxePr0qehoAP66LEmRZy0TEekKFqFE\nRESvYWhoCBsbG9y9e1d0FCKN9fz5c1y/fh3Hjx/H/v37sW7dOqU+r6ioSKnrEynS77//jlq1asHM\nzEx0FHqLcuXKYfr06UhNTcWTJ0/g5OSERYsWIS8vT2guExMTvHjxQmgGIiJNxMuSiIiI3qD45vhq\n1aqJjkKkFrKzs/HgwQM8ePAA9+/ff+1///3noqIi2NraolKlSrC1tYWNjQ309PSUstNaIpHAxsZG\n4esSKUtKSgqPctAgVatWxYoVKzBy5EiMHz8eS5YswTfffIOuXbsKuVCJO0KJiEqHRSgREdEbFBeh\nPLuNtFVOTs4bi83XvaygoOA/xWalSpX++XPdunVfeZm5ufkrBcHZs2dx5swZhX8s2dnZHDEmjcIb\n4zVTw4YNsX//fshkMowdOxYLFy7EggUL4ObmptIc3BFKRFQ6LEKJiIjeoLgIJdIUubm5r5SYb9u9\nmZeX90p5WfxfJyenV15nYWFR5p1P7du3x/nz55Gfn6+gj/ov1apVQ7ly5RS6JpEypaSk4MsvvxQd\ng0rJ09MTJ0+exObNm9GrVy80a9YMc+fORZ06dVTyfO4IJSIqHRahREREb2Bvb89b40movLy8txaZ\nL5edubm5rxSaxUVm7dq1X3lduXLlVD7SOWjQICxdulShRaiZmRlGjhypsPWIlK2goACpqalo0qSJ\n6ChUBnp6eujduze++OILREREwM3NDd26dcO0adNga2ur1GdzRygRUemwCCUiInoDe3t7HD16VHQM\n0iJ5eXl4+PBhic7XvH//PnJycmBjY/PaXZu1atV6pey0tLQUclbd+3ByckLz5s1x9OhRhV1upKen\nh759+ypkLSJVuHDhAncxaxETExOMGzcO/fr1w8yZM1G/fn2MHj0ao0ePVtplWIaGhigoKEBhYSH0\n9YklM+8AACAASURBVPWV8gwiIm3EIpSIiOgNOBpP75Kfn//aYvNNuzefP38OGxub1+7a/Oijj14p\nO62srNS+2CyNNWvWwNnZGdnZ2WVey9zcHIsWLYKlpaUCkhGpRkpKCs8H1UIVK1bEokWLMHz4cEyc\nOBFOTk6YMWMG+vTpo/CyUiKRwNjYGC9evFBa2UpEpI0kcrlcLjoEERGROrl16xa2bNmCgwcPIj4+\nHuXLl4e+vj5q1qwJd3d3dO7cGW3atNHKgkrXFRQU4OHDhyUeR8/MzETFihXfOI7+8susrKygp6cn\n+sNUC8uWLUNISEiZylBTU1O0adMGBw8e5OcjaZSRI0fC3t4eY8eOFR2FlOjYsWMYO3YsMjIyMG/e\nPPj4+Cj03yorKytcu3YNFSpUUNiaRETajkUoERHR3y5fvoyhQ4ciISEBcrn8tWdvSSQSmJubw9ra\nGvPnz0fXrl1ZwKixgoICPHr0qMS3omdkZLxSbL6u0Cz+b4UKFVhslsHMmTMxd+7cUpWhEokEH330\nEY4cOQJTU1MlpCNSHnd3d0yfPh2enp6io5CSyeVy7NmzB+PHj0f16tUxf/78Mp0N++DBAxw7dgwn\nT55EeHg4AgIC0KBBAzRv3hwtWrTg7ngiondgEUpERDpPLpcjIiICEydOxIsXL0p8bqGZmRnatGmD\nTZs2wdraWskpCQAKCwtfKTbftmszIyMDFSpUeGehWfznChUq8Kw1FZs1axbCwsJgYGBQ4guUTE1N\nUa9ePVSsWBEHDx7k/89IoxQWFsLKygq3bt2ClZWV6DikIvn5+VixYgVmzJgBHx8fzJo1C/b29iV+\n/4SEBMyePRtHjhyBsbExnj9/jsLCQgB/nRdqamqKvLw8fP7555g0aRIaN26srA+FiEijsQglIiKd\nJpfLMWLECKxevbpUu9KMjIxgZ2eH3377Tek3xGqjwsJCPH78+J2FZvGfnz59CktLy3cWmsX/tba2\nZkmmxnJzc9GsWTOMGjUKx44dw5YtW2BgYIDMzMxX3tbU1BRyuRwtW7ZEeHg4XFxc4O3tjdatW2P2\n7NkC0hOVzsWLF9GpUydcuXJFdBQS4NmzZ5g3bx6WLl2KgQMHYsKECW/dxZmRkYEhQ4Zgz549Jfo+\nRU9PD8bGxhgyZAjmzJkDY2NjRcYnItJ4LEKJiEinzZs3D9OnTy/TOYWGhoZwdHTE6dOnYWhoqMB0\nmqeoqAhPnjwp8a3oT548Qfny5Ut0vmZxsWlgwLsetcXEiRPxxx9/ICoqChKJBJmZmdi3bx+OHj2K\n48ePIzMzE0ZGRqhfvz7c3d3x6aefombNmv+8/4MHD9C8eXMsWrQIfn5+Aj8SopLbvHkzdu3ahaio\nKNFRSKDbt28jLCwM+/fvx5QpUzBo0CAYGRn9521u3rwJV1dXPHjw4LXH9byNqakpHB0dERcXx53H\nRET/wiKUiIh01vnz59G8eXPk5OSUeS0zMzOMGTMGM2fOVEAy9VFUVISnT5+W+Fb0x48fo1y5ciU6\nX7NSpUqwsbFhsamjTpw4AV9fX5w5cwaVK1cu9TonT57Ep59+iri4ONSrV0+BCYmUIyQkBJUqVcKE\nCRNERyE1cPbsWYwbNw5XrlzBN998g4CAAEgkEjx8+BCNGzdGenr6PyPw78vIyAh16tTB8ePHYWJi\nouDkRESaiUUoERHprNatW+PYsWNQ1JdCExMTpKWloXr16gpZTxnkcvl/is13jaM/fPgQFhYWJTpf\ns7jY1PVdsfRuubm5cHFxwdSpU9GjR48yr7dmzRqEh4fj+PHjKF++vAISEilPu3btMHHiRHh7e4uO\nQmokJiYGoaGhMDMzw4IFCzBnzhzExMQgLy+vTOuamppi0KBB+O677xSUlIhIs7EIJSIinXThwgW4\nuLgoZDdoMWNjY4waNQpz585V2JrvIpfLkZGRUaLzNYuLTTMzsxKdr1lcbL48qkdUVpMmTcLFixex\nY8cOSCQShaw5dOhQ/Pnnn9i5cyf09PQUsiaRohUVFcHa2hqXL1+GjY2N6DikZgoLC7Fx40aEhITg\n6dOnpd4J+jJTU1MkJiaiWbNmClmPiEiTsQglIiKdNHbsWCxevBgFBQUKXdfa2hqPHj0q9fvL5XI8\ne/asROdrFr/M1NS0ROdrFhebvDiBRDpx4gQ+++wznDlzBh988IHC1s3Ly0O7du3QsWNHTJkyRWHr\nEinS5cuX0b59e9y4cUN0FFJjjRo1wrlz5xS2nkQigZ+fH3bs2KGwNYmINBUP5SIiIp0UGxur8BIU\nALKzs/Hnn3+iatWqAP4qNjMzM0t0vmbx64yNjV9baNrb28PFxeU/r7OxseG5X6QxXrx4gcDAQCxa\ntEihJSjw11l4UVFRaNGiBVxcXNCxY0eFrk+kCCkpKdyVR2917tw5XL16VaFryuVyHDhwAI8ePULF\nihUVujYRkaZhEUpERDrp4sWLSlm3sLAQvr6+APBPuWlgYPDaHZp2dnZo0qTJK69jsUnaaubMmXB0\ndFTIuaCvU7VqVURGRsLf3x9Hjx5F7dq1lfIcotJiEUrvcuTIERQVFSl8XSMjI/z222/o1KmTwtcm\nItIkLEKJiEgnKfJs0H/T19dH+/bt0a1bt392bpqZmSnlWUSa5OTJk1ixYgXOnDmjsHNBX8fV1RXT\npk2Dv78/fv31V5ibmyvtWUTvKzk5GaNGjRIdg9RYfHw8cnNzFb7u8+fPceLECRahRKTzeJI8ERHp\nJGVdpmJoaIjmzZujefPmqFGjBktQIvz/SPzChQsVPhL/OkOGDIGLiwv69esHHodP6kIulyMlJQUu\nLi6io5Aau379ulLWLSwsxJUrV5SyNhGRJmERSkREOqlSpUpKWVcikcDBwUEpaxNpqlmzZqFWrVro\n2bOnSp4nkUiwdOlSXL58GQsXLlTJM4ne5ebNmzA2NlbJLwNIcynzlzfKGLknItI0LEKJiEgnKWtH\nTnZ2NpydnZWyNpEmSklJwfLly7Fs2TKljsS/zMTEBDt37sSCBQsgk8lU9lyiN+H5oFQSlStXVsq6\nEonkn4sciYh0GYtQIiLSSf7+/rCwsFD4uk2aNOFlR0R/y8vLQ9++ffHtt9+iSpUqKn9+9erVsWnT\nJvTq1Qs3b95U+fOJ/i05OZlj8fRObdq0gZGRkcLXtbCwQIsWLRS+LhGRpmERSkREOqlHjx4KHz+z\nsLDA+PHjFbomkSabNWsWHBwc0KtXL2EZPD09MXbsWPj7+yvtkjSikuCOUCoJV1dXpRSh+fn5aNWq\nlcLXJSLSNBI5T5AnIiIdNXXqVCxcuBDZ2dkKWa969eq4fPkyDA0NFbIekSZLSUlBhw4dcPr0aeHj\nmHK5HD179oSJiQlWr16t0hF9IuCvv4MffPABTp48CXt7e9FxSI3J5XLY29vjzp07Cl3X1dUViYmJ\nCl2TiEgTcUcoERHprKlTp6Jq1aoKKUVMTU0RFRXFEpQIf43EBwYGYsGCBcJLUOCvs/FWrlyJ5ORk\nLF26VHQc0kF//vkn5HI5qlWrJjoKqTmJRIKJEyfC3NxcYWuam5tjypQpCluPiEiTsQglIiKdZWRk\nhH379qFcuXJlWkcikSAkJIRnbxH9bc6cObC3t8f//vc/0VH+YW5ujl27dmH69Ok4evSo6DikY4rH\n4rkbmUpi8ODBqFmzJvT0yv7jupGREdq1a4cOHTooIBkRkeZjEUpERDqtbt26SEhIgLW1danO5DI1\nNYWPjw+ioqJw7949JSQk0iynT5/Gjz/+iOXLl6td6fPhhx9i7dq16NatG/7880/RcUiH8HxQeh/6\n+vpYsWJFmdeRSCSwtLTE6tWrFZCKiEg7sAglIiKd5+zsjLS0NHTo0AFmZmYlKm/Mzc1RpUoVREdH\n4+DBg+jduzc8PT1x//59FSQmUk/5+fno27cv5s+frxYj8a/TsWNHDBkyBF27dkVeXp7oOKQjeGM8\nvY/bt28jKCgIX375JSwsLEr1SyUDAwNYW1sjMTERlSpVUkJKIiLNxCKUiIgIgI2NDfbs2YNDhw7B\n19cXenp6MDExgZmZGQwNDWFsbIzy5cvD2NgYjo6OiIiIwJUrV+Dm5gYAmDJlCrp27Yr27dvj4cOH\ngj8aIjHmzJkDOzs7fPXVV6KjvNWkSZNQqVIljBo1SnQU0hHcEUollZaWBjc3NwQFBWHjxo04fvw4\n6tWrBzMzsxKvYW5ujtatW+PMmTNwcnJSYloiIs3DW+OJiIheIpfLYWdnh4iICDx58gTPnj2DoaEh\nateuDRcXF1SuXPmN7zd58mQcOHAAMpkM1tbWKk5OJM6ZM2fg5eWF06dPw87OTnScd3r27BlatGiB\n8ePHIzAwUHQc0mLp6emoW7cuHj9+rHbHRZB6SU5OxmeffYY5c+b859+lgoIC/PDDDwgPD0dWVhZy\ncnJQUFDwn/eVSCQwMjJCtWrVMG3aNPTu3Zt/34iIXoNFKBER0UvS0tLwySef4MaNG+/9Q4RcLse4\nceMgk8lw+PBhVKhQQUkpidRHfn4+WrRogREjRmhUqXjhwgW0bdsWBw4cQPPmzUXHIS118OBBLFiw\nAFKpVHQUUmMymQw9evTA8uXL0aVLl9e+TVFREeLi4pCYmIj4+Hjcu3cPenp6qFatGgwMDAAAu3fv\nZgFKRPQWBqIDEBERqRupVIr27duX6gcJiUSCefPmYfTo0fDx8UFMTAwsLS2VkJJIfcydOxdVqlRB\n3759RUd5L/Xq1cNPP/2EgIAAnDhxAra2tqIjkRZKSUnh+aD0Vjt37sTgwYOxbds2eHh4vPHt9PT0\n0K5dO7Rr1+6V1xX/EpeIiN6OZ4QSERG9RCqVwtPTs9TvL5FI8N1336FFixbo2LEjMjMzFZiOSL2c\nPXsWERERanlLfEn4+fmhd+/e6N69+yujpkSKwPNB6W1WrlyJ4OBgHDp06K0l6Ls4OTlBIpHg4sWL\nigtHRKSFWIQSERH9S1FREY4cOYL27duXaR2JRIKIiAg4Ozvj008/RVZWloISEqmP4lviw8PDUa1a\nNdFxSm3GjBkwNjbG+PHjRUchLcQilF5HLpcjPDwcs2fPRlxcHJo2bVqm9SQSCXx8fHDo0CEFJSQi\n0k4sQomIiP7l9OnTsLW1RdWqVcu8lp6eHn788UfUqVMHvr6+yM7OVkBCIvURHh4OW1tbjToX9HX0\n9fWxefNm7N69G1u2bBEdh7TIo0eP8PjxY9SuXVt0FFIjRUVFCA0NxYYNG5CYmAhHR0eFrOvj44Po\n6GiFrEVEpK1YhBIREf1LWcfiX6anp4fly5ejevXq+Pzzz5GTk6OwtYlESk1NxeLFi7FixQqNHIl/\nmbW1NXbu3IkRI0bg7NmzouOQljh16hSaNGkCPT3+2EV/KSgoQFBQEJKSkhAfHw87OzuFrd2+fXsk\nJiYiNzdXYWsSEWkbfkUmIiL6F5lMVuax+Jfp6elh9erVqFy5Mvz8/PgDCmm84pH4uXPnwt7eXnQc\nhWncuDEiIiLg5+eHx48fi45DWoBj8fRvOTk5CAgIQHp6OmJiYmBtba3Q9a2srNCwYUMkJiYqdF0i\nIm3CIpSIiOhveXl5OHr0aJkuK3gTfX19rFu3DpaWlvjiiy/w4sULhT+DSFXmz58PGxsbBAUFiY6i\ncF9++SU6d+6MXr16obCwUHQc0nDJycm8MZ4AABkZGejQoQPMzc2xZ88emJubK+U5PCeUiOjtWIQS\nERH97dixY3B0dFT4Do1iBgYG2LhxI4yNjdG9e3fk5+cr5TlEynTu3Dl89913WjMS/zrz5s1Dbm4u\npk2bJjoKaTjuCCUASE9Ph4eHB5ydnbFx40YYGRkp7Vk8J5SI6O1YhBIREf1NGWPxLzM0NMSWLVtQ\nVFSEL7/8kmUoaZSCggIEBgZizpw5qF69uug4SmNgYIDIyEhs2LABu3fvFh2HNFRGRgbu3r2LOnXq\niI5CAl27dg2urq7o0qULIiIilH5e7EcffYRbt27h7t27Sn0OEZGmYhFKRET0N6lUqvQiFACMjIwQ\nFRWFnJwc9O7dGwUFBUp/JpEizJ8/H1ZWVujfv7/oKEpna2uL7du3Y+DAgbh48aLoOKSBTp06hcaN\nG0NfX190FBIkNTUV7u7uGDNmDKZNm6aSXfT6+vpo3749d4USEb0Bi1AiIiIAz58/R0pKCtzc3FTy\nPGNjY+zYsQNPnz5Fnz59eBYhqb3ff/8dCxcuxMqVK7V2JP5lH330EebOnYsuXbrg2bNnouOQhuFY\nvG5LSkqCl5cXFixYgKFDh6r02TwnlIjozViEEhERAUhMTESzZs2UdnnB65iYmGD37t24d+8e+vXr\nh6KiIpU9m+h9FI/Ez549GzVq1BAdR6WCgoLQrl079OnTh5+j9F5YhOquAwcOoEuXLli/fj169Oih\n8uf7+PggJiaG/2YREb0Gi1AiIiL8NRbv6emp8ueamppi7969uH79OgYOHMgfWkgtffvtt7C0tMSA\nAQNERxFi8eLFSE9PxzfffCM6CmkQFqG6adOmTQgMDMTevXvh4+MjJIO9vT0qVaqEU6dOCXk+EZE6\nYxFKREQE1VyU9Cbm5ub4+eefkZaWhmHDhkEulwvJQfQ658+fx4IFC7T6lvh3MTIywvbt2/Hjjz/i\n4MGDouOQBsjKysKNGzdQv3590VFIhSIiIjBhwgTIZDK0atVKaBZvb2+OxxMRvQaLUCIi0nmPHz/G\npUuX0LJlS2EZLCwscODAAZw+fRojRoxgGUpqoXgkfubMmXBwcBAdR6iqVasiMjISffv2xZUrV0TH\nITV35swZNGjQAIaGhqKjkArI5XKEhYXh+++/R0JCAho0aCA6Es8JJSJ6AxahRESk844cOYKPP/4Y\nRkZGQnOUK1cOv/zyC44dO4aQkBCWoSTcwoULYWFhgYEDB4qOohbc3NwQFhYGPz8/PH/+XHQcUmMc\ni9cdhYWFGDp0KPbv34/ExES1+aVR27ZtkZKSwoveiIhewiKUiIh0nsix+JdZWlri0KFDiIuLw4QJ\nE1iGkjAXLlzA/PnzsWrVKujp8VvGYkOHDkWzZs3Qv39/fn7SGyUnJ8PFxUV0DFKyvLw89OzZExcv\nXkRsbCxsbW1FR/qHmZkZWrVqhSNHjoiOQkSkVvhdLRER6TypVKo2RSgAVKhQAdHR0Th06BCmTp3K\nsoVUrrCwEIGBgZgxY4ba7G5SFxKJBEuXLsWlS5fw3XffiY5Daoo7QrVfVlYWfH19kZeXh4MHD6J8\n+fKiI72C54QSEb2KRSgREem0O3fu4P79+2jcuLHoKP9RsWJFxMTEYPfu3ZgxY4boOKRjFi5cCDMz\nMwwaNEh0FLVkamqKnTt3Yv78+YiNjRUdh9RMTk4OLl++jIYNG4qOQkry6NEjeHl5oVq1aoiKioKJ\niYnoSK/Fc0KJiF7FIpSIiHRabGwsPDw8oK+vLzrKKypVqgSpVIqtW7dizpw5ouOQjrh48SLCw8M5\nEv8ONWrUwKZNm9CzZ0/cvHlTdBxSI2fPnkXdunVhbGwsOgopwe3bt+Hu7o62bdti5cqVMDAwEB3p\njRo1aoTs7Gxe8EZE9C/87paIiHSauo3Fv6xy5cqQyWRYt24d5s2bJzoOabnikfjp06ejZs2aouOo\nPU9PT4SEhCAgIAC5ubmi45Ca4Fi89kpLS4ObmxuCgoIQHh4OiUQiOtJbSSQSeHt7Izo6WnQUIiK1\nwSKUiIh0llwuh1Qqhaenp+gob1WlShXIZDIsX76cZxKSUi1atAgmJiYYMmSI6CgaIyQkBB9++CGG\nDh3K83wJAItQbZWcnAwPDw9MmzYNY8eOFR2nxHhOKBHRf7EIJSIinXXlyhUUFhaiTp06oqO8k52d\nHWQyGZYsWYIlS5aIjkNaKC0tDd988w1H4t+TRCLBqlWrcOLECSxbtkx0HFIDvDFe+8hkMnTs2BFL\nly5FYGCg6Djv5ZNPPsGRI0eQn58vOgoRkVpQ3wNNiIiIlKx4LF7dR9uKVa9eHTKZDB4eHjA0NMTg\nwYNFRyItUVhYiKCgIHz99deoVauW6Dgax9zcHLt27YKrqysaN26Mjz/+WHQkEuTFixe4ePEinJ2d\nRUchBdm5cycGDx6Mbdu2wcPDQ3Sc91apUiXUrl0bv/76K9q0aSM6DhGRcPx1PxER6SxNGIt/mYOD\nA2QyGebMmYOVK1eKjkNaYvHixTAwMMDQoUNFR9FYtWvXxpo1a9CtWzfcvXtXdBwS5Pfff8eHH34I\nU1NT0VFIAVauXIng4GAcOnRII0vQYj4+PjwnlIjobyxCiYhIJxUVFSE2NlatL0p6k1q1akEqlWL6\n9OlYt26d6Dik4S5duoQ5c+Zg9erVHIkvo08//RSDBg3CF198gby8PNFxSACeD6od5HI5wsPDMXv2\nbMTFxaFp06aiI5UJzwklIvp//G6XiIh0UmpqKipUqAB7e3vRUUrF0dERhw8fxqRJk7Bp0ybRcUhD\nFY/Eh4WF4cMPPxQdRytMnjwZNjY2GD16tOgoJADPB9V8RUVFCA0NxYYNG5CYmAhHR0fRkcqsdevW\nuHTpEh4+fCg6ChGRcCxCiYhIJ2niWPzL6tSpg5iYGISGhiIyMlJ0HNJAS5YsgZ6eHoKDg0VH0Rp6\nenpYv349pFIp1q5dKzoOqRh3hGq2goICBAUFISkpCfHx8bCzsxMdSSGMjIzg4eGBmJgY0VGIiIRj\nEUpERDpJJpNp5Fj8y+rXr49Dhw5h1KhR2LFjh+g4pEH++OMPzJo1iyPxSmBpaYldu3Zh3LhxOHny\npOg4pCL5+fk4d+4cmjRpIjoKlUJOTg4CAgKQnp6OmJgYWFtbi46kUDwnlIjoL/yul4iIdE5+fj4S\nEhLQrl070VEUolGjRjh48CCGDh2KPXv2iI5DGqCoqAhBQUGYOnUqateuLTqOVqpXrx6WLVuGgIAA\nPHjwQHQcUoELFy6gevXqsLCwEB2F3lNGRgY6dOgAc3Nz7NmzB+bm5qIjKZy3tzeio6Mhl8tFRyEi\nEopFKBER6ZwTJ06gZs2asLGxER1FYZo0aYIDBw5g4MCB2L9/v+g4pOaWLFkCABg+fLjgJNrN398f\nvXr1Qo8ePVBQUCA6DikZx+I1U3p6Ojw8PODs7IyNGzfCyMhIdCSlqF27NkxMTHDu3DnRUYiIhGIR\nSkREOkdbxuJf5uLigr179yIwMJC3w9IbXb58GTNnzuRIvIrMnDkThoaGmDBhgugopGQsQjXPtWvX\n4OrqCj8/P0RERGj9v4k+Pj78/oCIdJ52/0tPRET0GlKpVCuLUABo2bIldu/ejf/97384fPiw6Dik\nZopH4qdMmaIVNyFrAn19fWzevBk7d+7E1q1bRcchJeKN8ZolNTUV7u7uGDNmDMLCwiCRSERHUjqe\nE0pEBEjkPCSEiIh0SHZ2NmxtbXH37l2UK1dOdBylSUhIgL+/P6KiouDh4SE6DqmJJUuWIDIyEnFx\ncdDX1xcdR6ecOXMGXl5ekEqlcHZ2Fh2HFKywsBCWlpa4ffs2rKysRMehd0hKSoKfnx8WL16MHj16\niI6jMs+ePYOdnR3S09NhZmYmOg4RkRDcEUpERDolKSkJjRs31uoSFADc3d0RFRWFbt26ISEhQXQc\nUgNXrlzB9OnTsXr1apagAjRu3BiLFy+Gv78/njx5IjoOKdilS5fwwQcfsATVAAcOHECXLl2wfv16\nnSpBAaB8+fJo2rQp4uPjRUchIhKGRSgREekUbR6Lf5mHhwc2b96MgIAAJCUliY5DAhWPxE+aNAlO\nTk6i4+isnj17wtfXF7169UJhYaHoOKRAHIvXDJs2bUJgYCD27t0LHx8f0XGE4DmhRKTrWIQSEZFO\nkUql8PT0FB1DZby8vLBhwwZ06dIFx48fFx2HBPnxxx+Rn5+PkSNHio6i8+bNm4fs7Gx8/fXXoqOQ\nAvGiJPUXERGBCRMmQCaToVWrVqLjCMNzQolI17EIJSIinfH06VNcuHABrVu3Fh1FpXx8fLBmzRr4\n+voiOTlZdBxSsatXr+Lrr7/GmjVrOBKvBgwNDbFt2zasW7cOu3fvFh2HFIRFqPqSy+UICwvD999/\nj4SEBDRo0EB0JKGaNm2K+/fv49atW6KjEBEJwSKUiIh0RlxcHFq3bg1jY2PRUVSuU6dOWL58OTp1\n6oTTp0+LjkMqUlRUhH79+mHixImoU6eO6Dj0N1tbW2zfvh0DBw7ExYsXRcehMioqKsKpU6dYhKqh\nwsJCDBs2DPv370diYiIcHBxERxJOX18fXl5e3BVKRDqLRSgREekMXRuLf1nnzp3xww8/oGPHjkhN\nTRUdh1Rg2bJlyM3NxahRo0RHoZe0aNEC33zzDfz8/PDs2TPRcagMrly5ggoVKqBixYqio9C/5OXl\noWfPnrhw4QJiY2Nha2srOpLa4DmhRKTLWIQSEZHOkMlkOnNR0psEBARg0aJF8PHxwfnz50XHISW6\ndu0awsLCOBKvxvr164e2bduib9++KCoqEh2HSolj8eonKysLvr6+yMvLw8GDB1G+fHnRkdSKt7c3\nDh8+zEvbiEgnsQglIiKdcO/ePdy5c4c/rALo3r075s2bh08++QRpaWmi45ASFN8SP378eNStW1d0\nHHqLxYsX4+7du5g7d67oKFRKLELVy6NHj+Dl5YVq1aohKioKJiYmoiOpnapVq8LOzg4nT54UHYWI\nSOVYhBIRkU6QyWRo27Ytd8b9rXfv3pg9eza8vLxw+fJl0XFIwX766Sfk5ORgzJgxoqPQOxgbG2PH\njh344Ycf8Msvv4iOQ6WQnJwMFxcX0TEIwO3bt+Hu7o62bdti5cqVMDAwEB1JbXE8noh0FYtQIiLS\nCRyLf1Xfvn0xbdo0tG/fHlevXhUdhxTk+vXrHInXMFWrVkVkZCT69OnDz0UNI5fLuSNUTaSlpcHN\nzQ1BQUEIDw+HRCIRHUmtsQglIl3FIpSIiHSCVCplEfoa/fv3x4QJE+Dp6Ynr16+LjkNlJJfLxvAp\nRwAAIABJREFU0a9fP4SGhqJevXqi49B7cHNzw9SpU+Hn54fnz5+LjkMldOPGDZiamqJy5cqio+i0\n5ORkeHh4YNq0aRg7dqzoOBrB3d0dZ8+exdOnT0VHISJSKRahRESk9a5evYrc3FwWQ28wZMgQhISE\nwNPTE7du3RIdh8pg+fLlyMrK4ki8hho2bBiaNGmCAQMGQC6Xi45DJcCxePFiY2PRsWNHLF26FIGB\ngaLjaAwTExO4urpCJpOJjkJEpFIsQomISOvJZDJ4enpyTO4thg8fjuHDh8PT0xN37twRHYdK4fr1\n65gyZQrWrFnDc/E0lEQiwbJly5CWloZFixaJjkMlwLF4sXbu3Inu3btj27Zt6NKli+g4Gofj8USk\ni1iEEhGR1uNYfMmMHj0aAwYMgKenJ+7evSs6Dr0HuVyO/v37IyQkBPXr1xcdh8rA1NQUO3fuRHh4\nOGJjY0XHoXdgESrOypUrERwcjEOHDsHDw0N0HI1UXIRyBzoR6RIWoUREpNXkcvk/O0Lp3caNG4ev\nvvoK7du3R3p6uug4VEIrVqzAs2fPeDaelqhRowY2btyInj178rgKNSaXyzkaL4BcLkd4eDhmz56N\nuLg4NG3aVHQkjVWvXj0UFhbi0qVLoqMQEakMi1AiItJqv//+OywsLODg4CA6isaYPHkyunXrBi8v\nLzx8+FB0HHqHGzduYPLkyRyJ1zJeXl4YM2YM/P39kZubKzoOvcadO3cgkUhQtWpV0VF0RlFREUJD\nQ7FhwwYkJibC0dFRdCSNJpFI4O3tjejoaNFRiIhUhkUoERFpNY7Fl860adPw+eefw8vLC48fPxYd\nh95ALpdjwIABGDNmDBo0aCA6DinY2LFjUatWLQwbNoyjq2qoeCye50+rRkFBAYKCgpCUlIT4+HjY\n2dmJjqQVeE4oEekaFqFERKTVpFIpx+JLQSKRYNasWfD29sYnn3yCJ0+eiI5Er7Fy5Uo8fvwYoaGh\noqOQEkgkEqxatQrHjx/HTz/9JDoOvYTng6pOTk4OAgICkJ6ejpiYGFhbW4uOpDW8vLwQHx+PFy9e\niI5CRKQSLEKJiEhrFRQUID4+nkVoKUkkEoSHh6NNmzbw8fFBRkaG6Ej0Lzdv3sSkSZOwdu1ajsRr\nMQsLC+zatQvTpk3Dr7/+KjoO/QvPB1WNjIwMdOjQAebm5tizZw/Mzc1FR9Iq1tbWqF+/Po4ePSo6\nChGRSrAIJSIirZWcnIzq1avD1tZWdBSNJZFIsHDhQrRs2RIdO3ZEZmam6EiE/x+JHzVqFBo2bCg6\nDilZ7dq1sXr1anTt2hV3794VHYf+xh2hypeeng4PDw84Oztj48aNMDIyEh1JK/GcUCLSJSxCiYhI\na/G2eMWQSCSIiIiAs7MzPv30U2RlZYmOpPNWr16NR48eYfz48aKjkIp06tQJAwcORNeuXZGXlyc6\njs67d+8ecnNzUaNGDdFRtNa1a9fg6uoKPz8/REREQE+PP7oqC88JJSJdwq8mRESktXhRkuJIJBL8\n+OOPqFOnDnx9fZGdnS06ks66desWJkyYwFviddCUKVNQsWJFjBkzRnQUnceLkpQrNTUV7u7uGDNm\nDMLCwvh/ZyVr2bIlrl+/jvT0dNFRiIiUjkUoERFppdzcXBw7dgxt2rQRHUVr6OnpYfny5ahRowY+\n//xz5OTkiI6kc4pH4keOHIlGjRqJjkMqpqenh/Xr1yMmJgbr1q0THUencSxeeZKSkuDl5YUFCxZg\n6NChouPoBAMDA3h6enI8noh0AotQIiLSSr/++isaNGgAS0tL0VG0ip6eHlatWoUPPvgAXbp0QW5u\nruhIOmXNmjW4f/8+R+J1mKWlJXbt2oXQ0FAkJyeLjqOzWIQqx4EDB9ClSxesX78ePXr0EB1Hp/j4\n+LAIJSKdwCKUiIi0EsfilUdfXx9r165FhQoVEBAQgBcvXoiOpBNu376N8ePHY+3atTA0NBQdhwSq\nX78+li5dioCAADx48EB0HJ3EG+MVb9OmTQgMDMTevXvh4+MjOo7OKb4wqaioSHQUIiKlYhFKRERa\nSSqV8qIkJTIwMMCGDRtgamqKbt268fIWJZPL5Rg4cCCGDx8OZ2dn0XFIDQQEBODLL79Ejx49UFBQ\nIDqOTnn48CGePn2KWrVqiY6iNSIiIjBhwgTIZDK0atVKdByd5ODgACsrK5w5c0Z0FCIipWIRSkRE\nWufZs2dITU3Fxx9/LDqKVjM0NMTmzZsBAF9++SXy8/MFJ9Je69atw927dzFx4kTRUUiNzJo1CwYG\nBvx7oWKnTp1C06ZNeYu5AsjlcoSFheH7779HQkICGjRoIDqSTuPt8USkC/jVm4iItE58fDxatmwJ\nU1NT0VG0npGREbZt24bc3Fz07t2bO9OU4M6dOxg3bhxH4ukV+vr62Lx5M3bs2IHIyEjRcXQGx+IV\no7CwEMOGDcP+/fuRmJgIBwcH0ZF0Hs8JJSJdwCKUiIi0DsfiVcvY2Bg7duzA06dP0adPHxQWFoqO\npDWKR+KHDRuGxo0bi45DaqhixYrYuXMngoODkZqaKjqOTuBFSWWXl5eHnj174sKFC4iNjYWtra3o\nSASgbdu2OHHiBLKyskRHISJSGhahRESkdWQyGS9KUjETExPs3r0b6enp6NevHy9bUJD169fjzp07\nHH2mt2rSpAkWLVoEPz8/PHnyRHQcrccitGyysrLg6+uLvLw8HDx4EOXLlxcdif5mYWGBjz76CEeO\nHBEdhYhIaViEEhGRVrl//z5u3LiB5s2bi46ic0xNTbF3715cv34dAwcOZBlaRnfu3EFoaCjWrl0L\nIyMj0XFIzfXq1QufffYZevfuzc89JXr69CnS09Ph5OQkOopGevToEby8vFCtWjVERUXBxMREdCR6\nCc8JJSJtxyKUiIi0SmxsLNzd3WFgYCA6ik4yMzPDzz//jLS0NAwbNgxyuVx0JI0kl8sxaNAgDB06\nFE2aNBEdhzTE/Pnz8fz5c3z99deio2itU6dOoXHjxtDX1xcdRePcvn0b7u7uaNu2LVauXMmv02qK\nRSgRaTsWoUREpFU4Fi+ehYUFDhw4gNOnT2PEiBEsQ0th48aNuHXrFiZNmiQ6CmkQQ0NDREZGYu3a\ntdizZ4/oOFqJY/Glk5aWBjc3NwQFBSE8PBwSiUR0JHoDZ2dnZGRk4Nq1a6KjEBEpBYtQIiLSKlKp\nlEWoGihXrhx++eUXHDt2DCEhISxD38Off/6JkJAQjsRTqVSuXBnbt2/HgAEDkJaWJjqO1mER+v6S\nk5Ph4eGBadOmYezYsaLj0Dvo6enB29ubt8cTkdZiEUpERFrjxo0bePbsGRo0aCA6CgGwtLREdHQ0\n4uLiMH78eJahJVA8Ej948GA0bdpUdBzSUC1atMCcOXPg5+eHzMxM0XG0SnJyMlxcXETH0BixsbHo\n2LEjli1bhsDAQNFxqIQ4Hk9E2oxFKBERaQ2ZTAZPT0/o6fHLm7qwsrJCTEwMoqOjMWXKFJah77Bp\n0ybcuHEDU6ZMER2FNFz//v3h7u6Ovn378vNOQTIzM3Hr1i3Uq1dPdBSNsHPnTnTv3h1RUVHo3Lmz\n6Dj0Hj755BPIZDLk5+eLjkJEpHD8SZGIiLQGx+LVk7W1NQ4fPoy9e/di+vTpouOorbt372LMmDEc\niSeFiYiIwJ9//om5c+eKjqIVzpw5g4YNG/KSnxJYuXIlgoODcejQIbRt21Z0HHpPlStXRs2aNXH8\n+HHRUYiIFI5FKBERaQW5XA6pVApPT0/RUeg1bGxsIJVKsW3bNsyePVt0HLUjl8sxePBgDBo0iOcP\nksIYGxtj+/btWLJkCcdcFYBj8e8ml8sRHh6O2bNnIy4ujkd8aDCOxxORtmIRSkREWuHixYswNjZG\nrVq1REehN7C1tYVUKsX69esxb9480XHUypYtW3D16lWOxJPC2dnZITIyEl999RWuXr0qOo5G40VJ\nbyeXyxEaGooNGzYgMTERjo6OoiNRGbAIJSJtxSKUiIi0QvFYvEQiER2F3qJKlSqQyWRYsWIFvvvu\nO9Fx1MK9e/cwevRorFmzBsbGxqLjkBZyd3fHlClT4O/vj+zsbNFxNBaL0DcrKChAYGAgkpKSEB8f\nDzs7O9GRqIw+/vhjXLhwAY8ePRIdhYhIoViEEhGRVuBYvOaws7ODTCbDkiVLsGTJEtFxhCoeie/f\nvz+aN28uOg5pseDgYDg7O2PAgAG8PKkUsrOzceXKFTRs2FB0FLWTk5ODgIAApKenIyYmBtbW1qIj\nkQIYGxujTZs2kEqloqMQESkUi1AiItJ4hYWFiIuLYxGqQezt7SGTyfDtt99i2bJlouMIs3XrVly+\nfBlhYWGio5CWk0gk+Omnn3DhwgUsXrxYdByNc/bsWdSrV48Xmb0kIyMDHTp0gLm5Ofbs2QNzc3PR\nkUiBOB5PRNqIVx4SEZHGO3XqFKpUqYIqVaqIjkLvwcHBATKZDB4eHjAwMED//v1FR1Kp9PR0jBo1\nCvv37+dIPKmEqakpdu7ciVatWqFJkybw8PAQHUljcCz+Venp6ejQoQPc3NywePFi6Olxj4228fHx\nQXh4OORyOY8eIiKtwa9WRESk8TgWr7lq1aoFqVSK6dOnY926daLjqIxcLseQIUPQr18/jsSTSjk4\nOGDjxo3o2bMnbt26JTqOxmAR+l/Xrl2Dm5sb/Pz8EBERwRJUSzk6OsLQ0BDnz58XHYWISGH4FYuI\niDSeTCZD+/btRcegUnJ0dMThw4cxadIkbNq0SXQclYiMjERaWhqmTZsmOgrpIC8vL4waNQoBAQHI\nzc0VHUcjJCcnw8XFRXQMtZCamgp3d3eMHj0aYWFh3CmoxSQSCby9vREdHS06ChGRwrAIJSIijfbi\nxQskJSVxxFPD1alTBzExMQgNDUVkZKSwHHfu3EFQUBDs7OxgYmKCmjVrYvTo0Xj69KnCnpGeno6R\nI0fylngSKjQ0FA4ODggODublSe/w4sULpKWloVGjRqKjCJeUlAQvLy8sWLAAQ4cOFR2HVIDnhBKR\ntmERSkREGu23335D3bp1YWVlJToKlVH9+vURHR2NUaNGYceOHSp//tWrV9GsWTOsW7cOrVq1wpgx\nY/Dhhx9i8eLF+Pjjj/HkyZMyP0Mul2Po0KEIDAxEixYtFJCaqHQkEglWr16N3377DcuXLxcdR62d\nO3cOtWvXhqmpqegoQh04cABdunTB+vXr0aNHD9FxSEXat2+PpKQk5OTkiI5CRKQQvCyJiIg0Gsfi\ntUvDhg1x8OBBdOjQAQYGBujcubPKnj1kyBA8fPgQS5Ys+c9Op5CQEHz33XeYPHkyfvzxxzI9Iyoq\nChcuXNCZIwBIvVlYWGDXrl1wdXWFs7MzWrduLTqSWuJYPLBp0yaEhIRg7969aNWqleg4pEKWlpZw\ndnZGQkICvL29RcchIioz7gglIiKNJpVKWYRqmSZNmmD//v0YOHAgfv75Z5U88+rVq4iJiYGDg8Mr\n457Tp0+Hubk5NmzYUKYdMffv38eIESOwZs0amJiYlDUykUI4Ojpi9erV6NatG+7duyc6jlrS9YuS\nIiIiMGHCBEilUpagOornhBKRNmERSkREGisrKwunT5+Gq6ur6CikYC4uLti3bx+CgoLwyy+/KP15\nsbGxAPDa3S4WFhZwdXVFdnY2fvvtt1I/Y9iwYejTpw9atmxZ6jWIlOGzzz5D//790bVrV+Tl5YmO\no3Z0tQiVy+UICwvD999/j4SEBDRo0EB0JBKE54QSkTZhEUpERBorISEBzZs3h5mZmegopAQtWrTA\nnj178NVXX+Hw4cNKfVZaWhokEgmcnJxe+3pHR0cAwKVLl0q1flRUFM6dO4fp06eXOiORMk2dOhUV\nKlRASEiI6ChqJT8/H7///juaNGkiOopKFRYWYtiwYdi/fz8SExPh4OAgOhIJ1Lx5c/z555+4c+eO\n6ChERGXGIpSIiDQWx+K1X+vWrbFjxw707NkTR44cUdpzMjIyAPx1FtrrFL+8NLfHP3jwAMOHD+dI\nPKk1PT09bNiwAYcOHcK6detEx1Eb58+fR40aNWBubi46isrk5eWhZ8+euHDhAmJjY2Frays6Egmm\nr68PLy8vjscTkVZgEUpERBpLKpXC09NTdAxSMnd3d2zbtg3dunVDQkKC6DjvLTg4GP/73/94th6p\nPUtLS+zatQtjx45FcnKy6DhqQdfG4rOysuDr64u8vDwcPHgQ5cuXFx2J1ATPCSUibcEilIiINNKj\nR49w9epVtGjRQnQUUgEPDw9s3rwZAQEBSEpKUvj6xTs+i3eGvqz45VZWVu+17vbt23HmzBnMmDGj\nbAGJVKRBgwZYunQpAgIC8ODBA9FxhNOlG+MfPXoELy8v2NvbIyoqijvY6T98fHwQExODwsJC0VGI\niMqERSgREWmk2NhYuLm5wdDQUHQUUhEvLy9s2LABXbp0wfHjxxW6dp06dSCXy994Bugff/wBAG88\nQ/R1Hj58+M9IvKmpqUJyEqnCF198gR49eqBHjx4oKCgQHUcoXdkRevv2bbi7u8PDwwMrVqyAgYGB\n6EikZqpVq4bKlSsjJSVFdBQiojJhEUpERBqJY/G6ycfHB2vWrIGvr69CR3fbtWsHAK8d+8vKysLR\no0dhZmb2XuPtwcHB6NWrF1q3bq2wnESqMnv2bOjr62PixImiowhTWFiIs2fPav1FSWlpaXBzc0NQ\nUBDmzp0LiUQiOhKpKd4eT0TagEUoERFpJJlMxouSdFSnTp2wYsUKdOrUCadPn1bImrVq1YK3tzeu\nX7+O77///j+vCwsLw/Pnz/HVV1+VeGfnjh07cOrUKcycOVMh+YhUTV9fH1u2bMH27dsRGRkpOo4Q\naWlpqFKlyhsvUdMGycnJ8PDwwLRp0zB27FjRcUjN8ZxQItIGErlcLhcdgoiI6H3cvn0bTZo0wf37\n96Gnx9/p6aodO3YgODgY0dHRaNSoUZnXu3r1KlxdXXH//n18/vnnqFevHn777TccOXIEdevWxdGj\nR1GhQoV3rvPw4UM0atQI27dvh6ura5lzEYl06tQpeHt7QyaTKeTzTJNs2LAB+/fvx9atW0VHUYrY\n2Fh0794dK1asQOfOnUXHIQ2QnZ2NypUr486dO7xIi4g0Fn96JCIijSOVStGuXTuWoDouICAAixYt\ngo+PD86fP1/m9WrVqoWTJ0+ib9++OH78OBYuXIhr165h9OjR+PXXX0tUggLAiBEj0LNnT5agpBWa\nNm2K7777Dn5+fnjy5InoOCqlzeeD7ty5E927d0dUVBRLUCoxMzMztG7dGjKZTHQUIqJS4ynYRESk\ncTgWT8W6d++OgoICfPLJJ5BKpahbt26Z1rOzs8OqVatK/f67du3CyZMnFTayT6QOevfujRMnTqB3\n797Yt2+fzvwSKiUlBVOnThUdQ+FWrlyJsLAwHDp0CE2bNhUdhzRM8TmhXbp0ER2FiKhUOBpPREQa\nRS6Xw97eHrGxsXB0dBQdh9TEunXrMHnyZKF/Lx49eoRGjRph27ZtcHNzE5KBSFny8/Ph5eWFtm3b\nYsaMGaLjKF1RURGsrKxw/fp1WFtbi46jEHK5HPPmzcOyZcsQHR3Nr6FUKqmpqejcuTOuXLnCi7WI\nSCNxRygREWmUS5cuQSKRoHbt2qKjkBrp06cPCgoK0L59exw5cgS1atVSeYYRI0age/fuLEFJKxka\nGmLbtm1o3rw5XFxctH6c+vLly6hYsaJWlaChoaH45ZdfkJiYCDs7O9GRSEM1bNgQL168wJUrV/i9\nGBFpJBahRESkUYrH4rkLgV7Wr18/5Ofnw9PTE0eOHIGDg4PKnr17924cP34cZ86cUdkziVStcuXK\n2L59O3x9fVG3bl3UqVNHdCSl0abzQQsKCtC/f39cunQJ8fHxWlPukhgSiQTe3t44dOgQi1Ai0ki6\nccAPERFpDalUCk9PT9ExSE0NHjwYY8eOhaenJ27duqWSZz5+/BhDhw7F6tWrYWZmppJnEonSsmVL\nzJ49G35+fsjMzBQdR2mSk5Ph4uIiOkaZ5eTkICAgAOnp6YiJiWEJSgpRfE4oEZEmYhFKREQao6io\nCLGxsbwoid4qODgYw4cPh6enJ+7cuaP0540cORLdunWDu7u70p9FpA4GDBgANzc39O3bF9p63YA2\n7AjNyMhAhw4dYG5ujj179sDc3Fx0JNISXl5eiIuLQ15enugoRETvjUUoERFpjDNnzqBSpUo824ze\nafTo0RgwYAA8PT1x9+5dpT1n7969+PXXXzF79mylPYNIHS1ZsgR37tzB3LlzRUdROLlcrvFFaHp6\nOjw8PODs7IyNGzfCyMhIdCTSIjY2NnBycsKvv/4qOgoR0XtjEUpERBqDY/H0PsaNG4c+ffqgffv2\nSE9PV/j6jx8/xpAhQ7B69WrutCKdY2xsjO3bt2PJkiVaNyJ7/fp1WFhYwNbWVnSUUrl27Rrc3Nzg\n5+eHiIgI6OnxRz5SPI7HE5Gm4ldFIiLSGMUXJRGV1KRJk9C9e3d4eXnh4cOHCl171KhRCAgIQJs2\nbRS6LpGmqFatGrZu3YqvvvoKV69eFR1HYZKTkzV2N2hqairc3d0xevRohIWF8WJBUhoWoUSkqViE\nEhGRRsjLy0NiYiI8PDxERyENExYWhs6dO8PLywuPHz9WyJr79u3D0aNH8c033yhkPSJN1aZNG0ye\nPBn+/v7Izs4WHUchNHUsPikpCV5eXliwYAGGDh0qOg5puVatWuHKlSu4f/++6ChERO+FRSgREWmE\n48ePo3bt2qhYsaLoKKRhJBIJZs6cCR8fH3zyySd48uTJK2+Tm5uLTZs2IbB7dzg7OMDKzAzlTExQ\nw8YGndu1w9w5c3D79m0AwJMnTzgST/Qvw4cPR6NGjTBgwACtuDxJE4vQAwcOoEuXLli/fj169Ogh\nOg7pAENDQ3h4eODw4cOioxARvReJXBu+WyEiIq03Y8YMZGZmYv78+aKjkIaSy+UICQlBYmIiYmJi\nYGlpifz8fMz/5hssWrAATeRy+GdlwQXAhwD0ATwAkAIg1tgYkRIJvDw9UWRqiipVqmDJkiVCPx4i\ndZKdnQ1XV1f06dMHo0aNEh2n1ORyOWxtbXHmzBlUrVpVdJwS2bRpE0JCQrB79260atVKdBzSIUuX\nLsVvv/2GdevWiY5CRFRi3BFKREQaQSqV8nxQKhOJRIJvv/0WrVq1QocOHXDy5Em0aNAACeHhSMzM\nRHRWFgYD+AiANQBLALUBdAOw9MUL3MjNRYNDh3Bw5040athQ5IdCpHb+j737Dq/xbvwH/j7Z0wgy\nCCJDiGiTqPEQI0IipWhjxKalRamq0RaxV1DFgyqKGk0fexOcGCERmmUksVcRImSPk+Tcvz/6zflJ\nY2Sck/uck/frulxXcnKfz/0+fR6SvM9nmJiYYN++fVi8eDHOnDkjdpxy+/vvv6GrqwsbGxuxo5TK\nqlWr8MMPP0AqlbIEpUrn6+uLEydOaMVMcCKqOliEEhGR2svKykJUVBQ8PT3FjkIaTiKRYOXKlbC1\ntYVXmzb48vZtHM3ORuNSPNccwKzCQlwUBAR99x2WLlyo6rhEGsXOzg7btm3DgAED8OjRI7HjlEvR\nsnh1P2RIEATMnDkTq1evRlhYGJo1ayZ2JKqC7O3tYWpqiitXrogdhYio1FiEEhGR2rtw4QLc3d1h\nZmYmdhTSAsnJyQiXSrGxsBBjBAFlrTuaAziXnY1fFizAn8HBqohIpLG6du2Kb7/9Fv7+/sjNzRU7\nTplFRUWhRYsWYsd4p8LCQnz99dc4cuQIzp8/Dzs7O7EjURVWNCuUiEhTsAglIiK1x2XxpCyCIGDs\n8OEYnJWF/hUYpx6AXdnZmPDVV0hKSlJWPCKtMHXqVDRs2BDjxo3TuCWz6n5Qkkwmw6BBg5CQkIDT\np0/D0tJS7EhUxfn6+iIkJETsGEREpcYilIiI1J5UKkXnzp3FjkFa4PTp07hy7hzmyGQVHqsFgM9z\nczFj0qSKByPSIhKJBJs2bUJERATWr18vdpwyUeciNDMzE5988gny8vJw7NgxVKtWTexIRPDy8kJk\nZCSysrLEjkJEVCosQomISK29evUKN2/e5CEQpBSrFy/GpKwsGClpvO/y87Fn7168evVKSSMSaQdz\nc3Ps27cPgYGBiIiIEDtOqTx9+hQymQwNGjQQO0oJKSkp6NKlC+rXr49du3bByEhZ/4oRVYy5uTk8\nPDxw9uxZsaMQEZUKi1AiIlJrZ86cQdu2bWFgYCB2FNJwqampOHX2LAYpccw6AHx1dLB7924ljkqk\nHRo3boxNmzahb9++GrGFhLoelPT333+jffv26NSpEzZs2AA9PT2xIxEVw31CiUiTsAglIiK1xmXx\npCzR0dH4wMgIyj5yq312Ni6dOaPkUYm0Q48ePTBy5Ej07dsXMiVsSaFK6rgs/saNG/D09MTnn3+O\nxYsXq11JSwRwn1Ai0iwsQomISK2FhobyoCRSiri4OLip4BRrdwBxf/2l9HGJtMXMmTNRo0YNTFLz\n/XSjo6PV6sT4qKgodOrUCbNmzcLkyZPFjkP0Vu7u7khJScHDhw/FjkJE9F4sQomISG09efIESUlJ\ncHNzEzsKaYH09HRYqGBGmgWAtIwMpY9LpC10dHSwbds2hISEYOvWrWLHeauoqCi1mRF6+vRp+Pn5\nYd26dRgxYoTYcYjeSUdHB127duWsUCLSCCxCiYhIbYWGhqJTp07Q1dUVOwppAT09PeTrKP9HHxkA\nPf5/lOidatSogX379mHSpEmIjo4WO04JycnJSE9Ph729vdhRsHfvXvTv3x+7du1Cr169xI5DVCo+\nPj7cJ5SINAKLUCIiUltcFk/KZG9vj5umpkof9yaApKdP0bZtW4wcORLLly/H8ePH8fDhQwiCoPT7\nEWmqZs2aYe3atfjss8/w4sULseMUExMTA3d3d9H34Ny4cSPGjRuHkJAQdOzYUdQsRGVxow1bAAAg\nAElEQVTh4+MDqVSKgoICsaMQEb0TjxwkIiK1JAgCpFIppk6dKnYU0hItWrTAD3I5BADKrDqidHXx\nxbffokfPnoiPj0d8fDyOHj2K+Ph4pKeno2nTpnBxcVH8adq0KRo1asSZzlQl9e3bF3/99RcCAgJw\n/PhxtTkBPSoqStT9QQVBwJIlS7Bu3TqcPXsWTk5OomUhKg8bGxvUr18fly9fxn/+8x+x4xARvZVE\n4FQFIiJSQ7dv30aHDh3w+PFj0WfokHaQy+VwqlsXfzx7htbKGhOAk6kp/pBK0bp1yVFTU1ORkJCg\nKEiL/iQnJ6Nx48YlClJHR0fo6+srKR2ReiooKICfnx/c3d2xZMkSseMA+Keg/fTTTzFw4MBKv7cg\nCJgyZQqOHz+OkJAQ1KtXr9IzECnD1KlTYWJigtmzZ4sdhYjorViEEhGRWlq/fj3CwsKwbds2saOQ\nFvlm3Dg8+eUX7JbLlTLeUQCBTk7468aNMhX2mZmZSExMLFaOJiQk4NGjR3BwcChWjrq4uKBx48Yw\nMjJSSmYidZCSkoKPPvoIQUFB6NevX4XG2rNnD86ePYvY2FjExcUhIyMDgwcPfuPBTA8ePECjRo1K\nPC4IAiQSCQICAvDHH39UKE9ZFBQUYOTIkbh58yYOHz4MCwuLSrs3kbKdOnUKgYGBiIiIEDsKEdFb\nqcdaFCIion+RSqXw8/MTOwZpievXr2PGjBm4dOkSZEZGOJedjQ4VHDMHwEQTEywOCirzrGUzMzN8\n9NFH+Oijj4qPmZODmzdvKsrR3bt3Iz4+Hnfv3kWDBg2KlaMuLi5o0qQJTFWw7ymRqtWqVQt79+6F\nj48PXFxc4OrqWu6x5s+fjytXrsDMzAy2trZITEx873Pc3NzQu3dvAP/8vVu+fDmmTZuG5s2blztH\nWeXk5CAgIAAymQwnT57k32XSeJ6enrh+/TpevXqFmjVrih2HiOiNOCOUiIjUjlwuh5WVFaKiotCg\nQQOx45AGu3//PmbNmoVjx45h6tSp+Prrr3Hy5El8N2AAIrOzUauc4woAxhkY4GW3bgg+cECZkd9I\nJpPh9u3bipmjRUXpzZs3YW1tXaIgbdq0KapXr67yXEQVtW3bNsydOxeXL19GjRo1yjXG2bNnYWtr\nCwcHB5w9exZeXl7vnRE6fPhwbNq0CcA/B/PNmjULYWFhFXotZZGWloaePXuiXr162LJlCwwMDCrt\n3kSq9PHHH+Pzzz9Hnz59xI5CRPRGnBFKRERq59q1a6hRowZLUCq3Z8+eYf78+fjjjz/w9ddf49at\nW4pisGfPnrgwahR8N2xASDnKUAHADH19nKtXD+e2bFF29DcyMDBQlJyvKygowL179xTl6JkzZ7B2\n7VokJCSgZs2aJQ5qcnFxQa1a5a1/iZRvyJAhuHz5MgYNGoRDhw5BR0enzGNU9HT16OhoeHh4VGiM\nsnj27Bm6desGT09PrFy5slyvmUhd+fr6IiQkhEUoEaktFqFERKR2pFIpvL29xY5BGig1NRXLli3D\nL7/8giFDhiAhIQGWlpYlrlv888+YrqcHj19+wcbsbHQt5fhPAHxlYoKkhg0Revas6Ev/9PT04OTk\nBCcnJ/Ts2VPxuFwux8OHDxUF6aVLl7BlyxbEx8fD0NCwRDnq4uICKysrHkxGovjpp5/g7e2NOXPm\nYM6cOZVyzydPnmD9+vVISUnB3r17FcvkVe3evXvw8fHBkCFDEBgYyL9zpHV8fHzw008/KfbdJSJS\nNyxCiYhI7UilUgwdOlTsGKRBsrOzsXr1aixbtgw9evRAdHQ0GjZs+NbrJRIJFi5bhk4+Phg5aBA+\nzMnB2KwsdAWg+4brbwH4VV8fv+vpYeyECZg+e7ZaL2XV0dGBnZ0d7Ozsiu21KwgCnjx5oihIr127\nhp07dyI+Ph6FhYUlytGmTZuifv36/GWWVEpfXx87d+5Ey5Yt0aJFi2KlvqqcPHkSJ0+eBPDPGwdR\nUVGQSqX4/fffUb9+fZXc8+rVq/Dz88O0adMwduxYldyDSGxNmjQBANy4cUPxMRGROmERSkREaiU/\nPx9hYWHYvHmz2FFIA+Tn5+O3337DvHnz8J///Adnz55F06ZNS/18Hx8fxN+/j+A//sC0JUvQ7+FD\nuBkbw6GgAEJ+Ph7L5Ug0MECeri6Gf/45Ir/5Bvb29ip8RaolkUhQr1491KtXD126dCn2teTk5GKn\n2B8+fBgJCQnIyMgosf+oi4sL7OzsoKv7ptqYqOysra2xa9cu9OzZE2FhYXB2dlbJfUxMTDBz5kz0\n7t0b9vb2SE9Ph5OTE9q2bYvTp0+jS5cuiI2NhbGxsVLvGx4ejk8//RQrV65EQECAUscmUicSiUSx\nPJ5FKBGpIx6WREREaiUiIgJjxoxBbGys2FFIjcnlcvz555+YOXMm7O3tsXDhwhInsJfHy5cvER0d\njYcPH+LatWs4ceIEDhw4AHt7+yo7K/LVq1fFDmgq+jg5ORnOzs4lClIHBwfo6+uLHZs01Pr167Fi\nxQpERkbC3Ny8zM9/32FJ/xYWFoapU6fi/Pnz8PT0xKVLl7BixQqMHz++PPHf6OjRoxg+fDi2bdsG\nX19fpY1LpK52796NTZs24ejRo2JHISIqgTNCiYhIrUilUnTu3FnsGKSmBEHAkSNHMH36dBgbG2PD\nhg3w8vJS2vgWFhaKmZLXrl1DSEgIHBwclDa+JqpZsybatm2Ltm3bFns8IyMDiYmJinK0aA/Sx48f\nw8HBocRJ9o0bN4ahoaFIr4I0xZdffonLly9jxIgR2LVrl8rfgIiKioKHhwd0dXUxcuRIREZG4ty5\nc0orQnfs2IFJkybh4MGDaNOmjVLGJFJ33t7e+Pzzz5GbmwsjIyOx4xARFcMilIiI1EpoaCgmTZok\ndgxSQ+fOncO0adOQmpqKBQsWoGfPniotSaysrJCUlKSy8TWdubk5WrZsiZYtWxZ7PCcnBzdu3FAU\npEV7kN67dw8NGzYscZJ9kyZNYGJiItKrIHW0evVqdOjQAUFBQfjhhx9Ueq/o6GjFqfN16tQBAGRl\nZSll7FWrVmHp0qWQSqVo1qyZUsYk0gQ1a9ZEs2bNcOHCBR5+SURqh0UoERGpjZycHFy6dAkdOnQQ\nOwqpkZiYGEybNg2JiYmYO3cuBg4cWCl7U9aqVQvp6enIz8/nUu8yMDY2hpubG9zc3Io9LpPJcOvW\nLcXS+sOHD2PJkiW4desWbGxsShSkTZs2RbVq1UR6FSQmQ0ND7NmzB61atYKHhwd8fHxUdq/o6GhM\nnDgRwD9bswCo8D7AgiBg1qxZ+PPPPxEWFgY7O7uKxiTSOEX7hLIIJSJ1wyKUiIjUxoULF/DBBx+U\na1840j43b97EzJkzcfbsWUyfPh0HDhyo1JPadXR0ULt2bTx//hz16tWrtPtqKwMDAzRr1qzEzLiC\nggLcu3dPsQdpaGgoVq9ejcTERNSsWfONJ9nXqlVLpFdBlcXW1hbBwcHo168fLl68iEaNGill3JiY\nGLi5uUEikSArKwt3795Fs2bNIJVKsWLFCkgkEgwePLjc4xcWFmL8+PGIjIzE+fPnYWlpqZTcRJrG\n19cXo0ePxpIlS8SOQkRUDA9LIiIitTFt2jTo6upi3rx5YkchEf3999+YM2cO9u3bh++++w4TJkyA\nqampKFnc3NywadMmeHh4iHL/qkwul+Phw4fFTrIvWm5vZGT0xoLUysqqyh5qpa1WrlyJzZs3Izw8\n/K1bKBw4cAD79+8HACQlJSEkJAT29vZo3749AKB27dpYunQpAMDLywu3bt1C27ZtoaOjg9OnT6N5\n8+YIDQ2FRCLB/Pnz8eOPP5Yrq0wmw9ChQ/Hs2TMcOHCAM5qpSisoKECdOnUQHx8PGxsbseMQESmw\nCCUiIrXRunVrBAUFoVOnTmJHIRG8ePECixcvxubNmzFq1ChMnToVFhYWomby9fXFt99+Cz8/P1Fz\n0P8nCAKePHlSohy9fv06BEEoUY66uLjA1taWBamGEgQBQ4YMAQBs27btjf87zpkzB3Pnzn3rGHZ2\ndrhz5w4AYPPmzdi3bx+uXbuGp0+fIj8/H7a2tmjbti2+/vprtGvXrlw5MzMz4e/vDxMTEwQHB/OA\nGCIAffr0Qc+ePTF06FCxoxARKbAIJSIitZCamor69esjOTmZv0BWMRkZGfj555+xatUq9OvXD4GB\ngWoze2To0KHo3Lkzhg8fLnYUeg9BEJCcnFyiII2Pj0dWVpaiFH19L1I7Ozvo6OiIHZ3eIzs7G+3a\ntcPw4cMxYcIEpY37+eefo3Xr1vjqq68qNE5KSgq6d+8OV1dXrFu3Dnp63H2MCAA2bNiAM2fOYMeO\nHWJHISJS4HdpIiJSC+fOnUObNm1YglYhubm5WLduHRYvXowuXbogMjISDg4OYscqxsrKCs+ePRM7\nBpWCRCKBpaUlLC0tS8wqf/nypaIUTUhIQGhoKOLj45GSkgJnZ+cSBzU5ODiwzFIjJiYm2Lt3L9q0\naQM3NzfFKe8VFR0djTFjxlRojL///hs+Pj7o2bMnFi1axJnHRK/x8fHB9OnTIZfL+aYTEakN/oRH\nRERqQSqV8mTRKqKgoABbt27FnDlz8OGHH+LEiRP44IMPxI71RtbW1vj777/FjkEVZGFhgXbt2pVY\n9pyeno7ExERFSbpp0ybEx8fjyZMncHR0LFGQOjk5wdDQUKRXUbU1atQI27Ztw4ABA3Dp0iXY2tpW\naLzc3FzcvHkTzZs3L/cYN27cgK+vL8aNG4fJkydXKA+RNmrYsCEsLCwQExODFi1aiB2HiAgAi1Ai\nIlITUqkUmzZtEjsGqZAgCNizZw8CAwNhaWmJ4OBgtG3bVuxY72RlZYWoqCixY5CKVKtWDa1atUKr\nVq2KPZ6dnY0bN24oCtI///wT8fHxuH//Pho2bFjioCZnZ+e3HuRDyuPj44NvvvkG/v7+OHfuXIVK\n6atXr6Jx48blXoUQFRWFHj16YOHChRgxYkS5cxBpO19fX5w4cYJFKBGpDe4RSkREonv27BmaNGmC\nFy9eQFdXV+w4pGSCIODkyZOYNm0a5HI5Fi5cCF9fX41YQnry5EksXrwYUqlU7CikBvLy8nD79u0S\nJ9nfvn0bNjY2JQrSJk2a8ORwJRMEAX379kXNmjWxYcOGco/z66+/IjIyslxvwJ0+fRr9+/fHhg0b\n0KtXr3JnIKoKjh49iiVLluDMmTNiRyEiAsAZoUREpAZCQ0PRsWNHlqBa6OLFi5g2bRoeP36M+fPn\nw9/fX6P2CeMeofQ6Q0NDNGvWDM2aNSv2eEFBAe7evasoRk+dOoX//ve/SExMhIWFxRtPsrewsBDp\nVWg2iUSCzZs3o02bNli/fj2+/PLLco0THR0NDw+PMj9v7969GD16NHbt2qW0vUqJtFnHjh3Rv39/\nZGRkwNzcXOw4REQsQomISHyhoaHo3Lmz2DFIia5du4YZM2YgKioKs2bNwvDhwzXy8BkWoVQaenp6\naNy4MRo3bozevXsrHpfL5Xjw4IGiIA0PD8dvv/2G+Ph4mJiYlChHXVxcYGlpqRGzpcVkbm6Offv2\nwdPTEx988AHatGlT5jGio6MxfPjwMj1n48aNmDlzJkJCQuDu7l7mexJVRaampmjVqhVOnz6Nnj17\nih2HiIhL44mISHz29vY4dOhQiVlWpHnu3buHWbNmISQkBN9//z3Gjh1b7j341EFhYSGMjIyQk5Oj\nkUUuqSdBEPD48WNFQVq0F+n169chkUhKlKMuLi6oV68eC9J/OXjwIL7++mtcvnwZ1tbWpX6eTCZD\njRo1kJycDFNT0/deLwgClixZgnXr1uHEiRNwcnKqSGyiKmfJkiV4+PAhVq9eLXYUIiLOCCUiInHd\nu3cP2dnZcHFxETsKVUBSUhLmz5+P4OBgjB8/Hrdu3dKKvRF1dXVhYWGB5ORk2NjYiB2HtIREIoGt\nrS1sbW3h4+OjeFwQBDx//rxYOXrw4EHEx8cr/p38d0HasGFDjdpuQpl69uyJqKgo9OvXD1KpFPr6\n+qV6Xnx8PBo1alTqEnTKlCk4fvw4zp8/j3r16lU0NlGV4+vriz59+ogdg4gIAItQIiISWdGyeM50\n0kypqalYsmQJfv31VwwbNgyJiYmoU6eO2LGUqmh5PItQUjWJRAIrKytYWVnBy8ur2NdSUlKQkJCg\nKEhPnTqF+Ph4vHz5Ek2aNClRkNrb21eJWcyzZs1CVFQUJk2ahFWrVpXqOaXdH7SgoACjRo3CjRs3\ncO7cOe7rSlROH3zwATIyMnD37l3Y29uX+PqGDRvw22+/4fr16xAEAU2bNsXIkSPx5Zdf8udDIlI6\n7f/piIiI1JpUKoW3t7fYMaiMsrOzsWrVKvz000/o1asXYmNjUb9+fbFjqYS1tTWSkpLEjkFVXK1a\nteDp6QlPT89ij6enpyMxMVGxzH7jxo2Ij4/H06dP4ejoWOIkeycnJxgYGIj0KpRPR0cH27dvR8uW\nLbFt2zYMGTJE8bWUlBTs3r0bZ86cQVRUFDIzM6Gnp4fCwkK4urri8uXLaNmy5RvHzcnJQUBAAGQy\nGU6ePFmq2aNE9GYSiQQ+Pj4ICQnBmDFjin1t0KBBCA4OhpWVFQYOHAgTExOcPHkSY8aMQUREBLZs\n2SJOaCLSWtwjlIiIRCMIAmxsbBAREYFGjRqJHYdKQSaT4bfffsO8efPg6emJefPmwdnZWexYKjVk\nyBB06dIFw4YNEzsKUallZ2fjxo0bioK06M+DBw9gZ2dX4iR7Z2dnmJiYiB273K5duwYvLy+EhITA\n2toaEydOxMGDB6Gjo4Ps7OwS1+vq6sLQ0BD169fH0qVL8cknnyi+lpaWhp49e6JevXrYsmWLVhXH\nRGLZsWMHdu3ahf379yse27dvH/z9/eHg4IBLly6hZs2aAP6Zjf3ZZ5/hyJEj2LNnT7FD6IiIKopF\nKBERieb69ev45JNPcPfuXbGj0HsUFhYiODgYs2bNgqOjIxYuXIgWLVqIHatSTJ48GZaWlpg6darY\nUYgqLC8vD7du3SpWjiYkJOD27duoW7duiZPsmzZtCnNzc7Fjl8rOnTsxduxY5ObmIi8vDwUFBaV6\nnomJCbp164bffvsNeXl56NatGzw9PbFy5coqu/8qkbI9f/4cjRs3RnJysmI/32HDhmH79u1Ys2YN\nRo8eXez6uLg4uLu7o3Pnzjh16pQYkYlIS3FpPBERiYbL4tWfIAg4fPgwpk2bBjMzM/z222/o1KmT\n2LEqlZWVFZfGk9YwNDSEq6srXF1diz1eUFCAO3fuKMrRU6dOYdWqVUhMTETt2rVLFKQuLi6K2Vvq\nIiEhAWlpaaUuQItkZ2fjyJEjcHd3h0QiwfDhwxEYGMi9CYmUyNLSEg4ODrh48SLat28PAIrvrW9a\nFVS0l2hYWBgKCgqqxJ7HRFQ5+K8JERGJRiqVIiAgQOwY9BZnz57Fjz/+iIyMDCxYsACffPJJlSwG\nrKysEBcXJ3YMIpXS09ODs7MznJ2d8emnnyoeLywsxIMHDxQFaXh4uGIfUjMzszeeZF+nTp1K/7di\nw4YNWLJkSZlL0CJ5eXm4f/8+6tatix9//LFK/ltHpGpF+4QWFaG1a9cGANy7d6/EtUWrhQoKCnD3\n7l00bty48oISkVbj0ngiIhJFQUEB6tSpg8TERFhZWYkdh14THR2NadOm4ebNm5g7dy4GDBgAXV1d\nsWOJJiQkBMuWLcPJkyfFjkKkNgRBwN9//61YWv/6UnsdHZ0S5aiLiwvq1q2rkoLx/v37aNas2Rv3\nAi0rExMTTJ06FbNmzVJCMiJ63ZkzZzBlyhRcvnwZAPDHH39g8ODBcHR0RGRkZLE9Qv39/XHo0CFI\nJBKEh4ejdevWYkYnIi3CIpSIiERx6dIlfPHFF7h69arYUej/3LhxA4GBgTh//jxmzJiBkSNH8pAQ\nALGxsRg6dCiuXLkidhQitScIAp49e1aiHI2Pj0dubu4bC9IGDRpUaC9OHx8fhIaGorCwUCmvwdjY\nGImJiWjQoIFSxiOif8hkMtSpUwd37txB7dq1IZfL0aNHD4SEhMDS0hK9evWCkZERTp06haSkJJiZ\nmeHRo0e4ePEiWrZsKXZ8ItISXBpPRESikEql6Ny5s9gxCMCjR48wZ84cHDhwAJMmTcLmzZthamoq\ndiy1YWVlhWfPnokdg0gjSCQSWFtbw9raGl5eXsW+lpKSUqwgPXHiBOLj45GamgpnZ+cSBWmjRo3e\nuy/ggwcPEBYWprQSFPhnO4A1a9YgKChIaWMSEWBgYICOHTvi1KlTCAgIgI6ODg4dOoTly5dj+/bt\n2Lp1K4yMjODl5YW9e/fC398fwD/7ixIRKQtnhBIRkSi6du2K8ePHo2fPnmJHqbKSk5OxaNEi/P77\n7/jqq68wZcoUtTv8RB0UFBTA2NgYOTk5PKyBSAXS0tKQmJhY4iT7p0+fwsnJqVg52rRpUzg5OSlm\nq8+ePRuLFi2CTCZTaqYaNWrg5cuX3CuUSMlWr16NqKgobN68+Z3X5eXloXr16qhevTrfjCQipeJP\n80REVOlyc3Nx8eJF7N69W+woVVJ6ejqWL1+O1atXIyAgANevX4e1tbXYsdSWnp4eatasiRcvXvC/\nE5EKVK9eHa1bty6xB2BWVhZu3LihKEd37NiB+Ph4PHjwAI0aNYKLiwsuXbqk9BIU+KeEefToEZfH\nEymZr68vFi1aBEEQ3vlGQ3BwMGQyGQYOHFiJ6YioKmARSkRElS4iIgIuLi6oXr262FGqlNzcXPzy\nyy9YvHgxfHx8cOnSJdjb24sdSyMULY9nEUpUeUxNTeHh4QEPD49ij+fl5eHmzZuIj4/HkSNHVHJv\nfX19xMTEsAglUjJHR0cYGhri+vXrcHV1RUZGBszNzYtdExsbiylTpqBWrVr4/vvvRUpKRNqKRSgR\nEVW60NBQeHt7ix2jyigoKMDvv/+OOXPmwN3dHadOnULz5s3FjqVRuE8okfowNDRE8+bN0bx5cwwe\nPFgl9ygsLMSrV69UMjZRVSaRSODr64uQkBC4urqia9euMDY2hqurK8zNzZGQkIAjR47A1NQUhw4d\n4huQRKR05T+ekYiIqJykUimL0Eogl8uxa9cuuLq6Yvv27fjf//6HAwcOsAQtBxahROpJVXt4ymQy\nHDp0CBs3bsSRI0cQExODZ8+eQS6Xq+R+RFWJj48PQkJCAAB9+/ZFZmYmduzYgZ9//hlXr17F6NGj\ncf36dXh6eoqclIi0EQ9LIiKiSpWeno66desiOTkZxsbGYsfRSoIg4MSJE5g2bRokEgkWLlyIrl27\n8tCPCvjuu+9Qt25dTJ48WewoRPQaW1tbPH78WOnjGhkZoU+fPjAwMMCTJ08Uf9LS0mBpaYm6desq\n/tjY2JT4vHbt2tDR4ZwTojdJS0uDra0tnj17BhMTE7HjEFEVw6XxRERUqcLCwtCqVSuWoCoSERGB\nH3/8EUlJSZg/fz78/f1ZgCoBZ4QSqaePPvpIJUVoYWEh1qxZg2rVqhV7XCaTISkpSVGMPn36FE+e\nPMH58+eLfZ6eng5ra+u3FqVFH9eqVYv/RlOVU716dbi5uSEsLAy+vr5ixyGiKoZFKBERVSoui1eN\nq1evYvr06YiNjcXs2bMxdOhQ6Onx27yyWFtb4/r162LHIKJ/6d27N6RSKTIzM5U6rqOjY4kSFAAM\nDAzQoEGD9x6ilJubi6SkJEUxWvTn7NmzxT7PyspSFKbvmmFqYWHBwpS0StE+oSxCiaiy8TckIiKq\nVFKpFOvWrRM7hta4e/cuZs6ciVOnTuGHH37Azp07YWRkJHYsrWNlZYWkpCSxYxDRv/Tr1w/jxo1T\n6phmZmaYOnVqhcYwMjKCnZ0d7Ozs3nldbm5usbK06OPExMRin+fk5CgK0nfNMK1RowYLU9IIPj4+\nGDFihNgxiKgK4h6hRERUaZKTk+Hk5IQXL15wtmIFPX36FPPnz8f//vc/jB8/Ht999x3Mzc3FjqW1\nYmJiMHz4cMTFxYkdhYj+ZcaMGVi2bBny8vKUMp6lpSXu37+vVlu4ZGdn4+nTp28sTV//PC8v751F\nadHn1atXZ2FKoiosLISVlRViYmJQv359seMQURXC30KJiKjSnD59Gu3bt2cJWgGvXr1CUFAQNmzY\ngOHDhyMxMRG1a9cWO5bW4x6hROqpsLAQurq6yM/PV8p4JiYmCA4OVqsSFPgnl4ODAxwcHN55XVZW\nlqIgfb0ovXLlSrHPCwoK3lmUFn1sbm7OwpRUQldXF126dMHJkyfx+eefix2HiKoQ/iZKRESVRiqV\nonPnzmLH0EhZWVlYtWoVli9fjt69eyM2NpYzKCpRnTp1kJKSoihdiEh8Dx48wODBg2FgYICTJ0+i\nd+/eyMjIKPd4hoaGGD9+vEZ/nzI1NYWjoyMcHR3feV1mZuYbZ5TGxMQUewxAqWaYckUClYevry+O\nHz/OIpSIKhWXxhMRUaVxcnLCnj178MEHH4gdRWPIZDJs2LABCxYsQPv27TFv3jw0btxY7FhVUu3a\ntREfHw9LS0uxoxBVeX/++Se++eYbTJkyBZMmTYKOjg6io6Ph7e2NrKysMs8QNTIygkQiwYkTJ+Dp\n6ami1JonIyPjnUvxiz7W0dEp1QxTU1NTsV8SqZG///4bH374IZ4/f843GYmo0nBGKBERVYqHDx8i\nLS0Nrq6uYkfRCIWFhfjjjz8wa9YsODs74/Dhw/Dw8BA7VpVWtDyeRSiReDIyMjB+/HhERETg2LFj\naNGiheJrHh4eSEhIwNChQxEeHo6srKz3jmdsbAwjIyNs27YN+vr68Pf3R2hoKJo1a6bKl6ExzM3N\n4ezsDGdn57deIwgC0tPTSxSljx49QmRkZLHHDAwMSjXD1MTEpBJfJYnF1tYWNjY2+Ouvv9C6dWux\n4xBRFcEilIiIKoVUKoWXlxd0dHTEjqLWBEHAwYMHMWPGDFSrVg1btmxBhw4dxM+YANIAACAASURB\nVI5F+P9FaPPmzcWOQlQlRUZGYtCgQfDy8kJ0dPQbZxdaW1sjJCQEp06dQlBQEMLCwmBsbIyMjAzI\n5XLo6OjA1NQUhYWFqF69Or777juMGjUK1atXBwD89NNP8PPzw4ULF7j9SClJJBJUr14d1atXR5Mm\nTd56nSAISEtLKzGj9P79+wgPDy9WpBoZGb13hqmNjY3a7eVKZefr64sTJ06wCCWiSsMilIiIKkVo\naCi8vb3FjqHWTp8+jWnTpiE7OxuLFi1C9+7deUiFGrG2tkZSUpLYMYiqnMLCQixevBirVq3C2rVr\n4e/v/87rJRIJunbtiq5du+LVq1eIjo7GhAkT4OrqiubNm8PBwQEtWrSAg4NDiTfnBg8ejKSkJHTr\n1g3nz59HzZo1VfnSqhSJRIIaNWqgRo0acHFxeet1giDg1atXJWaY3rlzB2FhYcWKVFNT01LNMDU0\nNKzEV0pl4evrizlz5mDw4MGIj49HdnY2jIyM4OLiAnt7e/4cRERKxz1CiYhI5QRBQL169RAWFvbe\nE2+ror/++gvTpk3D3bt3MXfuXAQEBHDmrBqaOHEibG1tMWnSJLGjEFUZDx8+xJAhQ6Crq4utW7fC\n1ta2XOO0a9cOQUFBpd7/c9KkSbh06RJOnDjBWYdqShAEvHz58o17lr7+cVJSEszMzN47w9Ta2pqF\naSWLi4vDkiVL8Mcff8DY2Bj6+vqKrxUUFEAQBPTq1QuTJ08utg0GEVFFsAglIiKVS0hIQLdu3XD/\n/n2+s/+axMREBAYGIjw8HIGBgfjiiy+K/RJA6mXx4sV4+fIllixZInYUoiph165dGDduHL777jtM\nnjy5QoepNGnSBPv373/n0u3XyeVyDBkyBFlZWdi9ezf09LiQTlPJ5fJihenbDn5KSkpC9erV3zvD\n1Nramt+rK+jly5cYNWoUjh8/jry8PBQWFr71Wh0dHRgZGaFjx47YsmUL9+kmogpjEUpERCq3Zs0a\nREVFYdOmTWJHUQsPHz7EnDlzcPDgQUyePBnjx4/nwRAaYPPmzThz5gx+//13saMQabXMzEx88803\nCAsLwx9//IGWLVtWeMzatWsjISEBderUKfVzZDIZevTogUaNGmHdunV8I0/LyeVyvHjx4q1FadHH\nz58/R40aNd47w9TKyoqF6RtERUWha9euyMrKgkwmK/XzDAwMYGxsjKNHj6Jt27YqTEhE2o5vbRIR\nkcpJpdL37ulWFSQnJ2PhwoXYunUrRo8ejVu3bqFGjRpix6JSKjosiYhU5/Llyxg4cCA6dOiAmJgY\nmJmZVXjMwsJCpKamlnm/TwMDA+zZswdeXl6YM2cOZs+eXeEspL50dHRgaWkJS0tLuLm5vfW6wsJC\nRWH6elEaFxeHY8eOKT5PTk6GhYXFe2eYWlpaVpkZxzExMejUqRMyMzPL/FyZTAaZTIauXbtCKpWi\nTZs2KkhIRFUBZ4QSEZFKFRYWok6dOrh27Rrq1q0rdhxRpKen46effsLq1asxcOBATJ8+HdbW1mLH\nojKKiorCyJEjERMTI3YUIq1TWFiIJUuWYMWKFVi9ejX69u2rtLFTUlLg5OSEly9fluv5z549Q7t2\n7TBlyhR89dVXSstF2q2wsBDPnz9/7wzTFy9eoHbt2u+dYWppaVmh7SHElpmZCUdHR6W8oWhhYYE7\nd+7wzWQiKpeq8dYTERGJJjY2FtbW1lWyBM3JycHatWuxZMkSdOvWDX/99RcaNWokdiwqJ84IJVKN\nR48eYciQIQD+OTyufv36Sh2/qGgqLysrK4SEhKB9+/awsrJC7969lZiOtJWuri5sbGxgY2PzzusK\nCgoUhenrRenly5eLff7y5UvUqVPnvTNM69Spo5YHLn777bdIS0tTylhZWVkYM2YMgoODlTIeEVUt\nLEKJiEilpFIpvL29xY5RqQoKCrB582bMnTsXH330EUJDQ9GsWTOxY1EFWVpaIjk5GXK5XC1/ySTS\nRHv27MHYsWMxYcIEfP/99yqZ8ZaSklKhIhQAHBwccOjQIfj5+aFWrVpo3769ktJRVaenp6coMd8l\nPz8fz549KzGj9OLFi8U+T01NhaWl5VuL0qLHateuXWnfy54+fYodO3YgNzdXKePl5eVh//79uHfv\nHt9gJqIyYxFKREQqJZVKMXr0aLFjVAq5XI5du3YhMDAQtra22L17N1q3bi12LFISAwMDVKtWDSkp\nKWU6cIWISsrMzMS3336LM2fO4NChQ2jVqpXK7vXixQvUqlWrwuO0aNECO3bsQJ8+fSCVSuHq6qqE\ndESlo6+vD1tbW9ja2r7zOplMpihMX59ReuHChWIlalpammLFzrtmmdaqVavCB4X98ssvFXr+m8jl\ncqxatQo///yz0scmIu3GIpSIiFRGJpMhPDwcf/75p9hRVEoQBBw/fhzTp0+Hrq4u1qxZgy5duvCE\nYS1UtDyeRShR+f31118YOHAg2rVrh5iYGJibm6v0fhVdGv+6rl27YsWKFfj4449x/vx5NGjQQCnj\nEimLgYEB6tev/94tJvLy8pCUlFRi/9Jz584V+zwzM1NRmL5rH1MLC4u3/tyzc+dOpc0GLSKTybBn\nzx4WoURUZixCiYiohD179uDs2bOIjY1FXFwcMjIyMHjwYGzdurXEtQUFBVizZg3i4uIQExOD+Ph4\n5OfnY+PGjXB0dESTJk3KfFKvJrlw4QJ+/PFHJCcnY/78+fjss89YgGqxoiKUM8GIyk4ul2PZsmVY\ntmwZ/vvf/6J///6Vct+UlBSlzAgtMmDAACQlJaFbt244f/48LCwslDY2UWUxNDREw4YN0bBhw3de\nl5ubi6SkpBIHPZ0+fbrY59nZ2YqC9PWi1NLSEnfu3FHJa0hKSkJWVhZMTU1VMj4RaScWoUREVML8\n+fNx5coVmJmZwdbWFomJiW+9NisrCxMnToREIoGVlRVsbGzw6NEjAP8si+/cuXNlxa5UV65cwfTp\n03HlyhXMnj0bQ4YMgZ4ev61qOx6YRFQ+jx8/xtChQ5Gfn4/Lly+/t3xRJmXOCC0yceJEPHnyBD16\n9MCpU6dgYmKi1PGJ1IWRkRHs7OxgZ2f3zutycnLw9OnTEjNMIyMjIQiCyrLdvXsXzZs3V8n4RKSd\nuNM/ERGVsGLFCty8eRNpaWlYu3btO3+ANTExwbFjxxQ/8I4YMULxtdDQUK07KOnOnTsYNGgQfHx8\n0KVLF9y8eRMjRoxgCVpFsAglKrt9+/bBw8MDXl5eOH36dKWWoIByDkt6k6CgIDg4OCAgIAAFBQVK\nH59IkxgbG8Pe3h7t2rVD3759MWHCBAQFBWHx4sUqe6NAIpEgPz9fJWMTkfZiEUpERCV07NgRDg4O\npbpWX18fvr6+sLKyKvZ4bm4uYmJi4OnpqYqIle7JkycYM2YMWrdujSZNmuDWrVuYMGECDA0NxY5G\nlYhFKFHpZWVl4csvv8TkyZNx4MABzJgxQyWnwr+Psg5L+jcdHR1s2rQJMpkMY8aMUdmsNyJNZm5u\nrrKyUi6Xw8zMTCVjE5H2YhFKREQqcevWLbRo0ULjlwu+fPkS33//PZo3bw4zMzPcuHEDgYGBKj/c\ng9QTi1Ci0omOjkaLFi0Ub4q1adNGtCyqWBpfRF9fH7t370ZsbCxmzZqlknsQaTIbGxuVvQEik8lK\n/cY9EVERFqFERKQSCQkJGr0sPjMzEwsWLEDjxo2RmpqKuLg4LF26VCWzikhzWFtbswgleoeiA5G6\ndeuGWbNmYevWrahWrZqomZR9WNK/mZmZ4ciRIwgODsYvv/yisvsQaSKJRIIPP/xQJWM3adJElFnm\nRKTZuKEZERGpREJCAgIDA8WOUWZ5eXlYv349Fi5ciE6dOiEiIgJOTk5ixyI1YWVlhaSkJLFjEKml\nJ0+eYNiwYcjJycGlS5fee7hKZVHljNAilpaWCAkJQfv27WFlZYXPPvtMpfcj0iSjR4/GlStXkJmZ\nqbQxTU1NMXr0aKWNR0RVB2eEEhGRSjx//hwtW7YUO0apFRYWYuvWrWjSpAmOHTuGo0ePIjg4mCUo\nFcOl8URvduDAAXh4eMDT0xNnzpxRmxJULpcjNTUVFhYWKr+Xvb09Dh8+jNGjR+Ps2bMqvx+Rpujb\nty90dJRbPQiCgCFDhih1TCKqGliEEhGRSjg6OsLAwEDsGO8lCAL279+PDz/8EOvXr8fWrVtx9OhR\nuLu7ix2N1JClpSWSk5Mhl8vFjkKkFrKzszF69GhMnDgRe/fuxaxZs6Cnpz6LzlJTU2FmZlZpmdzd\n3REcHIy+ffvi6tWrlXJPInVnZGSElStXwtTUVCnjmZqaIigoiAclEVG5sAglIiKlEwQBTZs2FTvG\ne4WGhqJNmzaYNWsWgoKCEBYWhvbt24sdi9SYoaEhTE1N8erVK7GjEIkuNjYWLVq0QGZmJmJiYtC2\nbVuxI5VQGcvi/83b2xurVq3Cxx9/jAcPHlTqvYnU1bBhw9CmTRsYGhpWaBwDAwM0b94cY8eOVVIy\nIqpqWIQSEZHSqXsRevnyZXTt2hVffvklvv32W8TExKB79+6QSCRiRyMNwOXxVNXJ5XIsX74cPj4+\nmDFjBrZv347q1auLHeuNVH1Q0tsEBARg8uTJ6NatG1JSUir9/kTqRiKRYN++fWjcuHG5y1ADAwPY\n2dnh6NGjSl9qT0RVB//1ICIipUpPTwcA1K9fX+QkJSUkJMDf3x+ffvop+vTpg4SEBAwYMIA/TFOZ\nsAilquzp06fw8/PD7t27ERkZiUGDBokd6Z3EmBFaZMKECejVqxd69OiB7OxsUTIQqRNzc3OEh4fD\nx8cHJiYmZXquqakpOnbsiMjISNSsWVNFCYmoKlCfDXyIiEhtHDhwAPv37wcAxQnZ4eHhGDFiBACg\ndu3aWLp0qeL6oKAgJCYmAvhnuTkAbNmyBRcuXAAAeHp64osvvqi0/P/24MEDzJ49G0eOHMGUKVOw\nfft2GBsbi5aHNBuLUKqqDh06hFGjRuGrr75CYGCgWu0F+jYpKSmiFaEAsGjRIgwfPhz9+/fHvn37\nNOK/GZEqmZmZ4eDBg9i0aRNGjhwJExMTZGVlvfV6Q0NDmJub4+eff8agQYO4eoeIKozfiYmIqITY\n2Fhs3bpV8blEIsG9e/dw7949AICdnV2xIvT48eM4d+4cgH+WxUskEkRERCAiIkLxfDGK0OfPn2PB\nggXYvn07xo4di1u3bqnt8k3SHNbW1oo3CIiqguzsbEyePBnHjh3D7t274enpKXakUnvx4oUoS+OL\nSCQSbNy4Eb169cJXX32FjRs3ssghAnD37l2MHDkSn332GX7//XdERkbiwYMHip8jbW1tUb9+faSm\npiIuLg66urpiRyYiLSERBEEQOwQREWkHQRDQoEEDSKVSNG7cWLQcaWlpWLZsGdauXYtBgwZh+vTp\nsLKyEi0PaZcFCxYgMzMTixYtEjsKkcpduXIFAwYMwIcffoi1a9eiRo0aYkcqkx9++AHVq1fHjz/+\nKGqOrKwseHl5wcfHB/Pnzxc1C5HYXr16BUdHR0RFRcHOzk7xuCAIKCwshK6uLiQSCfLy8mBtbY34\n+HjY2NiIF5iItAo3RSMiIqW5desWBEGAk5OTKPfPycnB0qVL4eTkhEePHiEqKgqrVq1iCUpKxaXx\nVBXI5XKsWLEC3t7e+OGHH7Bjxw6NK0EB8Q5L+jdTU1McOXIEu3btwurVq8WOQySqlStXolevXsVK\nUOCfGdR6enqKWdOGhobo0aMH9u7dK0JKItJWXBpPRERKExoaCm9v70pf9pefn4/Nmzdj7ty5aNWq\nFc6cOQMXF5dKzUBVB4tQ0nZJSUkYPnw4UlNTcfHiRTg4OIgdqdzEPCzp3+rUqYPjx4/D09MTVlZW\n6Nu3r9iRiCpdWloaVq9ejYsXL5bq+r59+2L58uX4+uuvVZyMiKoKzgglIiKlkUql8Pb2rrT7yeVy\n/Pnnn3BxccHOnTuxd+9e7N27lyUoqRSLUNJmR44cgbu7O1q2bImwsDCNLkEB8Q9L+rdGjRrhyJEj\n+Prrr3HmzBmx4xBVutWrV+Pjjz+Go6Njqa738fFBXFwc9+YmIqXhjFAiIlIKuVyO06dP4+eff1b5\nvQRBwLFjxzB9+nTo6+tj3bp1lVrAUtXGIpS0UU5ODqZOnYqDBw9i586daN++vdiRlELsw5LexM3N\nDf/73//Qr18/nDx5Eh9++KHYkYgqRUZGBlauXImwsLBSP8fIyAjdu3fH3r17MXbsWBWmI6KqgjNC\niYhIKa5cuYJatWrB1tZWpfc5f/48OnTogMmTJ2PmzJmIjIxkCUqVysrKCs+fPwfPmyRtcfXqVbRq\n1QrJycmIi4vTmhIUUK+l8a/z8vLC6tWr0b17d9y/f1/sOESVYu3atejSpQucnZ3L9Ly+ffti165d\nKkpFRFUNi1AiIlIKVS+Lj42NRffu3TF48GCMHDkSV69exaefflrp+5ESGRkZwcjICKmpqWJHIaoQ\nQRCwatUqdO7cGZMnT0ZwcLBGHoj0NnK5HK9evYKFhYXYUd6oX79++P777+Hr64sXL16IHYdIpbKy\nsrB8+XJMnz69zM/19fVFbGwsl8cTkVKwCCUiIqWQSqXo3Lmz0se9ffs2BgwYAD8/P3Tr1g03btzA\nsGHDoKurq/R7EZWWtbU1fyEjjfbs2TN0794d27dvR0REBIYNG6Z1byylpaXB1NQU+vr6Ykd5q/Hj\nx8Pf3x/du3dHVlaW2HGIVObXX39Fhw4d0KxZszI/18jICB9//DFPjycipWARSkREFZafn4/z58/D\ny8tLaWM+fvwYo0ePRps2beDq6opbt25h/PjxMDQ0VNo9iMqL+4SSJjt69Cjc3d3h7u6OCxculPrQ\nEk2jbgclvc2CBQvg4uKCfv36IT8/X+w4REqXk5ODpUuXYsaMGeUeg8vjiUhZWIQSEVGFXbp0CY6O\njko5kCIlJQVTp07FBx98gGrVquHGjRuYPn06zMzMlJCUSDlYhJImys3NxYQJEzBmzBgEBwdjwYIF\naj1bsqLU8aCkN5FIJFi/fj0AYNSoUdx/mLTOhg0b0KZNmwodDObr64uYmBh+7yWiCuOp8UREVCqC\nIODMmTMIDQ3FuXPn8PjxYwiCgDp16kBXVxcNGzZEQUEB9PTK960lMzMTK1aswIoVK9CnTx9cuXIF\n9erVU/KrIFIOFqGkaa5du4aBAwfC2dkZsbGxqFmzptiRVE5dD0p6E319fezcuRPe3t6YNm0aFi1a\nJHYkIqXIzc1FUFAQDh48WKFxjI2NFcvjx4wZo6R0RFQVsQglIqJ3ksvlWL9+PebNm4f09HTk5OSg\nsLBQ8fW7d+8C+OcHVEtLS0ycOBFTp04t9RL2vLw8/Prrr1i0aBG8vLxw8eJFrV2mSdqDRShpCkEQ\nsGbNGsyZMwdBQUEYMWKE1u0F+jYpKSkaMSO0iKmpKQ4fPgxPT0/Y2Njgm2++ETsSUYVt2rQJ7u7u\naNGiRYXH6tu3L/773/+yCCWiCmERSkREb3X//n307dsXCQkJ7z3EIScnBzk5OVi8eDE2b96MvXv3\nws3N7a3XFxYWYtu2bZg9ezZcXV1x/PjxCi2ZIqpMVlZWuHTpktgxiN7p+fPn+OKLL5CUlITw8HA4\nOTmJHalSadKM0CK1a9dGSEgIPD09YW1tjX79+okdiajcZDIZFi9erLS9Pbt164YRI0bg+fPnsLS0\nVMqYRFT1cI9QIiJ6o+vXr8PDwwMxMTFlOsk2Ozsb9+7dg6enJ06fPl3i64IgYO/evWjevDk2bdqE\n7du34/DhwyxBSaNwRiipu5CQELi7u8PV1RUXLlyociUooDmHJf1bw4YNceTIEYwbNw6hoaFixyEq\nt99//x0uLi5o3bq1UsYzNjaGn58fT48nogphEUpERCUkJSWhQ4cOePXqVbFl8GWRlZWFTz75BFev\nXlU8durUKbRu3Rrz5s3DTz/9hLNnz8LT01NZsYkqDYtQUld5eXmYOHEiRo0ahe3bt2PRokUwMDAQ\nO5YoNOWwpDf54IMPsHPnTgQEBCA2NlbsOERllp+fj4ULFyIwMFCp4/L0eCKqKBahRERUjCAIGDp0\nKDIyMio8VnZ2Nvr06YPw8HB4e3tjzJgx+O677xAVFQU/P78qs08daR9ra2skJSWJHYOomPj4eLRu\n3RoPHz5EbGwsvLy8xI4kKk1cGv+6Tp06Ye3atejevTvu3bsndhyiMtm+fTvs7e3Rrl07pY7r5+eH\nqKgoPH/+XKnjElHVwSKUiIiKOXz4MMLDw5Gfn1/hsQRBwJ07d+Dn54f+/fsjPj4eAQEB0NHhtx/S\nbFZWVnj+/DkEQRA7ChEEQcAvv/yCjh07Yty4cdi9ezcsLCzEjiU6TTss6U369OmD6dOnw9fXF8nJ\nyWLHISqVgoICLFy4EDNnzlT62EXL4/ft26f0sYmoauBvokREVMyCBQvKtCfo+xQWFsLY2BgjR46E\nvr6+0sYlEpOxsTEMDAyQlpYmdhSq4pKTk9G7d29s3LgR58+fx8iRIznb/v9o+ozQImPHjkW/fv3Q\nvXt3ZGZmih2H6L3+/PNP1K1bFx07dlTJ+FweT0QVwSKUiIgUHj58iLi4OKWPm52djXPnzil9XCIx\ncZ9QEtvJkyfh5uaGJk2aICIiAs7OzmJHUiuaeljSm8ybNw/NmzdHnz59lLJig0hVCgsLMX/+fJXM\nBi3i5+eHv/76i7OkiahcWIQSEZHCxYsXVTJrMycnB+Hh4Uofl0hMLEJJLHl5eZg8eTJGjBiBrVu3\nIigoqMoeiPQ2giAgJSVFa7YIkEgk+PXXX6Gvr48vvviC23KQ2tq1axcsLCzQuXNnld3D2NgY3bp1\n4/J4IioXFqFERKRw+fJllSy7Kygo4IxQ0josQkkMiYmJaNOmDe7cuYO4uDh4e3uLHUktpaWlwcTE\nRKsKYj09Pfzvf//D7du38cMPP4gdh6gEuVyumA2q6i06uDyeiMqLRSgRESk8ffpUZbNMuHyJtA2L\nUKpMgiDg119/Rfv27TFmzBjs3btX4w8CUiVtOCjpTUxMTHDo0CEcPHgQK1asEDsOUTH79u2DiYkJ\nfH19VX4vPz8/XL58GS9evFD5vYhIu+iJHYCIiNSHnp7qvi3wpHjSNtbW1ixCqVK8ePECI0eOxMOH\nDxEWFoYmTZqIHUntactBSW9Sq1YthISEoF27drC2tkZAQIDYkYggl8sxd+5cLFiwoFIObCsqXPft\n24dRo0ap/H5EpD34WykRESk4ODiorAx1cHBQybhEYrGyskJSUpLYMUjLSaVSuLm5wcnJCRERESxB\nS0mbDkp6kwYNGuDo0aOYMGECTp06JXYcIhw6dAi6urro3r17pd2Ty+OJqDxYhBIRkULLli1hYmKi\n9HGNjY3Rvn17pY9LJCYujSdVkslkmDp1KoYNG4bNmzdj6dKlMDQ0FDuWxnjx4oVWLo1/XfPmzbFr\n1y4MHDgQ0dHRYsehKkwQBMydOxeBgYGVMhu0yMcff4zIyEgujyeiMmERSkRECm3atIFMJlPJ2F5e\nXioZl0gsLEJJVW7cuIH//Oc/uHHjBmJjY9G1a1exI2kcbV4a/7oOHTpg3bp16NGjB+7cuSN2HKqi\njh07hvz8fPTq1atS7/v68ngiotJiEUpERAo1atRA7969lb6fZ7169eDi4qLUMYnExiKUlE0QBGzY\nsAGenp4YNWoU9u/fXyXKPFXQ1sOS3uSzzz7DzJkz0a1bNzx//lzsOFTFvD4bVIz94Lk8nojKikUo\nEREVM336dBgYGChtPAMDA2RnZ6Nz586IiIhQ2rhEYisqQgVBEDsKaYGUlBT4+/tjzZo1OHfuHEaP\nHl2pS0y1TVWZEVpk9OjRGDBgAD7++GNkZmaKHYeqkJMnTyI9PR3+/v6i3J/L44morFiEEhFRMWlp\naTAwMFDKoUkGBgbo0aMHHjx4gEGDBqF///745JNPEBcXp4SkROIyNTWFrq4uMjIyxI5CGu706dNw\nc3ODnZ0dIiMj0bRpU7EjaTxtPyzpTebMmQMPDw/4+/urbJsbotcVzQadMWOGKLNBgX++F/v4+GD/\n/v2i3J+INA+LUCIiAgAUFBRg9uzZ6NOnDzZv3gwPD48KHcyhp6cHa2trbNiwAXp6evjiiy9w8+ZN\ndO3aFd26dUNAQABu3rypxFdAVPm4PJ4qQiaT4YcffsDgwYOxceNGLF++nAciKUlVOCzp3yQSCdau\nXQsjIyN8/vnnkMvlYkciLXfmzBkkJyejf//+oubg8ngiKgsWoUREhLt376JDhw6IiIhAdHQ0Pvvs\nM0ilUri5uZXrFHljY2PUr18fFy9ehIWFheJxIyMjfPPNN7h16xY+/PBDtGvXDl988QUePHigzJdD\nVGmsra2RlJQkdgzSQDdv3kTbtm1x/fp1xMbGwtfXV+xIWqWqLY0voqenh+DgYNy7dw/ff/+92HFI\ny82dOxfTp0+Hrq6uqDm6d++OixcvIiUlRdQcRKQZWIQSEVVhgiBg27ZtaN26Nfr164djx47BxsYG\nAGBmZoawsDBMnToVxsbGpVoqr6OjAxMTEwwZMgRXr15VjPVvZmZm+PHHH3Hr1i3Y2NjAw8MD33zz\nDQsl0jicEUplJQgCfvvtN7Rr1w4jRozAwYMHUadOHbFjaZ2qdFjSv5mYmODQoUM4evQoli9fLnYc\n+n/s3XdUlOf6NeA9oCCIjSBg1MSGPaHZsGFDQEBEAcVuYgdRY4s9CvaoiBV7jbEiKoKgCCgogg52\nBYxJTBR7QZAizPfH+cmXxIY6M8+Ufa111jpH4J09OQSHPff9PhoqPj4et2/fRu/evUVHQdmyZeHg\n4MD1eCIqERahRERa6unTp+jTpw/mz5+PY8eOYcyYMW/c36l06dKYOXMmUlNTMWjQIBgYGKB8+fIo\nU6ZM8efo6ekV/1n37t0RFxeHkJAQlC1b9oMZKlasiMDAQFy7dg26urpo2KQUpwAAIABJREFU1KgR\nJk+ejMePH8v9+RIpAotQ+hhPnjyBt7c3li1bhtjYWPj6+vJAJAWQyWRaXYQCgLGxMSIjIxEUFIQd\nO3aIjkMaKCAgAJMnT5bLPeXlgevxRFRSLEKJiLTQqVOnYGVlBWNjY6SkpMDS0vK9n1+3bl2sXbsW\njx49QmRkJBYtWgQHBwdYWlpi7ty5CAsLw71797Bnzx40adLko/OYmppi6dKlSE1NxaNHj1C3bl0E\nBgbyEBpSeSxCqaTi4uJgaWmJqlWr4uzZs2jUqJHoSBorKysLZcqU0fr7rVavXh0RERH44YcfEBUV\nJToOaZDTp08jPT0d/fr1Ex2lmIuLC06fPs0304nog1iEEhFpkYKCAkyfPh1eXl5YsWIFVqxYAQMD\ngxJ/vYGBAezs7ODn5wcPDw+0aNEC48aNQ7t27VC+fPnPzle9enWsXbsWp0+fxrVr12BhYYGlS5ci\nNzf3s69NpAgsQulDCgoKMGXKFPj4+CAkJARBQUH/mqon+dPGg5LepVGjRti3bx/69u2Lc+fOiY5D\nGuL1NKienp7oKMWMjIzQqVMnrscT0QexCCUi0hI3b95EmzZtkJKSAqlUCldX18+6niLXOS0sLLBj\nxw5ER0cjLi4OFhYWWLt2LQoKChT2mESfgkUovU9GRgZatWqFCxcuQCqVwtnZWXQkraCtByW9S+vW\nrbF27Vq4ubkhIyNDdBxSc8nJybh06RIGDhwoOsobvL29uR5PRB/EIpSISMPJZDJs2bIFLVq0QO/e\nvREeHg5zc3O5XVuRvvnmGxw4cAB79+7Fnj170KBBA+zYsQOFhYUKfVyikmIRSm8jk8mwefNm2NnZ\noV+/fjh8+DDMzMxEx9Ia2n5/0Lfp1q0bfvrpJzg5OfFnFn2WgIAATJo0SSVvPeHi4oLExESuxxPR\ne7EIJSLSYE+ePIGPjw8WLVqEmJgY+Pv7v3Eg0qdS5gEfzZs3R3R0NNatW4eVK1fCysoKBw4cUHgR\nS/QhLELpv548eYJevXph8eLFiImJwahRo3ggkpJxIvTthg4din79+qFLly68Bzd9EqlUipSUFAwe\nPFh0lLd6vR4fFhYmOgoRqTAWoUREGio+Ph5WVlYwNTVFcnIyvvnmG7k/hrKLyPbt2yMhIQHz58/H\nTz/9VFyQshAlUczNzZGZmcnvQQLw75+7Z8+eVcjPXfqwR48esQh9hxkzZqBJkybo3r078vPzRcch\nNRMYGIiJEyeq9H2OeXo8EX0Ii1AiIg1TUFCAqVOnolevXli9ejWCg4M/6kCkkhI14SSRSODi4oLz\n589j/Pjx8PPzKy5IiZTNyMgIEokEL168EB2FBCooKMC0adPQs2dPrFq1CsuXL1fIz10qGR6W9G4S\niQSrVq2CkZERBg4ciKKiItGRSE1cunQJiYmJGDp0qOgo7+Xq6oqEhAQ8efJEdBQiUlEsQomINMjr\ngzlSU1MhlUrRpUsXhT6eyCk4HR0deHt748qVKxgwYAB69+4NFxcXSKVSYZlIO3E9Xru9Poju3Llz\nkEqlcHFxER1J63E1/v10dXXxyy+/4Pbt2xg/fjwn2qlEAgMD8cMPP8DQ0FB0lPcyMjJCx44duR5P\nRO/EIpSISAPIZDJs2rQJdnZ26N+/v1IO5lCVe96VKlUKgwYNQlpaGpydndGlSxd4e3vj+vXroqOR\nlmARqp1kMhm2bt2KFi1awMfHR64H0dHn4WFJH2ZgYICDBw8iKioKixcvFh2HVNy1a9dw4sQJjBgx\nQnSUEuF6PBG9D4tQIiI19+TJE/Ts2RNLly7FiRMn4Ofnp7SSUpWmSPT19eHn54eMjAzY2NigTZs2\nGDRoEH7//XfR0UjDsQjVPk+fPkXv3r2xYMECHD9+HKNHj5bbQXT0+TgRWjKVKlVCZGQkli9fjm3b\ntomOQypszpw5GDt2LIyMjERHKRFXV1ecPHmS6/FE9FZ8xUZEpMZiY2NhaWmJL7/8EmfPnkXjxo2V\n9tiqMhH6X2XLlsWPP/6I9PR0VKtWDba2tvDz88Pdu3dFRyMNxSJUu5w6dQpWVlYwNjZGSkoKvv32\nW9GR6D94WFLJVatWDRERERg/fjwiIyNFxyEVlJaWhqNHj8LX11d0lBIrV64c1+OJ6J1YhBIRqaH8\n/HxMnjwZffr0wdq1axEUFCTkBE9Vmgj9r4oVKyIgIADXrl2Dnp4eGjdujEmTJuHRo0eio5GGYRGq\nHV69eoUZM2bA09MTy5cvx8qVK3kgkoriYUkfp2HDhggNDUW/fv2QnJwsOg6pmLlz52LUqFEoX768\n6CgfhevxRPQuLEKJiNRMWloaWrVqhcuXL0MqlcLJyUlIDlWdCP0vU1NTLFmyBBcuXMDTp09Rr149\nzJ49G1lZWaKjkYYwNzdnEarhbt26hbZt2yIpKQlSqRRubm6iI9E7yGQyFqGfoGXLltiwYQO6du2K\n9PR00XFIRfz22284fPgw/P39RUf5aG5ubjh58iSePn0qOgoRqRgWoUREakImk2HDhg1o1aoVBg0a\nhIMHD8LU1FR4JnVRrVo1hISEICkpCenp6ahTpw4WL16Mly9fio5Gas7MzAyZmZmiY5CCbN++Hc2a\nNYOXlxciIiJQpUoV0ZHoPV68eAE9PT0hWxLqrmvXrggICICTkxN/phEAYN68eRg5ciQqVqwoOspH\nK1euHDp06MD1eCJ6A4tQIiI18PjxY3h5eSE4OBixsbEYOXKk8IlM0Y//qWrXro1t27bh+PHjSEhI\ngIWFBdasWYP8/HzR0UhNcTVeMz179gx9+vTB3LlzER0djbFjx/JAJDXAg5I+z+DBgzFw4EA4Ozvj\n+fPnouOQQH/88Qf279+PMWPGiI7yybgeT0Rvw1dzREQqLiYmBpaWlvjqq69w9uxZNGrUSHSkYuo0\nEfpfjRs3xv79+xEaGorQ0FDUr18f27ZtQ2FhoehopGZYhGqexMREWFlZoXz58khJSYGVlZXoSFRC\nPCjp802bNg12dnbw8PBAXl6e6DgkyPz58zFs2DAYGxuLjvLJ3NzcEB8fz/V4IvoXFqFERCoqPz8f\nkyZNQr9+/bBhwwYsWbIE+vr6omMVU9eJ0P9q2rQpjh49ik2bNiEkJATffvst9u/fr9YlLykXi1DN\n8erVK/z000/o3r07goKCsHr1ahgaGoqORR+B9wf9fBKJBMuXL0fFihUxYMAAFBUViY5ESnb79m3s\n2rULY8eOFR3ls5QvXx7t27fHwYMHRUchIhXCIpSISAXduHEDdnZ2uH79OlJTU9G5c2fRkd5Kk8pC\ne3t7nDx5EosWLUJgYGBxQapJz5EUo1y5cigsLER2drboKPQZfv/9d9jb2yMhIQHnz5+Hu7u76Ej0\nCbgaLx+6urrYsWMH7t69ix9++IF/F2qZhQsX4vvvv0flypVFR/lsXI8nov9iEUpEpEJkMhnWrVuH\n1q1bY8iQIThw4IDKvgjVlInQf5JIJOjSpQtSUlIwadIkjBkzprggJXoXiUTCqVA198svv6BZs2bo\n3r07jh49ii+//FJ0JPpEjx494kSonJQpUwZhYWE4fvw4Fi5cKDoOKcndu3exY8cOjB8/XnQUuXBz\nc0NcXByePXsmOgoRqYhSogMQEdH/PHr0CEOGDMGtW7cQHx+PBg0aiI70QZo6IaKjowMvLy94eHhg\n+/bt6N+/P+rXr4/AwEDY2tqKjkcq6HURWqtWLdFR6CM8f/4cvr6+SE5ORmRkJGxsbERHos/EiVD5\nqlixIiIjI9GqVSuYm5tjwIABoiORgi1atAgDBgyAmZmZ6ChyUaFCBbRr1w4HDx5Ev379RMchIhXA\niVAiIhVw7NgxWFpaolatWjhz5oxalKCaOBH6X6VKlcLAgQNx/fp1uLq6ws3NDZ6enrh69aroaKRi\nzM3NkZmZKToGfYTTp0/DysoKhoaGOHfuHEtQDcHDkuSvatWqiIiIwKRJkxARESE6DinQvXv3sHnz\nZkyYMEF0FLny9vbmejwRFWMRSkQkUF5eHiZMmICBAwdi8+bN+Pnnn1XqQKQP0dSJ0P/S19eHr68v\nMjIy0KxZM7Rr1w4DBgzArVu3REcjFcHVePVRWFiIgIAAdOvWDYsXL0ZISAjKli0rOhbJCQ9LUowG\nDRogNDQU/fv3R1JSkug4pCCLFy9G7969Ne72IFyPJ6J/YhFKRCTI9evXYWdnh/T0dKSmpqJTp06i\nI30UbZgI/S9DQ0NMnDgR6enpqFGjBpo0aYKRI0fizp07oqORYCxC1cMff/yBdu3aITY2FufPn4eH\nh4foSCRnXI1XHDs7O2zatAndunVDWlqa6DgkZw8fPsSGDRswadIk0VHkrkKFCrC3t8ehQ4dERyEi\nFcAilIhIyWQyGUJCQtCmTRsMHz4coaGhavtLm7ZMhP5XhQoVMGvWLNy4cQNly5bFN998gwkTJuDh\nw4eio5EgLEJV36+//oqmTZuia9euiI6ORtWqVUVHIgXgYUmK5erqijlz5sDR0RF3794VHYfkaOnS\npfDy8kL16tVFR1EInh5PRK+xCCUiUqKHDx/Cw8MDa9euxcmTJzF06FC1naxU19zyZGJigkWLFuHi\nxYvIzs5G/fr1MWvWLDx//lx0NFIyFqGqKysrCwMHDsTMmTMRERGBCRMmQEeHL4E1FSdCFe+7777D\n4MGD4ezszFVjDfH48WOsWbMGP/74o+goCtO1a1fExsbyNRoRsQglIlKW6OhoWFlZoV69ejh9+jTq\n168vOtJn09aJ0P+qWrUqVq1ahbNnz+K3336DhYUFFi1ahJycHNHRSElYhKqmpKQkWFtbo3Tp0jh/\n/jxsbW1FRyIFkslknAhVkilTpqB169bo1q0b8vLyRMehz7Rs2TJ069YNNWrUEB1FYSpUqIC2bdty\nPZ6IWIQSESlaXl4exo0bh++++w5bt27FggULoKenJzrWZ+NE6Jtq1aqFLVu24MSJE0hKSoKFhQVW\nrVqF/Px80dFIwViEqpbCwkLMmTMHXbt2xYIFC7Bu3ToeiKQFsrOzoaurCwMDA9FRNJ5EIsGyZctg\nYmKCfv36obCwUHQk+kTPnj3DypUrMWXKFNFRFI7r8UQEsAglIlKoq1evonnz5rh16xZSU1PRoUMH\n0ZHkihOhb9ewYUPs3bsXBw8exKFDh1C/fn1s2bKFvyhqMBahquP27dvo0KEDjh07hnPnzqFHjx6i\nI5GScC1euXR1dbFt2zbcv38fY8aM4WsCNbV8+XK4uLigdu3aoqMoXNeuXRETE8P1eCItxyKUiEgB\nZDIZVq1aBXt7e/j5+WHfvn0at6rHidAPs7W1RUREBLZs2YL169fjm2++wd69e1FUVCQ6GslZhQoV\nkJ+fz9shCLZnzx7Y2trC2dkZx44dQ7Vq1URHIiXiWrzylSlTBmFhYYiPj8f8+fNFx6GPlJWVhWXL\nlmnFNCgAVKxYkevxRMQilIhI3h48eAB3d3ds3LgRCQkJGDx4sMaWhpz+KJk2bdogPj4eS5Yswbx5\n89C0aVNERETwn58GkUgknAoV6MWLF/juu+8wZcoUhIeH48cff4Surq7oWKRknAgVo0KFCoiIiMDa\ntWuxadMm0XHoI6xcuRIODg6oV6+e6ChKw/V4ImIRSkQkR0ePHoWVlRUaNWqExMRE1K1bV3QkhdHU\ncldRJBIJnJyckJKSgilTpmDcuHFo27Yt4uPjRUcjOWERKkZycjKsra0hkUgglUrRtGlT0ZFIkEeP\nHrEIFeTLL79EZGQkJk+ejPDwcNFxqASys7OxdOlSTJ06VXQUpXJ3d+d6PJGWYxFKRCQHubm5GDt2\nLIYMGYLt27dj3rx5GnEg0odwovHjSSQS9OjRA5cuXcKQIUMwcODA4oKU1BuLUOUqLCzE/Pnz4erq\nirlz52LDhg0wMjISHYsEevjwIVfjBapXrx7CwsIwcOBAnDlzRnQc+oA1a9bA3t4ejRo1Eh1FqSpW\nrIg2bdrg8OHDoqMQkSAsQomIPtOVK1fQvHlz/PXXX0hNTUX79u1FR1IKToR+Hl1dXfTv3x/Xr1+H\nu7s73N3d0aNHD1y5ckV0NPpELEKV56+//kKnTp0QERGBlJQUeHl5iY5EKoCr8eI1b94cmzdvRrdu\n3XD9+nXRcegdcnJy8PPPP2PatGmiowjB9Xgi7cYilIjoE8lkMqxYsQLt2rXD6NGjsXv3bhgbG4uO\npVScCP18enp6GDFiBDIyMmBnZ4cOHTqgf//++O2330RHo4/EIlQ59u3bB1tbWzg4OCAmJgbVq1cX\nHYlUBA9LUg0uLi6YP38+nJ2dcefOHdFx6C3WrVuHFi1a4NtvvxUdRQh3d3ccP34cWVlZoqMQkQAs\nQomIPsH9+/fh6uqKLVu2IDExEd99953WTUhq2/NVNAMDA4wfPx7p6emoU6cOmjVrhhEjRuDvv/8W\nHY1KiEWoYr148QKDBw/GpEmTcOjQIUyZMoUHItG/cCJUdQwcOBDDhg2Dk5MTnj59KjoO/UNubi4W\nLlyI6dOni44iTKVKldC6dWuuxxNpKRahREQfKSIiAlZWVrC0tERiYiIsLCxERxKGE6HyV758ecyY\nMQM3btxA+fLl8e2332L8+PF4+PCh6Gj0ASxCFefcuXOwsbFBYWEhpFIpmjVrJjoSqSAelqRaJk2a\nhHbt2qFbt27Izc0VHYf+z8aNG2FjYwMbGxvRUYTiejyR9mIRSkRUQrm5ufD398ewYcOwc+dOzJ07\nF6VLlxYdSxhOhCrWF198gQULFuDy5cvIzc1FvXr1MGPGDDx79kx0NHoHc3NzZGZmio6hUYqKirBw\n4UI4OzsjICAAmzZtQrly5UTHIhXFw5JUi0QiQVBQEMzMzNC3b18UFhaKjqT18vLyMH/+fK2eBn3t\n9Xr8ixcvREchIiVjEUpEVAKXLl1C06ZNkZmZiQsXLsDe3l50JJXAiVDFq1KlClasWIFz587h9u3b\nsLCwwIIFC5CTkyM6Gv0HJ0Ll6++//4aDgwMOHTqE5ORk9OzZU3QkUnFcjVc9Ojo62Lp1Kx4/fgx/\nf3++bhBsy5YtaNiwIafqARgbG6Nly5ZcjyfSQixCiYjeQyaTITg4GB06dMC4ceOwa9cuVKpUSXQs\nlcCJUOWqUaMGNm3ahLi4OJw7dw516tTBihUrkJeXJzoa/R8WofITGhoKGxsbtG/fHrGxsfj6669F\nRyIVJ5PJeFiSitLX10doaCgSEhIwd+5c0XG0VkFBAebOnYsZM2aIjqIyuB5PpJ1YhBIRvcO9e/fQ\npUsX7NixA6dPn8bAgQNZ/v0HJzuUr0GDBti9ezfCw8MRERGBevXqYdOmTXj16pXoaFqvYsWKyM3N\n5b3wPkN2djaGDRuG8ePHIywsDNOmTeOBSFQiOTk5kEgkMDQ0FB2F3qJChQqIiIjAhg0bsGHDBtFx\ntNK2bdtQp04dtGzZUnQUldGtWzccO3aM6/FEWoZFKBHRW4SHh8PKygq2trY4deoU6tSpIzqSymEp\nLJa1tTXCw8OxY8cObN68GY0bN8bu3btRVFQkOprWkkgkMDU15VToJ5JKpbC1tcXLly8hlUrRokUL\n0ZFIjfCgJNVXpUoVREZGYtq0aTh06JDoOFrl1atXnAZ9i9fr8eHh4aKjEJESsQglIvqHly9fws/P\nDyNHjsSuXbsQGBio1QcifQgnQsVr1aoVYmNjERwcjEWLFsHW1hbh4eH8/0YQrsd/vKKiIvz8889w\ndHTEzJkzsXXrVpQvX150LFIzPChJPdStWxdhYWH47rvvkJiYKDqO1ti5cyeqVauGtm3bio6icrge\nT6R9WIQSEf2fixcvokmTJnj48CEuXLjAF4sfwIlQ1SGRSNC5c2ecPXsWM2bMwKRJk9C6dWvExsaK\njqZ1WIR+nDt37sDR0REHDhzA2bNn4ePjIzoSqSkelKQ+mjVrhm3btqF79+64du2a6Dgar7CwEIGB\ngTwp/h26deuG6OhoZGdni45CRErCIpSItF5RURGCgoLQsWNHTJo0CTt37kTFihVFx1ILnDpULRKJ\nBB4eHrhw4QJGjBiB77//Hp07d0ZycrLoaFrD3NycRWgJhYWFwcbGpri0r1GjhuhIpMZ4UJJ6cXJy\nwsKFC+Hk5IS///5bdByNtnv3bpiYmKBDhw6io6gkY2Nj2NnZcT2eSIuUEh2AiEiku3fvYtCgQXj2\n7BnOnDmD2rVri46kNjgRqrp0dXXRt29f9OzZE5s2bYKHhweaNm2KgIAANG7cWHQ8jWZmZobMzEzR\nMVRaTk4Oxo0bh6NHj2L//v08uIPkghOh6qd///7IzMyEk5MT4uPjUalSJdGRNE5RURECAwOxZMkS\nvm57j9fr8d7e3qKjEJEScCKUiLTWoUOHYGNjg2bNmiE+Pp4lKGmc0qVLY+jQoUhPT0fbtm3RsWNH\n9O3bFxkZGaKjaSyuxr9famoqmjRpgqysLEilUpagJDc8LEk9TZgwAR07doS7uztevnwpOo7G2b9/\nP4yMjNC5c2fRUVRat27dEBUVxfV4Ii3BIpSItE5OTg5GjhwJf39/7NmzB7Nnz+aBSJ+Iq/HqwcDA\nAGPHjkVGRgbq16+PFi1aYOjQobh9+7boaBqHRejbFRUVYcmSJXBwcMCUKVOwfft2VKhQQXQs0iA8\nLEk9SSQSLFmyBFWrVkWfPn1QWFgoOpLGKCoqQkBAAKZPn85p0A/44osv0KJFC67HE2kJFqFEpFVe\nTyM9ffoUUqkUrVu3Fh1JbfFFtfopV64cpk2bhrS0NHzxxRewsrLC2LFjcf/+fdHRNAaL0DfdvXsX\nzs7O2Lt3L5KSktC3b1/RkUgDcTVefeno6GDz5s14/vw5/Pz8+CarnBw8eBC6urpwcXERHUUt8PR4\nIu3BIpSItMJ/p5F++eUXHogkB/xlRT0ZGxtj3rx5uHLlCgoLC9GgQQNMmzYNT58+FR1N7bEI/bdD\nhw7B2toaLVq0QHx8PGrVqiU6EmkoHpak3vT19bF//34kJSUhICBAdBy1J5PJEBAQgBkzZvCN6xLi\nejyR9mARSkQa786dO3BycsLevXtx9uxZTiPJCV9Yqz9zc3MEBwfj/PnzuHv3LiwsLDBv3jz+EvAZ\nWIT+z8uXL+Hr6wt/f3/s3bsXs2bNQqlSPKOTFIcToeqvfPnyOHLkCLZs2YJ169aJjqPWjhw5glev\nXqFr166io6gNExMTNG/eHEeOHBEdhYgUjEUoEWm0sLAw2NjYoFWrVoiPj0fNmjVFR9IonAjVDF9/\n/TU2bNiAkydPIjU1FXXq1EFwcDDy8vJER1M7lSpVQnZ2tlb/s7t48SKaNGmCx48f8xYkpDScCNUM\n5ubmiIyMxIwZMxAWFiY6jlqSyWSYPXs2pk2bBh0d/rr/MbgeT6Qd+JORiDRSTk4Ohg8fjrFjx2L/\n/v2YOXMmp5HkjBOhmqd+/frYtWsXIiIiEBUVhbp162LDhg149eqV6GhqQ0dHB6amplo5FVpUVISg\noCB07NgRkyZN4i1ISKk4Eao5LCwscOjQIQwZMgQJCQmi46idqKgovHjxAj169BAdRe14eHjg6NGj\nyMnJER2FiBSIRSgRaRypVAobGxtkZ2dDKpWiZcuWoiNpLE6EaiYrKyscPnwYO3fuxPbt29GoUSP8\n+uuvKCoqEh1NLWjjenxmZia6dOmCX3/9FWfOnEH//v35ZgkpTU5ODmQyGQwNDUVHITlp0qQJtm3b\nhu7du+PKlSui46gNToN+HhMTEzRr1ozr8UQajj8diUhjFBUV4eeff0bnzp0xY8YMbNu2DRUqVBAd\nS2Ox5NB8LVu2RExMDFauXIklS5bA2toahw4dYgH+AdpWhIaHh8Pa2hpNmzbFyZMnUbt2bdGRSMu8\nXovn30uaxdHREYsXL4azszNu374tOo5aOHHiBB4+fAhvb2/RUdQW1+OJNB/3RIlII/z9998YMGAA\ncnNzkZycjBo1aoiOpBVYiGk+iUSCTp06oWPHjjh48CCmTJmCuXPnYs6cOejQoYPoeCpJW4rQly9f\nYuLEiTh48CB2796NNm3aiI5EWopr8Zqrb9++yMzMhJOTE06dOoVKlSqJjqTSZs+ejalTp0JXV1d0\nFLXl4eGBCRMmICcnh1PmRBqKE6FEpPZCQ0NhY2MDe3t7xMbGsgRVEk7eaBeJRAJ3d3ekpqZi1KhR\nGDZsGDp16oSkpCTR0VSONhShly5dQrNmzXD//n2kpqayBCWheFCSZhs/fjycnJzQtWtXvHz5UnQc\nlRUXF4e//voLvXv3Fh1FrVWuXBlNmzZFRESE6ChEpCAsQolIbWVnZ2Po0KEYP348Dhw4gOnTp/NA\nJCXjRKj20dXVRe/evXH16lX07NkTnp6e6Nq1Ky5evCg6msrQ5CJUJpMhODgYHTp0wLhx4/Drr79y\nQouE40So5lu0aBG++uor+Pj48AC/dwgICMCUKVP4WlgOuB5PpNlYhBKRWjp37hxsbGyQl5cHqVQK\nOzs70ZG0DidCtVvp0qUxZMgQpKeno0OHDujcuTN8fHyQlpYmOppwmlqE3r9/H66urti+fTtOnz6N\ngQMH8ucAqQQWoZpPR0cHmzZtQk5ODnx9fflG7H8kJibi5s2b6Nevn+goGsHDwwORkZGcQCbSUCxC\niUitFBUVYeHChXB2dsasWbOwZcsWlC9fXnQsrcVfRKhMmTIYM2YMMjIy0LhxY7Rq1QqDBw/Gn3/+\nKTqaMJpYhEZERMDKygpWVlZISEhAnTp1REciKsbVeO2gp6eHffv2ISUlBbNmzRIdR6UEBARg8uTJ\nKF26tOgoGsHU1BRNmjThejyRhmIRSkRq46+//kKnTp1w+PBhJCcno1evXqIjaTVOgtE/GRkZYerU\nqUhLS4OpqSmsra0xevRojSsES8Lc3ByZmZk4fvw4PDw8UKVKFZQpUwZVq1aFk5MTIiMjRUcssdzc\nXIwePRrDhg3Dzp07MWfOHP6iTSqHE6Hao1y5cjhy5Ai2b9+ONWvC9XFMAAAgAElEQVTWiI6jEs6e\nPYsrV65gwIABoqNoFK7HE2kuFqFEpBb27dsHW1tbdOzYESdOnMDXX38tOhKBE6H0pkqVKmHu3Lm4\nevUqAKBhw4aYMmUKnjx5IjiZ8piZmeHWrVtwcHDA+fPn4e7ujvHjx8PV1RUPHz5EbGys6Iglcvny\nZTRr1gx37txBamoq7O3tRUcieitOhGoXMzMzHD16FLNnz0ZoaKjoOMIFBARg0qRJ0NfXFx1Fo3h4\neCAiIoLr8UQaiHdSJiKV9uLFC4wZMwaxsbE4ePAgmjdvLjoS/R9OhNL7mJmZYdmyZRg3bhxmz56N\nunXrYsyYMRg9ejSMjIxEx1Ooffv2IS8vDwMHDsS6deveOLiisLBQULKSkclkWLlyJWbNmoUFCxZg\n0KBB/PedVBonQrVP7dq1cejQITg7O8PExARt2rQRHUkIqVSK8+fPc3JRAUxNTWFra4vIyEh4eHiI\njkNEcsSJUCJSWcnJybCxsUFhYSGkUilLUBXEiVD6kK+++grr169HQkICLl++jDp16iAoKAi5ubmi\noylEfn4+ZsyYAV1dXcycOfOtp/fq6uoKSFYyDx48QNeuXbFlyxYkJibiu+++YwlKKo9FqHaytbXF\njh074OnpicuXL4uOI0RAQAAmTpyIMmXKiI6ikbgeT6SZWIQSkcopLCzEvHnz4OLigsDAQGzatAnl\nypUTHYv+g+UIfYy6deti586dOHr0KGJiYlC3bl2sX78eBQUFoqPJVXR0NB48eABjY2Pcv38f4eHh\nWLhwIYKDg3HmzBnR8d7r6NGjsLKyQuPGjZGQkAALCwvRkYhKhKvx2svBwQFLly5Fly5dtO6QvosX\nL+L06dMYMmSI6Cgaq3v37jhy5AjX44k0DFfjiUil3L59G/369YNMJkNKSgq++uor0ZHoPTgRSh/L\n0tISBw8exJkzZzB16lQsWLAAs2fPRs+ePaGjo/7vzyYnJ0MikcDIyAg9e/bEH3/8UfymgUwmQ9u2\nbbF3716Vml7Ly8vDjz/+iL1792L79u1o37696EhEH4UTodqtd+/eyMzMhJOTE06dOgVjY2PRkZQi\nMDAQ48aNg6GhoegoGsvU1BQ2NjZcjyfSMOr/GwcRaYw9e/bA1tYWjo6OiImJYQmq4jgRSp+jRYsW\nOH78OEJCQhAcHAwrKyscPHhQ7cv1+/fvQyaT4ffff0dhYSESEhKQlZWFixcvwtHREfHx8fD29hYd\ns9jVq1fRvHlz/Pnnn0hNTWUJSmrn5cuXePXqFcqWLSs6Cgn0ww8/wMXFBa6ursjJyREdR+GuXr2K\nuLg4DB8+XHQUjcf1eCLNwyKUiITLysrCd999hylTpiA8PByTJ09W6Xvo0f+n7qUVidehQwckJiZi\nzpw5mD59enFBqq6KiooA/O8+oL1794adnR0MDQ3RqFEj7N+/H9WqVUNcXBySkpKE5pTJZFi9ejXs\n7e3h5+eHvXv3crWY1NKjR49gYmLCN+cICxYsQO3atdGrVy+8evVKdByFmjNnDsaOHavxhw+qAq7H\nE2keFqFEJNTZs2dhY2MDiUQCqVSKpk2bio5EJcRfOkleJBIJ3NzcIJVKMXbsWIwYMQIdOnTA6dOn\nRUf7aBUrVgQAVKtW7Y0DoQwMDODo6Ajgfz/7RHn48CG6deuG9evX49SpUxg8eDD/fSa1xbV4ek1H\nRwcbNmxAXl4ehg8frrFv1t64cQNRUVHw9fUVHUUrmJmZwdraGkePHhUdhYjkhEUoEQlRWFiIOXPm\nwM3NDfPmzcOGDRv4rrYa0tRfMkgMHR0d9OrVC1evXkWfPn3Qq1cvuLm54cKFC6KjlVi9evUAABUq\nVMC9e/fe+HilSpUAQNhkSXR0NCwtLVG/fn2cPn26OC+RuuJBSfRPenp62LdvHy5cuICZM2eKjqMQ\nc+fOhb+/Pw8SVSKuxxNpFhahRKR0f/75J9q3b49jx44hJSUFnp6eoiPRJ+AEGSlKqVKl8P333yMt\nLQ0ODg5wcnJCr169cOPGDdHRPqhjx46QSCS4d+/eW4vQy5cvAwBq1qyp1Fx5eXkYP348Bg0ahK1b\nt2LBggXQ09NTagYiReBEKP2XkZERwsPDsXPnTqxevVp0HLm6efMmwsPDMWrUKNFRtEr37t0RHh7+\nxqYHEaknFqFEpFS7du1CkyZN0KVLFxw7dgzVq1cXHYk+AydCSZH09fXh7++PjIwMWFpaonXr1vj+\n++/xxx9/iI72Tl999RXc3Nxw//59XL169V8fi4qKwtGjR1GpUiU4OTkpLdP169fRokUL3Lx5Excu\nXEDHjh2V9thEisaJUHobU1NTHD16FIGBgdi/f7/oOHIzb948+Pr6Ft+GhZTD3NwcVlZWXI8n0hAs\nQolIKbKysjBgwABMnz4dR44cwY8//sgDkdQcJ0JJWcqWLYvJkycjPT0dVapUgY2NDfz9/ZGZmSk6\n2lutXLkSVatWxb179+Dg4ICJEyfC09MTLi4uKFWqFNavX6+UlUaZTIaQkBC0adMGI0aMwP79+1kY\nkcbhRCi9S61atXD48GEMHz4ccXFxouN8tt9//x2hoaEYPXq06ChaievxRJqDRSgRKdyZM2dgZWUF\nPT09nD9/Hk2aNBEdieSEE6GkTBUrVkRgYCCuXbsGXV1dNGrUCJMnT8bjx49FR/uXqlWrIiUlBRKJ\nBBkZGQgODkZ8fDzc3d2RkJCAbt26KTzDo0eP0L17d6xZswYnT57E0KFD+eYFaSQWofQ+1tbW+OWX\nX+Dl5YVLly7J/fr79u2Dv78/2rZtiwoVKkBHRwf9+/d/5+e/ePECU6dORYMGDWBgYABjY2M4OTkh\nJibmg481f/58DBs2DMbGxvJ8ClRCPXr04Ho8kYZgEUpEClNYWIiAgAC4u7tj4cKFWLduHQ9E0iAs\nVUgUU1NTLF26FKmpqXj06BHq1q2LwMBAZGVliY5WzNTUFKampkhISEBubi7u37+PvXv3KuWNoOPH\nj8PKygq1a9fGmTNnUL9+fYU/JpEoXI2nD+nUqROCg4PRpUsXud9aJTAwECtXrsSFCxdQrVq19742\nevr0KZo3b4558+ahdOnSGDFiBDw9PSGVStGpUyds2rTpnV97+/Zt7N69Gz/88INc81PJmZub49tv\nv0VUVJToKET0mViEEpFC/P7772jXrh1iY2Nx7tw59OjRQ3QkUgBOhJJI1atXx9q1a3HmzBlcu3YN\nFhYWWLp0qcpMa5ibmyt1fT8/Px8TJ07EgAEDsHHjRvz888/Q19dX2uMTicCJUCqJXr16Yfz48XBy\ncsKjR4/kdt2goCCkpaXh2bNnWLVq1XtfF82cORPXrl2Dp6cnUlNTsWTJEqxduxZXrlxB9erVMWrU\nKNy5c+etX7tw4UIMHjyY3+uCcT2eSDOwCCUiudu5cyeaNWuGrl27Ijo6GtWqVRMdiRSAE6GkKurU\nqYMdO3YgOjoa8fHxsLCwwNq1a1FQUCA0l5mZ2VtPjleEGzduwM7ODjdu3EBqaiocHByU8rhEonEi\nlEpq9OjR6Nq1K1xdXZGdnS2Xa9rb26N27dol+twDBw5AIpFg1qxZ0NH5/7+Gm5iY4IcffsDLly+x\ncePGN77uzp072LFjB8aNGyeXzPTpevTogcOHDyMvL090FCL6DCxCiUhunj9/jn79+uGnn35CREQE\nJkyY8K8XeqR5OBFKquSbb75BaGgo9u3bh71796JBgwbYsWMHCgsLheRRRhEqk8mwfv16tG7dGkOG\nDMGBAwc4MURahROh9DHmz5+PunXromfPnnj16pVSH/v1hkCtWrXe+FitWrUgk8lw/PjxNz62aNEi\nDBgwAGZmZgrPSO9XpUoVfPPNN1yPJ1JzbCiISC4SExNhZWUFQ0NDnD9/Hra2tqIjkYJxIpRUVbNm\nzRAVFYX169dj1apVsLS0RGhoqNKLe0UXoY8fP4anpydWrFiBuLg4DB8+nP9ektZhEUofQyKRYP36\n9SgsLMSwYcOU+vfC6+/TW7duvfGx3377DcD/pvv/KTMzE1u2bMGECRMUH5BKxNvbm+vxRGqORSgR\nfZZXr15h1qxZ8PDwwOLFixESEoKyZcuKjkVKwolQUmXt2rXDqVOnsGDBAsyePRvNmzdHVFSU0r5v\nFVmEnjhxApaWlvj666+RlJSEhg0bKuRxiFRZbm4u8vPzeRAjfZTSpUtjz549uHTpEqZNm6a0x3Vx\ncYFMJsPMmTNRVFRU/OcPHjzA0qVLAQBPnjz519csXrwYffr0wZdffqm0nPR+XI8nUn+lRAcgIvV1\n69Yt9O3bFwYGBpBKpXyRpmU4eUbqQCKRwMXFBc7Ozti7dy/8/f1hbm6OOXPmoFWrVgp9bDMzM0il\nUrleMz8/HzNmzMC2bduwceNGODo6yvX6ROrk0aNHMDEx4d9H9NGMjIwQHh6OVq1aoUqVKvDz81P4\nY86ePRtRUVHYu3cvrl27ho4dOyI7OxthYWGoVq0a/vzzz3/dUurBgwfYsGEDLl68qPBsVHJVqlRB\n48aNER0dDVdXV9FxiOgTcCKUiD7Jjh070KxZM3Tv3h1RUVEsQbUUJ0JJXejo6MDb2xuXL1/GgAED\n0KdPH7i4uMi9qPwneU+EpqWloWXLlrhy5QpSU1NZgpLW40FJ9DkqV66Mo0ePYt68eUpZdTY3N0dy\ncjJ8fX3x4sULrF69GkeOHIGPj0/x45uamhZ//tKlS+Ht7c1DR1UQT48nUm8sQonoozx79gx9+vRB\nYGAgoqKiMG7cOB6IpKU4gUPqqFSpUhg0aBBu3LgBZ2dnuLi4wNvbG9evX5f7Y8mrCJXJZNi4cSNa\ntWqFQYMG4eDBg6hcubIcEhKpN94flD5XzZo1ER4eDl9fX8TGxir88SpXrozg4GD89ttvyM3NxV9/\n/YWgoCD88ccfAP53j2vgf/eADgkJwY8//qjwTPTxevTogYMHD3I9nkhNsb0gkoN9+/bB398fbdu2\nRYUKFaCjo4P+/fu/92sSExPRpUsXfPHFFzA0NISlpSWWLVv2r3sGqZqEhARYWVmhfPnyOHfuHKyt\nrUVHIsE4EUrqSl9fH35+fkhPT4etrS3atm2LQYMG4ffff5fbY+jp6eH27duIjo7GqVOn8Pjx44++\nxpMnT+Dt7Y2goCDExsbC19eXb0IQ/R8WoSQPVlZW2LVrF7y9vXHhwgUhGbZs2QKJRILevXsDAIKC\nguDh4YEaNWoIyUPv9+WXXxavxxOR+mERSiQHgYGBWLlyJS5cuIBq1ap98JfUsLAw2Nvb49SpU+je\nvTtGjRqFgoICjB07Fj4+PkpKXXKvXr3CzJkz0aNHDwQFBWH16tUwNDQUHYsEYxlDmqBs2bKYNGkS\n0tPTUb16ddja2sLPzw937979pOtlZGRg1KhRMDU1hZWVFZ49ewYvLy+4urqiSpUqMDU1xZgxY4pP\nCH6fuLg4WFpaomrVqjh79iwaNWr0SZmINBVX40le2rdvjxUrVsDFxUWub4j9k0wmQ3Z29ht/vm3b\nNmzbtg2tWrWCu7s7nj59ilWrVmHy5MkKyUHywfV4IvXFw5KI5CAoKAjVqlVD7dq1ERcXh/bt27/z\nc7OysjBkyBCUKlUKcXFxxVOVAQEBaN++Pfbu3Yvdu3fD29tbWfHf67fffkOfPn1Qrlw5SKVSVKlS\nRXQkUiGcCCVNUaFCBcyePRujRo3C/Pnz0bhxYwwePBgTJ04sUdHy/Plz+Pn5Yc+ePSgsLERBQUHx\nx549e1b83x88eIBVq1YhJCQEvXv3xrJly9448bqgoAA//fQTNm3ahA0bNsDZ2Vl+T5RIg3AilOTJ\n29sb9+7dg6OjIxISEkr0vRUWFoYDBw4AADIzMwH8b+tr0KBBAAATExMsWrQIAJCTkwMzMzM4ODig\ndu3a0NHRQUJCAk6fPo1GjRph9+7dAIDly5fD1dUVtWvXVsTTJDnp0aMHfvrpJ+Tl5UFfX190HCL6\nCJwIJZIDe3v7Er9Y2bNnDx4+fAgfH59/rZbr6ekhMDAQMpkMq1evVlTUEpPJZNi2bRuaN28Ob29v\nREZGsgSlf+FEKGmiypUrY/Hixbh48SKeP3+OevXqYfbs2Xj+/Pk7v+bixYuoU6cO9uzZg9zc3H+V\noG9TUFCA3Nxc/PLLL6hTpw6uXr1a/LGMjAy0bt0aqampkEqlLEGJ3oMToSRvo0aNQvfu3eHi4vLW\n6c3/Sk1NxdatW7F161ZERUVBIpHg1q1bxX+2f//+4s/V19eHj48Prl+/jpCQEKxevRovX77EvHnz\nkJycDHNzczx//hzBwcGYMmWKIp8myUHVqlXRsGFDHDt2THQUIvpILEKJlOzEiROQSCRvPe23bdu2\nMDQ0RGJi4gd/kVakp0+fonfv3pg3bx6io6MxduxYHohEb8WJUNJUVatWxerVq5GUlISMjAxYWFhg\n8eLFePny5b8+79KlS2jTpg0ePHiA3Nzcj3qM3Nxc3L9/Hy1btsTVq1exefNm2NnZoW/fvjh8+DDM\nzMzk+ZSINA4nQkkR5s6di4YNG8LLy+uDr8dnzpyJwsLCd/7n5s2bxZ9bqlQprFu3DteuXUNWVhay\nsrJw/vx5TJo0CWXKlAEArFy5Ep07d0bdunUV+hxJPrgeT6Se2GwQKdmNGzcA4K0vcHR1dVGzZk28\nevWqRPePU4STJ0/CysoKxsbGSElJgZWVlZAcpPo4EUraoHbt2ti6dStiYmKQmJgICwsLrF69Gvn5\n+cjOzkbnzp3fOy36ITKZDM+fP0eTJk2wcOFCxMTEYNSoUfz3i6gEWISSIkgkEqxduxYSiQRDhgxR\n2pu+L168QFBQEKZOnaqUx6PP9/r0+Pz8fNFRiOgjsAglUrLX94qrUKHCWz/++s+fPn2qtEzA/1Y1\np0+fDm9vb6xYsQIrV67kgUj0QZwIJW3RqFEj7Nu3DwcOHEBYWBjq168PV1fXf93/81PJZDIUFBTA\n3t4e33zzjRzSEmkHrsaTopQuXRq7d+/G9evXlbamvmbNGrRr1w4NGzZUyuPR56tWrRoaNGjA9Xgi\nNcMilIhw8+ZNtGnTBsnJyZBKpXB1dRUdidQAJ9ZIGzVp0gSRkZFYtGgR4uLi3liV/1SvXr3C5s2b\nce/ePblcj0gbcCKUFKls2bI4fPgwQkNDERwcrNDHysnJwc8//4xp06Yp9HFI/rgeT6R+WIQSKdnr\nic93TRG9/vOKFSsqPItMJsOWLVvQokUL+Pj44MiRIzA3N1f445Lm4EQoaSupVAo9PT25X3ft2rVy\nvyaRpuJEKCmaiYkJIiMjsXDhwuJT3RVh7dq1aNmyJbcC1JCnpyfX44nUDItQIiWrV68eACAtLe2N\njxUWFuLWrVsoVaoUatWqpdAcT548gY+PDxYtWoTjx49j9OjRPBCJPgonQkmb7dy5E3l5eXK9Zm5u\nLnbs2CHXaxJpqry8POTm5qJ8+fKio5CGq1GjBo4cOQI/Pz/ExMTI/fq5ublYtGgRpk+fLvdrk+JV\nq1YN9erVw/Hjx0VHIaISYutBpGQdOnSATCZDZGTkGx+Li4tDTk4OWrVqhdKlSyssQ3x8PKysrFC5\ncmUkJyfj22+/VdhjkWbjRChpo9zcXPz5558KufZvv/32wVOKiej/T4PyTTlShm+//Ra7d+9Gr169\nkJqaKtdrb9iwAba2trC2tpbrdUl5uB5PpF5YhBIpmaenJ0xMTPDrr7/i3LlzxX+el5eHadOmQSKR\nYMSIEQp57IKCAkydOhU9e/bE6tWrsXz5chgYGCjksUjz8ZdP0la3bt1S2M9OPT09hZWsRJqEa/Gk\nbO3atcOqVavg4uKCW7duyeWaeXl5mD9/PqdB1ZynpyfCwsL4RiaRmiglOgCRJggLC8OBAwcAAJmZ\nmQCAxMREDBo0CMD/7i+0aNEiAEC5cuWwbt06eHl5oV27dujVqxeMjY1x8OBBpKWlwcvLC15eXnLP\nmJGRgd69e8PExASpqakwMzOT+2OQ9uFEKGmj/Px8hb0RoKOjw/uMEZUAD0oiETw9PXHv3j04Ojoi\nISEBlStX/qzrbd68GY0bN0bTpk3llJBEqF69OurWrYvjx4/DyclJdBwi+gAWoURykJqaiq1btxb/\nb4lEglu3bhW/W1yjRo3iIhQA3N3dERcXhzlz5mD//v3Izc1FnTp1sHTpUowaNUqu2WQyGTZv3oyJ\nEydixowZ8PPz4yQfyQW/j0hblS1bFoWFhQq5dmFhIQwNDRVybSJNwolQEsXX1xd3796Fi4sLYmJi\nYGRk9EnXKSgowLx587Bz5045JyQRvL29sWfPHhahRGpAIuM4D5HGevLkCYYNG4Zr165h586daNy4\nsehIpEESExMxfvx4JCYmio5CpFSvXr1C2bJlFTK5WaZMGWRnZ/PwOqIPWLNmDaRSKUJCQkRHIS0k\nk8kwePBg/P333zh06NAb9/YvLCyEVCpFSkoKUlNT8eLFCxgZGcHa2hpNmjSBtbU1Nm/ejJ07dyI6\nOlrQsyB5un37NqytrXH37l2FnvVARJ+PE6FEGio2Nhb9+/eHh4cHtm7dijJlyoiORBqI76WRNipV\nqhTq1q2Ly5cvy/3aDRs2ZAlKVAJcjSeRJBIJQkJC4OHhge+//x6bN2+Gjo4OsrKysHz5cgQFBSE3\nNxevXr3Cy5cvi7/O0NAQOjo6MDQ0REFBAX755ReBz4LkqXr16rCwsEBMTAwcHR1FxyGi9+ArbSIN\nk5+fj8mTJ6N3794ICQnBsmXLWIKSQnA1nrTZ0KFD5f6z1cDAAEOHDpXrNYk0FVfjSbRSpUph165d\nSE9Px+TJk3Hs2DHUrl0bgYGBePDgAbKysv5VggJATk4OXrx4gfv37+PZs2cYOHAgTpw4IegZkLzx\n9Hgi9cAilEiDpKWloVWrVrh06RJSU1Ph7OwsOhJpOE6EkjaSSqWIjIxEbm6uXK/78uVLHDlyBBcu\nXJDrdYk0ESdCSRUYGhri8OHD2LRpE7p06YIHDx68UX6+S1FREe7duwdXV1cEBwcrOCkpg6enJw4c\nOMDT44lUHItQIg0gk8mwYcMGtGrVCgMHDsShQ4dgamoqOhZpOE6EkrY5f/483N3d4eLiAgcHBwQG\nBqJs2bJyuXbZsmWxcOFCtG3bFk5OTujevTtSU1Plcm0iTcSJUFIVu3btQlZW1ieXXzk5OZg8eTLW\nr18v52SkbF999RXq1KmDmJgY0VGI6D1YhBKpucePH8PLywvBwcGIjY2Fr68vCypSGk6EkjY4d+4c\nunbtCjc3N3To0AE3b97EmDFj8OOPP8LCwuKzD0UoXbo0GjZsiHHjxmHcuHG4efMmWrduDWdnZ3h4\neEAqlcrpmRBpDk6Ekiq4du0axo8f/9kbAjk5OfD390d6erqckpEoXI8nUn0sQonUWExMDCwtLfHV\nV18hKSkJjRo1Eh2JtAgLd9J0KSkpcHNzQ9euXdGpUydkZGRg9OjRMDAwAADo6uri6NGjqFKlyieX\noaVLl0a1atUQERFRfEiSoaEhfvjhB9y8eRP29vZwcXFBt27dWIgS/QOLUBJNJpOhV69ecrtNSl5e\nHnr37i2Xa5E4XI8nUn0sQonUUH5+PiZNmoR+/fph/fr1WLJkCQ9EIiE4EUqaKDk5Ga6urujWrRsc\nHR1x8+ZN+Pv7Fxeg/2RqaoqUlBTY2Nh89Jp82bJl0axZM6SkpLx1xdfQ0BBjxozBzZs30b59e7i4\nuMDd3R3nz5//5OdGpCm4Gk+inTlzBjdv3pTba6GioiJcvXoV586dk8v1SIyvv/4atWvX5iFYRCqM\nRSiRmrlx4wbs7Oxw7do1pKamwtHRUXQk0lKcCCVNc/bsWbi4uMDDwwPOzs7IyMiAn5/fB99oqly5\nMhITE7FgwQKUK1cORkZG7/18IyMjlC9fHosXL8bJkydhbGz83s83MDDA6NGjcfPmTXTs2LF4SpW/\nLJO2ys/PR05ODipUqCA6CmmxpUuXIicnR67XzM3NRVBQkFyvScrH9Xgi1cYilEgJioqKkJGRgfPn\nz+PixYt4/vz5R19DJpNh3bp1aNWqFQYPHoywsDBUrlxZAWmJSo4ToaQJkpKS0KVLF/To0QMuLi7I\nyMiAr6/vR03a6+jowNfXFw8ePMCaNWvQoUMHGBsbo1SpUtDX1wcAVKxYEZ06dcLatWtx//59DBs2\n7KPeUDAwMIC/vz9u3rwJBwcHuLu7w83NDSkpKR/9nInU2ePHj2FsbMw35EioEydOyP11UFFREY4f\nPy7Xa5LycT2eSLWxCCVSkJycHGzcuBG2trYwNDSElZUV2rdvj9atW8PExARVq1bFyJEjcePGjQ9e\n69GjR+jRowdWrlyJ+Ph4jBgxgi/+STh+D5K6O3PmDJydneHp6Qk3NzdkZGRg5MiRn3WrEX19ffTp\n0wfHjx/Ho0eP8OTJE/z1119o1qwZDh06hOjoaPj4+BSXo5+iTJkyGDVqFDIyMuDo6Ihu3brBxcUF\nZ8+e/eRrEqkT3h+URHv06NEnDTaUxMOHDxV2bVKOGjVqoGbNmoiNjRUdhYjegkUokZzJZDJs3LgR\nZmZmGD16NM6fP4+8vDxkZ2fj+fPnyMrKQkFBAe7cuYP169fD2toabm5uePDgwVuvd+zYMVhaWqJm\nzZpISkpCw4YNlfyMiN6NE6Gkjk6fPg0nJyd4e3vD3d0dGRkZGDFixGeVk+9iZGQEExMTVK9eHXfu\n3JHrtcuUKQM/Pz9kZGQUT7R26dIFSUlJcn0cIlXDIpREu3PnjsLuz6+vr4/MzEyFXJuUh+vxRKqL\nRSiRHL148QIODg7w9/fHixcv8OLFi/d+fkFBAV6+fImoqCjUqVMHMTExxR/Ly8vDhAkTMGDAAGzc\nuBGLFy9WyC/pRJ+KE6GkbhITE+Ho6IhevXrBw8MD6enpGD58uFJ+tlatWhV///23Qq5dpkwZ+Pr6\nIiMjA66urvD09ISzszPOnDmjkMcjEo0HJZFoinwjWCKRoEGI3f4AACAASURBVKioSGHXJ+Xw9PRE\naGgo7t+/j/Xr16N79+6wsLCAoaEhKlasiDZt2mDjxo3v/F5KTExEly5d8MUXX8DQ0BCWlpZYtmwZ\nvzeI5IBFKJGcZGdno02bNkhISEB2dvZHfW1+fj6eP38ONzc3REdH4/r167Czs0NaWhouXLiAzp07\nKyg10efhRCipg4SEBHTu3Bk+Pj7o0aMH0tPTMWzYMKW+ufTll1/KfSL0v/T19TFy5EhkZGTA3d0d\n3t7ecHJywunTpxX6uETKxolQEq1y5crIz89XyLXz8vJ4DoAGqFmzJmrUqIG5c+di6NChOHv2LFq0\naIGxY8fC09MTV65cweDBg9GzZ883vjYsLAz29vY4deoUunfvjlGjRqGgoABjx46Fj4+PgGdDpFlY\nhBLJyaBBg3D9+nXk5uZ+8jVycnLg6uoKOzs7DBs2DAcOHOALfVJZnAglVXfq1Ck4ODigT58+8PLy\nQnp6OoYOHQo9PT2lZ1FGEfqavr4+hg8fjvT0dHh4eKBXr15wdHREYmKiUh6fSNE4EUqimZubK+zv\nEolEgqioKK7HawAvLy/cvHkThw4dwl9//YVt27Zhzpw5WL9+Pa5fv47q1atj3759CA0NLf6arKws\nDBkyBKVKlUJcXBzWrVuHBQsWIDU1FXZ2dti7dy92794t8FkRqT8WoURycPjwYYSHh39WCfpafn4+\natasiaFDh7JoIpXHiVBSRSdPnkSnTp3Qr18/9OzZE2lpaRgyZIiQAvQ1Ra7Gv4u+vj6GDRuG9PR0\n9OjRAz4+PujcuTMSEhKUmoNI3jgRSqJJJBK0aNFCIde2sLDArl270KBBAzRq1AijRo1CaGgoHj9+\nrJDHI8Xx8vJCUlISHB0d3/iYqakphg8fDplM9q9Dlfbs2YOHDx/Cx8cH1tbWxX+up6eHwMBAyGQy\nrF69WhnxiTQWi1CizySTyeDn54ecnBy5XTMtLe1f9wslUkUs6knVxMfHo2PHjujfvz98fHyQlpaG\nwYMHCy1AX1PmROh/6enpYejQoUhPT4eXlxf69OkDBwcHnDp1Skgeos/FIpRUwffffy/3v1+MjIyw\nbNkyHDhwAA8fPsSWLVtQvXp1hISE4P+xd99xNfb/H8BfV3vJrJtC47QncttESCpb9t57y7qp7CSZ\nqWQkVDbJ6q5kZKSiqRTlRqQkmhrn98f3Vw/u26xzus54Px+P+487+ZyXVee8zvt9XZqamrCwsMDy\n5ctx5coVfPr0iaePTXhPS0sLrVu3RmRk5Dd/XFpaGgAgJSVV87GIiAgwDPPN8rRHjx5QUFBAVFQU\nysvL+ROaEDFARSghdXTr1i3k5eXx9MyioiK4ubnx9ExC+IEmQokgiIyMhJWVFSZNmoSxY8ciLS0N\nU6dOrXmBIQiqi1A2/83IyMhg+vTpSEtLw8iRIzF+/Hj07t0bt27dYi0TIbVBq/GETfn5+Vi3bh1m\nz57N8+8zqqqq6NWrFwBAUlIS7du3h6OjI65evYrc3Fzs2rULysrK2LZtG1q0aIGuXbti7dq1iIiI\n4MlmGuG97909vrKyEn5+fmAYBjY2NjUfT01NBQDo6en95+dISkpCS0sLFRUVePbsGf9CEyLiqAgl\npI4CAgJ+++ZIvyI8PJxvF2EnhBdoIpSw7caNG+jVqxemTJmC8ePHIzU1FVOmTBGoArRagwYNAEAg\nJnhkZGQwbdo0pKWlYcyYMZg4cSKsrKxw8+ZNtqMR8ktoIpSw4f3791i7di10dXXx+vVrREdH4+rV\nq5CXl+fJ+fLy8ggICPju8ysZGRl069atpvh89+4d1q9fj6qqKqxevRoqKiro3bs3Nm3ahLt379LE\noIBwcHDAuXPnUFFR8dXHV6xYgaSkJNjZ2aFv3741Hy8oKAAANGzY8JvnVX/8w4cPfEpMiOijIpSQ\nOrp9+zZfJnzk5OSQmJjI83MJ4SWaCCVsuHHjBnr27Ilp06Zh4sSJePLkCSZPniyQBWg1hmFYuU7o\nj0hLS2Pq1KlITU3FuHHjMHnyZPTq1eu7K3yECAqaCCX16csCNDs7Gw8ePICvry+0tbXRrVs3zJkz\nBwoKCnV6DAUFBSxevBgdOnT45Z8jLy//VfH56tUrLFmyBO/fv8ecOXPQrFkz2NnZwd3dHXFxcaiq\nqqpTRlI72traaNWq1VdvNu7evRs7duyAkZERjh49ymI6QsQTFaGE1BG/1hK4XC6Sk5P5cjYhvEAT\noaQ+cblcREREwNLSEtOnT8fkyZPx5MkTTJo0SaAL0C+xeZ3QH5GWlsaUKVPw5MkTTJw4EVOnTkXP\nnj2/unkDIYKEJkJJfcjLy8Nff/0FXV1dvHnzBg8fPqwpQL/k5uaGUaNGQVFRsVaPo6CggAkTJmDj\nxo11yqusrPxV8ZmRkYEpU6bg2bNnGDNmDFRUVDBs2DDs27cPKSkp9GZ2PfpyPX7v3r1YtGgRTExM\nEB4ejkaNGn31udUTn9WTof9W/fF//zxCyK+jIpSQOuLX2klVVRVd64cIPHoSTfiNy+UiPDwclpaW\nmDlzJqZOnYqUlBRMnDjxq5sLCANBLUKrSUtLY9KkSTUTttOmTYOlpSUiIiLo3zoRGOXl5SgqKvru\n2ighdZWXl4c1a9ZAT08POTk5iImJwYEDB6ClpfXNz2cYBr6+vnB1dYWCggIkJSV/6XEkJSWhqKiI\nHTt2wNPTk+dvMDdr1uyr4jMhIQFDhw5FbGws+vfvDzU1NYwdOxYHDx7E8+fPefrY5GsODg44e/Ys\n3N3dsWDBApiZmSE8PByqqqr/+Vx9fX0A/7t57r9VVlbi+fPnkJKS+k8hTwj5dVSEElJHsrKyfDlX\nUlKSZ9ccIoQfaCKU8BOXy0VYWBh69OiBWbNmYfr06UhOTsaECROErgCtJmir8d8jJSVVc8mBqVOn\nYsaMGbC0tER4eDgVooR179+/R+PGjSEhQS9jCG99WYDm5uYiJiYGPj4+0NTU/OnPZRgGc+fORWJi\nIkaMGAE5OTkoKSn957kSwzBo0KAB5OTkMHr0aCQlJWHmzJn18pzqy+IzMzMTUVFRsLKyQnh4OLp0\n6QItLS1MnToVx48fF+g37YSRtrY2pKSksHz5crRr1w4RERHfnWq3srICl8vF1atX//NjkZGRKC4u\nRteuXYVmG4YQQUTPIAipIw6Hw7ezjY2N+XY2IbxApQjhNS6Xi7///hvdu3fHnDlzMHPmTCQnJ2P8\n+PFCW4BWE/SJ0H+TkpLChAkTkJKSgunTp2PWrFno0aMHwsLC6N8+YQ2txRNey83NxerVq78qQL29\nvX+pAP03LS0tnDhxAq9fv4a3tzfmzp2Lzp07Q1paGhYWFpg3bx68vb2RnZ0Nf39/aGho8P4X9BtZ\nvyw+L1++jHbt2uHs2bMwNTWFoaEh5s6dizNnziAvL4+1nKJgw4YNyM7OhoqKCv7++280btz4u587\nfPhwNGvWDIGBgYiJian5eFlZGf766y8wDIPZs2fXR2xCRBbDpWeyhNTJvHnz4OnpyfMXhdLS0igq\nKqJ3+4jAio+Px7hx4xAfH892FCICqgtQZ2dn5OXlYe3atRg1atQvrxgKg5MnT+LkyZM4ffo021Fq\npaKiAoGBgdiwYQNUVFTg5OSEPn360HQ4qVc3b97EmjVrcOvWLbajECGXm5sLd3d3+Pj4wMHBAatW\nreJbMamlpYWwsDChWWeurKxEfHw8wsPDER4ejtu3b0NLSwtWVlawsrJCjx49oKyszHZMoeDn54fJ\nkydDSkqqZir03xPtmpqamDhxYs3/X7hwAQ4ODpCVlcWoUaPQpEkTXLx4EWlpaXBwcEBgYGB9/zII\nESnCPVpBiAAYO3Ysjhw5gqKiIp6eq6uri6KiIroQNhFo9F4aqSsul4vQ0FA4OzsjPz8fa9euxciR\nI0WqAK0mLKvx3yMlJYVx48Zh9OjRCAwMxPz589G0aVM4OTmhb9++VIiSekEToaSuvixAR4wYgbi4\nOLRu3ZqvjykpKYnKykq+PgYvSUpKom3btmjbti2WLl2K8vJyPHz4EOHh4fDw8MCoUaNgYmJSU4x2\n6dIFCgoKbMcWSJmZmWAYBpWVlSgvL//mTbEsLS2/KkIHDRqEyMhIbNq0CWfPnkVpaSl0dHTg4eGB\n+fPn12d8QkQSTYQSUkdcLhd6enpIT0/n2Zny8vLo0qULYmNjMXr0aMybNw+GhoY8O58QXkhISMCY\nMWOQkJDAdhQihLhcLq5duwYXFxcUFBRg7dq1GDFihEgWoNWeP3+Onj17Iisri+0oPFFZWYmgoCBs\n2LABjRs3hpOTE6ytrakQJXzl4+OD6OhoHDhwgO0oRMi8e/cO7u7uOHDgAEaMGIFVq1bxvQCtpq+v\njwsXLsDAwKBeHo/fSktLcffu3ZqJ0cePH6N9+/Y1xWiHDh0gIyPDdkyBs2XLFvzzzz/w9PRkOwoh\nYo2uEUpIHTEMA09PT569CyolJYVOnTohNDQUiYmJaNasGXr16gVra2tcunQJVVVVPHkcQniB3ksj\nv4vL5eLKlSvo3LkzlixZgoULFyIhIQGjR48W6RIUAFq0aIHs7GyR+TouKSmJMWPGIDExEfPnz8fi\nxYvRpUsXXL16lb42EL7Jy8tD06ZN2Y5BhMi7d++wYsUK6Ovr4+PHj4iLi8P+/fvrrQQFhG8i9Gfk\n5OTQq1cvbNiwAXfu3EF2djZWrFiBT58+YeHChWjWrBlsbGywbds2PHz4UKR+7XVRffd4+v0ghF1U\nhBLCA3379sXw4cN5cpd3eXl5HDt2DAzDQE1NDS4uLsjKysL48ePh4uICPT097Ny5EwUFBTxITkjt\n0dQX+R1cLheXL19Gp06dsGzZMixevBgJCQkidx3QH5GTk0ODBg1E7qYTkpKSGD16NBISErBo0SIs\nXboUnTt3xpUrV6gQJTxHq/HkV+Xk5MDR0REGBgYoLCzE48eP4enpWa8FaDVRK0L/rUGDBujfvz/c\n3NwQExODzMxMzJw5E//88w8mTpyIZs2aYfDgwdi9ezcSExPF9nuDjo4OWrRoQdc4JoRlVIQSwiPe\n3t4wNzevUxmqoKCAkJAQqKmpffVxWVlZjB8/Hg8ePMCxY8fw4MEDaGlpYe7cuXjy5EldoxNSa+L6\nRJb8Oi6Xi5CQEHTs2BGOjo5YunQpEhISRPY6oD8j7NcJ/RFJSUmMHDkSCQkJWLJkCZYtW4aOHTvi\n8uXL9LWC8AxNhJKf+bIALSoqwqNHj7Bv3z60atWKtUyiXoT+W5MmTTBkyBDs2bMHSUlJSElJqfn+\nMGjQIDRv3hyjRo2Cj48P0tPTxep7hIODA06dOsV2DELEGhWhhPCInJwcwsPD0bdv399ek5eVlUWT\nJk0QGhqK7t27f/fzGIZBp06dcOLECSQmJqJp06bo2bMn+vXrh5CQEJFZtyTCgSZCyY9wuVxcunQJ\nHTp0wMqVK+Ho6Ij4+HiMGDHiP3dLFSdqamp4/fo12zH4SkJCAiNGjEBCQgKWL18OR0dHdOjQASEh\nIWL1YpfwB02Eku/JycnB8uXLYWBggOLiYsTHx7NegFYTtyL035o3b47Ro0fjwIEDyMjIwP3799Gv\nXz/cunULlpaW0NDQwKRJk3D06FG8fPmS7bh85eDggDNnzoj13wdC2Ca+r0QI4QN5eXlcuHABR44c\nQePGjaGkpPTDz5eRkYGsrCyGDh2KjIwMdOnS5ZcfS01NDevXr0dWVhbGjh0LJycnWpsn9Y5KDfJv\nXC4XwcHB+PPPP7F69WqsXLkSjx8/xvDhw8W6AK0mDkVoNQkJCTg4OCA+Ph4rVqzAqlWr0KFDBwQH\nB9PXDlJrVISSf3v79i2WLVsGAwMDlJaWIj4+Hnv37kXLli3ZjlZD3IvQf9PU1MTkyZPh7++Ply9f\nIjQ0FB07dkRwcDDatGkDPT09zJo1CydPnkROTg7bcXlKV1cXzZs3x+3bt9mOQojYolckhPCBg4MD\n3rx5g4MHD6JHjx5o0KBBzbXhFBUVAQCtW7fGokWLkJaWhhMnTqBRo0a1eixZWVlMmDAB0dHR8Pf3\nx/3796GlpYV58+bR2jzhK5oIJV/icrm4ePEi2rdvj7/++gurV6/Go0ePMGzYMCpAvyDKq/HfIyEh\ngeHDh+PRo0dYuXIl/vrrL7Rv3x4XL16kQpT8NlqNJ9WqC1BDQ0OUlZUhPj4ee/bsEagCtBoVod/H\nMAz09fUxe/ZsnDp1Cjk5OTh16hT09fXh7+8PXV1dmJmZYdGiRbh48SI+fPjAduQ6o/V4QtjFcOkZ\nKCF8x+Vy8fbtWxQUFEBaWhqLFi3CuHHjMGLECL483uvXr+Hl5QUfHx+0adMGCxYsgI2NDZURhKeS\nk5MxbNgwpKSksB2FsKi6AHVxcUFVVRWcnJwwaNAg+nrzHfv378ejR4/g7e3NdhTWVFVV4fz583Bx\ncYGkpCScnJwwcOBAenOF/JLGjRsjIyMDTZo0YTsKYcmbN2/g5uaGw4cPY9y4cVixYgXU1dXZjvVD\n3bp1w5YtW354CSzybRUVFYiJiUF4eDjCw8Nx7949GBoawsrKClZWVujatWvNoImwSEtLg6WlJV6+\nfCmW10snhG30KoWQesAwDJo3bw59fX1oa2ujTZs2SEhI4NvjVa/NZ2ZmYsyYMVi7di309fWxa9cu\nWpsnPEOlhXjjcrk4f/48LCws4OzsjHXr1iE2NhZDhgyhEvQHxGk1/nskJCQwdOhQxMXFYe3atXB2\ndoaFhQXOnz9PE6LkhyoqKvDp06dab9EQ4fbmzRssWbIERkZGqKioQGJiInbv3i3wJShAE6F1ISUl\nhY4dO2LVqlUIDQ1Fbm4u3NzcICsriw0bNuCPP/5Ajx494OzsjJs3b6KsrIztyD+lp6cHVVVV3Llz\nh+0ohIgleqVCCAtMTU35WoRWk5OTw4QJE/Dw4UP4+fnh7t270NLSwvz585Gamsr3xyeij0oL8VNV\nVYVz586hXbt2cHFxgZOTE2JjYzF48GAqQH+BOK7Gf4+EhASGDBmC2NhYODk5wcXFBW3btsW5c+fo\n5n/km96/f4/GjRvT1xox82UBWllZicTEROzatQtqampsR/tlVITyjqysLCwtLeHi4oJbt27h7du3\nWLNmDUpKSrB06VI0a9YM1tbW2Lp1Kx48eICKigq2I3/Tl+vxhYWFePbsGTIyMvDx40eWkxEi+uhZ\nBCEsqK8itBrDMOjSpQsCAwORkJCARo0aoUePHrCxscHly5fpBSepFZoIFS9VVVU4e/Ys2rVrhw0b\nNsDFxQWxsbEYNGgQ/V34DTQR+l8Mw2DQoEGIjY3F+vXrsWHDBrRt2xZnz56l70/kK3SjJPGSnZ2N\nxYsXw8jICFVVVUJZgFajIpR/FBUV0a9fP7i6uiI6OhovXrzA3LlzkZ2djalTp6JZs2YYOHAgdu7c\nifj4eIH5vmJsbAxfX1+0bNkSTZo0gbm5Odq0aYNmzZqhRYsWGDlyJKKiomjogBA+oGuEEsKCiooK\nKCsrIycn56d3lueX0tJSBAUFYffu3fj48SPmz5+PSZMmQVlZmZU8RPg8efIEgwYNouliEVc9Abp+\n/XpISUnB2dkZ9vb2VH7WUkVFBeTl5VFcXAxpaWm24wgkLpeLS5cuwdnZGeXl5XBycqJLLhAAwK1b\nt7Bq1Sq627KIy87OhqurK44ePYqJEyfC0dERLVq0YDtWndjY2GDhwoXo378/21HEztu3b3Hjxg2E\nh4cjIiIC+fn56NWrV801RnV1dev1Oc3Tp08xduxYJCUlobi4+LufxzAMFBQU0Lp1awQEBMDc3Lze\nMhIi6ugZJSEskJKSgoGBAZKTk1nLICcnh4kTJ9aszUdFRUFTU5PW5skvoyJMtFVVVeH06dNo06YN\ntmzZgk2bNuHhw4cYMGAA/dnXgZSUFFRUVPD27Vu2owgshmEwYMAAPHz4EJs3b8aWLVtgbm6O06dP\nC8wkD2EHTYSKtuzsbCxatAjGxsaQkJBAUlISPDw8hL4EBWgilE1//PEHRo4cCW9vb6SlpSE2Nhb2\n9va4d+8eevfujVatWmHChAk4cuQIXrx4wdcsPj4+MDc3R0xMzA9LUOB/bwoWFRXhyZMn6Ny5M7Zs\n2ULToYTwCBWhhLCkvtfjv+fLtfn4+Hg0bNgQPXr0QP/+/XHlyhV60Ul+iJ6QiZ6qqiqcOnUK5ubm\ncHV1xZYtWxAdHU1ToDxE1wn9NQzDwN7eHtHR0di6dSu2bdsGc3NznDx5kr43iam8vDw0bdqU7RiE\nx16/fo2FCxfWFKDJycnYsWOHSBSg1SQkJOjrloD4d/F548YNdOvWDVevXkX79u2ho6ODGTNmIDAw\nkKdvWu7YsQOLFy9GSUnJb/1d4HK5KCkpwcaNG7FixQqe5SFEnFERSghLBKUI/VLLli2xceNGZGVl\nYdSoUVizZg0MDAxq1ucJ+RKVYqKlqqoKJ0+ehJmZGdzc3ODq6ooHDx7Azs6O/qx5jK4T+nsYhoGd\nnR3u378PV1dXuLu7w8zMDEFBQTRhJWZoIlS0vHr1CgsWLICJiQmkpKRqCtDmzZuzHY3naCJUMDEM\n85/i8/z58zAxMUFgYCAMDAxgYmKCBQsW4Pz588jPz6/V44SGhmLt2rU/nQL9keLiYuzbtw/Hjx+v\n9RmEkP+hIpQQlghiEVqtem0+JiYGhw8fxp07d6CpqYkFCxYgLS2N7XhEgNBEqPCrrKxEUFAQTE1N\n4e7uDjc3N9y/fx+2trZUgPIJFaG1wzAMbG1tce/ePbi5ucHDwwNmZmYIDAykgkFMUBEqGl69eoX5\n8+fD1NQUMjIySElJgbu7u0gWoNWoCBUODMN8VXzm5ubiyJEjaNmyJby8vKChoYH27dvD0dERV69e\nRWFh4U/P/PjxI8aMGVOnErRacXExZs+ejTdv3tT5LELEGRWhhLBEkIvQagzDoGvXrggKCkJ8fDwa\nNGiA7t27w9bWltbmCZVkQq6yshKBgYEwNTWFh4cH3N3dce/ePfTv35/+bPmMVuPrhmEY9O/fH3fv\n3sWOHTuwa9cumJqaIiAggIoGEUer8cLtywJUVlYWKSkp2L59O/744w+2o/EdFaHCSVJS8qviMzc3\nFzt37oSSkhK2bt2K5s2bo1u3bli3bh1u3LiB0tLS/5yxc+fOXypMf1VpaSmcnZ15dh4h4oiKUEJY\n0qJFC1RUVAjNDTNatmyJTZs2ISsrCyNGjKhZm9+zZw+tzYsxmggVPpWVlQgICICJiQl27doFDw8P\n3L17FzY2NlSA1hOaCOUNhmHQr18/REVFYefOndizZw9MTExw4sQJKhxEFE2ECqeXL19i3rx5MDU1\nhZycnFgVoNWoCBUNMjIyXxWfOTk5cHZ2RkVFBVauXAkVFRX06dMHmzdvxr1791BaWordu3d/syCt\nrfLycvj7+6OoqIhnZxIibqgIJYQlDMMIxVTov8nJyWHSpEmIiYnBoUOHcPv2bWhqamLhwoV4+vQp\n2/FIPaLSTLhUVlbixIkTMDExwZ49e7Br1y5ERUWhX79+9GdZz6gI5S2GYWBtbY07d+5g9+7d2Ldv\nH4yNjXH8+HEqHkQMTYQKl3/++Qdz586FmZkZFBQU8OTJE7i5uYlVAVqNilDRpKCg8FXx+fLlSyxa\ntAi5ubmYNWsWmjZtioKCAp4/rpSUFEJDQ3l+LiHigopQQlgkjEVoNYZh0K1bt5q1eSUlJXTt2hW2\ntra4evUqrc2LCZoIFXyVlZU4fvw4jI2NsW/fPuzevRt37tyBtbU1FaAsUVdXpyKUDxiGQd++fXH7\n9m3s3bsX+/fvh5GREY4dO4aKigq24xEeoIlQ4VBdgLZp0waKiop48uQJtm3bBlVVVbajsYaKUPHQ\nsGFD2NvbY8eOHXj06BHWrFnDl+dahYWFuH//Ps/PJURcUBFKCIuEuQj90pdr8w4ODli1ahUMDQ2x\nd+9efPr0ie14hE+oRBNsFRUVOHbsGIyMjLB//37s3bsXt2/fRt++fenPjmVqamp0jVA+YhgGffr0\nwa1bt+Dp6Qlvb28YGRnB39+fClEhR0WoYPvnn38wZ84cmJubQ0lJiQrQL1ARKp5SU1NRXl7O83Or\nqqoQHR3N83MJERdUhBLCIlEpQqvJy8tj8uTJiI2Nha+vL27evElr8yKOJkIFT0VFBfz9/WFkZARv\nb294enri1q1b6NOnDxWgAqJJkyYoKSnhyR1kyfcxDIPevXvj5s2b8PLywoEDB2BoaAg/Pz8qRIVQ\nRUUFPn78iEaNGrEdhfzLixcvMHv2bJibm0NZWRmpqalwdXWFiooK29EEBhWh4omXN0n6N7pGKCG1\nR0UoISwyMTFBcnKyyD0xYhgG3bt3x8mTJ/Ho0SMoKiqia9eusLOzw7Vr12htXkRQqSZYKioqcPTo\nURgaGuLAgQPw8vLCzZs30bt3b/qzEjAMw6BFixbIzs5mO4pYYBgGVlZWiIyMhI+PDw4dOgQDAwMc\nOXKEClEhkp+fj0aNGkFSUpLtKOT/VRegbdu2RcOGDZGamoqtW7dSAfoNVISKJ0VFRb6dLS8vz7ez\nCRF1VIQSwiJlZWWoqKjg2bNnbEfhm1atWmHz5s3IysrC8OHDsWLFClqbJ4SHKioq4OfnB0NDQxw8\neBA+Pj6IjIyElZUVFaACTF1dndbj6xnDMOjVqxciIyPh6+sLPz8/GBgY4PDhw3xZXSS8RTdKEhxZ\nWVmYNWsW2rZti0aNGlEB+guoCBVPbdu2haysLM/PlZCQgIWFBc/PJURcUBFKCMtEbT3+e6rX5uPi\n4uDr64vIyEhoampi0aJFSE9PZzseqSVajWdPRUUFjhw5UlPkHDhwAJGRkejVqxcVoEKA7hzPrp49\neyIiIgIHDx6Ev78/DAwMcOjQISpEBRhdH5R9WVlZmDlzJtq1a4cmTZogNTUVW7ZsoT+XX0BFqHhq\n3749X4pQRUVFdOzYkefnEiIuqAglhGXiUoRWq16bP0WlLQAAIABJREFUP3XqFB49egQFBQV06dIF\n9vb2tDYvZKhsY0d5eTkOHz4MfX19+Pn5wdfXFzdu3EDPnj3ZjkZ+AxWhgsHS0hLh4eE4fPgwjh8/\nDn19fRw8eJAKUQFERSh7MjMzMWPGDLRr1w5NmzZFamoqNm/eTH8ev4GKUPHUqVMnSEjwvnKpqKiA\ntbU1z88lRFxQEUoIy0xNTZGYmMh2DFZ8uTY/dOhQrFixAkZGRti3bx+tzQsJmgitP+Xl5Th06BD0\n9fVx7NgxHD58GBEREVSACilajRcsPXr0QFhYGPz8/BAQEAA9PT34+vri8+fPbEcj/49W4+tfdQFq\nYWEBFRUVpKWlUQFaS1SEiidpaWnMnj2bp1OhUlJSGDFiBJSVlXl2JiHihopQQlgmbhOh3yIvL48p\nU6YgLi4OPj4+iIiIgKamJhYvXkxr8wKMJkLrR3l5OQ4ePAh9fX2cOHECfn5+CAsLQ48ePdiORuqA\nJkIFU/fu3fH333/D398fQUFB0NPTw4EDB6gQFQA0EVp/nj9/junTp8PCwgKqqqpIS0vDpk2bqIiu\nAypCxdeyZct4emMjWVlZrF+/nmfnESKOqAglhGX6+vp48eIFSkpK2I7COoZh0KNHD5w+fRpxcXGQ\nk5ND586dYW9vj+vXr9P0oQCiPxP++fz5M3x9faGnp4fAwEAcPXoUf//9N7p37852NMIDVIQKtm7d\nuiE0NBTHjx/HqVOnoKenB29vbypEWUQTofz3/PlzTJs2De3bt0fz5s3x9OlTbNy4kX7feYCKUPHV\npEkTHDlyBAoKCnU+S1FREe7u7mjdujUPkhEivqgIJYRl0tLS0NHRQUpKCttRBErr1q2xZcsWvHjx\nAkOGDMHy5cthZGQET09PFBYWsh2PgCZC+eXz58/w8fGBnp4eTp48iWPHjiE0NBTdunVjOxrhIXV1\ndSpChUDXrl1x/fp1nDhxAmfPnoWuri68vLxQVlbGdjSxQxOh/PPs2bOaArRFixZ4+vQpNmzYgCZN\nmrAdTWRQESreBg0ahGnTptXpDAUFBYwdOxYzZszgUSpCxBcVoYQIAFqP/z55eXlMnToVjx49gre3\nN8LDw6GhoYHFixcjIyOD7XhijyZCeefz58/w9vaGnp4ezpw5gxMnTuD69evo2rUr29EIH7Ro0QKv\nXr2if0NCokuXLrh27RoCAwNx4cIF6OrqYv/+/VSI1iMqQnnv2bNnmDp1Kv7880+oqalRAcpHVISK\ntzdv3iAkJASDBg2CvLz8bw8TyMvLY+7cufDy8qJBBEJ4gIpQQgSAiYkJFaE/8eXafGxsLGRlZdGp\nUycMGDAAoaGhVCawgJ6I8UZZWRm8vLygq6uLc+fOISAgANeuXUOXLl3Yjkb4qEGDBpCSkkJBQQHb\nUchv6Ny5M65cuYJTp04hODgYurq68PT0pEK0HtBqPO9kZGRgypQp6NChA1q2bIn09HSsX7+eClA+\noiJUfH348AE2NjaYMGECzp8/j7t370JPTw9KSko/fS6tpKSEVq1aITQ0FNu2baPn3oTwCBWhhAgA\nmgj9PRoaGti6dSuysrIwaNAgLFu2DMbGxrQ2zwIqoGuvrKwM+/fvh66uLi5cuICgoCBcvXoVnTt3\nZjsaqSd0nVDh1bFjR1y+fBmnT59GSEgIdHR0sG/fPpSWlrIdTWTRRGjdVRegHTt2RKtWrfD06VO4\nuLigcePGbEcTeVSEiqfi4mLY29vD0tISa9euBQCYm5sjJSUFwcHBsLOzQ8OGDSEnJwdlZWUoKytD\nXl4eDRo0QJ8+fRAUFITMzEzaDiKEx6gIJUQAUBFaOwoKCpg2bRoePXqE/fv3IywsDBoaGliyZAmt\nzdcDele6dsrKyuDp6QldXV0EBwfj1KlTuHLlCjp16sR2NFLP1NXV8erVK7ZjkDro0KEDQkJCcObM\nGVy5cgU6OjrYu3cvFaJ8QBOhtZeeno7JkyejY8eOaN26NRWgLKAiVPyUl5fDwcEBWlpa8PDw+Op5\nM8Mw6NmzJ4KDg/Hhwwc8e/YMN27cQHh4OFJTU1FQUIDQ0FDY2tpCQoIqG0J4jf5VESIAWrdujaKi\nIuTl5bEdRSgxDANLS0ucOXMGsbGxkJGRobX5ekK/t7+utLQU+/btg46ODkJCQnD69GlcvnwZHTt2\nZDsaYQlNhIqODh064NKlSzh37hyuXbsGDoeDPXv2UCHKI5WVlfjw4QMVd78pPT0dkyZNQqdOnaCp\nqYn09HQ4OzvT7yMLqAgVL1VVVZg0aRIkJSVx6NChn5aZLVq0QNu2bWFhYYFWrVrRsAEhfEZFKCEC\ngGEYuk4oj/x7bX7p0qUwNjbG/v37aW2ex+hJ2q8pLS3F3r17oaOjgytXruDMmTMICQlBhw4d2I5G\nWEZFqOj5888/ERwcjIsXLyI0NBQcDge7d+9GSUkJ29GEWn5+Pho2bAgpKSm2owiFLwtQLS0tpKen\nw8nJCY0aNWI7mtiiIlR8cLlcLFiwAC9fvkRQUBCkpaXZjkQI+RcqQgkRELQez1vVa/OPHz+Gp6cn\nQkNDoaGhgaVLl+LZs2dsxxMZNBH6faWlpdizZw90dHRw7do1nDt3DpcuXaIClNSg1XjRZWFhgYsX\nL+LixYsICwsDh8PBzp07qRCtJVqL/zVPnz7FxIkT0alTJ2hra1MBKkCoCBUfzs7OiIqKwsWLFyEv\nL892HELIN1ARSoiAoCKUP6qvwXP27FnExsZCSkoKHTt2xMCBA/H3339Tkfeb3r9/D19fXwwdOhTd\nu3dHdnY2GjVqhO7du+PQoUP0+wmgpKQEu3fvBofDQWhoKM6fP4/g4GD8+eefbEcjAoYmQkWfhYUF\nLly4gJCQEERGRoLD4cDDwwPFxcVsRxMqdKOkH0tLS8OECRPQpUsX6OjoICMjA+vWraMCVIBQESoe\ndu/ejYCAAFy9ehUNGzZkOw4h5DuoCCVEQFARyn8aGhpwdXVFVlYWBgwYgMWLF8PExAReXl4oKipi\nO55QOHXqFGbMmIEHDx6gXbt2UFBQwPDhw5GUlIRp06Zh5MiRbEdkTUlJCXbt2gUdHR2EhYXVTIO1\nb9+e7WhEQFERKj7atm2Lc+fO4fLly7h16xY4HA527NhBhegvoonQb0tNTcX48ePRtWtX6OrqIj09\nHWvXrqUCRgBRESr6/P39sX37doSGhkJVVZXtOISQH6AilBABYWpqiqSkJFRVVbEdReQpKChg+vTp\niI+Px759+3D9+nW0bt2a1uZ/gb6+PoKDg/Hy5Uvs2bMHysrK8PX1xZMnT9CqVSucOXMG586dYztm\nvSopKcHOnTvB4XAQERGB4OBgXLhwARYWFmxHIwJOXV2dilAx06ZNG5w9exZXr17FnTt3wOFw4O7u\nTm/G/QRNhH6tugDt1q0b9PX1qQAVAlSEirbg4GAsX74cV69ehYaGBttxCCE/QUUoIQKicePGUFZW\nxosXL9iOIja+XJuPiYmBpKQkOnbsiEGDBiEsLIzWvL+hZ8+esLOzq/n/6t8jVVVVzJo1C1wuFzdu\n3GApXf0qLi6Gh4cHOBwOIiMjERISgvPnz6Ndu3ZsRyNConnz5njz5g29ASaGzM3NcebMGVy7dg13\n794Fh8PB9u3bxb4Q5XK5CAoKgpWVFVq2bAkFBYWa66t+/vyZ7XisS01Nxbhx49CtWzcYGBggIyMD\nf/31FxWgQoCKUNF18+ZNTJ06FRcvXoSRkRHbcQghv4CKUEIECK3Hs0dTUxPbtm1DVlYW7O3tsWjR\nIlqb/4l/3zW++q6Yon5X3+LiYuzYsQMcDge3bt3C5cuXce7cObRt25btaETIyMrKomHDhnj37h3b\nUQhLzMzMcPr0aVy/fh33798Hh8OBm5ub2H7fmT59OkaPHo3ExETY2tpi0aJFsLCwQHJyMgICAnDi\nxAm2I7LiyZMnGDt2LLp37w4jIyNkZGRgzZo1UFZWZjsa+UVUhIqmuLg4DB8+HAEBAXQzTEKECBWh\nhAgQKkLZ9+Xa/N69e3Ht2jVoaGhg2bJleP78OdvxBE71RGhlZSX8/PzAMAxsbGxYTsUfRUVFcHd3\nB4fDwZ07d3D16lWcPXsWbdq0YTsaEWJ0nVAC/K8QPXXqFEJDQxEdHQ1tbW1s27YNhYWFbEerNy9e\nvMChQ4fQvHlzpKSkwMfHB5s3b8bJkydhbW0NAFi3bh3LKetXdQHao0cPGBsbIz09HatXr6YCVAhR\nESp60tLSYGdnBy8vL/Tu3ZvtOISQ30BFKCEChIpQwcEwDHr16oVz584hOjoaDMPgzz//xODBg2lt\n/v99ORG6YsUKJCUlwc7ODn379mUxFe8VFRVh+/bt4HA4uHv3Lq5du4YzZ87A3Nyc7WhEBKirq+PV\nq1dsxyACwtTUFCdPnkRYWBhiYmLA4XDg6uoqFoVo9WR0x44d/3NjJBkZGcjLy4vN9HRKSgrGjBmD\nHj16wMTEBBkZGVSACjkqQkXLy5cvYW1tjQ0bNmDo0KFsxyGE/CYqQgkRIFSECiYtLS24ubkhKysL\ntra2WLhwIUxNTeHt7S2264vVuFwudu/ejR07dsDIyAhHjx5lOxLPFBUVwc3NDRwOB/fv30doaChO\nnz4NMzMztqMREUIToeRbTExMEBQUhPDwcMTFxUFbWxtbtmzBp0+f2I7GN8bGxmjevDkePHiAvLy8\nr34sIyMDJSUlIvdG278lJydj9OjRsLS0hJmZGTIyMrBq1So0aNCA7WikjqgIFR25ubmwtrbG3Llz\nMXXqVLbjEEJqgYpQQgRI9YXv6YYAgklRUREzZsxAQkICdu/eXXNnSHFdm2cYBsXFxTXXUw0PD0ej\nRo3YjlVnhYWF2LZtG7S1tREdHY3Q0FCcOnUKpqambEcjIoiKUPIjxsbGCAwMxI0bN5CQkAAOh4PN\nmzfj48ePbEfjOTk5OVy4cAGKioowMjLCzJkzsXr1aowYMQJJSUno2rUrvLy82I7JF9UFaM+ePWFu\nbo6MjAysXLmSClARQkWoaPj06RNsbW0xcOBALF++nO04hJBaoiKUEAEiJycHTU1NPHnyhO0o5AcY\nhoGVldU31+bDw8PFZm3ex8cHhYWFMDMzQ3h4OFRVVdmOVCeFhYVwdXUFh8NBTEwMwsLCcPLkSSpA\nCV/Rajz5FUZGRjhx4gQiIyORlJQEHR0dbNq0SeQKUTMzM0yePBmlpaXw9fWFq6srzpw5AwkJCYwb\nNw7NmjVjOyJPJSUlYdSoUejVqxfatGlDBagIoyJU+JWWlmLw4MFo06YNtmzZwnYcQkgdUBFKiICh\n9Xjh8uXafP/+/TF//nyYmprCx8dHpNfmXV1d4eTkBCkpKURERAj1i9NPnz5h69at0NbWRlxcHMLD\nwxEUFAQTExO2oxExQBOh5HcYGhri+PHjuHnzJlJSUsDhcLBx40YUFBSwHa3OKisrYWVlhTVr1mDG\njBnIyMhAUVERoqOjUVlZidmzZ2PlypVsx+SJpKQkjBw5ElZWVmjXrh0yMjKwYsUKKkBFGBWhwq2i\nogJjxoxB06ZNsX///q+uk08IET5UhBIiYKgIFU6KioqYOXMmEhMTsWvXLly+fBkaGhpYvnw5MjMz\n2Y7HUxs2bMCqVavQpk0bKCsro3HjxmxHqpVPnz5hy5Yt4HA4ePz4MW7cuIHAwEAYGxuzHY2IESpC\nSW0YGBjg2LFjuH37NlJTU6Gjo4MNGzYIdSHq7++Pu3fvYtiwYXBzc4OmpmbNpkzDhg2hrq4Od3d3\nof6empiYWFOAWlhYICMjA46OjlBSUmI7GuEzCQkJVFVVsR2D1AKXy8XMmTNRWFgIf39/SEpKsh2J\nEFJHVIQSImCoCBVuDMOgd+/eOH/+PB48eAAul4v27dtjyJAhiIiIEPq1eT8/v5pJ0A4dOqC0tBQu\nLi5f/efn58d2zB/6+PEjNm/eDA6Hg8TERERGRiIgIABGRkZsRyNiiIpQUhf6+vrw9/fHnTt38PTp\nU3A4HKxfvx4fPnxgO9pvi4mJAcMw6Nmz51cfz83NhYqKCjp06ICqqirExcWxE7AOEhMTMWLECPTu\n3Rvt27enAlQM0USocOJyuXB0dERycjLOnj0LWVlZtiMRQniAilBCBAwVoaJDW1sb27dvR1ZWFmxs\nbDBv3jyYmZnBx8cHxcXFbMerlczMTDAMg8rKShw4cADFxcVYv379V/8JahH68eNHbNq0CRwOB8nJ\nybh58yaOHz8OQ0NDtqMRMaaqqor8/Hy6SR6pEz09PRw9ehR3795FRkYGdHR04OLiIlSFqIyMDLhc\nLt69e/fVx3Nzc9GsWbOaj8vIyLARr1YSEhLg4OCAPn36oEOHDnj27BmWL19OBagYoiJUOLm6uuLK\nlSsICQmhf7eEiBAqQgkRMFpaWnj//r1QvXghP/bl2vzOnTsREhKC1q1bw9HRUehW/JycnFBZWYnK\nykq8ffsWTZs2rfn/6v/Cw8PZjvmVgoICbNy4ERwOB0+ePMHt27dx7NgxGBgYsB2NEEhKSkJVVRVv\n3rxhOwoRAbq6uvDz88Pdu3fx/Plz6OjowMnJCfn5+WxH+6nevXsD+N+N+L6cks7Ly0NlZSXu3LkD\nOTk5dOnSha2Ivyw+Ph7Dhw9H37590bFjR2RkZGDZsmVQVFRkOxphCRWhwsfHxwc+Pj64fv06mjRp\nwnYcQggPURFKiICRkJCAsbExEhMT2Y5CeKx6bf7ChQt48OABqqqq0L59ewwdOlRo1+YFOXNBQQE2\nbNgAHR0dpKWl4c6dO/D394e+vj7b0Qj5Cq3HE17T1dXFkSNHcO/ePfzzzz/Q1dXFunXrBLoQtbW1\nxZAhQ/D27VsYGhpi0qRJWLlyJdatW4eHDx8C+N90liBfl7q6ALW2tkbnzp2pACU1qAgVLqdOnYKL\niwuuX78ONTU1tuMQQniMilBCBBCtx4u+6rX5zMxMWFtbY+7cuTAzM6tZNxcGgnrHzA8fPmD9+vXQ\n0dFBeno67ty5g6NHj0JPT4/taIR8k7q6Ol69esV2DCKCdHR0cOjQIdy/fx+vXr2Cjo4O1q5di/fv\n37Md7ZtOnz4NT09PmJqa4vz589ixYwfS09Ohra2N69evY968eWxH/KbHjx9j2LBh6NevH7p06YJn\nz55h6dKlVICSGlSECo/qrzWXL1+Gjo4O23EIIXxARSghAsjU1JQmQsWEkpISZs2ahaSkJHh4eCA4\nOBgaGhpYsWIFsrKy2I73U4I0Efrhwwe4uLhAR0cHz549Q1RUFPz8/KgAJQKPJkIJv3E4HBw8eBDR\n0dHIzs6Grq4u/vrrL4ErRBmGwcyZM3H79m18+PABnz9/xty5czF16tSa1XlB8ujRIwwdOhQ2Njbo\n2rUrMjIysGTJEigoKLAdjQgYKkKFw7179zB27FicOXMG5ubmbMchhPAJFaGECCCaCBU/DMOgT58+\nuHjxIu7fv4+Kigq0a9cOQ4cOxY0bNwSqcKwmKBOhHz58gLOzM3R0dJCZmYl79+7hyJEj0NXVZTsa\nIb+EilBSX7S1teHr64uHDx/i7du30NXVxZo1a5CXl8d2tO+qvlmSIKkuQPv374/u3btTAUp+iopQ\nwZeYmIhBgwbBz88P3bp1YzsOIYSPqAglRABVF6GCWH4R/tPW1oa7uzuysrLQt29fzJkzB+bm5gK5\nNs/m39H8/Hw4OTlBR0cHL168wP3793H48GFaYyJCh1bjSX3T0tLCgQMHEBMTg3fv3kFPTw+rV69G\nbm4u29H+Iy8vD02bNmU7BgAgLi4OQ4YMga2tLXr06IGMjAwsXryYClDyU1SECrbnz5/DxsYGHh4e\nsLW1ZTsOIYTPqAglRACpqKhAVlaWXhiLOSUlJcyePRtJSUnYsWOHwK3NszUR+v79e6xbtw66urp4\n+fIl7t+/j0OHDoHD4bCSh5C6oolQwhZNTU34+PggJiYGeXl50NfXx6pVqwSqEBWEidC4uDgMHjwY\ndnZ26NmzJzIyMrBo0SIqQMkvoyJUcL158wZ9+/bFqlWrMGbMGLbjEELqARWhhAgoWo8n1b5cm793\n7x7Ky8vRrl07DBs2DJGRkaxOZdbnY79//x5r166Frq4uXr9+jQcPHuDgwYNUgBKhR0UoYZumpia8\nvb0RGxuL/Px86OnpYeXKlXj37h3b0VidCP2yAO3VqxcyMjKwcOFCyMvLs5KHCC8qQgXThw8fYGNj\ngwkTJmDu3LlsxyGE1BMqQgkRUCYmJlSEkv/gcDjYsWMHsrKy0Lt3b8yaNQtt2rSBr69vva/N19dE\naF5eHtasWQNdXV28efMGDx8+hK+vL7S1tevl8QnhNypCiaDQ0NCAl5cXHj16hI8fP0JfXx+Ojo7I\nyclhLRMbE6GxsbEYNGgQ7O3tYWVlRQUoqTMqQgVPcXEx7O3tYWlpibVr17IdhxBSj6gIJURA0UQo\n+RElJSXMmTMHycnJ2L59Oy5cuAANDQ2sXLkSL168qLcc/JwIzc3NxerVq6Gnp4d3794hJiYGBw4c\ngJaWFt8ekxA2NG7cGGVlZSgqKmI7CiEAgNatW8PT0xOPHz9GUVERDAwMsHz58novRKuqqpCfn48m\nTZrUy+PFxMRg4MCBGDBgAPr06YP09HQsWLCAClBSZ1SECpby8nI4ODhAS0sLHh4eAnMDUEJI/aAi\nlBABRUUo+RUMw6Bv374IDg7GvXv38PnzZ7Rt25Zva/OlpaW4fv06Nm/ejPHjx6OoqAgODg7Ytm0b\nIiIi8Pnz5zo/Rm5uLlatWgV9fX3k5eUhJiYGPj4+0NTUrPsvgBABxDAMTYUSgdSqVSvs27cP8fHx\nKCkpgYGBAZYtW4a3b9/Wy+MXFBRAUVER0tLSfH2chw8fYsCAARg4cCD69u2LjIwMzJ8/nwpQwjNU\nhAqOqqoqTJo0CZKSkjh06BAkJKgSIUTc0L96QgSUsbExUlNTUV5eznYUIiS+tzZ/8OBBlJSU1Ons\nt2/fYvHixVBRUYGDgwOcnZ1x6dIlVFZW4vTp01i7di0GDx4MVVVVrFq1Cvn5+b/9GO/evcPKlSuh\nr6+P/Px8xMbGwtvbmwpQIhaoCCWCrGXLlti7dy/i4+NRVlYGQ0NDLF26FG/evOHr4/J7Lb66AB08\neDD69etXU4DKycnx7TGJeKIiVDBwuVwsWLAAL1++RFBQEN/fZCGECCYqQgkRUAoKCmjZsiWePn3K\ndhQiZKrX5pOSkuDm5obz589DQ0MDq1atqtXafGBgIHR1deHp6YnCwkJ8/PjxPwX958+f8fHjRxQU\nFMDDwwMcDgeXLl36pfPfvXuHFStWwMDAAAUFBYiLi4OXlxc0NDR+OyshwkpdXR2vXr1iOwYhP9Sy\nZUvs2bMHCQkJKC8vh5GREZYsWcLTQpTL5eLly5eIiIhASEgIZGRk8OHDB56dDwDR0dGwt7fH4MGD\nYWNjg/T0dMybN48KUMI3VIQKBmdnZ0RFReHixYs08U2IGKMilBABRuvxpC4kJCRgbW2N4OBgREVF\nobS0FG3btsXw4cNx8+bNn67Nc7lcLFu2DFOnTsWnT59+ee29rKwM+fn5GDlyJLZs2fLdz8vJyYGj\noyMMDAzw6dMnxMXFYf/+/WjduvVv/ToJEQU0EUqEibq6Onbv3o3ExERUVlbCyMgIixcvRnZ2dq3P\njI2NxdixY9GwYUPo6upiyJAhWLNmDZ4+fQpVVVWoqalhzZo1ePnyZa0f48GDB7Czs8PQoUPRv39/\npKenY+7cuVSAEr6jIpR9u3btQkBAAK5evYqGDRuyHYcQwiIqQgkRYFSEEl7R0dGBh4cHMjMz0atX\nL8yYMQNt27bFoUOHvrs2v379euzfv7/Wd6MvLi7Gxo0bsXfv3q8+npOTg+XLl8PAwABFRUV49OgR\nPD09qQAlYo2KUCKM1NTUsGvXLiQmJoLL5cLY2BiLFi36rUI0Ozsb1tbW6N69O4KCgvDp0yeUlpai\noKAAxcXFqKioQHl5ObKzs+Hu7g5dXV0sX74cZWVlv/wY1QXosGHDYGdnRwUoqXdUhLLr6NGjcHd3\nR2hoKFRVVdmOQwhhGRWhhAgwKkIJrzVo0ABz585FcnIytm3bhrNnz9aszf/zzz81nxcdHQ1XV9da\nl6DViouL4ejoiCdPnuDt27dYtmwZDAwMUFJSgvj4eOzbtw+tWrWq6y+LEKFHq/FEmKmpqWHnzp1I\nSkoCwzAwNjbGggULfvp3OiwsDPr6+rhx4waKi4t/WhSVlZWhtLQUnp6eMDIy+ur71rfcv38ftra2\nGDZsGOzt7ZGeno45c+ZAVlb2t3+NhNQFFaHsuXjxIhwdHXHt2jW67BIhBAAVoYQINCpCCb9Ur81f\nunQJd+7cQUlJCdq0aQMHBwfcuHEDo0aNqvMNlqqVlZXB0tIShoaGKCsrQ3x8PPbu3YuWLVvy5HxC\nRAFNhBJR0KJFC3h4eCA5ORlSUlIwNTXF/Pnzv1mIhoWFYeDAgfj06dNv3xiyuLgYWVlZ+PPPP795\n9r1799C/f384ODhgwIABSE9Px+zZs6kAJayhIpQdkZGRmDZtGoKDg2FoaMh2HEKIgGC4P7tIHCGE\nNZWVlWjQoAFycnKgpKTEdhwi4j59+oSjR49iy5YtyM7ORlVVFc/OlpKSwoULF2Bra8uzMwkRJU+f\nPoWNjQ0yMjLYjkIIz7x9+xZubm44dOgQxowZg5UrV6Jly5Z4/fo19PX1UVhYWKfzpaSkYGJigocP\nH0JSUhL37t2Di4sLkpKSsHr1akyePJnKTyIQCgoK0KpVK3z8+JHtKGIjNjYWNjY2CAgIQO/evdmO\nQwgRIDQRSogAk5SUhKGhIZKSktiOQsRA9dq8iYkJT0tQAKiqqkJAQABPzyRElFRPhNL700SU/PHH\nH9i+fTtSUlIgLy8PMzMzzJ07Fw4ODigtLa3z+RUVFXj69CkWLVoEGxsbjBw5EoMHD8bTp08xa9Ys\nKkGJwKCJ0PqVlpYGOzs7eHt7UwlKCPkPKkLBJv3JAAAgAElEQVQJEXC0Hk/qE5fLRVRUFM/Praqq\nQkREBM/PJURUKCoqQlZWFvn5+WxHIYTn/vjjD7i5ueHJkyf4+PEjoqKiUFFRwZOzi4qKsG/fPtjb\n2yMtLQ0zZ86kApQIHCpC688///wDa2trbNq0CUOGDGE7DiFEAFERSoiAoyKU1KfXr1//9rXaflVO\nTk6d1yAJEWV0nVAi6lRVVfH582dISPD2JYiioiKUlZWpACUCi4rQ+pGbmwtra2vMmzcPU6ZMYTsO\nIURAURFKiICjIpTUpzdv3vDthaSsrCzevXvHl7MJEQVUhBJRx+VyERISwvPLrxQWFuL48eM8PZMQ\nXqIilP8+ffqE/v37Y/DgwVi2bBnbcQghAoyKUEIEnImJCRISEui6caRe8PvvGf09JuT71NXVv3kH\nbEJERWZmJt++D8TFxfHlXEJ4QUJCAlwul54H8UlpaSkGDx6Mdu3aYfPmzWzHIYQIOCpCCRFwLVq0\nQFVVFd6+fct2FCIGmjdvjs+fP/Pl7LKyMqioqPDlbEJEAU2EElGXlpYGaWlpvpydl5fHt+9fhNQV\nwzCQkJCgqVA+qKiowJgxY9C0aVN4enqCYRi2IxFCBBwVoYQIOIZhaD2e1Bt1dXVISUnx5WwVFRU0\naNCAL2cTIgqoCCWijp9FpYSEBBWhRKDRejzvcblczJw5E4WFhfD394ekpCTbkQghQoCKUEKEABWh\npL68f/8eampqPD9XQkICPXv25Pm5hIgSWo0nok5RUZFvZ3O5XMjJyfHtfELqiopQ3uJyuXB0dERy\ncjLOnj1LN0sjhPwyKkIJEQJUhBJ+Ki8vx6VLlzB8+HBoa2ujefPmkJeX5+ljyMrKYuHChTw9kxBR\nQxOhRNSZmJigtLSUL2erqanxbaOBEF6gIpS3XF1dceXKFYSEhEBJSYntOIQQIUJFKCFCgIpQwg8J\nCQlYunQpWrVqhc2bN8Pa2hpZWVkIDw+Hqqoqzx6HYRh8/vwZPj4+NO1GyA9QEUpEnaqqKt8Ki06d\nOvHlXEJ4hYpQ3vHx8YGPjw+uX7+OJk2asB2HECJkqAglRAiYmJggJSWFnjyROsvNzcWePXtgYWEB\nW1tbyMnJITIyElFRUZgxYwYaNWoECQkJBAYG8mwqVE5ODrdu3UKzZs1gZmaG1atX48OHDzw5mxBR\n0rx5c+Tk5NDXeiLSxo8fDxkZGZ6eqaSkhKlTp/L0TEJ4jYpQ3jh16hRcXFxw/fp1vlzOiRAi+qgI\nJUQINGjQAKqqqsjIyGA7ChFC5eXlCA4OxrBhw6Cjo4N79+5h69atyMzMxKZNm6Cvr/+fn9OpUycs\nWbKkztdzU1BQwKZNm9C5c2ds3boVjx8/Rk5ODvT09LBjxw6+rUgSIoxkZGTQuHFj5OTksB2FEL5Z\nsGABJCR4+xKkqKgIUVFRKCgo4Om5hPASFaF1d/36dcybNw+XL1+Gjo4O23EIIUKKilBChAStx5Pf\nFR8fjyVLlqBly5ZwdXVF//79kZWVhePHj6Nv374/vbPmhg0bMGXKFCgoKNTq8RUUFODo6IjFixfX\nfKxly5bw9fVFREQEIiMjoa+vj6NHj9ILA0L+H63HE1GXn58PRUVFnpWhCgoK8PLyQmZmJnR0dLB5\n82YUFhby5GxCeImK0Lq5e/cuxo4dizNnzsDc3JztOIQQIUZFKCFCwtTUFImJiWzHIAIuNzcXu3fv\nRrt27WBvbw8FBQXcvn0bt2/fxrRp09CwYcNfPothGOzatQteXl5QUlKCtLT0L/08GRkZNGzYEMeO\nHYOTk9M3P8fY2BgXLlzA8ePH4e3tjXbt2uHKlSvgcrm/nI8QUURFKBFVJSUlWLFiBfr374+tW7dC\nTU2tzmWovLw87O3tMWPGDPj5+eHWrVtISEgAh8OBm5sbiouLeZSekLqjIrT2EhMTMXjwYPj5+aFb\nt25sxyGECDkqQgkREjQRSr6nvLwcFy5cwJAhQ6Cjo4Po6Gi4ubkhMzMTGzduhK6ubq3PZhgG48eP\nR1paGqZNmwZFRUUoKyv/Z5pUSkoKysrKaNCgAebNm4f09HQMGTLkp+d369YNt2/fxvr167FkyRJY\nWVnhwYMHtc5LiLBTV1enm4oRkRMZGQlzc3NkZWUhPj4e06ZNQ2RkJBo3blzrMlReXh5mZmbw8/Or\n+ZiBgQECAgIQFhaG+/fvg8PhYNeuXXQZFiIQJCQkqAithWfPnsHGxgY7d+6Era0t23EIISKA4dL4\nDSFCISkpCUOHDkVqairbUYiAePz4MY4cOYITJ05AT08PkyZNgoODA5SVlfn2mMXFxYiIiEB0dDQe\nPnyI4uJiKCoqokOHDujQoQN69uwJWVnZWp1dUVGBI0eOwNnZGZ07d8bmzZvrVOISIoycnZ1RVVWF\n9evXsx2FkDorKCiAo6MjQkJC4OnpiYEDB37148+fP4ednR2ysrJ+a3pTQUEB/fv3h7+//w9v7Pfo\n0SM4OTkhJiYGq1atwrRp02r9PYqQutLU1ERERAS0tLTYjiI03rx5g27dumHJkiWYM2cO23EIISKC\nilBChMT/sXefUVFd/9fA9wCCFMUeFTvSm4C9ISpYsICF2BVF7JXYC1bEChYkYscKKihWiA0LCBYE\nBhWwxCgW1FipAvO8+C3z/JOYBGWGO2V/1sqLmJlzN1kCM3u+59zPnz9DX18fb968kdrdvEnxZGVl\nYf/+/di1axfevn2L4cOHY9iwYUp1YHxOTg42bNiAtWvXon///li4cCFq1qwpdCyiMhEcHIyEhARs\n27ZN6ChEpRIZGYkJEyage/fuWLVq1T8ezVJYWIgVK1bAz88PIpEI2dnZ/7imnp4edHV1ERwc/LdS\n9d/cuHEDPj4+SElJwfz58+Hh4VHi416IpMXQ0BBRUVFK9ZpNlt69ewcHBwf069cPCxYsEDoOESkR\nbo0nUhDlypWDkZER7ty5I3QUKmMFBQU4evQoXF1dYWxsjFu3bmHdunV49OgRlixZonQvqHV0dDB7\n9mzcu3cPOjo6sLCwwMKFC/HhwwehoxHJHLfGk6LLysrCgAED4O3tjT179mDLli3/ej61hoYGFixY\ngKysLKxbtw4ODg6oVKnSH0ewqKuro3bt2nB1dUVYWBiePXv2TSUoADRt2hQnT55EaGgoDh8+DBMT\nE+zcuROFhYWl+lqJvgXPCC25nJwc9OjRA46Ojpg/f77QcYhIybAIJVIglpaWPCdURUgkEiQmJmLK\nlCmoU6cO/P390bt3bzx58gQhISHo2LGj1O64K6+qVq2KNWvW4NatW3j8+DGMjY2xYcMGFBQUCB2N\nSGZ4syRSVBKJBHv27IGVlRXq16+P5ORkdOjQocTP19XVhZeXFy5evIi3b98iISEBhoaGyM7ORmZm\nJiIiItCtW7dS/e5r1aoVoqOjsWvXLuzevRtmZmbYu3cvyykqEyxCS6agoAD9+vVDo0aNsG7dOohE\nIqEjEZGSUe530URKhjdMUn5ZWVnw9/dHkyZN4ObmhkqVKiEuLg4xMTHw8PBAhQoVhI5Y5urXr4/d\nu3cjOjoaUVFRf9wMo7i4WOhoRFLHIpQU0ePHj9GtWzesXbsWp06dwsqVK0t9jI+Ojg40NDRkcqZn\n+/btceHCBWzZsgVBQUGwsrJCWFgYf6+QTLEI/W/FxcUYMWIENDQ0sH37dqX/0J+IhMGfLEQKhEWo\nciooKEB4eDh69eoFY2Nj3L59GwEBAXj48CEWL14MQ0NDoSPKBWtra5w8eRI7duxAQEAAmjZtil9+\n+UXoWERSVb16dbx//x75+flCRyH6T8XFxdi4cSPs7e3h4OCA69evw97eXipry/o2BiKRCB07dsSV\nK1fg7++PNWvWoEmTJoiIiJD5tUk1sQj9dxKJBJMmTUJmZiZCQ0N5ji8RyQyLUCIFwiJUeUgkEty6\ndQuTJ0+GgYEBNmzYgD59+uDJkyfYvXs3HB0d+Sn4P+jQoQOuXbuGefPmYcKECXBycsLNmzeFjkUk\nFWpqaqhZsyaeP38udBSif3Xnzh20bdsWYWFhuHr1KubMmSP14qIstsSKRCJ06dIF8fHx8PX1xdKl\nS2Fvb48TJ06wECWpYhH673x8fBAXF4fIyEjeGJaIZIrvsokUSN26dZGbm4vXr18LHYW+04sXL7B2\n7VpYW1ujb9++qFKlCuLj43Hx4kWMGDFCJbe+fw+RSIS+ffsiNTUVffv2Rc+ePTFo0CA8fPhQ6GhE\npcbt8STPCgoKsHTpUjg4OGDIkCGIiYmBiYmJ1K9T1iWkSCRCjx49cPPmTSxYsABz5sxBy5YtERUV\nxUKUpIJF6D9bv349QkNDcebMmX+9uRoRkTSwCCVSICKRiDdMUkD5+fk4cuQIevbsCVNTU4jFYmza\ntAkPHjzAokWL0KhRI6EjKqxy5cph7NixSE9Ph7m5OZo3b47JkycjKytL6GhE341FKMmr69evo2nT\nprh27Rpu3ryJ8ePHy3T3ghA3SRGJRHBzc0NSUhK8vb0xdepUtGvXDufPny/zLKRcWIR+XUhICNau\nXYvo6GjUqFFD6DhEpAJYhBIpGG6PVwwSiQQ3b97EpEmTUKdOHQQGBqJfv354+vQpdu7cCQcHB259\nlyI9PT3Mnz8fd+/ehZqaGszNzbFkyRJ8+vRJ6GhE38zAwACZmZlCxyD6Q05ODn766Sf07NkTs2fP\nxokTJ1CvXj2ZXlPoKUw1NTW4u7tDLBZj7NixGDNmDBwdHXH58mVBc5HiYhH6d5GRkZg5cyaioqJQ\nv359oeMQkYrgu3AiBcMiVL69ePECa9asgZWVFfr374/q1avj+vXrOH/+PIYPHw49PT2hIyq16tWr\nIyAgAAkJCUhLS4OxsTGCgoLw+fNnoaMRlRgnQkmenD9/HlZWVnj+/DlSUlIwaNCgMpvUFGIi9K/U\n1dUxZMgQ3L17F8OGDcOwYcPg7OyMa9euCR2NFAyL0D+LiYmBp6cnjh8/DjMzM6HjEJEKYRFKpGBY\nhMqf/Px8HD58GD169ICZmRnu3LmDzZs34/79+1i4cCEaNGggdESV06hRI+zbtw8nT57E0aNHYW5u\njkOHDgk+YURUEixCSR68e/cOnp6eGDFiBDZs2IB9+/ahevXqZXZ9eft5raGhAQ8PD6SlpaFfv35w\nd3eHi4sLbty4IXQ0UhAsQv+/W7duoX///jhw4ACaNWsmdBwiUjEsQokUwJEjRzB58mS0b98eLi4u\niI+Px9ChQ7/6WA8PD6ipqf3rP05OTmX8FSgfiUSC69evY8KECTAwMEBQUBB+/PFHPH36FDt27ED7\n9u259V0O2NraIioqCkFBQfDz80OLFi1w4cIFoWMR/StujSehRUREwMLCAlpaWhCLxXBxcSnzDBKJ\nRC4mQv9KU1MTXl5eyMjIQPfu3dG7d2+4uroiKSlJ6Ggk51iE/k96ejpcXFywZcsWdOrUSeg4RKSC\nNIQOQET/bdmyZUhOToaenh7q1q2LO3fuIDs7+6uPdXNzQ8OGDb/630JCQvDo0SN0795dlnGV2vPn\nz7F3717s2rUL+fn5GD58OG7evMlzjeRc586dcf36dYSFhcHT0xPGxsbw8/ODjY2N0NGI/oYToSSU\nFy9eYOLEiUhJScHBgwfRrl07QfPIYxH6hZaWFiZMmICRI0diy5Yt6Nq1K9q2bYtFixbBwsJC6Hgk\nh1iEAk+ePIGzszOWL18ONzc3oeMQkYoSSeRt3wkR/U1MTAzq1KkDQ0NDxMTEoEOHDujQocM3Tba9\nf/8etWvXRnFxMTIzM1GlShUZJlYueXl5iIyMxK5duxAXF4e+fftixIgRaNOmjVy/SaOvKygoQHBw\nMJYtWwZnZ2csWbKExxeQXHn37h3q1auHDx8+CB2FVIREIsGuXbswa9YseHp6YuHChShfvrygmZKT\nkzF48GCFOQ4oOzsbmzdvxpo1a9CpUyf4+PjAxMRE6FgkR7p3744JEyYIMmEtD16/fo127dph1KhR\n+Omnn4SOQ0QqjPs2iRSAg4MDDA0N//Rn7969+6Y1QkJCkJubi759+7IELQGJRIKEhASMHz8eBgYG\nCA4OxqBBg/D06VNs27YNbdu2ZQmqoDQ1NTFx4kRkZGSgYcOGsLe3x/Tp0/H69WuhoxEBAPT19VFY\nWIiPHz8KHYVUwMOHD+Hs7IxNmzYhOjoavr6+gpegXyjS71ldXV3MmDED9+/fh6WlJdq2bYvhw4fj\nwYMHQkcjOaHKE6EfP35Et27d4OrqyhKUiATHIpRIAYlEIrx9+/abnrN161aIRCJ4eXnJKJVyyMzM\nxMqVK2Fubo7BgwfDwMAAiYmJOHv2LIYMGQJdXV2hI5KUVKhQAYsXL0Zqairy8/NhamoKX19f5OTk\nCB2NVJxIJIKBgQG3x5NMFRUVwd/fH82bN4eTkxPi4+PRpEkToWP9QVE3rVWoUAFz587F/fv30ahR\nI7Ro0QKenp54/Pix0NFIYKpahObl5aF3796ws7ODr6+v0HGIiFiEEimqb5kIvXbtGsRiMUxMTNC+\nfXsZplJMeXl5CA0NRbdu3WBpaYn79+9j27ZtSE9Px7x581CvXj2hI5IM1axZE4GBgYiLi0NSUhKM\njY2xdetWFBYWCh2NVBjPCSVZEovFaNOmDY4dO4a4uDjMnDkTGhryd+sARZoI/St9fX34+PggIyMD\nNWvWhJ2dHcaNG4enT58KHY0EoopFaGFhIQYOHIhq1aph8+bNCv09TUTKg0UokYL6+PEj8vPzS/TY\nLVu2QCQSYfTo0TJOpTgkEgmuXbuGsWPHwsDAANu3b8eQIUOQmZmJrVu38vxPFWRkZITQ0FBERETg\nwIEDsLS0REREhMJOJZFiYxFKspCfn49FixbB0dERI0eOxPnz52FkZCR0rK9Slp+9lStXxrJly5CW\nloaKFSvC2toakydPxvPnz4WORmVM1YpQiUQCLy8v5OTkYM+ePVBXVxc6EhERABahRAqrQoUKuHfv\n3n8+7sOHDzh06BA0NTUxfPjwMkgm3zIzM+Hn5wczMzMMGzYM9erVw+3btxEdHY3BgwdDR0dH6Igk\nsGbNmuHcuXMICAjAokWL0Lp1a1y+fFnoWKRiDAwMkJmZKXQMUiLXrl2DnZ0dEhMTcfv2bXh5eUFN\nTb7fCijTB5LVqlXDypUrcffuXWhoaMDCwgLe3t7IysoSOhqVEVUqQiUSCWbMmIG7d+8iPDwcWlpa\nQkciIvqDfL/6IaJ/VKlSpRLdSXXPnj3IyclR6Zsk5ebm4sCBA+jSpQusrKzw6NEj7NixA2lpaZg7\ndy7q1q0rdESSMyKRCF27dkViYiImTJiAoUOHolevXhCLxUJHIxXBiVCSlk+fPmHq1Klwc3ODj48P\njh49CgMDA6Fj/SdlmQj9qx9++AHr1q2DWCxGQUEBzMzMMHv2bLx580boaCRjqlSE+vn5ISoqCidP\nnuT5+kQkd1iEEimokhahX26SNGbMmDJIJT8kEgni4uIwZswYGBgYYNeuXRgxYgQyMzOxZcsWtG7d\nWqkmTUg21NTUMGTIEKSlpcHR0REdO3bEyJEj8eTJE6GjkZJjEUrSEB0dDSsrK7x9+xZisRju7u4K\n87tPIpEoTNbvUbt2bWzcuBGJiYl49+4djI2NsWDBgm++GSYpDlUpQrds2YKtW7ciKipKZYcwiEi+\nsQglUlCVK1f+zyI0ISEBycnJMDExQbt27coombCePn2KFStWwNTUFB4eHmjYsCGSk5MRFRWFgQMH\nQltbW+iIpIC0tLQwbdo0ZGRkoFatWmjSpAlmzpzJN6wkM7xrPJXG77//jhEjRsDLywtBQUHYvXs3\nqlatKnSsb6bMRegX9erVw88//4wbN27g2bNnMDIywpIlS/Dhwweho5GUqUIRGhYWhiVLliA6Ohq1\na9cWOg4R0VexCCVSUCWZCP1ykyQvL68ySiWMnJwc7N+/H87OzrC2tsbjx4+xe/du3L17F7Nnz0ad\nOnWEjkhKQl9fH8uXL0dKSgrev38PY2NjrF69Grm5uUJHIyVTu3ZtnhFK30wikeDQoUOwtLRExYoV\nkZKSgq5duwod67so69b4f9KwYUNs374dcXFxuH//Pho3bowVK1bg06dPQkcjKVH2IjQqKgqTJk3C\n6dOn0bhxY6HjEBH9IxahRArg2LFj8PDwgIeHB/z8/AAAKSkpePHiBQYNGoQZM2b87TkfP35EaGgo\ntLS0MGzYsLKOLHMSiQRXr16Fl5cX6tSpg5CQEIwcORKZmZn4+eef0bJlS5WYJCFh1K5dG1u2bMHl\ny5cRFxcHY2Nj7Ny5U6nf4FDZqlWrFp49e6ZyZRB9v2fPnqFPnz5YuHAhDh8+jA0bNqBChQpCxyoV\nVfw9bmRkhJCQEMTExCApKQmGhoZYs2YNcnJyhI5GpaTMRWhcXByGDBmC8PBwWFtbCx2HiOhfsQgl\nUgC3b99GSEgIQkJCEB0dDZFIhEePHqGwsBChoaEIDw//23P27duH3Nxc9OnTR6nO5/ntt9+wfPly\nmJiYwNPTE4aGhkhJScGZM2cwYMAAbn2nMmVqaorw8HCEhYVhx44dsLGxwfHjx1leUanp6OhAR0cH\nv//+u9BRSM5JJBJs27YNNjY2sLKywu3bt9G6dWuhY5Waqv8cNTMzw8GDB3H27FnExcWhcePG2LBh\nA/Ly8oSORt9JWYtQsVgMV1dXhISEoE2bNkLHISL6TyxCiRSAj48PioqK/vaPp6cnNm3ahAcPHvzt\nOWPHjkVRURH27t0rQGLpysnJwd69e+Hk5ARbW1s8ffoUe/bswZ07dzBr1iyFuPstKbdWrVrh0qVL\n8PPzw5w5c9C+fXvExsYKHYsUnIGBAbfH07+6f/8+OnXqhODgYJw7dw5LliyBlpaW0LGkRhUnQv/K\nysoKR44cwcmTJ3H27FkYGRkhKCgI+fn5Qkejb6SMRejDhw/RtWtXBAQEoFu3bkLHISIqERahRArM\nysqqRHeOV0QSiQRXrlyBp6cnDAwMsH//fowePRqZmZkICgpCixYt+AaJ5IpIJEKPHj2QlJSEUaNG\nYcCAAXBzc8O9e/eEjkYKineOp39SWFiINWvWoGXLlujRowfi4uKUbjuqqk+E/pWtrS0iIyMRHh6O\nyMhImJiYYNu2bfj8+bPQ0aiElK0IffHiBZydnTF37lwMHDhQ6DhERCXGIpRIgSljEfr48WMsW7YM\nRkZG8PLygrGxMVJTU3Hq1Cm4u7ujfPnyQkck+lfq6uoYMWIE0tLS0Lp1a7Rr1w5eXl4stOibsQil\nr0lKSkKrVq1w+vRpxMfHY/r06VBXVxc6lkzwA8+/a9asGU6fPo39+/fj4MGDMDU1xe7du1FYWCh0\nNPoPylSEvnv3Dl26dMHw4cMxfvx4oeMQEX0TFqFECuxLEaroUxPZ2dnYs2cPOnXqBDs7Ozx//hwH\nDhxAamoqZs6cidq1awsdkeibaWtrY8aMGUhPT0flypVhZWWFuXPn4t27d0JHIwXBrfH0f+Xl5WH+\n/PlwcnLC2LFjcfbsWRgaGgodS2YU/bWNrLVu3Rpnz57Fjh07sH37dlhYWGD//v1KU7QpI2UpQnNy\nctCjRw84Ojpi/vz5QschIvpmLEKJFFi1atWgra2Np0+fCh3lm0kkEly6dAmjRo1CnTp1cPDgQYwd\nOxaZmZkIDAxEs2bNOAlCSqFy5cpYuXIlbt++jZcvX8LY2Bjr1q3j+W70nzgRSl9cvXoVtra2uHPn\nDm7fvo1Ro0Yp/e9IiUSi9F+jNDg4OCAmJgaBgYHYuHEjrK2tcejQIRQXFwsdjf5CGYrQgoIC9OvX\nD40aNcK6dev4PUpEColFKJGCs7S0VKjt8b/++iuWLFmCxo0bY9y4cTAzM8OdO3dw8uRJ9O/fn1vf\nSWnVrVsX27dvx/nz53Hx4kWYmJhgz549Cv+miGSHRSh9/PgRkyZNQv/+/bFs2TKEh4er1C4Jliwl\nIxKJ0LlzZ8TGxmLt2rVYtWoVbG1tcfToUU7WyhFFL0KLi4sxYsQIaGhoYPv27VBTY5VARIqJP72I\nFJwinBP66dMn7N69Gx07dkTTpk2RlZWF0NBQiMVi/PTTT6hVq5bQEYnKjKWlJSIjI7Fnzx4EBQXB\nzs4OZ86c4ZtV+hsDAwMWoSrs9OnTsLS0RHZ2NsRiMfr27St0pDLFn4nfTiQSoWvXrkhISMCyZcuw\nePFiNG3aFCdPnuT/TzmgyEWoRCLBpEmTkJmZidDQUJQrV07oSERE341FKJGCk9citLi4GDExMfDw\n8EDdunVx+PBhTJgwAZmZmdi0aROaNm3KSQ9Sae3atcPVq1exePFiTJs2DZ06dcL169eFjkVypHbt\n2jwjVAW9fv0aQ4cOxYQJE7Bt2zbs2LEDVapUETqWIPg64fuIRCL07NkTN2/exLx58zB79my0atUK\n0dHRLEQFpMhFqI+PD+Li4hAZGQltbW2h4xARlQqLUCIFJ29F6KNHj7B48WI0btwYEydOhKWlJe7e\nvYvjx4+jb9++0NLSEjoikdwQiURwdXVFSkoKBg4cCFdXV7i7uyMjI0PoaCQHfvjhB7x69Yp3g1YR\nEokEBw8ehJWVFapXr46UlBQ4OTkJHUswLOxKT01NDX369EFSUhKmTZuGKVOmoH379rhw4YLQ0VSS\nohahAQEBCA0NxZkzZ6Cvry90HCKiUmMRSqTgzM3NkZ6ejs+fPwuW4dOnT9i1axccHR3RvHlzvHnz\nBocPH0ZycjK8vb1Rs2ZNwbIRKQINDQ2MHj0aGRkZsLW1RatWrTB+/Hi8ePFC6GgkoHLlyqFq1arI\nysoSOgrJ2NOnT9GrVy8sW7YMR48exbp166Crqyt0LMFxIlQ61NTU8OOPP0IsFsPLywujR49Gx44d\ncfXqVaGjqRQ1NTWFu4lVSEgI1q1bh+joaNSoUUPoOEREUsEilEiBJSUlwcfHBxKJBJUrV4ampiZ0\ndHRgamqKkSNH4vTp0zJ7wVVcXIyLF26vUuAAACAASURBVC9ixIgRqFOnDsLDwzFp0iQ8ffoUGzZs\ngJ2dHd/AEH0jHR0dzJkzB2lpadDW1oaFhQV8fHzw4cMHoaORQAwMDLg9XokVFxfj559/hq2tLZo1\na4Zbt26hRYsWQseSC5wIlT51dXUMHToU9+7dw5AhQzBkyBB06dIF8fHxQkdTCYo2ERoZGYmZM2ci\nKioK9evXFzoOEZHUsAglUkAJCQlo0qQJWrduDX9/f+Tn5yM7OxufP39Gbm4u0tLSsHPnTri7u6N2\n7doICQmR2huKhw8fwsfHB4aGhpg8eTKsra2RlpaGyMhI9OnTh1vfiaSgatWqWLt2LW7duoVHjx7B\n2NgYGzduREFBgdDRqIzxzvHKKz09HY6Ojti9ezcuXryIhQsXQlNTU+hYcoUfqMqGhoYGRo4cibS0\nNPTp0wf9+/dHjx49cPPmTaGjKTVFKkIvXrwIT09PnDhxAmZmZkLHISKSKhahRAqkqKgIM2bMQIcO\nHZCUlIScnJx/fUH16dMnvHz5EuPHj0enTp3w5s2b77rux48fsXPnTjg4OKBFixZ49+4dwsPDkZSU\nhOnTp+OHH3743i+JiP5F/fr1ERISgqioKJw+fRpmZmY4cOCAwm2to+/HIlT5fP78GX5+fmjdujX6\n9OmDK1euwMLCQuhYcocTobKnqamJMWPGICMjA127dkWvXr3g5uaG5ORkoaMpJUUpQm/evAl3d3cc\nPHgQTZs2FToOEZHUsQglUhBFRUXo168fNm/ejNzc3G96bnZ2Nq5cuQJ7e/sSnzVXXFyM8+fPY9iw\nYahbty6OHj2KqVOnIjMzE+vXr4etrS0nNYjKiI2NDU6dOoVt27bB398fzZo1w9mzZ4WORWWAW+OV\nS2JiIlq0aIHz58/j+vXrmDJlCtTV1YWOJZckEglfZ5QRLS0tTJw4Effv30f79u3h7OwMd3d33Llz\nR+hoSkURitC0tDT06NEDwcHB6Nixo9BxiIhkgkUokYKYNm0aoqOjkZOT813P//z5M549e4aOHTv+\n642VHjx4gIULF6Jhw4aYNm0a7OzskJ6ejmPHjsHNzY3b9ogE5OjoiPj4eMyZMwfjx4+Hs7Mzbt26\nJXQskiFOhCqH3NxczJkzB127dsWUKVMQFRWFhg0bCh1L7rEILVva2tqYNm0aHjx4gKZNm6JDhw4Y\nPHgw0tPThY6mFOS9CH3y5AmcnZ3h6+sLV1dXoeMQEckMi1AiBXDlyhVs27btu0vQLz5//oxHjx7B\n19f3T3/+4cMHbN++He3bt0erVq3w4cMHHDt2DElJSZg6dSrvEkkkR0QiEfr164fU1FS4ubnBxcUF\ngwYNwsOHD4WORjLAIlTxXbp0CTY2Nnjw4AGSk5MxfPhwFnwlwK3xwtHV1cXMmTPx4MEDmJubo02b\nNhgxYgR/z5SSPBehr1+/hrOzMyZPngwPDw+h4xARyRSLUCI5J5FI4OHh8c3b4f9JTk4O/Pz88OzZ\nM5w7dw5Dhw5FvXr1cOLECXh7e+Pp06cICAhAkyZNpHI9IpKNcuXKYdy4ccjIyICZmRmaN2+OKVOm\n4NWrV0JHIyliEaq4Pnz4gHHjxmHQoEFYtWoVwsLCeKb2N2JhLKwKFSpg3rx5yMjIQIMGDdC8eXOM\nHj0ajx8/FjqaQpLXIvTjx4/o1q0b3Nzc4O3tLXQcIiKZYxFKJOdiY2Px/Plzqa5ZWFgIMzMz/PTT\nT2jatCkyMjIQERGB3r17c+s7kYLR09PDggUL/jjLzczMDEuXLsWnT58ETkbSwDNCFdPx48dhYWGB\noqIiiMVibjP9DpwIlR+VKlXCokWLkJ6ejho1asDOzg7jx4/nz6ZvJI9FaF5eHnr37g17e3ssX75c\n6DhERGWCRSiRnAsODi71lvi/KiwshKamJhITEzFlyhRUr15dqusTUdmrUaMG1q9fj/j4eNy9exfG\nxsYICgr61zOBSf5VrVoVHz9+RF5entBRqASysrIwcOBATJs2DSEhIQgODkalSpWEjqWwOBEqX6pU\nqYLly5fj3r170NPTg5WVFaZMmYIXL14IHU0hyFsRWlhYiIEDB6J69eoIDAzk9xsRqQwWoURy7urV\nqzKZinj//j3evXsn9XWJSFiGhobYv38/Tpw4gfDwcFhYWODQoUOcrlJQampqqFWrltR3BpB0SSQS\n7N27F1ZWVqhTpw6Sk5Ph6OgodCyFxp9Z8qt69epYtWoV7ty5A5FIBHNzc8yYMYNHs/wHeSpCJRIJ\nvLy8kJOTgz179kBdXV3oSEREZYZFKJEcKyoqktk5TDo6OkhKSpLJ2kQkPDs7O/zyyy8IDAzEihUr\n0KJFC1y8eFHoWPQduD1evv32229wcXHB6tWrcfLkSaxevRo6OjpCx1IKnFCTbzVr1kRAQABSUlKQ\nm5sLU1NTzJ07F2/evBE6mlySlyJUIpFgxowZuHv3LsLDw3ksFhGpHBahRHJM1lshP3z4INP1iUh4\nTk5OuHHjBqZNm4aRI0eie/fuSE5OFjoWfQPeMEk+FRcXIzAwEPb29mjTpg1u3LiBpk2bCh1LaXAi\nVHEYGBhg06ZNSExMxJs3b2BiYgIfHx/uPPoLeSlC/fz8EBUVhZMnT0JXV1foOEREZY5FKJEc09DQ\nQHFxsUzXJyLlp6amhoEDB+LevXvo1q0bnJ2dMWzYMPz6669CR6MSYBEqf+7du4f27dvjwIEDuHz5\nMubNm4dy5coJHUupSCQSToQqmHr16mHLli1ISEjAkydPYGRkhGXLlqn8B++zZs1C586dMW7cOBw7\ndgxVqlSBjY0N5s+fj5cvX5Zpli1btmDr1q2IiopClSpVyvTaRETygkUokRzT0tKCvr6+TNYuLCyE\noaGhTNYmIvmkqamJSZMmIT09HQ0bNoS9vT2mT5/ObYxyjlvj5cfnz5+xfPlytGvXDgMHDsSlS5dg\namoqdCylxSJUMTVq1Ag7duxAbGws0tLS0LhxY6xcuRLZ2dlCRxNEQEAAcnJy0KRJEzRs2BBDhw5F\n+fLl4evrCysrK9y/f79McoSFhWHJkiWIjo5G7dq1y+SaRETyiEUokZyzsbGRybpFRUVo3LixTNYm\nIvlWsWJFLF68GKmpqcjLy4OJiQlWrFiBnJwcoaPRV3AiVD582fp+9epV3Lx5ExMmTICaGl9Kywq3\nxis+IyMj7NmzBzExMbh16xYMDQ2xbt065ObmCh2tTH38+BGxsbGYOnUqjIyMsH79esTHx2Pu3Ll4\n/fo1/Pz8ZJ4hKioKkyZNwunTp/n6n4hUHl+9Ecm5/v37S/38HpFIhI4dO/INHJGKq1mzJjZv3ozY\n2FgkJibC2NgYW7duRWFhodDR6P9gESqsnJwczJgxAy4uLpgxYwZOnjyJevXqCR1LJXAiVDmYmZkh\nNDQUv/zyC65cuYLGjRtj48aNMj8LX158uRnRX88IdXd3BwCZT/zHxcVhyJAhCA8Ph7W1tUyvRUSk\nCNiCEMm5oUOHSv2cUF1dXcyYMUOqaxKR4jI2NkZYWBjCw8Oxf/9+WFpaIiIighNZcoJFqHAuXLgA\na2trPH36FCkpKRgyZAjLuTLCnz/Kx8rKCuHh4Th+/Diio6NhZGSEn3/+GQUFBUJHKxN/LUIjIyMh\nEong6Ogos2umpKTA1dUVISEhaNOmjcyuQ0SkSFiEEsm5ChUqYPLkydDR0ZHKempqajAyMoKDg4NU\n1iMi5dG8eXOcP38e/v7+8PHxQZs2bXDlyhWhY6m8L2eEshgqO+/evcPo0aMxbNgw+Pv748CBA6hR\no4bQsVQOS2flZGdnh+PHj+PIkSM4evQojI2NsX37dnz+/FnoaDJ15MgRPHjwANOnT0e7du2wZMkS\neHp6Ytq0aTK53sOHD9G1a1cEBASgW7duMrkGEZEiYhFKpAAWL16MH374QSpvCMqXL4+wsDC+uSCi\nrxKJROjWrRsSExMxbtw4DBkyBL169UJqaqrQ0VRWhQoVAPzvnDmSvaNHj8LS0hIaGhoQi8Xo2bOn\n0JFUEot/5de8eXOcOXMG+/btw/79+2FmZoaQkBClPZ7l0KFD+PXXX7F+/XrExsaiZcuWGDBgAMqV\nKyf1az1//hxOTk6YP38+Bg4cKPX1iYgUGYtQIgWgpaWFU6dOoWLFiqVaR1tbG9u2beMh6UT0n9TV\n1TF06FDcu3cPHTp0gKOjI0aOHIknT54IHU3liEQibo8vAy9fvoS7uztmzpyJffv2ISgoCPr6+kLH\nUmn80FY1tGnTBufOncO2bduwdetWWFpa4sCBA3/aRq4Mjh07htatW+PFixcIDw9HVlYWnJycsG/f\nPqle5+3bt+jSpQs8PDwwbtw4qa5NRKQMWIQSKQhTU1NcuXIFVatWhZaW1jc9VyQSQVtbG8HBwfxU\nmIi+Sfny5TF9+nSkp6ejZs2aaNKkCWbNmoW3b98KHU2lfNkeT9InkUiwe/duWFtbw9DQEElJSTw+\nRg5wIlT1dOjQAZcuXcLGjRuxfv162NjY4PDhw1I/K18oX84IrV69Onr37o3o6GhoaGjA29tbatfI\nzs5Gjx490KlTJ8ybN09q6xIRKRMWoUQKxNLSEhkZGejVqxd0dHRKdNd3PT09mJub48aNGxgyZEgZ\npCQiZVSpUiX4+voiOTkZb9++hbGxMVavXo3c3Fyho6kEToTKxq+//oquXbti/fr1OHPmDFasWAFt\nbW2hYxH+V4RyIlT1iEQiODk5IS4uDqtWrYKfnx/s7Oxw7NgxhS/H/3qzpHr16sHc3ByvXr3Cy5cv\nS71+QUEB+vXrByMjI6xdu5bfP0RE/4BFKJGCqVy5MsLCwnDp0iUMGDAAWlpa0NPTQ4UKFaCjowM9\nPT1UrFgRGhoaaN26Nfbt24ekpCSYm5sLHZ2IlICBgQGCg4Nx6dIlxMbGwsTEBDt37lS6LYzyhkWo\ndBUVFWH9+vVo2rQpOnbsiPj4eNja2godi/6CRY7qEolE6N69O65fv47Fixdj4cKFaNasGU6dOqWw\nhehfi1AAePbsGUQiEfT09Eq1dlFREYYPHw5NTU1s27atRMMSRESqSkPoAET0fezt7bFv3z6EhITg\n3r17SElJwadPn6CpqQljY2PY2NhwqoWIZMbMzAwRERGIjY3FrFmzsHbtWvj5+cHFxYXlhQwYGBjg\n4cOHQsdQCqmpqRg1ahS0tLQQGxsLY2NjoSPRVyhq2UXSJRKJ0Lt3b/Ts2RPh4eGYMWMGli5diiVL\nlqBz585y//smIyMDP/zwAypWrPinIlQikWD+/PnIyspCly5doKur+93XkEgkmDRpEp4/f44zZ85A\nQ4Nv8YmI/g1/ShIpOHV1dVhYWMDCwkLoKESkglq3bo1Lly7hxIkTmDVrFlatWoWVK1eiVatWQkdT\nKrVr18aVK1eEjqHQCgoKsGLFCmzatAlLly6Fl5cXp6bknLyXXFR21NTU0K9fP7i5uSEsLAwTJ07E\nDz/8gKVLl8r1mb6nTp3CnDlz0LZtW+jr6+PZs2cYNWoUYmJi8PDhQzRo0ABBQUGlusbChQsRHx+P\nCxcuoHz58lJKTkSkvPjqj4iIiEpFJBKhZ8+eSE5OhoeHB9zd3dGnTx/cu3dP6GhKg1vjSyc+Ph72\n9va4ceMGEhMTMXbsWJagco4TofQ16urqGDhwIFJTU+Hp6YlRo0ahU6dOiI2NFTraV3Xu3Bmenp54\n/fo1zp8/j9evXyMiIgI1atT449ztBg0afPf6AQEBCAsLw+nTp1GxYkXpBSciUmJ8BUhERERSoa6u\nDg8PD6Snp6Nly5Zo164dxowZwwJPCliEfp/s7GxMnz4drq6umDdvHiIjI1GnTh2hY1EJcSKU/omG\nhgaGDRuGu3fvYtCgQRg0aBC6du2KhIQEoaP9iYWFBTZs2IBbt24hNjYWRkZG+P333xEbG4vZs2eX\n6mzQkJAQrFu3Dr/88gtq1KghxdRERMqNRSgRERFJlba2NmbOnIm0tDTo6+vDysoK8+bNw/v374WO\nprBq166N58+fo7i4WOgoCuPs2bOwsrLCq1evkJKSggEDBrBYUyCcCKWSKFeuHEaNGoX09HS4urqi\nb9++6NmzJxITE4WO9jdfu1nS94qMjMTMmTMRFRWFevXqSWVNIiJVwSKUiIiIZKJKlSpYtWoVbt++\njefPn8PY2Bj+/v7Iz88XOprCKV++PPT09PDmzRuho8i9t2/fYuTIkRg1ahQCAwOxZ88eVKtWTehY\n9B1YXFNJaWpqYuzYscjIyICzszNcXFzQp08fpKSkCB3tD9IqQi9evAhPT0+cOHECZmZmUkhGRKRa\nWIQSERGRTNWtWxc7duzAuXPncP78eZiYmGDv3r2cbvxG3B7/344cOQILCwvo6upCLBajW7duQkei\n78SJUPoe5cuXx6RJk/DgwQO0bdsWTk5O+PHHH3H37l2ho0mlCL158ybc3d1x8OBBNG3aVErJiIhU\nC4tQIiIiKhOWlpY4fvw4QkJCEBgYCDs7O5w5c4aFRwkZGBggMzNT6Bhy6fnz5+jTpw/mz5+PQ4cO\nYePGjahQoYLQsagUJBIJJ0Lpu2lra2P69Om4f/8+7Ozs4ODggKFDhyIjI0OwTKUtQtPS0tCjRw8E\nBwejY8eOUkxGRKRaWIQSERFRmWrfvj1iY2Ph4+ODqVOnolOnTrh+/brQseQeJ0L/TiKRYPv27bCx\nsYG5uTkSExPRpk0boWORlLAIpdLS09PDrFmzcP/+fZiYmKB169YYOXIkHj16VOZZSlOEPnnyBM7O\nzvD19YWrq6uUkxERqRYWoURERFTmRCIR3NzcIBaLMWDAALi6usLd3V3QaR15xyL0zx48eIDOnTsj\nKCgIv/zyC5YtW4by5csLHYukhJPiJE0VK1bE/PnzkZGRgbp166JZs2YYM2YMfvvttzLL8L1F6OvX\nr+Hs7IzJkyfDw8NDBsmIiFQLi1AiIiISjIaGBry8vJCeno4mTZqgVatWmDBhAl6+fCl0NLnDrfH/\nU1RUhLVr16JFixbo1q0brl27BhsbG6FjkQxwIpSkrVKlSli8eDHS0tJQtWpV2NraYuLEiWXys/V7\nitAPHz6gW7ducHNzg7e3t4ySERGpFhahREREJDhdXV3MnTsX9+7dg5aWFszNzeHj44OPHz8KHU1u\ncCIUSElJQatWrXDy5EnEx8fjp59+goaGhtCxSAY4EUqyVLVqVfj6+uLu3bsoX748rKysMG3aNLx4\n8UJm11RTU/ummwTm5eXB1dUV9vb2WL58ucxyERGpGhahREREJDeqVauGdevW4ebNm3j48CGMjIyw\nceNGFBQUCB1NcKpchObn52PBggXo1KkTvLy8cO7cORgaGgodi2SME6EkazVq1MCaNWuQmpqK4uJi\nmJubY+bMmXj9+rXUr/UtE6GFhYUYMGAAqlevjsDAQH4vEBFJEYtQIiIikjsNGjTAnj17cObMGZw6\ndQpmZmY4ePDgN03TKBtVLUJjY2Nha2sLsViM27dvw9PTk6WACuBEKJWlWrVqYf369UhOTsanT59g\nYmKCefPm4ffffy/Vuo8fP8bq1avh4uICMzMzZGdno3bt2ujQoQN8fHxw+/btvz2nuLgYo0ePRm5u\nLvbs2QN1dfVSZSAioj8TSfgqg4iIiOTc+fPnMWvWLBQXF2PlypXo3Lmz0JHKXGFhIbS1tZGTk4Ny\n5coJHUfmPn36hLlz5+Lw4cPYsGED+vbtywJUhURGRmLbtm2IjIwUOgqpoMePH2PZsmWIiIjAxIkT\nMXXqVFSqVKnEz79z5w4mTpyIuLg4SCQS5Ofn/+0x6urq0NLSQqNGjeDv74/OnTtDIpHgp59+Qlxc\nHH755Rfo6upK88siIiJwIpSIiIgUQMeOHZGQkIBZs2Zh7NixcHZ2xq1bt4SOJTNHjhzB5MmT0b59\ne+jr60NNTQ0jR45E9erVS3QjKU9PT6ipqUFNTQ0PHz4sg8TSdebMGVhaWuLjx48Qi8Xo168fS1AV\nw1kNElL9+vWxdetWxMfH49dff4WRkRGWL1/+n+dWSyQS+Pr6omnTprh48SLy8vK+WoIC/7vxW05O\nDsRiMXr37o3hw4dj8eLFiI6OxokTJ1iCEhHJCItQIiIiUggikQju7u64e/cuXF1d4eLigsGDBytk\n0fdfli1bhsDAQCQlJaFOnTp/lIAl2R5//Phx7NixAxUqVFC48vDNmzcYNmwYxo0bh+DgYOzcuRNV\nqlQROhYJQCKRKNzfX1I+hoaG2LVrF65evYo7d+6gcePGWLVqFbKzs//22OLiYgwfPhzLly9Hbm7u\nN5X5OTk5OHDgAFasWIGIiAj+3CMikiEWoURERKRQypUrh/HjxyMjIwMmJiZo1qwZpkyZglevXgkd\nTWoCAgKQnp6O9+/fY/PmzX+8oTYwMEBmZuY/Pu/169fw8vLCgAEDYGdnV1ZxS00ikSA0NBSWlpao\nUqUKUlJS4OzsLHQsEhiLUJIXxsbG2LdvH86fP48bN26gcePG8Pf3R25u7h+PmT17No4cOYKcnJzv\nusbnz58BAKNHj+ZENBGRDLEIJSIiIoWkp6eHhQsX4u7duyguLoapqSmWLl2KT58+CR2t1BwcHL56\nV/T/mggdPXo0RCIRAgMDZRlPqjIzM+Hq6oolS5YgIiICAQEB0NPTEzoWCYxFEMkjCwsLhIWFISoq\nCpcuXULjxo2xadMmXLp0CZs2bfruEvSLgoICXL9+HVu3bpVSYiIi+isWoURERKTQatSogY0bNyIh\nIQF37tyBsbExfv755z+ma5TJvxWhu3btQmRkJIKDg1G5cuUyTvbtiouLsWXLFjRp0gS2tra4desW\nWrZsKXQskiOcCCV5ZW1tjYiICERGRuL06dPo1KnTn6ZDSyM7OxvTp0/Hhw8fpLIeERH9GYtQIiIi\nUgqGhoY4cOAAjh8/jsOHD8PCwgKHDx9Wqsmyf9oa//jxY0ydOhVDhw5Fjx49BEj2bTIyMtCxY0fs\n2LEDFy5cwKJFi6ClpSV0LJIjyvR9S8rL3t4e8+bNQ7ly5aS+dkhIiNTXJCIiFqFERESkZOzt7XH2\n7FkEBgbC19cXLVu2xMWLF4WOJRVfmwiVSCQYPnw4KlSogPXr1wuUrGQKCwuxatUqtGrVCq6uroiN\njYWlpaXQsUhOcSKUFEFQUBDy8vKkumZ2djY2bNgg1TWJiOh/NIQOQERERCQLTk5O6NSpEw4ePIiR\nI0fC1NQUfn5+sLa2Fjrad/taEbpu3TpcvnwZp06dgr6+vkDJ/tvt27cxatQoVKlSBdevX0fDhg2F\njkRyjBOhpCguX74sk7+vjx49Ql5eHsqXLy/1tYmIVBknQomIiEhpqampYdCgQbh79y66du0KJycn\nDB8+HI8fPxY62nf5axGakZGB+fPnw8PDA126dBEw2T/Ly8vD3Llz4ezsjIkTJyI6OpolKJUIJ0JJ\n3uXn53/1uBJp0NHRgVgslsnaRESqjEUoERERKT0tLS1MnjwZGRkZqF+/Puzs7ODt7Y03b94IHe2b\nVK1aFTk5OX/clOPOnTvIz8/Hjh07oKam9qd/YmJiAACNGzeGmpoaIiMjyzzv5cuXYWNjg4yMDCQn\nJ8PDw4PlFpUIJ0JJEXz69Anq6uoyWVskEuHdu3cyWZuISJVxazwRERGpjIoVK2LJkiUYN24clixZ\nAhMTE3h7e2PKlCnQ0dEROt5/EolEqFWrFp49ewZDQ0M0aNAAnp6eX33siRMn8PLlS7i7u6NixYpo\n0KBBmeX88OED5syZg6NHj2LTpk1wc3Mrs2uTcpBIJCzNSe6pq6vLtLRXU+PcEhGRtLEIJSIiIpVT\nq1YtBAUFYdq0aZg3bx6MjY2xaNEijBgxAhoa8v3y6Mv2eENDQ9jY2CA4OPirj3N0dMTLly/h6+uL\nRo0alVm+kydPYty4cXB2doZYLEblypXL7NqkXFiEkrzT19eX2UTo58+fy/QDLCIiVcGPmIiIiEhl\nGRsb49ChQzhy5Aj27t0LKysrHD16VPBtuceOHYOHhwc8PDzg5+cHAIiNjYWHhweePXuGVatWCZrv\na169eoXBgwdj8uTJ2LlzJ7Zt28YSlL6b0N+DRCUhEolgbm4us/V5njIRkfTJ98gDERERURlo0aIF\nLly4gNOnT2P27NlYvXo1Vq5cibZt2wqS5/bt2wgJCfnj30UiER49eoRHjx5BIpHg48ePJVqnLCbq\nJBIJ9u/fD29vbwwZMgQpKSkKccwAyT9OhJIicHV1RWpqKvLy8qS6roODA78HiIhkQCThx61ERERE\nfygqKsK+ffuwYMECNGnSBL6+vrCwsBA61h9WrVqFrKwsrFmzRugoePLkCcaOHYsnT55g+/btaNas\nmdCRSEmEhobiyJEjCAsLEzoK0b96+fIlGjRoINUiVE9PD5GRkXB0dJTamkRE9D/cGk9ERET0f6ir\nq2PYsGFIS0uDg4MDHB0dMWrUKDx9+lToaAAAAwMDZGZmCpqhuLgYmzdvhq2tLVq2bIkbN26wBCWp\n4zQcKYJq1arB2tpaauuJRCI0bNgQHTp0kNqaRET0/7EIJSIiIvqK8uXLY/r06UhPT0eNGjVgbW2N\nWbNm4e3bt4Lm+nKzJKF8KYj37t2LS5cuYcGCBdDU1BQsDyknblojRZCUlIRWrVqhXLlyUjsTuXz5\n8ggLC+MHAUREMsIilIiIiOhfVKpUCStWrEBKSgp+//13GBsbY82aNVI/D66khCpCP3/+DF9fX7Rp\n0wbu7u64fPmyTG8SQsQiiORVTk4OZs6cCScnJ4wdOxaXL1/GyZMnS30+so6ODjZu3AhTU1MpJSUi\nor9iEUpERERUAgYGBti6dSsuXbqEK1euwNjYGLt27UJRUVGZ5vhShJblxNzNmzfRrFkzXL58GTdv\n3sSkSZOgrq5eZtcn1cOJUJJXUVFRsLS0RGZmJsRiMUaOHAmRSIRWrVrh1KlT0NPT+66fj9ra2liz\nZg1GjRolg9RERPQFi1AiIiKikITTUQAAIABJREFUb2BmZoajR4/iwIED2LZtG2xsbHDixIkyK24q\nVKgAdXV1vH//XubX+jL11L17d3h7e+PUqVOoX7++zK9LJJFIOBFKciUrKwuDBw/GuHHjEBQUhH37\n9qFGjRp/eoyDgwPEYjGaN28OXV3dEq2rq6uL+vXrIyYmBuPGjZNFdCIi+j9YhBIRERF9hzZt2uDy\n5cvw9fXFzJkz0aFDB1y7dq1Mrl0W2+MvXrwIGxsb/Pbbb0hJScHQoUNZTFGZ4t83kgcSiQTbt2+H\npaUl6tSpA7FYjC5duvzj4+vXr4+rV68iNDQU7dq1g6amJipWrAgtLS2oq6v/6d9NTU0RGBiItLQ0\n3nCOiKiMaAgdgIiIiEhRiUQi9OrVC927d0dISAj69++P5s2bw9fXFyYmJjK77pciVBZndL5//x4z\nZ87EqVOnEBgYiF69ekn9GkT/hVvjSR6kpaVhzJgxyMnJQXR0NJo0aVKi54lEIri4uMDFxQXv3r1D\nYmIiUlJSkJ2dDU1NTZiamsLe3h41a9aU8VdARER/xSKUiIiIqJQ0NDQwcuRIDBgwABs3bkSbNm3Q\nt29f+Pj4oHbt2lK/noGBATIzM6W+bmRkJMaPH48ePXpALBZDX19f6tcgKilOhJJQ8vPz4efnh02b\nNmHhwoUYP378d5+LXKlSJTg6OsLR0VHKKYmI6HtwazwRERGRlOjo6GDWrFlIT09HxYoVYWVlhfnz\n50v9PE9pb41/+fIlfvzxR3h7e2Pfvn34+eefWYKSoDgRSkK5fPkymjRpgsTERNy6dYs3hyMiUjIs\nQomIiIikrEqVKli9ejUSExORmZkJY2NjBAQEID8/v9RrFxUVQU1NDVevXkVERATOnTuHV69efdda\nEokEISEhsLa2RoMGDZCcnAwHB4dSZySSBk6EUll6+/YtRo8ejYEDB8LX1xdHjx5F3bp1hY5FRERS\nxiKUiIiISEbq1auHnTt34uzZszh79ixMTU2xd+9eFBcXf9M6xcXFOHv2LJydnaGrq4uAgABERUVh\nxIgR6Nu3L+rUqYMaNWpgzpw5ePr0aYnWfPz4Mbp16wZ/f3+cPn0aK1euhLa29vd8mURSx4lQKisS\niQQHDx6EhYUFtLS0kJqaCjc3N6FjERGRjLAIJSIiIpIxKysrnDhxArt27cKmTZtgZ2eHqKioEpU9\naWlpsLW1hZubG3755Rfk5+cjLy8PhYWF+PDhA96/f4+CggK8evUK/v7+MDIywty5c1FQUPDV9YqK\nirBhwwbY29vDwcEBCQkJsLOzk/aXTFRqnAglWfv111/h4uKC5cuXIzw8HJs2beKxIERESo5FKBER\nEVEZcXBwQFxcHBYuXIjJkyejc+fOuH79+j8+fu/evbC1tYVYLManT5/+c/0vJen69ethbW2N58+f\n/+m/37lzB23btsWhQ4dw9epVzJkzB+XKlSv110UkbZwIJVkqLCzEmjVr0LRpU7Rv3x63bt1Cy5Yt\nhY5FRERlgEUoERERURkSiUTo06cPxGIx3N3d0bt3b/z444+4f//+nx4XEhICLy8v5ObmfvNW+pyc\nHDx48ADNmzdHVlYWCgoKsGTJErRv3x7Dhg1DTEwMTExMpPllEUmVRCLhRCjJxI0bN9C8eXNERUUh\nPj4es2fP5gdCREQqhEUoERERkQDKlSuHMWPGICMjA9bW1mjZsiUmTJiAly9fIjU1FWPHjkVubu53\nr19YWIiXL1+iS5cusLOzQ0JCAhITEzFu3DioqfElIMk/FqEkTR8/fsTUqVPRo0cPTJ8+HdHR0TA0\nNBQ6FhERlTG+CiYiIiISkK6uLubNm4d79+5BU1MTZmZm6NChA/Ly8kq99ufPn3H79m20bt0ax48f\n5x2QSWFwazxJ0/Hjx2FhYYH3798jNTUVQ4YMYdFORKSiWIQSERERyYFq1arB398fK1euxLt376Ra\nBEVERHzz9noiobGootJ69uwZ+vXrB29vb+zatQs7d+5E1apVhY5FREQCYhFKREREJEd27dqFwsJC\nqa6Zn5+PU6dOSXVNIlniRCiVRnFxMYKCgmBjYwMzMzMkJyejY8eOQsciIiI5oCF0ACIiIiL6n9zc\nXCQkJEh93Y8fP+LQoUPo2bOn1NcmkhVOhNL3EIvFGDNmDADg4sWLsLCwEDgRERHJE06EEhEREcmJ\npKQk6OjoyGTta9euyWRdIlngRCh9q9zcXMybNw+Ojo4YNmwYLl++zBKUiIj+hhOhRERERHLi/v37\nMjvL88mTJzJZl0hWOBFKJXXu3DmMHTsWtra2SE5ORq1atYSOREREcopFKBEREZGcKCgokNkknLTP\nHSWSJU6EUkm8fv0a3t7eiImJwaZNm9CjRw+hIxERkZzj1ngiIiIiOaGnpwc1Ndm8PCtfvrxM1iWS\nBYlEwolQ+kcSiQQhISGwtLRE1apVIRaLWYISEVGJcCKUiIiISE5YWVnJbGu8iYmJTNYlkhUWofQ1\n9+/fx9ixY/H777/j5MmTsLe3FzoSEREpEE6EEhEREckJY2NjFBUVSX1ddXV1tG/fXurrEskKt8bT\nXxUUFMDX1xctW7ZE9+7dkZCQwBKUiIi+GYtQIiIiIjmhrq6O/v37Q11dXerrDh06VKprEskaJ0Lp\ni7i4ONjb2+Pq1au4ceMGpk+fDg0Nbm4kIqJvxyKUiIiISI54e3tDS0tLqmt+KUL37dvHmyaRQuBE\nKAHA+/fvMX78ePTt2xcLFizAiRMn0KBBA6FjERGRAmMRSkRERCRHbGxs4OrqKrWbG2lrayMmJgZr\n167Fli1bYGJiguDgYOTn50tlfSJZ4USo6pJIJDhy5AjMzc1RVFSE1NRUuLu78+8EERGVGotQIiIi\nIjmzefNmVKxYsdRv+nV0dDBx4kQ0a9YMXbp0waVLl7Br1y5ERETA0NAQ/v7+yM7OllJqIunhRKjq\nevLkCXr37o0FCxYgNDQUW7ZsQeXKlYWORURESoJFKBEREZGc0dfXx8WLF6Gvr//dZaiOjg66du2K\nFStW/OnP27Vrh9OnT+PYsWO4evUqGjVqhOXLl+Pdu3fSiE4kNZz+Uy1FRUVYv349bG1t0axZMyQm\nJqJt27ZCxyIiIiXDIpSIiIhIDpmZmSEhIQFGRkbQ0dH5pudqa2vDy8sLYWFh/3jjJXt7exw+fBgX\nLlxAeno6DA0NMXfuXGRlZUkjPlGpcCJUtSQmJqJly5Y4evT/sXffUVpW9/q476FJsSN2o1LUSBHr\noILMqFiixhKDvUWNsRsjiTWx92M0sddj78auSBRQQcRKE0XAGKMiokZEpL+/P84x35NfLKgzPMMz\n17WWawm87Od+0bV4557P3vv+DB06NKecckqdn5UMAIkiFACgwerUqVNGjx6dfv36pXXr1mnTps3X\nvraqqipt2rTJmmuumSeffDJ//OMf5+v2+bXXXjs33nhjXnzxxXzyySdZa621cvTRR+edd96py7cC\n30mlUjER2gh8/vnn6devX7bZZpscdthheeqpp7LGGmsUHQuAElOEAgA0YM2bN8+pp56ayZMn55JL\nLslWW22VZZZZJknSpEmTNGvWLMsuu2w6dOiQJ598MmPHjs3GG2/8nZ+z+uqr54orrsjo0aPTvHnz\nrLPOOjnooIMyfvz4un5LMF8UoeX2+OOPp0uXLnn//fczatSoHHDAAf6bA1DvFKEAAAuBNm3a5MAD\nD0z//v3z4YcfZs6cOZk+fXpmzZqVwYMHZ/bs2amurv7BRcKKK66YCy+8MG+++WZWWmml9OjRI3vu\nuWdGjRpVR+8Evp2t8eX1wQcfZI899sjhhx+eq666KrfcckuWXXbZomMB0EgoQgEAFkJNmzbNIoss\nkqqqqqy55pqZOXNm3nrrrTpbv23btjnttNMyceLEdO/ePVtttVV23HHHDB8+vM6eAd/EdGC5zJs3\nL9dee226du2aVVddNaNGjcpWW21VdCwAGhlFKADAQq6qqio1NTUZOHBgna+9+OKL57e//W0mTpyY\nPn36ZNddd82WW26ZgQMHmtqj3vh/q1zGjh2bmpqaXHPNNRkwYEDOPffc73wJHADUBUUoAEAJ1NbW\nZtCgQfW2fqtWrXLEEUdk/Pjx2WuvvfKrX/0qm266aR5++GGlFfXCROjCb+bMmTn11FPTq1ev9O3b\nN0OHDs0666xTdCwAGjFFKABACdTW1i6QKc0WLVrkgAMOyGuvvZZjjjkmJ598crp3754777wzc+fO\nrddn03go1xd+gwcPzjrrrJMRI0bk1VdfzRFHHJGmTZsWHQuARk4RCgBQAh07dsy8efMyYcKEBfK8\npk2bpm/fvnnllVdy1lln5eKLL87aa6+dG264IbNmzVogGSg3E6ELp48//jgHHXRQ9t5775x77rn5\ny1/+kpVXXrnoWACQRBEKAFAKVVVV9b49/uueu/3222fo0KG58sorc9ttt6VTp0659NJL88UXXyzQ\nLJSHidCFT6VSyW233ZbOnTunVatWGTNmTHbaaaeiYwHAv1GEAgCUxJfb44vwZRE7YMCA3HXXXRkw\nYEDat2+f8847L1OnTi0kEwuvSqViInQh8tZbb2XbbbfNueeem/vvvz9//vOfs/jiixcdCwD+gyIU\nAKAkvrw5vuhpuurq6jzwwAN54oknMmLEiLRv3z6///3v89FHHxWai4WLIrThmz17di644IJsuOGG\nqa2tzUsvvZTq6uqiYwHA11KEAgCURPv27dOsWbO8+eabRUdJknTt2jW33XZbhg0blvfffz+dOnXK\ncccdl/fee6/oaDRwRZf5fLsXXnghG264YQYMGJDnn38+v/vd79K8efOiYwHAN1KEAgCUxJfb04va\nHv91OnbsmGuuuSYjRozInDlz0qVLlxx66KF56623io5GA2YitGH67LPPcvTRR2eHHXZIv3790r9/\n/3To0KHoWAAwXxShAAAl8uX2+IZolVVWycUXX5zXX389Sy21VDbYYIPsu+++GTt2bNHRaGBMhDZM\nDz74YDp37pzPPvssY8aMyV577aWwBmChoggFACiRL2+Ob8hF0rLLLpuzzz47EyZMyJp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hhx1y8skn56677sr8+fOLHRcAYJOlCAUACsrReP7RrFmz0r1795x11lm57LLL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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -631,8 +631,8 @@ }, "outputs": [], "source": [ - "eight_queens_csp = NQueensCSP(8)\n", - "backtracking_instru_queen = make_instru(eight_queens_csp)\n", + "twelve_queens_csp = NQueensCSP(12)\n", + "backtracking_instru_queen = make_instru(twelve_queens_csp)\n", "result = backtracking_search(backtracking_instru_queen)" ] }, @@ -663,9 +663,9 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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iIiIOKDBFREQcUGCKiIg4oMAUERFx4CsfXLBtV+tQ1eFY6ayB+ebsgaa1ciYW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\nRBxQYIqIiDigwBQREXFAgSkiIuLA/wGx9HtR0bJVGAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -696,8 +696,7 @@ }, "outputs": [], "source": [ - "twelve_queens_csp = NQueensCSP(12)\n", - "conflicts_instru_queen = make_instru(eight_queens_csp)\n", + "conflicts_instru_queen = make_instru(twelve_queens_csp)\n", "result = min_conflicts(conflicts_instru_queen)" ] }, @@ -728,9 +727,9 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"90d3a46fba824550b06d512a7ee51ba6": { "views": [] }, "929017ae984f46629bc194a2779327eb": { "views": [] }, + "985e23a5c55f42289a39080a8d378ab8": { + "views": [] + }, + "98ad0614d4624fe5928d71bfe1e32da1": { + "views": [] + }, + "9a5c64c0a0f04c6392b7884ff64c65f7": { + "views": [] + }, + "a18c9ddb3c0d4ce886b8f3b31e8dbb92": { + "views": [] + }, + "a3548933fc7e4c859037055d8d1fc0ab": { + "views": [] + }, + "a4fbd325f3eb4628b81345772c57e5be": { + "views": [] + }, + "a9e0b9d7f7bd444a85722a69a6035dde": { + "views": [] + }, + "b0016f7111c14e79b5be2c5aaca24c63": { + "views": [] + }, + "b07f7653ba0343b281dbc670942de37f": { + "views": [ + { + "cell_index": 51 + } + ] + }, "b3dd25b3195f46658527feef84c2caef": { "views": [] }, "b3fc0e0db39242939d56957cd645c96b": { "views": [] }, + "b4c71fb938374a2fb5fd6995e7936601": { + "views": [] + }, + "b73ac2d4487a47e79812fb369af615bb": { + "views": [] + }, "b7a0fd44074240c8882527d80c2f6c6d": { "views": [] }, + "b8ec601ed4f24bbbacf9761a1254662d": { + "views": [] + }, "bb2927544b334a1b9309336da6bec4c3": { "views": [] }, - "bdfa8758560342bd878ae5b06b45b4b8": { + "bbd54feed3b74f43ab727c3a413d7ead": { "views": [] }, - "c6b8efa97cfa4321b65590aed95875a5": { + "bca8595123d242c6a6d485f7cb0a5534": { "views": [] }, - "cfbfd71eacc649b590d5f512934de608": { + "bddf733ec5b64f8690a308d3b15419d5": { "views": [ { "cell_index": 46 } ] }, + "bdfa8758560342bd878ae5b06b45b4b8": { + "views": [] + }, + "c6b8efa97cfa4321b65590aed95875a5": { + "views": [] + }, + "cfbfd71eacc649b590d5f512934de608": { + "views": [] + }, + "cfda977df1534943a7f51597e2a1608c": { + "views": [] + }, "d0da7774d5ce443e835242bb77b21365": { "views": [] }, "d32bcd4e31b84d7b952ba19960d84906": { "views": [] }, + "d38292e6eaea477689c1d2a632d0820e": { + "views": [] + }, + "d54665321f9e4804801ab6a8b795455b": { + "views": [] + }, "d6ddae211b524deab64833883a14f28f": { "views": [] }, "d789cb6d104145ebbe9a5d2b77afe718": { "views": [] }, + "d96f52b5aeb849a081b28ed31bca6904": { + "views": [] + }, "d9e723f5807d4bb7a1722c564978a337": { "views": [] }, @@ -911,14 +1042,26 @@ "e4f69c894d1742549ea3b5d1c576d780": { "views": [] }, + "e6c8f0ab5727415a8ef87df1c499789f": { + "views": [] + }, "eaa04091ba7e49d4a62c3d6e6845ca3f": { "views": [] }, + "f28d6245207f411f850824961ae6cdfa": { + "views": [] + }, + "f3d39f32e5d64f32880f64d2a8f36813": { + "views": [] + }, "fb4ee56210f24757b93f94f392de1a9f": { "views": [] }, "fcd462cccda040a68f002169df257f3a": { "views": [] + }, + "fe05ed9854354e3e9d436ea7ab7b7302": { + "views": [] } }, "version": "1.1.1" From 030c27fb367a70bbf8daf6506cabf9f23d578a27 Mon Sep 17 00:00:00 2001 From: SnShine Date: Mon, 13 Jun 2016 20:56:17 +0530 Subject: [PATCH 314/513] used sgb-words from aimadata rather than downloading a local copy --- search-4e.ipynb | 227 ++++++++++++++++++++++-------------------------- 1 file changed, 104 insertions(+), 123 deletions(-) diff --git a/search-4e.ipynb b/search-4e.ipynb index 4ef222b75..100e0bcda 100644 --- a/search-4e.ipynb +++ b/search-4e.ipynb @@ -328,7 +328,7 @@ "\n", "`green` → `greed` → `treed` → `trees` → `tress` → `cress` → `crass` → `grass`\n", "\n", - "We will need a dictionary of words. I'll make a local copy of the list of 5-letter words from the [Stanford GraphBase](http://www-cs-faculty.stanford.edu/~uno/sgb.html) project (the `!` indicates that these are shell commands, not Python):" + "We will need a dictionary of words. We'll use 5-letter words from the [Stanford GraphBase](http://www-cs-faculty.stanford.edu/~uno/sgb.html) project for this purpose. Let's get that file from aimadata." ] }, { @@ -345,41 +345,8 @@ }, "outputs": [], "source": [ - "! [ -e sgb-words.txt ] || curl -O http://www-cs-faculty.stanford.edu/~uno/sgb-words.txt" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "which\r\n", - "there\r\n", - "their\r\n", - "about\r\n", - "would\r\n", - "these\r\n", - "other\r\n", - "words\r\n", - "could\r\n", - "write\r\n" - ] - } - ], - "source": [ - "! head sgb-words.txt" + "from search import *\n", + "sgb_words = DataFile(\"EN-text/sgb-words.txt\")" ] }, { @@ -398,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "button": false, "collapsed": false, @@ -415,13 +382,13 @@ "5757" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "WORDS = set(open('sgb-words.txt').read().split())\n", + "WORDS = set(sgb_words.read().split())\n", "len(WORDS)" ] }, @@ -441,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "button": false, "collapsed": false, @@ -471,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "button": false, "collapsed": false, @@ -488,7 +455,7 @@ "{'cello', 'hallo', 'hells', 'hullo', 'jello'}" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -499,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -510,7 +477,7 @@ "{'would'}" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -535,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "button": false, "collapsed": false, @@ -567,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "button": false, "collapsed": false, @@ -581,10 +548,10 @@ { "data": { "text/plain": [ - "['green', 'greed', 'treed', 'trees', 'tress', 'cress', 'crass', 'grass']" + "['green', 'greed', 'treed', 'trees', 'treys', 'greys', 'grays', 'grass']" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -595,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "button": false, "collapsed": false, @@ -621,7 +588,7 @@ " 'brain']" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -632,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "button": false, "collapsed": false, @@ -650,15 +617,15 @@ " 'flown',\n", " 'flows',\n", " 'slows',\n", - " 'slots',\n", - " 'slits',\n", - " 'spits',\n", - " 'spite',\n", - " 'smite',\n", + " 'stows',\n", + " 'stoas',\n", + " 'stoae',\n", + " 'stole',\n", + " 'stile',\n", " 'smile']" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "button": false, "collapsed": false, @@ -739,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "button": false, "collapsed": false, @@ -776,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "button": false, "collapsed": true, @@ -812,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "button": false, "collapsed": false, @@ -881,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "button": false, "collapsed": false, @@ -922,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "button": false, "collapsed": true, @@ -989,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "button": false, "collapsed": false, @@ -1030,7 +997,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -1069,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "button": false, "collapsed": false, @@ -1104,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": { "button": false, "collapsed": false, @@ -1121,7 +1088,7 @@ "" ] }, - "execution_count": 27, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1134,7 +1101,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -1145,7 +1112,7 @@ "['Suck', 'E', 'Suck']" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1156,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1167,7 +1134,7 @@ "[('W', '*', '*'), ('W', ' ', '*'), ('E', ' ', '*'), ('E', ' ', ' ')]" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1178,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": { "button": false, "collapsed": false, @@ -1195,7 +1162,7 @@ "['Suck']" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1224,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": { "button": false, "collapsed": false, @@ -1274,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "button": false, "collapsed": false, @@ -1291,7 +1258,7 @@ "(2, 13)" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1303,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "button": false, "collapsed": false, @@ -1320,7 +1287,7 @@ "[('Pour', 0, 1), ('Fill', 0), ('Pour', 0, 1)]" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1346,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "button": false, "collapsed": false, @@ -1392,7 +1359,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": { "button": false, "collapsed": false, @@ -1422,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -1454,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -1471,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -1503,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { "button": false, "collapsed": true, @@ -1525,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": { "button": false, "collapsed": false, @@ -1587,7 +1554,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": { "collapsed": false }, @@ -1598,31 +1565,31 @@ "{(0, 0): [(0, 1), (1, 0)],\n", " (0, 1): [(0, 2), (0, 0), (1, 1)],\n", " (0, 2): [(0, 3), (0, 1), (1, 2)],\n", - " (0, 3): [(0, 4), (0, 2)],\n", + " (0, 3): [(0, 4), (0, 2), (1, 3)],\n", " (0, 4): [(0, 3), (1, 4)],\n", " (1, 0): [(1, 1), (2, 0), (0, 0)],\n", " (1, 1): [(1, 2), (1, 0), (2, 1), (0, 1)],\n", - " (1, 2): [(1, 1), (2, 2), (0, 2)],\n", - " (1, 3): [(1, 4), (1, 2), (0, 3)],\n", - " (1, 4): [(2, 4), (0, 4)],\n", + " (1, 2): [(1, 3), (1, 1), (2, 2), (0, 2)],\n", + " (1, 3): [(1, 4), (1, 2), (2, 3), (0, 3)],\n", + " (1, 4): [(1, 3), (2, 4), (0, 4)],\n", " (2, 0): [(2, 1), (3, 0), (1, 0)],\n", " (2, 1): [(2, 2), (2, 0), (3, 1), (1, 1)],\n", - " (2, 2): [(2, 1), (3, 2), (1, 2)],\n", - " (2, 3): [(2, 4), (2, 2), (3, 3)],\n", - " (2, 4): [(3, 4), (1, 4)],\n", + " (2, 2): [(2, 3), (2, 1), (3, 2), (1, 2)],\n", + " (2, 3): [(2, 4), (2, 2), (1, 3)],\n", + " (2, 4): [(2, 3), (1, 4)],\n", " (3, 0): [(3, 1), (4, 0), (2, 0)],\n", " (3, 1): [(3, 2), (3, 0), (4, 1), (2, 1)],\n", - " (3, 2): [(3, 3), (3, 1), (4, 2), (2, 2)],\n", - " (3, 3): [(3, 4), (3, 2), (4, 3)],\n", - " (3, 4): [(3, 3), (4, 4), (2, 4)],\n", + " (3, 2): [(3, 1), (4, 2), (2, 2)],\n", + " (3, 3): [(3, 2), (4, 3), (2, 3)],\n", + " (3, 4): [(4, 4), (2, 4)],\n", " (4, 0): [(4, 1), (3, 0)],\n", " (4, 1): [(4, 2), (4, 0), (3, 1)],\n", " (4, 2): [(4, 3), (4, 1), (3, 2)],\n", - " (4, 3): [(4, 4), (4, 2), (3, 3)],\n", - " (4, 4): [(4, 3), (3, 4)]}" + " (4, 3): [(4, 4), (4, 2)],\n", + " (4, 4): [(4, 3)]}" ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1651,7 +1618,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": { "collapsed": true }, @@ -1670,7 +1637,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": { "collapsed": false }, @@ -1681,7 +1648,7 @@ "text": [ "\n", "uniform_cost_search:\n", - "no solution after 12 results and 3 goal checks\n" + "no solution after 132 results and 33 goal checks\n" ] } ], @@ -1708,7 +1675,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { "button": false, "collapsed": false, @@ -1728,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { "button": false, "collapsed": false, @@ -1745,7 +1712,7 @@ "3" ] }, - "execution_count": 45, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1756,7 +1723,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -1767,7 +1734,7 @@ "[('Pour', 0, 1), ('Fill', 0), ('Pour', 0, 1)]" ] }, - "execution_count": 46, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1778,7 +1745,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": { "button": false, "collapsed": false, @@ -1795,7 +1762,7 @@ "((0, 0), (7, 9), {8})" ] }, - "execution_count": 47, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1814,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -1849,7 +1816,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": { "button": false, "collapsed": false, @@ -1890,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": { "button": false, "collapsed": true, @@ -1919,7 +1886,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": { "button": false, "collapsed": false, @@ -1965,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": { "button": false, "collapsed": false, @@ -1982,7 +1949,7 @@ "'test_frontier ok'" ] }, - "execution_count": 52, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -2045,7 +2012,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": { "button": false, "collapsed": false, @@ -2060,7 +2027,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2077,7 +2044,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": { "button": false, "collapsed": false, @@ -2090,9 +2057,9 @@ "outputs": [ { "data": { - "image/png": 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ZWTU0NKiyslJVVVX6+OOP13PstM3MzOiLL77Q559/rh9++OGV87/88ou+/vpr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\nobwBADAM5Q0AgGH+P3KmhkzzUJPZAAAAAElFTkSuQmCC\n", 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K169fV3FxsRoaGtTe3q5QKJS6Zvv27Tp37pwuX76cdq/H41FfX5/q6uoUi8VUX1+vlpaW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\nz+d2u7W0tJTrMbLG6evn5Pc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2129,12 +2096,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ + "import collections\n", "class defaultkeydict(collections.defaultdict):\n", " \"\"\"Like defaultdict, but the default_factory is a function of the key.\n", " >>> d = defaultkeydict(abs); d[-42]\n", @@ -2144,6 +2112,15 @@ " self[key] = self.default_factory(key)\n", " return self[key]" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -2163,6 +2140,10 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" + }, + "widgets": { + "state": {}, + "version": "1.1.1" } }, "nbformat": 4, From cd24621acb6763fd1ab00d3282060fa32f869b19 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 14 Jun 2016 00:21:44 +0530 Subject: [PATCH 315/513] adds skeletal romania map in search notebook --- search.ipynb | 125 ++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 114 insertions(+), 11 deletions(-) diff --git a/search.ipynb b/search.ipynb index 0fa3575b9..3f3c5575c 100644 --- a/search.ipynb +++ b/search.ipynb @@ -6,8 +6,7 @@ "collapsed": true }, "source": [ - "# The search.py module\n", - "*Date: 14 March 2016*" + "# The search.py module" ] }, { @@ -43,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 81, "metadata": { "collapsed": false }, @@ -85,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -138,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -156,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -167,7 +166,7 @@ "['Cat', 'Monkey']" ] }, - "execution_count": 19, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -211,7 +210,7 @@ "['Dog', 'Bear', 'Monkey']" ] }, - "execution_count": 20, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -230,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -241,7 +240,7 @@ "(18, 17)" ] }, - "execution_count": 21, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -259,6 +258,106 @@ "The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former passes through two roads compared to the three roads in the `ucs_node` solution." ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Romania map visualisation\n", + "\n", + "Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem to reach 'Bucharest' starting from 'Arad'. This is how the problem is defined:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Rimnicu': (233, 410), 'Oradea': (131, 571), 'Zerind': (108, 531), 'Mehadia': (168, 339), 'Timisoara': (94, 410), 'Arad': (91, 492), 'Bucharest': (400, 327), 'Lugoj': (165, 379), 'Sibiu': (207, 457), 'Drobeta': (165, 299), 'Vaslui': (509, 444), 'Hirsova': (534, 350), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Craiova': (253, 288), 'Urziceni': (456, 350), 'Eforie': (562, 293), 'Pitesti': (320, 368), 'Iasi': (473, 506), 'Fagaras': (305, 449)}\n" + ] + } + ], + "source": [ + "romania_locations = romania_map.locations\n", + "print(romania_locations)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Pmk7BNXI4HAoKCtK8efM0ZswY0zkAADS4mpoarVq1SqmpqbJarXruuecUGRl5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\nB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = nx.Graph()\n", + "\n", + "for n, p in romania_locations.items():\n", + "# print(n)\n", + " # add nodes from romania_locations\n", + " G.add_node(n)\n", + " \n", + "# print(p)\n", + " # add positions for each node\n", + " G.node[n]['pos'] = p\n", + " \n", + "# add edges between cities in romania map - UndirectedGraph defined in search.py\n", + "for node in romania_map.nodes():\n", + "# print(node)\n", + " connections = romania_map.get(node)\n", + "# print((connections))\n", + " for connection in connections.keys():\n", + " G.add_edge(node, connection)\n", + " \n", + "\n", + "# draw the graph with locations from romania_locations\n", + "plt.figure(figsize=(15,10))\n", + "nx.draw(G, romania_locations)\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -286,6 +385,10 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" + }, + "widgets": { + "state": {}, + "version": "1.1.1" } }, "nbformat": 4, From d7bcf3a5feb377742f9e6980de0a0fef1fbf6635 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 14 Jun 2016 12:22:08 +0530 Subject: [PATCH 316/513] adds node labels, edge labels to romania_map --- search.ipynb | 67 +++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 48 insertions(+), 19 deletions(-) diff --git a/search.ipynb b/search.ipynb index 3f3c5575c..ff27e8cdc 100644 --- a/search.ipynb +++ b/search.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -280,9 +280,16 @@ "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Have a look at `romania_locations`. We will use these location values to draw the romania graph using **networkx**." + ] + }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -291,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Rimnicu': (233, 410), 'Oradea': (131, 571), 'Zerind': (108, 531), 'Mehadia': (168, 339), 'Timisoara': (94, 410), 'Arad': (91, 492), 'Bucharest': (400, 327), 'Lugoj': (165, 379), 'Sibiu': (207, 457), 'Drobeta': (165, 299), 'Vaslui': (509, 444), 'Hirsova': (534, 350), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Craiova': (253, 288), 'Urziceni': (456, 350), 'Eforie': (562, 293), 'Pitesti': (320, 368), 'Iasi': (473, 506), 'Fagaras': (305, 449)}\n" + "{'Lugoj': (165, 379), 'Hirsova': (534, 350), 'Urziceni': (456, 350), 'Bucharest': (400, 327), 'Timisoara': (94, 410), 'Oradea': (131, 571), 'Iasi': (473, 506), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Arad': (91, 492), 'Zerind': (108, 531), 'Neamt': (406, 537), 'Pitesti': (320, 368), 'Eforie': (562, 293), 'Drobeta': (165, 299), 'Vaslui': (509, 444), 'Mehadia': (168, 339), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Craiova': (253, 288)}\n" ] } ], @@ -302,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -315,16 +322,16 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Pmk7BNXI4HAoKCtK8efM0ZswY0zkAADS4mpoarVq1SqmpqbJarXruuecUGRl5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\nB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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -334,27 +341,49 @@ "source": [ "G = nx.Graph()\n", "\n", + "# use this while labeling nodes in the map\n", + "node_labels = dict()\n", + "\n", "for n, p in romania_locations.items():\n", - "# print(n)\n", " # add nodes from romania_locations\n", " G.add_node(n)\n", - " \n", - "# print(p)\n", - " # add positions for each node\n", - " G.node[n]['pos'] = p\n", - " \n", + " # add nodes to node_labels\n", + " node_labels[n] = n\n", + "\n", + "# positions for node labels\n", + "node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n", + "\n", + "# use thi whiel labeling edges\n", + "edge_labels = dict()\n", + "\n", "# add edges between cities in romania map - UndirectedGraph defined in search.py\n", "for node in romania_map.nodes():\n", - "# print(node)\n", " connections = romania_map.get(node)\n", - "# print((connections))\n", " for connection in connections.keys():\n", + " distance = connections[connection]\n", + " # add edges to the graph\n", " G.add_edge(node, connection)\n", - " \n", + " # add distances to edge_labels\n", + " edge_labels[(node, connection)] = distance\n", "\n", + " \n", + "# initial colors for all the nodes\n", + "node_colors = [\"w\" for i in G.nodes()]\n", + " \n", + "# set the size of the plot\n", + "plt.figure(figsize=(18,13))\n", "# draw the graph with locations from romania_locations\n", - "plt.figure(figsize=(15,10))\n", - "nx.draw(G, romania_locations)\n", + "nx.draw(G, pos = romania_locations, node_color = node_colors)\n", + "\n", + "# draw labels for nodes\n", + "node_label_handles = nx.draw_networkx_labels(G, pos = node_label_pos, labels = node_labels, font_size = 14)\n", + "# add a white bounding box behind the node labels\n", + "[label.set_bbox(dict(facecolor='white', edgecolor='none')) for label in node_label_handles.values()]\n", + "\n", + "# add edge lables to the graph\n", + "nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n", + "\n", + "# show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", "plt.show()" ] }, From a3bdaf6cddc37f440b841f6e77e80d13f3f89037 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 14 Jun 2016 17:44:48 +0530 Subject: [PATCH 317/513] adds visualisation for breadth_first_tree_search on romania map in notebook --- search.ipynb | 647 ++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 613 insertions(+), 34 deletions(-) diff --git a/search.ipynb b/search.ipynb index ff27e8cdc..e8b9e8256 100644 --- a/search.ipynb +++ b/search.ipynb @@ -277,19 +277,19 @@ }, "outputs": [], "source": [ - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + "romania_problem = GraphProblem('Oradea', 'Fagaras', romania_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Have a look at `romania_locations`. We will use these location values to draw the romania graph using **networkx**." + "Have a look at `romania_locations`. It is a dictionary defined in search module. We will use these location values to draw the romania graph using **networkx**." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Lugoj': (165, 379), 'Hirsova': (534, 350), 'Urziceni': (456, 350), 'Bucharest': (400, 327), 'Timisoara': (94, 410), 'Oradea': (131, 571), 'Iasi': (473, 506), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Arad': (91, 492), 'Zerind': (108, 531), 'Neamt': (406, 537), 'Pitesti': (320, 368), 'Eforie': (562, 293), 'Drobeta': (165, 299), 'Vaslui': (509, 444), 'Mehadia': (168, 339), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Craiova': (253, 288)}\n" + "{'Neamt': (406, 537), 'Giurgiu': (375, 270), 'Vaslui': (509, 444), 'Lugoj': (165, 379), 'Fagaras': (305, 449), 'Sibiu': (207, 457), 'Bucharest': (400, 327), 'Iasi': (473, 506), 'Oradea': (131, 571), 'Craiova': (253, 288), 'Rimnicu': (233, 410), 'Drobeta': (165, 299), 'Hirsova': (534, 350), 'Eforie': (562, 293), 'Pitesti': (320, 368), 'Arad': (91, 492), 'Zerind': (108, 531), 'Urziceni': (456, 350), 'Mehadia': (168, 339), 'Timisoara': (94, 410)}\n" ] } ], @@ -317,38 +317,35 @@ "source": [ "%matplotlib inline\n", "import networkx as nx\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "from networkx.readwrite import json_graph\n", + "from copy import copy, deepcopy\n", + "import time" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 10, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "# initialise a graph\n", "G = nx.Graph()\n", "\n", "# use this while labeling nodes in the map\n", "node_labels = dict()\n", + "node_colors = dict()\n", "\n", "for n, p in romania_locations.items():\n", " # add nodes from romania_locations\n", " G.add_node(n)\n", " # add nodes to node_labels\n", " node_labels[n] = n\n", + " # node_colors to color nodes while exploring romania map\n", + " node_colors[n] = \"w\"\n", "\n", "# positions for node labels\n", "node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n", @@ -364,27 +361,214 @@ " # add edges to the graph\n", " G.add_edge(node, connection)\n", " # add distances to edge_labels\n", - " edge_labels[(node, connection)] = distance\n", + " edge_labels[(node, connection)] = distance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def show_map(node_colors):\n", + " # set the size of the plot\n", + " plt.figure(figsize=(18,13))\n", + " # draw the graph with locations from romania_locations\n", + " nx.draw(G, pos = romania_locations, node_color = [node_colors[node] for node in G.nodes()])\n", + "\n", + " # draw labels for nodes\n", + " node_label_handles = nx.draw_networkx_labels(G, pos = node_label_pos, labels = node_labels, font_size = 14)\n", + " # add a white bounding box behind the node labels\n", + " [label.set_bbox(dict(facecolor='white', edgecolor='none')) for label in node_label_handles.values()]\n", + "\n", + " # add edge lables to the graph\n", + " nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n", + "\n", + " # show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", + "# plt.clf()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_map(node_colors)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def tree_search(problem, frontier):\n", + " \"\"\"Search through the successors of a problem to find a goal.\n", + " The argument frontier should be an empty queue.\n", + " Don't worry about repeated paths to a state. [Figure 3.7]\"\"\"\n", + " \n", + " global iterations\n", + " iterations = 0\n", + " global all_node_colors\n", + " all_node_colors = []\n", + " \n", + " frontier.append(Node(problem.initial))\n", + "\n", + " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", + " for n in frontier_list:\n", + " node_colors[n.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " \n", + " while frontier:\n", + " node = frontier.pop()\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " \n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " \n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " return node\n", + " frontier.extend(node.expand(problem))\n", + "\n", + " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", + " for n in frontier_list:\n", + " # modified node color category to 'is_frontier'\n", + " node_colors[n.state] = \"blue\"\n", + " \n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", "\n", + " # modify node color category to 'already_explored'\n", + " node_colors[node.state] = \"gray\"\n", " \n", - "# initial colors for all the nodes\n", - "node_colors = [\"w\" for i in G.nodes()]\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", " \n", - "# set the size of the plot\n", - "plt.figure(figsize=(18,13))\n", - "# draw the graph with locations from romania_locations\n", - "nx.draw(G, pos = romania_locations, node_color = node_colors)\n", + " return None\n", + "\n", + "def breadth_first_tree_search(problem):\n", + " \"Search the shallowest nodes in the search tree first.\"\n", + " return tree_search(problem, FIFOQueue())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Sibiu', 'Fagaras']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "breadth_first_tree_search(romania_problem).solution()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "83\n", + "83\n" + ] + } + ], + "source": [ + "print(len(all_node_colors))\n", + "print(iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from ipywidgets import interact\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", "\n", - "# draw labels for nodes\n", - "node_label_handles = nx.draw_networkx_labels(G, pos = node_label_pos, labels = node_labels, font_size = 14)\n", - "# add a white bounding box behind the node labels\n", - "[label.set_bbox(dict(facecolor='white', edgecolor='none')) for label in node_label_handles.values()]\n", + "def slider_callback(iteration):\n", + " show_map(all_node_colors[iteration])\n", "\n", - "# add edge lables to the graph\n", - "nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n", + "def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KlMjTbMVJQkKCGjRooK1btzILBQCAApCSkqKZM2dqzpw5evrppzV27FhVrlzZ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\nCQAAAAAsyU8AAAAAYEl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", + "w = widgets.interactive(slider_callback, iteration = slider)\n", + "display(w)\n", "\n", - "# show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", - "plt.show()" + "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "a = widgets.interactive(visualize_callback, Visualize = button)\n", + "display(a)" ] }, { @@ -416,7 +600,402 @@ "version": "3.5.1" }, "widgets": { - "state": {}, + "state": { + "00a017b52bdf4b9ba44bfd439576c502": { + "views": [] + }, + "0110891787e744f7b7e7bb869b7c811d": { + "views": [] + }, + "028b36de20cf414fa15ecb9e96c2fede": { + "views": [] + }, + "03d2cf1d8a6f4c2895749174929e491a": { + "views": [] + }, + "0816711892aa474590a633b9b6dbabfe": { + "views": [] + }, + "081abfe146f749e0b26f3af9e4d90e2c": { + "views": [] + }, + "08eb9ee40f9c4c618cf5ec4e488c5bdb": { + "views": [] + }, + "0fc05cebf88a4f93bcd88a2274f6de16": { + "views": [] + }, + "10c29a41c87b4aa38e5376ca1845cba7": { + "views": [] + }, + "1105d7a1fcf64222ac3885d6ef20c475": { + "views": [] + }, + "11c4c53376784a61a7148ab8b968f75f": { + "views": [] + }, + 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355924c0db3881626363f03b1654d23130355540 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 14 Jun 2016 19:59:30 +0530 Subject: [PATCH 318/513] adds interactive visual for breadth first search --- search.ipynb | 677 ++++++++++++++++++++++++++++----------------------- 1 file changed, 375 insertions(+), 302 deletions(-) diff --git a/search.ipynb b/search.ipynb index e8b9e8256..e65585db6 100644 --- a/search.ipynb +++ b/search.ipynb @@ -264,7 +264,7 @@ "collapsed": true }, "source": [ - "# Romania map visualisation\n", + "# Romania map visualisations\n", "\n", "Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem to reach 'Bucharest' starting from 'Arad'. This is how the problem is defined:" ] @@ -277,7 +277,7 @@ }, "outputs": [], "source": [ - "romania_problem = GraphProblem('Oradea', 'Fagaras', romania_map)" + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" ] }, { @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Neamt': (406, 537), 'Giurgiu': (375, 270), 'Vaslui': (509, 444), 'Lugoj': (165, 379), 'Fagaras': (305, 449), 'Sibiu': (207, 457), 'Bucharest': (400, 327), 'Iasi': (473, 506), 'Oradea': (131, 571), 'Craiova': (253, 288), 'Rimnicu': (233, 410), 'Drobeta': (165, 299), 'Hirsova': (534, 350), 'Eforie': (562, 293), 'Pitesti': (320, 368), 'Arad': (91, 492), 'Zerind': (108, 531), 'Urziceni': (456, 350), 'Mehadia': (168, 339), 'Timisoara': (94, 410)}\n" + "{'Neamt': (406, 537), 'Craiova': (253, 288), 'Fagaras': (305, 449), 'Drobeta': (165, 299), 'Timisoara': (94, 410), 'Sibiu': (207, 457), 'Urziceni': (456, 350), 'Giurgiu': (375, 270), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Rimnicu': (233, 410), 'Arad': (91, 492), 'Mehadia': (168, 339), 'Hirsova': (534, 350), 'Oradea': (131, 571), 'Zerind': (108, 531), 'Pitesti': (320, 368), 'Lugoj': (165, 379), 'Bucharest': (400, 327), 'Eforie': (562, 293)}\n" ] } ], @@ -307,6 +307,13 @@ "print(romania_locations)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in notebook and we use ipywidgets to interact with the map to see how the searching algorithm works." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -318,15 +325,23 @@ "%matplotlib inline\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", - "import pickle\n", - "from networkx.readwrite import json_graph\n", - "from copy import copy, deepcopy\n", + "\n", + "from ipywidgets import interact\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", "import time" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get started by initializing an empty graph. We will add nodes, place the nodes in their location as shown in the book, add edges to the graph." + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -337,6 +352,8 @@ "\n", "# use this while labeling nodes in the map\n", "node_labels = dict()\n", + "# use this to modify colors of nodes while exploring the graph.\n", + "# This is the only dict we send to `show_map(node_colors)` while drawing the map\n", "node_colors = dict()\n", "\n", "for n, p in romania_locations.items():\n", @@ -345,8 +362,10 @@ " # add nodes to node_labels\n", " node_labels[n] = n\n", " # node_colors to color nodes while exploring romania map\n", - " node_colors[n] = \"w\"\n", + " node_colors[n] = \"white\"\n", "\n", + "initial_node_colors = dict(node_colors)\n", + " \n", "# positions for node labels\n", "node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n", "\n", @@ -364,9 +383,16 @@ " edge_labels[(node, connection)] = distance" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have completed building our graph based on romania_map and its locations. It's time to display it here in the notebook. This function `show_map(node_colors)` helps us do that. We will be calling this function later on to display the map at each and every interval step while searching using variety of algorithms from the book." + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 32, "metadata": { "collapsed": true }, @@ -387,13 +413,19 @@ " nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n", "\n", " # show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", - "# plt.clf()\n", " plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can simply call the function with node_colors dictionary object to display it." + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "metadata": { "collapsed": false }, @@ -402,7 +434,7 @@ "data": { "image/png": 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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -413,9 +445,65 @@ "show_map(node_colors)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! You see, the romania map as shown in the Figure[3.2] in the book. Now, see how different searching algorithms perform with our problem statements." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Searching algorithms\n", + "\n", + "In this section, we have visualisations of the following searching algorithms:\n", + "\n", + "1. breadth_first_tree_search\n", + "2. depth_first_tree_search\n", + "3. depth_first_graph_search\n", + "4. breadth_first_search\n", + "5. best_first_graph_search\n", + "6. uniform_cost_search\n", + "7. depth_limited_search\n", + "8. iterative_deepening_search\n", + "9. astar_search\n", + "10. recursive_best_first_search\n", + "\n", + "We add the colors to the nodes to have a nice visualisation when displaying. So, these are the different colors we are using in these visuals:\n", + "* Un-explored nodes - white\n", + "* Frontier nodes - blue\n", + "* Currently exploring node - red\n", + "* Already explored nodes - gray\n", + "* Goal node - green" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Breadth first tree search\n", + "\n", + "We have a working implementation in search module. But as we want to interact with the graph while it is searching, we need to modify the implementation. Here's the modified breadth first tree search.\n", + "\n", + "Let's define a problem statement:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)" + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -426,48 +514,47 @@ " The argument frontier should be an empty queue.\n", " Don't worry about repeated paths to a state. [Figure 3.7]\"\"\"\n", " \n", + " # we use these two variables at the time of visualisations\n", " global iterations\n", " iterations = 0\n", " global all_node_colors\n", " all_node_colors = []\n", " \n", " frontier.append(Node(problem.initial))\n", - "\n", + " \n", + " # modify the color of frontier nodes to blue\n", " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", " for n in frontier_list:\n", " node_colors[n.state] = \"blue\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", - " \n", " while frontier:\n", " node = frontier.pop()\n", " \n", + " # modify the currently searching node to red\n", " node_colors[node.state] = \"red\"\n", - " \n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", " if problem.goal_test(node.state):\n", + " # modify goal node to green after reaching the goal\n", " node_colors[node.state] = \"green\"\n", - " \n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " \n", " return node\n", + " \n", " frontier.extend(node.expand(problem))\n", - "\n", + " \n", + " # modify the color of frontier nodes to blue\n", " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", " for n in frontier_list:\n", - " # modified node color category to 'is_frontier'\n", " node_colors[n.state] = \"blue\"\n", - " \n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", "\n", - " # modify node color category to 'already_explored'\n", + " # modify the color of explored nodes to gray\n", " node_colors[node.state] = \"gray\"\n", - " \n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", @@ -478,31 +565,181 @@ " return tree_search(problem, FIFOQueue())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's call the `modified breadth_first_tree_search` with our problem statement. Print `iterations` to see how many intermediate steps we have got " + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90\n", + "90\n" + ] + } + ], + "source": [ + "breadth_first_tree_search(romania_problem).solution()\n", + "\n", + "print(len(all_node_colors))\n", + "print(iterations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback function which are called when we interact with slider and the button.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def slider_callback(iteration):\n", + " show_map(all_node_colors[iteration])\n", + "\n", + "def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "image/png": 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oeXt7Gx0LAACYHOUnkM+aNGmrHTv6SXo222O4ujbRypVhatu2be4FK4Leffdd\nffHFF9q0aRObHwEAAAAAYEIPstgggFxw5swZSQ/naIz09If/3zjIiZCQEMXFxWn16tVGRwEAAAAA\nAHmA8hPIZ7duJUtyytEYGRlOSk5Ozp1ARZidnZ3mzJmj1157TUlJSUbHAQAAAAAAuYzyE8hnLi7u\nkhJzNIad3RW5u7vnTqAirkWLFmrWrJnefvtto6MAAIC/uXnzptERAACACVB+AvmsceN6srHZnIMR\nUpWe/u1977CKfxcREaEFCxbo6NGjRkcBAAD/T/Xq1bVw4UKlpqYaHQUAABRilJ9APnvttQEqVmyB\npPRsjvC5atasptq1a+dmrCLtoYce0ogRIzRkyBCjowAAkGO9e/eWjY2NJk+enOX4tm3bZGNjo8uX\nLxuU7C+LFy+Wq6vrv163cuVKrVixQrVq1dKyZcuUnp7d350AAEBRRvkJ5LP69eurUqVykr7O1v0u\nLnP1+usDczcUNGTIEP3+++9at26d0VEAAMgRi8UiJycnRURE6NKlS3ecM5rVar2vHI0bN9aWLVv0\n/vvva86cOapTp47WrFkjq9WaDykBAIBZUH4CBggPHy1n54GSHmzHdlvbmSpd+oL+85//5E2wIszB\nwUHvvvuuhgwZwhpjAIBCLzAwUD4+PpowYcI9rzl48KA6dOggNzc3lStXTkFBQYqLi8s8v2fPHrVt\n21ZlypSRu7u7mjdvrh07dmQZw8bGRgsWLFCXLl1UvHhx1axZU999953Onj2rdu3aycXFRY888oj2\n7t0r6a/Zp3379lVSUpJsbGxka2v7jxklqWXLlvrpp580ZcoUjR8/Xg0bNtTGjRspQQEAwH2h/AQM\n0LFjR40aFSpn55aSjt3XPba2M1WixDR9993XcnBwyNuARVTbtm3l7++vadOmGR0FAIAcsbGx0ZQp\nU7RgwQKdOHHijvN//vmnWrRooYCAAO3Zs0dbtmxRUlKSOnfunHnNtWvX9MILL+jHH3/U7t279cgj\nj+jpp59WQkJClrEmT56soKAg7d+/Xw0aNNBzzz2nfv36aeDAgdq7d6+8vLzUu3dvSVLTpk01c+ZM\nOTs7Ky4uTufPn9ewYcP+9f1YLBZ16NBB0dHRGj58uAYPHqwWLVro+++/z9kPCgAAmJ7FykemgGHm\nzJmvESPGKi2tj1JTB0iq/D9XpEv6SsWLz1Hp0me0bdt6VapUyYCkRceJEyfUoEEDRUdHq2LFikbH\nAQDggfVN4/EWAAAgAElEQVTp00eXLl3SF198oZYtW8rT01NRUVHatm2bWrZsqfj4eM2cOVM///yz\nNm3alHlfQkKCSpUqpV27dql+/fp3jGu1WvXQQw9p6tSpCgoKkvRXyfrmm29q0qRJkqSYmBj5+/tr\nxowZGjx4sCRleV0PDw8tXrxYgwYN0tWrV7P9HtPS0rR06VKNHz9eNWvW1OTJk/Xoo49mezwAAGBe\nzPwEDBQaOkD79v2kRo2iZWcXIFfXNnJ0HCQ7u+Fydu4nZ+cq8vV9S/Pm9dShQ9EUn/mgcuXKGjRo\nkIYOHWp0FAAAciw8PFwrV67Ur7/+muV4dHS0tm3bJldX18yvihUrymKx6Nixv55KiY+PV3BwsGrW\nrKkSJUrIzc1N8fHxOnXqVJax/P39M/9crlw5ScqyMePtYxcuXMi192VnZ6fevXvr8OHD6tSpkzp1\n6qRu3bopJiYm114DAACYg53RAYCirlq1akpMjNMXX3yqpKQknTt3Tjdv3lSJEtVVv36I6tWrZ3TE\nImfEiBHy9fXV5s2b1bp1a6PjAACQbQ0aNFDXrl01fPhwjRkzJvN4RkaGOnTooGnTpt2xdubtsvKF\nF15QfHy8Zs2apUqVKsnR0VEtW7ZUSkpKluvt7e0z/3x7I6P/PWa1WpWRkZHr78/BwUEhISHq3bu3\n5s2bp8DAQLVt21ZhYWGqWrVqrr8eAAAofCg/AYNZLBb99ttvRsfA3zg5OWnmzJkaNGiQ9u3bxxqr\nAIBC7a233pKvr682bNiQeaxevXpauXKlKlasKFtb27ve9+OPP2r27Nlq166dJGWu0Zkdf9/d3cHB\nQenp6dka516cnZ01bNgwvfTSS5oxY4YaNWqkbt26acyYMfL29s7V1wIAAIULj70DwF106tRJPj4+\nmj17ttFRAADIkapVqyo4OFizZs3KPDZw4EBduXJF3bt3165du3TixAlt3rxZwcHBSkpKkiTVqFFD\nS5cuVWxsrHbv3q0ePXrI0dExWxn+PrvUx8dHN2/e1ObNm3Xp0iUlJyfn7A3+jZubm8aNG6fDhw+r\nRIkSCggI0KuvvvrAj9zndjkLAACMQ/kJAHdhsVg0a9Ysvf3229me5QIAQEExZswY2dnZZc7ALF++\nvH788UfZ2trqqaeeUu3atTVo0CAVK1Yss+BctGiRrl+/rvr16ysoKEj//e9/5ePjk2Xcv8/ovN9j\nTZo00csvv6wePXqobNmyioiIyMV3+pdSpUopPDxcMTExSktLU61atTRq1Kg7dqr/X2fPnlV4eLh6\n9eqlN998U7du3cr1bAAAIH+x2zsA/IM33nhDZ86c0ZIlS4yOAgAAsumPP/7QhAkTtGHDBp0+fVo2\nNnfOAcnIyFCXLl3022+/KSgoSN9//70OHTqk2bNn6//8n/8jq9V612IXAAAUbJSfAPAPrl+/rlq1\namn58uVq1qyZ0XEAAEAOXLlyRW5ubnctMU+dOqUnn3xSr7/+uvr06SNJmjJlijZs2KCvv/5azs7O\n+R0XAADkAh57BwqwPn36qFOnTjkex9/fXxMmTMiFREWPi4uLpk6dqtDQUNb/AgCgkHN3d7/n7E0v\nLy/Vr19fbm5umccqVKig48ePa//+/ZKkmzdv6t13382XrAAAIHdQfgI5sG3bNtnY2MjW1lY2NjZ3\nfLVq1SpH47/77rtaunRpLqVFdnXv3l0lS5bUe++9Z3QUAACQB37++Wf16NFDsbGxevbZZxUSEqKt\nW7dq9uzZqlKlisqUKSNJOnz4sN544w2VL1+e3wsAACgkeOwdyIG0tDRdvnz5juOff/65BgwYoE8/\n/VRdu3Z94HHT09Nla2ubGxEl/TXz89lnn9XYsWNzbcyi5sCBA2rZsqViYmIy/wMEAAAKvxs3bqhM\nmTIaOHCgunTposTERA0bNkzu7u7q0KGDWrVqpcaNG2e5JzIyUmPGjJHFYtHMmTP1zDPPGJQeAAD8\nG2Z+AjlgZ2ensmXLZvm6dOmShg0bplGjRmUWn+fOndNzzz0nDw8PeXh4qEOHDjp69GjmOOPHj5e/\nv78WL16satWqqVixYrpx44Z69+6d5bH3wMBADRw4UKNGjVKZMmVUrlw5DR8+PEum+Ph4de7cWc7O\nzqpcubIWLVqUPz8Mk6tdu7aCgoI0atQoo6MAAIBcFBUVJX9/f40cOVJNmzZV+/btNXv2bJ05c0Z9\n+/bNLD6tVqusVqsyMjLUt29fnT59Wj179lT37t0VEhKipKQkg98JAAC4G8pPIBdduXJFnTt3VsuW\nLTV+/HhJUnJysgIDA1W8eHF9//332rFjh7y8vNS6dWvdvHkz894TJ05o+fLlWrVqlfbt2ydHR8e7\nrkkVFRUle3t7/fzzz5o7d65mzpypTz75JPP8iy++qOPHj2vr1q1au3atPv74Y/3xxx95/+aLgLCw\nMH355Zc6dOiQ0VEAAEAuSU9P1/nz53X16tXMY15eXvLw8NCePXsyj1ksliy/m3355Zf69ddf5e/v\nry5duqh48eL5mhsAANwfyk8gl1itVvXo0UOOjo5Z1ulcvny5JOnDDz+Un5+fatSoofnz5+v69eta\nt25d5nWpqalaunSp6tatK19f33s+9u7r66uwsDBVq1ZNzzzzjAIDA7VlyxZJ0pEjR7RhwwYtXLhQ\njRs3Vp06dbR48WLduHEjD9950VGiRAnt3btXNWvWFCuGAABgDi1atFC5cuUUHh6uM2fOaP/+/Vq6\ndKlOnz6thx9+WJIyZ3xKfy17tGXLFvXu3VtpaWlatWqV2rRpY+RbAAAA/8DO6ACAWbzxxhvauXOn\ndu/eneWT/+joaB0/flyurq5Zrk9OTtaxY8cyv/f29lbp0qX/9XUCAgKyfO/l5aULFy5Ikg4dOiRb\nW1s1aNAg83zFihXl5eWVrfeEO5UtW/aeu8QCAIDC5+GHH9ZHH32kkJAQNWjQQKVKlVJKSopef/11\nVa9ePXMt9tv//r/zzjtasGCB2rVrp2nTpsnLy0tWq5XfDwAAKKAoP4FcsGLFCk2fPl1ff/21qlSp\nkuVcRkaGHnnkEX3yySd3zBb08PDI/PP9Piplb2+f5XuLxZI5E+Hvx5A3HuRne/PmTRUrViwP0wAA\ngNzg6+ur7777Tvv379epU6dUr149lS1bVtL/34jy4sWL+uCDDzRlyhT1799fU6ZMkaOjoyR+9wIA\noCCj/ARyaO/everXr5/Cw8PVunXrO87Xq1dPK1asUKlSpeTm5panWR5++GFlZGRo165dmYvznzp1\nSufOncvT10VWGRkZ2rRpk6Kjo9WnTx95enoaHQkAANyHgICAzKdsbn+47ODgIEl65ZVXtGnTJoWF\nhSk0NFSOjo7KyMiQjQ0riQEAUJDxLzWQA5cuXVKXLl0UGBiooKAgxcXF3fH1/PPPq1y5curcubO2\nb9+ukydPavv27Ro2bFiWx95zQ40aNdS2bVsFBwdrx44d2rt3r/r06SNnZ+dcfR38MxsbG6WlpenH\nH3/UoEGDjI4DAACy4XapeerUKTVr1kzr1q3TpEmTNGzYsMwnOyg+AQAo+Jj5CeTAV199pdOnT+v0\n6dN3rKt5e+2n9PR0bd++Xa+//rq6d++uK1euyMvLS4GBgSpZsuQDvd79PFK1ePFi9e/fX61atVLp\n0qU1btw4xcfHP9DrIPtSUlLk4OCgp59+WufOnVNwcLC++eYbNkIAAKCQqlixooYOHary5ctnPllz\nrxmfVqtVaWlpdyxTBAAAjGOxsmUxAORYWlqa7Oz++jzp5s2bGjZsmJYsWaL69etr+PDhateuncEJ\nAQBAXrNarapTp466d++uwYMH37HhJQAAyH88pwEA2XTs2DEdOXJEkjKLz4ULF8rHx0fffPONJk6c\nqIULF6pt27ZGxgQAAPnEYrFo9erVOnjwoKpVq6bp06crOTnZ6FgAABRplJ8AkE3Lli1Tx44dJUl7\n9uxR48aNNWLECHXv3l1RUVEKDg5WlSpV2AEWAIAipHr16oqKitLmzZu1fft2Va9eXQsWLFBKSorR\n0QAAKJJ47B0Asik9PV2lSpWSj4+Pjh8/rubNm2vAgAF67LHH7ljP9eLFi4qOjmbtTwAAiphdu3Zp\n9OjROnr0qMLCwvT888/L1tbW6FgAABQZlJ8AkAMrVqxQUFCQJk6cqF69eqlixYp3XPPll19q5cqV\n+vzzzxUVFaWnn37agKQAAMBI27Zt06hRo3T58mVNmDBBXbt2Zbd4AADyAeUnAORQnTp1VLt2bS1b\ntkzSX5sdWCwWnT9/Xu+9957Wrl2rypUrKzk5Wb/88ovi4+MNTgwAAIxgtVq1YcMGjR49WpI0adIk\ntWvXjiVyAADIQ3zUCAA5FBkZqdjYWJ05c0aSsvwHxtbWVseOHdOECRO0YcMGeXp6asSIEUZFBQAA\nBrJYLHrqqae0Z88evfnmmxo6dKiaN2+ubdu2GR0NAADTYuYnkItuz/hD0XP8+HGVLl1av/zyiwID\nAzOPX758Wc8//7x8fX01bdo0bd26VW3atNHp06dVvnx5AxMDAACjpaenKyoqSmFhYapataomT56s\nBg0aGB0LAABTsQ0LCwszOgRgFn8vPm8XoRSiRUPJkiUVGhqqXbt2qVOnTrJYLLJYLHJycpKjo6OW\nLVumTp06yd/fX6mpqSpevLiqVKlidGwAAGAgGxsb1alTRyEhIbp165ZCQkK0fft2+fn5qVy5ckbH\nAwDAFHjsHcgFkZGReuutt7Icu114UnwWHU2aNNHOnTt169YtWSwWpaenS5IuXLig9PR0ubu7S5Im\nTpyoVq1aGRkVAAAUIPb29goODtbvv/+uxx9/XK1bt1ZQUJB+//13o6MBAFDoUX4CuWD8+PEqVapU\n5vc7d+7U6tWr9cUXXygmJkZWq1UZGRkGJkR+6Nu3r+zt7TVp0iTFx8fL1tZWp06dUmRkpEqWLCk7\nOzujIwIAgALMyclJr732mo4ePSpfX181adJE/fr106lTp4yOBgBAocWan0AORUdHq2nTpoqPj5er\nq6vCwsI0f/58JSUlydXVVVWrVlVERISaNGlidFTkgz179qhfv36yt7dX+fLlFR0drUqVKikyMlI1\na9bMvC41NVXbt29X2bJl5e/vb2BiAABQUCUkJCgiIkLvvfeenn/+eb355pvy9PQ0OhYAAIUKMz+B\nHIqIiFDXrl3l6uqq1atXa82aNXrzzTd1/fp1rV27Vk5OTurcubMSEhKMjop8UL9+fUVGRqpt27a6\nefOmgoODNW3aNNWoUUN//6zp/Pnz+uyzzzRixAhduXLFwMQAAKCgKlmypN566y0dPHhQNjY28vPz\n0xtvvKHLly8bHQ0AgEKDmZ9ADpUtW1aPPvqoxowZo2HDhql9+/YaPXp05vkDBw6oa9eueu+997Ls\nAo6i4Z82vNqxY4deffVVeXt7a+XKlfmcDAAAFDanT5/WxIkT9dlnn2nw4MEaMmSIXF1djY4FAECB\nxsxPIAcSExPVvXt3SdKAAQN0/PhxPf7445nnMzIyVLlyZbm6uurq1atGxYQBbn+udLv4/N/PmVJS\nUnTkyBEdPnxYP/zwAzM4AADAv6pQoYLef/997dixQ4cPH1a1atU0bdo0JScnGx0NAIACi/ITyIFz\n585pzpw5mjVrlvr3768XXnghy6fvNjY2iomJ0aFDh9S+fXsDkyK/3S49z507l+V76a8Nsdq3b6++\nffuqV69e2rdvnzw8PAzJCQAACp9q1app6dKl2rJli3788UdVr15d8+fPV0pKitHRAAAocCg/gWw6\nd+6cnnjiCUVFRalGjRoKDQ3VpEmT5Ofnl3lNbGysIiIi1KlTJ9nb2xuYFkY4d+6cBgwYoH379kmS\nzpw5o8GDB+vxxx9Xamqqdu7cqVmzZqls2bIGJwUAAIVR7dq19dlnn2nt2rX6/PPP9fDDD2vx4sVK\nT083OhoAAAUG5SeQTVOnTtXFixfVr18/jRs3TleuXJGDg4NsbW0zr/n111914cIFvf766wYmhVG8\nvLyUlJSk0NBQvf/++2rcuLFWr16thQsXatu2bXr00UeNjggAAEygfv362rBhgz766CN98MEHql27\ntlauXKmMjIz7HuPKlSuaM2eOnnzyST3yyCOqU6eOAgMDFR4erosXL+ZhegAA8hYbHgHZ5ObmpjVr\n1ujAgQOaOnWqhg8frldeeeWO65KTk+Xk5GRAQhQE8fHxqlSpkm7evKnhw4frzTfflLu7u9GxAACA\nSVmtVm3cuFGjR49WRkaGJk6cqPbt299zA8bz589r/Pjx+uSTT9SmTRv17NlTDz30kCwWi+Li4vTp\np59qzZo16tixo8aNG6eqVavm8zsCACBnKD+BbFi7dq2Cg4MVFxenxMRETZkyRREREerbt68mTZqk\ncuXKKT09XRaLRTY2TLAu6iIiIjR16lQdO3ZMLi4uRscBAABFgNVq1Zo1azRmzBiVKFFCkydP1hNP\nPJHlmtjYWD311FN69tln9dprr6l8+fJ3Hevy5cuaN2+e5s6dqzVr1qhx48b58A4AAMgdlJ9ANjRv\n3lxNmzZVeHh45rEPPvhAkydPVteuXTVt2jQD06EgKlGihMaMGaOhQ4caHQUAABQh6enpWr58ucLC\nwlS5cmVNmjRJjRo10unTp9W0aVNNnDhRvXv3vq+xvvrqK/Xt21dbt27Nss49AAAFGeUn8ICuXbsm\nDw8PHT58WFWqVFF6erpsbW2Vnp6uDz74QK+99pqeeOIJzZkzR5UrVzY6LgqIffv26cKFC2rVqhWz\ngQEAQL5LTU3VokWLNHHiRNWrV08XLlxQly5dNHLkyAcaZ8mSJXr77bcVExNzz0fpAQAoSCg/gWxI\nTExUiRIl7npu9erVGjFihPz8/LR8+XIVL148n9MBAAAAd3fz5k2NGzdOCxcuVFxcnOzt7R/ofqvV\nqjp16mjGjBlq1apVHqUEACD3MP0IyIZ7FZ+S1K1bN02fPl0XL16k+AQAAECBUqxYMSUlJWnQoEEP\nXHxKksViUUhIiObNm5cH6QAAyH3M/ATySEJCgkqWLGl0DBRQt//q5XExAACQnzIyMlSyZEkdPHhQ\nDz30ULbGuHbtmry9vXXy5El+3wUAFHjM/ATyCL8I4p9YrVZ1795d0dHRRkcBAABFyNWrV2W1WrNd\nfEqSq6urPD099eeff+ZiMgAA8gblJ5BDTJ5GdtjY2Khdu3YKDQ1VRkaG0XEAAEARkZycLCcnpxyP\n4+TkpOTk5FxIBABA3qL8BHIgPT1dP//8MwUosqVPnz5KS0vTkiVLjI4CAACKCHd3d125ciXHv78m\nJibK3d09l1IBAJB3KD+BHNi0aZMGDx7Muo3IFhsbG82dO1evv/66rly5YnQcAABQBDg5Oaly5cr6\n4Ycfsj3GkSNHlJycrAoVKuRiMgAA8gblJ5ADH374of773/8aHQOFWIMGDdShQweFhYUZHQUAABQB\nFotFAwYMyNFu7QsWLFDfvn3l4OCQi8kAAMgb7PYOZFN8fLyqV6+uP/74g0d+kCPx8fHy8/PT1q1b\nVbt2baPjAAAAk0tMTFTlypUVGxsrT0/PB7o3KSlJlSpV0p49e+Tj45M3AQEAyEXM/ASyacmSJerc\nuTPFJ3KsTJkyGjdunAYNGsT6sQAAIM+VKFFCAwYMUND/Ze8+o6Os9rePf2cmCWmU0BGBACHURJpU\nQSFipEsdRIqAoocuCCi9iSC92OgKHBi6dJQgIqFL+0PoEgKShN5SSTLPCx+zDgKhJdwJc33WYsHM\n7L3v684SmfnNLq1bEx8f/9j9kpKS6NixIw0aNFDhU0REMgwVP0Wegt1u15J3SVUfffQR169fZ8mS\nJUZHEREREQcwcuRIvLy8aNKkCXfu3Hlk+/j4eN5//33Cw8P57rvvnkNCERGR1KHip8hT2LVrF3fv\n3qVGjRpGR5EXhJOTE9OnT+fTTz99rA8gIiIiIs/CYrGwePFi8uXLxyuvvMKkSZO4fv36fe3u3LnD\nd999xyuvvMKtW7fYuHEjrq6uBiQWERF5OtrzU+QpfPDBBxQrVoz+/fsbHUVeMG3btqVAgQKMHj3a\n6CgiIiLiAOx2O8HBwXz77besW7eOt956i/z582MymYiMjGTDhg2ULl2asLAwTp8+jbOzs9GRRURE\nnoiKnyJP6Pbt2xQsWPCpNogXeZTw8HD8/PzYsWMHvr6+RscRERERB3Lp0iU2btzIlStXSEpKIkeO\nHAQEBFCgQAGqV69Oly5daNOmjdExRUREnoiKnyJPaPbs2axZs4ZVq1YZHUVeUOPHjycoKIj169dj\nMpmMjiMiIiIiIiKSYWnPT5EnpIOOJK316NGD0NBQ1qxZY3QUERERERERkQxNMz9FnkBISAhvvvkm\nYWFhODk5GR1HXmC//PILH330EUePHsXNzc3oOCIiIiIiIiIZkmZ+ijyB2bNn8/7776vwKWmuTp06\nlC9fnnHjxhkdRURERERERCTD0sxPkccUHx9PgQIFCA4OxsfHx+g44gDOnTtH+fLl+eOPP/D29jY6\njoiIiIiIiEiGo5mfIo9pzZo1lCxZUoVPeW4KFSrEJ598Qu/evY2OIiIiInKP4cOH4+/vb3QMERGR\nR9LMT5HHVLduXd577z3atGljdBRxILGxsZQuXZpvvvmGwMBAo+OIiIhIBtahQweuXr3K6tWrn3ms\n6Oho4uLi8PLySoVkIiIiaUczP0Uew/nz59mzZw/NmjUzOoo4GFdXV6ZMmUKPHj2Ij483Oo6IiIgI\nAO7u7ip8iohIhqDip8hjmDdvHlarVaduiyEaNGhAsWLFmDJlitFRRERE5AWxb98+AgMDyZUrF1mz\nZqVGjRrs2rXrnjbff/89xYsXx83NjVy5clG3bl2SkpKAv5e9+/n5GRFdRETkiaj4KfIISUlJzJkz\nhw8++MDoKOLAJk+ezNixY/nrr7+MjiIiIiIvgNu3b9OuXTuCg4PZu3cv5cqVo379+ly/fh2AP/74\ng27dujF8+HBOnjzJli1bePvtt+8Zw2QyGRFdRETkiTgZHUAkvbhz5w4LFixk06btXL16AxcXZwoW\nzIufXzGyZs1K+fLljY4oDszHx4ePPvqIfv36sXDhQqPjiIiISAZXq1atex5PmTKFZcuWsWHDBlq3\nbk1YWBienp40bNgQDw8PChQooJmeIiKSIan4KQ4vNDSU0aMnsGDBQszmN4iKagRkB+Ixmc5isUwl\nSxY733zzLZ07f4iTk/7aiDEGDBhAyZIl2bZtGzVr1jQ6joiIiGRgly9fZtCgQWzdupXIyEgSExOJ\njY0lLCwMgDp16lCoUCG8vb0JDAzkrbfeomnTpnh6ehqcXERE5Mlo2bs4tB07dvDKK1WYOzczMTGH\niYpaAbwPNAKaY7f3JSHhT65dm0vfvkuoU6cxd+7cMTa0OCwPDw8mTJhAt27dSEhIMDqOiIiIZGDt\n2rXjjz/+YMqUKezcuZNDhw6RP3/+5AMWPT092b9/P0uXLqVQoUKMGTOGEiVKEBERYXByERGRJ6Pi\npzis/fv389Zbjbl1ay4JCaOBlx/S0gTUIjr6Z3buzM1bbzXRqdtimObNm5MrVy6+/fZbo6OIiIhI\nBhYcHEz37t15++23KVmyJB4eHoSHh9/Txmw288Ybb/DFF19w6NAhoqKiWLt2rUGJRUREno6Kn+KQ\nYmNjeeutxkRFfQ/UfcxezsTFzeLgQTc++2xoWsYTeSiTycS0adMYMWIEly5dMjqOiIiIZFC+vr4s\nWLCAY8eOsXfvXt59910yZcqU/Pq6deuYOnUqBw8eJCwsjIULF3Lnzh1KlSplYGoREZEnp+KnOKSl\nS5cSF1cKaPqEPS3ExExlxoyZREdHp0U0kUcqVaoU7dq14/PPPzc6ioiIiGRQc+bM4c6dO1SsWJHW\nrVvTqVMnvL29k1/Pli0bq1atok6dOpQsWZKJEycye/ZsqlWrZlxoERGRp2Cy2+12o0OIPG9+ftU4\ncqQ/0Pip+nt6NmTq1KZ06NAhdYOJPKZbt25RokQJVq5cSeXKlY2OIyIiIiIiIpIuaeanOJyQkBD+\n/PM8UP+px7hz5z9MnDgr9UKJPKEsWbIwduxYunbtSmJiotFxRERERERERNIlFT/F4fz55584O/sD\nTs8wSlnCws6kViSRp9KmTRtcXV2ZM2eO0VFERERERERE0iUVP8Xh3Llzh6Qkj2ccxZPY2Dupkkfk\naZlMJqZPn87gwYO5du2a0XFERERERERE0h0VP8XhZMmSBbP59jOOcgs3tyypkkfkWZQtW5ZmzZox\nZMgQo6OIiIiIJNu9e7fREURERAAVP8UBlShRgri4P4DYZxhlBwULFkmtSCLPZOTIkSxdupSDBw8a\nHUVEREQEgMGDBxsdQUREBFDxUxxQkSJFKFu2LLDsqcdwdp5IWNgRypcvz5gxYzh79mzqBRR5Qtmz\nZ2fkyJF069YNu91udBwRERFxcHfv3uXMmTP89ttvRkcRERFR8VMcU//+Xcic+Zun7H0UD48wIiIi\nmDBhAqGhoVSqVIlKlSoxYcIEzp8/n6pZRR5Hp06diI2NZeHChUZHEREREQfn7OzM0KFDGTRokL6Y\nFRERw5ns+tdIHFBCQgI+Pv6cP9+NpKQuT9AzBnf3AAYObMKAAX3vGW/Lli3YbDZWrVpF8eLFsVqt\ntGjRgpdeein1b0DkAXbt2kWzZs04duwYWbJoT1oRERExTmJiImXKlGHy5MkEBgYaHUdERByYip/i\nsP78808qVHiNmzdHYrd3eowet3F3b0FgYA6WL1+AyWR6YKv4+Hg2b96MzWZj9erV+Pv7Y7Vaadas\nGXny5EndmxD5l44dO5I9e3bGjx9vdBQRERFxcEuXLuWrr75iz549D33vLCIiktZU/BSHdvLkSV5/\nvS43b1YhJqY7UBn49xuzaMCGh8c4mjSpzty53+Lk5PRY48fFxbFp0yZsNhvr1q2jQoUKWK1WmjZt\nSlyh6tgAACAASURBVM6cOVP5bkQgMjKSMmXK8Ntvv1GqVCmj44iIiIgDS0pKonz58gwbNox33nnH\n6DgiIuKgVPwUh3f9+nVmzpzNxInfEhWVlTt3GgHZgXicnUOxWBZTuXIV+vXrQt26dZ/6W+uYmBjW\nr1/PkiVL2LhxI1WqVMFqtdKkSRO8vLxS9Z7EsU2dOpXVq1fzyy+/aJaFiIiIGGrNmjUMGDCAQ4cO\nYTbryAkREXn+VPwU+f+SkpL4+eef+f33YLZu3cGNG9do164VLVu2pHDhwql6raioKNauXYvNZiMo\nKIgaNWpgtVpp1KgRWbNmTdVrieNJSEigXLlyDB06lObNmxsdR0RERByY3W6natWq9OrVi1atWhkd\nR0REHJCKnyIGu3XrFmvWrMFms7F161Zq166N1WqlYcOGeHp6Gh1PMqjffvuNdu3aERISgoeHh9Fx\nRERExIFt3ryZrl27cvTo0cfePkpERCS1qPgpko7cuHGDVatWsWTJEoKDg6lTpw5Wq5X69evj7u5u\ndDzJYFq3bk3RokUZOXKk0VFERETEgdntdmrVqkX79u3p0KGD0XFERMTBqPgpkk5dvXqVlStXYrPZ\n2Lt3L3Xr1qVly5bUrVsXV1dXo+NJBvDXX3/xyiuvsGvXLnx8fIyOIyIiIg5s+/bttGnThpMnT+Li\n4mJ0HBERcSAqfopkAJcuXWLFihXYbDYOHjxIgwYNsFqtvPXWW3rzKCkaO3Ys27dvZ82aNUZHERER\nEQdXt25dGjZsSJcuXYyOIiIiDkTFT5EMJjw8nGXLlmGz2QgJCaFx48ZYrVYCAgJwdnY2Op6kM3Fx\ncfj7+zNhwgQaNGhgdBwRERFxYPv27aNx48acPn0aNzc3o+OIiIiDUPFTJJU0bNiQXLlyMWfOnOd2\nzQsXLrB06VJsNhtnzpyhSZMmWK1WXn/9dW0mL8k2bdpE165dOXLkiLZMEBEREUM1bdqU1157jd69\nexsdRUREHITZ6AAiae3AgQM4OTlRo0YNo6OkupdffplPPvmEXbt2sXfvXooVK0b//v3Jnz8/Xbp0\n4bfffiMxMdHomGKwwMBA/Pz8mDBhgtFRRERExMENHz6csWPHcvv2baOjiIiIg1DxU154s2bNSp71\nduLEiRTbJiQkPKdUqc/b25u+ffuyb98+goODefnll+nZsycFChSgR48eBAcHk5SUZHRMMcjEiROZ\nNGkSYWFhRkcRERERB+bn50dAQABTp041OoqIiDgIFT/lhRYbG8t///tfOnfuTLNmzZg1a1bya+fO\nncNsNrN48WICAgLw8PBgxowZXLt2jdatW1OgQAHc3d0pU6YM8+bNu2fcmJgY3n//fTJnzky+fPn4\n8ssvn/OdpczHx4cBAwZw8OBBtmzZQs6cOencuTOFChWiT58+7NmzB+144VgKFy5M9+7d6dOnj9FR\nRERExMENGzaMyZMnc/36daOjiIiIA1DxU15oS5cuxdvbm9KlS9O2bVt+/PHH+5aBDxgwgK5duxIS\nEsI777xDbGwsFSpUYP369YSEhNCrVy8+/vhjfv311+Q+ffr0ISgoiJUrVxIUFMSBAwfYtm3b8769\nx1KiRAmGDBnC0aNH2bBhAx4eHrRt25YiRYrQv39/9u/fr0Kog+jXrx/79u1j8+bNRkcRERERB+br\n60ujRo2YOHGi0VFERMQB6MAjeaHVqlWLRo0a8cknnwBQpEgRxo8fT9OmTTl37hyFCxdm4sSJ9OrV\nK8Vx3n33XTJnzsyMGTOIiooiR44czJs3j1atWgEQFRXFyy+/TJMmTZ7rgUdPy263c+jQIWw2G0uW\nLMFsNmO1WmnZsiV+fn6YTCajI0oa+emnn/jss884dOgQLi4uRscRERERBxUaGkqFChU4fvw4uXLl\nMjqOiIi8wDTzU15Yp0+fZvv27bz77rvJz7Vu3ZrZs2ff065ChQr3PE5KSuKLL77glVdeIWfOnGTO\nnJmVK1cm75V45swZ7t69S5UqVZL7eHh44Ofnl4Z3k7pMJhNly5blyy+/5PTp0yxatIi4uDgaNmxI\nqVKlGDZsGMeOHTM6pqSBRo0a4e3tzbRp04yOIiIiIg7M29ubVq1aMXbsWKOjiIjIC87J6AAiaWXW\nrFkkJSVRoECB+17766+/kv/s4eFxz2vjxo1j0qRJTJ06lTJlyuDp6cnnn3/O5cuX0zyzEUwmExUr\nVqRixYp89dVX7Nq1iyVLlvDmm2+SPXt2rFYrVquVYsWKGR1VUoHJZGLKlClUq1aN1q1bky9fPqMj\niYiIiIMaOHAgZcqUoXfv3rz00ktGxxERkReUZn7KCykxMZEff/yRMWPGcOjQoXt++fv7M3fu3If2\nDQ4OpmHDhrRu3Rp/f3+KFCnCyZMnk18vWrQoTk5O7Nq1K/m5qKgojhw5kqb39DyYTCaqVq3KpEmT\nOH/+PN988w0RERHUqFGD8uXLM2bMGM6ePWt0THlGvr6+fPjhh/Tv39/oKCIiIuLAXnrpJbp06cLV\nq1eNjiIiIi8wzfyUF9LatWu5evUqH3zwAV5eXve8ZrVa+f7772nTps0D+/r6+rJkyRKCg4PJkSMH\n06dP5+zZs8njeHh40KlTJ/r370/OnDnJly8fI0eOJCkpKc3v63kym83UqFGDGjVqMGXKFLZt24bN\nZqNSpUoULlw4eY/QB82slfRv4MCBlCxZku3bt/Paa68ZHUdEREQc1MiRI42OICIiLzjN/JQX0pw5\nc6hdu/Z9hU+AFi1aEBoayubNmx94sM+gQYOoVKkS9erV44033sDT0/O+Qun48eOpVasWTZs2JSAg\nAD8/P2rWrJlm92M0i8VCrVq1+O677wgPD2fUqFEcO3aMsmXLUq1aNaZMmcLFixeNjilPwNPTk3Hj\nxtGtWzcSExONjiMiIiIOymQy6bBNERFJUzrtXUSeWnx8PJs3b8Zms7F69Wr8/f1p2bIlzZs3J0+e\nPEbHk0ew2+3UqlWLli1b0qVLF6PjiIiIiIiIiKQ6FT9FJFXExcWxadMmbDYb69ato0KFClitVpo2\nbUrOnDmfetykpCTi4+NxdXVNxbTyj//7v/8jICCAo0ePkitXLqPjiIiIiNxn586duLu74+fnh9ms\nxYsiIvJkVPwUkVQXExPD+vXrWbJkCRs3bqRKlSpYrVaaNGnywK0IUnLs2DGmTJlCREQEtWvXplOn\nTnh4eKRRcsfUq1cvoqOjmTFjhtFRRERERJJt27aNjh07EhERQa5cuXjjjTf46quv9IWtiIg8EX1t\nJiKpzs3NjWbNmmGz2bh48SIdO3Zk7dq1eHt706BBA+bPn8/Nmzcfa6ybN2+SO3duChYsSK9evZg+\nfToJCQlpfAeOZdiwYaxZs4a9e/caHUVEREQE+Ps9YNeuXfH392fv3r2MHTuWmzdv0q1bN6OjiYhI\nBqOZnyLy3Ny+fZvVq1djs9nYunUrtWvXxmazkSlTpkf2XbVqFf/5z39YvHgxr7/++nNI61jmzZvH\nt99+y86dO7WcTERERAwRFRWFi4sLzs7OBAUF0bFjR5YsWULlypWBv1cEValShcOHD1OoUCGD04qI\nSEahT7gi8txkzpyZ9957j9WrVxMWFsa7776Li4tLin3i4+MBWLRoEaVLl8bX1/eB7a5cucKXX37J\n4sWLSUpKSvXsL7p27dphNpuZN2+e0VFERETEAUVERLBgwQJOnToFQOHChfnrr78oU6ZMchs3Nzf8\n/Py4deuWUTFFRCQDUvFT5CFatWrFokWLjI7xwsqWLRtWqxWTyZRiu3+Ko7/88gtvv/128h5PSUlJ\n/DNxfd26dQwdOpSBAwfSp08fdu3albbhX0Bms5np06czYMAAbty4YXQcERERcTAuLi6MHz+e8+fP\nA1CkSBGqVatGly5diI6O5ubNm4wcOZLz58+TP39+g9OKiEhGouKnyEO4ubkRGxtrdAyHlpiYCMDq\n1asxmUxUqVIFJycn4O9inclkYty4cXTr1o1mzZrx6quv0rhxY4oUKXLPOH/99RfBwcGaEfoIFSpU\n4J133mHo0KFGRxEREREHkz17dipVqsQ333xDTEwMAD/99BMXLlygRo0aVKhQgQMHDjBnzhyyZ89u\ncFoREclIVPwUeQhXV9fkN15irHnz5lGxYsV7ipp79+6lQ4cOrFixgp9//hk/Pz/CwsLw8/Mjb968\nye0mTZpEvXr1aN++Pe7u7nTr1o3bt28bcRsZwhdffMGiRYs4fPiw0VFERETEwUycOJFjx47RrFkz\nli5dypIlSyhWrBjnzp3DxcWFLl26UKNGDVatWsWIESO4cOGC0ZFFRCQDUPFT5CFcXV0189NAdrsd\ni8WC3W7n119/vWfJ+2+//Ubbtm2pWrUqO3bsoFixYsyePZvs2bPj7++fPMbatWsZOHAgAQEB/P77\n76xdu5bNmzfz888/G3Vb6V6OHDkYPnw43bt3R+fhiYiIyPOUJ08e5s6dS9GiRenRowfTpk3jxIkT\ndOrUiW3btvHBBx/g4uLC1atX2b59O59++qnRkUVEJANwMjqASHqlZe/GuXv3LmPHjsXd3R1nZ2dc\nXV2pXr06zs7OJCQkcPToUc6ePcv3339PXFwc3bt3Z/PmzdSsWZPSpUsDfy91HzlyJE2aNGHixIkA\n5MuXj0qVKjF58mSaNWtm5C2ma507d2bGjBksXryYd9991+g4IiIi4kCqV69O9erV+eqrr7h16xZO\nTk7kyJEDgISEBJycnOjUqRPVq1enWrVqbN26lTfeeMPY0CIikq5p5qfIQ2jZu3HMZjOenp6MGTOG\nnj17EhkZyZo1a7h48SIWi4UPPviA3bt38/bbb/P999/j7OzM9u3buXXrFm5ubgDs37+fP/74g/79\n+wN/F1Th78303dzckh/L/SwWC9OnT6dv377aIkBEREQM4ebmhsViSS58JiYm4uTklLwnfIkSJejY\nsSPffvutkTFFRCQDUPFT5CE089M4FouFXr16cenSJc6fP8+wYcOYO3cuHTt25OrVq7i4uFC2bFm+\n+OILjhw5wscff0y2bNn4+eef6d27N/D30vj8+fPj7++P3W7H2dkZgLCwMLy9vYmPjzfyFtO96tWr\nExAQwKhRo4yOIiIiIg4mKSmJOnXqUKZMGXr16sW6deu4desW8Pf7xH9cvnyZrFmzJhdERUREHkTF\nT5GH0J6f6UP+/PkZMmQIFy5cYMGCBeTMmfO+NgcPHuSdd97h8OHDfPXVVwDs2LGDwMBAgORC58GD\nB7l69SqFChXCw8Pj+d1EBjV27Fhmz57N8ePHjY4iIiIiDsRsNlO1alUuXbpEdHQ0nTp1olKlSrRv\n35758+cTHBzM8uXLWbFiBYULF76nICoiIvJvKn6KPISWvac/Dyp8/vnnn+zfv5/SpUuTL1++5KLm\nlStX8PHxAcDJ6e/tjVeuXImLiwtVq1YF0IE+j5A3b14GDhxIjx499LMSERGR52ro0KFkypSJ9u3b\nEx4ezogRI3B3d2fUqFG0atWKNm3a0LFjRz7//HOjo4qISDpnsusTrcgDLViwgI0bN7JgwQKjo8hD\n2O12TCYToaGhODs7kz9/fux2OwkJCfTo0YP9+/cTHByMk5MTN27coHjx4rz//vsMHjwYT0/P+8aR\n+929e5eyZcsyatQomjRpYnQcERERcSADBw7kp59+4siRI/c8f/jwYXx8fHB3dwf0Xk5ERFKm4qfI\nQyxbtozFixezbNkyo6PIU9i3bx/t2rXD398fX19fli5dipOTE0FBQeTOnfuetna7nW+++Ybr169j\ntVopVqyYQanTpy1bttCxY0dCQkKSP2SIiIiIPA+urq7MmzePVq1aJZ/2LiIi8iS07F3kIbTsPeOy\n2+1UrFiRRYsW4erqyrZt2+jSpQs//fQTuXPnJikp6b4+ZcuWJTIykpo1a1K+fHnGjBnD2bNnDUif\n/tSuXZvKlSszduxYo6OIiIiIgxk+fDibN28GUOFTRESeimZ+ijxEUFAQo0ePJigoyOgo8hwlJiay\nbds2bDYbK1aswNvbG6vVSosWLShYsKDR8Qxz/vx5ypUrx549eyhSpIjRcURERMSBnDhxAl9fXy1t\nFxGRp6KZnyIPodPeHZPFYqFWrVp89913XLx4kS+++IJjx45Rrlw5qlWrxpQpU7h48aLRMZ+7AgUK\n0KdPH3r37m10FBEREXEwxYsXV+FTRESemoqfIg+hZe/i5OREnTp1mDVrFuHh4QwaNCj5ZPnXX3+d\nr7/+msjISKNjPje9e/fm6NGjbNiwwegoIiIiIiIiIo9FxU+Rh3Bzc9PMT0nm4uJCvXr1+OGHH4iI\niKBPnz7s2LGD4sWLExAQwIwZM7hy5YrRMdNUpkyZmDJlCj179iQuLs7oOCIiIuKA7HY7SUlJei8i\nIiKPTcVPkYfQzE95mEyZMtGoUSMWLlxIeHg4Xbt2JSgoiKJFixIYGMicOXO4fv260THTRL169ShR\nogSTJk0yOoqIiIg4IJPJRNeuXfnyyy+NjiIiIhmEDjwSeYiLFy9SoUIFwsPDjY4iGURUVBRr167F\nZrMRFBREjRo1aNmyJY0bNyZr1qxGx0s1Z86coXLlyhw8eJCXX37Z6DgiIiLiYP78808qVarEiRMn\nyJEjh9FxREQknVPxU+Qhrl+/TpEiRV7YGXyStm7fvs3q1aux2Wxs3bqV2rVrY7VaadiwIZ6enkbH\ne2ZDhgzh5MmTLF682OgoIiIi4oD+85//kCVLFsaOHWt0FBERSedU/BR5iJiYGLy8vLTvpzyzGzdu\nsGrVKpYsWUJwcDB16tTBarVSv3593N3djY73VKKjoylVqhRz586lVq1aRscRERERB3PhwgVeeeUV\njh49St68eY2OIyIi6ZiKnyIPkZSUhMViISkpCZPJZHQceUFcvXqVlStXYrPZ2Lt3L3Xr1qVly5bU\nrVsXV1dXo+M9kRUrVjBkyBAOHDiAs7Oz0XFERETEwXzyySckJiYydepUo6OIiEg6puKnSApcXV25\nceNGhitKScZw6dIlVqxYgc1m4+DBgzRo0ACr1cpbb72Fi4uL0fEeyW63ExgYSL169ejVq5fRcURE\nRMTBREZGUqpUKQ4cOEDBggWNjiMiIumUip8iKciWLRtnz57Fy8vL6CjyggsPD2f58uXYbDaOHj1K\n48aNsVqtBAQEpOtZlcePH6dGjRocOXKEPHnyGB1HREREHMyAAQO4cuUKM2bMMDqKiIikUyp+iqQg\nb968HDhwgHz58hkdRRzIhQsXWLp0KTabjdOnT9OkSROsVitvvPEGTk5ORse7T79+/bh8+TJz5841\nOoqIiIg4mGvXruHr68uuXbvw8fExOo6IiKRDKn6KpKBw4cJs2bKFwoULGx1FHFRoaGhyIfT8+fM0\na9YMq9XKa6+9hsViMToe8PfJ9iVLlmTp0qVUrVrV6DgiIiLiYEaMGMGpU6eYP3++0VFERCQdUvFT\nJAUlS5Zk+fLllCpVyugoIpw+fZolS5awZMkSLl26RPPmzbFarVStWhWz2WxotoULFzJx4kT27NmT\nboqyIiIi4hhu3bqFj48PW7du1ft2ERG5j7GflkXSOVdXV2JjY42OIQKAj48PAwYM4ODBg2zZsoWc\nOXPSuXNnChUqRJ8+fdi9ezdGfZ/VunVr3N3dmTVrliHXFxEREceVJUsW+vbty9ChQ42OIiIi6ZBm\nfoqkoFq1aowfP55q1aoZHUXkoY4ePYrNZsNmsxEfH0/Lli2xWq2UK1cOk8n03HIcOnSIt956i5CQ\nEHLkyPHcrisiIiISHR2Nj48P69ato1y5ckbHERGRdEQzP0VS4OrqSkxMjNExRFJUunRpRowYwfHj\nx1m5ciVms5kWLVrg6+vLwIEDOXz48HOZEfrKK6/QsmVLBg0alObXEhEREflf7u7uDBgwgMGDBxsd\nRURE0hkVP0VSoGXvkpGYTCbKli3Ll19+yenTp1m0aBHx8fE0bNiQUqVKMWzYMEJCQtI0w4gRI1i5\nciX79+9P0+uIiIiI/NuHH37I//3f/7Fz506jo4iISDqi4qdICtzc3FT8lAzJZDJRsWJFxo0bR2ho\nKHPnzuXmzZu89dZb+Pn5MWrUKE6dOpXq1/Xy8uKLL76gW7duJCUlpfr4IiIiIg+TKVMmBg8erFUo\nIiJyDxU/RVKgZe/yIjCZTFSpUoVJkyYRFhbGN998Q2RkJDVr1qR8+fKMGTOGP//8M9Wu16FDBxIS\nEpg/f36qjSkiIiLyONq3b09YWBhbtmwxOoqIiKQTKn6KpEDL3uVFYzabqVGjBtOmTePChQtMmDCB\n0NBQqlSpQqVKlRg/fjxhYWHPfI2vv/6azz77jGvXrrF+/XoCAhqTL58vWbPmJU+eolSuXCd5Wb6I\niIhIanF2dmbYsGEMHjz4uex5LiIi6Z9OexdJQbdu3ShRogTdunUzOopImkpISODXX3/FZrOxcuVK\nihcvjtVqpUWLFrz00ktPPJ7dbqd69ZocPHgCi6UAd+50AV4DMgNRwEEyZ/4Ok+koPXp0YejQATg5\nOaXyXYmIiIgjSkxMxN/fn/Hjx1O3bl2j44iIiME081MkBVr2Lo7CycmJOnXqMGvWLMLDwxk0aBD7\n9++ndOnSvP7663z99ddERkY+1liJiYm8//7HHDp0m5iYNdy5sw/oBBQHXgKKAS24fTuIW7d+ZeLE\n7dSp05jo6Oi0u0ERERFxGBaLhZEjRzJo0CDN/hQREc38FEnJpk2bcHNzo2bNmkZHETFEXFwcmzZt\nwmazsW7dOipUqIDVaqVp06bkzJnzgX26dPmEH37YT3T0Wv6e6fkod3F1bU+NGtFs2LAci8WSqvcg\nIiIijsdut1OhQgUGDRpE06ZNjY4jIiIGUvFTJAX//PUwmUwGJxExXkxMDBs2bMBms7Fx40aqVKmC\n1WqlSZMmeHl5ARAUFESjRp2Jjt4HeD3B6PG4u9dm4sR2fPRR5zTJLyIiIo5l/fr19OvXj0OHDunL\nVRERB6bip4iIPLGoqCjWrl2LzWZj8+bN1KhRA6vVyrx5y/j113rAx08x6mYKF+7DmTMH9YWDiIiI\nPDO73c5rr71Gly5deO+994yOIyIiBlHxU0REnsnt27dZvXo18+bNY/PmHUAEj7fc/d+S8PAoyaZN\nc6hevXoqpxQRERFH9Ouvv9K5c2dCQkJwdnY2Oo6IiBhABx6JiMgzyZw5M++99x5169bFxaU1T1f4\nBDATHd2J2bMXpmY8ERERcWC1atWiYMGC/Pjjj0ZHERERg6j4KSIiqSIsLJz4+GLPNIbd7kNoaHgq\nJRIRERGBUaNGMWLECOLi4oyOIiIiBlDxU+QZ3L17l4SEBKNjiKQL0dGxQKZnHCUTf/55loULFxIU\nFMSRI0e4cuUKSUlJqRFRREREHFDVqlXx8/Nj5syZRkcREREDOBkdQCQ927RpE1WqVCFr1qzJz/3v\nCfDz5s0jKSmJjz76yKiIIulG7txewLVnHOU6JlMSa9euJSIigsjISCIiIrhz5w65cuUiT5485M2b\nN8Xfvby8dGCSiIiI3GPEiBE0aNCAjh074u7ubnQcERF5jnTgkUgKzGYzwcHBVK1a9YGvz5w5kxkz\nZrB9+3YyZXrWGW8iGdv69etp1Woot2/vfeox3N3fZfToqvTs2eOe5+Pj47l06dI9BdGH/R4dHU2e\nPHkeq1CaNWvWDF8otdvtzJw5k23btuHq6kpAQACtWrXK8PclIiKS2po3b06VKlX49NNPjY4iIiLP\nkYqfIinw8PBg0aJFVKlShZiYGGJjY4mJiSEmJoa4uDh2797N559/ztWrV/Hy8jI6roihEhMTyZfP\nh8uXlwCvPsUIEbi6liQiIvSe2dZPKjY2lsjIyEcWSSMjI4mPj3+sImnevHnx9PRMdwXFqKgoevTo\nwc6dO2ncuDERERGcPHmSVq1a0b17dwCOHj3KyJEj2bVrFxaLhXbt2jF06FCDk4uIiDx/ISEh1KpV\ni1OnTpElSxaj44iIyHOi4qdICvLly0dkZCRubm7A30vdzWYzFosFi8WCh4cHAAcPHlTxUwQYPXos\no0YdJSbmyU9UtVhG0Lr1BX78cUYaJHuw6OjoxyqURkREYLfb7yuKPqxQ+s//G9JacHAwdevWZe7c\nuTRr1gyAb7/9lqFDh3LmzBkuXrxIQEAAlSpVom/fvpw8eZIZM2bw+uuvM3r06OeSUUREJD1p27Yt\nvr6+DB482OgoIiLynKj4KZKCPHny0LZtW958800sFgtOTk44Ozvf83tiYiL+/v44OWkLXZFr165R\nokR5rlwZhd3e5gl6/oanZwv++GM7vr6+aZbvWdy5c+exZpNGRERgsVgeazZpnjx5kr9ceRo//PAD\nAwYM4PTp07i4uGCxWDh37hwNGjSgR48emM1mhg0bxvHjx5MLsnPmzGH48OHs37+fHDlypNaPR0RE\nJEM4ffo0VapU4eTJk2TPnt3oOCIi8hyoWiOSAovFQsWKFXn77beNjiKSIWTPnp1ff11HtWoB3L4d\nj93e8TF6bcLdvS2rVi1Kt4VPAE9PTzw9PSlatGiK7ex2O7dv335gYXTfvn33Pe/q6pribFJfX198\nfX0fuOQ+a9asxMbGsnr1aqxWKwAbNmzg+PHj3Lp1C4vFQrZs2fDw8CA+Ph4XFxeKFy9OXFwc27dv\np3HjxmnysxIREUmvfHx8aNq0KePHj9cqCBERB6Hip0gKOnTogLe39wNfs9vt6W7/P5H0oHTp0uzZ\n8xu1atXn9u3/cudOF6AR9/6TYwe2YLFMxNPzD9atW0n16tWNCZzKTCYTWbJkIUuWLBQrVizFtna7\nnZs3bz5w9uiuXbuIiIigdu3a9O7d+4H93377bTp27EiPHj2YPXs2uXPn5sKFCyQmJpIrVy7y5cvH\nhQsXWLhwIe+99x63b99m2rRpXL58mejo6LS4fYeRmJhISEgIV69eBf4u/JcuXRqLxWJwMhERQC4h\njgAAIABJREFUeZRBgwZRrlw5evXqRe7cuY2OIyIiaUzL3kWewfXr17l79y45c+bEbDYbHUckXYmL\ni2PFihWMGfM1p0+H4uRUmcTELJjNd7DbD5MjhzM3bvzF6tU/UbNmTaPjZlg3b97k999/Z/v27cmH\nMq1cuZLu3bvTvn17Bg8ezIQJE0hMTKRkyZJkyZKFyMhIRo8enbxPqDy+y5cvM3PWTCZ/PZmYpBgs\nmS1ggsRbibjiSs+uPen8YWd9mBYRSed69OiBk5MTEydONDqKiIikMRU/RVKwdOlSihYtSvny5e95\nPikpCbPZzLJly9i7dy/du3fn5ZdfNiilSPp35MiR5KXYHh4eFC5cmFdffZVp06axZcsWVq1aZXTE\nF8aIESNYs2YNM2bMoFy5cgDcunWLY8eOkS9fPmbNmsXmzZv56quveO211+7pm5iYSPv27R+6R2nO\nnDkddmaj3W5n3PhxDBk+BHNJMzHlYiD/vxpdBNcDrthD7AwZNITP+3+uFQIiIulUREQEpUuX5tCh\nQ3ofLyLyglPxUyQFFSpUoGHDhgwbNuyBr+/atYtu3boxfvx43njjjeeaTUTkwIEDJCQkJBc5ly9f\nTteuXenbty99+/ZN3p7jf2em16hRg0KFCjFt2jS8vLzuGS8xMZGFCxcSGRn5wD1Lr1+/To4cOVI8\nwOmfP+fIkeOFmhHfq08vZtpmEt0iGrI9ovFNcF/qzvtN3mf6lOkqgIqIpFP9+/fn1q1bfPvtt0ZH\nERGRNKQ9P0VSkC1bNi5cuMDx48eJiooiJiaGmJgYoqOjiY+P56+//uLgwYOEh4cbHVVEHFBkZCSD\nBw/m1q1b5MqVixs3btC2bVu6deuG2Wxm+fLlmM1mXn31VWJiYvj88885ffo048aNu6/wCX8f8tau\nXbuHXi8hIYHLly/fVxS9cOECf/zxxz3P/5PpcU68z549e7ouEE6ZNoWZi2cS3SYa3B+jQ1aIbhPN\nvPnzKFyoMJ/2+TTNM4qIyJPr168fxYsXp1+/fhQuXNjoOCIikkY081MkBe3atWPBggW4uLiQlJSE\nxWLByckJJycnnJ2dyZw5M3fv3mXOnDm8+eabRscVEQcTFxfHyZMnOXHiBFevXsXHx4eAgIDk1202\nG0OHDuXs2bPkzJmTihUr0rdv3/uWu6eF+Ph4Ll269MAZpP9+Lioqity5cz+ySJo3b16yZs36XAul\nUVFR5H4pN9HtoyHHE3a+Bm5z3Yj8K5LMmTOnST4REXk2w4YNIzQ0lHnz5hkdRURE0oiKnyIpaNmy\nJdHR0YwbNw6LxXJP8dPJyQmz2UxiYiJeXl5kypTJ6LgiIslL3f9XbGws165dw9XVlezZsxuU7OFi\nY2MfWij99+9xcXHJy+sfVSjNnDnzMxdKZ8+eTc/JPYlqHvVU/T1WeDDu43H85z//eaYcIiKSNm7e\nvImPjw+///47JUqUMDqOiIikARU/RVLQvn17AH744QeDk4hkHLVq1cLPz4+pU6cCULhwYbp3707v\n3r0f2udx2ogAxMTEPFaRNDIykoSEhMeaTZonTx48PT3vu5bdbqe4X3FOlT0FxZ4y8Bnw3u3Nn8f/\nTNdL+0VEHNmYMWM4ePAgixcvNjqKiIikAe35KZKC1q1bExcXl/z4f2dUJSYmAmA2m/WBVhzKlStX\nGDJkCBs2bCA8PJxs2bLh5+fHZ599RkBAACtXrsTZ2fmJxty3bx8eHh5plFheJG5ubnh7e+Pt7f3I\ntlFRUQ8sjB4+fJhffvnlnufNZvN9s0mzZcvGn6f+hGbPELgwXFxxkatXr5IzZ85nGEhERNJK9+7d\n8fHx4fDhw/j7+xsdR0REUpmKnyIpCAwMvOfx/xY5LRbL844jki40bdqU2NhY5s6dS9GiRbl06RK/\n/fYbV69eBf4+KOxJ5cjxpJspijyah4cHRYoUoUiRIim2s9vt3Llz574i6bFjxzC5muBZDq03g0tm\nF65fv67ip4hIOuXh4cFnn33G4MGD+emnn4yOIyIiqUzL3kUeITExkWPHjnH69Gm8vb0pW7YssbGx\n7N+/n+joaMqUKUPevHmNjinyXNy8eRMvLy82b95M7dq1H9jmQcve33//fU6fPs2qVavw9PTk008/\npU+fPsl9/r3s3Ww2s2zZMpo2bfrQNiJp7fz585QoV4Lo7tHPNI7H1x783+7/00nCIiLpWGxsLMWK\nFWP58uVUqlTJ6DgiIpKKnmUug4hDGDt2LP7+/rRq1YqGDRsyd+5cbDYb9evXp0WLFnz22WdERkYa\nHVPkufD09MTT05PVq1ffsyXEo0yaNInSpUtz4MABRowYwYABA1i1alUaJhV5djly5CD+TjzEP8Mg\ndyH+drxmN4uIpHOurq4MGjSIwYMHc+DAATp37kz58uUpWrQopUuXJjAwkAULFjzR+x8REUkfVPwU\nScG2bdtYuHAhY8aMITY2lsmTJzNhwgRmzpzJ9OnT+eGHHzh27Bjff/+90VFFnguLxcIPP/zAggUL\nyJYtG9WqVaNv377s2bMnxX6VK1fms88+w8fHhw8//JB27doxceLE55Ra5Om4u7vz2uuvwdFnGCQE\nXq36KlmyZEm1XCIikjby5cvHH3/8QcOGDfH29mbGjBls2rQJm83Ghx9+yPz58ylYsCADBw4kNjbW\n6LgiIvKYVPwUScGFCxfIkiVL8vLcZs2aERgYiIuLC++99x6NGjXinXfeYffu3QYnFXl+mjRpwsWL\nF1m7di316tVj586dVKlShTFjxjy0T9WqVe97HBISktZRRZ5Zv179yHw481P3z3w4M/179U/FRCIi\nkhYmT55Mly5dmDVrFufOnWPAgAFUrFgRHx8fypQpQ/Pmzdm0aRPbt2/nxIkT1KlTh2vXrhkdW0RE\nHoOKnyIpcHJyIjo6+p7DjZydnblz507y4/j4eOLjn2VNpEjG4+LiQkBAAIMGDWL79u106tSJYcOG\nkZCQkCrjm0wm/r0l9d27d1NlbJEnERgYiHuCO5x6is5nwCXKhfr166d6LhERST2zZs1i+vTp7Nix\ng3feeSfFg02LFSvGkiVLKFeuHI0bN9YMUBGRDEDFT5EUFChQAICFCxcCsGvXLnbu3InFYmHWrFks\nX76cDRs2UKtWLSNjihiuZMmSJCQkPPQDwK5du+55vHPnTkqWLPnQ8XLlykV4eHjy48jIyHseizwv\nZrMZ23wbbmvd4En+E4wEtzVu2BbYUvwQLSIixjp79iyfffYZ69evp2DBgo/Vx2w2M3nyZHLlysUX\nX3yRxglFRORZORkdQCQ9K1u2LPXr16dDhw7MmzeP0NBQypYty4cffsi7776Lq6srr776Kh9++KHR\nUUWei2vXrtGiRQs6duyIv78/mTNnZu/evYwbN44333wTT0/PB/bbtWsXY8eOpVmzZvz6668sWLCA\n//73vw+9Tu3atfn666+pWrUqZrOZgQMH4ubmlla3JZKi119/nfmz59OuUzuiA6OhBA//+jgJOAmZ\n1mdizow5BAQEPMekIiLypL7//nvat2+Pr6/vE/Uzm82MHj2aN954g8GDB+Pi4pJGCUVE5Fmp+CmS\nAjc3N4YPH07lypUJCgqicePGfPzxxzg5OXHo0CFOnTpF1apVcXV1NTqqyHPh6elJ1apVmTp1KqdP\nnyYuLo78+fPTpk0bBg4cCPy9ZP1/mUwmevfuzeHDhxk1ahSenp6MHDmSJk2a3NPmf02YMIEPPviA\nWrVqkSdPHr766iuOHz+e9jco8hDNmjUjT548dPioA+Hbwol+JRp7GTt4/P8G0WA6YsL9kDueTp5Y\nPC00qN/A0MwiIpKyuLg45s6dy/bt25+qf4kSJShdujQrVqygVatWqZxORERSi8n+703VREREROSB\n7HY7u3fvZvyU8axft57YqL+3enB1d+Xtem/zac9PqVq1Kh06dMDV1ZXvvvvO4MQiIvIwq1evZvLk\nyWzZsuWpx1i8eDHz589n3bp1qZhMRERSk2Z+ijymf74n+N8Zana7/b4ZayIi8uIymUxUqVKFZVWW\nASQf8uXkdO9bqilTpvDKK6+wbt06HXgkIpJO/fXXX0+83P3ffH19uXjxYiolEhGRtKDip8hjelCR\nU4VPERHH9u+i5z+yZs1KaGjo8w0jIiJPJDY29pm3r3J1dSUmJiaVEomISFrQae8iIiIiIiLicLJm\nzcr169efaYwbN26QLVu2VEokIiJpQcVPERERERERcTivvvoqQUFB3L1796nH2LhxIxUrVkzFVCIi\nktpU/BR5hISEBC1lERERERF5wfj5+VG4cGHWrFnzVP3j4+OZOXMm//nPf1I5mYiIpCYVP0UeYd26\ndbRq1croGCIiIiIiksq6dOnC9OnTkw83fRIrV66kePHilC5dOg2SiYhIalHxU+QRtIm5SPoQGhpK\njhw5uHbtmtFRJAPo0KEDZrMZi8WC2WxO/vPhw4eNjiYiIulIs2bNuHLlChMnTnyifmfOnKFXr14M\nHjw4jZKJiEhqUfFT5BFcXV2JjY01OoaIw/P29uadd95hypQpRkeRDKJOnTpEREQk/woPD6dMmTKG\n5XmWPeVERCRtuLi4sG7dOqZOncq4ceMeawbo0aNHCQgIYOjQoQQEBDyHlCIi8ixU/BR5BDc3NxU/\nRdKJAQMG8PXXX3Pjxg2jo0gGkClTJnLlykXu3LmTf5nNZjZs2ECNGjXw8vIiR44c1KtXj5MnT97T\nd8eOHZQrVw43NzcqV67Mxo0bMZvN7NixA/h7P+hOnTpRpEgR3N3dKV68OBMmTLhnjLZt29KkSRO+\n/PJLXn75Zby9vQH48ccfefXVV8mSJQt58+alVatWREREJPe7e/cu3bp146WXXsLV1ZVChQppZpGI\nSBoqUKAA27dvZ/78+VSrVo0lS5Y88AurI0eO0LVrV2rWrMmoUaP4+OOPDUgrIiJPysnoACLpnZa9\ni6QfRYsWpX79+kybNk3FIHlq0dHRfPrpp/j5+REVFcWIESNo1KgRR48exWKxcPv2bRo1akSDBg1Y\ntGgR58+fp1evXphMpuQxEhMTKVSoEMuWLSNnzpzs2rWLzp07kzt3btq2bZvcLigoiKxZs/LLL78k\nzyZKSEhg1KhRFC9enMuXL9OvXz9at27Nli1bAJg4cSLr1q1j2bJlFChQgAsXLnDq1Knn+0MSEXEw\nBQoUICgoiKJFizJx4kR69epFrVq1yJo1K7GxsZw4cYKzZ8/SuXNnDh8+TP78+Y2OLCIij8lkf5qd\nnUUcyMmTJ6lfv74+eIqkEydOnKBly5bs27cPZ2dno+NIOtWhQwcWLFiAq6tr8nM1a9Zk3bp197W9\ndesWXl5e7Ny5k0qVKvH1118zfPhwLly4gIuLCwDz58/n/fff5/fff6datWoPvGbfvn05evQo69ev\nB/6e+RkUFERYWBhOTg//vvnIkSP4+/sTERFB7ty56dq1K2fOnGHjxo3P8iMQEZEnNHLkSE6dOsWP\nP/5ISEgI+/fv58aNG7i5ufHSSy/x5ptv6r2HiEgGpJmfIo+gZe8i6Uvx4sU5ePCg0TEkA3j99deZ\nOXNm8oxLNzc3AE6fPs2QIUPYvXs3V65cISkpCYCwsDAqVarEiRMn8Pf3Ty58AlSuXPm+feC+/vpr\n5s2bx7lz54iJieHu3bv4+Pjc08bPz+++wue+ffsYOXIkhw4d4tq1ayQlJWEymQgLCyN37tx06NCB\nwMBAihcvTmBgIPXq1SMwMPCemaciIpL6/ndVSalSpShVqpSBaUREJLVoz0+RR9Cyd5H0x2QyqRAk\nj+Tu7k7hwoUpUqQIRYoUIV++fADUq1eP69evM2vWLPbs2cP+/fsxmUzEx8c/9tgLFy6kb9++fPDB\nB/z8888cOnSIjz766L4xPDw87nl8584d3n77bbJmzcrChQvZt29f8kzRf/pWrFiRc+fO8cUXX5CQ\nkECbNm2oV6/es/woREREREQclmZ+ijyCTnsXyXiSkpIwm/X9ntzv0qVLnD59mrlz51K9enUA9uzZ\nkzz7E6BEiRLYbDbu3r2bvLxx9+7d9xTcg4ODqV69Oh999FHyc4+zPUpISAjXr1/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DAAAg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\nuqElWwAAAABJRU5ErkJggg==\n", "text/plain": [ - "['Sibiu', 'Fagaras']" + "" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "breadth_first_tree_search(romania_problem).solution()" + "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", + "w = widgets.interactive(slider_callback, iteration = slider)\n", + "display(w)\n", + "\n", + "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "a = widgets.interactive(visualize_callback, Visualize = button)\n", + "display(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Breadth first search\n", + "\n", + "Let's change all the node_colors to starting position and difine a different problem statement." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "node_colors = dict(initial_node_colors)\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def breadth_first_search(problem):\n", + " \"[Figure 3.11]\"\n", + " \n", + " # we use these two variables at the time of visualisations\n", + " global iterations\n", + " iterations = 0\n", + " global all_node_colors\n", + " all_node_colors = []\n", + " \n", + " node = Node(problem.initial)\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return node\n", + " \n", + " frontier = FIFOQueue()\n", + " frontier.append(node)\n", + " \n", + " # modify the color of frontier nodes to blue\n", + " node_colors[node.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " explored = set()\n", + " while frontier:\n", + " node = frontier.pop()\n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " explored.add(node.state) \n", + " \n", + " for child in node.expand(problem):\n", + " if child.state not in explored and child not in frontier:\n", + " if problem.goal_test(child.state):\n", + " node_colors[child.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return child\n", + " frontier.append(child)\n", + "\n", + " node_colors[child.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " node_colors[node.state] = \"gray\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return None" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 59, "metadata": { "collapsed": false }, @@ -511,28 +748,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "83\n", - "83\n" + "26\n", + "26\n" ] } ], "source": [ + "breadth_first_search(romania_problem).solution()\n", + "\n", "print(len(all_node_colors))\n", "print(iterations)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 60, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "from ipywidgets import interact\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", "def slider_callback(iteration):\n", " show_map(all_node_colors[iteration])\n", "\n", @@ -545,16 +780,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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KlMjTbMVJQkKCGjRooK1btzILBQCAApCSkqKZM2dqzpw5evrppzV27FhVrlzZ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\nCQAAAAAsyU8AAAAAYEl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UAAByQXx8vGbMmKHZs2frhRde0OjRo+Xt7W10LAAAYHKUn0Aua9asmUqVKiU/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\nu0c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}, - "fc5bf1bb183445bab2d512ccbee0be37": { + "f7e777fdf53a4e08853e9d7c411cc3c6": { "views": [] }, - "fd087cde55544ddea3dbacb22191c707": { - "views": [] + "fa03910d8f3747e49cf2e1ddc004afc0": { + "views": [ + { + "cell_index": 44 + } + ] }, - "ff818912a24a4517a8f34ea8a4614423": { + "fa7f2272527648a5b5db0fe941eac78b": { "views": [] }, - "fff756571d314c5f9c4070fa1ff66ace": { + "ffa5da7ffc384b9faeeb78b71e94d9fd": { "views": [] } }, From 54e4693d79c02c2ec19d426cd2a8750e631f6184 Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 14 Jun 2016 20:31:14 +0530 Subject: [PATCH 319/513] adds visuals for uniform cost search and A-star search --- search.ipynb | 583 ++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 554 insertions(+), 29 deletions(-) diff --git a/search.ipynb b/search.ipynb index e65585db6..affda83e9 100644 --- a/search.ipynb +++ b/search.ipynb @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Neamt': (406, 537), 'Craiova': (253, 288), 'Fagaras': (305, 449), 'Drobeta': (165, 299), 'Timisoara': (94, 410), 'Sibiu': (207, 457), 'Urziceni': (456, 350), 'Giurgiu': (375, 270), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Rimnicu': (233, 410), 'Arad': (91, 492), 'Mehadia': (168, 339), 'Hirsova': (534, 350), 'Oradea': (131, 571), 'Zerind': (108, 531), 'Pitesti': (320, 368), 'Lugoj': (165, 379), 'Bucharest': (400, 327), 'Eforie': (562, 293)}\n" + "{'Giurgiu': (375, 270), 'Arad': (91, 492), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Craiova': (253, 288), 'Mehadia': (168, 339), 'Neamt': (406, 537), 'Rimnicu': (233, 410), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Bucharest': (400, 327), 'Fagaras': (305, 449), 'Oradea': (131, 571), 'Sibiu': (207, 457), 'Drobeta': (165, 299), 'Lugoj': (165, 379), 'Zerind': (108, 531), 'Hirsova': (534, 350), 'Pitesti': (320, 368), 'Eforie': (562, 293)}\n" ] } ], @@ -341,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": { "collapsed": false }, 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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -503,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -574,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -583,8 +583,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "90\n", - "90\n" + "86\n", + "86\n" ] } ], @@ -605,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -623,16 +623,16 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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oeXt7Gx0LAACYHOUnkM+aNGmrHTv6SXo222O4ujbRypVhatu2be4FK4Leffdd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DAAAg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\nkp8AAAAAwJL8BAAAAAC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KlMjTbMVJQkKCGjRooK1btzILBQCAApCSkqKZM2dqzpw5evrppzV27FhVrlzZ\n6FgAAMDkKD+BAtaqVSft2DFA0tM5HsPNrZWio0PVqVOnvAtWDL377rv68ssvtXHjRjY/AgAAAADA\nhO5lsUEAeeD06dOS7s/VGFbr/f87DnIjODhYCQkJWrlypdFRAAAAAABAPqD8BArY9etpkpxzNUZm\nprPS0tLyJlAx5uDgoLlz5+rVV19Vamqq0XEAAAAAAEAeo/wECpira2lJybkaw8EhRaVLl86bQMVc\n27Zt1aZNG7311ltGRwEAAH9z7do1oyMAAAAToPwECljLlk1kZ7cpFyOky2r99q53WMW/Cw8P18KF\nC3X06FGjowAAgP9Vp04dRUREKD093egoAACgCKP8BArYq68OkpPTQknWHI7wherVq60GDRrkZaxi\n7b777tOoUaP0yiuvGB0FAIBc69u3r+zs7DR16tRsx7du3So7OztdunTJoGR/Wbx4sdzc3P71uujo\naH3yySeqX7++li1bJqs1p787AQCA4ozyEyhgTZs2VbVqFSV9naP7XV3n6bXXBudtKOiVV17Rr7/+\nqjVr1hgdBQCAXLFYLHJ2dlZ4eLguXrx4yzmj2Wy2u8rRsmVLbd68We+//77mzp2rhg0batWqVbLZ\nbAWQEgAAmAXlJ2CAsLCxcnEZLOnedmy3t5+lcuXO64knnsifYMWYo6Oj3n33Xb3yyiusMQYAKPIC\nAgJUvXp1TZo06Y7XHDp0SF27dpW7u7sqVqyo3r17KyEhIev8nj171KlTJ5UvX16lS5fWgw8+qB07\ndmQbw87OTgsXLlT37t1VqlQp1atXT999953OnDmjzp07y9XVVY0bN9a+ffsk/TX7tH///kpNTZWd\nnZ3s7e3/MaMktWvXTj/++KOmTZumiRMnqnnz5tqwYQMlKAAAuCuUn4ABHn/8cY0ZEyIXl3aSjt3V\nPfb2s1SmzAx9993XcnR0zN+AxVSnTp3k5+enGTNmGB0FAIBcsbOz07Rp07Rw4UKdOHHilvN//PGH\n2rZtK39/f+3Zs0ebN29WamqqunXrlnXNlStX9Pzzz2v79u3avXu3GjdurMcee0xJSUnZxpo6dap6\n9+6tAwcOqFmzZnrmmWc0YMAADR48WPv27ZO3t7f69u0rSWrdurVmzZolFxcXJSQk6Ny5cxoxYsS/\nvh+LxaKuXbsqJiZGI0eO1NChQ9W2bVt9//33uftBAQAA07PY+MgUMMzcuQs0atR4ZWT0U3r6IEk1\n/s8VVkmjAaXqAAAgAElEQVRrVarUXJUrd1pbt65TtWrVDEhafJw4cULNmjVTTEyMqlatanQcAADu\nWb9+/XTx4kV9+eWXateunby8vBQVFaWtW7eqXbt2SkxM1KxZs/TTTz9p48aNWfclJSWpbNmy2rVr\nl5o2bXrLuDabTffdd5+mT5+u3r17S/qrZH3jjTc0ZcoUSVJsbKz8/Pw0c+ZMDR06VJKyva6np6cW\nL16sIUOG6PLlyzl+jxkZGVq6dKkmTpyoevXqaerUqXrggQdyPB4AADAvZn4CBgoJGaT9+39UixYx\ncnDwl5tbR5UsOUQODiPl4jJALi415ePzpubP76P4+BiKzwJQo0YNDRkyRMOHDzc6CgAAuRYWFqbo\n6Gjt3bs32/GYmBht3bpVbm5uWV9Vq1aVxWLRsWN/PZWSmJiooKAg1atXT2XKlJG7u7sSExN18uTJ\nbGP5+fll/blixYqSlG1jxpvHzp8/n2fvy8HBQX379tXhw4cVGBiowMBAPfnkk4qNjc2z1wAAAObg\nYHQAoLirXbu2kpMT9OWXnyk1NVVnz57VtWvXVKZMHTVtGqwmTZoYHbHYGTVqlHx8fLRp0yZ16NDB\n6DgAAORYs2bN1KNHD40cOVLjxo3LOp6ZmamuXbtqxowZt6ydebOsfP7555WYmKjZs2erWrVqKlmy\npNq1a6cbN25ku75EiRJZf765kdH/PWaz2ZSZmZnn78/R0VHBwcHq27ev5s+fr4CAAHXq1EmhoaGq\nVatWnr8eAAAoeig/AYNZLBb98ssvRsfA3zg7O2vWrFkaMmSI9u/fzxqrAIAi7c0335SPj4/Wr1+f\ndaxJkyaKjo5W1apVZW9vf9v7tm/frjlz5qhz586SlLVGZ078fXd3R0dHWa3WHI1zJy4uLhoxYoRe\nfPFFzZw5Uy1atNCTTz6pcePGqXLlynn6WgAAoGjhsXcAuI3AwEBVr15dc+bMMToKAAC5UqtWLQUF\nBWn27NlZxwYPHqyUlBT17NlTu3bt0okTJ7Rp0yYFBQUpNTVVklS3bl0tXbpUcXFx2r17t3r16qWS\nJUvmKMPfZ5dWr15d165d06ZNm3Tx4kWlpaXl7g3+jbu7uyZMmKDDhw+rTJky8vf317Bhw+75kfu8\nLmcBAIBxKD8B4DYsFotmz56tt956K8ezXAAAKCzGjRsnBweHrBmYlSpV0vbt22Vvb69HH31UDRo0\n0JAhQ+Tk5JRVcC5atEh//vmnmjZtqt69e+u///2vqlevnm3cv8/ovNtjrVq10ksvvaRevXqpQoUK\nCg8Pz8N3+peyZcsqLCxMsbGxysjIUP369TVmzJhbdqr/v86cOaOwsDA999xzeuONN3T9+vU8zwYA\nAAoWu70DwD94/fXXdfr0aS1ZssToKAAAIId+//13TZo0SevXr9epU6dkZ3frHJDMzEx1795dv/zy\ni3r37q3vv/9e8fHxmjNnjv7nf/5HNpvttsUuAAAo3Cg/AeAf/Pnnn6pfv76WL1+uNm3aGB0HAADk\nQkpKitzd3W9bYp48eVKPPPKIXnvtNfXr10+SNG3aNK1fv15ff/21XFxcCjouAADIAzz2DhRi/fr1\nU2BgYK7H8fPz06RJk/IgUfHj6uqq6dOnKyQkhPW/AAAo4kqXLn3H2Zve3t5q2rSp3N3ds45VqVJF\nx48f14EDByRJ165d07vvvlsgWQEAQN6g/ARyYevWrbKzs5O9vb3s7Oxu+Wrfvn2uxn/33Xe1dOnS\nPEqLnOrZs6c8PDz03nvvGR0FAADkg59++km9evVSXFycnn76aQUHB2vLli2aM2eOatasqfLly0uS\nDh8+rNdff12VKlXi9wIAAIoIHnsHciEjI0OXLl265fgXX3yhQYMG6bPPPlOPHj3ueVyr1Sp7e/u8\niCjpr5mfTz/9tMaPH59nYxY3Bw8eVLt27RQbG5v1HyAAAFD0Xb16VeXLl9fgwYPVvXt3JScna8SI\nESpdurS6du2q9u3bq2XLltnuiYyM1Lhx42SxWDRr1iw99dRTBqUHAAD/hpmfQC44ODioQoUK2b4u\nXryoESNGaMyYMVnF59mzZ/XMM8/I09NTnp6e6tq1q44ePZo1zsSJE+Xn56fFixerdu3acnJy0tWr\nV9W3b99sj70HBARo8ODBGjNmjMqXL6+KFStq5MiR2TIlJiaqW7ducnFxUY0aNbRo0aKC+WGYXIMG\nDdS7d2+NGTPG6CgAACAPRUVFyc/PT6NHj1br1q3VpUsXzZkzR6dPn1b//v2zik+bzSabzabMzEz1\n799fp06dUp8+fdSzZ08FBwcrNTXV4HcCAABuh/ITyEMpKSnq1q2b2rVrp4kTJ0qS0tLSFBAQoFKl\nSun777/Xjh075O3trQ4dOujatWtZ9544cULLly/XihUrtH//fpUsWfK2a1JFRUWpRIkS+umnnzRv\n3jzNmjVLn376adb5F154QcePH9eWLVu0evVqffzxx/r999/z/80XA6Ghofrqq68UHx9vdBQAAJBH\nrFarzp07p8uXL2cd8/b2lqenp/bs2ZN1zGKxZPvd7KuvvtLevXvl5+en7t27q1SpUgWaGwAA3B3K\nTyCP2Gw29erVSyVLlsy2Tufy5cslSR9++KF8fX1Vt25dLViwQH/++afWrFmTdV16erqWLl2qRo0a\nycfH546Pvfv4+Cg0NFS1a9fWU089pYCAAG3evFmSdOTIEa1fv14RERFq2bKlGjZsqMWLF+vq1av5\n+M6LjzJlymjfvn2qV6+eWDEEAABzaNu2rSpWrKiwsDCdPn1aBw4c0NKlS3Xq1Cndf//9kpQ141P6\na9mjzZs3q2/fvsrIyNCKFSvUsWNHI98CAAD4Bw5GBwDM4vXXX9fOnTu1e/fubJ/8x8TE6Pjx43Jz\nc8t2fVpamo4dO5b1feXKlVWuXLl/fR1/f/9s33t7e+v8+fOSpPj4eNnb26tZs2ZZ56tWrSpvb+8c\nvSfcqkKFCnfcJRYAABQ9999/vz766CMFBwerWbNmKlu2rG7cuKHXXntNderUyVqL/ea//2+//bYW\nLlyozp07a8aMGfL29pbNZuP3AwAACinKTyAPfPLJJ3rnnXf09ddfq2bNmtnOZWZmqnHjxvr0009v\nmS3o6emZ9ee7fVSqRIkS2b63WCxZMxH+fgz5415+tteuXZOTk1M+pgEAAHnBx8dH3333nQ4cOKCT\nJ0+qSZMmqlChgqT/vxHlhQsX9MEHH2jatGkaOHCgpk2bppIlS0ridy8AAAozyk8gl/bt26cBAwYo\nLCxMHTp0uOV8kyZN9Mknn6hs2bJyd3fP1yz333+/MjMztWvXrqzF+U+ePKmzZ8/m6+siu8zMTG3c\nuFExMTHq16+fvLy8jI4EAADugr+/f9ZTNjc/XHZ0dJQkvfzyy9q4caNCQ0MVEhKikiVLKjMzU3Z2\nrCQGAEBhxr/UQC5cvHhR3bt3V0BAgHr37q2EhIRbvp599llVrFhR3bp107Zt2/Tbb79p27ZtGjFi\nRLbH3vNC3bp11alTJwUFBWnHjh3at2+f+vXrJxcXlzx9HfwzOzs7ZWRkaPv27RoyZIjRcQAAQA7c\nLDVPnjypNm3aaM2aNZoyZYpGjBiR9WQHxScAAIUfMz+BXFi7dq1OnTqlU6dO3bKu5s21n6xWq7Zt\n26bXXntNPXv2VEpKiry9vRUQECAPD497er27eaRq8eLFGjhwoNq3b69y5cppwoQJSkxMvKfXQc7d\nuHFDjo6Oeuyxx3T27FkFBQXpm2++YSMEAACKqKpVq2r48OGqVKlS1pM1d5rxabPZlJGRccsyRQAA\nwDgWG1sWA0CuZWRkyMHhr8+Trl27phEjRmjJkiVq2rSpRo4cqc6dOxucEAAA5DebzaaGDRuqZ8+e\nGjp06C0bXgIAgILHcxoAkEPHjh3TkSNHJCmr+IyIiFD16tX1zTffaPLkyYqIiFCnTp2MjAkAAAqI\nxWLRypUrdejQIdWuXVvvvPOO0tLSjI4FAECxRvkJADm0bNkyPf7445KkPXv2qGXLlho1apR69uyp\nqKgoBQUFqWbNmuwACwBAMVKnTh1FRUVp06ZN2rZtm+rUqaOFCxfqxo0bRkcDAKBY4rF3AMghq9Wq\nsmXLqnr16jp+/LgefPBBDRo0SP/5z39uWc/1woULiomJYe1PAACKmV27dmns2LE6evSoQkND9eyz\nz8re3t7oWAAAFBuUnwCQC5988ol69+6tyZMn67nnnlPVqlVvuearr75SdHS0vvjiC0VFRemxxx4z\nICkAADDS1q1bNWbMGF26dEmTJk1Sjx492C0eAIACQPkJALnUsGFDNWjQQMuWLZP012YHFotF586d\n03vvvafVq1erRo0aSktL088//6zExESDEwMAACPYbDatX79eY8eOlSRNmTJFnTt3ZokcAADyER81\nAkAuRUZGKi4uTqdPn5akbP+Bsbe317FjxzRp0iStX79eXl5eGjVqlFFRAQCAgSwWix599FHt2bNH\nb7zxhoYPH64HH3xQW7duNToaAACmxcxPIA/dnPGH4uf48eMqV66cfv75ZwUEBGQdv3Tpkp599ln5\n+PhoxowZ2rJlizp27KhTp06pUqVKBiYGAABGs1qtioqKUmhoqGrVqqWpU6eqWbNmRscCAMBU7END\nQ0ONDgGYxd+Lz5tFKIVo8eDh4aGQkBDt2rVLgYGBslgsslgscnZ2VsmSJbVs2TIFBgbKz89P6enp\nKlWqlGrWrGl0bAAAYCA7Ozs1bNhQwcHBun79uoKDg7Vt2zb5+vqqYsWKRscDAMAUeOwdyAORkZF6\n8803sx27WXhSfBYfrVq10s6dO3X9+nVZLBZZrVZJ0vnz52W1WlW6dGlJ0uTJk9W+fXsjowIAgEKk\nRIkSCgoK0q+//qqHHnpIHTp0UO/evfXrr78aHQ0AgCKP8hPIAxMnTlTZsmWzvt+5c6dWrlypL7/8\nUrGxsbLZbMrMzDQwIQpC//79VaJECU2ZMkWJiYmyt7fXyZMnFRkZKQ8PDzk4OBgdEQAAFGLOzs56\n9dVXdfToUfn4+KhVq1YaMGCATp48aXQ0AACKLNb8BHIpJiZGrVu3VmJiotzc3BQaGqoFCxYoNTVV\nbm5uqlWrlsLDw9WqVSujo6IA7NmzRwMGDFCJEiVUqVIlxcTEqFq1aoqMjFS9evWyrktPT9e2bdtU\noUIF+fn5GZgYAAAUVklJSQoPD9d7772nZ599Vm+88Ya8vLyMjgUAQJHCzE8gl8LDw9WjRw+5ublp\n5cqVWrVqld544w39+eefWr16tZydndWtWzclJSUZHRUFoGnTpoqMjFSnTp107do1BQUFacaMGapb\nt67+/lnTuXPn9Pnnn2vUqFFKSUkxMDEAACisPDw89Oabb+rQoUOys7OTr6+vXn/9dV26dMnoaAAA\nFBnM/ARyqUKFCnrggQc0btw4jRgxQl26dNHYsWOzzh88eFA9evTQe++9l20XcBQP/7Th1Y4dOzRs\n2DBVrlxZ0dHRBZwMAAAUNadOndLkyZP1+eefa+jQoXrllVfk5uZmdCwAAAo1Zn4CuZCcnKyePXtK\nkgYNGqTjx4/roYceyjqfmZmpGjVqyM3NTZcvXzYqJgxw83Olm8Xn//2c6caNGzpy5IgOHz6sH374\ngRkcAADgX1WpUkXvv/++duzYocOHD6t27dqaMWOG0tLSjI4GAEChRfkJ5MLZs2c1d+5czZ49WwMH\nDtTzzz+f7dN3Ozs7xcbGKj4+Xl26dDEwKQrazdLz7Nmz2b6X/toQq0uXLurfv7+ee+457d+/X56e\nnobkBAAARU/t2rW1dOlSbd68Wdu3b1edOnW0YMEC3bhxw+hoAAAUOpSfQA6dPXtWDz/8sKKiolS3\nbl2FhIRoypQp8vX1zbomLi5O4eHhCgwMVIkSJQxMCyOcPXtWgwYN0v79+yVJp0+f1tChQ/XQQw8p\nPT1dO3fu1OzZs1WhQgWDkwIAgKKoQYMG+vzzz7V69Wp98cUXuv/++7V48WJZrVajowEAUGhQfgI5\nNH36dF24cEEDBgzQhAkTlJKSIkdHR9nb22dds3fvXp0/f16vvfaagUlhFG9vb6WmpiokJETvv/++\nWrZsqZUrVyoiIkJbt27VAw88YHREAABgAk2bNtX69ev10Ucf6YMPPlCDBg0UHR2tzMzMux4jJSVF\nc+fO1SOPPKLGjRurYcOGCggIUFhYmC5cuJCP6QEAyF9seATkkLu7u1atWqWDBw9q+vTpGjlypF5+\n+eVbrktLS5Ozs7MBCVEYJCYmqlq1arp27ZpGjhypN954Q6VLlzY6FgAAMCmbzaYNGzZo7NixyszM\n1OTJk9WlS5c7bsB47tw5TZw4UZ9++qk6duyoPn366L777pPFYlFCQoI+++wzrVq1So8//rgmTJig\nWrVqFfA7AgAgdyg/gRxYvXq1goKClJCQoOTkZE2bNk3h4eHq37+/pkyZoooVK8pqtcpiscjOjgnW\nxV14eLimT5+uY8eOydXV1eg4AACgGLDZbFq1apXGjRunMmXKaOrUqXr44YezXRMXF6dHH31UTz/9\ntF599VVVqlTptmNdunRJ8+fP17x587Rq1Sq1bNmyAN4BAAB5g/ITyIEHH3xQrVu3VlhYWNaxDz74\nQFOnTlWPHj00Y8YMA9OhMCpTpozGjRun4cOHGx0FAAAUI1arVcuXL1doaKhq1KihKVOmqEWLFjp1\n6pRat26tyZMnq2/fvnc11tq1a9W/f39t2bIl2zr3AAAUZpSfwD26cuWKPD09dfjwYdWsWVNWq1X2\n9vayWq364IMP9Oqrr+rhhx/W3LlzVaNGDaPjopDYv3+/zp8/r/bt2zMbGAAAFLj09HQtWrRIkydP\nVpMmTXT+/Hl1795do0ePvqdxlixZorfeekuxsbF3fJQeAIDChPITyIHk5GSVKVPmtudWrlypUaNG\nydfXV8uXL1epUqUKOB0AAABwe9euXdOECRMUERGhhIQElShR4p7ut9lsatiwoWbOnKn27dvnU0oA\nAPIO04+AHLhT8SlJTz75pN555x1duHCB4hMAAACFipOTk1JTUzVkyJB7Lj4lyWKxKDg4WPPnz8+H\ndAAA5D1mfgL5JCkpSR4eHkbHQCF1869eHhcDAAAFKTMzUx4eHjp06JDuu+++HI1x5coVVa5cWb/9\n9hu/7wIACj1mfgL5hF8E8U9sNpt69uypmJgYo6MAAIBi5PLly7LZbDkuPiXJzc1NXl5e+uOPP/Iw\nGQAA+YPyE8glJk8jJ+zs7NS5c2eFhIQoMzPT6DgAAKCYSEtLk7Ozc67HcXZ2VlpaWh4kAgAgf1F+\nArlgtVr1008/UYAiR/r166eMjAwtWbLE6CgAAKCYKF26tFJSUnL9+2tycrJKly6dR6kAAMg/lJ9A\nLmzcuFFDhw5l3UbkiJ2dnebNm6fXXntNKSkpRscBAADFgLOzs2rUqKEffvghx2McOXJEaWlpqlKl\nSh4mAwAgf1B+Arnw4Ycf6r///a/RMVCENWvWTF27dlVoaKjRUQAAQDFgsVg0aNCgXO3WvnDhQvXv\n31+Ojo55mAwAgPzBbu9ADiUmJqpOnTr6/fffeeQHuZKYmChfX19t2bJFDRo0MDoOAAAwueTkZNWo\nUUNxcXHy8vK6p3tTU1NVrVo17dmzR9WrV8+fgAAA5CFmfgI5tGTJEnXr1o3iE7lWvnx5TZgwQUOG\nDGH9WOD/sXef0VFW+9vHvzOThDRK6IhAgBBqIk2qoBAx0qUOIkVAUemCgNKbCNKLja7AgaFLRwki\nErq0P4QuISBJ6C2VJPO88DHrcIDQEu6EuT5rsWBm9t73dWeJzPxmFxERSXPZsmXjk08+oXXr1sTH\nxz92v6SkJDp27EiDBg1U+BQRkQxDxU+Rp2C327XkXVLVRx99xPXr11myZInRUURERMQBjBw5Ei8v\nL5o0acKdO3ce2T4+Pp7333+f8PBwvv/+++eQUEREJHWo+CnyFHbt2sXdu3epUaOG0VHkBeHk5MT0\n6dP57LPPHusDiIiIiMizsFgsLF68mHz58vHKK68wadIkrl+/fl+7O3fu8P333/PKK69w69YtNm7c\niKurqwGJRUREno72/BR5Ch988AHFihWjf//+RkeRF0zbtm0pUKAAo0ePNjqKiIiIOAC73U5wcDDf\nffcd69at46233iJ//vyYTCYiIyPZsGEDpUuXJiwsjNOnT+Ps7Gx0ZBERkSei4qfIE7p9+zYFCxZ8\nqg3iRR4lPDwcPz8/duzYga+vr9FxRERExIFcunSJjRs3cuXKFZKSksiRIwcBAQEUKFCA6tWr06VL\nF9q0aWN0TBERkSei4qfIE5o9ezZr1qxh1apVRkeRF9T48eMJCgpi/fr1mEwmo+OIiIiIiIiIZFja\n81PkCemgI0lrPXr0IDQ0lDVr1hgdRURERERERCRD08xPkScQEhLCm2++SVhYGE5OTkbHkRfYr7/+\nykcffcTRo0dxc3MzOo6IiIiIiIhIhqSZnyJPYPbs2bz//vsqfEqaq1OnDuXLl2fcuHFGRxERERER\nERHJsDTzU+QxxcfHU6BAAYKDg/Hx8TE6jjiAc+fOUb58ef7880+8vb2NjiMiIiIiIiKS4Wjmp8hj\nWrNmDSVLllThU56bQoUK8emnn9K7d2+jo4iIiIjcY/jw4fj7+xsdQ0RE5JE081PkMdWtW5f33nuP\nNm3aGB1FHEhsbCylS5fm22+/JTAw0Og4IiIikoF16NCBq1evsnr16mceKzo6mri4OLy8vFIhmYiI\nSNrRzE+Rx3D+/Hn27NlDs2bNjI4iDsbV1ZUpU6bQo0cP4uPjjY4jIiIiAoC7u7sKnyIikiGo+Cny\nGObNm4fVatWp22KIBg0aUKxYMaZMmWJ0FBEREXlB7Nu3j8DAQHLlykXWrFmpUaMGu3btuqfNDz/8\nQPHixXFzcyNXrlzUrVuXpKQk4J9l735+fkZEFxEReSIqfoo8QlJSEnPmzOGDDz4wOoo4sMmTJzN2\n7Fj+/vtvo6OIiIjIC+D27du0a9eO4OBg9u7dS7ly5ahfvz7Xr18H4M8//6Rbt24MHz6ckydPsmXL\nFt5+++17xjCZTEZEFxEReSJORgcQSS/u3LnDggUL2bRpO1ev3sDFxZmCBfPi51eMrFmzUr58eaMj\nigPz8fHho48+ol+/fixcuNDoOCIiIpLB1apV657HU6ZMYdmyZWzYsIHWrVsTFhaGp6cnDRs2xMPD\ngwIFCmimp4iIZEgqforDCw0NZfToCSxYsBCz+Q2iohoB2YF4TKazWCxTyZLFzrfffkfnzh/i5KS/\nNmKMAQMGULJkSbZt20bNmjWNjiMiIiIZ2OXLlxk0aBBbt24lMjKSxMREYmNjCQsLA6BOnToUKlQI\nb29vAgMDeeutt2jatCmenp4GJxcREXkyWvYuDm3Hjh288koV5s7NTEzMYaKiVgDvA42A5tjtfUlI\n+Itr1+bSt+8S6tRpzJ07d4wNLQ7Lw8ODCRMm0K1bNxISEoyOIyIiIhlYu3bt+PPPP5kyZQo7d+7k\n0KFD5M+fP/mARU9PT/bv38/SpUspVKgQY8aMoUSJEkRERBicXERE5Mmo+CkOa//+/bz1VmNu3ZpL\nQsJo4OWHtDQBtYiO/oWdO3Pz1ltNdOq2GKZ58+bkypWL7777zugoIiIikoEFBwfTvXt33n77bUqW\nLImHhwfh4eH3tDGbzbzxxht8+eWXHDp0iKioKNauXWtQYhERkaej4qc4pNjYWN56qzFRUT8AdR+z\nlzNxcbM4eNCNzz8fmpbxRB7KZDIxbdo0RowYwaVLl4yOIyIiIhmUr68vCxYs4NixY+zdu5d3332X\nTJkyJb++bt06pk6dysGDBwkLC2PhwoXcuXOHUqVKGZhaRETkyan4KQ5p6dKlxMWVApo+YU8LMTFT\nmTFjJtHR0WkRTeSRSpUqRbt27fjiiy+MjiIiIiIZ1Jw5c7hz5w4VK1akdevWdOrUCW9v7+TXs2XL\nxqpVq6hTpw4lS5Zk4sSJzJ49m2rVqhkXWkRE5CmY7Ha73egQIs+bn181jhzpDzR+qv6eng2ZOrUp\nHTp0SN1gIo/p1q1blChRgpUrV1K5cmWj44iIiIiIiIikS5r5KQ4nJCSEv/46D9R/6jHu3PmEiRNn\npV4okSeUJUsWxo4dS9euXUlMTDQ6joiIiIiIiEi6pOKnOJy//voLZ2d/wOkZRilLWNiZ1Iok8lTa\ntGmDq6src+bMMTqKiIiIiIiISLqk4qc4nDt37pCU5PGMo3gSG3snVfKIPC2TycT06dMZPHgw165d\nMzqOiIiIiIiISLqj4qc4nCxZsmA2337GUW7h5pYlVfKIPIuyZcvSrFkzhgwZYnQUERERkWS7d+82\nOoKIiAig4qc4oBIlShAX9ycQ+wyj7KBgwSKpFUnkmYwcOZKlS5dy8OBBo6OIiIiIADB48GCjI4iI\niAAqfooDKlKkCGXLlgWWPfUYzs4TCQs7Qvny5RkzZgxnz55NvYAiTyh79uyMHDmSbt26YbfbjY4j\nIiIiDu7u3bucOXOG33//3egoIiIiKn6KY+rfvwuZM3/7lL2P4uERRkREBBMmTCA0NJRKlSpRqVIl\nJkyYwPnz51M1q8jj6NSpE7GxsSxcuNDoKCIiIuLgnJ2dGTp0KIMGDdIXsyIiYjiTXf8aiQNKSEjA\nx8ef8+e7kZTU5Ql6xuDuHsDAgU0YMKDvPeNt2bIFm83GqlWrKF68OFarlRYtWvDSSy+l/g2IPMCu\nXbto1qwZx44dI0sW7UkrIiIixklMTKRMmTJMnjyZwMBAo+OIiIgDU/FTHNZff/1FhQqvcfPmSOz2\nTo/R4zbu7i0IDMzB8uULMJlMD2wVHx/P5s2bsdlsrF69Gn9/f6xWK82aNSNPnjypexMi/6Njx45k\nz56d8ePHGx1FREREHNzSpUv5+uuv2bNnz0PfO4uIiKQ1FT/FoZ08eZLXX6/LzZtViInpDlQG/veN\nWUdHdjQAACAASURBVDRgw8NjHE2aVGfu3O9wcnJ6rPHj4uLYtGkTNpuNdevWUaFCBaxWK02bNiVn\nzpypfDciEBkZSZkyZfj9998pVaqU0XFERETEgSUlJVG+fHmGDRvGO++8Y3QcERFxUCp+isO7fv06\nM2fOZuLE74iKysqdO42A7EA8zs6hWCyLqVy5Cv36daFu3bpP/a11TEwM69evZ8mSJWzcuJEqVapg\ntVpp0qQJXl5eqXpP4timTp3K6tWr+fXXXzXLQkRERAy1Zs0aBgwYwKFDhzCbdeSEiIg8fyp+ivx/\nSUlJ/PLLL/zxRzBbt+7gxo1rtGvXipYtW1K4cOFUvVZUVBRr167FZrMRFBREjRo1sFqtNGrUiKxZ\ns6bqtcTxJCQkUK5cOYYOHUrz5s2NjiMiIiIOzG63U7VqVXr16kWrVq2MjiMiIg5IxU8Rg926dYs1\na9Zgs9nYunUrtWvXxmq10rBhQzw9PY2OJxnU77//Trt27QgJCcHDw8PoOCIiIuLANm/eTNeuXTl6\n9Ohjbx8lIiKSWlT8FElHbty4wapVq1iyZAnBwcHUqVMHq9VK/fr1cXd3NzqeZDCtW7emaNGijBw5\n0ugoIiIi4sDsdju1atWiffv2dOjQweg4IiLiYFT8FEmnrl69ysqVK7HZbOzdu5e6devSsmVL6tat\ni6urq9HxJAP4+++/eeWVV9i1axc+Pj5GxxEREREHtn37dtq0acPJkydxcXExOo6IiDgQFT9FMoBL\nly6xYsUKbDYbBw8epEGDBlitVt566y29eZQUjR07lu3bt7NmzRqjo4iIiIiDq1u3Lg0bNqRLly5G\nRxEREQei4qdIBhMeHs6yZcuw2WyEhITQuHFjrFYrAQEBODs7Gx1P0pm4uDj8/f2ZMGECDRo0MDqO\niIiIOLB9+/bRuHFjTp8+jZubm9FxRETEQaj4KZJKGjZsSK5cuZgzZ85zu+aFCxdYunQpNpuNM2fO\n0KRJE6xWK6+//ro2k5dkmzZtomvXrhw5ckRbJoiIiIihmjZtymuvvUbv3r2NjiIiIg7CbHQAkbR2\n4MABnJycqFGjhtFRUt3LL7/Mp59+yq5du9i7dy/FihWjf//+5M+fny5duvD777+TmJhodEwxWGBg\nIH5+fkyYMMHoKCIiIuLghg8fztixY7l9+7bRUURExEGo+CkvvFmzZiXPejtx4kSKbRMSEp5TqtTn\n7e1N37592bdvH8HBwbz88sv07NmTAgUK0KNHD4KDg0lKSjI6phhk4sSJTJo0ibCwMKOjiIiIiAPz\n8/MjICCAqVOnGh1FREQchIqf8kKLjY3lP//5D507d6ZZs2bMmjUr+bVz585hNptZvHgxAQEBeHh4\nMGPGDK5du0br1q0pUKAA7u7ulClThnnz5t0zbkxMDO+//z6ZM2cmX758fPXVV8/5zlLm4+PDgAED\nOHjwIFu2bCFnzpx07tyZQoUK0adPH/bs2YN2vHAshQsXpnv37vTp08foKCIiIuLghg0bxuTJk7l+\n/brRUURExAGo+CkvtKVLl+Lt7U3p0qVp27YtP/30033LwAcMGEDXrl0JCQnhnXfeITY2lgoVKrB+\n/XpCQkLo1asXH3/8Mb/99ltynz59+hAUFMTKlSsJCgriwIEDbNu27Xnf3mMpUaIEQ4YM4ejRo2zY\nsAEPDw/atm1LkSJF6N+/P/v371ch1EH069ePffv2sXnzZqOjiIiIiAPz9fWlUaNGTJw40egoIiLi\nAHTgkbzQatWqRaNGjfj0008BKFKkCOPHj6dp06acO3eOwoULM3HiRHr16pXiOO+++y6ZM2dmxowZ\nREVFkSNHDubNm0erVq0AiIqK4uWXX6ZJkybP9cCjp2W32zl06BA2m40lS5ZgNpuxWq20bNkSPz8/\nTCaT0REljfz88898/vnnHDp0CBcXF6PjiIiIiIMKDQ2lQoUKHD9+nFy5chkdR0REXmCa+SkvrNOn\nT7N9+3befffd5Odat27N7Nmz72lXoUKFex4nJSXx5Zdf8sorr5AzZ04yZ87MypUrk/dKPHPmDHfv\n3qVKlSrJfTw8PPDz80vDu0ldJpOJsmXL8tVXX3H69GkWLVpEXFwcDRs2pFSpUgwbNoxjx44ZHVPS\nQKNGjfD29mbatGlGRxEREREH5u3tTatWrRg7dqzRUURE5AXnZHQAkbQya9YskpKSKFCgwH2v/f33\n38l/9vDwuOe1cePGMWnSJKZOnUqZMmXw9PTkiy++4PLly2me2Qgmk4mKFStSsWJFvv76a3bt2sWS\nJUt48803yZ49O1arFavVSrFixYyOKqnAZDIxZcoUqlWrRuvWrcmXL5/RkURERMRBDRw4kDJlytC7\nd29eeuklo+OIiMgLSjM/5YWUmJjITz/9xJgxYzh06NA9v/z9/Zk7d+5D+wYHB9OwYUNat26Nv78/\nRYoU4eTJk8mvFy1aFCcnJ3bt2pX8XFRUFEeOHEnTe3oeTCYTVatWZdKkSZw/f55vv/2WiIgIatSo\nQfny5RkzZgxnz541OqY8I19fXz788EP69+9vdBQRERFxYC+99BJdunTh6tWrRkcREZEXmGZ+ygtp\n7dq1XL16lQ8++AAvL697XrNarfzwww+0adPmgX19fX1ZsmQJwcHB5MiRg+nTp3P27NnkcTw8POjU\nqRP9+/cnZ86c5MuXj5EjR5KUlJTm9/U8mc1matSoQY0aNZgyZQrbtm3DZrNRqVIlChcunLxH6INm\n1kr6N3DgQEqWLMn27dt57bXXjI4jIiIiDmrkyJFGRxARkRecZn7KC2nOnDnUrl37vsInQIsWLQgN\nDWXz5s0PPNhn0KBBVKpUiXr16vHGG2/g6el5X6F0/Pjx1KpVi6ZNmxIQEICfnx81a9ZMs/sxmsVi\noVatWnz//feEh4czatQojh07RtmyZalWrRpTpkzh4sWLRseUJ+Dp6cm4cePo1q0biYmJRscRERER\nB2UymXTYpoiIpCmd9i4iTy0+Pp7Nmzdjs9lYvXo1/v7+tGzZkubNm5MnTx6j48kj2O12atWqRcuW\nLenSpYvRcURERERERERSnYqfIpIq4uLi2LRpEzabjXXr1lGhQgWsVitNmzYlZ86cTz1uUlIS8fHx\nuLq6pmJa+df//d//ERAQwNGjR8mVK5fRcURERETus3PnTtzd3fHz88Ns1uJFERF5Mip+ikiqi4mJ\nYf369SxZsoSNGzdSpUoVrFYrTZo0eeBWBCk5duwYU6ZMISIigtq1a9OpUyc8PDzSKLlj6tWrF9HR\n0cyYMcPoKCIiIiLJtm3bRseOHYmIiCBXrly88cYbfP311/rCVkREnoi+NhORVOfm5kazZs2w2Wxc\nvHiRjh07snbtWry9vWnQoAHz58/n5s2bjzXWzZs3yZ07NwULFqRXr15Mnz6dhISENL4DxzJs2DDW\nrFnD3r17jY4iIiIiAvzzHrBr1674+/uzd+9exo4dy82bN+nWrZvR0UREJIPRzE8ReW5u377N6tWr\nsdlsbN26ldq1a2Oz2ciUKdMj+65atYpPPvmExYsX8/rrrz+HtI5l3rx5fPfdd+zcuVPLyURERMQQ\nUVFRuLi44OzsTFBQEB07dmTJkiVUrlwZ+GdFUJUqVTh8+DCFChUyOK2IiGQU+oQrIs9N5syZee+9\n91i9ejVhYWG8++67uLi4pNgnPj4egEWLFlG6dGl8fX0f2O7KlSt89dVXLF68mKSkpFTP/qJr164d\nZrOZefPmGR1FREREHFBERAQLFizg1KlTABQuXJi///6bMmXKJLdxc3PDz8+PW7duGRVTREQyIBU/\nRR6iVatWLFq0yOgYL6xs2bJhtVoxmUwptvu3OPrrr7/y9ttvJ+/xlJSUxL8T19etW8fQoUMZOHAg\nffr0YdeuXWkb/gVkNpuZPn06AwYM4MaNG0bHEREREQfj4uLC+PHjOX/+PABFihShWrVqdOnShejo\naG7evMnIkSM5f/48+fPnNzitiIhkJCp+ijyEm5sbsbGxRsdwaImJiQCsXr0ak8lElSpVcHJyAv4p\n1plMJsaNG0e3bt1o1qwZr776Ko0bN6ZIkSL3jPP3338THBysGaGPUKFCBd555x2GDh1qdBQRERFx\nMNmzZ6dSpUp8++23xMTEAPDzzz9z4cIFatSoQYUKFThw4ABz5swhe/bsBqcVEZGMRMVPkYdwdXVN\nfuMlxpo3bx4VK1a8p6i5d+9eOnTowIoVK/jll1/w8/MjLCwMPz8/8ubNm9xu0qRJ1KtXj/bt2+Pu\n7k63bt24ffu2EbeRIXz55ZcsWrSIw4cPGx1FREREHMzEiRM5duwYzZo1Y+nSpSxZsoRixYpx7tw5\nXFxc6NKlCzVq1GDVqlWMGDGCCxcuGB1ZREQyABU/RR7C1dVVMz8NZLfbsVgs2O12fvvtt3uWvP/+\n+++0bduWqlWrsmPHDooVK8bs2bPJnj07/v7+yWOsXbuWgQMHEhAQwB9//MHatWvZvHkzv/zyi1G3\nle7lyJGD4cOH0717d3QenoiIiDxPefLkYe7cuRQtWpQePXowbdo0Tpw4QadOndi2bRsffPABLi4u\nXL16le3bt/PZZ58ZHVlERDIAJ6MDiKRXWvZunLt37zJ27Fjc3d1xdnbG1dWV6tWr4+zsTEJCAkeP\nHuXs2bP88MMPxMXF0b17dzZv3kzNmjUpXbo08M9S95EjR9KkSRMmTpwIQL58+ahUqRKTJ0+mWbNm\nRt5iuta5c2dmzJjB4sWLeffdd42OIyIiIg6kevXqVK9ena+//ppbt27h5OREjhw5AEhISMDJyYlO\nnTpRvXp1qlWrxtatW3njjTeMDS0iIumaZn6KPISWvRvHbDbj6enJmDFj6NmzJ5GRkaxZs4aLFy9i\nsVj44IMP2L17N2+//TY//PADzs7ObN++nVu3buHm5gbA/v37+fPPP+nfvz/wT0EV/tlM383NLfmx\n3M9isTB9+nT69u2rLQJERETEEG5ublgsluTCZ2JiIk5OTsl7wpcoUYKOHTvy3XffGRlTREQyABU/\nRR5CMz+NY7FY6NWrF5cuXeL8+fMMGzaMuXPn0rFjR65evYqLiwtly5blyy+/5MiRI3z88cdky5aN\nX375hd69ewP/LI3Pnz8//v7+2O12nJ2dAQgLC8Pb25v4+HgjbzHdq169OgEBAYwaNcroKCIiIuJg\nkpKSqFOnDmXKlKFXr16sW7eOW7duAf+8T/zX5cuXyZo1a3JBVERE5EFU/BR5CO35mT7kz5+fIUOG\ncOHCBRYsWEDOnDnva3Pw4EHeeecdDh8+zNdffw3Ajh07CAwMBEgudB48eJCrV69SqFAhPDw8nt9N\nZFBjx45l9uzZHD9+3OgoIiIi4kDMZjNVq1bl0qVLREdH06lTJypVqkT79u2ZP38+wcHBLF++nBUr\nVlC4cOF7CqIiIiL/S8VPkYfQsvf050GFz7/++ov9+/dTunRp8uXLl1zUvHLlCj4+PgA4Of2zvfHK\nlStxcXGhatWqADrQ5xHy5s3LwIED6dGjh35WIiIi8lwNHTqUTJky0b59e8LDwxkxYgTu7u6MGjWK\nVq1a0aZNGzp27MgXX3xhdFQREUnnTHZ9ohV5oAULFrBx40YWLFhgdBR5CLvdjslkIjQ0FGdnZ/Ln\nz4/dbichIYEePXqwf/9+goODcXJy4saNGxQvXpz333+fwYMH4+nped84cr+7d+9StmxZRo0aRZMm\nTYyOIyIiIg5k4MCB/Pzzzxw5cuSe5w8fPoyPjw/u7u6A3suJiEjKVPwUeYhly5axePFili1bZnQU\neQr79u2jXbt2+Pv74+vry9KlS3FyciIoKIjcuXPf09Zut/Ptt99y/fp1rFYrxYoVMyh1+rRlyxY6\nduxISEhI8ocMERERkefB1dWVefPm0apVq+TT3kVERJ6Elr2LPISWvWdcdrudihUrsmjRIlxdXdm2\nbRtdunTh559/Jnfu3CQlJd3Xp2zZskRGRlKzZk3Kly/PmDFjOHv2rAHp05/atWtTuXJlxo4da3QU\nERERcTDDhw9n8+bNACp8iojIU9HMT5GHCAoKYvTo0QQFBRkdRZ6jxMREtm3bhs1mY8WKFXh7e2O1\nWmnRogUFCxY0Op5hzp8/T7ly5dizZw9FihQxOo6IiIg4kBMnTuDr66ul7SIi8lQ081PkIXTau2Oy\nWCzUqlWL77//nosXL/Lll19y7NgxypUrR7Vq1ZgyZQoXL140OuZzV6BAAfr06UPv3r2NjiIiIiIO\npnjx4ip8iojIU1PxU+QhtOxdnJycqFOnDrNmzSI8PJxBgwYlnyz/+uuv88033xAZGWl0zOemd+/e\nHD16lA0bNhgdRUREREREROSxqPgp8hBubm6a+SnJXFxcqFevHj/++CMRERH06dOHHTt2ULx4cQIC\nApgxYwZXrlwxOmaaypQpE1OmTKFnz57ExcUZHUdEREQckN1uJykpSe9FRETksan4KfIQmvkpD5Mp\nUyYaNWrEwoULCQ8Pp2vXrgQFBVG0aFECAwOZM2cO169fNzpmmqhXrx4lSpRg0qRJRkcRERERB2Qy\nmejatStfffWV0VFERCSD0IFHIg9x8eJFKlSoQHh4uNFRJIOIiopi7dq12Gw2goKCqFGjBi1btqRx\n48ZkzZrV6Hip5syZM1SuXJmDBw/y8ssvGx1HREREHMxff/1FpUqVOHHiBDly5DA6joiIpHMqfoo8\nxPXr1ylSpMgLO4NP0tbt27dZvXo1NpuNrVu3Urt2baxWKw0bNsTT09PoeM9syJAhnDx5ksWLFxsd\nRURERBzQJ598QpYsWRg7dqzRUUREJJ1T8VPkIWJiYvDy8tK+n/LMbty4wapVq1iyZAnBwcHUqVMH\nq9VK/fr1cXd3NzreU4mOjqZUqVLMnTuXWrVqGR1HREREHMyFCxd45ZVXOHr0KHnz5jU6joiIpGMq\nfoo8RFJSEhaLhaSkJEwmk9Fx5AVx9epVVq5cic1mY+/evdStW5eWLVtSt25dXF1djY73RFasWMGQ\nIUM4cOAAzs7ORscRERERB/Ppp5+SmJjI1KlTjY4iIiLpmIqfIilwdXXlxo0bGa4oJRnDpUuXWLFi\nBTabjYMHD9KgQQOsVitvvfUWLi4uRsd7JLvdTmBgIPXq1aNXr15GxxEREREHExkZSalSpThw4AAF\nCxY0Oo6IiKRTKn6KpCBbtmycPXsWLy8vo6PICy48PJzly5djs9k4evQojRs3xmq1EhAQkK5nVR4/\nfpwaNWpw5MgR8uTJY3QcERERcTADBgzgypUrzJgxw+goIiKSTqn4KZKCvHnzcuDAAfLly2d0FHEg\nFy5cYOnSpdhsNk6fPk2TJk2wWq288cYbODk5GR3vPv369ePy5cvMnTvX6CgiIiLiYK5du4avry+7\ndu3Cx8fH6DgiIpIOqfgpkoLChQuzZcsWChcubHQUcVChoaHJhdDz58/TrFkzrFYrr732GhaLxeh4\nwD8n25csWZKlS5dStWpVo+OIiIiIgxkxYgSnTp1i/vz5RkcREZF0SMVPkRSULFmS5cuXU6pUKaOj\niHD69GmWLFnCkiVLuHTpEs2bN8dqtVK1alXMZrOh2RYuXMjEiRPZs2dPuinKioiIiGO4desWPj4+\nbN26Ve/bRUTkPsZ+WhZJ51xdXYmNjTU6hggAPj4+DBgwgIMHD7JlyxZy5sxJ586dKVSoEH369GH3\n7t0Y9X1W69atcXd3Z9asWYZcX0RERBxXlixZ6Nu3L0OHDjU6ioiIpEOa+SmSgmrVqjF+/HiqVatm\ndBSRhzp69Cg2mw2bzUZ8fDwtW7bEarVSrlw5TCbTc8tx6NAh3nrrLUJCQsiRI8dzu66IiIhIdHQ0\nPj4+rFu3jnLlyhkdR0RE0hHN/BRJgaurKzExMUbHEElR6dKlGTFiBMePH2flypWYzWZatGiBr68v\nAwcO5PDhw89lRugrr7xCy5YtGTRoUJpfS0REROS/ubu7M2DAAAYPHmx0FBERSWdU/BRJgZa9S0Zi\nMpkoW7YsX331FadPn2bRokXEx8fTsGFDSpUqxbBhwwgJCUnTDCNGjGDlypXs378/Ta8jIiIi8r8+\n/PBD/u///o+dO3caHUVERNIRFT9FUuDm5qbip2RIJpOJihUrMm7cOEJDQ5k7dy43b97krbfews/P\nj1GjRnHq1KlUv66Xlxdffvkl3bp1IykpKdXHFxEREXmYTJkyMXjwYK1CERGRe6j4KZICLXuXF4HJ\nZKJKlSpMmjSJsLAwvv32WyIjI6lZsybly5dnzJgx/PXXX6l2vQ4dOpCQkMD8+fNTbUwRERGRx9G+\nfXvCwsLYsmWL0VFERCSdUPFTJAVa9i4vGrPZTI0aNZg2bRoXLlxgwoQJhIaGUqVKFSpVqsT48eMJ\nCwt75mt88803fP7551y7do3169cTENCYfPl8yZo1L3nyFKVy5TrJy/JFREREUouzszPDhg1j8ODB\nz2XPcxERSf902rtICrp160aJEiXo1q2b0VFE0lRCQgK//fYbNpuNlStXUrx4caxWKy1atOCll156\n4vHsdjvVq9fk4METWCwFuHOnC/AakBmIAg6SOfP3mExH6dGjC0OHDsDJySmV70pEREQcUWJiIv7+\n/owfP566desaHUdERAymmZ8iKdCyd3EUTk5O1KlTh1mzZhEeHs6gQYPYv38/pUuX5vXXX+ebb74h\nMjLyscZKTEzk/fc/5tCh28TErOHOnX1AJ6A48BJQDGjB7dtB3Lr1GxMnbqdOncZER0en3Q2KiIiI\nw7BYLIwcOZJBgwZp9qeIiGjmp0hKNm3ahJubGzVr1jQ6iogh4uLi2LRpEzabjXXr1lGhQgWsVitN\nmzYlZ86cD+zTpcun/PjjfqKj1/LPTM9HuYura3tq1Ihmw4blWCyWVL0HERERcTx2u50KFSowaNAg\nmjZtanQcERExkIqfIin496+HyWQyOImI8WJiYtiwYQM2m42NGzdSpUoVrFYrTZo0wcvLC4CgoCAa\nNepMdPQ+wOsJRo/H3b02Eye246OPOqdJfhEREXEs69evp1+/fhw6dEhfroqIODAVP0VE5IlFRUWx\ndu1abDYbmzdvpkaNGlitVubNW8Zvv9UDPn6KUTdTuHAfzpw5qC8cRERE5JnZ7XZee+01unTpwnvv\nvWd0HBERMYiKnyIi8kxu377N6tWrmTdvHps37wAieLzl7v8rCQ+PkmzaNIfq1aunckoRERFxRL/9\n9hudO3cmJCQEZ2dno+OIiIgBdOCRiIg8k8yZM/Pee+9Rt25dXFxa83SFTwAz0dGdmD17YWrGExER\nEQdWq1YtChYsyE8//WR0FBERMYiKnyIikirCwsKJjy/2TGPY7T6EhoanUiIRERERGDVqFCNGjCAu\nLs7oKCIiYgAVP0Wewd27d0lISDA6hki6EB0dC2R6xlEy8ddfZ1m4cCFBQUEcOXKEK1eukJSUlBoR\nRURExAFVrVoVPz8/Zs6caXQUERExgJPRAUTSs02bNlGlShWyZs2a/Nx/nwA/b948kpKS+Oijj4yK\nKJJu5M7tBVx7xlGuYzIlsXbtWiIiIoiMjCQiIoI7d+6QK1cu8uTJQ968eVP83cvLSwcmiYiIyD1G\njBhBgwYN6NixI+7u7kbHERGR50gHHomkwGw2ExwcTNWqVR/4+syZM5kxYwbbt28nU6ZnnfEmkrGt\nX7+eVq2Gcvv23qcew939XUaPrkrPnj3ueT4+Pp5Lly7dUxB92O/R0dHkyZPnsQqlWbNmzfCFUrvd\nzsyZM9m2bRuurq4EBATQqlWrDH9fIiIiqa158+ZUqVKFzz77zOgoIiLyHKn4KZICDw8PFi1aRJUq\nVYiJiSE2NpaYmBhiYmKIi4tj9+7dfPHFF1y9ehUvLy+j44oYKjExkXz5fLh8eQnw6lOMEIGra0ki\nIkLvmW39pGJjY4mMjHxkkTQyMpL4+PjHKpLmzZsXT0/PdFdQjIqKokePHuzcuZPGjRsTERHByZMn\nadWqFd27dwfg6NGjjBw5kl27dmGxWGjXrh1Dhw41OLmIiMjzFxISQq1atTh16hRZsmQxOo6IiDwn\nKn6KpCBfvnxERkbi5uYG/LPU3Ww2Y7FYsFgseHh4AHDw4EEVP0WA0aPHMmrUUWJinvxEVYtlBK1b\nX+Cnn2akQbIHi46OfqxCaUREBHa7/b6i6MMKpf/+vyGtBQcHU7duXebOnUuzZs0A+O677xg6dChn\nzpzh4sWLBAQEUKlSJfr27cvJkyeZMWMGr7/+OqNHj34uGUVERNKTtm3b4uvry+DBg42OIiIiz4mK\nnyIpyJMnD23btuXNN9/EYrHg5OSEs7PzPb8nJibi7++Pk5O20BW5du0aJUqU58qVUdjtbZ6g5+94\nerbgzz+34+vrm2b5nsWdO3ceazZpREQEFovlsWaT5smTJ/nLlafx448/MmDAAE6fPo2LiwsWi4Vz\n587RoEEDevTogdlsZtiwYRw/fjy5IDtnzhyGDx/O/v37yZEjR2r9eERERDKE06dPU6VKFU6ePEn2\n7NmNjiMiIs+BqjUiKbBYLFSsWJG3337b6CgiGUL27Nn57bd1VKsWwO3b8djtHR+j1ybc3duyatWi\ndFv4BPD09MTT05OiRYum2M5ut3P79u0HFkb37dt33/Ourq4pzib19fXF19f3gUvus2bNSmxsLKtX\nr8ZqtQKwYcMGjh8/zq1bt7BYLGTLlg0PDw/i4+NxcXGhePHixMXFsX37dho3bpwmPysREZH0ysfH\nh6ZNmzJ+/HitghARcRAqfoqkoEOHDnh7ez/wNbvdnu72/xNJD0qXLs2ePb9Tq1Z9bt/+D3fudAEa\nce8/OXZgCxbLRDw9/2TdupVUr17dmMCpzGQykSVLFrJkyUKxYsVSbGu327l58+YDZ4/u2rWLiIgI\nateuTe/evR/Y/+2336Zjx4706NGD2bNnkzt3bi5cuEBiYiK5cuUiX758XLhwgYULF/Lee+9xfvrx\nXQAAIABJREFU+/Ztpk2bxuXLl4mOjk6L23cYiYmJhISEcPXqVeCfwn/p0qWxWCwGJxMRkUcZNGgQ\n5cqVo1evXuTOndvoOCIiksa07F3kGVy/fp27d++SM2dOzGaz0XFE0pW4uDhWrFjBmDHfcPp0KE5O\nlUlMzILZfAe7/TA5cjhz48bfrF79MzVr1jQ6boZ18+ZN/vjjD7Zv3558KNPKlSvp3r077du3Z/Dg\nwUyYMIHExERKlixJlixZiIyMZPTo0cn7hMrju3z5MjNnzWTyN5OJSYrBktkCJki8lYgrrvTs2pPO\nH3bWh2kRkXSuR48eODk5MXHiRKOjiIhIGlPxUyQFS5cupWjRopQvX/6e55OSkjCbzSxbtoy9e/fS\nvXt3Xn75ZYNSiqR/R44cSV6K7eHhQeHChXn11VeZNm0aW7ZsYdWqVUZHfGGMGDGCNWvWMGPGDMqV\nKwfArVu3OHbsGPny5WPWrFls3ryZr7/+mtdee+2evomJibRv3/6he5TmzJnTYWc22u12xo0fx5Dh\nQzCXNBNTLgby/0+ji+B6wBV7iJ0hg4bwRf8vtEJARCSdioiIoHTp0hw6dEjv40VEXnAqfoqkoEKF\nCjRs2JBhw4Y98PVdu3bRrVs3xo8fzxtvvPFcs4mIHDhwgISEhOQi5/Lly+natSt9+/alb9++ydtz\n/PfM9Bo1alCoUCGmTZuGl5fXPeMlJiaycOFCIiMjH7hn6fXr18mRI0eKBzj9++ccOXK8UDPie/Xp\nxUzbTKJbREO2RzS+Ce5L3Xm/yftMnzJdBVARkXSqf//+3Lp1i++++87oKCIikoa056dICrJly8aF\nCxc4fvw4UVFRxMTEEBMTQ3R0NPHx8fz9998cPHiQ8PBwo6OKiAOKjIxk8ODB3Lp1i1y5cnHjxg3a\ntm1Lt27dMJvNLF++HLPZzKuvvkpMTAxffPEFp0+fZty4cfcVPuGfQ97atWv30OslJCRw+fLl+4qi\nFy5c4M8//7zn+X8zPc6J99mzZ0/XBcIp06Ywc/FMottEg/tjdMgK0W2imTd/HoULFeazPp+leUYR\nEXly/fr1o3jx4vTr14/ChQsbHUdERNKIZn6KpKBdu3YsWLAAFxcXkpKSsFgsODk54eTkhLOzM5kz\nZ+bu3bvMmTOHN9980+i4IuJg4uLiOHnyJCdOnODq1av4+PgQEBCQ/LrNZmPo0KGcPXuWnDlzUrFi\nRfr27Xvfcve0EB8fz6VLlx44g/R/n4uKiiJ37tyPLJLmzZuXrFmzPtdCaVRUFLlfyk10+2jI8YSd\nr4HbXDci/44kc+bMaZJPRESezbBhwwgNDWXevHlGRxERkTSi4qdIClq2bEl0dDTjxo3DYrHcU/x0\ncnLCbDaTmJiIl5cXmTJlMjquiEjyUvf/Fhsby7Vr13B1dSV79uwGJXu42NjYhxZK//f3uLi45OX1\njyqUZs6c+ZkLpbNnz6bn5J5ENY96qv4eKzwY9/E4Pvnkk2fKISIiaePmzZv4+Pjwxx9/UKJECaPj\niIhIGlDxUyQF7du3B+DHH380OIlIxlGrVi38/PyYOnUqAIULF6Z79+707t37oX0ep40IQExMzGMV\nSSMjI0lISHis2aR58uTB09PzvmvZ7XaK+xXnVNlTUOwpA58B793e/HX8r3S9tF9ExJGNGTOGgwcP\nsnjxYqOjiIhIGtCenyIpaN26NXFxccmP/3tGVWJiIgBms1kfaMWhXLlyhSFDhrBhwwbCw8PJli0b\nfn5+fP755wQEBLBy5UqcnZ2faMx9+/bh4eGRRonlReLm5oa3tzfe3t6PbBsVFfXAwujhw4f59ddf\n73nebDbfN5s0W7Zs/HXqL2j2DIELw8UVF7l69So5c+Z8hoFERCStdO/eHR8fHw4fPoy/v7/RcURE\nJJWp+CmSgsDAwHse/3eR02KxPO84IulC06ZNiY2NZe7cuRQtWpRLly7x+++/c/XqVeCfg8KeVI4c\nT7qZosijeXh4UKRIEYoUKZJiO7vdzp07d+4rkh47dgyTqwme5dB6M7hkduH69esqfoqIpFMeHh58\n/vnnDB48mJ9//tnoOCIiksq07F3kERITEzl27BinT5/G29ubsmXLEhsby/79+4mOjqZMmTLkzZvX\n6Jgiz8XNmzfx8vJi8+bN1K5d+4FtHrTs/f333+f06dOsWrUKT09PPvvsM/r06ZPc53+XvZvNZpYt\nW0bTpk0f2kYkrZ0/f54S5UoQ3T36mcbx+MaD/9v9fzpJWEQkHYuNjaVYsWIsX76cSpUqGR1HRERS\n0bPMZRBxCGPHjsXf359WrVrRsGFD5s6di81mo379+rRo0YLPP/+cyMhIo2OKPBeenp54enqyevXq\ne7aEeJRJkyZRunRpDhw4wIgRIxgwYACrVq1Kw6Qizy5HjhzE34mH+GcY5C7E347X7GYRkXTO1dWV\nQYMGMXjwYA4cOEDnzp0pX748RYsWpXTp0gQGBrJgwYInev8jIiLpg4qfIinYtm0bCxcuZMyYMcTG\nxjJ58mQmTJjAzJkzmT59Oj/++CPHjh3jhx9+MDqqyHNhsVj48ccfWbBgAdmyZaNatWr07duXPXv2\npNivcuXKfP755/j4+PDhhx/Srl07Jk6c+JxSizwdd3d3Xnv9NTj6DIOEwKtVXyVLliyplktERNJG\nvnz5+PPPP2nYsCHe3t7MmDGDTZs2YbPZ+PDDD5k/fz4FCxZk4MCBxMbGGh1XREQek4qfIim4cOEC\nWbJkSV6e26xZMwIDA3FxceG9996jUaNGvPPOO+zevdvgpCLPT5MmTbh48SJr166lXr167Ny5kypV\nqjBmzJiH9qlatep9j0NCQtI6qsgz69erH5kPZ37q/pkPZ6Z/r/6pmEhERNLC5MmT6dKlC7NmzeLc\nuXMMGDCAihUr4uPjQ5kyZWjevDmbNm1i+/btnDhxgjp16nDt2jWjY4uIyGNQ8VMkBU5OTkRHR99z\nuJGzszN37txJfhwfH098/LOsiRTJeFxcXAgICGDQoEFs376dTp06MWzYMBISElJlfJPJxP9uSX33\n7t1UGVvkSQQGBuKe4A6nnqLzGXCJcqF+/fqpnktERFLPrFmzmD59Ojt27OCdd95J8WDTYsWKsWTJ\nEsqVK0fjxo01A1REJANQ8VMkBQUKFABg4cKFAOzatYudO3disViYNWsWy5cvZ8OGDdSqVcvImCKG\nK1myJAkJCQ/9ALBr1657Hu/cuZOSJUs+dLxcuXIRHh6e/DgyMvKexyLPi9lsxjbfhttaN3iS/wQj\nwW2NG7YFthQ/RIuIiLHOnj3L559/zvr16ylYsOBj9TGbzUyePJlcuXLx5ZdfpnFCERF5Vk5GBxBJ\nz8qWLUv9+vXp0KED8+bNIzQ0lLJly/Lhhx/y7rvv4urqyquvvsqHH35odFSR5+LatWu0aNGCjh07\n4u/vT+bMmdm7dy/jxo3jzTffxNPT84H9du3axdixY2nWrBm//fYbCxYs4D//+c9Dr1O7dm2++eYb\nqlatitlsZuDAgbi5uaXVbYmk6PXXX2f+7Pm069SO6MBoKMHDvz5OAk5CpvWZmDNjDgEBAc8xqYiI\nPKkffviB9u3b4+vr+0T9zGYzo0eP5o033mDw4MG4uLikUUIREXlWKn6KpMDNzY3hw4dTuXJlgoKC\naNy4MR9//DFOTk4cOnSIU6dOUbVqVVxdXY2OKvJceHp6UrVqVaZOncrp06eJi4sjf/78tGnThoED\nBwL/LFn/byaTid69e3P48GFGjRqFp6cnI0eOpEmTJve0+W8TJkzggw8+oFatWuTJk4evv/6a48eP\np/0NijxEs2bNyJMnDx0+6kD4tnCiX4nGXsYOHv+/QTSYjphwP+SOp5MnFk8LDeo3MDSziIikLC4u\njrlz57J9+/an6l+iRAlKly7NihUraNWqVSqnExGR1GKy/++maiIiIiLyQHa7nd27dzN+ynjWr1tP\nbNQ/Wz24urvydr23+aznZ1StWpUOHTrg6urK999/b3BiERF5mNWrVzN58mS2bNny1GMsXryY+fPn\ns27dulRMJiIiqUkzP0Ue07/fE/z3DDW73X7fjDUREXlxmUwmqlSpwrIqywCSD/lycrr3LdWUKVN4\n5ZVXWLdunQ48EhFJp/7+++8nXu7+v3x9fbl48WIqJRIRkbSg4qfIY3pQkVOFTxERx/a/Rc9/Zc2a\nldDQ0OcbRkREnkhsbOwzb1/l6upKTExMKiUSEZG0oNPeRURERERExOFkzZqV69evP9MYN27cIFu2\nbKmUSERE0oKKnyIiIiIiIuJwXn31VYKCgrh79+5Tj7Fx40YqVqyYiqlERCS1qfgp8ggJCQlayiIi\nIiIi8oLx8/OjcOHCrFmz5qn6x8fHM3PmTD755JNUTiYiIqlJxU+RR1i3bh2tWrUyOoaIiIiIiKSy\nLl26MH369OTDTZ/EypUrKV68OKVLl06DZCIiklpU/BR5BG1iLpI+hIaGkiNHDq5du2Z0FMkAOnTo\ngNlsxmKxYDabk/98+PBho6OJiEg60qxZM65cucLEiROfqN+ZM2fo1asXgwcPTqNkIiKSWlT8FHkE\nV1dXYmNjjY4h4vC8vb155513mDJlitFRJIOoU6cOERERyb/Cw8MpU6aMYXmeZU85ERFJGy4uLqxb\nt46pU6cybty4x5oBevToUQICAhg6dCgBAQHPIaWIiDwLFT9FHsHNzU3FT5F0YsCAAXzzzTfcuHHD\n6CiSAWTKlIlcuXKRO3fu5F9ms5kNGzZQo0YNvLy8yJEjB/Xq1ePkyZP39N2xYwflypXDzc2NypUr\ns3HjRsxmMzt27AD+2Q+6U6dOFClSBHd3d4oXL86ECRPuGaNt27Y0adKEr776ipdffhlvb28Afvrp\nJ1599VWyZMlC3rx5adWqFREREcn97t69S7du3XjppZdwdXWlUKFCmlkkIpKGChQowPbt25k/fz7V\nqlVjyZIlD/zC6siRI3Tt2pWaNWsyatQoPv74YwPSiojIk3IyOoBIeqdl7yLpR9GiRalfvz7Tpk1T\nMUieWnR0NJ999hl+fn5ERUUxYsQIGjVqxNGjR7FYLNy+fZtGjRrRoEEDFi1axPnz5+nVqxcmkyl5\njMTERAoVKsSyZcvImTMnu3btonPnzuTOnZu2bdsmtwsKCiJr1qz8+uuvybOJEhISGDVqFMWLF+fy\n5cv069eP1q1bs2XLFgAmTpzIunXrWLZsGQUKFODChQucOnXq+f6QREQcTIECBQgKCqJo0aJMnDiR\nXr16UatWLbJmzUpsbCwnTpzg7NmzdO7cmcOHD5M/f36jI4uIyGMy2Z9mZ2cRB3Ly5Enq16+vD54i\n6cSJEydo2bIl+/btw9nZ2eg4kk516NCBBQsW4OrqmvxczZo1Wbdu3X1tb926hZeXFzt37qRSpUp8\n8803DB8+nAsXLuDi4gLA/Pnzef/99/njjz+oVq3aA6/Zt29fjh49yvr164F/Zn4GBQURFhaGk9PD\nv28+cuQI/v7+REREkDt3brp27cqZM2fYuHHjs/wIRETkCY0cOZJTp07x008/ERISwv79+7lx4wZu\nbm689NJLvPnmm3rvISKSAWnmp8gjaNm7SPpSvHhxDh48aHQMyQBef/11Zs6cmTzj0s3NDYDTp08z\nZMgQdu/ezZUrV0hKSgIgLCyMSpUqceLECfz9/ZMLnwCVK1e+bx+4b775hnnz5nHu3DliYmK4e/cu\nPj4+97Tx8/O7r/C5b98+Ro4cyaFDh7h27RpJSUmYTCbCwsLInTs3HTp0IDAwkOLFixMYGEi9evUI\nDAy8Z+apiIikvv9eVVKqVClKlSplYBoREUkt2vNT5BG07F0k/TGZTCoEySO5u7tTuHBhihQpQpEi\nRciXLx8A9erV4/r168yaNYs9e/awf/9+TCYT8fHxjz32woUL6du3Lx988AG//PILhw4d4qOPPrpv\nDA8Pj3se37lzh7fffpusWbOycOFC9u3blzxT9N++FStW5Ny5c3z55ZckJCTQpk0b6tWr9yw/ChER\nERERh6WZnyKPoNPeRTKepKQkzGZ9vyf3u3TpEqdPn2bu3LlUr14dgD179iTP/gQoUaIENpuNu3fv\nJi9v3L179z0F9+DgYKpXr85HH32U/NzjbI8SEhLC9evX+eqrr5L3i3vQTGZPT0+aN29O8+bNadOm\nDa+99hqhoaHJhyaJiIiIiMjj0SdDkUfQsneRjCMpKYlly5ZhtVrp378/O3fuNDqSpDM5c+Yke/bs\nzJgxgzNnzrB161a6deuGxWJJbtO2bVsSExP58MMPOX78OL/++itjx44FSC6A+vr6sm/fPn755RdO\nnz7N8OHDk0+CT4m3tzcuLi5MnTqV0NBQ1q5dy7Bhw+5pM2HCBGw2GydOnODUqVP85z//IVu2bLz0\n0kup94MQEREREXEQKn6KPMK/e7XdvXvX4CQi8jD/Lhfev38//fr1w2KxsHfvXjp16sTNmzcNTifp\nidlsZsmSJezfvx8/Pz969uzJmDFj7jnAInPmzKxdu5bDhw9Trlw5vvjiC4YPH47dbk8+QKlLly40\nbdqUVq1aUblyZS5evMinn376yOvnzp2befPmsXz5ckqVKsXo0aOZNGnSPW08PT0ZO3Ysr776KpUq\nVSIkJIRNmzbdswepiIgYJzExEbPZzOrVq9O0j4iIpA6d9i7yGDw9PQkPDydz5sxGRxGR/xIdHc2g\nQYPYsGEDRYsWpUyZMoSHhzNv3jwAAgMD8fHx4dtvvzU2qGR4y5cvp1WrVly5coWsWbMaHUdERB6i\ncePGREVFsXnz5vteO3bsGKVLl+aXX37hzTfffOprJCYm4uzszKpVq2jUqNFj97t06RJeXl46MV5E\n5DnTzE+Rx6Cl7yLpj91up1WrVuzZs4fRo0dTvnx5NmzYQExMTPKBSD179uSPP/4gLi7O6LiSwcyb\nN4/g4GDOnTvHmjVr6NOnD02aNFHhU0QknevUqRNbt24lLCzsvtdmz56Nt7f3MxU+n0Xu3LlV+BQR\nMYCKnyKPQSe+i6Q/J0+e5NSpU7Rp04YmTZowYsQIJk6cyPLlywkNDSUqKorVq1eTK1cu/f2VJxYR\nEcF7771HiRIl6NmzJ40bN06eUSwiIulX/fr1yZ07N3Pnzr3n+f/H3r3HxZT/fwB/zRRdJXJZad1K\nKKLIvc39vsvii+iicguFXdcoikQIaxffKFHWumRbrG/4srLrGsJGqUSRiEgSaZrz+2O/5ifXojrN\n9Ho+Hvt47Jw558xreuRM8z7vz+cjk8kQHh4OV1dXAMCsWbPQrFkzaGtro0mTJpg3b16Raa7S0tIw\nePBgGBgYQEdHB+bm5oiIiHjna964cQNSqRRXrlxRbHtzmDuHvRMRiYervRMVA1d8J6p4dHV18fz5\nc9jY2Ci2WVtbo2nTphg/fjzu3r0LdXV12NvbQ19fX8SkpIzmzp2LuXPnih2DiIhKSE1NDU5OTggN\nDcXChQsV2/ft24esrCw4OzsDAKpXr45t27ahXr16uHr1KiZOnAhtbW14eXkBACZOnAiJRIITJ05A\nV1cXCQkJRRbHe9OrBfGIiKjiYecnUTFw2DtRxVO/fn2YmZlh9erVKCwsBPDPF5unT5/Cz88PHh4e\ncHFxgYuLC4B/VoInIiIi1efq6orU1NQi836GhISgT58+MDQ0BAAsWLAAHTp0QIMGDdC/f3/MmTMH\nO3bsUOyflpYGGxsbmJubo2HDhujbt+8Hh8tzKQ0iooqLnZ9ExcBh70QV08qVKzF8+HD06NEDbdq0\nwcmTJ/HNN9+gffv2aN++vWK//Px8aGhoiJiUiIiIyouJiQlsbW0REhKCXr164e7duzh06BB27dql\n2Gfnzp1Yt24dbty4gdzcXMhksiKdndOmTcPUqVNx4MAB9OzZE0OHDkWbNm3EeDtERPSZ2PlJVAzs\n/CSqmMzMzLBu3Tq0bNkSV65cQZs2beDj4wMAePjwIfbv34+RI0fCxcUFq1evRnx8vMiJiYiIqDy4\nuroiMjIS2dnZCA0NhYGBgWJl9r/++gv29vYYNGgQDhw4gEuXLsHX1xcvX75UHD9hwgTcvHkTY8eO\nxfXr19GxY0csXbr0na8llf7ztfr17s/X5w8lIiJxsfhJVAyc85Oo4urZsyd++uknHDhwAJs3b0ad\nOnUQEhKCr776CkOHDsXjx49RUFCALVu2YNSoUZDJZGJHJvqoBw8ewNDQECdOnBA7ChGRUho+fDg0\nNTURFhaGLVu2wMnJSdHZeerUKTRq1Ahz585F27ZtYWxsjJs3b751jvr162P8+PHYuXMnvL29ERQU\n9M7Xql27NgAgIyNDsS02NrYM3hUREX0KFj+JioHD3okqtsLCQujo6ODOnTvo1asXJk2ahK+++grX\nr1/Hf/7zH+zcuRPnzp2DhoYGlixZInZcoo+qXbs2goKC4OTkhJycHLHjEBEpHU1NTdjZ2WHRokVI\nSUlRzAEOAKampkhLS8Mvv/yClJQU/Pjjj9i9e3eR4z08PHD48GHcvHkTsbGxOHToEMzNzd/5Wrq6\numjXrh2WLVuG+Ph4/PXXX5gzZw4XQSIiqiBY/CQqBg57J6rYXnVy/PDDD3j48CH++9//YuPGjWjS\npAmAf1Zg1dTURNu2bXH9+nUxoxIV26BBg9C7d2/MmDFD7ChEREpp3LhxyM7ORpcuXdCsWTPF9iFD\nhmDGjBmYNm0aLC0tceLECfj6+hY5trCwEFOnToW5uTn69++PL7/8EiEhIYrn3yxsbt26FTKZDNbW\n1pg6dSr8/PzeysNiKBGROCQCl6Uj+qixY8eiW7duGDt2rNhRiOg90tPT0atXL4wePRpeXl6K1d1f\nzcP19OlTtGjRAnPmzIG7u7uYUYmKLTc3F61bt0ZgYCAGDx4sdhwiIiIiIqXDzk+iYuCwd6KKLz8/\nH7m5ubCzswPwT9FTKpUiLy8Pu3btQo8ePVCnTh2MGjVK5KRExaerq4tt27Zh0qRJuH//vthxiIiI\niIiUDoufRMXAYe9EFV+TJk1Qv359+Pr6IikpCc+fP0dYWBg8PDywatUqGBkZYe3atYpFCYiURZcu\nXeDs7Izx48eDA3aIiIiIiEqGxU+iYuBq70TKYcOGDUhLS0OHDh1Qq1YtBAYG4saNGxgwYADWrl0L\nGxsbsSMSfZJFixbh9u3bReabIyIiIiKij1MXOwCRMuCwdyLlYGlpiYMHD+Lo0aPQ0NBAYWEhWrdu\nDUNDQ7GjEX2WqlWrIiwsDN27d0f37t0Vi3kREREREdGHsfhJVAxaWlp4+PCh2DGIqBi0tbXx9ddf\nix2DqNS1bNkS8+bNg6OjI6Kjo6GmpiZ2JCIiIiKiCo/D3omKgcPeiYioIpg+fTqqVq2KFStWiB2F\niIiIiEgpsPhJVAwc9k5ERBWBVCpFaGgoAgMDcenSJbHjEBFVaA8ePICBgQHS0tLEjkJERCJi8ZOo\nGLjaO5FyEwSBq2STymjQoAFWrlwJBwcHfjYREX3AypUrMXLkSDRo0EDsKEREJCIWP4mKgcPeiZSX\nIAjYvXs3oqKixI5CVGocHBzQrFkzLFiwQOwoREQV0oMHD7Bp0ybMmzdP7ChERCQyFj+JioHD3omU\nl0QigUQiwaJFi9j9SSpDIpFg48aN2LFjB44fPy52HCKiCmfFihUYNWoUvvzyS7GjEBGRyFj8JCoG\nDnsnUm7Dhg1Dbm4uDh8+LHYUolJTq1YtbNq0CWPHjsWTJ0/EjkNEVGFkZmZi8+bN7PokIiIALH4S\nFQs7P4mUm1QqxYIFC+Dj48PuT1IpAwYMQL9+/TBt2jSxoxARVRgrVqyAnZ0duz6JiAgAi59ExcI5\nP4mU34gRI5CVlYVjx46JHYWoVK1cuRInT57E3r17xY5CRCS6zMxMBAcHs+uTiIgUWPwkKgYOeydS\nfmpqaliwYAF8fX3FjkJUqnR1dREWFobJkyfj3r17YschIhJVQEAARo8eDSMjI7GjEBFRBcHiJ1Ex\ncNg7kWqws7NDeno6oqOjxY5CVKo6duyI8ePHY9y4cZzagYgqrfv37yMkJIRdn0REVASLn0TFwGHv\nRKpBXV0d8+fPZ/cnqSRvb29kZGRg06ZNYkchIhJFQEAAxowZg/r164sdhYiIKhCJwPYAoo969OgR\nTExM8OjRI7GjENFnKigogKmpKcLCwtC1a1ex4xCVqmvXruGrr77CmTNnYGJiInYcIqJyc+/ePZiZ\nmeHvv/9m8ZOIiIpg5ydRMXDYO5HqqFKlCjw9PbF48WKxoxCVOjMzM3h5ecHR0REymUzsOERE5SYg\nIAD29vYsfBIR0VvY+UlUDHK5HOrq6igsLIREIhE7DhF9ppcvX6Jp06bYuXMnOnbsKHYcolIll8vR\np08f9OjRA56enmLHISIqc6+6PuPi4mBoaCh2HCIiqmBY/CQqJg0NDeTk5EBDQ0PsKERUCjZs2IAD\nBw7g999/FzsKUam7ffs22rZti6ioKFhZWYkdh4ioTH333XcoLCzE2rVrxY5CREQVEIufRMVUvXp1\npKamQl9fX+woRFQK8vPzYWxsjMjISLRr107sOESlbvv27Vi6dCnOnz8PLS0tseMQEZWJjIwMmJub\n4+rVq6hXr57YcYiIqALinJ9ExcQV34lUi4aGBubMmcO5P0lljR49Gi1btuTQdyJSaQEBAXB0dGTh\nk4iI3oudn0TF1KhRIxw/fhyNGjUSOwoRlZLnz5/D2NgYv//+OywtLcWOQ1TqHj16BAsLC2zbtg09\nevQQOw4RUali1ycRERUHOz+JiokrvhOpHi0tLcyaNQtLliwROwpRmahZsyY2b94MZ2dnZGdnix2H\niKhULV++HE5OTix8EhHRB7Hzk6iY2rRpgy1btrA7jEjF5OXloUmTJjhy5AhatWoldhyiMjFlyhTk\n5OQgLCxM7ChERKXi7t27aNmyJa5du4YvvvhC7DhERFSBsfOTqJi0tLQ45yeRCtLW1sYIMrUwAAAg\nAElEQVT333/P7k9SaQEBATh79ix2794tdhQiolKxfPlyjB07loVPIiL6KHWxAxApCw57J1Jdbm5u\nMDY2xrVr12BmZiZ2HKJSp6Ojg7CwMHzzzTfo2rUrh4gSkVJLT09HWFgYrl27JnYUIiJSAuz8JCom\nrvZOpLp0dXUxY8YMdn+SSuvQoQMmTZoEFxcXcNYjIlJmy5cvh7OzM7s+iYioWFj8JComDnsnUm1T\npkzBkSNHkJCQIHYUojKzYMECPHz4EBs3bhQ7ChHRJ0lPT0d4eDhmz54tdhQiIlISLH4SFROHvROp\ntmrVqmHatGlYunSp2FGIykyVKlUQFhYGb29vJCUliR2HiKjEli1bBhcXF9StW1fsKEREpCQ45ydR\nMXHYO5Hqc3d3h7GxMZKTk2FiYiJ2HKIy0bx5c3h7e8PBwQF//fUX1NX55yARKYc7d+5g+/btHKVB\nREQlws5PomLisHci1Ve9enVMnTqV3Z+k8qZMmQI9PT34+/uLHYWIqNiWLVsGV1dX1KlTR+woRESk\nRHirn6iYOOydqHKYNm0aTExMcPPmTTRu3FjsOERlQiqVYsuWLbC0tET//v3Rrl07sSMREX3Q7du3\n8fPPP7Prk4iISoydn0TFxGHvRJVDjRo14Obmxo44Unn169fHDz/8AAcHB97cI6IKb9myZRg3bhy7\nPomIqMRY/CQqJg57J6o8ZsyYgT179iA1NVXsKERlatSoUWjTpg3mzp0rdhQiove6ffs2duzYgZkz\nZ4odhYiIlBCLn0TF8OLFC7x48QJ3797F/fv3UVhYKHYkIipDBgYGmDBhApYvXw4AkMvlyMzMRFJS\nEm7fvs0uOVIpP/30E/bu3YsjR46IHYWI6J38/f0xfvx4dn0SEdEnkQiCIIgdgqiiunDhAlatWo+9\ne3dDLtcEoAE1tRfQ1KyKqVMnwM1tPAwNDcWOSURlIDMzE6amppgwwQ1btuxAbm4u1NX1IZe/gEz2\nBAMHDsbMmZPRqVMnSCQSseMSfZYjR47AxcUFV65cQY0aNcSOQ0SkkJaWBktLSyQkJKB27dpixyEi\nIiXE4ifRO6SmpuKbb0bjxo27eP58EuRyFwCv/7H1NzQ0NkAi+QXDhw/H5s3roKGhIVZcIiplMpkM\nHh6zERS0CcC3KCycBqDta3s8hkQSCm3tDTA01MX+/TvQrFkzkdISlQ4PDw88fPgQP//8s9hRiIgU\n3NzcUL16dSxbtkzsKEREpKRY/CR6w7Vr19C1a2/k5MxEYaEHALUP7J0DLS0XtGyZhePHf4e2tnZ5\nxSSiMvLy5Uv07z8MZ84UIC/vZwA1P7C3HBJJMHR1vXDs2AGumE1KLS8vD1ZWVvDx8cHIkSPFjkNE\nhNTUVFhZWeH69euoVauW2HGIiEhJsfhJ9JqMjAy0bt0JDx8uhiA4FPOoQmhqjsVXX+XiP/+JgFTK\nqXSJlJUgCBg1yhn79z/G8+d7AFQp5pG/QV/fDRcvnkTjxo3LMiJRmYqJicGgQYNw8eJF1K9fX+w4\nRFTJTZo0CTVq1IC/v7/YUYiISImx+En0mvHj3REaWhUy2aoSHvkSOjrW2LXLHwMGDCiTbERU9k6d\nOoU+fRzw7NkVADolOlYqXYwhQxIRERFWNuGIyomvry9OnjyJqKgozmdLRKJh1ycREZUWFj+J/ic3\nNxd16jTA8+dXABh9whlCYGu7F8ePHyjtaERUToYOtUdkpBUE4btPOPoRNDWNkZaWyAUZSKnJZDJ0\n6dIFjo6OmDJlithxiKiSmjhxIgwMDLB06VKxoxARkZLj+Fyi/wkP3w6ptBs+rfAJAKNw9uwZ3Lx5\ns/RCEVG5yczMxMGDByAIYz/xDDUhkXyLTZtCSjMWUblTV1dHWFgYFi5ciOvXr4sdh4gqodTUVOzZ\nswfff/+92FGIiEgFsPhJ9D87dhzAs2ejP+MM2pBIBuPgwYOllomIys9///tfVKnSAx9e4OjDnj8f\ngx079pdeKCKRmJqawtfXFw4ODigoKBA7DhFVMn5+fpg0aRIMDAzEjkJERCqAxU+i/3n4MAtAvc86\nx4sX9fDo0aPSCURE5SorKwsFBZ93DQC+wOPHvAaQanBzc0PNmjXh5+cndhQiqkRu3bqFiIgIfPfd\np0xBQ0RE9DYWP4mIiIjoLRKJBCEhIdiwYQPOnTsndhwiqiT8/Pzg5ubGrk8iIio16mIHIKooatUy\nAJDxWefQ1MxAzZpWpROIiMqVgYEBqlTJQH7+55zlHmrU+PRh80QVjaGhIdatWwcHBwfExsZCW1tb\n7EhEpMJu3ryJvXv3IikpSewoRESkQtj5SfQ/dnaDoKPz82ecIQ+C8BsGDBhQapmIqPz06tULBQXH\nAHz6sHUtre2ws/u69EIRVQAjRoyAtbU1Zs+eLXYUIlJxfn5+mDx5MmrW5I1EIiIqPRJBEASxQxBV\nBLm5uahTpwGeP7+CT1vxPQSGhgE4d+4o6tevX9rxiKgcDB1qj8hIKwjCp8wz9ghVqjTC7dtJqFu3\nbqlnIxJTdnY2LCwssGnTJvTt21fsOESkglJSUtC+fXskJiay+ElERKWKnZ9E/6Orqwt7+zFQV1/9\nCUe/hLb2GrRv3wKtWrXClClTkJaWVuoZiahszZw5GdraPwF4VuJjpdIfoaNTDQMHDsTRo0dLPxyR\niPT19bFlyxa4urpyYT8iKhPs+iQiorLC4ifRa3x956NGjQhIJNtKcFQhNDVd0bWrMSIiIpCQkIBq\n1arB0tISEyZMwM2bN8ssLxGVrk6dOmHgQBtoaY0GUFCCIyOhp7cR58+fwKxZszBhwgT069cPly9f\nLquoROWuZ8+eGD58ONzc3MCBQ0RUmlJSUvDbb79hxowZYkchIiIVxOIn0Wu++OILHD9+EPr686Cm\nFgig8CNH5EBLawRatbqDX3/dDqlUijp16mDZsmVITExE3bp10a5dOzg7O3PidiIlIJFIEBYWhM6d\nBWhrDwKQ9ZEj5JBINkFPbxKOHNkHY2NjjBw5EvHx8Rg4cCD69OkDBwcHpKamlkd8ojLn7++Pv//+\nGzt27BA7ChGpkCVLlmDKlCmoUaOG2FGIiEgFsfhJ9AYzMzPExp6CuXkEtLWNIZUuA5D5xl5/Q0PD\nDZqajTB8eC38+WfUWyvgGhgYYPHixbhx4wYaN26Mzp07w97eHvHx8eX2Xoio5KpWrYqoqL1wcjKH\npqYJtLRcAVx4Y69HkEgCoaPTDCYmG3DuXDTatWtX5Bzu7u5ISkpCo0aNYGlpie+//x5ZWR8rphJV\nbFpaWggPD8f06dNx+/ZtseMQkQq4ceMG9u3bh+nTp4sdhYiIVBSLn0Tv0LBhQ1y+fBInTkRg1Khk\naGiYQEurHnR1TaCpWRs1avTH7Nn1cONGHLZt+zc0NDTeey59fX14e3vjxo0bMDc3R7du3TBy5Ej8\n/fff5fiOiKgk1NXVsX59INLSErFggSlq1RoGDQ0D6OqaQF29NtTUjPDtt7E4cmQbrl+/gGbNmr3z\nPHp6eli8eDGuXr2KZ8+eoXnz5li+fDmeP39ezu+IqPRYWVnBw8MDzs7OkMvlYschIiW3ZMkSTJ06\nlV2fRERUZrjaO1Ex5Ofn4+HDh8jLy0P16tVhYGAANTW1TzpXbm4uNm7ciFWrVqFTp07w8vKCpaVl\nKScmotIkl8uRlZWF7Oxs7Nq1CykpKQgODi7xeRISEuDp6YmYmBj4+vrC0dHxk68lRGKSyWSwsbGB\nnZ0dPDw8xI5DREoqOTkZHTt2RHJyMvT19cWOQ0REKorFTyIiIiIqseTkZHTq1AknTpxAixYtxI5D\nREpo3bp1yMrKwqJFi8SOQkREKozFTyIiIiL6JP/+97+xadMmnD59GlWqVBE7DhEpkVdfQwVBgFTK\n2diIiKjs8FOGiIiIiD7JhAkTULduXSxevFjsKESkZCQSCSQSCQufRERU5tj5SURERESfLCMjA5aW\nloiMjETHjh3FjkNEREREVARvs5FKkUql2Lt372edY+vWrdDT0yulRERUUTRu3BiBgYFl/jq8hlBl\nU69ePfz0009wcHDAs2fPxI5DRERERFQEOz9JKUilUkgkErzr11UikcDJyQkhISHIzMxEjRo1Pmve\nsfz8fDx9+hS1atX6nMhEVI6cnZ2xdetWxfA5Q0NDDBw4EEuXLlWsHpuVlQUdHR1oamqWaRZeQ6iy\ncnJygra2NjZs2CB2FCKqYARBgEQiETsGERFVUix+klLIzMxU/P/+/fsxYcIE3Lt3T1EM1dLSQrVq\n1cSKV+oKCgq4cARRCTg7O+Pu3bsIDw9HQUEBrl27BhcXF9jY2GD79u1ixytV/AJJFdWTJ09gYWGB\njRs3on///mLHIaIKSC6Xc45PIiIqd/zkIaVQp04dxX+vurhq166t2Paq8Pn6sPfU1FRIpVLs3LkT\n3bp1g7a2NqysrPD333/j6tWr6NKlC3R1dWFjY4PU1FTFa23durVIIfXOnTsYMmQIDAwMoKOjAzMz\nM+zatUvxfFxcHHr37g1tbW0YGBjA2dkZOTk5iufPnz+Pvn37onbt2qhevTpsbGxw5syZIu9PKpVi\n/fr1GDZsGHR1dTF//nzI5XKMGzcOTZo0gba2NkxNTbFixYrS/+ESqQgNDQ3Url0bhoaG6NWrF0aM\nGIHDhw8rnn9z2LtUKsXGjRsxZMgQ6OjooFmzZjh+/DjS09PRr18/6OrqwtLSErGxsYpjXl0fjh07\nhlatWkFXVxc9evT44DUEAA4ePIiOHTtCW1sbtWrVwuDBg/Hy5ct35gKA7t27w8PD453vs2PHjoiO\njv70HxRRGalevTpCQ0Mxbtw4PHz4UOw4RCSywsJCnD17FlOmTIGnpyeePn3KwicREYmCnz6k8hYt\nWoR58+bh0qVL0NfXh52dHTw8PODv74+YmBi8ePHirSLD611Vbm5ueP78OaKjo3Ht2jWsWbNGUYDN\ny8tD3759oaenh/PnzyMyMhKnTp2Cq6ur4vinT5/C0dERJ0+eRExMDCwtLTFw4EA8fvy4yGv6+vpi\n4MCBiIuLw5QpUyCXy2FkZIQ9e/YgISEBS5cuhb+/P7Zs2fLO9xkeHg6ZTFZaPzYipZaSkoKoqKiP\ndlD7+flh9OjRuHLlCqytrTFq1CiMGzcOU6ZMwaVLl2BoaAhnZ+cix+Tn52PZsmUIDQ3FmTNnkJ2d\njUmTJhXZ5/VrSFRUFAYPHoy+ffvi4sWLOHHiBLp37w65XP5J783d3R1OTk4YNGgQ4uLiPukcRGWl\ne/fuGDVqFNzc3N45VQ0RVR6rVq3C+PHjce7cOURERKBp06Y4ffq02LGIiKgyEoiUzJ49ewSpVPrO\n5yQSiRARESEIgiDcunVLkEgkwqZNmxTPHzhwQJBIJEJkZKRiW2hoqFCtWrX3PrawsBB8fX3f+XpB\nQUGCvr6+8OzZM8W248ePCxKJRLhx48Y7j5HL5UK9evWE7du3F8k9bdq0D71tQRAEYe7cuULv3r3f\n+ZyNjY1gYmIihISECC9fvvzouYhUydixYwV1dXVBV1dX0NLSEiQSiSCVSoW1a9cq9mnUqJGwatUq\nxWOJRCLMnz9f8TguLk6QSCTCmjVrFNuOHz8uSKVSISsrSxCEf64PUqlUSEpKUuyzfft2QVNTU/H4\nzWtIly5dhNGjR783+5u5BEEQunXrJri7u7/3mBcvXgiBgYFC7dq1BWdnZ+H27dvv3ZeovD1//lww\nNzcXwsLCxI5CRCLJyckRqlWrJuzfv1/IysoSsrKyhB49egiTJ08WBEEQCgoKRE5IRESVCTs/SeW1\natVK8f9169aFRCJBy5Yti2x79uwZXrx48c7jp02bhsWLF6Nz587w8vLCxYsXFc8lJCTAwsIC2tra\nim2dO3eGVCrFtWvXAAAPHjzAxIkT0axZM+jr60NPTw8PHjxAWlpakddp27btW6+9ceNGWFtbK4b2\nr169+q3jXjlx4gQ2b96M8PBwmJqaIigoSDGslqgysLW1xZUrVxATEwMPDw8MGDAA7u7uHzzmzesD\ngLeuD0DReYc1NDRgYmKieGxoaIiXL18iOzv7na8RGxuLHj16lPwNfYCGhgZmzJiBxMRE1K1bFxYW\nFpgzZ857MxCVJ01NTYSFheG7775772cWEam21atXo0OHDhg0aBBq1qyJmjVrYu7cudi3bx8ePnwI\ndXV1AP9MFfP639ZERERlgcVPUnmvD3t9NRT1XdveNwTVxcUFt27dgouLC5KSktC5c2f4+vp+9HVf\nndfR0REXLlzA2rVrcfr0aVy+fBn169d/qzCpo6NT5PHOnTsxY8YMuLi44PDhw7h8+TImT578wYKm\nra0tjh49ivDwcOzduxcmJib46aef3lvYfR+ZTIbLly/jyZMnJTqOSEza2tpo3LgxzM3NsWbNGjx7\n9uyj/1aLc30QBKHI9eHVF7Y3j/vUYexSqfSt4cEFBQXFOlZfXx/+/v64cuUKHj58CFNTU6xatarE\n/+aJSpulpSVmzJiBsWPHfvK/DSJSToWFhUhNTYWpqaliSqbCwkJ07doV1atXx+7duwEAd+/ehbOz\nMxfxIyKiMsfiJ1ExGBoaYty4cfjll1/g6+uLoKAgAECLFi3w999/49mzZ4p9T548CUEQYGZmpnjs\n7u6Ofv36oUWLFtDR0UFGRsZHX/PkyZPo2LEj3Nzc0KZNGzRp0gTJycnFytulSxdERUVhz549iIqK\ngrGxMdasWYO8vLxiHX/16lUEBASga9euGDduHLKysop1HFFFsnDhQixfvhz37t37rPN87pcyS0tL\nHD169L3P165du8g14cWLF0hISCjRaxgZGSE4OBh//PEHoqOj0bx5c4SFhbHoRKKaPXs28vPzsXbt\nWrGjEFE5UlNTw4gRI9CsWTPFDUM1NTVoaWmhW7duOHjwIABgwYIFsLW1haWlpZhxiYioEmDxkyqd\nNzusPmb69Ok4dOgQbt68iUuXLiEqKgrm5uYAgDFjxkBbWxuOjo6Ii4vDiRMnMGnSJAwbNgyNGzcG\nAJiamiI8PBzx8fGIiYmBnZ0dNDQ0Pvq6pqamuHjxIqKiopCcnIzFixfjxIkTJcrevn177N+/H/v3\n78eJEydgbGyMlStXfrQg0qBBAzg6OmLKlCkICQnB+vXrkZ+fX6LXJhKbra0tzMzMsGTJks86T3Gu\nGR/aZ/78+di9eze8vLwQHx+Pq1evYs2aNYruzB49emD79u2Ijo7G1atX4erqisLCwk/Kam5ujn37\n9iEsLAzr16+HlZUVDh06xIVnSBRqamrYtm0bli5diqtXr4odh4jKUc+ePeHm5gag6Gekvb094uLi\ncO3aNfz8889YtWqVWBGJiKgSYfGTVMqbHVrv6tgqaReXXC6Hh4cHzM3N0bdvX3zxxRcIDQ0FAGhp\naeHQoUPIyclBhw4d8O2336JLly4IDg5WHL9lyxbk5uaiXbt2GD16NFxdXdGoUaOPZpo4cSJGjBiB\nMWPGoH379khLS8PMmTNLlP0VKysr7N27F4cOHYKamtpHfwY1atRA3759cf/+fZiamqJv375FCrac\nS5SUxffff4/g4GDcvn37k68PxblmfGif/v3749dff0VUVBSsrKzQvXt3HD9+HFLpPx/B8+bNQ48e\nPTBkyBD069cPNjY2n90FY2Njg1OnTsHb2xseHh7o1asXLly48FnnJPoUxsbGWLp0Kezt7fnZQVQJ\nvJp7Wl1dHVWqVIEgCIrPyPz8fLRr1w5GRkZo164devToASsrKzHjEhFRJSER2A5CVOm8/ofo+54r\nLCxEvXr1MG7cOMyfP18xJ+mtW7ewc+dO5ObmwtHREU2bNi3P6ERUQgUFBQgODoavry9sbW3h5+eH\nJk2aiB2LKhFBEPDNN9/AwsICfn5+YschojLy9OlTuLq6ol+/fujWrdt7P2smT56MjRs3Ii4uTjFN\nFBERUVli5ydRJfShLrVXw20DAgKgqamJIUOGFFmMKTs7G9nZ2bh8+TKaNWuGVatWcV5BogqsSpUq\nmDRpEhITE9GiRQtYW1tj2rRpePDggdjRqJKQSCTYvHkzgoODcerUKbHjEFEZCQsLw549e7Bu3TrM\nmjULYWFhuHXrFgBg06ZNir8xfX19ERERwcInERGVG3Z+EtE7ffHFF3BycoKXlxd0dXWLPCcIAs6e\nPYvOnTsjNDQU9vb2iiG8RFSxZWZmYvHixdixYwdmzJiB6dOnF7nBQVRWfv31V8yaNQuXLl1663OF\niJTfhQsXMHnyZIwZMwYHDx5EXFwcunfvDh0dHWzbtg3p6emoUaMGgA+PQiIiIiptrFYQkcKrDs6V\nK1dCXV0dQ4YMeesLamFhISQSiWIxlYEDB75V+MzNzS23zERUMnXq1MG6detw5swZXLlyBaampggK\nCoJMJhM7Gqm4b7/9FjY2Nvj+++/FjkJEZaBt27bo2rUrnjx5gqioKPz444/IyMhASEgIjI2Ncfjw\nYdy4cQNAyefgJyIi+hzs/CQiCIKA//73v9DV1UWnTp3w5ZdfYuTIkVi4cCGqVav21t35mzdvomnT\nptiyZQscHBwU55BIJEhKSsKmTZuQl5cHe3t7dOzYUay3RUTFEBMTg9mzZ+PevXvw9/fH4MGD+aWU\nykxOTg5at26NdevWYdCgQWLHIaJSdufOHTg4OCA4OBhNmjTBrl27MGHCBLRs2RK3bt2ClZUVtm/f\njmrVqokdlYiIKhF2fhIRBEHAH3/8gS5duqBJkybIzc3F4MGDFX+YviqEvOoMXbJkCczMzNCvXz/F\nOV7t8+zZM1SrVg337t1D586d4ePjU87vhohKwtraGseOHcOqVavg5eWFrl274uTJk2LHIhWlp6eH\nrVu3YsGCBew2JlIxhYWFMDIyQsOGDbFw4UIAwKxZs+Dj44O//voLq1atQrt27Vj4JCKicsfOTyJS\nSElJgb+/P4KDg9GxY0esXbsWbdu2LTKs/fbt22jSpAmCgoLg7Oz8zvPI5XIcPXoU/fr1w4EDB9C/\nf//yegtE9BkKCwsRHh4OLy8vWFlZwd/fHy1atBA7FqkguVwOiUTCLmMiFfH6KKEbN27Aw8MDRkZG\n+PXXX3H58mXUq1dP5IRERFSZsfOTiBSaNGmCTZs2ITU1FY0aNcL69eshl8uRnZ2N/Px8AICfnx9M\nTU0xYMCAt45/dS/l1cq+7du3Z+GTVNqTJ0+gq6sLVbmPqKamBicnJ1y/fh1dunTBV199hQkTJuDu\n3btiRyMVI5VKP1j4fPHiBfz8/LBr165yTEVEJZWXlweg6CghY2NjdO3aFSEhIfD09FQUPl+NICIi\nIipvLH4S0Vu+/PJL/Pzzz/j3v/8NNTU1+Pn5wcbGBlu3bkV4eDi+//571K1b963jXv3hGxMTg717\n92L+/PnlHZ2oXFWvXh06OjrIyMgQO0qp0tLSwqxZs3D9+nVUr14drVq1woIFC5CTkyN2NKok7ty5\ng/T0dHh7e+PAgQNixyGid8jJyYG3tzeOHj2K7OxsAFCMFho7diyCg4MxduxYAP/cIH9zgUwiIqLy\nwk8gInqvqlWrQiKRwNPTE8bGxpg4cSLy8vIgCAIKCgreeYxcLsfatWvRunVrLmZBlULTpk2RlJQk\ndowyUbNmTaxYsQKxsbG4c+cOmjZtih9++AEvX74s9jlUpSuWyo8gCDAxMUFgYCAmTJiA8ePHK7rL\niKji8PT0RGBgIMaOHQtPT09ER0criqD16tWDo6Mj9PX1kZ+fzykuiIhIVCx+EtFH1ahRAzt27EBm\nZiamT5+O8ePHw8PDA48fP35r38uXL2P37t3s+qRKw9TUFImJiWLHKFMNGjRAaGgojhw5gqioKDRv\n3hw7duwo1hDGly9f4uHDhzh9+nQ5JCVlJghCkUWQqlatiunTp8PY2BibNm0SMRkRvSk3NxenTp3C\nxo0bMX/+fERFReFf//oXPD09cfz4cTx69AgAEB8fj4kTJ+Lp06ciJyYiosqMxU8iKjY9PT0EBgYi\nJycHQ4cOhZ6eHgAgLS1NMSfomjVrYGZmhm+//VbMqETlRpU7P99kYWGBgwcPIjg4GIGBgWjfvj1u\n3rz5wWMmTJiAr776CpMnT8aXX37JIhYVIZfLkZ6ejoKCAkgkEqirqys6xKRSKaRSKXJzc6Grqyty\nUiJ63Z07d9C2bVvUrVsXkyZNQkpKChYvXoyoqCiMGDECXl5eiI6OhoeHBzIzM7nCOxERiUpd7ABE\npHx0dXXRu3dvAP/M97R06VJER0dj9OjRiIiIwLZt20ROSFR+mjZtiu3bt4sdo1x1794dZ8+eRURE\nBL788sv37rdmzRr8+uuvWLlyJXr37o0TJ05gyZIlaNCgAfr27VuOiakiKigoQMOGDXHv3j3Y2NhA\nS0sLbdu2haWlJerVq4eaNWti69atuHLlCho1aiR2XCJ6jampKebMmYNatWoptk2cOBETJ07Exo0b\nERAQgJ9//hlPnjzBtWvXRExKREQESAROxkVEn0kmk2Hu3LkICQlBdnY2Nm7cCDs7O97lp0rhypUr\nsLOzw9WrV8WOIgpBEN47l5u5uTn69euHVatWKbZNmjQJ9+/fx6+//grgn6kyWrduXS5ZqeIJDAzE\nzJkzsXfvXpw/fx5nz57FkydPcPv2bbx8+RJ6enrw9PTE+PHjxY5KRB8hk8mgrv7/vTXNmjWDtbU1\nwsPDRUxFRETEzk8iKgXq6upYuXIlVqxYAX9/f0yaNAmxsbFYvny5Ymj8K4IgIC8vD9ra2pz8nlSC\niYkJUlJSIJfLK+VKtu/7d/zy5Us0bdr0rRXiBUGApqYmgH8Kx5aWlujevTs2bNgAU1PTMs9LFct3\n332Hbdu24eDBgwgKClIU03Nzc3Hr1i00b968yO9YamoqAKBhw4ZiRSai93hV+JTL5YiJiUFSUhIi\nIyNFTkVERMQ5P4moFL1aGV4ul8PNzQ06Ojrv3G/cuHHo3Lkz/vOf/3AlaFJ62jPd+/gAACAASURB\nVNraMDAwwO3bt8WOUqFUrVoVtra22LVrF3bu3Am5XI7IyEicPHkS1apVg1wuh4WFBe7cuYOGDRui\nRYsWGDVq1DsXUiPVtm/fPmzduhV79uyBRCJBYWEhdHV10bJlS6irq0NNTQ0A8PDhQ4SHh2POnDlI\nSUkROTURvY9UKsWzZ88we/ZstGjRQuw4RERELH4SUdmwsLBQfGF9nUQiQXh4OKZPn45Zs2ahffv2\n2LdvH4ugpNQqw4rvJfHq3/OMGTOwYsUKuLu7o2PHjpg5cyauXbuG3r17QyqVQiaTwdDQECEhIYiL\ni8OjR49gYGCAoKAgkd8BlacGDRogICAArq6uyMnJeednBwDUqlULNjY2kEgkGD58eDmnJKKS6N69\nO5YuXSp2DCIiIgAsfhKRCNTU1DBy5EhcuXIF8+bNg7e3NywtLREREQG5XC52PKISq0wrvn+MTCbD\n0aNHkZGRAeCf1d4zMzMxZcoUmJubo0uXLvjXv/4F4J9rgUwmA/BPB23btm0hkUiQnp6u2E6Vw7Rp\n0zBnzhxcv379nc8XFhYCALp06QKpVIpLly7h8OHD5RmRiN5BEIR33sCWSCSVcioYIiKqmPiJRESi\nkUqlGDp0KGJjY7F48WIsW7YMFhYW+OWXXxRfdImUAYuf/y8rKws7duyAj48Pnjx5guzsbLx8+RK7\nd+9Geno65s6dC+CfOUElEgnU1dWRmZmJoUOHYufOndi+fTt8fHyKLJpBlcO8efNgbW1dZNurooqa\nmhpiYmLQunVrHD9+HFu2bEH79u3FiElE/xMbG4thw4Zx9A4REVV4LH4SkegkEgm+/vprnDt3DitX\nrsQPP/wAc3NzhIeHs/uLlAKHvf+/unXrws3NDWfOnIGZmRkGDx4MIyMj3LlzB4sWLcLAgQMB/P/C\nGHv27EH//v2Rn5+P4OBgjBo1Ssz4JKJXCxslJiYqOodfbVu8eDE6deoEY2NjHDp0CI6OjtDX1xct\nKxEBPj4+sLW1ZYcnERFVeBKBt+qIqIIRBAHHjh2Dj48P7t69i/nz58Pe3h5VqlQROxrRO8XHx2Pw\n4MEsgL4hKioKN27cgJmZGSwtLYsUq/Lz83HgwAFMnDgR1tbW2Lhxo2IF71crflPltGHDBgQHByMm\nJgY3btyAo6Mjrl69Ch8fH4wdO7bI75FcLmfhhUgEsbGxGDRoEJKTk6GlpSV2HCIiog9i8ZOIKrTo\n6Gj4+voiJSUF8+bNg5OTEzQ0NMSORVREfn4+qlevjqdPn7JI/x6FhYVFFrKZO3cugoODMXToUHh5\necHIyIiFLFKoWbMmWrZsicuXL6N169ZYsWIF2rVr997FkHJzc6Grq1vOKYkqr8GDB6Nnz57w8PAQ\nOwoREdFH8RsGEVVotra2OHr0KMLDw7F37140bdoUP/30E168eCF2NCIFDQ0NGBoa4tatW2JHqbBe\nFa3S0tIwZMgQ/Pjjjxg3bhz+/e9/w8jICABY+CSFgwcP4q+//sLAgQMRGRmJDh06vLPwmZubix9/\n/BEBAQH8XCAqJxcvXsT58+cxfvx4saMQEREVC79lEJFS6NKlC6KiorBnzx5ERUXB2NgYa9asQV5e\nntjRiABw0aPiMjQ0hImJCbZu3YolS5YAABc4o7d07NgR3333HY4ePfrB3w9dXV0YGBjgzz//ZCGG\nqJwsWrQIc+fO5XB3IiJSGix+EpFSad++Pfbv34/9+/fjxIkTaNKkCVasWIHc3Fyxo1ElZ2pqyuJn\nMairq2PlypUYNmyYopPvfUOZBUFATk5OecajCmTlypVo2bIljh8//sH9hg0bhoEDB2L79u3Yv39/\n+YQjqqQuXLiAixcv8mYDEREpFRY/iUgpWVlZYe/evThy5AjOnz8PY2NjLF26lIUSEk3Tpk254FEZ\n6N+/PwYNGoS4uDixo5AIIiIi0K1bt/c+//jxY/j7+8Pb2xuDBw9G27Ztyy8cUSX0qutTU1NT7ChE\nRETFxuInESm1Vq1aYefOnTh+/DiuXbsGY2Nj+Pr6Ijs7W+xoVMlw2Hvpk0gkOHbsGHr27IkePXrA\nxcUFd+7cETsWlSN9fX3Url0bz549w7Nnz4o8d/HiRXz99ddYsWIFAgMD8euvv8LQ0FCkpESq7/z5\n84iNjcW4cePEjkJERFQiLH4SkUpo0aIFwsPDcerUKdy8eRMmJibw8vJCVlaW2NGokjA1NWXnZxnQ\n0NDAjBkzkJiYiC+++AKtW7fGnDlzeIOjktm1axfmzZsHmUyGvLw8rFmzBra2tpBKpbh48SImTZok\ndkQilbdo0SLMmzePXZ9ERKR0JIIgCGKHICIqbSkpKVi2bBkiIiIwfvx4fPfdd6hTp47YsUiFyWQy\n6OrqIjs7m18My1B6ejoWLlyIffv2Yc6cOZgyZQp/3pVARkYG6tevD09PT1y9ehW///47vL294enp\nCamU9/KJylpMTAyGDh2KpKQkXnOJiEjp8K9FIlJJTZo0QVBQEGJjY/H06VM0b94c33//PTIyMsSO\nRipKXV0dDRs2REpKithRVFr9+vWxefNm/PHHH4iOjkbz5s0RFhYGuVwudjQqQ/Xq1UNISAiWLl2K\n+Ph4nD59GgsWLGDhk6icsOuTiIiUGTs/iahSSE9PR0BAAMLCwmBvb4/Zs2fDyMioROd48eIF9uzZ\ng2PHjuHRo0eoWrUq6tevjzFjxqBdu3ZllJyUyddffw1XV1cMGTJE7CiVxp9//onZs2fj+fPnWL58\nOfr06QOJRCJ2LCojI0eOxK1bt3Dy5Emoq6uLHYeoUjh37hyGDRuG5ORkaGhoiB2HiIioxHi7nIgq\nhfr162Pt2rW4du0aqlatCgsLC7i5uSE1NfWjx969exezZs2CoaEh/P39cf/+fairq6OgoACXL1/G\ngAED0Lp1a4SGhqKwsLAc3g1VVFz0qPzZ2Njg1KlT8Pb2hoeHB3r16oULFy6IHYvKSEhICK5evYq9\ne/eKHYWo0njV9cnCJxERKSt2fhJRpfTgwQMEBgYiKCgI3377LebNmwdjY+O39rt48SL69+8PExMT\ntG3bFgYGBm/tI5fLkZycjNOnT8Pc3Bw7d+6EtrZ2ebwNqmA2bNiA2NhYBAUFiR2lUiooKEBwcDB8\nfX1ha2sLPz8/NGnSROxYVMri4+Mhk8nQqlUrsaMQqbyzZ89i+PDh7PokIiKlxs5PIqqUateuDX9/\nfyQmJsLQ0BAdOnSAk5NTkdW64+Li0KtXL3Tr1g19+vR5Z+ETAKRSKUxNTTFmzBikp6dj8ODBkMlk\n5fVWqALhiu/iqlKlCiZNmoTExES0aNEC1tbWmDZtGh48eCB2NCpFLVq0YOGTqJwsWrQInp6eLHwS\nEZFSY/GTiCo1AwMD+Pr6Ijk5GSYmJujSpQtGjx6NS5cuoX///ujRowfMzMyKdS51dXUMGjQId+7c\ngbe3dxknp4qIw94rBl1dXXh7eyM+Ph5yuRwtWrSAn58fnj17JnY0KkMczERUus6cOYOrV6/CxcVF\n7ChERESfhcVPIiIA+vr68PLywo0bN2BhYQFbW1tIpdISdxepqamhT58+2LBhA54/f15GaamiMjIy\nwuPHj5Gbmyt2FAJQp04drFu3DmfOnMGVK1dgamqKoKAgdmarIEEQEBkZyXmXiUoRuz6JiEhVsPhJ\nRPQaPT09zJ07F82aNUOHDh0+6Rw1a9ZE/fr1sWvXrlJORxWdVCqFsbExkpOTxY5CrzExMcHOnTsR\nGRmJHTt2oFWrVoiMjGSnoAoRBAHr1q1DQECA2FGIVMLp06cRHx/Prk8iIlIJLH4SEb0hMTERycnJ\naN68+Sefw8LCAj/++GMppiJlwaHvFZe1tTWOHTuGVatWwcvLC127dsXJkyfFjkWlQCqVIjQ0FIGB\ngYiNjRU7DpHSe9X1WbVqVbGjEBERfTYWP4mI3pCcnAxDQ0Ooqal98jnq1auHlJSUUkxFysLU1JTF\nzwpMIpFgwIABuHTpEiZMmAA7Ozt8++23SEhIEDsafaYGDRogMDAQ9vb2ePHihdhxiJTWqVOnkJCQ\nAGdnZ7GjEBERlQoWP4mI3pCbm/vZnQ4aGhrIy8srpUSkTJo2bcoV35WAmpoanJyccP36dXTu3Bk2\nNjaYOHEiMjIyxI5Gn8He3h5mZmaYP3++2FGIlNaiRYswf/58dn0SEZHKYPGTiOgN1apVw8uXLz/r\nHPn5+dDR0SmlRKRMOOxduWhpaWHWrFm4fv069PT00LJlSyxYsAA5OTliR6NPIJFIsHHjRvzyyy/4\n448/xI5DpHROnjyJxMREjB07VuwoREREpYbFTyKiN5iamuLOnTuftSJ0eno6TExMSjEVKQtTU1N2\nfiqhmjVrYsWKFYiNjcWdO3dgamqKH3744bNvhFD5MzAwwObNmzF27Fg8efJE7DhESsXHx4ddn0RE\npHJY/CQieoOxsTFatWqF+Pj4Tz7H5cuX4e7uXoqpSFnUrVsXL168QHZ2tthR6BM0aNAAoaGhOHz4\nMKKiotCiRQv88ssvkMvlYkejEujfvz8GDBgADw8PsaMQKY2TJ08iKSkJTk5OYkchIiIqVSx+EhG9\nw4wZM3D58uVPOvbhw4fIzMzE8OHDSzkVKQOJRMKh7yrAwsICBw8exObNm7Fq1Sq0b98eR48eFTsW\nlcDKlStx6tQpREREiB2FSClwrk8iIlJVLH4SEb3DN998A5lMhosXL5boOJlMhkOHDsHd3R0aGhpl\nlI4qOg59Vx3du3fH2bNnMWvWLEyYMAH9+vX75BsjVL50dHQQFhaGKVOmcCEroo/466+/kJyczK5P\nIiJSSSx+EhG9g7q6Og4dOoSTJ0/i77//LtYxBQUF+O2332BqagovL68yTkgVGTs/VYtUKsXIkSMR\nHx+PQYMGoW/fvnB0dERqaqrY0egjOnbsiPHjx8PV1RWCIIgdh6jCWrRoERYsWIAqVaqIHYWIiKjU\nsfhJRPQepqamiI6OxunTp/H777/j3r1779xPJpMhLi4OYWFhaN68OSIiIqCmplbOaakiYfFTNVWt\nWhVTp05FYmIiGjVqBCsrK8ycOROPHj0SOxp9gLe3NzIzMxEUFCR2FKIK6c8//0RKSgocHR3FjkJE\nRFQmJAJvgxMRfdCDBw+wfv16rF+/Hnp6emjUqBG0tbVRWFiIJ0+e4OrVq2jevDlmzJiBYcOGQSrl\nfaXK7syZM3B3d0dMTIzYUagMZWRkwMfHBxEREZg5cyY8PDygpaUldix6h/j4eNjY2OD06dNo2rSp\n2HGIKpSePXtizJgxcHFxETsKERFRmWDxk4iomGQyGfbt24fo6Gikp6fj0KFDmD59Ouzs7GBmZiZ2\nPKpAsrKyYGxsjMePH0MikYgdh8rY9evX4enpiZiYGPj4+MDR0ZHd3xXQDz/8gB07duDPP/+Eurq6\n2HGIKoQTJ07A2dkZCQkJHPJOREQqi8VPIiKiMlCzZk1cv34dtWvXFjsKlZPTp09j9uzZyM7OxrJl\nyzBgwAAWvysQuVyOPn36oHv37pg/f77YcYgqhB49esDBwQHOzs5iRyEiIiozHJtJRERUBrjie+XT\nqVMnnDhxAn5+fpg1a5ZipXiqGKRSKUJDQ7F27VpcuHBB7DhEoouOjkZaWhocHBzEjkJERFSmWPwk\nIiIqA1z0qHKSSCT45ptvcOXKFdjb22PYsGH417/+xd+FCsLIyAhr1qyBg4MDnj9/LnYcIlG9WuGd\n00AQEZGqY/GTiIioDLD4Wbmpq6tj3LhxSExMhJWVFTp16oQpU6bg/v37Yker9Ozs7NCqVSvMmzdP\n7ChEojl+/Dhu374Ne3t7saMQERGVORY/iYiIygCHvRMAaGtrY968eUhISEDVqlVhZmYGHx8f5Obm\nFvscd+/eha+vL/r164eOHTviq6++wsiRIxEZGQmZTFaG6VWTRCLBhg0bsGfPHhw9elTsOESiWLRo\nEby8vNj1SURElQKLn0REIvDx8YGFhYXYMagMsfOTXlerVi2sXr0a58+fR2JiIpo2bYr169ejoKDg\nvcdcvnwZI0aMgLm5OTIyMuDu7o7Vq1dj8eLF6Nu3LwICAtC4cWP4+fnhxYsX5fhulF/NmjURHBwM\nZ2dnZGdnix2HqFz98ccfSE9Px5gxY8SOQkREVC642jsRVTrOzs7IysrCvn37RMuQl5eH/Px81KhR\nQ7QMVLZycnJgaGiIp0+fcsVvesvFixcxZ84cpKamYunSpRg2bFiR35N9+/bB1dUVCxYsgLOzM/T0\n9N55ntjYWCxcuBDZ2dn47bffeE0poalTpyI7Oxvh4eFiRyEqF4IgoFu3bnB1dYWjo6PYcYiIiMoF\nOz+JiESgra3NIoWK09PTg66uLu7evSt2FKqArKyscOTIEfz000/w8/NTrBQPAEePHsX48eNx8OBB\nTJs27b2FTwCwtLREZGQk2rRpg0GDBnERnxIKCAhATEwMdu3aJXYUonLxxx9/ICMjA6NHjxY7ChER\nUblh8ZOI6DVSqRR79+4tsq1x48YIDAxUPE5KSoKtrS20tLRgbm6OQ4cOoVq1ati2bZtin7i4OPTu\n3Rva2towMDCAs7MzcnJyFM/7+PigVatWZf+GSFQc+k4f07t3b1y4cAHu7u5wcnJCv379MGLECOza\ntQvW1tbFOodUKsWaNWtgZGQELy+vMk6sWrS1tREWFgZ3d3feqCCVJwgC5/okIqJKicVPIqISEAQB\nQ4YMQdWqVXHu3DmEhIRg4cKFePnypWKfvLw89O3bF3p6ejh//jwiIyNx6tQpuLq6FjkXh0KrPi56\nRMUhlUoxZswYJCQkQEdHBx06dICtrW2JzxEQEIAtW7bg2bNnZZRUNbVv3x5ubm5wcXEBZ4MiVXbs\n2DHcu3cPdnZ2YkchIiIqVyx+EhGVwOHDh5GUlISwsDC0atUKHTp0wOrVq4ssWrJ9+3bk5eUhLCwM\nZmZmsLGxQVBQECIiIpCSkiJieipv7PykkqhatSoSEhIwa9asTzq+YcOG6Nq1K3bs2FHKyVTf/Pnz\nkZWVhQ0bNogdhahMvOr69Pb2ZtcnERFVOix+EhGVwPXr12FoaIgvvvhCsc3a2hpS6f9fThMSEmBh\nYQFtbW3Fts6dO0MqleLatWvlmpfExeInlcT58+chk8nQrVu3Tz7HxIkTsWXLltILVUlUqVIF4eHh\n8Pb2Zrc2qaSjR48iMzMTo0aNEjsKERFRuWPxk4joNRKJ5K1hj693dZbG+any4LB3Kom0tDSYm5t/\n1nXC3NwcaWlppZiq8mjWrBkWLVoEBwcHyGQyseMQlRp2fRIRUWXH4icR0Wtq166NjIwMxeP79/+P\nvfsOr/H+/zj+PCeRjSDUjkRFYhPEqj2KotRMSK3Uqi02TWJWjaB2EXukiNolBI0tIVZKZaBmjUTI\nPvfvj/6cb1PaJpHkTuT9uK5zXdzn/nzu1x2Rk/M+n/Eoxd/t7e25f/8+Dx8+1B87f/48Op1O/3cH\nBweuXLmSYt29wMBAFEXBwcEhk+9AZCdly5YlPDyc5ORktaOIHODVq1cpRoynh7m5Oa9fv86gRLnP\n4MGDsbS0ZObMmWpHESLDHDlyhD/++ENGfQohhMi1pPgphMiVoqOjuXz5copHZGQkTZs2ZcmSJVy8\neJHg4GD69OmDqampvl2LFi2ws7PD1dWVkJAQzpw5w+jRo8mTJ49+tJaLiwtmZma4urpy9epVTpw4\nwcCBA/niiy+wtbVV65aFCszMzLCysuLu3btqRxE5gKWlJVFRUe/VR1RUFPnz58+gRLmPVqtlzZo1\nfP/995w/f17tOEK8t7+O+jQwMFA7jhBCCKEKKX4KIXKlkydPUqNGjRQPd3d35s+fj42NDU2aNKFr\n1664ublRpEgRfTuNRoOfnx8JCQk4OTnRp08fJk2aBICJiQkApqamHDp0iOjoaJycnOjYsSP169dn\n9erVqtyrUJdMfRepVblyZc6cOUNsbGy6+zh27BhVq1bNwFS5T4kSJVi8eDG9evWSUbQixzty5AjP\nnj2jW7duakcRQgghVKNR/r64nRBCiDS5fPky1atX5+LFi1SvXj1VbSZOnEhAQACnTp3K5HRCbQMH\nDqRy5coMGTJE7SgiB2jdujU9evTA1dU1zW0VRaFGjRp8++23tGzZMhPS5S7Ozs4UKlSIxYsXqx1F\niHRRFIX69eszdOhQevTooXYcIYQQQjUy8lMIIdLIz8+Pw4cPExERwbFjx+jTpw/Vq1dPdeHz9u3b\n+Pv7U6lSpUxOKrID2fFdpMXgwYNZsmTJWxuvpcaZM2eIjIyUae8ZZMmSJezevZvDhw+rHUWIdDl8\n+DAvXryga9euakcRQgghVCXFTyGESKOXL1/y9ddfU7FiRXr16kXFihU5ePBgqtpGRUVRsWJFTExM\nmDJlSiYnFdmBTHsXadGmTRsSEhL47rvv0tTu+fPn9OvXj88//5yOHTvSu3fvFJu1ibQrUKAAa9as\noW/fvjx79kztOEKkiaIofPPNN7LWpxBCCIFMexdCCCEyVWhoKO3atZPRnyLV7t27p5+qOnr0aP1m\nav/k0aNHfPbZZ3zyySfMnz+f6OhoZs6cyQ8//MDo0aMZOXKkfk1ikXbDhg3jyZMnbNmyRe0oQqTa\noUOHGDlyJFeuXJHipxBCiFxPRn4KIYQQmcjW1pa7d++SmJiodhSRQ5QsWZKlS5fi5eVF69atOXDg\nADqd7q3znjx5wuzZs3F0dKRt27bMmzcPgHz58jF79mzOnj3LuXPnqFChAjt37kzXVHoBs2fP5tKl\nS1L8FDnGm1Gf33zzjRQ+hRBCCGTkpxBCCJHpypYty4EDB7Czs1M7isgBoqOjcXR0ZOrUqSQlJbFk\nyRKeP39OmzZtKFiwIPHx8YSFhXH48GE6derE4MGDcXR0/Mf+/P39GTFiBFZWVnh7e8tu8Olw4cIF\n2rRpQ1BQECVLllQ7jhD/6uDBg4wePZqQkBApfgohhBBI8VMIIYTIdJ9++ilDhw6lbdu2akcR2Zyi\nKPTo0QNLS0uWL1+uP37u3DlOnTrFixcvMDY2pmjRonTo0IGCBQumqt+kpCRWrVqFh4cHHTt2ZNq0\naRQuXDizbuODNG3aNE6ePMnBgwfRamXylMieFEWhTp06jB49WjY6EkIIIf6fFD+FEEKITDZs2DBs\nbGwYOXKk2lGEEOmUlJREgwYNcHFxYejQoWrHEeKdDhw4gLu7OyEhIVKkF0IIIf6fvCIKIUQmiYuL\nY/78+WrHENlAuXLlZMMjIXI4Q0ND1q9fj6enJ6GhoWrHEeItf13rUwqfQgghxP/Iq6IQQmSQvw+k\nT0xMZMyYMbx8+VKlRCK7kOKnEB8GOzs7pk2bRq9evWQTM5HtHDhwgNjYWL744gu1owghhBDZihQ/\nhRAinXbu3Mmvv/5KVFQUABqNBoDk5GSSk5MxMzPD2NiYFy9eqBlTZAN2dnbcvHlT7RhCiAwwcOBA\nrKysmD59utpRhNCTUZ9CCCHEP5M1P4UQIp0cHBy4c+cOzZs359NPP6VSpUpUqlSJAgUK6M8pUKAA\nx44do1q1aiomFWpLSkrCwsKCFy9eYGJionYcIVIlKSkJQ0NDtWNkS/fv36d69er89NNPODk5qR1H\nCPbt28f48eO5fPmyFD+FEEKIv5FXRiGESKcTJ06wePFiXr9+jYeHB66urnTr1o2JEyeyb98+AAoW\nLMjjx49VTirUZmhoSJkyZbh9+7baUUQ2EhkZiVarJSgoKFteu3r16vj7+2dhqpyjePHifP/99/Tq\n1YtXr16pHUfkcoqi4OHhIaM+hRBCiH8gr45CCJFOhQsXpm/fvhw+fJhLly4xduxYLC0t2bNnD25u\nbjRo0IDw8HBiY2PVjiqyAZn6njv16dMHrVaLgYEBRkZGlC1bFnd3d16/fk3p0qV5+PChfmT48ePH\n0Wq1PHv2LEMzNGnShGHDhqU49vdrv4unpydubm507NhRCvfv0KVLF5ycnBg7dqzaUUQut2/fPuLj\n4+nUqZPaUYQQQohsSYqfQgjxnpKSkihWrBiDBg1i+/bt7N69m9mzZ+Po6EiJEiVISkpSO6LIBmTT\no9yrRYsWPHz4kPDwcGbMmMHSpUsZO3YsGo2GIkWK6EdqKYqCRqN5a/O0zPD3a79Lp06duH79OrVr\n18bJyYlx48YRHR2d6dlyksWLF7Nnzx4OHjyodhSRS8moTyGEEOK/ySukEEK8p7+uiZeQkICtrS2u\nrq4sXLiQo0eP0qRJExXTiexCip+5l7GxMYULF6ZEiRJ0796dnj174ufnl2LqeWRkJE2bNgX+HFVu\nYGBA37599X3MmTOHjz/+GDMzM6pWrcqmTZtSXMPLy4syZcpgYmJCsWLF6N27N/DnyNPjx4+zZMkS\n/QjUO3fupHrKvYmJCRMmTCAkJIRHjx5hb2/PmjVr0Ol0GftFyqEsLS3x8fGhf//+PH36VO04Ihfa\nu3cviYmJdOzYUe0oQgghRLYlq9gLIcR7unfvHmfOnOHixYvcvXuX169fkydPHurWrctXX32FmZmZ\nfkSXyL3s7OzYsmWL2jFENmBsbEx8fHyKY6VLl2bHjh107tyZGzduUKBAAUxNTQGYNGkSO3fuZNmy\nZdjZ2XH69Gnc3NwoWLAgrVu3ZseOHcybN49t27ZRqVIlHj9+zJkzZwBYuHAhN2/exMHBgVmzZqEo\nCoULF+bOnTtp+plUvHhxfHx8OH/+PMOHD2fp0qV4e3vToEGDjPvC5FBNmzalS5cuDBo0iG3btsnP\nepFlZNSnEEIIkTpS/BRCiPfwyy+/MHLkSCIiIihZsiRFixbFwsKC169fs3jxYg4ePMjChQspX768\n2lGFymTkpwA4d+4cmzdvpmXLlimOazQaChYsCPw58vPNn1+/fs2CBQs4fPgw9evXB8Da2pqzZ8+y\nZMkSWrduzZ07dyhevDgtWrTAwMCAkiVLUqNGDQDy5cuHkZERZmZmFC5cj2rt9AAAIABJREFUOMU1\n0zO9vlatWgQGBrJlyxZ69OhBgwYN+PbbbyldunSa+/qQzJw5E0dHRzZv3oyLi4vacUQusWfPHpKT\nk/n888/VjiKEEEJka/IRoRBCpNNvv/2Gu7s7BQsW5MSJEwQHB3PgwAF8fX3ZtWsXK1asICkpiYUL\nF6odVWQDJUqU4MWLF8TExKgdRWSxAwcOkDdvXkxNTalfvz5NmjRh0aJFqWp7/fp14uLi+PTTT8mb\nN6/+sXz5csLCwoA/N96JjY2lTJky9O/fnx9//JGEhIRMux+NRoOzszOhoaHY2dlRvXp1vvnmm1y9\n67mpqSkbN25k5MiR3L17V+04IheQUZ9CCCFE6skrpRBCpFNYWBhPnjxhx44dODg4oNPpSE5OJjk5\nGUNDQ5o3b0737t0JDAxUO6rIBrRaLa9evcLc3FztKCKLNWrUiJCQEG7evElcXBy+vr5YWVmlqu2b\ntTX37t3L5cuX9Y9r165x6NAhAEqWLMnNmzdZuXIl+fPnZ8yYMTg6OhIbG5tp9wRgbm6Op6cnwcHB\n+qn1mzdvzpINm7KjGjVqMHz4cHr37i1roopM99NPP6Eoioz6FEIIIVJBip9CCJFO+fPn5+XLl7x8\n+RJAv5mIgYGB/pzAwECKFSumVkSRzWg0GlkPMBcyMzPDxsaGUqVKpfj58HdGRkYAJCcn649VqFAB\nY2NjIiIisLW1TfEoVapUiratW7dm3rx5nDt3jmvXruk/eDEyMkrRZ0YrXbo0W7ZsYfPmzcybN48G\nDRpw/vz5TLtedjZu3DhiY2NZvHix2lHEB+yvoz7lNUUIIYT4b7LmpxBCpJOtrS0ODg7079+fyZMn\nkydPHnQ6HdHR0URERLBz506Cg4PZtWuX2lGFEDmAtbU1Go2Gffv28dlnn2FqaoqFhQVjxoxhzJgx\n6HQ6GjZsSExMDGfOnMHAwID+/fuzbt06kpKScHJywsLCgq1bt2JkZES5cuUAKFOmDOfOnSMyMhIL\nCwsKFSqUKfnfFD19fHzo0KEDLVu2ZNasWbnqAyBDQ0PWr19PnTp1aNGiBRUqVFA7kvgA7d69G4AO\nHTqonEQIIYTIGWTkpxBCpFPhwoVZtmwZ9+/fp3379gwePJjhw4czYcIEVqxYgVarZc2aNdSpU0ft\nqEKIbOqvo7aKFy+Op6cnkyZNomjRogwdOhSAadOm4eHhwbx586hUqRItW7Zk586d2NjYAGBpacnq\n1atp2LAhlStXZteuXezatQtra2sAxowZg5GRERUqVKBIkSLcuXPnrWtnFK1WS9++fQkNDaVo0aJU\nrlyZWbNmERcXl+HXyq4+/vhjZs6cSa9evTJ17VWROymKgqenJx4eHjLqUwghhEgljZJbF2YSQogM\n9Msvv3DlyhXi4+PJnz8/pUuXpnLlyhQpUkTtaEIIoZrbt28zZswYLl++zNy5c+nYsWOuKNgoikK7\ndu2oVq0a06dPVzuO+IDs2rWLadOmcfHixVzxf0kIIYTICFL8FEKI96QoirwBERkiLi4OnU6HmZmZ\n2lGEyFD+/v6MGDECKysrvL29qVq1qtqRMt3Dhw+pVq0au3btom7dumrHER8AnU5HjRo18PLyon37\n9mrHEUIIIXIMWfNTCCHe05vC598/S5KCqEirNWvW8OTJEyZPnvyvG+MIkdM0a9aM4OBgVq5cScuW\nLenYsSPTpk2jcOHCakfLNEWLFmXp0qW4uroSHByMhYWF2pFEDhEWFsaNGzeIjo7G3NwcW1tbKlWq\nhJ+fHwYGBrRr107tiCIbe/36NWfOnOHp06cAFCpUiLp162JqaqpyMiGEUI+M/BRCCCGyyOrVq2nQ\noAHlypXTF8v/WuTcu3cvEyZMYOfOnfrNaoT40Dx//hxPT082bdrExIkTGTJkiH6n+w/Rl19+iamp\nKcuXL1c7isjGkpKS2LdvH0uXLiU4OJiaNWuSN29eXr16xZUrVyhatCj3799nwYIFdO7cWe24Ihu6\ndesWy5cvZ926ddjb21O0aFEUReHBgwfcunWLPn36MGDAAMqWLat2VCGEyHKy4ZEQQgiRRcaPH8+x\nY8fQarUYGBjoC5/R0dFcvXqV8PBwrl27xqVLl1ROKkTmKVCgAN7e3pw4cYJDhw5RuXJl9u/fr3as\nTLNo0SIOHjz4Qd+jeD/h4eFUq1aN2bNn06tXL+7evcv+/fvZtm0be/fuJSwsjClTplC2bFmGDx/O\n+fPn1Y4sshGdToe7uzsNGjTAyMiICxcu8Msvv/Djjz+yY8cOTp06xZkzZwCoU6cOEydORKfTqZxa\nCCGyloz8FEIIIbJIhw4diImJoXHjxoSEhHDr1i3u379PTEwMBgYGfPTRR5ibmzNz5kzatm2rdlwh\nMp2iKOzfv59Ro0Zha2vL/PnzcXBwSHX7xMRE8uTJk4kJM0ZAQADOzs6EhIRgZWWldhyRjfz22280\natSI8ePHM3To0P88/6effqJfv37s2LGDhg0bZkFCkZ3pdDr69OlDeHg4fn5+FCxY8F/P/+OPP2jf\nvj0VKlRg1apVskSTECLXkJGfQgjxnhRF4d69e2+t+SnE39WrV49jx47x008/ER8fT8OGDRk/fjzr\n1q1j79697N69Gz8/Pxo1aqR2VJEOCQkJODk5MW/ePLWj5BgajYa2bdty5coVWrZsScOGDRkxYgTP\nnz//z7ZvCqcDBgxg06ZNWZA2/Ro3boyzszMDBgyQ1wqhFxUVRevWrfnmm29SVfgEaN++PVu2bKFL\nly7cvn07kxNmDzExMYwYMYIyZcpgZmZGgwYNuHDhgv75V69eMXToUEqVKoWZmRn29vZ4e3urmDjr\neHl5cevWLQ4dOvSfhU8AKysrDh8+zOXLl5k1a1YWJBRCiOxBRn4KIUQGsLCw4MGDB+TNm1ftKCIb\n27ZtG4MHD+bMmTMULFgQY2NjzMzM0Grls8gPwZgxY/j111/56aefZDRNOj158oQpU6awa9cuLl68\nSIkSJf7xa5mYmIivry9nz55lzZo1ODo64uvrm203UYqLi6NWrVq4u7vj6uqqdhyRDSxYsICzZ8+y\ndevWNLedOnUqT548YdmyZZmQLHvp1q0bV69eZfny5ZQoUYINGzawYMECbty4QbFixfjqq684evQo\na9asoUyZMpw4cYL+/fuzevVqXFxc1I6faZ4/f46trS3Xr1+nWLFiaWp79+5dqlatSkREBPny5cuk\nhEIIkX1I8VMIITJAqVKlCAwMpHTp0mpHEdnY1atXadmyJTdv3nxr52edTodGo5GiWQ61d+9ehgwZ\nQlBQEIUKFVI7To7366+/Ymdnl6r/DzqdjsqVK2NjY8PixYuxsbHJgoTpc+nSJVq0aMGFCxewtrZW\nO45QkU6nw97eHh8fH+rVq5fm9vfv36dixYpERkZ+0MWruLg48ubNy65du/jss8/0x2vWrEmbNm3w\n8vKicuXKdO7cmW+++Ub/fOPGjalSpQqLFi1SI3aWWLBgAUFBQWzYsCFd7bt06UKTJk0YPHhwBicT\nQojsR4aaCCFEBihQoECqpmmK3M3BwYFJkyah0+mIiYnB19eXK1euoCgKWq1WCp851N27d+nXrx9b\ntmyRwmcGKV++/H+ek5CQAICPjw8PHjzg66+/1hc+s+tmHtWqVWP06NH07t0722YUWcPf3x8zMzPq\n1q2brvbFixenRYsWrF+/PoOTZS9JSUkkJydjbGyc4ripqSm//PILAA0aNGDPnj3cu3cPgFOnTnH5\n8mVat26d5XmziqIoLFu27L0Kl4MHD2bp0qWyFIcQIleQ4qcQQmQAKX6K1DAwMGDIkCHky5ePuLg4\nZsyYwSeffMKgQYMICQnRnydFkZwjMTGR7t27M2rUqHSN3hL/7N8+DNDpdBgZGZGUlMSkSZPo2bMn\nTk5O+ufj4uK4evUqq1evxs/PLyvippq7uzuJiYm5Zk1C8W6BgYG0a9fuvT70ateuHYGBgRmYKvux\nsLCgbt26TJ8+nfv376PT6di4cSOnT5/mwYMHACxatIgqVapQunRpjIyMaNKkCd9+++0HXfx8/Pgx\nz549o06dOunuo3HjxkRGRhIVFZWByYQQInuS4qcQQmQAKX6K1HpT2DQ3N+fFixd8++23VKxYkc6d\nOzNmzBhOnTola4DmIFOmTCF//vy4u7urHSVXefP/aPz48ZiZmeHi4kKBAgX0zw8dOpRWrVqxePFi\nhgwZQu3atQkLC1MrbgoGBgasX7+eWbNmcfXqVbXjCJU8f/48VRvU/JuCBQvy4sWLDEqUfW3cuBGt\nVkvJkiUxMTHh+++/x9nZWf9auWjRIk6fPs3evXsJCgpiwYIFjB49mp9//lnl5JnnzffP+xTPNRoN\nBQsWlN9fhRC5gry7EkKIDCDFT5FaGo0GnU6HsbExpUqV4smTJwwdOpRTp05hYGDA0qVLmT59OqGh\noWpHFf/h4MGDbNq0iXXr1knBOgvpdDoMDQ0JDw9n+fLlDBw4kMqVKwN/TgX19PTE19eXWbNmceTI\nEa5du4apqWm6NpXJLLa2tsyaNYuePXvqp++L3MXIyOi9/+0TEhI4deqUfr3onPz4t6+FjY0Nx44d\n49WrV9y9e5czZ86QkJCAra0tcXFxTJw4ke+++442bdpQqVIlBg8eTPfu3Zk7d+5bfel0OpYsWaL6\n/b7vw8HBgWfPnr3X98+b76G/LykghBAfIvlNXQghMkCBAgUy5JdQ8eHTaDRotVq0Wi2Ojo5cu3YN\n+PMNSL9+/ShSpAhTp07Fy8tL5aTi3/z+++/06dOHTZs2ZdvdxT9EISEh3Lp1C4Dhw4dTtWpV2rdv\nj5mZGQCnT59m1qxZfPvtt7i6umJlZYWlpSWNGjXCx8eH5ORkNeOn0K9fP0qXLo2Hh4faUYQKihYt\nSnh4+Hv1ER4eTrdu3VAUJcc/jIyM/vN+TU1N+eijj3j+/DmHDh3i888/JzExkcTExLc+gDIwMHjn\nEjJarZYhQ4aofr/v+4iOjiYuLo5Xr16l+/snKiqKqKio9x6BLIQQOYGh2gGEEOJDINOGRGq9fPkS\nX19fHjx4wMmTJ/n111+xt7fn5cuXABQpUoRmzZpRtGhRlZOKf5KUlISzszNDhgyhYcOGasfJNd6s\n9Td37ly6detGQEAAq1atoly5cvpz5syZQ7Vq1Rg0aFCKthEREZQpUwYDAwMAYmJi2LdvH6VKlVJt\nrVaNRsOqVauoVq0abdu2pX79+qrkEOro3LkzNWrUYN68eZibm6e5vaIorF69mu+//z4T0mUvP//8\nMzqdDnt7e27dusXYsWOpUKECvXv3xsDAgEaNGjF+/HjMzc2xtrYmICCA9evXv3Pk54cib968NGvW\njC1bttC/f/909bFhwwY+++wzTExMMjidEEJkP1L8FEKIDFCgQAHu37+vdgyRA0RFRTFx4kTKlSuH\nsbExOp2Or776inz58lG0aFGsrKzInz8/VlZWakcV/8DT0xMjIyMmTJigdpRcRavVMmfOHGrXrs2U\nKVOIiYlJ8XM3PDycPXv2sGfPHgCSk5MxMDDg2rVr3Lt3D0dHR/2x4OBgDh48yNmzZ8mfPz8+Pj6p\n2mE+o3300UcsW7YMV1dXLl26RN68ebM8g8h6kZGRLFiwQF/QHzBgQJr7OHHiBDqdjsaNG2d8wGwm\nKiqKCRMm8Pvvv1OwYEE6d+7M9OnT9R9mbNu2jQkTJtCzZ0+ePXuGtbU1M2bMeK+d0HOCwYMHM378\nePr165fmtT8VRWHp0qUsXbo0k9IJIUT2olEURVE7hBBC5HSbN29mz549bNmyRe0oIgcIDAykUKFC\nPHr0iObNm/Py5UsZeZFDHDlyhC+//JKgoCA++ugjtePkajNnzsTT05NRo0Yxa9Ysli9fzqJFizh8\n+DAlSpTQn+fl5YWfnx/Tpk2jbdu2+uM3b97k4sWLuLi4MGvWLMaNG6fGbQDQt29fDAwMWLVqlWoZ\nROa7fPky3333HQcOHKB///5Ur16db775hnPnzpE/f/5U95OUlESrVq34/PPPGTp0aCYmFtmZTqej\nfPnyfPfdd3z++edpartt2za8vLy4evXqe22aJIQQOYWs+SmEEBlANjwSaVG/fn3s7e355JNPuHbt\n2jsLn+9aq0yo68GDB7i6urJhwwYpfGYDEydO5I8//qB169YAlChRggcPHhAbG6s/Z+/evRw5coQa\nNWroC59v1v20s7Pj1KlT2Nraqj5CzNvbmyNHjuhHrYoPh6IoHD16lE8//ZQ2bdpQtWpVwsLC+Pbb\nb+nWrRvNmzfniy++4PXr16nqLzk5mYEDB5InTx4GDhyYyelFdqbVatm4cSNubm6cOnUq1e2OHz/O\n119/zYYNG6TwKYTINaT4KYQQGUCKnyIt3hQ2tVotdnZ23Lx5k0OHDrFr1y62bNnC7du3ZffwbCY5\nORkXFxe++uormjZtqnYc8f/y5s2rX3fV3t4eGxsb/Pz8uHfvHgEBAQwdOhQrKytGjBgB/G8qPMDZ\ns2dZuXIlHh4eqk83z5cvH+vWrWPAgAE8efJE1SwiYyQnJ+Pr60vt2rUZMmQIXbt2JSwsDHd3d/0o\nT41Gw8KFCylRogSNGzcmJCTkX/sMDw+nU6dOhIWF4evrS548ebLiVkQ25uTkxMaNG+nQoQM//PAD\n8fHx/3huXFwcy5cvp0uXLmzdupUaNWpkYVIhhFCXTHsXQogM8Ouvv9KuXTtu3rypdhSRQ8TFxbFs\n2TKWLFnCvXv3SEhIAKB8+fJYWVnxxRdf6As2Qn1eXl4cO3aMI0eO6ItnIvvZvXs3AwYMwNTUlMTE\nRGrVqsXs2bPfWs8zPj6ejh07Eh0dzS+//KJS2reNHTuWW7dusXPnThmRlUPFxsbi4+PD3LlzKVas\nGGPHjuWzzz771w+0FEXB29ubuXPnYmNjw+DBg2nQoAH58+cnJiaGS5cusWzZMk6fPo2bmxteXl6p\n2h1d5B7BwcG4u7tz9epV+vXrR48ePShWrBiKovDgwQM2bNjAihUrqF27NvPmzaNKlSpqRxZCiCwl\nxU8hhMgAjx8/pmLFijJiR6Ta999/z5w5c2jbti3lypUjICCA2NhYhg8fzt27d9m4cSMuLi6qT8cV\nEBAQQI8ePbh48SLFixdXO45IhSNHjmBnZ0epUqX0RURFUfR/9vX1pXv37gQGBlKnTh01o6YQHx9P\nrVq1GDVqFL1791Y7jkiDp0+fsnTpUr7//nvq1q2Lu7s79evXT1MfiYmJ7Nmzh+XLl3Pjxg2ioqKw\nsLDAxsaGfv360b17d8zMzDLpDsSHIDQ0lOXLl7N3716ePXsGQKFChWjXrh0nT57E3d2drl27qpxS\nCCGynhQ/hRAiAyQmJmJmZkZCQoKM1hH/6fbt23Tv3p0OHTowZswYTExMiIuLw9vbG39/fw4fPszS\npUtZvHgxN27cUDturvb48WNq1KjBmjVraNmypdpxRBrpdDq0Wi3x8fHExcWRP39+nj59yieffELt\n2rXx8fFRO+JbQkJCaNasGefPn6dMmTJqxxH/ISIiggULFrBhwwY6derE6NGjcXBwUDuWEG/ZtWsX\n3333XZrWBxVCiA+FFD+FECKDWFhY8ODBA9XXjhPZX2RkJNWqVePu3btYWFjojx85coS+ffty584d\nfv31V2rVqkV0dLSKSXM3nU5H69atqVmzJjNmzFA7jngPx48fZ9KkSbRr147ExETmzp3L1atXKVmy\npNrR3um7775jz549HDt2TJZZEEIIIYR4T7KbghBCZBDZ9EiklrW1NYaGhgQGBqY47uvrS7169UhK\nSiIqKgpLS0uePn2qUkoxe/ZsYmNj8fT0VDuKeE+NGjXiyy+/ZPbs2UydOpU2bdpk28InwKhRowCY\nP3++ykmEEEIIIXI+GfkphBAZpEqVKqxfv55q1aqpHUXkADNnzmTlypXUqVMHW1tbgoODCQgIwM/P\nj1atWhEZGUlkZCROTk4YGxurHTfXOXnyJF26dOHChQvZukgm0s7LywsPDw9at26Nj48PhQsXVjvS\nO4WHh1O7dm38/f1lcxIhhBBCiPdg4OHh4aF2CCGEyMkSEhLYu3cv+/fv58mTJ9y/f5+EhARKliwp\n63+Kf1SvXj1MTEwIDw/nxo0bFCxYkKVLl9KkSRMALC0t9SNERdb6448/aNmyJT/88AOOjo5qxxEZ\nrFGjRvTu3Zv79+9ja2tLkSJFUjyvKArx8fG8fPkSU1NTlVL+OZugcOHCjB07lr59+8rPAiGEEEKI\ndJKRn0IIkU537tzh++9XsGLFahTFnlev7IB8GBu/RKs9RuHCJowdO5hevXqmWNdRiL+KiooiMTER\nKysrtaMI/lzns127dlSsWJE5c+aoHUeoQFEUli9fjoeHBx4eHri5ualWeFQUhY4dO1K+fHm+/fZb\nVTLkZIqipOtDyKdPn7JkyRKmTp2aCan+2bp16xg6dGiWrvV8/PhxmjZtypMnTyhYsGCWXVekTmRk\nJDY2Nly4cIEaNWqoHUcIIXIsWfNTCCHSYcuWrdjb12Dhwhiio4/x8mUAOt1KdLq5xMau4NWrUCIi\n5uPufghb20pcv35d7cgim8qfP78UPrORefPm8fz5c9ngKBfTaDQMGjSIn3/+me3bt1O9enX8/f1V\ny7Jy5UrWr1/PyZMnVcmQU7169SrNhc+IiAiGDx9OuXLluHPnzj+e16RJE4YNG/bW8XXr1r3Xpofd\nu3cnLCws3e3To379+jx48EAKnyro06cP7du3f+v4xYsX0Wq13Llzh9KlS/Pw4UNZUkkIId6TFD+F\nECKNVq9eS//+Y4mNPUpCwkLA4R1naYHmvHq1iz/+mEadOk24du1aFicVQqTF6dOnmTt3Llu3biVP\nnjxqxxEqq1q1KkePHsXT0xM3Nzc6duzI7du3szxHkSJFWLlyJa6urlk6IjCnun37Nl26dKFs2bIE\nBwenqs2lS5dwcXHB0dERU1NTrl69yg8//JCu6/9TwTUxMfE/2xobG2f5h2GGhoZvLf0g1Pfm+0ij\n0VCkSBG02n9+256UlJRVsYQQIseS4qcQQqRBYGAgQ4eO5/Xrw0DqNqBQlF7ExMynSZO2REVFZW5A\nIUS6PHv2jB49erBq1SpKly6tdhyRTWg0Gjp16sT169epXbs2Tk5OjB8/npcvX2Zpjnbt2tG8eXNG\njhyZpdfNSa5evUqzZs1wcHAgPj6eQ4cOUb169X9to9PpaNWqFW3btqVatWqEhYUxe/Zsihcv/t55\n+vTpQ7t27ZgzZw6lSpWiVKlSrFu3Dq1Wi4GBAVqtVv/o27cvAD4+Pm+NHN2/fz916tTBzMwMKysr\nOnToQEJCAvBnQXXcuHGUKlUKc3NznJyc+Pnnn/Vtjx8/jlar5ejRo9SpUwdzc3Nq1aqVoij85pxn\nz5699z2LjBcZGYlWqyUoKAj437/XgQMHcHJywsTEhJ9//pl79+7RoUMHChUqhLm5ORUqVGD79u36\nfq5evUqLFi0wMzOjUKFC9OnTR/9hyuHDhzE2Nub58+cprj1x4kT9iNNnz57h7OxMqVKlMDMzo1Kl\nSvj4+GTNF0EIITKAFD+FECINJk2aRWzsTKB8mtopiguvXjmxbt36zAkmhEg3RVHo06cPnTp1eucU\nRCFMTEyYMGECISEhPHz4kPLly7N27Vp0Ol2WZZg/fz4BAQHs3r07y66ZU9y5cwdXV1euXr3KnTt3\n+Omnn6hatep/ttNoNMyYMYOwsDDc3d3Jnz9/huY6fvw4V65c4dChQ/j7+9O9e3cePnzIgwcPePjw\nIYcOHcLY2JjGjRvr8/x15OjBgwfp0KEDrVq1IigoiBMnTtCkSRP9913v3r05efIkW7du5dq1a3z5\n5Ze0b9+eK1eupMgxceJE5syZQ3BwMIUKFaJnz55vfR1E9vH3LTne9e8zfvx4ZsyYQWhoKLVr12bw\n4MHExcVx/Phxrl+/jre3N5aWlgC8fv2aVq1akS9fPi5cuICfnx+nTp2iX79+ADRr1ozChQvj6+ub\n4hpbtmyhV69eAMTFxeHo6Mj+/fu5fv06I0aMYODAgRw7diwzvgRCCJHxFCGEEKkSFhammJgUUuCV\nAko6HseVkiXtFZ1Op/atiGwkLi5OiYmJUTtGrrZgwQKlVq1aSnx8vNpRRA5x9uxZpW7duoqjo6Py\nyy+/ZNl1f/nlF6Vo0aLKw4cPs+ya2dXfvwaTJk1SmjVrply/fl0JDAxU3NzcFA8PD+XHH3/M8Gs3\nbtxYGTp06FvHfXx8lLx58yqKoii9e/dWihQpoiQmJr6zj0ePHillypRRRo0a9c72iqIo9evXV5yd\nnd/Z/vbt24pWq1Xu3r2b4vjnn3+uDBkyRFEURQkICFA0Go1y+PBh/fOBgYGKVqtVfv/9d/05Wq1W\nefr0aWpuXWSg3r17K4aGhoqFhUWKh5mZmaLVapXIyEglIiJC0Wg0ysWLFxVF+d+/6a5du1L0VaVK\nFcXLy+ud11m5cqViaWmpvHr1Sn/sTT+3b99WFEVRRo0apTRs2FD//MmTJxVDQ0P998m7dO/eXXFz\nc0v3/QshRFaSkZ9CCJFKS5asRKdzBczS2cMnvHhhIJ+SixTGjh3LihUr1I6Ra50/f56ZM2eybds2\njIyM1I4jcojatWsTGBjIqFGj6N69Oz169PjXDXIySv369enduzdubm5vjQ7LLWbOnEnFihXp0qUL\nY8eO1Y9y/PTTT3n58iX16tWjZ8+eKIrCzz//TJcuXZg2bRovXrzI8qyVKlXC0NDwreOJiYl06tSJ\nihUrMnfu3H9sHxwcTNOmTd/5XFBQEIqiUKFCBfLmzat/7N+/P8XatBqNhsqVK+v/Xrx4cRRF4fHj\nx+9xZyKjNGrUiJCQEC5fvqx/bN68+V/baDQaHB0dUxwbPnw406ZNo169ekyZMkU/TR4gNDSUKlWq\nYGb2v99f69Wrh1ar1W/I2bNnTwIDA7l79y4AmzdvplGjRvolIHTMEf1IAAAgAElEQVQ6HTNmzKBq\n1apYWVmRN29edu3alSU/94QQIiNI8VMIIVLpl1+CSEho/h49aEhIaJHqDRhE7lCuXDlu3bqldoxc\n6cWLF3Tr1o3ly5djY2OjdhyRw2g0GpydnQkNDcXOzo7q1avj4eHB69evM/W6np6e3LlzhzVr1mTq\ndbKbO3fu0KJFC3bs2MH48eNp06YNBw8eZPHixQA0aNCAFi1a8NVXX+Hv78/KlSsJDAzE29ubtWvX\ncuLEiQzLki9fvneu4f3ixYsUU+fNzc3f2f6rr74iKiqKrVu3pnvKuU6nQ6vVcuHChRSFsxs3brz1\nvfHXDdzeXC8rl2wQ/8zMzAwbGxtsbW31j5IlS/5nu79/b/Xt25eIiAj69u3LrVu3qFevHl5eXv/Z\nz5vvh+rVq1O+fHk2b95MUlISvr6++invAN999x0LFixg3LhxHD16lMuXL6dYf1YIIbI7KX4KIUQq\n/flGx/K9+khIyM+LF7LpkfgfKX6qQ1EU+vXrR9u2benUqZPacUQOZm5ujqenJ0FBQYSGhmJvb8+W\nLVsybWSmkZERGzduZPz48YSFhWXKNbKjU6dOcevWLfbs2UOvXr0YP3485cuXJzExkdjYWAD69+/P\n8OHDsbGx0Rd1hg0bRkJCgn6EW0YoX758ipF1b1y8eJHy5f99TfC5c+eyf/9+9u3bh4WFxb+eW716\ndfz9/f/xOUVRePDgQYrCma2tLcWKFUv9zYgPRvHixenfvz9bt27Fy8uLlStXAuDg4MCVK1d49eqV\n/tzAwEAURcHBwUF/rGfPnmzatImDBw/y+vVrvvjiixTnt2vXDmdnZ6pUqYKtrS03b97MupsTQoj3\nJMVPIYRIJRMTUyD2vfowMIjFzMw0YwKJD4KdnZ28gVDBkiVLiIiI+Ncpp0KkhbW1NVu3bmXz5s3M\nnTuXBg0acOHChUy5VqVKlRg/fjyurq4kJydnyjWym4iICEqVKqUvdMKf08fbtGmDqemfr6tlypTR\nT9NVFAWdTkdiYiIAT58+zbAsgwYNIiwsjGHDhhESEsLNmzdZsGAB27ZtY+zYsf/Y7siRI0yaNIml\nS5dibGzMo0ePePTokX7X7b+bNGkSvr6+TJkyhRs3bnDt2jW8vb2Ji4ujXLlyODs707t3b3bs2EF4\neDgXL15k3rx5+Pn56ftITRE+ty6hkJ3927/Ju54bMWIEhw4dIjw8nEuXLnHw4EEqVqwIgIuLC2Zm\nZvpNwU6cOMHAgQP54osvsLW11ffh4uLCtWvXmDJlCu3atUtRnLezs8Pf35/AwEBCQ0P5+uuvCQ8P\nz8A7FkKIzCXFTyGESCUbm5JA6Hv1YWoamqrpTCL3KF26NE+ePEnxhl5krqCgILy8vNi2bRvGxsZq\nxxEfmAYNGnD+/Hn69etH+/bt6dOnDw8ePMjw64wcOZI8efLkmgJ+586diYmJoX///gwYMIB8+fJx\n6tQpxo8fz8CBA/n1119TnK/RaNBqtaxfv55ChQrRv3//DMtiY2PDiRMnuHXrFq1atcLJyYnt27fz\n448/0rJly39sFxgYSFJSEl27dqV48eL6x4gRI955fuvWrdm1axcHDx6kRo0aNGnShICAALTaP9/C\n+fj40KdPH8aNG4eDgwPt2rXj5MmTWFtbp/g6/N3fj8lu79nPX/9NUvPvpdPpGDZsGBUrVqRVq1YU\nLVoUHx8fAExNTTl06BDR0dE4OTnRsWNH6tevz+rVq1P0Ubp0aRo0aEBISEiKKe8AkydPpnbt2rRp\n04bGjRtjYWFBz549M+huhRAi82kU+ahPCCFS5ciRI3TsOJqYmEtAet4o3MPUtAqPHkWSN2/ejI4n\ncjAHBwd8fX2pVKmS2lE+eNHR0dSoUYOZM2fStWtXteOID1x0dDQzZsxg9erVjB49mpEjR2JiYpJh\n/UdGRlKzZk0OHz5MtWrVMqzf7CoiIoKffvqJ77//Hg8PD1q3bs2BAwdYvXo1pqam7N27l9jYWDZv\n3oyhoSHr16/n2rVrjBs3jmHDhqHVaqXQJ4QQQuRCMvJTCCFSqWnTpuTLFwecSld7Q8NVODs7S+FT\nvEWmvmcNRVFwc3OjefPmUvgUWSJfvnx8++23nDlzhrNnz1KhQgV27dqVYdOMra2tmTdvHr169SIu\nLi5D+szOypQpw/Xr16lTpw7Ozs4UKFAAZ2dn2rZty507d3j8+DGmpqaEh4cza9YsKleuzPXr1xk5\nciQGBgZS+BRCCCFyKSl+CiFEKmm1WsaO/RozswlAWne3DCNPnuWMGjU4M6KJHE42PcoaK1euJDQ0\nlAULFqgdReQyH3/8MX5+fqxatYqpU6fSrFkzQkJCMqTvXr16YWdnx+TJkzOkv+xMURSCgoKoW7du\niuPnzp2jRIkS+jUKx40bx40bN/D29qZgwYJqRBVCCCFENiLFTyGESIOvvx5MgwaFMDHpReoLoPcw\nM2vN7NlTqVChQmbGEzmUFD8z3+XLl5k8eTLbt2/Xb44iRFZr1qwZwcHBdO7cmRYtWjBo0CCePHny\nXn1qNBpWrFjB5s2bCQgIyJig2cTfR8hqNBr69OnDypUrWbhwIWFhYXzzzTdcunSJnj17YmZmBkDe\nvHlllKcQQggh9KT4KYQQaWBgYICf32Y++SQeM7NWwPl/OTsJ2IGZWT2mTHFj2LAhWZRS5DQy7T1z\nvXz5kq5du+Lt7U358uXVjiNyOUNDQwYPHkxoaCjGxsZUqFABb29v/a7k6WFlZcWqVavo3bs3UVFR\nGZg26ymKgr+/Py1btuTGjRtvFUD79+9PuXLlWLZsGc2bN2ffvn0sWLAAFxcXlRILIYQQIruTDY+E\nECIdkpOTmT9/IXPnfk9sbCFevhwAVATMgSgMDI5hbLyScuVsmDlzAm3atFE5scjO7t27R61atTJl\nR+jcTlEUvv76a+Lj4/nhhx/UjiPEW27cuMHIkSOJiIhg/vz57/V6MWDAAOLj4/W7POckSUlJ7Nix\ngzlz5hAXF4e7uzvOzs4YGRm98/xff/0VrVZLuXLlsjipEEIIIXIaKX4KIcR7SE5O5tChQyxevJYT\nJwIxNzenSJGPqF27CiNGDKRKlSpqRxQ5gE6nI2/evDx8+FA2xMpgiqKg0+lITEzM0F22hchIiqKw\nf/9+Ro0aRdmyZZk/fz729vZp7icmJoZq1aoxZ84cOnXqlAlJM97r169Zu3Yt8+bNo2TJkowdO5Y2\nbdqg1coENSGEEEJkDCl+CiGEENlA1apVWbt2LTVq1FA7ygdHURRZ/0/kCAkJCSxZsoSZM2fi4uLC\nN998Q4ECBdLUx+nTp+nYsSOXLl2iaNGimZT0/T19+pQlS5awZMkS6tWrx9ixY9/ayEgIkfX8/f0Z\nPnw4V65ckddOIcQHQz5SFUIIIbIB2fQo88ibN5FTGBkZMXLkSK5fv05cXBz29vYsW7aMpKSkVPdR\nt25d+vfvT//+/d9aLzM7iIiIYNiwYZQrV467d+9y/Phxdu3aJYVPIbKJpk2botFo8Pf3VzuKEEJk\nGCl+CiGEENmAnZ2dFD+FEAAULlyY5cuX8/PPP7N9+3Zq1KjB0aNHU91+6tSp3L9/n1WrVmViyrQJ\nDg7G2dmZmjVrYm5uzrVr11i1alW6pvcLITKPRqNhxIgReHt7qx1FCCEyjEx7F0IIIbKBtWvXcuzY\nMdavX692lBzlt99+4/r16xQoUABbW1tKlCihdiQhMpSiKOzcuRN3d3eqVq3K3LlzKVu27H+2u379\nOg0bNuTMmTN8/PHHWZD0bW92bp8zZw7Xr19n5MiRuLm5kS9fPlXyCCFSJzY2ljJlynDy5Ens7OzU\njiOEEO9NRn4KIYQQ2YBMe0+7gIAAOnXqxMCBA/n8889ZuXJliufl813xIdBoNHzxxRdcv36d2rVr\n4+TkxPjx43n58uW/tqtQoQKTJ0/G1dU1TdPmM0JSUhJbt27F0dGR4cOH4+LiQlhYGKNHj5bCpxA5\ngKmpKV999RWLFi1SO4oQQmQIKX4KIUQaaLVadu7cmeH9zps3DxsbG/3fPT09Zaf4XMbOzo6bN2+q\nHSPHeP36Nd26daNz585cuXKFadOmsWzZMp49ewZAfHy8rPUpPigmJiZMmDCBkJAQHj58SPny5Vm7\ndi06ne4f2wwbNgxTU1PmzJmTJRlfv37NkiVLsLOzY+nSpXh5eXHlyhW+/PJLjIyMsiSDECJjDBo0\niM2bN/P8+XO1owghxHuT4qcQ4oPWu3dvtFotbm5ubz03btw4tFot7du3VyHZ2/5aqHF3d+f48eMq\nphFZrXDhwiQlJemLd+Lffffdd1SpUoWpU6dSqFAh3NzcKFeuHMOHD8fJyYnBgwdz9uxZtWMKkeGK\nFy+Oj48Pfn5+rFq1itq1axMYGPjOc7VaLWvXrsXb25vg4GD98WvXrrFo0SI8PT2ZPn06K1as4MGD\nB+nO9Mcff+Dp6YmNjQ3+/v5s2rSJEydO8Nlnn6HVytsNIXKi4sWL07ZtW1avXq12FCGEeG/y24gQ\n4oOm0WgoXbo027dvJzY2Vn88OTmZDRs2YG1trWK6f2ZmZkaBAgXUjiGykEajkanvaWBqakp8fDxP\nnjwBYPr06Vy9epXKlSvTvHlzfvvtN1auXJni/70QH5I3Rc9Ro0bRvXt3evTowZ07d946r3Tp0syf\nPx8XFxc2btxI48aNadGiBTdu3CA5OZnY2FgCAwOpUKECXbt2JSAgINVLRoSHhzN06FDs7Oy4d+8e\nJ06cYOfOnbJzuxAfiBEjRrB48eIsXzpDCCEymhQ/hRAfvMqVK1OuXDm2b9+uP7Zv3z5MTU1p3Lhx\ninPXrl1LxYoVMTU1xd7eHm9v77feBD59+pSuXbtiYWFB2bJl2bRpU4rnJ0yYgL29PWZmZtjY2DBu\n3DgSEhJSnDNnzhyKFStGvnz56N27NzExMSme9/T0pHLlyvq/X7hwgVatWlG4cGHy58/PJ598wpkz\nZ97nyyKyIZn6nnpWVlYEBwczbtw4Bg0axLRp09ixYwdjx45lxowZuLi4sGnTpncWg4T4UGg0Gpyd\nnQkNDcXOzo4aNWrg4eHB69evU5zXunVroqOjWbhwIUOGDCEyMpJly5bh5eXFjBkzWL9+PZGRkTRq\n1Ag3NzcGDBjwr8WO4OBgevToQa1atbCwsNDv3F6+fPnMvmUhRBZydHSkdOnS+Pn5qR1FCCHeixQ/\nhRAfPI1GQ79+/VJM21mzZg19+vRJcd6qVauYPHky06dPJzQ0lHnz5jFnzhyWLVuW4rxp06bRsWNH\nQkJC6NatG3379uXevXv65y0sLPDx8SE0NJRly5axbds2ZsyYoX9++/btTJkyhWnTphEUFISdnR3z\n589/Z+43Xr58iaurK4GBgZw/f57q1avTtm1bWYfpAyMjP1Ovb9++TJs2jWfPnmFtbU3lypWxt7cn\nOTkZgHr16lGhQgUZ+SlyBXNzczw9Pbl48SKhoaHY29uzZcsWFEXhxYsXNGnShK5du3L27Fm6dOlC\nnjx53uojX758DBkyhKCgIO7evYuLi0uK9UQVReHIkSO0bNmSdu3aUbNmTcLCwpg1axbFihXLytsV\nQmShESNGsHDhQrVjCCHEe9EoshWqEOID1qdPH54+fcr69espXrw4V65cwdzcHBsbG27dusWUKVN4\n+vQpP/30E9bW1sycORMXFxd9+4ULF7Jy5UquXbsG/Ll+2sSJE5k+fTrw5/T5fPnysWrVKpydnd+Z\nYcWKFcybN08/oq9+/fpUrlyZ5cuX689p0aIFt2/fJiwsDPhz5OeOHTsICQl5Z5+KolCiRAnmzp37\nj9cVOc/GjRvZt28fW7ZsUTtKtpSYmEhUVBRWVlb6Y8nJyTx+/JhPP/2UHTt28PHHHwN/btQQHBws\nI6RFrnTy5ElGjBiBiYkJBgYGVKlShcWLF6d6E7C4uDhatmxJs2bNmDRpEj/++CNz5swhPj6esWPH\n0qNHD9nASIhcIikpiY8//pgff/yRmjVrqh1HCCHSxVDtAEIIkRUsLS3p2LEjq1evxtLSksaNG1Oy\nZEn983/88Qd3795lwIABDBw4UH88KSnprTeLf52ObmBgQOHChXn8+LH+2I8//sjChQv57bffiImJ\nITk5OcXomRs3bry1AVPdunW5ffv2P+Z/8uQJkydPJiAggEePHpGcnExcXJxM6f3A2NnZsWDBArVj\nZEubN29m9+7dHDhwgM6dO7Nw4ULy5s2LgYEBRYsWxcrKirp169KlSxcePnzIuXPnOHXqlNqxhVDF\nJ598wrlz55g2bRpLlizh6NGjqS58wp87y2/YsIEqVaqwZs0arK2t8fLyok2bNrKBkRC5jKGhIUOH\nDmXhwoVs2LBB7ThCCJEuUvwUQuQaffv25csvv8TCwkI/cvONN8XJFStW/OdGDX+fLqjRaPTtz5w5\nQ48ePfD09KRVq1ZYWlqye/du3N3d3yu7q6srT548YeHChVhbW2NsbEzTpk3fWktU5Gxvpr0ripKm\nQsWH7tSpUwwdOhQ3Nzfmzp3L119/jZ2dHePHjwf+/D+4e/dupk6dyuHDh2nRogWjRo2idOnSKicX\nQj0GBgbcv3+f4cOHY2iY9l/5ra2tcXJywtHRkVmzZmVCQiFETtGvXz9sbW25f/8+xYsXVzuOEEKk\nmRQ/hRC5RrNmzTAyMuLZs2d06NAhxXNFihShePHi/PbbbymmvafVqVOnKFmyJBMnTtQfi4iISHGO\ng4MDZ86coXfv3vpjp0+f/td+AwMDWbx4MZ9++ikAjx494sGDB+nOKbKnAgUKYGRkxOPHj/noo4/U\njpMtJCUl4erqysiRI5k8eTIADx8+JCkpidmzZ2NpaUnZsmVp0aIF8+fPJzY2FlNTU5VTC6G+6Oho\nfH19uXHjRrr7GD16NBMnTpTipxC5nKWlJS4uLixbtoxp06apHUcIIdJMip9CiFzlypUrKIryzs0e\nPD09GTZsGPnz56dNmzYkJiYSFBTE77//rh9h9l/s7Oz4/fff2bx5M3Xr1uXgwYNs3bo1xTnDhw/n\nyy+/pGbNmjRu3BhfX1/OnTtHoUKF/rXfjRs3Urt2bWJiYhg3bhzGxsZpu3mRI7zZ8V2Kn39auXIl\nDg4ODBo0SH/syJEjREZGYmNjw/379ylQoAAfffQRVapUkcKnEP/v9u3bWFtbU7Ro0XT30aRJE/3r\npoxGFyJ3GzFiBKdPn5afB0KIHEkW7RFC5Crm5uZYWFi887l+/fqxZs0aNm7cSLVq1WjYsCGrVq3C\n1tZWf867ftn767HPPvsMd3d3Ro4cSdWqVfH393/rE/KuXbvi4eHB5MmTqVGjBteuXWP06NH/mnvt\n2rXExMRQs2ZNnJ2d6devH2XKlEnDnYucQnZ8T8nJyQlnZ2fy5s0LwKJFiwgKCsLPz4+AgAAuXLhA\neHg4a9euVTmpENlLVFQU+fLle68+jIyMMDAwIDY2NoNSCSFyqrJly+Li4iKFTyFEjiS7vQshhBDZ\nyPTp03n16pVMM/2LxMRE8uTJQ1JSEvv376dIkSLUqVMHnU6HVqulZ8+elC1bFk9PT7WjCpFtnDt3\njsGDB3PhwoV095GcnIyRkRGJiYmy0ZEQQgghciz5LUYIIYTIRt5Me8/tXrx4of/zm81aDA0N+eyz\nz6hTpw4AWq2W2NhY/o+9O4+qOX/8B/6890Z7KRWF0oqhLMk6GHvWiWZCDJV9HcYyfAwjS2bGFhFG\nCsPYM8puhslYk5KloiJLKkuhReu9vz/83O80RPu77n0+zukc99738uzOjLk9ey337t1DrVq1BMlJ\nVFXVr18f9+/fL9OozaioKJiYmLD4JCIiomqNn2SIiIiqEE57B2bMmAEvLy/cu3cPwNulJd5NVPl3\nCSOTyfD999/j5cuXmDFjhiBZiaoqExMTODg4YP/+/aW+xubNm+Hu7l6OqYhIUaWnp+PEiRMIDQ1F\nRkaG0HGIiArhtHciIqIqJCMjA0ZGRsjIyFDK0Vbbtm2Dh4cH1NXV0bt3b8yaNQsODg7vbVJ2+/Zt\neHt748SJE/jrr79gY2MjUGKiqisoKAheXl64fPlyic9NT0+HmZkZbty4gfr161dAOiJSFM+fP8eQ\nIUOQmpqKpKQk9OnTh2txE1GVonw/VREREVVhWlpaqFWrFhITE4WOUunS0tJw4MABLFu2DCdOnMCt\nW7cwevRo7N+/H2lpaYWObdCgAVq0aIFff/2VxSdREfr164fnz59j7969JT530aJF6NGjB4tPInqP\nVCpFUFAQ+vbti8WLF+PUqVNISUnBqlWrEBgYiMuXL8Pf31/omEREcipCByAiIqLC3k19b9CggdBR\nKpVYLEavXr1gYWGBTp06ISoqCq6urpg4cSKmTJkCDw8PWFpaIjMzE4GBgXB3d4eGhobQsYmqLIlE\ngoMHD6Jnz57Q0dFBnz59PnmOTCbDL7/8gqNHj+LixYuVkJKIqptRo0bh6tWrGDFiBC5cuICdO3ei\nT58+6NatGwBg/PjxWL9+PTw8PAROSkT0Fkd+EhERVTHKuumRrq4uxo0bh/79+wN4u8HRvn37sGzZ\nMqxduxbTp0/HuXPnMH78eKxbt47FJ1ExNG/eHIcPH4a7uzs8PT3x9OnTIo+9e/cu3N3dsXPnTpw+\nfRr6+vqVmJSIqoM7d+4gNDQUY8eOxQ8//IDjx49jypQp2Ldvn/yY2rVrQ11d/aN/3xARVSaO/CQi\nIqpilHnTIzU1NfmfCwoKIJFIMGXKFHz++ecYMWIEBgwYgMzMTERGRgqYkqh6ad++PS5cuAAvLy+Y\nm5tjwIABGDp0KAwNDVFQUIBHjx5h27ZtiIyMhIeHB86fPw9dXV2hYxNRFZSXl4eCggK4uLjInxsy\nZAjmzJmDyZMnw9DQEH/88Qfatm0LIyMjyGQyiEQiARMTEbH8JCIiqnKsra1x/vx5oWMITiKRQCaT\nQSaToUWLFti+fTscHBywY8cONG3aVOh4RNWKpaUlFi1ahMDAQLRo0QJbtmxBamoqVFRUYGhoCDc3\nN3z11VdQVVUVOioRVWHNmjWDSCRCcHAwJk2aBAAICQmBpaUlTE1NcfToUTRo0ACjRo0CABafRFQl\ncLd3IiKiKub27dtwdnZGTEyM0FGqjLS0NLRr1w7W1tY4cuSI0HGIiIiUlr+/P7y9vdG1a1e0bt0a\ne/fuRd26deHn54ekpCTo6upyaRoiqlJYfhIRlcC7abjvcCoPVYTs7GzUqlULGRkZUFHhJA0AePHi\nBXx8fLBo0SKhoxARESk9b29v/Pbbb3j16hVq164NX19f2Nvby19PTk5G3bp1BUxIRPR/WH4SEZVR\ndnY2srKyoKWlhZo1awodhxSEmZkZzp49CwsLC6GjVJrs7GyoqqoW+QsF/rKBiIio6nj27BlevXoF\nKysrAG9naQQGBmLDhg1QV1eHnp4enJyc8NVXX6FWrVoCpyUiZcbd3omIiik3NxcLFy5Efn6+/Lm9\ne/di0qRJmDp1KhYvXowHDx4ImJAUibLt+J6UlAQLCwskJSUVeQyLTyIioqrDwMAAVlZWyMnJgaen\nJ6ytrTF27FikpaVh2LBhaNmyJfbv3w83NzehoxKRkuPITyKiYnr06BEaNWqEzMxMFBQUYPv27Zgy\nZQratWsHbW1thIaGQlVVFdeuXYOBgYHQcamamzRpEpo0aYKpU6cKHaXCFRQUoGfPnujcuTOntRMR\nEVUjMpkMP/74I/z9/dG+fXvo6+vj6dOnkEqlOHz4MB48eID27dvD19cXTk5OQsclIiXFkZ9ERMX0\n/PlzSCQSiEQiPHjwAOvWrcPcuXNx9uxZBAUF4ebNmzA2NsaKFSuEjkoKwNraGrGxsULHqBRLly4F\nACxYsEDgJESKxdPTE7a2tkLHICIFFh4ejpUrV2LGjBnw9fXF5s2bsWnTJjx//hxLly6FmZkZvvnm\nG6xevVroqESkxFh+EhEV0/Pnz1G7dm0AkI/+nD59OoC3I9cMDQ0xatQoXLp0SciYpCCUZdr72bNn\nsXnzZuzatavQZmJEis7d3R1isVj+ZWhoiAEDBuDOnTvlep+qulxESEgIxGIxUlNThY5CRGUQGhqK\nLl26YPr06TA0NAQA1KlTB127dkVcXBwAoEePHmjTpg2ysrKEjEpESozlJxFRMb18+RKPHz/GgQMH\n8Ouvv6JGjRryHyrflTZ5eXnIyckRMiYpCGUY+fn06VOMGDEC27dvh7GxsdBxiCpdz549kZKSguTk\nZJw+fRpv3rzB4MGDhY71SXl5eWW+xrsNzLgCF1H1VrduXdy6davQ59+7d+/Cz88PTZo0AQA4ODhg\n4cKF0NDQEComESk5lp9ERMWkrq6OOnXqYP369Thz5gyMjY3x6NEj+etZWVmIjo5Wqt25qeKYm5sj\nMTERubm5QkepEFKpFN988w3c3NzQs2dPoeMQCUJVVRWGhoYwMjJCixYtMGPGDMTExCAnJwcPHjyA\nWCxGeHh4oXPEYjECAwPlj5OSkjB8+HAYGBhAU1MTrVq1QkhISKFz9u7dCysrK+jo6GDQoEGFRluG\nhYWhd+/eMDQ0hK6uLjp16oTLly+/d09fX184OztDS0sL8+fPBwBERUWhf//+0NHRQZ06deDq6oqU\nlBT5ebdu3UKPHj2gq6sLbW1ttGzZEiEhIXjw4AG6desGADA0NIREIoGHh0f5vKlEVKkGDRoELS0t\nfP/999i0aRO2bNmC+fPno1GjRnBxcQEA1KpVCzo6OgInJSJlpiJ0ACKi6qJXr174559/kJKSgtTU\nVEgkEtSqVUv++p07d5CcnIw+ffoImJIURY0aNdCgQQPcu3cPjRs3FjpOufvpp5/w5s0beHp6Ch2F\nqEpIT0/Hnj17YGdnB1VVVQCfnrKelZWFzp07o27duggKCoKJiQlu3rxZ6Jj79+9j3759OHz4MDIy\nMjBkyBDMnz8fGzdulN935MiR8PHxAQCsX78e/fr1Q1xcHPIUEPAAACAASURBVPT09OTXWbx4Mby8\nvLBq1SqIRCIkJyejS5cuGDt2LFavXo3c3FzMnz8fX375pbw8dXV1RYsWLRAWFgaJRIKbN29CTU0N\npqamOHjwIL766itER0dDT08P6urq5fZeElHl2r59O3x8fPDTTz9BV1cXBgYG+P7772Fubi50NCIi\nACw/iYiK7dy5c8jIyHhvp8p3U/datmyJQ4cOCZSOFNG7qe+KVn7+888/WLduHcLCwqCiwo8ipLyO\nHz8ObW1tAG/XkjY1NcWxY8fkr39qSviuXbvw9OlThIaGyovKhg0bFjqmoKAA27dvh5aWFgBg3Lhx\n2LZtm/z1rl27Fjp+7dq1OHDgAI4fPw5XV1f580OHDi00OvPHH39EixYt4OXlJX9u27ZtqF27NsLC\nwtC6dWs8ePAAs2fPhrW1NQAUmhmhr68P4O3Iz3d/JqLqqU2bNti+fbt8gEDTpk2FjkREVAinvRMR\nFVNgYCAGDx6MPn36YNu2bXjx4gWAqruZBFV/irjp0fPnz+Hq6oqAgADUr19f6DhEgurSpQtu3LiB\nyMhIXL16Fd27d0fPnj2RmJhYrPOvX78OOzu7QiM0/8vMzExefAKAiYkJnj59Kn/87NkzjB8/Ho0a\nNZJPTX327BkePnxY6Dr29vaFHl+7dg0hISHQ1taWf5mamkIkEiE+Ph4A8N1332H06NHo3r07vLy8\nyn0zJyKqOsRiMYyNjVl8ElGVxPKTiKiYoqKi0Lt3b2hra2PBggVwc3PDzp07i/1DKlFJKdqmR1Kp\nFCNHjoSrqyuXhyACoKGhAXNzc1hYWMDe3h5btmzB69ev8euvv0Isfvsx/d+jP/Pz80t8jxo1ahR6\nLBKJIJVK5Y9HjhyJa9euYe3atbh06RIiIyNRr16999Yb1tTULPRYKpWif//+8vL23VdsbCz69+8P\n4O3o0OjoaAwaNAgXL16EnZ1doVGnRERERJWB5ScRUTGlpKTA3d0dO3bsgJeXF/Ly8jB37ly4ublh\n3759hUbSEJUHRSs/V61ahZcvX2Lp0qVCRyGqskQiEd68eQNDQ0MAbzc0eiciIqLQsS1btsSNGzcK\nbWBUUhcuXMDUqVPh6OiIJk2aQFNTs9A9i9KqVSvcvn0bpqamsLCwKPT176LU0tISU6ZMwZEjRzB6\n9Gj4+fkBAGrWrAng7bR8IlI8n1q2g4ioMrH8JCIqpvT0dKipqUFNTQ3ffPMNjh07hrVr18p3qR04\ncCACAgKQk5MjdFRSEIo07f3SpUtYuXIl9uzZ895INCJllZOTg5SUFKSkpCAmJgZTp05FVlYWBgwY\nADU1NbRr1w4///wzoqKicPHiRcyePbvQUiuurq4wMjLCl19+ifPnz+P+/fsIDg5+b7f3j7GxscHO\nnTsRHR2Nq1evYtiwYfINlz5m8uTJePXqFVxcXBAaGor79+/jzz//xPjx45GZmYns7GxMmTJFvrv7\nlStXcP78efmUWDMzM4hEIhw9ehTPnz9HZmZmyd9AIqqSZDIZzpw5U6rR6kREFYHlJxFRMWVkZMhH\n4uTn50MsFsPZ2RknTpzA8ePHUb9+fYwePbpYI2aIiqNBgwZ4/vw5srKyhI5SJqmpqRg2bBi2bNkC\nU1NToeMQVRl//vknTExMYGJignbt2uHatWs4cOAAOnXqBAAICAgA8HYzkYkTJ2LZsmWFztfQ0EBI\nSAjq16+PgQMHwtbWFosWLSrRWtQBAQHIyMhA69at4erqitGjR7+3adKHrmdsbIwLFy5AIpGgT58+\naNasGaZOnQo1NTWoqqpCIpEgLS0N7u7uaNy4MZydndGxY0esWrUKwNu1Rz09PTF//nzUrVsXU6dO\nLclbR0RVmEgkwsKFCxEUFCR0FCIiAIBIxvHoRETFoqqqiuvXr6NJkyby56RSKUQikfwHw5s3b6JJ\nkybcwZrKzWeffYa9e/fC1tZW6CilIpPJ4OTkBEtLS6xevVroOERERFQJ9u/fj/Xr15doJDoRUUXh\nyE8iomJKTk5Go0aNCj0nFoshEokgk8kglUpha2vL4pPKVXWf+u7t7Y3k5GT89NNPQkchIiKiSjJo\n0CAkJCQgPDxc6ChERCw/iYiKS09PT7777n+JRKIiXyMqi+q86VFoaCiWL1+OPXv2yDc3ISIiIsWn\noqKCKVOmYO3atUJHISJi+UlERFSVVdfy8+XLlxgyZAg2bdoEc3NzoeMQERFRJRszZgyCg4ORnJws\ndBQiUnIsP4mIyiA/Px9cOpkqUnWc9i6TyTB69Gj0798fgwcPFjoOERERCUBPTw/Dhg3Dxo0bhY5C\nREqO5ScRURnY2NggPj5e6BikwKrjyM8NGzYgISEBK1euFDoKERERCWjatGnYtGkTsrOzhY5CREqM\n5ScRURmkpaVBX19f6BikwExMTJCeno7Xr18LHaVYwsPDsXjxYuzduxeqqqpCxyEiIiIBNWrUCPb2\n9ti9e7fQUYhIibH8JCIqJalUivT0dOjq6godhRSYSCSqNqM/X79+DRcXF6xfvx5WVlZCxyFSKsuX\nL8fYsWOFjkFE9J7p06fD29ubS0URkWBYfhIRldKrV6+gpaUFiUQidBRScNWh/JTJZBg7dix69uwJ\nFxcXoeMQKRWpVIqtW7dizJgxQkchInpPz549kZeXh7///lvoKESkpFh+EhGVUlpaGvT09ISOQUrA\n2tq6ym96tHnzZty5cwdr1qwROgqR0gkJCYG6ujratGkjdBQioveIRCL56E8iIiGw/CQiKiWWn1RZ\nbGxsqvTIz8jISCxYsAD79u2Dmpqa0HGIlI6fnx/GjBkDkUgkdBQiog8aMWIELl68iLi4OKGjEJES\nYvlJRFRKLD+pslTlae/p6elwcXGBt7c3bGxshI5DpHRSU1Nx5MgRjBgxQugoRERF0tDQwNixY+Hj\n4yN0FCJSQiw/iYhKieUnVRYbG5sqOe1dJpNh4sSJ6NSpE4YPHy50HCKltGvXLvTt2xe1a9cWOgoR\n0UdNmjQJv/32G169eiV0FCJSMiw/iYhKieUnVRYDAwNIpVK8ePFC6CiF+Pv7IzIyEuvWrRM6CpFS\nkslk8invRERVXf369eHo6Ah/f3+hoxCRkmH5SURUSiw/qbKIRKIqN/X91q1bmDt3Lvbt2wcNDQ2h\n4xAppWvXriE9PR1du3YVOgoRUbFMnz4dPj4+KCgoEDoKESkRlp9ERKXE8pMqU1Wa+p6ZmQkXFxes\nXLkSTZo0EToOkdLy8/PD6NGjIRbzIz0RVQ9t2rRB3bp1ERwcLHQUIlIi/KRERFRKqamp0NfXFzoG\nKYmqNPJzypQpaNOmDUaNGiV0FCKllZmZiX379sHNzU3oKEREJTJ9+nR4e3sLHYOIlAjLTyKiUuLI\nT6pMVaX83LFjBy5fvoz169cLHYVIqe3fvx8dO3ZEvXr1hI5CRFQigwcPxr179xARESF0FCJSEiw/\niYhKieUnVaaqMO09OjoaM2fOxL59+6ClpSVoFiJlx42OiKi6UlFRwZQpU7B27VqhoxCRklAROgAR\nUXXF8pMq07uRnzKZDCKRqNLvn5WVBRcXFyxfvhy2traVfn8i+j/R0dGIj49H3759hY5CRFQqY8aM\ngZWVFZKTk1G3bl2h4xCRguPITyKiUmL5SZWpVq1aUFNTQ0pKiiD3//bbb2FnZ4fRo0cLcn8i+j9b\nt26Fm5sbatSoIXQUIqJS0dfXx9ChQ7Fp0yahoxCREhDJZDKZ0CGIiKojPT09xMfHc9MjqjQdO3bE\n8uXL0blz50q97++//w5PT0+EhYVBW1u7Uu9NRIXJZDLk5eUhJyeH/z0SUbUWExODL774AgkJCVBT\nUxM6DhEpMI78JCIqBalUivT0dOjq6godhZSIEJse3b17F99++y327t3LooWoChCJRKhZsyb/eySi\naq9x48Zo2bIl9uzZI3QUIlJwLD+JiErgzZs3CA8PR3BwMNTU1BAfHw8OoKfKUtnlZ3Z2NlxcXLB4\n8WK0aNGi0u5LREREymH69Onw9vbm52kiqlAsP4mIiiEuLg6zZs2Cqakp3N3dsXr1apibm6Nbt26w\nt7eHn58fMjMzhY5JCq6yd3z/7rvvYGNjgwkTJlTaPYmIiEh59OrVC7m5uQgJCRE6ChEpMJafREQf\nkZubi7Fjx6J9+/aQSCS4cuUKIiMjERISgps3b+Lhw4fw8vJCUFAQzMzMEBQUJHRkUmCVOfJz3759\nOHXqFLZs2SLI7vJERESk+EQiEb799lt4e3sLHYWIFBg3PCIiKkJubi6+/PJLqKioYPfu3dDS0vro\n8aGhoXBycsJPP/2EkSNHVlJKUiYZGRkwMjJCRkYGxOKK+/1lfHw82rdvj+PHj8Pe3r7C7kNERESU\nlZUFMzMzXL58GZaWlkLHISIFxPKTiKgIHh4eePHiBQ4ePAgVFZVinfNu18pdu3ahe/fuFZyQlFG9\nevVw6dIlmJqaVsj1c3Jy0KFDB7i5uWHq1KkVcg8i+rh3/+/Jz8+HTCaDra0tOnfuLHQsIqIKM2/e\nPLx584YjQImoQrD8JCL6gJs3b8LR0RGxsbHQ0NAo0bmHDh2Cl5cXrl69WkHpSJl98cUXWLBgQYWV\n69OmTUNiYiIOHDjA6e5EAjh27Bi8vLwQFRUFDQ0N1KtXD3l5eWjQoAG+/vprODk5fXImAhFRdfP4\n8WPY2dkhISEBOjo6QschIgXDNT+JiD7A19cX48aNK3HxCQADBw7E8+fPWX5ShajITY8OHTqE4OBg\nbN26lcUnkUDmzp0Le3t7xMbG4vHjx1izZg1cXV0hFouxatUqbNq0SeiIRETlrn79+ujduzf8/f2F\njkJECogjP4mI/uP169cwMzPD7du3YWJiUqpr/Pzzz4iOjsa2bdvKNxwpvRUrViApKQmrV68u1+sm\nJCSgTZs2CA4ORtu2bcv12kRUPI8fP0br1q1x+fJlNGzYsNBrT548QUBAABYsWICAgACMGjVKmJBE\nRBXkypUrGDZsGGJjYyGRSISOQ0QKhCM/iYj+IywsDLa2tqUuPgHA2dkZZ8+eLcdURG9VxI7vubm5\nGDJkCObOncvik0hAMpkMderUwcaNG+WPCwoKIJPJYGJigvnz52PcuHH466+/kJubK3BaIqLy1bZt\nW9SpUwdHjhwROgoRKRiWn0RE/5GamgoDA4MyXcPQ0BBpaWnllIjo/1TEtPd58+ahTp06mDFjRrle\nl4hKpkGDBhg6dCgOHjyI3377DTKZDBKJpNAyFFZWVrh9+zZq1qwpYFIioooxffp0bnpEROWO5ScR\n0X+oqKigoKCgTNfIz88HAPz5559ISEgo8/WI3rGwsMCDBw/k/46VVXBwMA4cOIBt27ZxnU8iAb1b\niWr8+PEYOHAgxowZgyZNmmDlypWIiYlBbGws9u3bhx07dmDIkCECpyUiqhiDBw9GXFwcrl+/LnQU\nIlIgXPOTiOg/Lly4gClTpiAiIqLU17h+/Tp69+6Npk2bIi4uDk+fPkXDhg1hZWX13peZmRlq1KhR\njt8BKbqGDRvir7/+gqWlZZmu8/DhQzg4OODQoUPo0KFDOaUjotJKS0tDRkYGpFIpXr16hYMHD+L3\n33/HvXv3YG5ujlevXuHrr7+Gt7c3R34SkcL6+eefERMTg4CAAKGjEJGCYPlJRPQf+fn5MDc3x5Ej\nR9C8efNSXWP69OnQ1NTEsmXLAABv3rzB/fv3ERcX997XkydPUL9+/Q8Wo+bm5lBVVS3Pb48UQK9e\nvTBjxgz06dOn1NfIy8tDly5d4OTkhDlz5pRjOiIqqdevX8PPzw+LFy+GsbExCgoKYGhoiO7du2Pw\n4MFQV1dHeHg4mjdvjiZNmnCUNhEptNTUVFhZWSE6Ohp16tQROg4RKQCWn0REH7BkyRIkJiZi06ZN\nJT43MzMTpqamCA8Ph5mZ2SePz83NRUJCwgeL0YcPH6JOnTofLEYtLS2hoaFRmm+PqrnJkyejUaNG\nmDZtWqmvMXfuXNy4cQNHjhyBWMxVcIiENHfuXPz999+YOXMmDAwMsH79ehw6dAj29vZQV1fHihUr\nuBkZESmVCRMmQFtbG/r6+jh37hzS0tJQs2ZN1KlTBy4uLnBycuLMKSIqNpafREQfkJSUhM8++wzh\n4eEwNzcv0bk///wzLly4gKCgoDLnyM/Px8OHDxEfH/9eMXrv3j3o6+sXWYzq6OiU+f6lkZWVhf37\n9+PGjRvQ0tKCo6MjHBwcoKKiIkgeReTt7Y34+Hj4+PiU6vzjx49j3LhxCA8Ph6GhYTmnI6KSatCg\nATZs2ICBAwcCeDvqydXVFZ06dUJISAju3buHo0ePolGjRgInJSKqeFFRUfj+++/x119/YdiwYXBy\nckLt2rWRl5eHhIQE+Pv7IzY2FmPHjsWcOXOgqakpdGQiquL4kygR0QcYGxtjyZIl6NOnD0JCQoo9\n5SYwMBBr167F+fPnyyWHiooKLCwsYGFhgZ49exZ6TSqVIjExsVAhumfPHvmftbS0iixG9fX1yyXf\nhzx//hxXrlxBVlYW1qxZg7CwMAQEBMDIyAgAcOXKFZw+fRrZ2dmwsrJC+/btYWNjU2gap0wm47TO\nj7CxscHx48dLdW5iYiLc3d2xb98+Fp9EVcC9e/dgaGgIbW1t+XP6+vqIiIjA+vXrMX/+fDRt2hTB\nwcFo1KgR/34kIoV2+vRpDB8+HLNnz8aOHTugp6dX6PUuXbpg1KhRuHXrFjw9PdGtWzcEBwfLP2cS\nEX0IR34SEX3EkiVLsG3bNuzZswcODg5FHpeTkwNfX1+sWLECwcHBsLe3r8SU75PJZEhOTv7gVPq4\nuDhIJJIPFqNWVlYwNDQs0w/WBQUFePLkCRo0aICWLVuie/fuWLJkCdTV1QEAI0eORFpaGlRVVfH4\n8WNkZWVhyZIl+PLLLwG8LXXFYjFSU1Px5MkT1K1bFwYGBuXyviiK2NhY9O7dG/fu3SvRefn5+ejW\nrRt69+6N+fPnV1A6IioumUwGmUwGZ2dnqKmpwd/fH5mZmfj999+xZMkSPH36FCKRCHPnzsXdu3ex\nd+9eTvMkIoV18eJFODk54eDBg+jUqdMnj5fJZPjf//6HU6dOISQkBFpaWpWQkoiqI5afRESf8Ntv\nv+GHH36AiYkJJk2ahIEDB0JHRwcFBQV48OABtm7diq1bt8LOzg6bN2+GhYWF0JE/SiaT4cWLF0UW\no7m5uUUWo8bGxiUqRo2MjDBv3jx8++238nUlY2NjoampCRMTE8hkMsycORPbtm3D9evXYWpqCuDt\ndKeFCxciLCwMKSkpaNmyJXbs2AErK6sKeU+qm7y8PGhpaeH169cl2hDrhx9+QGhoKE6cOMF1Pomq\nkN9//x3jx4+Hvr4+dHR08Pr1a3h6esLNzQ0AMGfOHERFReHIkSPCBiUiqiBv3ryBpaUlAgIC0Lt3\n72KfJ5PJMHr0aNSsWbNUa/UTkXJg+UlEVAwFBQU4duwYNmzYgPPnzyM7OxsAYGBggGHDhmHChAkK\nsxZbWlraB9cYjYuLQ3p6OiwtLbF///73pqr/V3p6OurWrYuAgAC4uLgUedyLFy9gZGSEK1euoHXr\n1gCAdu3aIS8vD5s3b0a9evXg4eGB7OxsHDt2TD6CVNnZ2Njg8OHDaNKkSbGOP336NNzc3BAeHs6d\nU4mqoLS0NGzduhXJyckYNWoUbG1tAQB37txBly5dsGnTJjg5OQmckoioYmzfvh179+7FsWPHSnxu\nSkoKGjVqhPv37783TZ6ICOCan0RExSKRSDBgwAAMGDAAwNuRdxKJRCFHz+np6aF169byIvLf0tPT\nER8fDzMzsyKLz3fr0SUkJEAsFn9wDaZ/r1n3xx9/QFVVFdbW1gCA8+fPIzQ0FDdu3ECzZs0AAKtX\nr0bTpk1x//59fPbZZ+X1rVZr1tbWiI2NLVb5mZSUhFGjRmHXrl0sPomqKD09PcyaNavQc+np6Th/\n/jy6devG4pOIFJqvry8WLFhQqnPr1KmDvn37Yvv27Zg+fXo5JyMiRaB4P7UTEVWCGjVqKGTx+Sna\n2tpo0aIF1NTUijxGKpUCAKKjo6Gjo/Pe5kpSqVRefG7btg2enp6YOXMmdHV1kZ2djVOnTsHU1BTN\nmjVDfn4+AEBHRwfGxsa4efNmBX1n1Y+NjQ3u3r37yeMKCgowfPhwjBs3Dl27dq2EZERUXrS1tdG/\nf3+sXr1a6ChERBUmKioKSUlJ6NOnT6mvMWHCBAQEBJRjKiJSJBz5SUREFSIqKgpGRkaoVasWgLej\nPaVSKSQSCTIyMrBw4UL88ccfmDp1KmbPng0AyM3NRXR0tHwU6LsiNSUlBQYGBnj9+rX8Wsq+27G1\ntTUiIyM/edzSpUsBoNSjKYhIWBytTUSK7uHDh2jcuDEkEkmpr9G0aVM8evSoHFMRkSJh+UlEROVG\nJpPh5cuXqF27NmJjY9GwYUPo6uoCgLz4vH79Or799lukp6dj8+bN6NmzZ6Ey8+nTp/Kp7e+WpX74\n8CEkEgnXcfoXa2trHDhw4KPHnD17Fps3b8a1a9fK9AMFEVUO/mKHiJRRVlYWNDQ0ynQNDQ0NZGZm\nllMiIlI0LD+JiKjcJCYmolevXsjOzkZCQgLMzc2xadMmdOnSBe3atcOOHTuwatUqdO7cGV5eXtDW\n1gYAiEQiyGQy6OjoICsrC1paWgAgL+wiIyOhrq4Oc3Nz+fHvyGQyrFmzBllZWfJd6S0tLRW+KNXQ\n0EBkZCT8/f2hqqoKExMTdOrUCSoqb//XnpKSghEjRmD79u0wNjYWOC0RFUdoaCgcHByUclkVIlJe\nurq68tk9pfXq1Sv5bCMiov9i+UlEVALu7u548eIFgoKChI5SJdWrVw979uxBREQEkpKScO3aNWze\nvBlXr17F2rVrMWPGDKSlpcHY2BjLly9Ho0aNYGNjg+bNm0NNTQ0ikQhNmjTBxYsXkZiYiHr16gF4\nuymSg4MDbGxsPnhfAwMDxMTEIDAwUL4zfc2aNeVF6LtS9N2XgYFBtRxdJZVKcfLkSfj6+uLSpUto\n3rw5zp07h5ycHMTGxuLp06cYP348PDw8MGrUKLi7u6Nnz55CxyaiYkhMTISjoyMePXok/wUQEZEy\naNq0Ka5fv4709HT5L8ZL6uzZs7CzsyvnZESkKESyd3MKiYgUgLu7O7Zv3w6RSCSfJt20aVN89dVX\nGDdunHxUXFmuX9by88GDBzA3N0dYWBhatWpVpjzVzd27dxEbG4t//vkHN2/eRFxcHB48eIDVq1dj\nwoQJEIvFiIyMhKurK3r16gVHR0ds2bIFZ8+exd9//w1bW9ti3Ucmk+HZs2eIi4tDfHy8vBB995Wf\nn/9eIfruq27dulWyGH3+/DmcnJyQlZWFyZMnY9iwYe9NEQsPD8fGjRuxd+9emJiY4NatW2X+d56I\nKoeXlxcePHiAzZs3Cx2FiKjSff311+jWrRsmTpxYqvM7deqEGTNmYPDgweWcjIgUActPIlIo7u7u\nePLkCXbu3In8/Hw8e/YMZ86cwbJly2BlZYUzZ85AXV39vfPy8vJQo0aNYl2/rOVnQkICLC0tcfXq\nVaUrP4vy33XuDh8+jJUrVyIuLg4ODg5YvHgxWrRoUW73S01N/WApGhcXh8zMzA+OFrWyskK9evUE\nmY767NkzdOrUCYMHD8bSpUs/meHmzZvo27cvfvjhB4wfP76SUhJRaUmlUlhbW2PPnj1wcHAQOg4R\nUaU7e/Yspk6dips3b5b4l9A3btxA3759kZCQwF/6EtEHsfwkIoVSVDl5+/ZttGrVCv/73//w448/\nwtzcHG5ubnj48CECAwPRq1cv7N27Fzdv3sR3332HCxcuQF1dHQMHDsTatWuho6NT6Ppt27aFj48P\nMjMz8fXXX2Pjxo1QVVWV3++XX37Br7/+iidPnsDa2hpz5szB8OHDAQBisVi+xiUAfPHFFzhz5gzC\nwsIwf/58hIeHIzc3F3Z2dlixYgXatWtXSe8eAcDr16+LLEZTU1Nhbm7+wWLU1NS0Qj5wFxQUoFOn\nTvjiiy/g5eVV7PPi4uLQqVMn7Nixg1Pfiaq4M2fOYMaMGbh+/XqVHHlORFTRZDIZPv/8c3Tv3h2L\nFy8u9nnp6eno3Lkz3N3dMW3atApMSETVGX8tQkRKoWnTpnB0dMTBgwfx448/AgDWrFmDH374Adeu\nXYNMJkNWVhYcHR3Rrl07hIWF4cWLFxgzZgxGjx6N/fv3y6/1999/Q11dHWfOnEFiYiLc3d3x/fff\nw9vbGwAwf/58BAYGYuPGjbCxscGlS5cwduxY6Ovro0+fPggNDUWbNm1w6tQp2NnZoWbNmgDefngb\nOXIkfHx8AADr169Hv379EBcXp/Cb91QlOjo6aNmyJVq2bPnea1lZWbh37568DL1x44Z8ndHk5GSY\nmpp+sBht2LCh/J9zSR0/fhx5eXlYtmxZic6zsrKCj48PFi1axPKTqIrz8/PDmDFjWHwSkdISiUQ4\ndOgQOnTogBo1auCHH3745N+Jqamp+PLLL9GmTRtMnTq1kpISUXXEkZ9EpFA+Ni193rx58PHxQUZG\nBszNzWFnZ4fDhw/LX9+yZQvmzJmDxMRE+VqKISEh6Nq1K+Li4mBhYQF3d3ccPnwYiYmJ8unzu3bt\nwpgxY5CamgqZTAYDAwOcPn0aHTt2lF97xowZiI2NxZEjR4q95qdMJkO9evWwcuVKuLq6ltdbRBUk\nJycH9+/f/+CI0cePH8PExOS9UtTS0hIWFhYfXIrhnb59+2LIkCEYNWpUiTPl5+ejYcOGOHr0KJo3\nb16Wb4+IKsiLFy9gaWmJe/fuQV9fX+g4RESCSkpKQv/+/aGnp4dp06ahX79+kEgkhY5JTU1FQEAA\n1q1bBxcXF/z888+CLEtERNUHR34SkdL477qSrVu3LvR6TEwM7OzsCm0i06FDB4jFYkRFRcHCwgIA\nYGdnV6isat++PXJzcxEfH4/s7GxkZ2fD0dGx0LXzU5XbswAAGdNJREFU8/Nhbm7+0XzPnj3DDz/8\ngL///hspKSkoKChAdnY2Hj58WOrvmSqPqqoqGjdujMaNG7/3Wl5eHh48eCAvQ+Pj43H27FnExcXh\n/v37MDQ0/OCIUbFYjKtXr+LgwYOlyqSiooLx48fD19eXm6gQVVG7du1Cv379WHwSEQEwNjbGxYsX\nsX//fvz000+YOnUqBgwYAH19feTl5SEhIQEnTpzAgAEDsHfvXi4PRUTFwvKTiJTGvwtMANDU1Cz2\nuZ+advNuEL1UKgUAHDlyBA0aNCh0zKc2VBo5ciSePXuGtWvXwszMDKqqqujWrRtyc3OLnZOqpho1\nasgLzf8qKCjA48ePC40UvXz5MuLi4nDnzh1069btoyNDP6Vfv37w8PAoS3wiqiAymQxbtmzBunXr\nhI5CRFRlqKqqYsSIERgxYgQiIiJw7tw5pKWlQVtbG927d4ePjw8MDAyEjklE1QjLTyJSCrdu3cKJ\nEyewcOHCIo9p0qQJAgICkJmZKS9GL1y4AJlMhiZNmsiPu3nzJt68eSMvpC5dugRVVVVYWlqioKAA\nqqqqSEhIQJcuXT54n3drPxYUFBR6/sKFC/Dx8ZGPGk1JSUFSUlLpv2mqFiQSCczMzGBmZobu3bsX\nes3X1xcRERFlur6enh5evnxZpmsQUcW4evUq3rx5U+T/L4iIlF1R67ATEZUEF8YgIoWTk5MjLw5v\n3LiB1atXo2vXrnBwcMDMmTOLPG/48OHQ0NDAyJEjcevWLZw7dw4TJkyAs7NzoRGj+fn58PDwQFRU\nFE6fPo158+Zh3LhxUFdXh5aWFmbNmoVZs2YhICAA8fHxiIyMxObNm+Hn5wcAMDIygrq6Ok6ePImn\nT5/i9evXAAAbGxvs3LkT0dHRuHr1KoYNG1ZoB3lSPurq6sjLyyvTNXJycvjvEVEV5efnBw8PD65V\nR0RERFSB+EmLiBTOn3/+CRMTE5iZmaFHjx44cuQIFi9ejJCQEPlozQ9NY39XSL5+/Rpt27bFoEGD\n0LFjR2zdurXQcV26dEHTpk3RtWtXODs7o0ePHvj555/lry9ZsgSLFi3CqlWr0KxZM/Tq1QuBgYHy\nNT8lEgl8fHzg5+eHevXqwcnJCQDg7++PjIwMtG7dGq6urhg9ejQaNmxYQe8SVQfGxsaIi4sr0zXi\n4uJQt27dckpEROUlIyMD+/fvh5ubm9BRiIiIiBQad3snIiKqonJzc2FmZoYzZ84UWnqhJJycnNC3\nb1+MGzeunNMRUVn4+/vjjz/+QFBQkNBRiIiIiBQaR34SERFVUTVr1sSYMWOwcePGUp3/8OFDnDt3\nDq6uruWcjIjKys/PD2PGjBE6BhEREZHCY/lJRERUhY0bNw67du3C3bt3S3SeTCbDjz/+iG+++QZa\nWloVlI6ISuP27dtISEhA3759hY5CRCSolJQU9OrVC1paWpBIJGW6lru7OwYOHFhOyYhIkbD8JCIi\nqsIaNGiAn376CX379sWjR4+KdY5MJoOnpyciIiKwdOnSCk5IRCW1detWuLm5QUVFRegoREQVyt3d\nHWKxGBKJBGKxWP7VoUMHAMCKFSuQnJyMGzduICkpqUz3WrduHXbu3FkesYlIwfATFxERURU3duxY\npKeno0OHDti0aRP69OlT5O7Qjx8/xsKFCxEeHo7jx49DW1u7ktMS0cfk5ORg586duHjxotBRiIgq\nRc+ePbFz5078e7uRmjVrAgDi4+Nhb28PCwuLUl+/oKAAEomEn3mIqEgc+UlERFQNfPfdd9iwYQMW\nLFgAa2trrFy5Erdu3UJiYiLi4+Nx8uRJODs7w9bWFhoaGjh37hyMjY2Fjk1E/xEUFIRmzZrByspK\n6ChERJVCVVUVhoaGMDIykn/VqlUL5ubmCAoKwvbt2yGRSODh4QEAePToEQYNGgQdHR3o6OjA2dkZ\niYmJ8ut5enrC1tYW27dvh5WVFdTU1JCVlQU3N7f3pr3/8ssvsLKygoaGBpo3b45du3ZV6vdORFUD\nR34SERFVEwMHDsSAAQMQGhoKX19fbN26FS9fvoSamhpMTEwwYsQIbNu2jSMfiKowbnRERPRWWFgY\nhg0bhtq1a2PdunVQU1ODTCbDwIEDoampiZCQEMhkMkyePBmDBg1CaGio/Nz79+9j9+7dOHDgAGrW\nrAlVVVWIRKJC158/fz4CAwOxceNG2NjY4NKlSxg7diz09fXRp0+fyv52iUhALD+JiIiqEZFIhLZt\n26Jt27ZCRyGiEkpISMC1a9dw+PBhoaMQEVWa/y7DIxKJMHnyZCxfvhyqqqpQV1eHoaEhAOD06dO4\ndesW7t27hwYNGgAAfv/9d1hZWeHMmTPo1q0bACAvLw87d+6EgYHBB++ZlZWFNWvW4PTp0+jYsSMA\nwMzMDFeuXMGGDRtYfhIpGZafRERERESVICAgAK6urlBTUxM6ChFRpenSpQu2bNlSaM3PWrVqffDY\nmJgYmJiYyItPADA3N4eJiQmioqLk5Wf9+vWLLD4BICoqCtnZ2XB0dCz0fH5+PszNzcvy7RBRNcTy\nk4iIiIioghUUFMDf3x9Hjx4VOgoRUaXS0NAol8Lx39PaNTU1P3qsVCoFABw5cqRQkQoANWrUKHMW\nIqpeWH4SEREREVWwU6dOwdjYGHZ2dkJHISKqspo0aYInT57g4cOHMDU1BQDcu3cPT548QdOmTYt9\nnc8++wyqqqpISEhAly5dKiouEVUTLD+JiIiIiCoYNzoiImWVk5ODlJSUQs9JJJIPTlvv0aMHbG1t\nMXz4cHh7e0Mmk2HatGlo3bo1vvjii2LfU0tLC7NmzcKsWbMglUrRuXNnZGRk4PLly5BIJPz7mEjJ\niIUOQERERKXj6enJUWRE1UBKSgr++usvDB06VOgoRESV7s8//4SJiYn8y9jYGK1atSry+KCgIBga\nGqJbt27o3r07TExMcOjQoRLfd8mSJVi0aBFWrVqFZs2aoVevXggMDOSan0RKSCT796rDREREVO6e\nPn2KZcuW4ejRo3j8+DEMDQ1hZ2eHKVOmlGm30aysLOTk5EBPT68c0xJReVuxYgWio6Ph7+8vdBQi\nIiIipcPyk4iIqAI9ePAAHTp0gK6uLpYsWQI7OztIpVL8+eefWLFiBRISEt47Jy8vj4vxEykImUyG\nxo0bw9/fHx07dhQ6DhEREZHS4bR3IiKiCjRx4kSIxWJcu3YNzs7OsLa2RqNGjTB58mTcuHEDACAW\ni+Hr6wtnZ2doaWlh/vz5kEqlGDNmDCwsLKChoQEbGxusWLGi0LU9PT1ha2srfyyTybBkyRKYmppC\nTU0NdnZ2CAoKkr/esWNHzJ49u9A10tPToaGhgT/++AMAsGvXLrRp0wY6OjqoU6cOXFxc8OTJk4p6\ne4gU3vnz5yEWi9GhQwehoxAREREpJZafREREFSQtLQ0nT57ElClToK6u/t7rOjo68j8vXrwY/fr1\nw61btzB58mRIpVLUr18fBw4cQExMDLy8vLB8+XIEBAQUuoZIJJL/2dvbG6tWrcKKFStw69YtDBo0\nCIMHD5aXrCNGjMCePXsKnX/gwAGoq6ujX79+AN6OOl28eDFu3LiBo0eP4sWLF3B1dS2394RI2bzb\n6Ojf/60SERERUeXhtHciIqIKcvXqVbRt2xaHDh3Cl19+WeRxYrEY06ZNg7e390evN2/ePFy7dg2n\nTp0C8Hbk58GDB+XlZv369TFx4kTMnz9ffk7Xrl3RoEED7NixA6mpqTA2NsaJEyfQtWtXAEDPnj1h\naWmJTZs2ffCeMTEx+Oyzz/D48WOYmJiU6PsnUnYvX75Ew4YNcffuXRgZGQkdh4iIiEgpceQnERFR\nBSnJ7xft7e3fe27Tpk1wcHCAkZERtLW1sWbNGjx8+PCD56enp+PJkyfvTa39/PPPERUVBQDQ19eH\no6Mjdu3aBQB48uQJzp49i2+++UZ+fHh4OJycnNCwYUPo6OjAwcEBIpGoyPsSUdF2796Nnj17svgk\nIiIiEhDLTyIiogpibW0NkUiE6OjoTx6rqalZ6PHevXsxY8YMeHh44NSpU4iMjMSkSZOQm5tb4hz/\nnm47YsQIHDx4ELm5udizZw9MTU3lm7BkZWXB0dERWlpa2LlzJ8LCwnDixAnIZLJS3ZdI2b2b8k5E\nREREwmH5SUREVEH09PTQu3dvrF+/HllZWe+9/urVqyLPvXDhAtq1a4eJEyeiRYsWsLCwQFxcXJHH\na2trw8TEBBcuXCj0/Pnz5/HZZ5/JHw8cOBAAEBwcjN9//73Qep4xMTF48eIFli1bhs8//xw2NjZI\nSUnhWoVEpRAREYHnz5+jR48eQkchIiIiUmosP4mIiCrQhg0bIJPJ0Lp1axw4cAB3797FnTt3sHHj\nRjRv3rzI82xsbBAeHo4TJ04gLi4OS5Yswblz5z56r9mzZ2PlypXYs2cPYmNjsXDhQpw/f77QDu+q\nqqoYPHgwli5dioiICIwYMUL+mqmpKVRVVeHj44P79+/j6NGjWLhwYdnfBCIltHXrVnh4eEAikQgd\nhYiIiEipqQgdgIiISJGZm5sjPDwcXl5emDt3LhITE1G7dm00a9ZMvsHRh0ZWjh8/HpGRkRg+fDhk\nMhmcnZ0xa9Ys+Pv7F3mvadOmISMjA99//z1SUlLQqFEjBAYGolmzZoWOGzFiBLZt24ZWrVqhcePG\n8ucNDAywfft2/O9//4Ovry/s7OywZs0aODo6ltO7QaQc3rx5g927dyMiIkLoKERERERKj7u9ExER\nERGVo507d2LXrl04fvy40FGIiIiIlB6nvRMRERERlSNudERERERUdXDkJxERERFRObl79y46deqE\nR48eoWbNmkLHISIiIlJ6XPOTiIiIiKgE8vPzceTIEWzevBk3b97Eq1evoKmpiYYNG6JWrVoYOnQo\ni08iIiKiKoLT3omIiIiIikEmk2H9+vWwsLDAL7/8guHDh+PixYt4/PgxIiIi4OnpCalUih07duC7\n775Ddna20JGJiIiIlB6nvRMRERERfYJUKsWECRMQFhaGrVu3omXLlkUe++jRI8ycORNPnjzBkSNH\nUKtWrUpMSkRERET/xvKTiIiIiOgTZs6ciatXr+LYsWPQ0tL65PFSqRRTp05FVFQUTpw4AVVV1UpI\nSURERET/xWnvREREREQf8c8//yAwMBCHDx8uVvEJAGKxGOvWrYOGhgbWrVtXwQmJiIiIqCgc+UlE\nRERE9BFDhw5Fhw4dMG3atBKfGxoaiqFDhyIuLg5iMccdEBEREVU2fgIjIiIiIipCcnIyTp48iZEj\nR5bqfAcHB+jr6+PkyZPlnIyIiIiIioPlJxERERFREQIDAzFw4MBSb1okEokwevRo7N69u5yTERER\nEVFxsPwkIiIiIipCcnIyzM3Ny3QNc3NzJCcnl1MiIiIiIioJlp9EREREREXIzc1FzZo1y3SNmjVr\nIjc3t5wSEREREVFJsPwkIiIiIiqCnp4eUlNTy3SN1NTUUk+bJyIiIqKyYflJRERERFSEjh07Ijg4\nGDKZrNTXCA4Oxueff16OqYiIiIiouFh+EhEREREVoWPHjlBVVcWZM2dKdf7z588RFBQEd3f3ck5G\nRERERMXB8pOIiIiIqAgikQiTJk3CunXrSnX+li1b4OTkhNq1a5dzMiIiIiIqDpGsLHN4iIiIiIgU\nXEZGBtq0aYPx48fj22+/LfZ5586dw1dffYVz586hcePGFZiQiIiIiIqiInQAIiIiIqKqTEtLC8eO\nHUPnzp2Rl5eHmTNnQiQSffSc48ePY+TIkdi9ezeLTyIiIiIBceQnEREREVExPH78GAMGDECNGjUw\nadIkDBkyBOrq6vLXpVIpTp48CV9fX4SFheHgwYPo0KGDgImJiIiIiOUnEREREVExFRQU4MSJE/D1\n9UVoaCjs7e2hq6uLzMxM3L59G/r6+pg8eTKGDh0KDQ0NoeMSERERKT2Wn0REREREpZCQkICoqCi8\nfv0ampqaMDMzg62t7SenxBMRERFR5WH5SURERERERERERApJLHQAIiIiIiIiIiIioorA8pOIiIiI\niIiIiIgUEstPIiIiIiIiIiIiUkgsP4mIiIiI/j9zc3OsXr26Uu4VEhICiUSC1NTUSrkfERERkTLi\nhkdEREREpBSePn2K5cuX4+jRo3j06BF0dXVhZWWFoUOHwt3dHZqamnjx4gU0NTWhpqZW4Xny8/OR\nmpoKIyOjCr8XERERkbJSEToAEREREVFFe/DgATp06IBatWph2bJlsLW1hbq6Om7fvg0/Pz8YGBhg\n6NChqF27dpnvlZeXhxo1anzyOBUVFRafRERERBWM096JiIiISOFNmDABKioquHbtGr7++ms0btwY\nZmZm6Nu3LwIDAzF06FAA7097F4vFCAwMLHStDx3j6+sLZ2dnaGlpYf78+QCAo0ePonHjxlBXV0e3\nbt2wb98+iMViPHz4EMDbae9isVg+7X3btm3Q1tYudK//HkNEREREJcPyk4iIiIgUWmpqKk6dOoUp\nU6ZU2HT2xYsXo1+/frh16xYmT56MR48ewdnZGQMGDMCNGzcwZcoUzJkzByKRqNB5/34sEonee/2/\nxxARERFRybD8JCIiIiKFFhcXB5lMBhsbm0LPN2jQANra2tDW1sakSZPKdI+hQ4fCw8MDDRs2hJmZ\nGTZu3AhLS0usWLEC1tbWGDx4MMaPH1+mexARERFRybH8JCIiIiKldP78eURGRqJNmzbIzs4u07Xs\n7e0LPY6JiYGDg0Oh59q2bVumexARERFRybH8JCIiIiKFZmVlBZFIhJiYmELPm5mZwcLCAhoaGkWe\nKxKJIJPJCj2Xl5f33nGampplzikWi4t1LyIiIiIqPpafRERERKTQ9PX10atXL6xfvx6ZmZklOtfQ\n0BBJSUnyxykpKYUeF6Vx48YICwsr9NyVK1c+ea+srCxkZGTIn4uIiChRXiIiIiIqjOUnERERESk8\nX19fSKVStG7dGnv27EF0dDRiY2Oxe/duREZGQkVF5YPndevWDRs2bMC1a9cQEREBd3d3qKurf/J+\nEyZMQHx8PGbPno27d+8iMDAQv/76K4DCGxj9e6Rn27ZtoampiXnz5iE+Ph4HDx7Exo0by/idExER\nESk3lp9EREREpPDMzc0REREBR0dHLFy4EK1atYK9vT28vb0xefJkrFmzBsD7O6uvWrUKFhYW6Nq1\nK1xcXDB27FgYGRkVOuZDu7Gbmpri4MGDCA4ORosWLbB27Vr8+OOPAFBox/l/n6unp4ddu3bh9OnT\nsLOzg5+fH5YuXVpu7wERERGRMhLJ/ruwEBERERERlbu1a9di0aJFSEtLEzoKERERkdL48PweIiIi\nIiIqE19fXzg4OMDQ0BCXLl3C0qVL4e7uLnQsIiIiIqXC8pOIiIiIqALExcXBy8sLqampqF+/PiZN\nmoQFCxYIHYuIiIhIqXDaOxERERERERERESkkbnj0/9qxAxkAAACAQf7W9/gKIwAAAABgSX4CAAAA\nAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAALMlPAAAAAGBJfgIA\nAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAAAAAsyU8AAAAAYEl+\nAgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJAAAAACzJTwAAAABg\nSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAALMlPAAAA\nAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAAAAAsyU8A\nAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJAAAAACzJ\nTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAA\nLMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAA\nAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJ\nAAAAACzJTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl\n+QkAAAAALMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAA\ngCX5CQAAAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEA\nAACAJfkJAAAAACzJTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/\nAQAAAIAl+QkAAAAALMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACw\nJD8BAAAAgCX5CQAAAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAA\nALAkPwEAAACAJfkJAAA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -657,12 +657,12 @@ "source": [ "## Breadth first search\n", "\n", - "Let's change all the node_colors to starting position and difine a different problem statement." + "Let's change all the node_colors to starting position and define a different problem statement." ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -674,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -739,7 +739,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -748,8 +748,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "26\n", - "26\n" + "24\n", + "24\n" ] } ], @@ -762,7 +762,356 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slider_callback(iteration):\n", + " show_map(all_node_colors[iteration])\n", + "\n", + "def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nAAAAluQnAAAAALAkPwE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", + "w = widgets.interactive(slider_callback, iteration = slider)\n", + "display(w)\n", + "\n", + "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "a = widgets.interactive(visualize_callback, Visualize = button)\n", + "display(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uniform cost search\n", + "\n", + "Let's change all the node_colors to starting position and define a different problem statement." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "node_colors = dict(initial_node_colors)\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def best_first_graph_search(problem, f):\n", + " \"\"\"Search the nodes with the lowest f scores first.\n", + " You specify the function f(node) that you want to minimize; for example,\n", + " if f is a heuristic estimate to the goal, then we have greedy best\n", + " first search; if f is node.depth then we have breadth-first search.\n", + " There is a subtlety: the line \"f = memoize(f, 'f')\" means that the f\n", + " values will be cached on the nodes as they are computed. So after doing\n", + " a best first search you can examine the f values of the path returned.\"\"\"\n", + " \n", + " # we use these two variables at the time of visualisations\n", + " global iterations\n", + " iterations = 0\n", + " global all_node_colors\n", + " all_node_colors = []\n", + " \n", + " f = memoize(f, 'f')\n", + " node = Node(problem.initial)\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return node\n", + " \n", + " frontier = PriorityQueue(min, f)\n", + " frontier.append(node)\n", + " \n", + " node_colors[node.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " explored = set()\n", + " while frontier:\n", + " node = frontier.pop()\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return node\n", + " \n", + " explored.add(node.state)\n", + " for child in node.expand(problem):\n", + " if child.state not in explored and child not in frontier:\n", + " frontier.append(child)\n", + " node_colors[child.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " elif child in frontier:\n", + " incumbent = frontier[child]\n", + " if f(child) < f(incumbent):\n", + " del frontier[incumbent]\n", + " frontier.append(child)\n", + " node_colors[child.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + "\n", + " node_colors[node.state] = \"gray\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return None\n", + "\n", + "def uniform_cost_search(problem):\n", + " \"[Figure 3.14]\"\n", + " return best_first_graph_search(problem, lambda node: node.path_cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41\n", + "41\n" + ] + } + ], + "source": [ + "uniform_cost_search(romania_problem).solution()\n", + "\n", + "print(len(all_node_colors))\n", + "print(iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def slider_callback(iteration):\n", + " show_map(all_node_colors[iteration])\n", + "\n", + "def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)" + ] + }, + { + "cell_type": "code", + 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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nAAAAWJKfAAAAAMCS/AQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", + "w = widgets.interactive(slider_callback, iteration = slider)\n", + "display(w)\n", + "\n", + "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "a = widgets.interactive(visualize_callback, Visualize = button)\n", + "display(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A* search\n", + "\n", + "Let's change all the node_colors to starting position and define a different problem statement." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "node_colors = dict(initial_node_colors)\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def best_first_graph_search(problem, f):\n", + " \"\"\"Search the nodes with the lowest f scores first.\n", + " You specify the function f(node) that you want to minimize; for example,\n", + " if f is a heuristic estimate to the goal, then we have greedy best\n", + " first search; if f is node.depth then we have breadth-first search.\n", + " There is a subtlety: the line \"f = memoize(f, 'f')\" means that the f\n", + " values will be cached on the nodes as they are computed. So after doing\n", + " a best first search you can examine the f values of the path returned.\"\"\"\n", + " \n", + " # we use these two variables at the time of visualisations\n", + " global iterations\n", + " iterations = 0\n", + " global all_node_colors\n", + " all_node_colors = []\n", + " \n", + " f = memoize(f, 'f')\n", + " node = Node(problem.initial)\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return node\n", + " \n", + " frontier = PriorityQueue(min, f)\n", + " frontier.append(node)\n", + " \n", + " node_colors[node.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " explored = set()\n", + " while frontier:\n", + " node = frontier.pop()\n", + " \n", + " node_colors[node.state] = \"red\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " \n", + " if problem.goal_test(node.state):\n", + " node_colors[node.state] = \"green\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return node\n", + " \n", + " explored.add(node.state)\n", + " for child in node.expand(problem):\n", + " if child.state not in explored and child not in frontier:\n", + " frontier.append(child)\n", + " node_colors[child.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " elif child in frontier:\n", + " incumbent = frontier[child]\n", + " if f(child) < f(incumbent):\n", + " del frontier[incumbent]\n", + " frontier.append(child)\n", + " node_colors[child.state] = \"blue\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + "\n", + " node_colors[node.state] = \"gray\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", + " return None\n", + "\n", + "def astar_search(problem, h=None):\n", + " \"\"\"A* search is best-first graph search with f(n) = g(n)+h(n).\n", + " You need to specify the h function when you call astar_search, or\n", + " else in your Problem subclass.\"\"\"\n", + " h = memoize(h or problem.h, 'h')\n", + " return best_first_graph_search(problem, lambda n: n.path_cost + h(n))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41\n", + "41\n" + ] + } + ], + "source": [ + "uniform_cost_search(romania_problem).solution()\n", + "\n", + "print(len(all_node_colors))\n", + "print(iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": { "collapsed": true }, @@ -780,16 +1129,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 32, "metadata": { "collapsed": false 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UAAByQXx8vGbMmKHZs2frhRde0OjRo+Xt7W10LAAAYHKUn0Aua9asmUqVKiU/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\nAGBJfgIAAAAAS/ITAAA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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nFMt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"25f28d1fc7954fbfbf949a7886c849be": { + "views": [] + }, + "272e8204dee44634ab340393f1ea9791": { + "views": [] + }, "365c7e5aea07404da04d6ffc25724e21": { "views": [] }, + "38204d73bd63477cb49e2ee34e8c3532": { + "views": [] + }, + "3c800341bd464d9593927d8f68458fcd": { + "views": [] + }, "3d8ad1c09c9148e98a897f66e2e07dab": { "views": [] }, + "3dec1ba740be4dc9a0ec8cb7e30bbfb2": { + "views": [] + }, + "3e44728dc0a645779e762d951a5ba923": { + "views": [] + }, + "3ec8dc05f28d44178f9b3007660fc055": { + "views": [] + }, "412a234d9d7d4366886b448558969d5e": { "views": [] }, @@ -902,6 +1287,16 @@ "453f8e1e43b44d87a0a8dbdf232a443e": { "views": [] }, + "486b24363dbd4a13a2a331f20907352c": { + "views": [] + }, + "4afffeb237594a49be1a133d1c27ebf3": { + "views": [ + { + "cell_index": 62 + } + ] + }, "4b5427b00ef5437b83ee3ceec19620a1": { "views": [] }, @@ -927,6 +1322,9 @@ "59ad241185f647b0ab783a514987ea99": { "views": [] }, + "5b82597037774040b6dd91864505a22d": { + "views": [] + }, "5c6b6bb6ef954d6687b1e882d2b955c5": { "views": [] }, @@ -940,9 +1338,32 @@ "65b158e1fba645f5a82573b9c7a2a426": { "views": [] }, + "6925b54ed60d4655afe35c00bc3b249a": { + "views": [] + }, + "69dfa4349d3c424fb09a2b17dae31382": { + "views": [ + { + "cell_index": 62 + } + ] + }, + "6bafca6f1a2149b68a4292399997e0b3": { + "views": [] + }, + "6c87dd3de38b4c9db59e0ad2e1ae2c23": { + "views": [ + { + "cell_index": 56 + } + ] + }, "7009ef53e4d849caa975213300a599ae": { "views": [] }, + "71ef3ee61a0f4c1a9cdcb1bc6085e19d": { + "views": [] + }, "72ebe1632d7049dbbdd6a64dbbf0e907": { "views": [] }, @@ -950,6 +1371,9 @@ "views": [] }, "76016c5e69554017aca4ab9b4c8a7a92": { + "views": [] + }, + "778433af1e9a43018a093b329a37a0d3": { "views": [ { "cell_index": 44 @@ -959,9 +1383,15 @@ "7a9e4f4ae801445b8fc213ec5fe3fc2d": { "views": [] }, + "7b028fce0cea441797275b9fbf9ace4f": { + "views": [] + }, "854bdd172d63494b93a73cc7dff71f9a": { "views": [] }, + "857efe999cbf48dea1abfe0ec21a7716": { + "views": [] + }, "8602d368e05a43f49af449f0668a16da": { "views": [ { @@ -976,12 +1406,24 @@ } ] }, + "8910f641820d4169843324a52a9c27bf": { + "views": [] + }, + "8c07f844cbd94ca5a9e9187e820d467e": { + "views": [] + }, "8ff3a2148f7141a58ca785d99eae436e": { "views": [] }, "9b4d43f4a5eb41b69d7f2c691429f809": { "views": [] }, + "9bfeeeeb5a0545a0ac5dca639466133e": { + "views": [] + }, + "9f4b1eeb781540f4b4fcc4e4ee17a2df": { + "views": [] + }, "a005a91075ab42b380ac8ff14f668130": { "views": [] }, @@ -995,18 +1437,49 @@ "a2bd6a5fb64240839c9f69666bea45a0": { "views": [] }, + "a314e82173d146628f431e6628a9c070": { + "views": [] + }, "a70aa3baef764c0e8afdf7e4acba36a7": { "views": [] }, + "a742ee20e9a3402dba08be56e8a08f05": { + "views": [] + }, + "a952a71cd75a4885b575114eb335e36e": { + "views": [ + { + "cell_index": 50 + } + ] + }, "aad7ddcdc9704479b00066132859cba1": { "views": [] }, + "ab5c6b3d56fe42b298dea83dd3a54618": { + "views": [] + }, + "acadb11780c34dd986eb0dd16ff3ca41": { + "views": [] + }, + "af138f3de46144ca931fb362cbd1ab00": { + "views": [] + }, "b516d7c7bf734def8cbdbc95491e2bcb": { "views": [] }, "b7efd13a1532423b82f6df27743570ff": { "views": [] }, + "bd9c5370555f4b8d992b653551c04778": { + "views": [] + }, + "c9108c033a1f4c6a81d1a12c24bbecd5": { + "views": [] + }, + "c91f90b2011f4fd08305c3014dcec5ab": { + "views": [] + }, "cb93b72fa07e48969a8061e3aee733d1": { "views": [] }, @@ -1019,6 +1492,16 @@ "d0d2f7da3afa4aba9566c44032db9990": { "views": [] }, + "d2cd070e7282411c840525baefb38a9b": { + "views": [ + { + "cell_index": 56 + } + ] + }, + "d3273606f9d5450f9c08abb914e545ad": { + "views": [] + }, "d4863215a8c44e06ad5175b0bb1fb2f2": { "views": [] }, @@ -1028,30 +1511,69 @@ "da986b94b7d446ddaf27d9c8eb9ea93c": { "views": [] }, + "dbeb1fd0755a4c1bb5a131f31db91c12": { + "views": [] + }, "dc2f0ff53c6c4a8596b477ebba555974": { "views": [] }, "dd8e399106e845cbaa0d27364c8a91a9": { "views": [] }, + "df0e0e9b02e74c55adade9e9ea86c321": { + "views": [] + }, + "dfca999fb6474224a5396239db66cf23": { + "views": [ + { + "cell_index": 50 + } + ] + }, "e5e7165259864c18a946ac2505ecd255": { "views": [] }, "e94c6cb5b6414bbba52a761a06010121": { "views": [] }, + "e97ec2f4dff544d0a81907d4e7de681c": { + "views": [] + }, "eb6cb661a9964d9e84186eb170e75764": { "views": [] }, + "ecdb12781a5f4ba69e771f564b749045": { + "views": [] + }, "ecdce3d7ba9149c793c17e021a2f3c78": { "views": [] }, + "effb5ec0a0f84e4f9ca23effc9dc3b13": { + "views": [ + { + "cell_index": 56 + } + ] + }, + "f11666cfb292409ba464137cb83a3a8c": { + "views": [] + }, + "f1815b70e6464d9893a6ab3a134b2ffd": { + "views": [ + { + "cell_index": 62 + } + ] + }, "f19d8ff62bb8417fa4c347cfa6595965": { "views": [] }, "f4c08a34dd6744db8a72304a81bcbaf0": { "views": [] }, + "f5590e2fec5943c6bbdad5088d98b5ae": { + "views": [] + }, "f7e777fdf53a4e08853e9d7c411cc3c6": { "views": [] }, @@ -1065,6 +1587,9 @@ "fa7f2272527648a5b5db0fe941eac78b": { "views": [] }, + "fd1051fc24744ed2aa963590f4c682e8": { + "views": [] + }, "ffa5da7ffc384b9faeeb78b71e94d9fd": { "views": [] } From 8623520231f3dd338f2e7ae8c6ddc732f2e0c99a Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Wed, 15 Jun 2016 00:28:59 +0530 Subject: [PATCH 320/513] Added Visualize and Time Delay to Applets --- csp.ipynb | 512 +++++++++--- mdp.ipynb | 2337 ++++++++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 2554 insertions(+), 295 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index fdb8fd399..9b08fb9d2 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -145,9 +145,9 @@ { "data": { "text/plain": [ - "(,\n", - " ,\n", - " )" + "(,\n", + " ,\n", + " )" ] }, "execution_count": 7, @@ -421,7 +421,8 @@ "%matplotlib inline\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", - "import matplotlib" + "import matplotlib\n", + "import time" ] }, { @@ -471,6 +472,19 @@ " plt.show()\n", "\n", " return update_step # <-- this is a function\n", + "\n", + "def make_visualize(slider):\n", + " ''' Takes an input a slider and returns \n", + " callback function for timer and animation\n", + " '''\n", + " \n", + " def visualize_callback(Visualize, time_step):\n", + " if Visualize is True:\n", + " for i in range(slider.min, slider.max + 1):\n", + " slider.value = i\n", + " time.sleep(time_step)\n", + " \n", + " return visualize_callback\n", " " ] }, @@ -514,7 +528,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click." + "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step." ] }, { @@ -526,9 +540,9 @@ "outputs": [ { "data": { - "image/png": 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hhx1y8skn56677sr8+fOLHRcAYJOlCAUACsrReP7RrFmz0r1795x11lm57LLL\n6nWnY//+/Tfq4/Gf1TbbbJPBgwfnpptuyuzZszNlypR86UtfyvDhw7PXXnulY8eO+c53vpMxY8Zk\n2bJlxY4LALDJcDQeACioiy66KC1atMhFF11U7CgU2Z/+9KccfvjhueKKK3L66afX+/y33nor++23\nXxYsWLBe941uzFatWpWnn356zaNLzz33XA4++OA1Dy916tQpDRrY6wAA8El8lwQAFFR5ebkdoeTp\np59Onz598uMf/7ggJWiS7Ljjjtlpp50ybdq0gszfGFRUVKRLly657LLLMmXKlMybNy/nnntu3nrr\nrZxwwglp06ZNjj/++Nx222158803ix2XDeiCCy5Inz590r59+zRt2jTbbrtt9ttvv1x66aVZsGBB\nseMBwEbBjlAAoKCuuOKKf/ojm58pU6Zk8ODBueWWW3LUUUcVdK1LLrkktbW1+cEPflDQdTZWc+fO\nzfjx4zN27NhMmDAhLVu2TGVlZSorK9OzZ89sueWWxY5IgTRu3DgHHnhgOnTokNatW2fp0qWZNm1a\nnnrqqbRs2TKPPfZYdtttt2LHBICiUoQCAAV19dVXZ/ny5bn66quLHYUiGDNmTE466aTce++96dOn\nT8HXe+yxx3LmmWdmxowZBV9rY1dbW5vnnntuzWv0Tz75ZPbff/81xWjnzp1L9gqBzVFNTU0aNWr0\nf3790ksvzTXXXJNTTz01t9xySxGSAcDGw9F4AKCgPJa0+aqurs5Xv/rVDB8+fIOUoElyyCGHZN68\neY6FJ2nQoEEOOOCAXHjhhZkwYUIWLFiQiy++OIsXL84ZZ5yR1q1bZ/DgwfnVr36VOXPmFDsu6+mT\nStAk+cpXvpIkefvttzdkHADYKClCAYCCckfo5unuu+/Of/7nf+ahhx7KoYceusHWLS8vz+GHH54H\nHnhgg625qWjatGn69euX66+/PjNmzMiLL76YgQMHZurUqTn00EOz66675qyzzsqwYcOyePHiYsel\nnowcOTJlZWXp1atXsaMAQNE5Gg8AFNRPf/rTvPHGGxkyZEixo7CB3HTTTbn66qszduzYdOjQYYOv\n/9vf/jb33XdfRo4cucHX3lTV1dXlT3/605rX6KdOnZp99tlnzTH6Ll26pGHDhsWOyWfw4x//OEuX\nLs0HH3yQp556Kk888UROOeWU3Hjjjf4eArDZU4QCAAV1ww03ZObMmbnhhhuKHYUN4Lrrrssvf/nL\njB8/PruzfWAfAAAgAElEQVTssktRMrz33nvZeeeds3DhwjRp0qQoGTZ1y5cvz2OPPbbmftFZs2al\nR48ea4rRPffcM2VlZcWOySdo27ZtFi5cuObPDz300Fx55ZV2hAJAHI0HAArMHaGbh7q6ulx++eW5\n9dZbM3ny5KKVoEmy7bbbpmPHjpk0aVLRMmzqmjRpkt69e+eHP/xhnnnmmcyaNSsnnHBCnnvuufTt\n2zef+9zn8vWvfz333XdfFi1aVOy4/IN33nknq1evzvz58zNs2LAsXLgwlZWVueeee4odDQCKzo5Q\nAKCgbr755jz55JO5+eabix2FAqmrq8t3vvOdTJw4MWPHjk3r1q2LHSk/+MEP8s477+TnP/95saOU\nnLq6urzyyisZN25cxo0bl0ceeSS77bZbKisr07dv3xx66KFp3LhxsWPyN3Pnzs0ee+yRrbfeOvPn\nzy92HAAoKjtCAYCC8lhSaVu9enXOOOOMPPbYY3n44Yc3ihI0SQYMGJDRo0fHz/zrX1lZWb7whS/k\n7LPPzsiRI7No0aIMGTIkjRo1ysUXX5yWLVumX79++clPfpIXXnjB34Mia9++fTp06JB33303CxYs\nKHYcACgqRSgAUFAVFRVZvXp1sWNQACtXrsxXv/rVzJ49O+PGjcs222xT7Ehr7Lvvvqmpqckrr7xS\n7Cglr2HDhunWrVuuuuqqTJs2LXPnzs0ZZ5yRmTNnZuDAgWnXrl1OPvnk3HXXXXnnnXeKHXezNG/e\nvJSVlaV58+bFjgIARaUIBQAKyh2hpWn58uX58pe/nCVLlmT06NHZcsstix3pn5SVlaV///4ZPXp0\nsaNsdrbZZpsMGjQoN910U2bPnp1HH300X/rSlzJ8+PB06NAhHTt2zHe+85089NBDWbZsWbHjloRX\nX301S5Ys+T+/XldXl0suuWTNPaHNmjUrQjoA2Hi4IxQAKKihQ4fmD3/4Q4YOHVrsKNSTpUuX5uij\nj862226bu+++O40aNSp2pE80cuTIDBkyJBMnTix2FP5m1apVefrpp9fcLzp9+vQcfPDBa+4X7dSp\nUxo0sFdjbf3sZz/LRRddlK5du+bzn/98tttuuyxYsCCPPPJI5syZk5133jkTJ07MzjvvXOyoAFBU\nilAAoKDuv//+3HPPPRk2bFixo1APFi9enAEDBmTPPffMzTffnPLy8mJH+lRLly7N9ttvn7fffjst\nWrQodhw+wYcffphJkyatKUYXLVqUww47LH379k1lZWV22mmnYkfcJLz44ov51a9+lUcffTRvvfVW\nFi9enObNm+cLX/hCjjrqqHzrW99yLB4AoggFAApsxIgRue222zJixIhiR2E9vfvuuzn88MPTtWvX\nDBkyZJPYudevX7+cfvrpGTx4cLGj8Bm8+eabGTduXMaOHZsJEyZku+22W1OK9uzZc6O7ggEA2LRs\n/N+9AgCbNHeEloZ58+alR48eOeKII/Kzn/1skyhBk7gndBOz00475dRTT819992XBQsW5Le//W12\n2GGHDBkyJO3atUu3bt3y/e9/P9OmTfPvFQBgrdkRCgAU1JgxY/KTn/wkY8aMKXYU1tFrr72WPn36\n5PTTT8+FF15Y7DhrZfbs2Tn00EMzb968Taa85ZMtW7YsU6ZMydixYzNu3Li89dZb6dWr15r7RXfZ\nZZdiRwQANnK+GwQACqq8vNzOrU3Yyy+/nB49euS8887b5ErQJNl1112z9dZb59lnny12FNZT06ZN\nc/jhh+f666/PjBkz8uKLL2bgwIF57LHHcuihh2bXXXfNmWeemfvvvz/vv/9+seMCABshRSgAUFCO\nxm+6nn/++fTu3TtXXXVVvvnNbxY7zjobMGCA4/ElqG3btvnqV7+aO++8M/Pmzcvw4cOzxx575JZb\nbkn79u3TpUuXXHbZZZkyZUpWrlxZ7LgAwEZAEQoAFFRFRUVWr15d7BispWnTpqVv3775+c9/nq99\n7WvFjrNeBgwYkAceeKDYMSigsrKy7LvvvjnvvPPy4IMP5t13380111yTVatW5dxzz03Lli1z5JFH\n5oYbbsjLL78ct4MBwObJHaEAQEFNmzYt5557bqZNm1bsKHxGEydOzHHHHZfbb789/fv3L3ac9VZT\nU5PWrVtn5syZad26dbHjUASLFi3KhAkT1twvmiSVlZWprKzMYYcdllatWhU5IQCwIdgRCgAUlKPx\nm5bRo0fnuOOOy9ChQ0uiBE2SRo0a5bDDDsuDDz5Y7CgUScuWLXPsscfm1ltvzRtvvJFx48alU6dO\n+e1vf5vddtstBx54YC688MJMmDAhy5cvL3ZcAKBAFKEAQEF5LGnTMXTo0Jx66qkZNWpUevbsWew4\n9co9ofxdWVlZ9txzz5x99tkZOXJkFi1alCFDhqRRo0a59NJL06pVq/Tr1y/XX399XnjhBcfoAaCE\nOBoPABTUCy+8kBNOOCEvvPBCsaPwL/zmN7/JJZdckoceeigdO3Ysdpx6N3/+/Oy1115ZuHBhGjZs\nWOw4bMTef//9PPzwwxk3blzGjh2bZcuWpU+fPunbt2/69OmTtm3bFjsiALCOFKEAQEG99NJLGTRo\nUF566aViR+FT3HDDDbnuuusybty47LnnnsWOUzCdO3fO9ddfnx49ehQ7CpuQOXPmrClFH3744eyw\nww7p27dvKisr07179zRt2rSo+erq6jJp0qT8cdiwPPPoo3n19ddTs2pVttxii3TcZ58c1KtXjjvh\nhOy6665FzQkAGwNFKABQUK+++mr69++fV199tdhR+ATXXHNNbrvttowfPz4777xzseMU1OWXX56P\nP/441157bbGjsIlavXp1nn766TXF6PTp03PwwQeveXhp//33T4MGG+b2sbq6uvx+6NBcfv75KV+8\nOMcvXZqD6uqyV5LGSRYneT7J1IYNc095eQ466KD8+Kab0qFDhw2SDwA2RopQAKCg5syZk8MOOyyv\nvfZasaPwD+rq6nLxxRdn1KhRGTdu3GZx3PeJJ57IqaeemhdffLHYUSgRH374YR555JE1r9EvWrQo\nhx122JpitH379gVZ9/33389pJ5yQlyZPzo3LlqVXkrJ/8fEfJ7mtrCxXNGmS7156ab570UUpK/tX\nnwEApUkRCgAU1Ny5c9O1a9fMnTu32FH4m9ra2pxzzjl5/PHHM2bMmLRs2bLYkTaI2trabL/99nny\nySdLfvcrxfHmm29m3LhxGTduXMaPH5/tttsulZWV6du3b3r27Jktt9xyvddYtGhReh9ySHq89Vau\nq6lJk7X43DeSHNO0aQ78ylfyi9tuU4YCsNlRhAIABTVv3rx07tw58+bNK3YUkqxatSqnn356Xn31\n1YwePTpbbbVVsSNtUP/xH/+Rgw8+ON/85jeLHYUSV1tbm+eee25NMfrEE0+kU6dOa+4X7dy5cyoq\nKtZq5sqVK9PtgAPS65VXcs3Klf9yF+in+TBJZdOm6X/eefne97+/DhMAYNOlCAUACmrhwoXZZ599\nsnDhwmJH2ezV1NTkxBNPzAcffJDq6uo0a9as2JE2uKFDh+b222/PAw88UOwobGaWLVuWKVOmrLlf\n9M0330yvXr3WFKOf5TGja666KpN+9KOMWbZsnUrQv5uXpNMWW+ShRx/NAQccsB6TAGDToggFAArq\nvffey2677Zb33nuv2FE2ax9//HEGDx6cxo0b57777kvjxo2LHakoFi9enPbt22f+/PlFf+2bzdv8\n+fMzfvz4NfeLbrHFFmtK0d69e2ebbbb5p4+fN29e9tl11zy3fHnq4+bR25Pc3LFjpj7/fD1MA4BN\nw4Z50hAA2GyVl5dn1apVxY6xWfvwww9zxBFHZNttt83QoUM32xI0SbbeeusccMABefjhh4sdhc3c\n9ttvn5NOOil33nln5s2bl5EjR2aPPfbILbfcks997nPp0qVLLrvsskyePDk1NTW5+Ze/zLF1dfVS\ngibJSUnenDUr06dPr6eJALDxsyMUACiopUuXpnXr1lm6dGmxo2yW3nvvvRxxxBHp1KlTfvnLX6ZB\nAz8Hv/baa/P666/nF7/4RbGjwCdasWJFpk6duuZ+0VdffTUVy5dnfE1N9q/Hda4sL8/7p5+eIb/8\nZT1OBYCNlyIUACioFStWpEWLFlmxYkWxo2x2FixYsOao7XXXXeeF6L958cUX079//7z++uv+mrBJ\neOWVV3LQPvtk8apV9Xqk7+Ekl3TokMdefLEepwLAxsuWAACgoCoqKhyNL4I333wz3bt3z6BBg5Sg\n/0uHDh1SVlaWF5U/bCLeeuut7N+sWb3/z9sBSZ579dV6ngoAGy9FKABQUA0aNEhtbW0cQtlwZs2a\nle7du+cb3/hGLr/8ciXo/1JWVpYBAwZ4OZ5NxuLFi7NdAf4dulWSFatWZeXKlfU+GwA2RopQAKCg\nysrKUl5entWrVxc7ymbhT3/6U3r27JmLLroo5513XrHjbLQGDBiQ0aNHFzsGfCYVFRUpRFVZm6S2\nri7l5eUFmA4AGx9FKABQcI7HbxjPPPNM+vTpk2uvvTZnnHFGseNs1Hr27Jnp06fn/fffL3YU+Lc+\n//nPZ1YBdoTOStJ+u+08ogbAZsN/8QCAglOEFt6jjz6aI444IjfddFNOOOGEYsfZ6DVt2jTdunXL\n2LFjix0F/q0OHTpk7vLlWVLPc59J0nn/+nyHHgA2bopQAKDgysvLFaEFNG7cuAwaNCj33HNPBg4c\nWOw4mwzH49lUVFRUpGeXLhlWz3N/36xZ+g4eXM9TAWDjpQgFAArOjtDCGT58eE488cQMGzYslZWV\nxY6zSRkwYEAeeuih1NbWFjsK/Fv/ecEFubF589TXAfm5SSasXJnj7SAHYDOiCAUACq6iosJjSQVw\nzz335Mwzz8yDDz6Yrl27FjvOJudzn/tcWrdunaeeeqrYUeDf6tevX2rbtctvysrWe1Zdkv9s3Dhb\nbbttevbsmXHjxq1/QADYBChCAYCCsyO0/v3617/OBRdckAkTJuTAAw8sdpxNluPxbCrKy8tz++9/\nnwuaNMms9Zx1W1lZ3mjXLjNfey0XXXRRvvnNb6aysjLPPPNMvWQFgI2VIhQAKDhFaP26/vrr84Mf\n/CCTJk3K3nvvXew4m7T+/fsrQtlkdOzYMSd8/evpmmT2Os74fZJLttwy940alSZNmuSYY47Jiy++\nmMGDB+fII4/M8ccfn9mz13U6AGzcFKEAQMF5LKl+1NXV5Yorrsivf/3rTJ48ObvttluxI23yvvSl\nL2XOnDl55513ih0F/q1Ro0bl3t/9Lv9x/vn50hZb5LfJZ74zdFmS8xo1yn9tu23GTJ78Tz9Eadiw\nYc4888zMnDkze++9dw455JCcffbZWbBgQSG+DAAoGkUoAFBw7ghdf3V1dTn//PNTXV2dyZMnZ6ed\ndip2pJLQsGHD9O3bNw888ECxo8C/VF1dndNOOy2jR4/Oj667LqMnT85/f+5zOax581Qn+bQfNb2f\n5CdlZdmnWbO8069fnnvlley3336f+LHNmzfPpZdempdeeinl5eXp0KFDrrjiinz44YeF+rIAYINS\nhAIABedo/PpZvXp1vvGNb2Tq1Kl5+OGH06ZNm2JHKikDBgxQhLJR+8Mf/pCzzjorDz74YA466KAk\nSefOnTN95syc9qtf5cf77pttGjZMtxYtcsYWW+Tsxo1zcrNm6bjllmlbVpY/Hnxw7h47NveOGJGW\nLVv+2/VatWqVIUOG5Omnn87s2bOz++6758Ybb0xNTU2hv1QAKKiyurq6z3qaAgBgnXTs2DF33313\nOnbsWOwom5yVK1fma1/7WubNm5eRI0dmyy23LHakkrNw4cLsscceWbhwYRo1alTsOPBPfve73+Xc\nc8/NQw899Kk7OZPk/fffz7PPPpuZM2dm5cqVad68eTp27JhHH300zz77bO688851zvDcc8/loosu\nysyZM/Pf//3f+cpXvpIGDeypAWDTowgFAApu//33z6233poDDjig2FE2KStWrMixxx6bmpqa3H//\n/dliiy2KHalkHXLIIbnmmmty2GGHFTsKrHH33Xfn//2//5exY8dmn332WacZb7/9dvbdd9/Mnz9/\nvYv+iRMn5oILLkhtbW1+9KMfpU+fPus1DwA2ND/GAwAKzh2ha2/p0qU58sgj07BhwwwfPlwJWmAD\nBgzwejwblTvuuCMXXHBBxo8fv84laJLssMMO2XPPPTNp0qT1ztS7d+88+eSTufDCC3PWWWelsrIy\nzzzzzHrPBYANRREKABScO0LXzgcffJDDDz88O+ywQ+69917HtTcARSgbk1tvvTWXXnppJk6cmA4d\nOqz3vEGDBmXYsGH1kCwpKyvLMccckz//+c8ZNGhQjjzyyBx//PGZPXt2vcwHgEJShAIABacI/ewW\nLVqU3r17r7lOoKKiotiRNgv7779/lixZklmzZhU7Cpu5m266KVdeeWUefvjh7LnnnvUys6qqKsOH\nD6/XnfkNGzbMWWedlZkzZ2bvvffOIYcckrPPPjsLFiyotzUAoL4pQgGAgisvL1eEfgbz5s1Ljx49\ncvjhh+fnP/+5x0g2oAYNGqR///5ej6eobrzxxvzwhz/MpEmTsttuu9Xb3N122y1t2rTJ448/Xm8z\n/6558+a59NJL89JLL6W8vDwdOnTIFVdckQ8//LDe1wKA9eW7awCg4OwI/fdef/31dO/ePSeddFKu\nueaalJWVFTvSZsfxeIrppz/9aX7yk59k0qRJ2WWXXep9fn0ej/8krVq1ypAhQ/L0009n1qxZ2X33\n3XPjjTempqamYGsCwNpShAIABeexpH/tlVdeSffu3fPtb387F110UbHjbLb69OmTxx57LB999FGx\no7CZue666/KLX/wijzzySHbeeeeCrPH3IrSurq4g8//u85//fO6+++489NBDGT16dPbaa6/cd999\nqa2tLei6APBZKEIBgIKzI/TTPf/88+nVq1euvPLKnH322cWOs1lr0aJFDj744EyYMKHYUdiMXHPN\nNbnlllsyadKk7LTTTgVbZ5999knDhg0zffr0gq3xjzp16pQHH3wwN998c66//vocdNBBGT9+/AZZ\nGwA+jSIUACg4RegnmzZtWvr27Zuf/exnOeWUU4odhzgez4Z11VVX5a677sqkSZOyww47FHStsrKy\ngh+P/yS9e/fOk08+mQsvvDBnnXVW+vbtm2effXaDZgCAv1OEAgAF57Gk/2vSpEk58sgjc9ttt+WY\nY44pdhz+ZsCAAXnggQcKfnyYzVtdXV2+973vZejQoZk0aVLatm27QdYtRhGa/LWEPeaYY/LnP/85\nVVVVGTBgQI4//vjMnj17g2cBYPOmCAUACs4dof/sgQceyFe+8pUMHTo0AwYMKHYc/sEee+yRJk2a\nZMaMGcWOQomqq6vLxRdfnOHDh+fhhx9OmzZtNtjaBx10UJYsWZKXXnppg635jxo2bJizzjorr776\navbee+8ccsghOfvss7Nw4cKi5AFg86MIBQAKztH4//H73/8+p5xySkaOHJlevXoVOw7/S1lZmePx\nFExdXV2++93v5qGHHsrEiRPTqlWrDbp+gwYNUlVVVZRdof+oefPmufTSS/PSSy+lvLw8e+21V664\n4op8+OGHRc0FQOlThAIABacI/avbb7893/72tzN27Nh06dKl2HH4FP3791eEUu/q6uryX//1X5k0\naVImTJiQli1bFiXH4MGDi16E/l2rVq0yZMiQPP300/8/e3ceV3P+eA/83NuNUNYk6yiJZBBTttGK\nNkMXGWXGYGyNfT6foWEwtrENg7FHRox9KSWh3RJFtlSk7FsYS2l1u78/5qvfx4wZ1L33Vd3zfDz8\n4d73fb3PnYeJe+5rwbVr19C8eXOsXLkSBQUFoqMREVEFxSKUiIiI1I57hAIrV67EjBkzEBUVhbZt\n24qOQ//Czs4OSUlJePLkiegoVEEUFRVh3LhxOHXqFMLDw1G7dm1hWT799FPcunULN27cEJbhr0xM\nTLB161YcOnQIISEhsLCwwI4dO1BUVCQ6GhERVTAsQomIiEjttH1G6Pz58/HLL78gNjYWLVq0EB2H\n3kFPTw/29vY4fPiw6ChUARQVFcHHxwfnzp3DkSNHULNmTaF5ZDIZ+vTpg/379wvN8TZWVlYICwuD\nn58flixZAmtra4SHh4uORUREFQiLUCIiIlI7bT0s6fWhKFu3bsWxY8fQtGlT0ZHoPXGfUFIFhUKB\nESNGICUlBWFhYahevbroSADEnR7/vhwdHREfHw9fX1/4+PigZ8+eSExMFB2LiIgqABahREREpHba\nOCO0qKgIEyZMwOHDhxETE4MGDRqIjkQfwM3NDYcPH9bKAp9UQ6FQYNiwYcjIyMChQ4dgYGAgOlIx\nJycnJCUl4cGDB6Kj/COJRAJPT08kJydDLpfD3d0dXl5eSE9PFx2NiIjKMRahREREpHbaVoQqFAp8\n/fXXSExMRGRkpLBDUajkGjVqhIYNG+LUqVOio1A59OrVKwwePBj37t3DwYMHUa1aNdGR3lC5cmW4\nuroiKChIdJR30tXVhY+PD9LS0tCqVSvY2Nhg3LhxyMzMFB2NiIjKIRahREREpHbadFhSQUEBvLy8\ncOfOHRw+fBg1atQQHYlKiMvjqSQKCwsxaNAgPHnyBAcOHEDVqlVFR3qrsr48/q/09fUxffp0pKam\nQkdHBxYWFpg1axaysrJERyMionKERSgRERGpnbbsEZqbmwu5XI78/HwEBweXuVlg9GHc3d0RGhoq\nOgaVIwUFBRg4cCBevnyJwMBAVKlSRXSkf+Ti4oK4uDg8ffpUdJQPUrduXSxbtgxnzpxBWloazM3N\nsXLlShQUFIiORkRE5QCLUCIiIlI7bVgan5WVBXd3d9SoUQN79uyBnp6e6EhUSp06dcKdO3dw584d\n0VGoHMjPz4enpycUCgX27t1b5n8G6Ovrw9HRESEhIaKjlIiJiQm2bt2K0NBQhISEwMLCAjt27EBR\nUZHoaEREVIaxCCUiIiK1q+hF6NOnT9GjRw+YmZlhy5Yt0NXVFR2JVEBHRwfOzs6cFUrvlJeXh379\n+kEmk2HXrl2oXLmy6Ejvpbwtj38bKysrhIWFwc/PD0uWLIG1tTXCw8NFxyIiojKKRSgRERGpXUXe\nI/Thw4ewt7dH165dsW7dOujo6IiORCrEfULpXXJzc+Hh4YGqVatix44dqFSpkuhI761Xr16IiIjA\ny5cvRUcpNUdHR8THx8PX1xc+Pj7o2bMnEhMTRcciIqIyhkUoERERqV1F3SP09u3bsLOzg1wux88/\n/wyJRCI6EqmYs7MzoqKikJeXJzoKlUE5OTno3bs36tSpg23btpW72eC1a9dGp06dEBYWJjqKSkgk\nEnh6eiI5ORlyuRzu7u7w8vJCenq66GhERFRGsAglIiIitauIS+PT09Nha2uLESNG4Mcff2QJWkHV\nqVMHH3/8MWJiYkRHoTLm5cuX6NWrFxo0aICAgADIZDLRkUqkIiyP/ytdXV34+PggLS0NrVq1go2N\nDcaNG4fMzEzR0YiISDAWoURERKR2Fa0ITU5Ohp2dHXx9ffGf//xHdBxSM54eT3+VlZUFV1dXmJiY\nwN/fv1xvidGnTx+EhoYiPz9fdBSV09fXx/Tp05GamgodHR1YWFhg1qxZyMrKEh2NiIgEYRFKRERE\naleR9ghNTEyEk5MTFixYgFGjRomOQxrwep9QpVIpOgqVAS9evICLiwssLCzg5+dXrktQAKhfvz4s\nLS0RGRkpOora1K1bF8uWLcOZM2eQlpYGc3NzrFq1CgUFBaKjERGRhrEIJSIiIrWrKDNCT5w4ARcX\nF6xevRpffPGF6DikIW3atEFeXh6uXr0qOgoJ9uzZM/Ts2RPt2rXDmjVrIJVWjI9TFXF5/NuYmJhg\n69atCA0NRXBwMFq1aoUdO3agqKhIdDQiItKQivE3NxEREZVpFeGwpKNHj8LDwwNbt26FXC4XHYc0\nSCKRwM3NjafHa7k//vgDPXr0QKdOnbBy5coKU4ICgFwuR1BQULn/Of2+rKysEBYWhvXr12PJkiWw\ntrZGeHi46FhERKQBFedvbyIiIiqzyvuM0KCgIAwaNAj79u1Dz549RcchAV4vjyft9OTJEzg5OcHO\nzg6//PJLhTsczcTEBI0aNcLx48dFR9EoR0dHxMfHw9fXFz4+PujZsycSExNFxyIiIjViEUpERERq\nV56L0O3bt2PUqFEIDQ1Ft27dRMchQZycnBAfH48XL16IjkIa9ujRIzg4OMDFxQWLFy+ucCXoa9qy\nPP6vJBIJPD09kZycDLlcDnd3d3h7eyM9PV10NCIiUgMWoURERKR25fWwJD8/P/z3v/9FeHg4Pvnk\nE9FxSCB9fX106dKFy2e1zMOHD+Hg4AAPDw/89NNPFbYEBf5/Eaqth4Lp6urCx8cHaWlpsLCwgI2N\nDcaNG4fMzEzR0YiISIVYhBIREZHalcc9QpcuXYp58+YhJiYGrVu3Fh2HygAuj9cu9+/fh729PQYM\nGIDZs2dX6BIUACwsLFCtWjWcOXNGdBSh9PX1MX36dKSmpkJHRwcWFhaYNWsWsrKyREcjIiIVYBFK\nREREaleelsYrlUrMmjULa9euxbFjx2BmZiY6EpURbm5uCA0N5QnTWuDu3buwt7fHl19+iRkzZoiO\noxESiURrl8e/Td26dbFs2TIkJCQgLS0N5ubmWLVqFQoKCkRHIyKiUmARSkRERGpXXopQpVKJ7777\nDnv37kVsbCwaN24sOhKVIWZmZqhevTrOnTsnOgqp0e3bt2FnZ4fhw4dj6tSpouNoVN++fbF3716t\nXR7/Nqampti6dStCQ0MRHByMVq1aYefOnfxChIionGIRSkRERGpXHvYILSoqgo+PD44dO4bo6GgY\nGxuLjkRlEJfHV2w3btyAnZ0dxowZg++++050HI3r0KED8vLykJycLDpKmWNlZYWwsDCsW7cOixcv\nhomou0MAACAASURBVLW1NfcMJiIqh1iEEhERkdqV9T1CX716hcGDByM1NRXh4eGoXbu26EhURrm7\nuyM0NFR0DFKDjIwM2NvbY9KkSZg0aZLoOEJwefy7OTk5IT4+HlOmTIGPjw969uyJxMRE0bGIiOg9\nsQglIiIitSvLS+Pz8/Ph6emJP/74A6GhoTAwMBAdicqwbt26ITU1FY8ePRIdhVQoLS0N9vb28PX1\nxbhx40THEYpF6LtJpVIMGDAAycnJkMvlcHd3h7e3NzIyMkRHIyKid2ARSkRERGpXVovQly9f4rPP\nPoOOjg4CAwNRtWpV0ZGojKtUqRKcnJxw6NAh0VFIRa5cuQJHR0fMmDEDo0ePFh1HuK5du+LevXss\n9d6Drq4ufHx8kJaWBgsLC9jY2GD8+PHIzMwUHY2IiP4Bi1AiIiIqlb1792L8+PGwtbVFjRo1IJVK\nMXjw4Deu+bc9QocPHw6pVAqpVKrRD97Pnz+Hi4sL6tevjx07dqBSpUoauzeVb25ubtwntIJITk6G\no6Mj5syZg+HDh4uOUybo6OigT58+2L9/v+go5Ya+vj6mT5+OlJQUSKVSWFhYYNasWcjKyhIdjYiI\n/oJFKBEREZXK3LlzsWrVKly4cAGNGjWCRCL52zX/NCM0ODgY/v7+MDAweOvr1OXJkydwcnJCmzZt\nsGnTJshkMo3dm8o/Nzc3HDlyBIWFhaKjUCkkJSWhe/fuWLhwIYYMGSI6TpnC5fElU7duXSxbtgwJ\nCQlIS0uDubk5Vq1ahYKCAtHRiIjo/7AIJSIiolJZtmwZrl69iufPn2P16tVQKpV/u+ZthyU9fvwY\nI0eOxMCBA9G+fXtNxcX9+/dhZ2eH7t27Y+XKlZBK+c8h+jD169eHqakp4uLiREehErpw4QJ69OiB\npUuX4osvvhAdp8xxdHREcnIy7t+/LzpKuWRqaoqtW7ciNDQUwcHBaNWqFXbu3ImioiLR0YiItB7/\n5U9ERESlYmdnh2bNmv3rNW+bETpixAhIJBKsWrVKnfHecPPmTXTr1g3e3t5YsGCBRmehUsXi7u7O\n5fHlVGJiIpydnfHrr79i4MCBouOUSZUqVYK7uzsCAwNFRynXrKysEBYWhnXr1mHx4sWwsbFBRESE\n6FhERFqNRSgRERGp3V+L0N9++w0HDhzA+vXrUatWLY1kuHr1KmxtbTF+/HhMnTpVI/ekiotFaPmU\nkJAAV1dXrFmzBv379xcdp0zj8njVcXJyQnx8PCZPnozRo0ejZ8+eSExMFB2LiEgrsQglIiIitfvf\nw5Ju3ryJiRMn4ssvv0SvXr00cv+LFy/C3t4eM2fOxPjx4zVyT6rYrK2tkZmZiZs3b4qOQu/p1KlT\ncHd3x4YNGyCXy0XHKfOcnZ0RHx+PP/74Q3SUCkEqlWLAgAFITk6GXC6Hu7s7vL29NXpIoKb88ccf\n2LBhA/r27YvmzZujatWqqFmzJrp16wZ/f/+3bqEDACdPnoSbmxvq1KmDqlWrom3btli+fDm3FCAi\nlWIRSkRERGr3eo9QpVKJr776CgYGBli+fLlG7h0fH48ePXrgl19+wbBhwzRyT6r4pFIpXFxcOCu0\nnDhx4gR69+6NzZs347PPPhMdp1yoVq0anJycEBwcLDpKhaKrqwsfHx+kpaXBwsIC1tbWGD9+PDIz\nM0VHU5ndu3dj5MiRiI+PR6dOnTBp0iT0798fly9fxvDhw/H555//7TVBQUGws7PD8ePH0bdvX4wb\nNw6FhYWYNGkSvLy8BLwLIqqoWIQSERGR2r1eGr906VIcO3YMGzZsQI0aNdR+35iYGPTq1QsbN258\n6wcvotJwd3dHaGio6Bj0DrGxsZDL5di6dStcXV1FxylXuDxeffT19TF9+nSkpKRAIpHAwsICs2bN\nQlZWluhopdaiRQsEBwfjzp072LJlC+bNm4cNGzYgNTUVjRs3xt69e7F///7i67OysjBixAjIZDLE\nxMTAz88PCxcuxPnz59G5c2fs2bMHu3btEviOiKgiYRFKREREaieTyZCbm4sffvgBQ4cOhbOzs9rv\neejQIXh6emLHjh0aW4JP2sXZ2RmxsbHIzc0VHYX+QWRkJPr374/t27ejZ8+eouOUO7169UJUVBSy\ns7NFR6mwjIyMsHz5ciQkJCAtLQ3m5uZYtWoVCgoKREcrMXt7e7i7u//tcSMjI4wePRpKpRLR0dHF\nj+/evRuPHz+Gl5cXrKysih+vVKkS5s6dC6VSiTVr1mgiOhFpARahREREpHY6OjrIz89Hfn4+/P39\nIZVK3/gVExMDADAzM4NUKsWBAwdKdb89e/ZgyJAhCAoKgqOjoyreAtHf1KxZE1ZWVoiKihIdhd7i\n6NGjGDhwIHbv3g0nJyfRccqlmjVrokuXLpz5rAGmpqbYunUrQkNDERwcjFatWmHnzp0Vbn9MXV1d\nAH9+QfpaVFQUJBLJW78ktbW1RdWqVXHy5EkUFhZqLCcRVVyyd19CREREVDqvP/AMHz78rc+HhITg\n4cOHGDBgAKpXr46mTZuW+F6bN2+Gr68vDh8+jHbt2pV4HKL38fr0eDc3N9FR6H+EhYVh8ODB2Ldv\nHz799FPRccq1fv36Yd++fRgwYIDoKFrBysoKYWFhiIiIwJQpU7B48WIsXLiwQpT5CoUCmzdvhkQi\ngYuLS/HjV65cAQCYm5v/7TU6OjowMTFBcnIyMjIy0KJFC43lJaKKiUUoERERqZ1MJoNEIsH69evf\n+ryDgwMePnyIn376CaampiW+z+rVqzF//nxERUWhZcuWJR6H6H25ubmhV69eWLlyJSQSieg4hD+/\nWBk2bBiCgoLQuXNn0XHKvT59+uC7775DXl4e9PT0RMfRGk5OToiPj8eePXswatQoNGvWDAsWLHhj\n6Xh5M2XKFFy+fBm9evVCjx49ih9//vw5APzj3uGvH3/27Jn6QxJRhccilIiIiEolKCgIgYGBAIAH\nDx4AAE6ePImhQ4cCAAwNDTF9+nS8evVKrTkWLlyI9evXIzY2FiYmJmq9F9FrlpaWUCqVSE5OhqWl\npeg4Wi8wMBCjRo1CSEgIbGxsRMepEIyMjNC2bVuEh4dzv2UNk0qlGDBgAORyOfz8/ODm5gYHBwfM\nnTu3VF8airBixQosXboUrVq1QkBAgOg4RKTFuEcoERERlcr58+cREBCAgIAAHDlyBBKJBNevXy9+\nbN++fcWnxv+bks6mUyqVmDZtGjZv3swSlDROIpHw9PgyYs+ePRg9ejQOHTrEElTFeHq8WLq6uvjm\nm2+QlpYGCwsLWFtbY/z48cjMzBQd7b2sXLkSEydOROvWrREZGYmaNWu+8fzrGZ+vZ4b+1evH//o6\nIqKSYBFKREREpTJz5kwoFIp//JWeng4dHZ1/LUKjoqLw6tWrD57hUlRUhIkTJ+LQoUOIiYlBw4YN\nS/t2iD7Y631CSZydO3di7NixCAsLQ/v27UXHqXDkcjkOHDig9pn99O/09fUxffp0pKSkQCKRwMLC\nArNnz0Z2drboaP9o2bJlGD9+PNq0aYPIyEgYGRn97ZrX+35evXr1b88pFApcv34dMpms3M2CJaKy\niUUoERERqZ1MJoNCoVDpmAqFAsOHD0dCQgIiIyNRt25dlY5P9L4cHByQmJjI/esE+f333zFp0iQc\nPXqUB6SpSZMmTWBiYoLY2FjRUQh/blewfPlyJCQk4MqVK2jevDlWrVqFgoIC0dHesHDhQnz77bdo\n3749oqKiYGho+NbrHB0doVQqERYW9rfnYmJikJOTg65duxafOE9EVBosQomIiEjtpFIpioqKUFRU\npJLxCgoK4O3tjVu3buHIkSNcLkdCVa1aFd26dcORI0dER9E6mzdvxuTJkxEeHo6PP/5YdJwKjcvj\nyx5TU1P8/vvvCA0NRXBwMFq1aoWdO3eq7O/a0pgzZw6+//57WFtbIzw8HLVq1frHa/v37w9DQ0Ps\n2LEDZ8+eLX48Pz8fP/zwAyQSCXx8fDQRm4i0gESpVCpFhyAiIqKKT1dXFzk5OaWe0ZGbmwtPT09I\npVLs2rWLpxhTmbBq1SrEx8dj8+bNoqNojY0bN2LmzJkIDw9Hy5YtRcep8K5cuQJHR0fcvn0bUinn\n05RFERERmDJlCoA/Z2M6OTkJybF582YMHToUMpkMY8eOfetp8E2bNsVXX31V/PugoCB4enqicuXK\nGDhwIGrXro0DBw7g6tWr8PT0xI4dOzT5FoioAmMRSkRERBqhp6eHp0+fokqVKiUeIzs7G71790a9\nevUQEBDAZXJUZty4cQM2NjZ48OABSyINWLduHebNm4eIiAg0b95cdBytYWlpiY0bN6JTp06io9A/\nKCoqwp49ezB16lQ0a9YMCxYsgJWVlUYzzJo1C7Nnz/7Xa+zs7BAZGfnGY3FxcZg3bx7i4uKQl5cH\nMzMzfP311xg3blyJD1QkIvorFqFERESkEfr6+njw4AH09fVL9PqnT5/Czc0NrVu3xtq1a6Gjo6Pi\nhESlY2lpiU2bNvHEcjVbtWoVFi9ejIiICDRr1kx0HK0yffp05OfnY9GiRaKj0DsUFhbCz88Pc+bM\ngaOjI+bMmcPDhoiIwD1CiYiISENkMlmJTxzOzMyEg4MDOnXqhPXr17MEpTKJp8er37Jly7BkyRJE\nR0ezBBXg9T6hnEtT9unq6uKbb75BWloaWrRoARsbG4wfPx6ZmZmioxERCcUilIiIiDSipEXonTt3\nYGdnh969e2Pp0qVcHkdlFotQ9fr555+xcuVKREdHo2nTpqLjaKV27dpBoVDg0qVLoqPQe9LX18eM\nGTOQnJwMiUQCCwsLzJ49G9nZ2aKjEREJwSKUiIiINEJHR+eDi9CMjAzY2tpi2LBhmD17NktQKtO6\ndOmC9PR03L9/X3SUCmf+/PlYv349oqOj0aRJE9FxtJZEIuHp8eWUkZERli9fjoSEBFy5cgXNmzfH\n6tWrUVhYKDoaEZFGsQglIiIijfjQGaHJycmws7PD5MmT8d1336kxGZFq6OrqokePHjh06JDoKBXK\n7NmzERAQgOjoaDRq1Eh0HK3HIrR8MzU1xe+//47Q0FAcOHAArVq1ws6dO1FUVCQ6GhGRRvCwJCIi\nIlKLW7duYcuWAJw8GYHz5y/i0aM/oKenh48+aoBPPukIF5c+kMvlqFSp0t9em5iYCHd3dyxatAhf\nfvmlgPREJbN582YEBwdjz549oqOUe0qlEjNnzsTevXsRGRmJevXqiY5E+PNU8oYNGyI2NhbNmzcX\nHYdKKSIiAlOmTAEALFy4EE5OToITERGpF4tQIiIiUqnr169j4sTRiI2NhaNjEdq0KYCZGVCjBqBQ\nAHfvAlevAidOGODWLSn++19fTJr0X8hkMgDAyZMnIZfLsWbNGvTt21fwuyH6MJmZmTA3N0dmZuZb\nS356P0qlEtOmTUNISAjCw8NhZGQkOhL9Dx8fH5iYmGDy5Mmio5AKFBUVYc+ePZg6dSqaNWuGBQsW\nwMrKSnQsIiK1YBFKREREKuPntx6+vpPQv38+PDwUqFLl36+/cQNYvboaioqaYufOIFy/fh1eXl7Y\nunUrnJ2dNZKZSNU6duyI+fPnw9HRUXSUckmpVGLy5MkIDw/H0aNHYWhoKDoS/cXRo0cxffp0nDp1\nSnQUUqHCwkL4+flhzpw5cHR0xJw5c2Bqaio6FhGRSrEIJSIiIpWYNWsGNm1aglmzcvDRR+//OqUS\n2LdPiu3bq6CoqBICAwNha2urvqBEajZ79mw8f/4cS5YsER2l3FEqlZg0aRKOHz+OI0eOoHbt2qIj\n0VsUFhbC2NgYFy5c4L6tFVB2djaWLl2K5cuX44svvsAPP/yAunXrio5FRKQSPCyJiIiISs3ffyM2\nbVqCpUs/rAQFAIkE6NevCCNGvESlSkVo3bq1ekISaYibmxsOHjwoOka5o1QqMW7cOMTFxSE8PJwl\naBmmq6uLXr16ITAwUHQUUgN9fX3MmDEDKSkpAAALCwvMnj0b2dnZKr2PUqkE52URkaaxCCUiIqJS\nuXXrFr77bgJmzMhBaXoLZ2fg00/zMHbsSNWFIxKgffv2ePbsGdLT00VHKTeKiorg4+ODxMREHDly\nBDVr1hQdid6Bp8dXfEZGRli+fDni4+Nx5coVNG/eHKtXr0ZhYWGJxnv48CEWLFgEW9teqFmzPnR0\ndCCV6qB69Xro2tUVs2fPw927d1X8LoiI3sSl8URERFQqAwd6oGrVgxg8+FWpx8rNBYYPr4rdu4+i\nS5cuKkhHJMawYcNgZWWFcePGiY5S5hUVFWHkyJG4cuUKQkNDYWBgIDoSvYfc3FwYGxsjPT2d+7hq\niXPnzsHX1xcZGRmYO3cuPD09IZW+e27VkydPMHbsZOzfvxcSST/k5bkD6ACg8f9dcQ/AWVSuHAZg\nB1xd3bB27VLUq1dPfW+GiLQWZ4QSERFRiT148AChoWHo27f0JSgAVKkCeHjkYsWKxSoZj0gUd3d3\nLo9/DwqFAkOHDkV6ejoOHTrEErQcqVKlCnr27IkDBw6IjkIaYmVlhcOHD2Pt2rVYvHgxbGxsEBER\n8a+vOXz4MMzM2mDfPgPk519HXt5GAH0BfIQ/6wgpgEYA+iA/fw3y82/i4MEmaN68Dfbv59YLRKR6\nLEKJiIioxHbu3IlPP5VAX191Y7q4KBESEoqcnBzVDUqkYT169MDJkyfx8uVL0VHKrFevXmHw4MG4\ne/cuDh48CH1V/iAhjeDyeO3k5OSE+Ph4TJ48GaNGjYKzszPOnTv3t+t2794DufwrPHu2DQUFywDU\neo/Rq6OwcD6ysg5g0KAx2LRps8rzE5F2YxFKREREJRYXF4mPP85T6ZgGBkCTJnq4cOGCSscl0qTq\n1avD2tr6nbOltFVhYSG++OILPH78GMHBwahataroSFQC7u7uiI2NxYsXL0RHIQ2TSqUYMGAAkpOT\n0adPH7i5uWHQoEHIyMgAAJw5cwZfffUNcnPDANiV4A4dkZsbibFjfRETE6PS7ESk3ViEEhERUYld\nvHgeZmaqH7dZs1csQqncc3NzQ2hoqOgYZU5BQQG8vLyQlZWFoKAgVKlSRXQkKqHq1aujW7du/HOu\nxSpVqoRvvvkGaWlpaNGiBaytrTFmzBj07fslcnOXA2hXitFbICfHD59/PlTlJ9YTkfZiEUpEREQl\n9vx5NtSxpZ++fgGeP3+u+oGJNOj1PqE8m/T/KygowIABA1BQUIB9+/ZBT09PdCQqJS6PJwDQ19fH\njBkzkJKSgkuXknD7dgMAA1Uwci88f94Zixf/ooKxiIhYhBIREVEpyGQ6UChUP+6rV1Lo6uqqfmAi\nDWrRogUqVaqES5cuiY5SJuTn56Nfv36QSqXYs2cPKleuLDoSqUDv3r1x+PBh5Obmio5CZYChoSHS\n0+8DmA1AopIx8/Im49df1+HVK9UczEhE2o1FKBEREZWYiclHuH1b9ePevasHU1NT1Q9MpEESiYSn\nx/+f3NxceHh4oEqVKti5cycqVaokOhKpSN26ddG+fXscPXpUdBQqA86ePYsXL3QAdFHhqG2hUDTk\nXqFEpBIsQomIiKjErK1tceWKav85oVQCFy68wNy5czF9+nRERUUhL0+1BzIRaQqLUCAnJwe9e/dG\nrVq1sG3bNs72roC4PJ5eS0hIgELxKVQ1G/S13NyuiI9PUOmYRKSdWIQSERFRibm59UJsbFWocgvE\n8+eBxo0bY8GCBSgqKsLUqVNRt25dODk5Yd68eYiLi0NhYaHqbkikRnZ2drh48SKePHkiOooQL1++\nRK9evVC/fn1s2bIFMplMdCRSAw8PDwQHB/NnM+H06YvIzS3NAUlvV1jYDidPXlT5uESkfViEEhER\nUYnZ29tDR6cmzp9X3ZgHDlTF2LGT0b179+Li8+7du/j222/xxx9/wMfHB4aGhnB3d8eSJUtw7tw5\nFBUVqS4AkQrp6enB3t4eR44cER1F47KysuDm5oaPPvoImzZtgo6OjuhIpCaNGzeGmZkZly4Tnj7N\nAlBDDSPXxPPnWWoYl4i0DYtQIiIiKjGJRIKZMxdg9epqUMVEoIQE4Nq1qvjqq6/eeLx69erFxef5\n8+eRnp6OoUOHIj09HV5eXqhbty769euHVatWISUlhad0U5mijcvjX7x4ARcXF7Ro0QIbN25kCaoF\nuDyeAKBSJV0ABWoYueD/xiYiKh0WoURERFQq3t7eaNGiMzZuLN0HlKdPgV9+qYqNG3+HgYHBv15r\naGiI/v37Y/Xq1UhNTcXFixchl8tx9uxZuLi4oEGDBhg0aBA2btyI69evlyoXUWm5ubkhLCwMCoVC\ndBSNePbsGXr27Im2bdti7dq1kEr5kUMbyOVy7N+/nzP0tVybNmaQyVJVPq5EkoI2bZqrfFwi0j78\nVwkRERGVikQiwaZN23DmjBF++w0l2i/0jz+AKVOq4uuvx6Nnz54f/PqGDRviiy++gL+/P27cuIET\nJ07AwcEBERER6Ny5M0xMTPD111/j999/x7179z48IFEpNG7cGA0bNsTp06dFR1G7p0+fokePHujY\nsSNWrVrFElSLmJubw9DQEKdOnRIdhQSytu6AqlXPqHxcff0z6NSpg8rHJSLtI1Fy7RgRERGVUl5e\nHuzt7XHjRiosLAoxblwOatd+v9fGxQHLl1fB6NH/wcyZsyGRqPakWaVSiZSUFERGRiIyMhLR0dEw\nNjaGo6MjHB0dYWdnhzp16qj0nkR/NXXqVEgkEsybN090FLV58uQJevToAQcHB/z8888q/3+Zyr6Z\nM2fi5cuX+Pnnn0VHIUGys7NhZNQEubkXATRS0ahPULlyM9y5cw2GhoYqGpOItBWLUCIiIioVhUKB\nAQMGQCaTYdOmTZg1azo2bFgDV9dXcHcvRP36b3vNn/uBBgfr486davjtt+1wcHDQWN4LFy4UF6PH\njx+HmZlZcTHarVu3dy7NJ/pQx48fx7hx43Du3DnRUdTi0aNH6N69O1xdXTF//nyWoFrqwoULkMvl\nSE9P558BLTZ8+Fhs3lwdr179pJLxpNKF6Ns3Gbt3b1bJeESk3ViEEhERUYkplUqMHTsWycnJCAsL\nQ+XKlQEAaWlpWL16BTZv3gQ9PcDcXIrq1RVQKCS4fl2BjIwCWFqaY+zYyRg4cCCqVKki7D0UFhYi\nISGhuBiNj49HmzZtiovRzp07C81HFcOrV69Qr149XLx4EQ0bNhQdR6UePnwIJycneHh4YM6cOSzA\ntJhSqYSZmRn27NkDKysr0XFIAKVSibVr12LMmP9CqTwLoGUpR7yBKlWscfZsLCwsLFQRkYi0HItQ\nIiIiKrF58+Zh9+7diImJQY0aNf72fFFRETIyMnDu3Dk8ffoUOjo6yM/Px+rVq5GUlCQg8bvl5uYi\nLi6uuBi9ePEibGxsiotRa2tr6Ory5Fr6cN7e3nBwcMCIESNER1GZ+/fvw8nJCZ9//jlmzJjBEpQw\nefJkVK5cGXPmzBEdhTTs1q1bGDNmDNLT09Gzpwv8/I4hJycGQNUSjpiPqlV74vvvXfHDD76qjEpE\nWoxFKBEREZXIxo0bMXfuXJw8eRL137b+/R/k5+ejVq1aePToEapVq6bGhKqRlZWFY8eOITIyEhER\nEUhPT8enn35aXIy2bdsWOjo6omNSOfD7779j9+7dCAwMFB1FJe7evQtHR0cMHjwY06ZNEx2HyohT\np07h66+/xuXLl0VHIQ159eoVVqxYgZ9++gkTJ07E5MmTIZPJMHDgUBw8eBc5OYEA9D9w1DxUqTIA\ntrYyhITsgkwmU0d0ItJCLEKJiIjogwUHB2PEiBGIiYlBixYtPvj1HTt2xOLFi2Fra6uGdOr15MkT\nREdHF88YffjwIezt7YuLUQsLC86Ko7d68uQJTE1NkZmZWbyNRHl1+/ZtODo6YsSIEZg8ebLoOFSG\nFBUVoXHjxoiIiEDLlqVdFk1l3ZkzZzBy5EjUqlULa9asgbm5efFzCoUCQ4d+g717o5CT4w/g0/cd\nFdWqDYGzczts3+6PSpUqqSU7EWknqegAREREVL7ExcVh2LBhCAoKKlEJCgA2NjY4ffq0ipNpRp06\nddCvXz+sWrUKKSkpSEpKQr9+/XDu3Dm4ubmhfv368Pb2xoYNG5CRkSE6LpUhderUgaWlJWJiYkRH\nKZWbN2/Czs4OPj4+LEHpb6RSKeRyOfbv3y86CqlRVlYWJk6ciF69emHixIkIDw9/owQFAB0dHQQE\nrMPWrQtRs+YAVKvmAeAIgMK3jPgKQBSqVRsAAwN3rF8/DXv2bGEJSkQqxyKUiIiI3ltKSgrkcjk2\nb96Mjh07lnicjh07Ij4+XoXJxGnQoAEGDRqEjRs34saNG4iLi4OTkxOioqLQtWtXNG3aFMOGDcPW\nrVtx79490XFJMHd3d4SGhoqOUWIZGRmwt7fHxIkT8e2334qOQ2VU3759sW/fPtExSE2CgoJgaWmJ\n58+fIykpCYMHD/7XlRByuRy3b1/FkiVuMDefCl3dmqhRoxMqV+4NXd3PUKNGF+jq1oSJyUTMn2+L\n27evwtvbi6sriEgtuDSeiIiI3svdu3fRtWtX/PjjjxgyZEipxkpLS4OTkxNu3bqlmnBllFKpRGpq\navEy+ujoaBgZGRUvo7e3t0edOnVExyQNOn/+PDw9PZGWliY6yge7du0anJyc4OvrCx8fH9FxqAx7\n9eoVjI2NkZiYiCZNmoiOQypy584djBs3DsnJyVi3bh3s7e1LNE5WVhbOnTsHf39/ZGZmYvLkybCy\nsnrroYtERKrGGaFERET0Ts+ePYOLiwtGjx5d6hIUAMzMzJCdnY379++XPlwZJpFIYGFhgTFjxmDv\n3r149OgRtm3bBlNTU/j7+8PU1BRWVlb4z3/+g4MHD+LFixeiI5OatW3bFrm5ubh69epbn1cqldi5\ncyccHR3RqFEjVK1aFc2aNcOAAQNw6tQpDaf9/65cuQIHBwf88MMPLEHpnWQyGXr37s3l8RWEjbaT\nxwAAIABJREFUQqHAihUr0K5dO7Rt2xYXL14scQkKAAYGBrC1tUW7du3QvHlz2NvbswQlIo1hEUpE\nRET/Ki8vD3369IGDgwOmTJmikjElEglsbGwqzPL49yWVSt8oPh8/fozVq1ejdu3aWLp0KRo0aIDO\nnTtj2rRpiIiIQG5urujIpGISiQRubm44ePDgW58fMWIEvLy8kJSUBDc3N0ycOBEdOnTAgQMH0LVr\nV2zbtk3Dif/cEsPR0RGzZ8/GiBEjNH5/Kp+4PL5iOHfuHDp16oR9+/bh+PHj+PHHH1V22JtEIgEX\nqBKRpnFpPBEREf0jhUKBzz//HFKpFNu3b4eOjo7Kxp45cyYKCwvx008/qWzM8i4vLw9xcXHFS+kv\nXLgAa2vr4qX01tbWPDiiAggKCsKvv/6K8PDwNx6/desWmjZtCmNjY1y6dOmNbRNiYmLg4OAAU1NT\nXLt2TWNZk5KS0LNnTyxcuBBffvmlxu5L5V9eXh6MjY1x5coV1KtXT3Qc+kDZ2dn48ccfsWXLFixY\nsABDhgxR+Z6dK1asQFpaGn799VeVjktE9G84I5SIiIjeSqlUYvz48Xjy5Am2bNmi0hIUgFbOCH0X\nPT09ODg4YM6cOThx4gTu37+PyZMn48WLFxg/fjwMDQ3h6uqKxYsX4+zZs1AoFKIjUwk4OTnh9OnT\nyMrKeuPxR48eAfjzMLG/7h1rZ2cHAwOD4ms04cKFC+jRoweWLFnCEpQ+mJ6eHlxcXHDgwAHRUegD\nHTx4EK1bt0ZmZiaSkpIwdOhQtRxcxBmhRCQCi1AiIiJ6q59++gknTpxAYGCgypbB/S8bGxskJCSg\nqKhI5WNXFAYGBm8Un9evX8eIESNw8+ZNfPnll6hbty7kcjl+/fVXXL58mR8oywl9fX107tz5bzNC\nLS0tYWxsjPj4eDx58uSN52JjY5GVlYUePXpoJOO5c+fg7OyMFStWwMvLSyP3pIqHy+PLl3v37sHT\n0xMTJkzAhg0bEBAQgLp166rtfjwVnohEYBFKREREf+Pv748NGzbg0KFDajvAoG7duqhTpw5SU1PV\nMn5FVKdOHfTt2xcrV65EcnIyLl++DE9PT1y4cAGfffYZ6tevDy8vL/j5+SE9PZ3FaBnm7u7+t31C\n9fT0EBQUhGrVqqFVq1YYNWoUpk6digEDBsDZ2RnOzs5Yu3at2rOdOXMGLi4uWL16NTw9PdV+P6q4\nXF1dceLECTx79kx0FPoXCoUCq1evRtu2bdGiRQtcunQJ3bt318i9+fcUEWmaTHQAIiIiKltCQkIw\ndepUxMTEoH79+mq9V8eOHREfH49WrVqp9T4VVf369eHt7Q1vb28AwPXr1xEVFYXIyEjMnDkTurq6\ncHJygqOjIxwcHNCwYUPBiek1d3d3LFy4EEql8o1ZUW3atMHQoUOxYMECbNiwofhxMzMzfPXVVzA0\nNFRrrtOnT6N3797w8/ND79691XovqvgMDAxgb2+PgwcPYtCgQaLj0FtcvHgRI0eOhEwmQ3R0NCwt\nLTV2by6NJyIROCOUiIiIisXFxWHo0KEICgpCixYt1H6/jh074vTp02q/j7YwMTHBsGHDsHXrVty9\nexeHDx/GJ598gsDAQLRp0wYtW7bEN998gz179uDx48ei42o1MzMzGBgY4Ny5c8WPKRQKODo6Ytq0\naRg5ciTS09Px8uVLnD17FiYmJvD29oavr6/aMp04cQKfffYZNm3axBKUVIbL48umnJwc+Pr6onv3\n7vj6668RGxur0RIUYBFKRGKwCCUiIiIAQEpKCuRyOTZv3oyOHTtq5J48MEl9JBLJG8Xno0ePsGPH\nDpiZmeG3335Ds2bN0K5dO3z77bcICQnBixcvREfWOm5ubggNDS3+/ZYtWxAXF4d+/fph8eLFaNq0\nKfT09NCuXTvs378fDRs2xJIlS3Djxg2VZ4mNjYWHhwe2bNkCNzc3lY9P2uuzzz5DeHg4cnJyREeh\n/xMWFobWrVvj1q1buHjxIkaMGAGpVPPVAItQIhKBRSgRERHh7t27cHV1xYIFCzRaglhZWSElJQW5\nubkau6e2kkqlbxSfjx8/xtq1a2FoaIhly5ahYcOG6NSpE6ZOncrSQkP+uk/o2bNnIZFIYG9v/7dr\nq1SpAhsbGxQVFb0xi1QVoqKi0L9/f+zYsQPOzs4qHZuoTp06sLa2xuHDh0VH0XoPHjyAl5cXvvnm\nG6xevRrbtm2DsbGxsDw8LImIRGARSkREpOWePXsGV1dXjBo1CkOGDNHovatUqYJWrVohMTFRo/cl\nQFdX943i89GjR1iwYAFkMhl+/PFHGBkZwd7eHrNnz8bx48dRUFAgOnKFY2tri5SUFDx69AgAUKlS\nJSiVyuLf/9X/Xqcq4eHh+Pzzz7Fr1y44OTmpbFyi/8Xl8WIVFRVh/fr1aNOmDZo2bYqkpCS4uLiI\njgWAhyURkeaxCCUiItJieXl56NOnD+zt7dW69+C/4fL4skFPT++N4vP+/fuYMmUKsrKyMGHCBBga\nGsLFxQWLFi3CmTNnoFAoREcu9ypVqgRHR0eEhYUBQHERuX79ety7d++Naw8dOoQTJ05AT08PXbp0\nUcn9w8LC4O3tjb179751FiqRqnh4eODgwYP8QkWAy5cvw9bWFps2bUJERATmz5+PqlWrio4FgEvj\niUgMFqFERERaSqFQ4IsvvkC9evXwyy+/CFuixgOTyiYDAwO4urpi8eLFOHv2LG7cuIFRo0bh9u3b\nxaeXe3h4YMWKFUhKSuKH2RL63+Xxbm5ukMvlePjwISwsLDBkyBD4+vqid+/e6NWrFwBg4cKFqFWr\nVqnve/DgQQwePBiBgYHo1q1bqccj+jcNGjRAy5YtERUVJTqK1sjNzcW0adNgb2+PQYMG4cSJE/j4\n449Fx3oDi1AiEkGi5E8eIiIiraNUKjFu3DhcvnwZYWFhqFy5srAsKSkpcHd3R0ZGhrAM9OEePHiA\nqKgoREZGIjIyEtnZ2XBwcICjoyMcHR3RrFkz7v/2Hu7du4fWrVsjMzMTMpkMSqUS69evx5YtW5CU\nlIScnBzUrl0bHTt2xPjx41WyfD0oKAgjR45EcHAwbGxsVPAuiN7t559/RlpaGtatWyc6SoUXHh6O\n0aNHo3379li2bBkaNGggOtJb+fn54fTp09iwYYPoKESkRViEEhERaaF58+Zh165diI2NRY0aNYRm\nKSoqQu3atZGWloa6desKzUIld+PGjTeKUZlMVlyKOjg4oFGjRqIjllnt27fH8uXLNTIzc+/evfjm\nm28QGhqKDh06qP1+RK+lp6ejS5cuuHfvHnR0dETHqZAyMzPxn//8B8eOHcOqVavg7u4uOtK/2rBh\nA+Li4rBx40bRUYhIi3BpPBERkZbx9/fHhg0bcOjQIeElKPDnaebW1tZcHl/ONW3aFEOHDsWWLVtw\n584dHDlyBDY2Njhw4ADatWuHFi1awMfHB7t37/7Hw4C01V9Pj1eXXbt2YcyYMQgLC2MJShrXrFkz\n1K9fHydPnhQdpcJRKpXw9/fHxx9/jHr16uHy5ctlvgQFeGo8EYnBIpSIiEiLhISEYOrUqQgLCytT\nS+V4YFLFIpFI3ig+MzMzsWvXLpibmyMgIABmZmZo27YtJk2ahODgYDx//lx0ZKE0UYRu27YNEyZM\nwJEjR2BlZaXWexH9E54er3qpqamwt7fH2rVrERYWhp9//hnVqlUTHeu9cYEqEWkai1AiIiItERcX\nh6FDhyIoKAgtWrQQHecNPDCpYpNKpW8Un0+ePMH69ethZGSEFStWoFGjRujYsSO+//57HD16FDk5\nOaIja5S1tTUePnyIW7duqWX8gIAAfPfddwgPD0ebNm3Ucg+i9/G6CGX5VXp5eXmYOXMmPv30U/Tv\n3x9xcXHl7ksOHpZERCKwCCUiItICqampkMvl+O2339CxY0fRcf7m9YzQoqIi0VFIA2Qy2RvF56NH\nj7Bw4ULo6upi1qxZMDIygp2dHWbNmoVjx46hoKBAdGS10tHRgYuLi1pmhfr7+2Pq1KmIiIiApaWl\nyscn+hCWlpaoXLkyEhMTRUcp16KiotC2bVtcunQJ58+fx7hx48rlvqssQolIBBahREREFdzdu3fh\n4uKCBQsWlNk9w4yNjVG9enVcu3ZNdBQSQE9PD/b29pg9ezaOHz+OBw8e4Pvvv8fLly8xadIk1KlT\nB87Ozli4cCESEhKgUChER1Y5d3d3hIaGqnTM9evXY+bMmYiMjETLli1VOjZRSUgkEi6PL4XHjx9j\nyJAh+Oqrr7Bo0SLs27evXB9ExyKUiERgEUpERFSBPXv2DK6urhg1ahSGDBkiOs6/4vJ4ek1fXx8u\nLi5YtGgRzpw5g1u3bsHHxwd3797F0KFDYWhoiD59+mD58uW4dOlShZhJ3LNnT8TExCA3N1cl461a\ntQrz5s1DdHQ0zM3NVTImkSqwCP1wSqUSAQEBaN26NWrWrInLly+jT58+omOVGg9LIiIRWIQSERFV\nUHl5efDw8IC9vT18fX1Fx3knHphE/6RWrVrw8PDAihUrkJSUhJSUFHh5eeHy5cuQy+UwNjbG559/\njvXr1+PatWvlcoZRrVq10K5dO0RHR5d6rOXLl+Pnn39GdHQ0mjVrVvpwRCr0ySefIDs7GykpKaKj\nlAtXr15F9+7dsWzZMoSEhGDZsmUwMDAQHUtlyuPPayIq31iEEhERVUAKhQJffPEFjIyM8Msvv5SL\nWRecEUrvy9jYGAMHDiwuPhMSEuDq6orjx4/Dzs4OH330EYYMGYKAgADcvn1bdNz3porT45csWYIV\nK1YgOjoaJiYmKkpGpDpSqRRyuZyzQt8hPz8fc+bMQZcuXdCrVy/Ex8fjk08+ER1Lpbg0nohEYBFK\nRERUwSiVSkyYMAFPnjxBQEBAuTlAoX379khKSkJeXp7oKFTO/G/xeefOHYSHh6NTp04ICQlB+/bt\nYW5ujtGjR2PXrl3IzMwUHfcfvS5ClUolioqKkJOTg9zc3PcuChYsWIC1a9ciJiYGH330kZrTEpUc\nl8f/u2PHjsHKygoJCQlITEzEpEmTIJPJRMdSORahRCQCi1AiIqIKZv78+Th27BgCAwOhp6cnOs57\nq1atGszNzXHhwgXRUagck0gkbxSfDx8+xJ49e9CyZUts3boV5ubmaNOmDSZOnIgDBw7g2bNnoiMX\n09HRwePHL2Bubo0qVaqjevXaMDCoCQMDQ3Ts2BNz587Hw4cP3/raOXPm4LfffkNMTEy5PjyFtMOn\nn36KW7du4caNG6KjlCl//PEHRowYAS8vL8ydOxdBQUFo0qSJ6FhqwyKUiERgEUpERFSB+Pv7w8/P\nD4cOHUKNGjVEx/lgXB5PqiaVSt8oPh8/fowNGzbA2NgYK1euROPGjWFjYwNfX18cOXIEL1++1HjG\ntLQ0dO3qjA4dHPHy5de4dm0xCgruQKHIg0KRj5cvkxAfPx7z5mWgadOWGDRoOJ4+fQrgzxngM2fO\nxPbt2xEdHY0GDRpoPD/Rh5LJZOjTpw/2798vOkqZoFQq8fvvv8PS0hJ6enq4fPky+vbtWy62tSmN\niv7+iKhsYhFKRERUQYSEhGDq1KkICwsrt2WIjY0Ni1BSK5lM9kbx+fjxYyxevBiVK1fGnDlzUK9e\nPdja2uLHH39EbGws8vPz1Zpn1aq1aNu2M06dckFu7k0olYsAOACo+T9X1QfQC3l5fsjLu469e6ug\nWbOPERERgR9++AH79u1DdHQ0jI2N1ZqVSJVeL4/fu3cvxo8fD1tbW9SoUQNSqRSDBw/+x9dlZ2dj\n2rRpsLCwQJUqVVC7dm24uLggMjJSg+lVJz09HS4uLli0aBECAwPx66+/lssvMkuKM0KJSNMkSv7k\nISIiKvfi4uLQu3dvhISEoGPHjqLjlFhSUhLkcjnS0tJERyEt9fLlSxw/fhyRkZGIjIxEamoqOnfu\nDEdHRzg6OqJ9+/Yq26tv1qyfsGjRZuTkBAMw/8BXh0Mm80TjxrUQHx8PQ0NDlWQi0pT8/HwYGxuj\nUaNGSE5Ohr6+Pho1aoTU1FQMGjQIAQEBf3vNs2fP0LVrV6SkpKB169bo3r07srOzERQUhEePHmHj\nxo0YOnSogHfz4QoKCrBkyRIsWbIEU6ZMwcSJE6Grqys6lkZt27YNwcHB2L59u+goRKRFKt6Oy0RE\nRFomNTUVcrkcv/32W7kuQQHAwsICDx8+xJMnT1CnTh3RcUgLVatWDc7OznB2dgYAPH36FLGxsYiM\njMTXX3+N27dvw9bWFk5OTnB0dISlpSWk0g9fZLVjx04sWuSPnJzjAEoyk7M7Xr06jAcP3PHw4UMW\noVTuVK5cGa6urqhfvz4CAwPRrFkzxMTEwMHB4R9fM3PmTKSkpKB///7YsWNH8f97P/30Ezp06IBx\n48bB2dm5zK+KOHnyJEaNGoXGjRvjzJkzaNq0qehIQnCPUCISgUvjiYiIyrF79+7BxcUFCxYsgLu7\nu+g4paajo4MOHTogISFBdBQiAECtWrXQp08fLF++HJcuXcKVK1cwaNAgJCcno2/fvjA2Nsbnn3+O\ndevWIS0t7b0+1D948AAjR45HTs52lKwEfc0GeXnz4Ok5BK9evSrFOERi9O3bF0lJSWjWrNl7XR8Y\nGAiJRIJZs2a98QWEoaEhvv32W+Tm5sLf319dcUvt2bNn8PHxQf/+/TF9+nQcPHhQa0tQgEUoEYnB\nIpSIiKicevbsGVxcXDBq1CgMGTJEdByV4YFJVJbVq1fvjeLzzJkzcHNzw8mTJ+Hg4IAmTZrgq6++\nwubNm3H79u23jjFt2hzk5X0JwLrUeZTKEbh1qxq2bt1a6rGINM3FxQWnTp0qPvzrXR48eAAAMDU1\n/dtzpqamUCqViIiIUGlGVVAqldi1axcsLS0BAMnJyRgwYIDWHxak7e+fiMRgEUpERFQO5eXlwcPD\nA3Z2dvD19RUdR6VYhFJ58tfiMzIyEl26dEFoaCjat2+P5s2bY9SoUdi5cycyMzPx4sULbN++DYWF\nk1SUQIKXL/+LRYvWqGg8Is3R19eHo6MjgoOD3+v611tAXL9+/W/PZWRkAACuXLmiuoAqcP36dbi7\nu2P27NnYvXs31qxZg5o1a777hVqCM0KJSNNYhBIREZUzCoUCX375JYyMjLBs2bIKN6PCxsYG8fHx\n/HBE5Y5EInmj+Hz48CH27duHVq1aYdu2bTA3N4elpSVevfoEQEMV3tkVN27cxrVr11Q4JpFmvD49\n/n24u7tDqVRi5syZKCoqKn780aNH+OWXXwDgvWeXqlthYSEWL14Ma2trdOvWDYmJiejSpYvoWGUK\nl8YTkQgsQomIiMoRpVKJCRMm4PHjxwgICICOjo7oSCrXsGFD6OnpFc/uISqvpFIpPv74Y0yYMAFB\nQUF4/PgxOnWyRWGhk4rvpAOZrDP31qVyqVevXoiMjER2dvY7r509ezaaNGmCPXv2oF27dpg0aRJG\njhyJ1q1bFx+wV5LDy1Tt9OnT+OSTT3D06FGcPn0a33//PSpVqiQ6VpnDIpSIRBD/twQRERG9t/nz\n5+PYsWMIDAyEnp6e6DhqY2Njw+XxVOHIZDLcvJkJoJ3Kx87Obovz5y+pfFwidatVqxY6d+6MsLCw\nd15rbGyMhIQEjBkzBtnZ2VizZg1CQ0Ph5eWF3bt3AwCMjIzUHfkfvXjxAmPHjoWHhwemTJmCw4cP\nv/dBUNqIRSgRicAilIiIqJzw9/eHn58fDh06hBo1aoiOo1YdO3ZEfHy86BhEKvfnrLfqKh9XqayO\nZ8/ePaOOqCz6kOXxdevWxYoVK5CRkYG8vDzcuXMHy5Ytw82bNwH8+UWapimVSuzduxetWrVCfn4+\nLl++DG9v7wq3dY2q8b8PEYnAIpSIiKgcCAkJwdSpUxEWFoYGDRqIjqN2PDCJKqo/l8fmq2HkAty6\ndR2nT5/Go0ePOMuKypU+ffrg0KFDKCgoKPEYmzdvhkQigbe3twqTvdutW7fQp08fTJ8+Hdu3b4ef\nnx9q166t0QzlGX9WEZGmyUQHICIion936tQpDB06FCEhIWjRooXoOBrRoUMHXLx4EQUFBdxXjSoU\nS8vmuHAhBYCDSseVyc7j0aP7GDNmDDIyMlBYWAhTU1OYmJjA1NS0+JeJiQmaNm2KKlWqqPT+RKVh\nbGyM1q1bIzEx8V+vUyqVyMnJQbVq1d54fMuWLdiyZQu6du2KPn36qDNqsVevXuHXX3/FvHnz8P/Y\nu/e4mu/HD+Cv00U3VMotmvtlLtswxYgsqRRy+yIrojJjirnFNrOZIne2kHR1zaWQklCMkcswfDFy\nLxkSuqnO+f2xL7+Zy0rnnPe5vJ6Ph8ej6/vzOvt+O5fXeV/8/f0RFxcHAwMDpVxbU3BpPBGJwCKU\niIhIhV28eBHu7u6IjIyEra2t6DhKU61aNTRu3Bhnz57Fxx9/LDoOkdx07doB8fHHUFDwhVzHNTI6\ng1WrotGhQwcAwKNHj3Dt2jVkZmYiMzMTFy5cwK5du5CZmYmbN2/CwsLitSVp48aNUbduXZU4cIa0\nQ0JCAuLj41FcXIwVK1YAAI4cOQJvb28AgKWlJUJCQgAABQUFqF27NhwdHdGkSRPo6Ojg8OHD+PXX\nX9G6dWts3rxZKZlPnjwJPz8/mJmZ4ciRI2jevLlSrqtpWIQSkQgsQomIiFRUVlYWnJ2dERQUBFdX\nV9FxlO758ngWoaRJevfujUmTvgaQD8Dk3368nH5DlSpP8OGHH774ipmZGdq1a4d27dq98tNlZWXI\nysp6UZJmZmYiJSXlxcd5eXlo2LDhG4vSatWqySk3EXD69GlER0cDAKRSKXR0dHDt2jVcu3YNANCw\nYcMXRaiBgQGGDRuGX375BampqQCAZs2aISgoCP7+/go/RPDJkyf45ptvsHHjRsyfPx+enp7c57IS\nWIQSkQgSGe95iIiIVM6jR4/QrVs3DBs2DIGBgaLjCLF69WocPnwYUVFRoqMQydWnn/bFgQO9AXwu\nl/EMDUdh+vTGmDXra7mMl5+fj+vXr79UlP59dqmJickry+6ff2xtbQ09Pc61oHfToUMHLFy4EPb2\n9qKjvCIhIQFffvklHBwcEBISAktLS9GR1F5CQgLWrl2LhIQE0VGISIuwCCUiIlIxRUVFcHZ2Rtu2\nbbFs2TKtnW1y5swZDBkyBBcvXhQdhUiuTpw4gW7dXFFYeBZA7UqO9gvMzP6DK1d+h4WFhTzivZVM\nJsO9e/feWJLm5OSgfv36b5xNWqNGDa29T6N/9+OPPyInJwfLli0THeWF27dvY8KECTh//jxWrlyJ\nHj3ku7+vNktISEB4eDh27NghOgoRaREWoURERCqkrKwMQ4cOhUQiwYYNG6Crqys6kjClpaUwMzPD\nrVu3YG5uLjoOkVxNnjwDoaGnUVCwA+++W9V9GBt3wrp1C+Du7i7PeO+suLgYN2/efGNRKpPJ3nqI\nEw+b0W7//e9/4ejoiJs3bwrfp7asrAw///wzvv/+e4wbNw7Tp09X+NJ7bbNjxw6EhYVh586doqMQ\nkRbhuhUiIiIVIZPJ4O/vj/v37yMpKUmrS1AA0NPTQ/v27XHixAk4OjqKjkMkV3PnfoeEhI64erU/\nZLItACpaAObAxMQZn3/+H5UpQYG/9nBs1qwZmjVr9trv5+bmvlSSnj17FvHx8cjMzMStW7dQq1at\nN84mrVOnDmeTarj3338f1apVw4kTJ2BjYyMsx+nTp+Hn5wcjIyMcOnQILVu2FJZFk3GPUCISgUUo\nERGRiggKCsKhQ4dw8OBBzjr5n+cHJrEIJU0TExODoqKHsLe3RkZGR+TnRwJoX87f3g4jo3EICPgc\nP/zwjQJTyp+5uTk6dOjw4nT7vystLcWdO3deKkp379794uOnT5+iUaNGr92btFGjRqhataqAW0Ty\nNmDAAGzbtk1IEZqfn49Zs2YhOjoawcHBGDlypPCZqZqMRSgRicAilIiISAVEREQgLCwMhw8fhqmp\nqeg4KsPGxgYxMTGiYxDJVWxsLGbNmoUDBw6gadOmiI1dhy++cIZM1g35+WMB2AGo8o/fegpgFwwN\nl6JGjQfYtGkzunbtqvzwCqSnp4cGDRqgQYMGr92H8enTpy8ts8/MzERqauqL5ffVq1d/42zS+vXr\na/0se3UxYMAADB06FEFBQUqdAZyYmIhx48bBzs4O586dQ61atZR2bW3FIpSIROAeoURERILt2rUL\nPj4+SE9PR4sWLUTHUSk3b95Ex44dcffuXS6JJY0QFxeHCRMmYN++fWjVqtWLrz9+/BgxMbFYsmQN\nrl+/CGPjVpBI6gCQQiq9jqKiG7C2bo6qVaU4ceIE9PX1xd0IFSSVSpGTk/PGvUn//PNPvPfee28s\nSrkPseqQyWRo2LAhEhMT0aZNG4VfLysrCwEBATh16hRCQ0O5AkGJdu/ejRUrVmD37t2ioxCRFuGM\nUCIiIoGOHj0Kb29v7Nq1iyXoa1hbW0NHRwc3btxAw4YNRcchqpQdO3Zg/PjxSElJeakEBYDq1atj\n3LgvMG7cF8jPz8fZs2dx//596OjooF69emjdujXKyspgbW2N27dvo1GjRoJuhWrS0dFB3bp1Ubdu\nXXTp0uWV7xcVFeHGjRsvlaRHjx598bmuru4bS9IGDRqgSpV/ztAlRZFIJC+WxyuyCJVKpVi5ciVm\nzZqFMWPGICoqCkZGRgq7Hr0e52URkbKxCCUiIhLk4sWLcHd3R2RkJGxtbUXHUUkSiQQ2NjbIyMhg\nEUpqbc+ePfDx8UFiYiI+/PDDt/6siYkJOnfu/MrX9fX1MXz4cISHh2POnDmKiqqRDA0N0aJFi9e+\n4SSTyfDw4cOXZpOeOnUKW7ZsQWZmJu7cuYM6deq8sSitVasWZ6zL2YABA/Dll1/i22+/Vcj4v//+\nO/z8/KCjo4O0tDS0bt1aIdeht+PSeCISgUUoERGRAFlZWXBxcUFQUBBcXV1Fx1Fpzw96QkPWAAAg\nAElEQVRM+s9//iM6CtE7OXDgADw9PREfH4+OHTtWaixfX1/06tUL3333HfT0+FReHiQSCSwsLGBh\nYfHa/31KS0tx69atl2aT7tix48XnhYWFLxWj/zzEydjYWMCtUm+ffPIJsrOzcfXqVRgbG+PSpUso\nLi6GiYkJWrVqhRo1arzTuAUFBfj++++xdu1azJkzBz4+PjwMSSAWoUQkAp89ERERKdmjR4/g7OwM\nPz8/eHt7i46j8mxsbPDdd9+JjkH0Tg4fPoz//Oc/iIuLwyeffFLp8Vq3bv1i/8R+/frJISH9Gz09\nvRen1Ts4OLzy/cePH7+0H+nly5eRnJyMzMxMXL9+Hebm5m+cTWplZcVDnF7jwoULMLM0Q5t2bSCD\nDIZ1Df965VoMFGQVwNzCHCOGj8D4L8bjvffeK9eYe/bswdixY2Fra4uzZ8+iTp06ir0R9K9YhBKR\nCDwsiYiISImKiorg7OyMtm3bYtmyZVxOWQ55eXmoV68ecnNzeUAMqZWMjAy4ubkhNjYWvXr1ktu4\nkZGR2LJlC3bt2iW3MUkxpFIpsrOzX3uAU2ZmJh4+fIgGDRq8sSg1NTUVfROU6v79+xj9+WikHkhF\n8QfFKPugDDAH8PeHSimAe0CV36tA56wOfEb5YH7Q/Dfu75mTk4OJEyfi6NGj+Pnnn+Hs7KyMm0Ll\nkJKSggULFiAlJUV0FCLSIixCiYiIlKSsrAxDhw4FAGzcuJGzgCqgVatWWLduHdq1ayc6ClG5nD59\nGk5OTlizZg369Okj17Hz8/NhbW2Ns2fPon79+nIdm5SrsLAQ169ff2NRamBg8MaS9L333tOoN4eO\nHDmC3v16o7BlIZ51fwaU56blA0Z7jWD52BIHUg6gSZMmL74llUoRHh6OmTNnYtSoUfj222+5TYGK\nSUlJQUhICPbu3Ss6ChFpES6NJyIiUgKZTAZ/f3/cv38fSUlJLEEr6PmBSSxCSR2cP38eLi4u+Pnn\nn+VeggJ/HaY0dOhQrF27VmGHyZByGBkZ4f3338f777//yvdkMhnu37//UjGakZGBjRs3IjMzE9nZ\n2bCysnrt3qSNGzeGpaWl2qw6OHLkCBx7O6LArQBoVoFfNAEK3Qtx58Qd2HaxxfFfj6NRo0Y4f/48\nxowZg9LSUqSmpuKDDz5QWHZ6d1waT0QicEYoERGREsydOxebNm3CwYMHtW6pozyEhobi+PHjWLt2\nregoRG91+fJl9OjRAyEhIfDw8FDYdX777Te4u7sjMzOTb6xoqZKSEty8efONs0lLSkreOJu0YcOG\nb1xKrmwPHjxA0/eb4lGvRxUrQf9B56gOmtxqgoF9BmLNmjWYPXs2xowZw78PFZaamoqgoCDs27dP\ndBQi0iKcEUpERKRgERERCAsLw+HDh1mCviNbW1usWLFCdAyit8rMzETPnj0xZ84chZagANCuXTvU\nrFkTe/fu5Z6HWkpfXx9NmjR5aTn43z169OilcvTChQvYtWsXMjMzcfPmTVhYWLyxKK1bt67STlP3\nGeuDwuaFlSpBAUBqK8WV/17Bjl07cObMGVhZWcknICkMZ4QSkQgsQomIiBQoMTERgYGBSE9P54uy\nSmjbti2uX7+Ox48fo3r16qLjEL3i5s2bcHBwQGBgILy9vZVyTT8/P4SFhbEIpdcyMzNDu3btXrul\nSFlZGbKysl6aTZqSkvLi87y8PDRo0OC1JWmjRo3kdj984cIF7Endg+KxxZUfTALI+stwLfwaqlWr\nVvnxSOHUZesGItIsLEKJiIgU5OjRoxg5ciR27tyJFi1aiI6j1vT19fHRRx/hxIkT+PTTT0XHIXpJ\nVlYWHBwcMGHCBIwdO1Zp1x02bBimTZuGu3fvok6dOkq7Lqk/XV1dWFtbw9raGt27d3/l+/n5+a8c\n4pSWlvbiY2Nj4zfOJrW2toaeXvleZi5dsRQlH5YAVeR0w8wAnUY6iI6Oxrhx4+Q0KCkSZ4QSkbJx\nj1AiIiIFuHjxIuzt7REeHg5XV1fRcTTCxIkTUatWLQQGBoqOQvTCvXv3YG9vD09PTyH/3/Tx8UHT\npk0xffp0pV+btJNMJsO9e/feuDdpTk4O6tev/8ZDnGrUqPFiJqCllSUeDHgA1JRjwP8CnXM648iB\nI3IclBThwIEDmD17NtLS0kRHISItwhmhREREcpaVlQUXFxcEBQWxBJUjW1tbbNq0SXQMohcePHiA\nnj17YtCgQcIKel9fXwwfPhxTp05V2p6OpN0kEglq166N2rVro3Pnzq98v7i4+JVDnE6cOPHic5lM\nhkaNGqFevXp49PARYCHngPWA3/f8DplMxqXXKo57hBKRCCxCiYiI5OjRo0dwdnaGn5+f0vYJ1Ba2\ntraYNGkSX9ySSnj06BGcnJzg7OyM2bNnC8thY2MDY2NjpKWlcdsIUgkGBgZo1qwZmjV7/elHubm5\nyMzMxO7du7H/7H6U6ZTJN0A14NmzZ3j06BHMzc3lOzbJFYtQIhKBbxsTERHJSVFREdzd3dG9e3cu\nU1WAhg0boqSkBHfu3BEdhbTckydP0Lt3b3zyySeYN2+e0GJeIpHA19cXYWFhwjIQVYS5uTk6dOiA\nrl27wsDEQP4XkAC6VXRRXCyHA5hIofimJhGJwCKUiIhIDsrKyuDp6YmaNWtiyZIlfHKvABKJBLa2\ntjh27JjoKKTFCgoK4ObmhjZt2mDp0qUq8bf+2WefISkpCffv3xcdhajcTExMICtWwGxAKVBaWAoT\nExP5j01yxxmhRKRsLEKJiIgqSSaTwd/fH/fv30dMTAx0dXVFR9JYLEJJpOezvhs0aICVK1eqRAkK\n/DXDrm/fvoiOjhYdhajcWrVqhYLsAkDOK+PxEDC1MEW1atXkPDDJG5fGE5EILEKJiIgqKTg4GIcO\nHUJ8fDwMDQ1Fx9FoNjY2yMjIEB2DtNCzZ88waNAgmJubY+3atSp3MNHz5fEsFUhdVK1aFXXr1wWy\n5TzwLaBDhw5yHpQUgUUoEYmgWs/giIiI1ExERARWr16NpKQkmJqaio6j8Tp27IiTJ0+irEzeU4iI\n3qy0tBTDhg2Dnp4eYmNjoaeneueNdu3aFQBw+PBhwUmIys/b0xuGv8v3DcRq56vBd4SvXMckxWAR\nSkQisAglIiJ6R4mJiQgMDERycjKsrKxEx9EKNWrUQN26dXHhwgXRUUhLlJWVwcvLC4WFhdi0aRP0\n9fVFR3otiUQCHx8fHppEauVzv8+B8wCeyGnAW4Benh769u0rpwFJkVRlexEi0i4sQomIiN7BsWPH\nMHLkSMTHx6NFixai42gV7hNKyiKVSuHj44OcnBxs3boVBgYKOOFajry8vJCQkIBHjx6JjkJULnXr\n1oX/l/4w3mMMVHZiYClgkmSCFUtWqOwbFvQqzgglImVjEUpERFRBly5dQr9+/RAZGYlOnTqJjqN1\nWISSMshkMowbNw5XrlzBjh07YGRkJDrSv6pZsyacnJywbt060VGIyu37775H3bK60D1aiYMGZYBk\npwSdP+yMYcOGyS8cKRSXxhORCCxCiYiIKiArKwtOTk4ICgqCq6ur6DhaiQcmkaLJZDJMmjQJv/32\nGxITE2FiYiI6Urn5+flh9erVLBdIbVSpUgUH9hyA5XlL6B7SBaQVHKAEMNhtAPMcczzMeYgHDx4o\nJCfJH4tQIhKBRSgREVE55eXlwcXFBX5+fvD29hYdR2t9+OGHuHLlCp4+fSo6CmkgmUyGGTNmID09\nHUlJSahevbroSBXSo0cPPH36FMePHxcdhajcrK2tcfLoSbTJawOT9SbA/XL+4k3AOMIYn9b9FNcv\nX4eTkxO6deuG27dvKzQvyQeLUCISgUUoERFRORQVFaFfv37o1q0bAgMDRcfRagYGBmjbti1Onjwp\nOgppoB9++AG7du1CSkoKzM3NRcepMB0dHR6aRGqpXr16OHn0JL4d8y10VuvAcIMh8DuAh/j//UOl\nAO4BOAlUi6kGi90WWLtoLRLjE1GtWjXMnTsX3t7esLOzwx9//CHstlD58LAkIhKBRSgREdG/KCsr\ng6enJ2rWrIklS5bwibsK4PJ4UoT58+dj/fr1SE1NhaWlpeg472zkyJHYsmULnjyR11HcRMqhq6uL\nrp90hXVdayyfvBw98nvAfJM59IL0YLDAALpBuqizqw5c9VwRvSAad2/dxZAhQ156XJ4yZQpmzpwJ\ne3t7nDlzRuCtofLgjFAiUjY90QGIiIhUmUwmQ0BAAP78808kJydDV7cShzmQ3Nja2mL79u2iY5AG\nWbZsGVavXo309HTUrl1bdJxKqVu3Luzt7bFx40b4+vqKjkNUISEhIZg8eTJ8fHzg4+MDAMjPz0dx\ncTGMjY1haGj4r2P4+PjAzMwMvXr1wrZt29ClSxdFx6Z3wKXxRCQCZ4QSERG9RXBwMA4ePIiEhIRy\nvfgi5eCMUJKn1atXY9GiRdi3bx/q1asnOo5c+Pr6cnk8qZ3Lly/j8OHDr+zDbWJigho1alTocXjQ\noEGIiYlB//79kZycLO+oJAcsQolIBBahREREbxAZGYnVq1cjKSkJpqamouPQ3zRt2hT5+fnIzs4W\nHYXUXFRUFH744QekpqaiQYMGouPIjZOTE+7evYvTp0+LjkJUbgsXLsTYsWNhYmIil/F69eqFhIQE\njBgxAps2bZLLmCQ/LEKJSAQWoURERK+RmJiI6dOnIzk5GVZWVqLj0D9IJBLY2Njg2LFjoqOQGtu4\ncSMCAwOxd+9eNG3aVHQcudLV1cXo0aM5K5TURk5ODjZv3ozx48fLddzOnTsjNTUVkyZNwqpVq+Q6\nNlUO91wnIhFYhBIREf3DsWPHMHLkSMTHx6NFixai49AbcHk8Vcb27dsREBCAPXv2oGXLlqLjKMSo\nUaOwceNGFBQUiI5C9K+WL1+OoUOHombNmnIfu23btjh48CDmzZuH4OBguY9P744zQolI2ViEEhER\n/c2lS5fQr18/REZGolOnTqLj0FvY2tpyRii9k927d+Pzzz/H7t270bZtW9FxFMba2hqdOnVCXFyc\n6ChEb/X06VOsWrUKX331lcKu0aRJE/zyyy+IjY3FtGnTWMCpAC6NJyIRWIQSERH9T1ZWFpycnBAU\nFARXV1fRcehf2NjY4MSJEygrKxMdhdRIamoqRo4ciYSEBLRv3150HIXjoUmkDsLDw9G9e3eFb1Fh\nZWWF9PR0pKWlwc/Pj48fgrEIJSIRWIQSEREByMvLg4uLC/z8/F45rZZUk6WlJSwtLXHp0iXRUUhN\nHDx4EB4eHti6davWzPh2dXVFZmYmLly4IDoK0WuVlpZi8eLFmDJlilKuZ2FhgX379uHatWsYOnQo\niouLlXJdehWLUCISgUUoERFpvaKiIri7u6Nbt24IDAwUHYcqgMvjqbyOHj2KQYMGYcOGDbCzsxMd\nR2n09fXh7e3NWaGksuLi4tCgQQPY2toq7ZpVq1ZFYmIipFIp+vbti/z8fKVdm/4fi1AiEoFFKBER\nabWysjJ4enrC0tISS5Ys4QmmaoYHJlF5nDp1Cn379kVkZCQcHBxEx1G60aNHIzY2FkVFRaKjEL1E\nJpNh/vz5SpsN+ncGBgbYtGkT6tWrB0dHR+Tm5io9g7bjcy4iEoFFKBERaS2ZTIaAgAD8+eefiImJ\nga6uruhIVEGcEUr/5vfff0fv3r2xevVq9O7dW3QcIRo3boyPPvoI27dvFx2F6CX79u1DcXGxsL9N\nPT09hIeHo3PnzujevTuys7OF5NBmnBFKRMrGIpSIiLRWcHAwDh48iISEBBgaGoqOQ++gXbt2uHjx\nIgoKCkRHIRV08eJFODk5YenSpXB3dxcdRygemkSqKCQkBFOmTIGOjriXpRKJBAsWLMCQIUNgZ2eH\nzMxMYVm0DZfGE5EILEKJiEgrRUZGYtWqVUhKSoKpqanoOPSODA0N0apVK/z222+io5CKuXLlChwd\nHREcHIwhQ4aIjiNcv379cO7cOVy5ckV0FCIAwOnTp3Hu3Dl4eHiIjgKJRIKZM2di0qRJ6NatG86d\nOyc6klZgEUpEIrAIJSIirZOYmIjp06cjOTkZVlZWouNQJXF5PP3TjRs30LNnT3zzzTfw8vISHUcl\nGBgYwMvLC2vWrBEdhQgAsGDBAkyYMAEGBgaio7zwxRdfICQkBD179uTjihKwCCUiEViEEhGRVjl2\n7BhGjhyJ+Ph4tGzZUnQckgMWofR3d+7cgYODAyZNmgQ/Pz/RcVSKr68vIiMj8ezZM9FRSMvduHED\nSUlJGDNmjOgorxg2bBjCw8PRp08fpKamio6j0XhYEhGJwCKUiIi0xqVLl9CvXz9ERkaiU6dOouOQ\nnPDkeHru7t27cHBwwJgxYzBhwgTRcVROixYt0KJFC+zcuVN0FNJyS5Ysgbe3N8zMzERHeS1XV1ds\n3boVHh4e2LZtm+g4Go0zQolI2ViEEhGRVsjKyoKzszOCgoLg6uoqOg7JUfPmzZGbm4t79+6JjkIC\n3b9/Hz179oSHhwemTJkiOo7K4qFJJFpubi6ioqLg7+8vOspb2dnZYc+ePRg/fjwiIiJEx9FIXBpP\nRCKwCCUiIo2Xl5cHFxcX+Pr6wtvbW3QckjMdHR107NiRs0K1WG5uLnr16oW+ffvim2++ER1HpQ0c\nOBAnTpzA9evXRUchLbVy5Ur06dMH1tbWoqP8q3bt2iEtLQ2zZ8/GokWLRMfROCxCiUgEFqFERKTR\niouL4e7uDjs7OwQGBoqOQwrCfUK11+PHj+Hs7Izu3bvjxx9/5J5z/8LIyAgeHh5Yu3at6CikhYqK\nirBs2TJMnjxZdJRya968OQ4dOoSwsDB8/fXXLO7kiEUoEYnAIpSIiDRWWVkZPD09YWlpiaVLl7Ig\n0WAsQrVTfn4+XF1d0aFDByxatIh/4+Xk6+uLtWvXorS0VHQU0jKxsbH46KOP0LZtW9FRKsTa2hoH\nDx5EcnIyxo8fD6lUKjqSRuB9NhGJwCKUiIg0kkwmQ0BAAO7du4eYmBjo6uqKjkQKZGNjg+PHj/PF\nqRYpLCxE37590axZM6xYsYIvqCugbdu2sLa2RlJSkugopEWkUikWLFiAqVOnio7yTmrWrIn9+/fj\n/Pnz8PT0RElJiehIGoEzQolI2ViEEhGRRgoODkZ6ejri4+NhaGgoOg4pWO3atWFqaoo//vhDdBRS\nguLiYgwYMAC1a9dGWFgYdHT4lLaieGgSKdvOnTtRtWpV2Nvbi47yzqpXr46kpCQ8efIE/fv3R0FB\ngehIao1L44lIBD5rJCIijRMZGYlVq1YhOTkZZmZmouOQknB5vHYoKSnBkCFDYGJigujoaM72fkdD\nhgzBL7/8gjt37oiOQloiJCQEU6ZMUfvZ20ZGRti6dSvMzc3h7OyMvLw80ZHUFotQIhKBRSgREWmU\n3bt3Y/r06UhOToaVlZXoOKRENjY2PDlew5WWluKzzz5DWVkZ1q9fDz09PdGR1JaJiQn+85//ICIi\nQnQU0gJHjhxBVlYWBg4cKDqKXOjr6yMqKgoffvgh7O3tce/ePdGR1BKLUCISgUUoERFpjGPHjmHE\niBGIj49Hy5YtRcchJeOMUM0mlUoxatQo5ObmIi4uDlWqVBEdSe35+voiPDyce+uSwoWEhGDSpEka\n9eaFjo4Oli1bhr59+8LOzg43b94UHUntqPvsYCJSTyxCiYhII1y6dAn9+vVDREQEOnXqJDoOCdC+\nfXucP38eRUVFoqOQnEmlUowZMwY3b97kvr9y1KFDB5ibmyM1NVV0FNJgly9fxuHDh+Ht7S06itxJ\nJBLMnj0bX3zxBezs7HDx4kXRkdQOZ4QSkbKxCCUiIrWXlZUFZ2dnBAUFwc3NTXQcEsTY2BgtWrTA\n6dOnRUchOZLJZPD398f58+exc+dOGBsbi46kUfz8/LB69WrRMUiDLVy4EGPHjoWJiYnoKArj7++P\n77//Hj169MDJkydFx1EbXBpPRCKwCCUiIrWWl5eH3r17w9fXVyNnm1DFcHm8ZpHJZJg6dSqOHj2K\npKQkVKtWTXQkjePh4YF9+/YhJydHdBTSQDk5Odi8eTPGjx8vOorCjRgxAqGhoXBxcUF6erroOGqB\nRSgRicAilIiI1FZxcTHc3d3RtWtXBAYGio5DKoAHJmmWWbNmISUlBXv27IGpqanoOBqpevXq6N+/\nP6KiokRHIQ20fPlyDB06FDVr1hQdRSnc3d2xceNGDB48GDt37hQdR+WxCCUiEViEEhGRWpJKpfD0\n9ISlpSWWLl3KDfcJAGeEapK5c+diy5Yt2Lt3L2rUqCE6jkbz9fXFmjVrWEiQXD19+hQrV67EV199\nJTqKUn366adITEyEr68vYmNjRcdRaXzuRkQisAglIiK1I5PJEBAQgHv37iEmJga6urqiI5GKaNmy\nJe7du4f79++LjkKVsGjRIkRERGDfvn2oVauW6Dgar1OnTqhSpQqX85JchYeHw97eHk2bNhUdRek6\nduyI/fv3IzAwECtWrBAdR6XxDRgiUjYWoUREpHbmzZuHtLQ0nh5Nr9DV1cXHH3+M48ePi45C7+jn\nn3/G8uXLsX//ftStW1d0HK0gkUjg6+uLsLAw0VFIQ5SUlGDx4sWYMmWK6CjCtGrVCocOHcLSpUvx\nww8/sPB7DS6NJyIRWIQSEZFaiYyMxMqVK5GcnAwzMzPRcUgFcXm8+lq7di2Cg4Oxf/9+WFtbi46j\nVTw9PZGYmIgHDx6IjkIaIC4uDg0aNICtra3oKEI1bNgQhw4dwpYtWzBx4kRIpVLRkVQKi1AiEoFF\nKBERqY3du3dj+vTpSE5OhpWVleg4pKJ4YJJ6WrduHb755hukpqaiUaNGouNonRo1asDNzQ0xMTGi\no5Cak8lkCAkJ0erZoH9Xp04dpKen4/jx4xg1ahRKS0tFR1IZLEKJSAQWoUREpBaOHTuGESNGID4+\nHi1bthQdh1SYra0tMjIy+OJKjWzZsgWTJ09GSkoKmjdvLjqO1nq+PJ5/O1QZ+/btw7Nnz9C7d2/R\nUVSGmZkZUlJSkJOTg0GDBqGoqEh0JJXAw5KISAQWoUREpPIuXbqEfv36ISIiAp06dRIdh1SclZUV\njIyMcPXqVdFRqBx27tyJcePGISkpCa1btxYdR6t169YNpaWl+PXXX0VHITU2f/58TJ48GTo6fKn5\ndyYmJkhISIChoSF69+6NJ0+eiI6kEvjGCxEpGx+diIhIpWVlZcHZ2RlBQUFwc3MTHYfUBJfHq4eU\nlBSMHj0au3btwkcffSQ6jtaTSCTw8fHhoUn0zk6fPo3z58/Dw8NDdBSVVKVKFaxbtw7NmzeHg4OD\n1u/Jy6XxRCQCi1AiIlJZeXl56N27N3x9feHt7S06DqkRHpik+tLS0jB8+HBs374dHTt2FB2H/uf5\nFiR5eXmio5AaWrBgAfz9/WFgYCA6isrS1dVFaGgoHBwcYGdnh9u3b4uOJAyLUCISgUUoERGppOLi\nYri7u6Nr164IDAwUHYfUjI2NDYtQFXb48GEMHjwYmzdvRpcuXUTHob+pVasWevbsifXr14uOQmrm\nxo0bSEpKwpgxY0RHUXkSiQRBQUEYOXIk7Ozs8Mcff4iOJASLUCISgUUoERGpHKlUCk9PT1haWmLp\n0qXcTJ8q7OOPP8bvv/+OZ8+eiY5C/3D8+HH0798fsbGx6NGjh+g49Bp+fn5YvXo1CwqqkCVLlsDb\n2xumpqaio6iNqVOnYsaMGbC3t8eZM2dEx1E6Pr8jIhFYhBIRkUqRyWQICAjAvXv3EBMTA11dXdGR\nSA1VrVoVTZo00coXlqrszJkzcHNzQ3h4OJycnETHoTdwcHBAXl4eTp48KToKqYnc3FxERUUhICBA\ndBS14+vriyVLlqBXr144fPiw6DhKxzdciEjZWIQSEZFKmTdvHtLS0hAfHw9DQ0PRcUiN8cAk1XLh\nwgU4Ozvjp59+Qp8+fUTHobfQ0dHB6NGjeWgSlVtoaCj69OmD+vXri46ilgYPHoyYmBj0798fycnJ\nouMoDZfGE5EILEKJiEhlREZGYuXKlUhOToaZmZnoOKTmeGCS6rh8+TIcHR2xYMECDBo0SHQcKgdv\nb2/ExcXh6dOnoqOQiisqKsLy5csxefJk0VHUWq9evZCQkIARI0Zg06ZNouMoBYtQIhKBRSgREamE\n3bt3Y/r06UhOToaVlZXoOKQBWISqhmvXrqFnz574/vvvMXz4cNFxqJysrKxgZ2enNYUMvbvY2Fi0\na9cObdu2FR1F7XXu3Bl79+7FpEmTsHr1atFxFI5FKBGJwCKUiIiEO3bsGEaMGIH4+Hi0bNlSdBzS\nEK1atUJWVhZyc3NFR9Fat27dgoODA6ZPn47Ro0eLjkMV5Ovry+Xx9FZSqRQLFizAlClTREfRGB98\n8AHS09MRHByM4OBg0XEUikUoEYnAIpSIiIS6fPky3N3dERERgU6dOomOQxpET08P7du3x/Hjx0VH\n0UrZ2dn49NNPMX78eHzxxRei49A7cHZ2xp07d3D27FnRUUhF7dy5E1WrVoW9vb3oKBqladOmOHTo\nEGJiYjBt2jSNLQt5ajwRicAilIiIhMnOzoaTkxN+/PFHuLm5iY5DGogHJolx7949ODg4wNvbG5Mm\nTRIdh96Rnp4eRo0axVmh9EYhISGYOnUqCy0FqFevHg4ePIi0tDT4+fmhrKxMdCSF0NSSl4hUF4tQ\nIiISIi8vDy4uLvDx8cGoUaNExyENxX1Cle/hw4dwdHTEwIEDMWPGDNFxqJJGjRqF9evXo7CwUHQU\nUjFHjhxBVlYWBgwYIDqKxrKwsMC+fftw7do1DBs2DMXFxaIjyRWXxhORCCxCiYhI6YqLi+Hu7o6u\nXbuyKCGFel6E8oWWcuTl5aFXr17o1asXvv/+e9FxSA4aNGgAGxsbbNmyRXQUUjEhISGYNGkS9PT0\nREfRaFWrVkViYiJKS0vRt29f5Ofni44kNyxCiUgEFqFERKRUUqkUnp6esLS0xD+2TE0AACAASURB\nVNKlS7mcjhSqfv360NXVxY0bN0RH0XhPnjyBi4sLOnfujPnz5/NvW4Pw0CT6p0uXLuHw4cPw9vYW\nHUUrGBgYYPPmzbCysoKjo6PGHALIIpSIRGARSkRESiOTyRAQEIB79+4hJiYGurq6oiORhpNIJFwe\nrwQFBQXo06cPWrduzTc4NFCfPn3wxx9/4OLFi6KjkIpYuHAhxo4dCxMTE9FRtIaenh7Cw8PRqVMn\ndO/eHdnZ2aIjVRofK4hIBBahRHKwdetWTJgwAd26dYOpqSl0dHTg5eX12p/19vaGjo7OW/85Ojoq\n+RYQKce8efOQlpaG+Ph4GBoaio5DWoJFqGIVFRWhf//+eO+997By5Uro6PDppabR19fHyJEjOSuU\nAAA5OTmIi4vD+PHjRUfROjo6Oli4cCGGDBkCOzs7XLt2TXSkSuOMUCJSNm7oQiQHc+bMwdmzZ1G1\nalXUr1//rTMm+vfvj0aNGr32e9HR0bh27Rp69+6tqKhEwkRGRmLlypU4cuQIzMzMRMchLWJjY4NZ\ns2aJjqGRnj17hsGDB8PU1BRr167lLG8N5uPjg86dO2Pu3LkwMDAQHYcEWr58OYYNG4aaNWuKjqKV\nJBIJZs6cCXNzc3Tr1g3Jyclo3bq16FjvhEvjiUgEiYz3PESVlp6ejvr166NJkyZIT09Hjx498Nln\nnyE6OrrcY+Tl5cHKygpSqRR37txBjRo1FJiYSLmSkpLg7e2NtLQ0tGzZUnQc0jLP718fPXoEfX19\n0XE0RmlpKYYOHYrS0lLExcXxv60WcHBwgJ+fH4YMGSI6Cgny9OlTNGzYEEePHkXTpk1Fx9F669ev\nx6RJk5CQkABbW1vRcSqsoKAAFhYWKCwsFB2FiLQI1y4RyUH37t3RpEmTSo0RHR2NwsJCDBw4kCUo\naZRjx47By8sL27dvZwlKQpiamqJBgwY4d+6c6Cgao6ysDCNGjEB+fj42bdrEElRL8NAkCg8Ph729\nPUtQFeHh4YHw8HD06dMHqampouNUGGeEEpEILEKJVERYWBgkEgn8/PxERyGSm8uXL8Pd3R0RERHo\n3Lmz6DikxbhPqPxIpVL4+voiOzsb27Zt4zJpLdK/f3+cOXMGV69eFR2FBCgpKcHixYsxZcoU0VHo\nb1xdXbFlyxZ4eHhg27ZtouNUCA9LIiIRWIQSqYCjR4/i3LlzaNGiBbp16yY6DpFcZGdnw8nJCT/+\n+CPc3NxExyEtxyJUPmQyGcaPH48//vgDO3fuhJGRkehIpEQGBgbw9PREeHi46CgkQFxcHBo0aKCW\nS7A13fO9QseNG4eIiAjRcSqEM0KJSNlYhBKpgFWrVkEikcDX11d0FCK5yMvLg4uLC3x8fDBq1CjR\ncYhgY2ODjIwM0THUmkwmw1dffYWTJ08iMTERJiYmoiORAL6+voiIiEBJSYnoKKREMpkMISEhnA2q\nwtq3b4+0tDR89913WLx4seg45cKl8UQkAotQIsEeP36MuLg4VKlSBSNGjBAdh6jSiouL0b9/f3Tt\n2hUzZswQHYcIANC2bVvcuHEDjx8/Fh1FLclkMsycORMHDhxAcnIyqlevLjoSCfL++++jadOm2LVr\nl+gopET79u3Ds2fP0Lt3b9FR6C1atGiBX375BatWrcLXX3+t8iUji1AiEoFFKJFgMTExKCgo4CFJ\npBGkUim8vLxQo0YNLF26lHs/kcrQ19fHRx99hOPHj4uOopbmzJmDHTt2YO/evTA3NxcdhwTjoUna\nZ/78+Zg8eTJ0dPjyUdVZW1vj0KFDSEpKwvjx4yGVSkVHeiMWoUQkAh/JiAR7fkjSmDFjREchqhSZ\nTIaAgADk5OQgNjYWurq6oiMRvYTL499NSEgIYmNjkZqaCktLS9FxSAUMGjQIx44dw82bN0VHISU4\nffo0zp8/Dw8PD9FRqJxq1qyJAwcO4Ny5c/D09FTZrSz4hjkRicAilEigjIwMnD17Fi1atICdnZ3o\nOKRmHj58iDVr1mDAgAFo1qwZjI2NYWZmBjs7O6xdu1bp77DPnz8faWlpiI+Ph6GhoVKvTVQePDCp\n4pYvX46VK1di//79qFOnjug4pCKMjY0xbNgwrF27VnQUUoIFCxbA398fBgYGoqNQBVSvXh3Jycl4\n/Pgx+vfvj4KCAtGRXoszQolI2ViEEgn0/JAkPz8/0VFIDcXFxcHPzw8ZGRno1KkTJk6ciEGDBuH8\n+fPw8fHBkCFDlJYlKioKoaGhSE5OhpmZmdKuS1QRNjY2OHbsGF90ldPq1auxYMEC7N+/H/Xq1RMd\nh1SMr68vwsPDUVZWJjoKKdCNGzeQlJTElUtqysjICNu2bYOZmRmcnZ2Rl5cnOhKAv1bEderUCebm\n5pBKpejYsSNWrVrFx2ciUgqJjPc2RJWWkJCA+Ph4AMDdu3exZ88eNG7c+MUsT0tLS4SEhLz0O0+e\nPEHdunUhlUpx+/Zt7g9KFZaWlob8/Hy4urq+9PV79+6hY8eOuH37NrZs2YL+/fsrNEdSUhK8vb2R\nlpaGli1bKvRaRJUhk8lQu3ZtnDx5EtbW1qLjqLTo6GjMmDEDaWlpaNq0qeg4pKJsbW3x7bffvvI4\nRJpj4sSJ0NXVxYIFC0RHoUqQSqXw9/fH4cOHkZycjFq1agnLMnz4cGzYsAG1a9dGnz59EBYWhtat\nW+PChQvw8vJCZGSksGxEpB04I5RIDk6fPo3o6GhER0cjJSUFEokE165de/G1bdu2vfI769atQ2Fh\nIQYMGMASlN6Jvb39a1981qpVC59//jlkMhnS0tIUmiEjIwNeXl7Yvn07S1BSeRKJhMvjy2HTpk2Y\nPn069u7dyxKU3oqHJmm23NxcREVFISAgQHQUqiQdHR0sW7YMffr0gZ2dnbD9fbdv344NGzagSZMm\nuHDhAlatWgXgr9dSbm5uiImJeTG5hIhIUViEEsnBrFmzUFZW9sZ/V69efeV3Pv/8c5SVlSE2NlZA\nYtJ0+vr6AAA9PT2FXePy5cvo168fIiIi0LlzZ4Vdh0ieeGDS28XHx8Pf3x/Jycl4//33RcchFTd0\n6FCkp6cjOztbdBRSgNDQUPTp0wf169cXHYXkQCKRYPbs2Rg7dizs7Oxw8eJFpWeIj4+HRCLBV199\nBXNz8xeHJenp6eGHH36ATCbDihUrlJ6LiLQLi1AiIg1TVlaGqKgoSCQSODs7K+Qa2dnZcHZ2xo8/\n/gg3NzeFXINIETgj9M12796NMWPGIDExER988IHoOKQGqlatisGDByMiIkJ0FJKzoqIiLF++HJMn\nTxYdheQsICAAs2fPRo8ePXDy5EmlXvvu3bsAgEaNGr30dZlMhsaNGwMADh06hNLSUqXmIiLtwiKU\niEjDTJs2DefPn4erqyscHR3lPn5eXh5cXFwwevRojBo1Su7jEylSx44dcerUKb7I+od9+/Zh5MiR\nSEhIQIcOHUTHITXi6+uLNWvWQCqVio5CchQbG4t27dqhbdu2oqOQAowcORKhoaFwcXFBenq60q5r\naWkJALh27dpLX5fJZMjMzAQAlJaWvviYiEgRWIQSEWmQZcuWYdGiRWjVqhWio6PlPn5xcTH69++P\nrl27YsaMGXIfn0jRzM3NYWVlhQsXLoiOojIOHTqEYcOGYcuWLejUqZPoOKRmPv74Y1SvXh379+8X\nHYXkRCqVYsGCBZgyZYroKKRA7u7u2LBhAwYPHoxdu3Yp5Zqurq6QyWRYtGgRcnNzAfy1ZL+kpATf\nfvvti597/j0iIkVgEUpEpCFWrFiBgIAAtGnTBvv374eZmZlcx5dKpfDy8kKNGjWwdOnSF/s6Eakb\nLo//f8eOHcPAgQOxfv16dOvWTXQcUkMSiQR+fn5YvXq16CgkJzt37kTVqlVhb28vOgopmIODA3bt\n2gUfHx+sW7dO4dcbOnQonJ2dcfXqVbRq1erF4Z4dOnTA4cOH8d577wH463AnIiJF4T0MEZEGWLJk\nCSZMmIAPPvgA+/fvR61ateQ6vkwmw8SJE5GTk4PY2Fjo6urKdXwiZeKBSX85deoU+vbti4iICPTs\n2VN0HFJjw4cPR0pKCv7880/RUUgO5s+fj6lTp/INTy1hY2OD/fv3Y/r06Qo/qEhHRwc7d+5EcHAw\natWq9WL1UvPmzXHkyBFUq1YNAOT+PJaI6O8kMplMJjoEERG9u3nz5iEwMBDt27fH3r17YW5urpBr\nrFu3DgcPHpT7TFMiZTt+/DhGjx6Ns2fPio4izO+//w5HR0eEhoaif//+ouOQBhg5ciTatGnDw3XU\n3JEjR/DZZ5/h8uXL0NPTEx2HlOj69etwdHSEl5cXvv76a6UV4Xp6eigsLIRUKoWpqSlMTU2Rk5Oj\nlGsTkXbijFAiIjX2ww8/IDAwEB07dkRqaqpCStCoqCiEhoYiKSmJJShphA8//BBXr17F06dPRUcR\n4uLFi3BycsKSJUtYgpLcPD80iXMs1FtISAgmTZrEElQLNWzYEIcOHcKWLVswadIkpR2AJpFIIJPJ\nsGHDBjx79gweHh5KuS4RaS/OCCUiUlNRUVHw9vaGnp4exo8fD1NT01d+pmHDhhgxYsQ7XyMpKQne\n3t5IS0tDy5YtKxOXSKV06tQJ8+bNQ/fu3UVHUaqrV6/C3t4ec+bMqdR9A9E/yWQytG7dGitXruR+\ns2rq0qVLsLOzw7Vr12BiYiI6DgmSm5sLNzc3NG/eHGFhYXIvxZ88efJiCTwAVKlSBb/88gtcXV0B\n/LVioU6dOnK9JhHR3/GtPiIiNXX9+nVIJBKUlZVh6dKlr/2Z7t27v3PZkZGRAS8vL+zYsYMlKGmc\n5wcmaVMReuPGDTg4OODrr79mCUpyJ5FI4Ovri7CwMBahamrhwoUYO3YsS1AtZ25ujpSUFAwaNAiD\nBw/Ghg0bYGhoKLfxHR0dYWRkhDZt2qBatWooLS1F165dYWJigp07d7IEJSKF44xQIgW7f/8+Tpw4\ngTNnzuDRo8fQ19dD06ZN0KFDB7Rs2ZKHzpBKunz5Mrp3746wsDC4ubmJjkMkd+vXr8fWrVuxdetW\n0VGU4s6dO+jevTu+/PJL+Pv7i45DGurBgwdo0qQJMjMzUaNGDdFxqALu3r2LVq1a4dKlS6hZs6bo\nOKQCnj17Bk9PT/z5559ISEh4aRZnZSxcuBAbN27E1atXUVhYiKKiIowdOxZff/01rKys5HINIqK3\nYRFKpAAymQxJSUkIDv4JGRmHYWjYAfn5H6G01BxACapWvQzgBAwNn8Hf/3OMHesHCwsL0bGJAADZ\n2dno0qULZs6cidGjR4uOQ6QQV65cQY8ePXDr1i3RURQuJycH3bt3h7e3N6ZNmyY6Dmk4Dw8PdOrU\nCRMmTBAdhSpg5syZyM3Nxc8//yw6CqmQsrIyfPHFF/jtt9+QlJSkkNcrhoaGyM3NhZGRkdzHJiJ6\nHRahRHKWlZWFzz7zQ0bGdeTnTwYwBMCbHthPwtBwBQwMkhEevgIDBw5UYlKiV+Xl5aF79+4YPHgw\nZs6cKToOkcLIZDJYWlri999/1+gZKPfv30ePHj0waNAgzJo1S3Qc0gIHDhzAhAkTcPbsWaWdOk2V\n8/TpUzRs2BBHjx5F06ZNRcchFSOTyRAYGIidO3ciJSUF9erVk+v4RkZGePDgAYyNjeU6LhHRm/DU\neCI5OnHiBFq16oBDhzogP/8UgJF4cwkKAB1QVBSBvLyt8PIKxPjxX/G0VRKmuLgYAwYMQNeuXTFj\nxgzRcYgUSiKRwMbGBhkZGaKjKMyjR4/Qq1cvuLm54dtvvxUdh7SEvb09ioqKcOzYMdFRqJzCw8PR\no0cPlqD0WhKJBMHBwfDy8kLXrl1x5coVuY/P1z9EpEwsQonk5Ny5c/j0U1fk5a1EaelsAFUq8Nuf\noKDgGCIifsHEidMVFZHojaRSKby8vGBubo6lS5dyFg9phecHJmmiJ0+ewNnZGd26dcPcuXP5N01K\nI5FI4OPjg7CwMNFRqBxKSkqwaNEiTJkyRXQUUnHTpk3DjBkz0L17d5w5c0Zu47IIJSJlYxFKJAfF\nxcXo23cYnjwJBtDvHUcxR0FBEsLCNiE5OVme8YjeSiaTYeLEibh79y5iY2N5gBdpDU0tQvPz8+Hq\n6op27dph8eLFLEFJ6UaOHIlt27bh8ePHoqPQv4iLi0PDhg1hY2MjOgqpAV9fXyxZsgS9evXC4cOH\n5TImi1AiUjYWoURy8MMPwcjJaYy/lsJXRg0UFKzBZ5/5IT8/Xw7JiP7d/PnzceDAASQkJMDQ0FB0\nHCKl6dixI06cOIGysjLRUeSmsLAQffv2RZMmTfDTTz+xBCUhateuDQcHB2zYsEF0FHoLmUyGkJAQ\nTJ06VXQUUiODBw9GdHQ03N3d5TJ5g49TRKRsLEKJKqmwsBBLlixHQcFiAPJ4IO+JoqKPsG7dejmM\nRfR2UVFRCA0NRVJSEszMzETHIVIqS0tL1KpVCxcvXhQdRS6Ki4sxcOBA1KpVC2vWrIGODp/mkTi+\nvr5YvXq16Bj0FqmpqXj27BlcXFxERyE14+TkhISEBIwYMQKbNm2q9HicEUpEysRnyESVtHnzZkgk\ntgAay23M/PxxCAkJldt4RK+TlJSEadOmITk5We4ngBKpC01ZHl9SUoKhQ4fCyMgI0dHR3OKChHN0\ndMSDBw9w6tQp0VHoDUJCQjB58mS+aULv5JNPPsHevXsxadKkSr3pwaXxRKRsfNQjqqQdO/bh6VN3\nOY/aEzdu/IHc3Fw5j0v0l4yMDHh5eWH79u1o2bKl6DhEwmjCyfFlZWXw9PRESUkJNmzYAH19fdGR\niKCjo4PRo0fz0CQVdfr0aZw/fx4eHh6io5Aa++CDD5Ceno7g4GDMmzfvncZgEUpEyqYnOgCRujt+\n/BSAADmPqgsjo49w6tQpODg4yHls0iTFxcU4ffo0Tpw4gds3bkBaVoYatWqhXbt2+Pjjj1GjRo1X\nfufy5cvo168fIiIi0LlzZwGpiVSHra0tIiMjRcd4Z1KpFKNGjcKDBw+wc+dOVKlSRXQkohe8vb3x\nwQcfYMGCBTAxMREdh/4mJCQE/v7+MDAwEB2F1FzTpk1x6NAh9OrVCw8fPkRwcHCF9v1kEUpEysYi\nlKiS/vzzFuS5LP65srLGuHXrltzHJc1w/fp1LF+4EFEREbDW1cXHJSVoVFgIHQA5+vqYa2yM34qK\n0OvTTzEhMBB2dnYAgOzsbDg7O2POnDlwc3MTeyOIVMBHH32ES5cuoaCgAMbGxqLjVIhMJsPYsWNx\n/fp1JCUl8bAzUjn169dHly5dsHnzZnh7e4uOQ/9z48YNJCcn4+effxYdhTREvXr1cPDgQfTu3Rt+\nfn5YuXLlW7dokclkuHXrFs6dO4eSkhLs3r0b7du3R/Pmzbm1CxEpnETGt1+IKsXAoCqePcsCUF2u\n4+rqesDOLhtdunSBubk5zM3NYWZm9uLj559Xr16dpy1qEalUimWLF2PON99gVGkpPi8peWMN/xhA\njESCECMj9OjTB9/Nm4d+/fph8ODBmDlzpjJjE6m0jh07YvHixejatavoKOUmk8kQEBCAjIwMpKSk\noFq1aqIjEb3Wjh07EBwcjCNHjoiOQv8zceJE6OnpISQkRHQU0jBPnjyBu7s7LCwsEBMT88qM44sX\nLyJ0yRJsXL8ektJSfKivDzx+DP1q1fBfmQx/lpTAtVcvfDFlCrp27crXOESkECxCiSrJwuI9PHx4\nAEATuY5rZOQEDw9rvPfee8jNzcWjR4+Qm5v74t/zzwsLC2FqavraovRN5enfP+a7ruqjqKgIQ/r0\nwYNff0VEfj6alfP3ngKYamCADTIZXAcPRkxMDJ9YEv3N+PHj0ahRI3z11Veio5SLTCbDtGnTsG/f\nPuzbtw9mZmaiIxG9UWlpKRo0aIA9e/agTZs2ouNovdzcXDRp0gRnz55F/fr1RcchDVRUVIRhw4ah\noKAA27Ztg4mJCZ48eYKpEyZg26ZN8C0pwajSUjQC8M9now8ArJNI8JOxMRp8+CHWbNiA9957T8Ct\nICJNxiKUqJJ69OiHtLTPAAyW67hGRnVx4cKvaNiw4Vt/rqSkBI8ePXpjUfqmz3Nzc/H48WOYmJhU\nqDz9++fcV0p5pFIpBjg7Q/+XX7CusBDvsgvgcgALa9bEkdOnYWVlJe+IRGorOjoaiYmJ2LRpk+go\n5TJr1ixs374dBw4cgIWFheg4RP/qm2++wePHj7F06VLRUbTe3LlzcenSJURFRYmOQhqstLQUvr6+\nuHTpEpYsWYJh/frB/tEjLCwqQnneuisFMF9PD4sNDBAdFwcXFxdFRyYiLcIilKiS5s4Nxvff30Bx\ncagcR/0vTE0/RW5ulkJn7kmlUjx+/Ljc5ek/P9fX169wefr8n7GxMWclVsDSRYsQ9+232J+f/04l\n6HPf6unhxCefIDEtjf/9if7n0qVLcHJywvXr10VH+VdBQUGIjo5Geno6atWqJToOUblcv34dH3/8\nMW7fvs29bAUqKipCo0aNkJKSgrZt24qOQxpOKpXCx8cHcVFRWCqTYdQ71A6/AuhnZITobdvg7Ows\n/5BEpJVYhBJV0p07d9C0aVsUFd0AIJ892qpU8Ye/f1XMn/+jXMZTBJlMhoKCgldmmZa3TC0tLa1w\nefr88+rVq0NHR0f0fwKluXHjBj5u1Qq/FhSgaSXHKgFga2KCiT//DE8vL3nEI1J7UqkUFhYWuHjx\nImrXrv3S9/bt24cVK1bg6NGjyM3NhYWFBdq2bYuAgAClvyhbsmQJfvrpJ6Snp3NWN6kdJycneHl5\nYfjw4aKjaK2wsDBs374du3fvFh2FtMCzZ89g07o1fK9exbhKVA5HALhXrYrfLl5EvXr15BeQiLQW\ni1AiOXBzG4I9e1qhtHSWHEa7CSOj9vjvf0+iQYMGchhPNRUXF5d7Cf8/v1ZQUIDq1au/00xUMzMz\n6Onpib75FTJ5wgTorFyJ+SUlchlvPwD/hg1xNjOTs0KJ/qdXr1748ssv0adPnxdfmzp1KhYsWABr\na2u4uLjA0tISf/75J06ePImePXsiODhYaflCQ0Mxf/58pKenc780UktbtmzBihUrkJaWJjqKVpJK\npWjVqhVCQ0PRo0cP0XFIC8z++mscX7wYOwsKXtkLtMJj6ekho0sXJPL+g4jkgEUokRzcvn0bLVu2\nQ35+KoAPKzGSDMbGzpg6tRtmzeKp3m9SWlqKvLy8Cu2H+vxreXl5MDIyqlB5+vevKXtJX3FxMepb\nWuLY06dvPB2+omQA3jcxQfiePejSpYucRiVSb9988w1kMhnmzJkD4K+ZU2PGjIG3tzdWrVr1yhso\nZWVlSjtsbu3atZg1axbS09PRuLG87gmIlOvZs2ewtrb+P/buPJ7q9P0f+OsQWYo2tKAs1WhDC01K\nSbYotA6tJNOoaddMm/aVtqnGR6S9tFOSLUWptIkpJQ4to1BR2Zdz3r8/Pt/6TZ+WSQ73Oc71fDzm\nMcZx7vdLM3OW69zXdePy5cvo1KkT6zhSJywsDKtXr8aNGzfoQ1BS54qKitBeQwN3y8ogio/uKgF0\nVlbGsYsX0adPHxGsSAiRZlQIJURE9u8/iF9+8UFpaTwAre9YgYO8vDd++OEqbt2Kh5ycnKgjEvx3\nR0RRUVGN56G+/2cZGZkaF0/f/6WsrFzjNx83btyAp5UV7r57J9I/h98aNUKTJUuwdJkodjETIvnO\nnj2L7du3Izo6+kPBRklJCRkZGUx3kR8+fBje3t6Ii4tD586dmeUgRBR+++03CIVC+Pr6so4idczM\nzDBr1iyMGTOGdRQiBfz//BMXFizAiZISka25QUYGD0eNwh4JOdiQECK+JKs/lBAxNnHieOTlvcTy\n5QNQWnoUgGkN7l2Mxo1no0OHZFy8GENF0DokIyMDVVVVqKqq1nj0AMdxKCsr++qu0ydPnuDu3buf\n/ZnKysoat/JHRUWhp4ha4v+pV3U1DsfHi3xdQiSVqakpJk6cCKFQiJiYGLx8+RJz584Fj8fDuXPn\ncP/+fSgoKMDExAR9+/atl0wnT57EvHnzEBsbS0VQ0iB4eHigf//+WLNmDeTla3P0H6mJq1ev4sWL\nFxgxYgTrKERKnDtyBG4iLIICgKtQiN7nz4PjONrVTAipFSqEEiJC3t5zoKOjhSlTHFFWNh5VVfMA\ntPnKPaoBnIWS0jw4OJgjMPAiVFRU6iktqSkejwclJSUoKSl917D2ysrKj4qj/1sozcvLQ3p6+kff\ne5yVhXllZSL/XXQAPHv6VOTrEiKp1NXV0axZMzx69Ag3b94Ej8eDvLw8jI2Nce/evQ9vujiOg7m5\nOU6cOIFWrVrVWZ7w8HB4eXkhKioKXbt2rbPrEFKfOnbsiC5duiAsLAyjR49mHUdq+Pr6Yt68eRI3\nI51IJo7jcDs1FX+KeF1NAKiuxt9//w0tre/pviOEkP+iZ0NCRGzUqFEwNzfHb78tQ0hIFzRqNBjF\nxQMAGAFogf9OuXkEOblbkJM7AV1dTWzYsANDhw5lG5zUOXl5eairq0NdXf2b7/O7tzdk/PxEnkUW\n/x0TQAj5/0xMTHDjxg3k5+eD4zj4+vqia9euSExMhKGhIbKzszF//nxERUVhzJgxiIuLq5Mc0dHR\ncHd3R3h4OIyMjOrkGoSwMnXqVAQGBlIhtJ6kp6cjMTERhw4dYh2FSImSkhK8LS39rkFhX8MD8IO8\nPDIyMqgQSgipFSqEElIH1NXVsWePP7ZuXY/Tp0/j8uWbSEo6jqKid8jLy4OhoSGcnCxhaxsGY2Nj\n1nGJGGupro4cOTlAxO3xeQDycnMxZswY6OnpQU9PD/r6+tDT00O7du0gIyMj0usRIglMTU2RlJT0\n4UMCOTk5nD179sMbrq5du+LUqVPo3Lkz4uPjkZSUBFPTmoxB+XeXLl3CuHHjcPr0aZiYmIh0bULE\nwYgRIzBr1ixkZ2dDR0eHdZwGb9OmTfjll1+gpKTEOgqREpWVlWgsKwteZaP0HwAAIABJREFUdbXI\n124MoKoORkYRQqQLFUIJqUOqqqqYPHkyJk+e/OF7Dg4O8PT0xPDhw9kFIxKjZ8+eOKuoKPJC6G0e\nD0McHTHU0RF8Ph+JiYnYv38/+Hw+CgoKoKOj80mBVE9PDx06dKC5bqTBMjU1xZEjR2BpaQkAMDY2\n/mTXiaKiImxsbBAcHIwbN26ItBB69epVjB49GkePHkX//v1Fti4h4kRBQQHjxo3D7t27sXr1atZx\nGrTc3FycOHEC6enprKMQKaKsrIyy6mpUARD1qQdvOA5NmzYV8aqEEGlDhVBC6pmGhgby8/NZxyAS\nolevXkitqMBbAKoiXPdikyaYPnr0Zw9OKC0tRVZWFvh8PjIzM5GWloYzZ86Az+cjJycHbdq0+aRA\nqq+vD11dXTRp0kSEKQmpX8bGxrh//z6mTp0KAGjWrNlnf6558+YAgDIRzu+9desWnJyccODAAQwe\nPFhk6xIijqZOnQorKyssX76c5lbWoe3bt+Onn36Cmpoa6yhEijRu3Bg6rVsjLScHhiJctxrA/bIy\ndOvWTYSrEkKkEb3yIKSeaWhoIC8vj3UMIiGaNWsGWysrHDh3DjM4TiRrpgO4z+PB3t7+s7crKSmh\nW7dun32hWVVVhSdPnoDP538olF65cgV8Ph9ZWVlQUVH5pED6/uuWLVvSKZ9ErCkpKeGHH36Ampoa\neDwe0tLSPvtz9+7dAwCRtfWmpKTA3t4eQUFBsLW1FcmahIizrl27QkdHB+fOnYOjoyPrOA1ScXEx\nAgICcP36ddZRiBTqY2qKxFOnRFoITQagra5OB8sSQmqNCqGE1DN1dXVkZWWxjkEkyMyFC+EaF4dJ\npaUQRTPQCkVFeHp5oXHjxjW+r5ycHPT19aGvr//JbUKhEC9evPhQIOXz+Thz5syHrzmO+2yBVF9f\nH23btqW5pEQsmJiY4OnTpxg2bBjOnj2LrVu3Yvbs2R9uj46ORlRUFJo3by6SomVaWhpsbW2xY8cO\nGplCpMr7Q5OoEFo3du/eDQsLi88+XxNS18Z5esL7/Hn8UlYGUX0EHqSggHEeHiJajRAizXgcJ6It\nRoSQb3LkyBGEhYUhJCSEdRQiQaa4uqLRqVMIqKio1TqnAfzWti3uZmTU+8EJBQUFH4qi/yyW8vl8\nFBYWQkdH57O7STt06AA5OVFPmSLk8/bs2YOYmBj4+vrCzMwMz549w+DBg2FsbIysrCyEhYVBRkYG\nR48ehZOTU62ulZGRAQsLC6xfvx7jx48X0W9AiGQoKSmBlpYWUlNToampyTpOg1JVVQV9fX0cP36c\nDl0j9YrjOFy8eBHLly9HytWrOCEQwEoE6+YA6K6ggLTsbLRu3VoEKxJCpBkVQgmpZxcuXMDq1atx\n8eJF1lGIBHn69CkMO3bEsspKzP73H/+sWwCGKioiNDYW/fr1E2W8WispKfloLuk//56Tk4N27dp9\ncS6psrIy6/ikAUlLS8OwYcPA5/Px+vVrrFy5EmfOnMGLFy+goqICc3Nz/P777+jdu3etrpOdnY1B\ngwZh6dKl8KAdLkRKeXl5oXXr1vDx8WEdpUE5fPgwAgICEB8fzzoKkRIcxyEmJgYrV67Ey5cvsWTJ\nEjRv3hy/jh2Lv0pLUZsJ8hwAeyUl9J07Fz6rVokqMiFEilEhlJB6du/ePYwdOxb3799nHYVIiOfP\nn8POzg6Ghoa4Eh0N19ev4VNdjZqc3R4KwFNREUEhIRLXfltZWfnJXNL3X2dlZaFZs2ZfnEvaokUL\nmktKakQgEKBFixbg8/lo1apVnVzj2bNnGDhwIObNm4fp06fXyTUIkQTJyclwcnJCVlYWZGVlWcdp\nEDiOQ8+ePbF69eovzgInRFQ4jsP58+excuVKvHv3DkuXLsWYMWM+/P/sMW4cCk6fxrGysu+eybem\nUSOc1tfHtdRU6hAihIgEzQglpJ6pq6vTYUnkmz18+BC2trbw9PTEwoULkZubCw8XF5jcugXfkhJY\nAvjaZM2H+O9M0FvNmyP0+HGx2wn6LeTl5dGxY0d07Njxk9uEQiGeP3/+UYE0NDT0w9c8Hu+Lc0nb\ntGlDc0nJJ2RlZdG7d2/cuHEDQ4cOFfn6L168gKWlJaZPn05FUCL1jI2Noa6ujujoaNjZ2bGO0yDE\nxsaisrKS/jxJneI4DmfPnsXKlStRUVGBpUuXYuTIkZ98oLEzOBjOf/+N0TdvYm9ZGVRrcI1qAMsb\nNcIxDQ3Ex8VREZQQIjK0I5SQeiYQCKCgoICysjI0akSfRZAvu379OpycnLBu3Tq4ubl9+D7HcTh8\n6BA2+PigPD8fjhUV6F1dDR0AsgDyAdzi8RAuI4Mnysr4efp0/LZkSb3PBGWN47ivziV9+/btF+eS\ntm/fnl5wS7FFixZBXl4ey5cvF+m6L1++xKBBg+Dq6orFixeLdG1CJNWuXbsQGRmJU6dOsY7SIFhb\nW8PFxeWj1w2EiIpQKERoaChWrVoFjuPg4+MDJyenr36wXFFRgVmenog4fhw7y8rgAPzrAUp3AXgq\nK0Ole3ccCg2FhoaGKH8NQoiUo0IoIQxoaGjg7t27aNOmDesoREyFh4fDzc0Ne/fu/WJrG8dxuHbt\nGuJiY3E7Ph5/P3sGgUCAli1bomufPggIDsazZ8/qrL1X0hUXF38yl/T918+fP4empuZnd5Pq6elJ\nXVFZ2oSGhiIgIADnz58X2ZoFBQUYPHgwHBwcsHr1apGtS4ikKyoqgra2Nh48eECHoNTS3bt3YW9v\nj6ysLDRu3Jh1HNKACAQCnDx5EqtWrULjxo3h4+ODYcOG1Wj8UExMDOZMnQrB69eYUlKCHzkOhgCU\nAVQBSANwE8DBpk2RISuLZWvXwnPaNBpxRAgROSqEEsJAjx49cODAARgaGrKOQsRQcHAwFi1ahNDQ\nUPTt2/e717GysoKXlxecnZ1FmE46VFZW4vHjx5/dSZqdnY3mzZt/tt3+/VxSItlevHiBbt264dWr\nVyJ5A/b27VtYWVnB3Nwcvr6+9KaOkP/h4eEBfX19/P7776yjSLRx48bB0NAQCxYsYB2FNBACgQBH\njx7F6tWroaKiAh8fH9jZ2X338xjHcbh8+TKO7NmDW4mJuJedjYrqasjIyKBT27bo1acPhru4wMnJ\niTpzCCF1hgqhhDAwZMgQLFiwANbW1qyjEDHCcRzWrl2LoKAgREZGonPnzrVab9u2bUhNTcXu3btF\nlJAA/20Ly8nJ+exOUj6fDxkZmS8e3kRzSSWHlpYWLl68CH19/VqtU1xcDBsbGxgbG2P79u1UBCXk\nM5KSkjBu3Dg8evSIHiO/05MnT2BsbIzs7GyoqtZkEiMhn6qursbhw4exZs0atGrVCsuWLYOVlZXI\nn8PevXuHtm3boqioiJ4fCSH1hgYUEsKAhoYG8vPzWccgYkQgEGDmzJm4cuUKEhMT0bZt21qv6eDg\ngHXr1kEoFNIbSxGSkZGBlpYWtLS0MGjQoI9u4zgOr1+//qhAevHiRQQFBYHP5+Pdu3fQ1dX97G5S\nbW1t2v0gRkxNTZGUlFSrQmhpaSmGDRsGAwMD/PHHH/Qmj5AvMDExgZKSEi5duoTBgwezjiORtmzZ\ngilTplARlNRKVVUVDh48iDVr1kBTUxP+/v6wsLCos+cvgUAAOTk5en4khNQrKoQSwgCdHE/+qby8\nHOPHj8fr16+RkJAgsjcxenp6aNasGe7cuYPevXuLZE3ydTweD61atUKrVq0+O9agqKgIWVlZHwql\nKSkpOHXqFDIzM/HixQtoamp+KJD+s1Cqq6tLc0nr2ftC6Lhx477r/uXl5XB2doampiYCAgLowwhC\nvoLH48HT0xO7du2iQuh3KCwsxP79+5Gamso6CpFQlZWV2LdvH9auXQs9PT3s3r0bAwcOrPPrVldX\nf3LSPCGE1DUqhBLCAO0IJe+9efMGjo6O0NDQQGRkpMgPN3BwcEB4eDgVQsVE06ZNYWho+Nn5wO/n\nkr4vkmZmZiIuLu7DXNKWLVt+cS5p8+bNGfw2DZuJiQlOnjz5XfetrKzEmDFjoKqqij179tCbPEK+\nwbhx47BkyRK8evWKDvmrIX9/fwwbNgyampqsoxAJU1FRgeDgYKxfvx4GBgY4ePAgzMzM6u36AoGA\nniMJIfWOZoQSwkBwcDASEhKwd+9e1lEIQzk5ObC1tYWFhQW2bt1aJzvGLl26hPnz5+PWrVsiX5vU\nH4FA8NW5pI0aNfrqXFJqOau54uJiqKuro7CwsEYfUFRXV8PFxQWVlZU4ceIEjTsgpAYmTpwIIyMj\nzJ07l3UUiVFeXg4dHR1ER0eje/furOMQCVFWVoagoCBs2LABRkZGWLp0KUxNTes9R05ODkxMTJCT\nk1Pv1yaESC/aEUoIA7QjlDx48AC2trb45Zdf8Ntvv9VZocrMzAx8Ph8vXrxAmzZt6uQapO7JyspC\nW1sb2trasLCw+Og2juPw6tWrjwqkFy5cwK5du8Dn81FcXPzVuaSNGtFLgf8lEAiQkJAApSZKMDQ1\nxNs3b8FxHFq0aIG+ffrCYoAFRowYAWVl5U/uN2nSJBQVFSEsLIyKoITU0NSpU+Hp6Yk5c+bQBzjf\n6MCBAzA2NqYiKPkmpaWlCAgIgK+vL/r06YOwsDD06tWLWR5qjSeEsEA7Qglh4ObNm5g2bRpu377N\nOgph4Nq1a3B2dsb69esxefLkOr/e2LFjYW1tjSlTptT5tYj4effu3UdzSf+5kzQ3NxdaWlqf3U2q\nq6sLRUVF1vHrlUAgwI6dO7B6w2pUNK5AUYcioC2A95MHSgA8B5rkNIHwqRBT3Kdgzco1aNq0KYRC\nIaZOnYrs7GyEh4fTTFdCvgPHcejSpQsCAwPRv39/1nHEnlAohIGBAQICAj45vI+QfyouLoa/vz82\nbdoEMzMzLFmyBMbGxqxjISsrC5aWlsjOzmYdhRAiRWgbCCEM0I5Q6XX27Fm4u7tj//79sLOzq5dr\nOjg44NSpU1QIlVIqKiowMjKCkZHRJ7dVVFQgOzv7owLphQsXwOfz8fjxY7Rq1eqz7fb6+vpo1qwZ\ng9+m7vD5fIxyGYWMNxkosS8B2n3mh1oBaA8UoxgoBAITA3HU4ChCDoTgxIkTSE9PR2RkJBVBCflO\nPB4PHh4eVAj9RmfOnIGKikq9HGpDJNO7d++wc+dObN26FYMGDUJMTIxY7R4WCATUmUIIqXe0I5QQ\nBsrLy6Gqqory8nJq/ZIiQUFBWLJkCc6cOQMTE5N6u+7Lly+hr6+P/Px8kR/GRBougUCAv//++4tz\nSeXl5T8qjv6zWNq6dWuJemy7d+8eBlgMwLte7yA0FQI1GdebATQ63Qjt27THnTt3oKKiUmc5CZEG\nr169gr6+PrKzs+kguH9hZmaGWbNmYcyYMayjEDHz5s0bbN++HX/88Qesra2xePFidOnShXWsTzx4\n8ADOzs54+PAh6yiEEClCH78QwoCCggIUFBTw5s0bepEvBTiOw+rVqz8cktWpU6d6vb6amhq6du2K\n+Ph4WFtb1+u1ieSSlZVF+/bt0b59ewwePPij2ziOw8uXLz8qkMbGxiIgIAB8Ph8lJSVf3EmqpaUl\nVrs/8vLyMNByIN4MfAN8zyaZjkD1hGo8D3mO1NRU2sVGSC21atUKtra2OHToEGbMmME6jthKTExE\nbm4uRowYwToKESOFhYXYunUrdu7cCXt7e1y5cgWdO3dmHeuL6NR4QggL4vNOhBAp8749ngqhDZtA\nIMCMGTNw/fp1XL16ldmBRQ4ODggPD6dCKBEJHo8HdXV1qKuro1+/fp/c/u7du492kN6+fRtHjx4F\nn89Hfn7+F+eS6ujo1OtcUo7jMMljEooMir6vCPpeG6DMrgxjxo1BRlrGJ4coEUJqZurUqZg7dy6m\nT58uUbvL65Ovry/mzp0rVh8sEXZev36NLVu2wN/fH05OTrh+/Tr09fVZx/pXVAglhLBAz5yEMKKu\nro68vDyx/pSW1E5ZWRnGjRuHt2/fIj4+nmnLrL29PZydnbFt2zZ6U0nqnIqKCoyNjT97EEN5efkn\nc0ljYmLA5/Px5MkTqKmpfXY3qZ6ensjnkkZGRuLKnSuocq+q/WI/AG/S32DNujVYu3pt7dcjRIpZ\nWFiguLgYN2/erNdRMpIiPT0dV69exeHDh1lHIYzl5+dj8+bNCAwMxKhRo3Dr1i3o6OiwjvXNqqur\nqZhPCKl39KhDCCN0YFLDVlhYCEdHR7Rt2xYRERHMZ3P26NEDVVVVePjwIQwMDJhmIdJNQUEBBgYG\nn/3vUCAQ4NmzZx/NIj169OiHrxUUFD7bbq+npwcNDY0aF/nXbVqHkj4lIns1VNavDH8G/InlPssh\nLy8vmkUJkUIyMjIfDk2iQuinNm3aBC8vLzqYTYrl5ubCz88PwcHBcHFxQXJyMrS1tVnHqjHaEUoI\nYYEKoYQw8n5HKGl4/v77b9ja2mLIkCHYvHkzZGRqcvJK3eDxeB/a46kQSsSVrKwsOnTogA4dOsDS\n0vKj2ziOQ35+/kdzSaOjo+Hv7w8+n4+ysrLPHtykp6cHbW3tT95o5ebm4kbSDWCWCH8BNUDYQojI\nyEgMHz5chAsTIn0mT56MLl26YPPmzWjatCnrOGIjNzcXx48fx6NHj1hHIQw8f/4cGzduxP79+zFh\nwgT89ddfaNeuHetY340KoYQQFqgQSggjtCO0YUpLS4OdnR2mT58Ob29vsWpDd3BwgK+vL7y9vVlH\nIaTGeDweNDQ0oKGhATMzs09uf/v27Uft9jdv3kRISAgyMzPx8uVLaGtrf1Qgff36NeQ05VAhVyHS\nnCVtS3Dl6hUqhBJSS23atIGFhQWOHDkCT09P1nHExvbt2+Hi4gI1NTXWUUg9evbsGTZs2IDDhw/D\nzc0N9+/fZzZ3XpQEAgG1xhNC6h096hDCiLq6OlJTU1nHICKUmJiIESNGwNfXFxMnTmQd5xMWFhZw\ncXFBYWEhHdJFGhxVVVX07NkTPXv2/OS2srKyT+aSRkZFolijWOQ5hK2FuHLjisjXJUQaTZ06FT4+\nPlQI/T/FxcXYtWsXrl27xjoKqSePHz/G+vXrcezYMXh4eODhw4dQV1dnHUtkqquraUcoIaTese/X\nJERK0Y7QhiUsLAxOTk7Yt2+fWBZBAUBJSQnm5uaIiopiHYWQeqWoqIguXbpg2LBhmD17Nnbs2AEr\nGyugSR1cTAkoLCisg4UJkT7W1tbIz8/H3bt3WUcRC0FBQRg0aJBEnAZOaicrKwseHh7o1asXWrRo\ngfT0dGzcuLFBFUEBao0nhLBBhVBCGKEZoQ3Hrl27MG3aNERERMDW1pZ1nK9ycHDAuXPnWMcghDm5\nRnKAsA4W5gAZWXp5RYgoyMrKwt3dHYGBgayjMFdVVYUtW7bQeJsGLiMjA5MnT4aJiQnatm2LjIwM\nrF27tsGOQqDWeEIIC/RKnRBGaEeo5OM4DitWrMCGDRuQkJCAPn36sI70r4YOHYrz589DIBCwjkII\nU/q6+lB4pyD6hQsAPR090a9LiJRyd3dHSEgISktLWUdh6vjx49DR0YGJiQnrKKQOPHz4EBMmTEC/\nfv2gq6uLzMxMrFy5Ei1atGAdrU5RazwhhAUqhBLCCO0IlWwCgQC//PILwsLCkJiYiI4dO7KO9E20\ntbXRrl07XL9+nXUUQpjq1asX5PPlRb6u7HNZ6Gvrg+M4ka9NiDTS0tJC3759cfz4cdZRmOE4Dhs3\nbqTdoA3Q/fv34eLiAnNzcxgYGIDP58PHxwfNmjVjHa1eUGs8IYQFKoQSwoiqqioqKipQVlbGOgqp\nobKyMowaNQqZmZm4dOkSWrduzTpSjTg4OCA8PJx1DEKY6tmzJwQFAkCU4zwFAC+dh+PHj6NDhw74\n9ddfERsbi6qqKhFehBDp4+npKdXt8e8fR+zs7FhHISKSkpKC0aNHw9LSEsbGxuDz+Vi0aBFUVFRY\nR6tX1BpPCGGBCqGEMMLj8aCurk7t8RKmoKAAVlZWUFRUREREhES+YLW3t6c5oUTqKSoqYvKkyZC7\nIye6RR8BP+j/gKdPnyIiIgJt2rTB4sWLoaGhAVdXVxw9ehTv3r0T3fUIkRL29vbIysrC/fv3WUdh\nwtfXF97e3pCRobduku7OnTtwdnaGra0tfvzxR/D5fCxYsABNmzZlHY0Jao0nhLBAz6aEMERzQiXL\ns2fPMGDAAJiYmODgwYOQlxd9W219MDU1xYsXL/DkyRPWUQhhav6c+ZBLkQNeiWCxSkDpkhLWLFsD\nHo+Hrl27YtGiRUhKSsK9e/cwcOBA7Nu3D5qamrCxsYG/vz9ycnJEcGFCGr5GjRrBzc0NQUFBrKPU\nu+TkZKSlpcHV1ZV1FFILN27cwLBhwzBs2DBYWFggKysLc+fOhbKyMutoTFFrPCGEBSqEEsIQzQmV\nHPfv34eZmRnc3d2xefNmid6VISsrCzs7O9oVSqRehw4dsHrlaihHKAPVtVtL/qI8rAZYYfjw4Z/c\n1rZtW/z888+IiIhATk4OPDw8kJiYiO7du6NPnz5YvXo1/vrrL5orSshXTJkyBQcPHkR5eTnrKPXK\nz88PM2fOlNgPX6XdtWvXYGdnh1GjRsHOzg58Ph8zZ86EoqIi62higVrjCSEsSO47eUIaAA0NDSqE\nSoDLly/DwsICa9euxbx581jHEQlqjyfkv2b9Ogt99PoAIfi+YigHNEpohDav2iA4IPhff7xp06YY\nPXo0Dh48iLy8PGzYsAEvX77EsGHDoKenhzlz5uDSpUuorq5lZZaQBkZXVxdGRkY4ffo06yj15smT\nJ4iMjMTPP//MOgqpocuXL8PKygouLi5wdnZGRkYGvLy8oKCgwDqaWKHWeEIIC1QIJYQhao0Xf6Gh\noRgxYgQOHjyI8ePHs44jMjY2NkhISEBJSQnrKIQwlZeXh2dZz2CgZADlA8pATR6SSwDFMEW0f9Ee\n1+KvoUWLFjW6tpycHAYPHoxt27YhOzsbp0+fRvPmzTF37ly0bt0akyZNwqlTp1BcXFyzX4qQBmrq\n1KlSdWjSli1b4O7uDlVVVdZRyDfgOA4XL16EhYUFJk2ahJ9++gmPHj2Cp6cnGjduzDqeWKLWeEII\nC1QIJYQhao0Xb//5z3/g5eWF8+fPw9ramnUckWrWrBl69+6NuLg41lEIYebly5cYMmQIJk+ejPt3\n78P3N18oH1ZG46jGXy+IFgGyl2WhFKSEKYOm4K/bf6FNmza1ysLj8WBoaAgfHx/cuXMHd+7cQZ8+\nfeDv74+2bdvCwcEBgYGByM3NrdV1CJFkjo6OuHfvHjIyMlhHqXOFhYXYv38/Zs2axToK+RccxyEm\nJgbm5ubw9PTE5MmTkZ6ejilTptBIg39BhVBCCAtUCCWEIdoRKp44jsOyZcvg5+eHhIQE9O7dm3Wk\nOkHt8USaFRYWwtraGo6Ojli8eDF4PB5++eUXPLr/CHMs5qDZsWZo8p8maHKmCWQvyELmggzkz8ij\n0c5GUAhQgKu2K65duobtW7bXyaw3bW1tzJgxAzExMXj69CnGjRuHCxcuwMDAAD/++CPWr1+Phw8f\nivy6hIizxo0bY9KkSVJxaJK/vz+GDx8OTU1N1lHIF3Ach8jISJiZmWHmzJmYNm0aHjx4gEmTJkFO\nTo51PIlAM0IJISzwOJrMTwgzMTExWL9+PS5cuMA6Cvk/1dXV8PLywu3btxEREQENDQ3WkerMw4cP\nMWTIEDx79gw8Ho91HELqTVFREaysrNC3b19s2bLls//9CwQCPHz4ELdv30ZOTg6EQiGqqqoQFBSE\nzMxMZnPeKisrcenSJYSGhuLMmTNQVlaGo6MjHB0d0bdvX9pZQxq89PR0DBw4EE+fPm2wu+3Ky8uh\no6ODmJgYdOvWjXUc8j84jsO5c+ewcuVKlJaWYunSpRg1ahQ9/n6HnTt34v79+/jzzz9ZRyGESBH6\n+IUQhmhHqHgpLS2Fi4sLysrKcOnSJTRt2pR1pDrVuXNnKCgoICUlBUZGRqzjEFIvSktLYW9vD0ND\nwy8WQQFAVlYWXbt2RdeuXT98TyAQwNfXFxUVFcwKofLy8rC2toa1tTV27tyJ27dvIywsDL/88gvy\n8vLg4OAAR0dHWFlZ0anEpEHq3LkzOnfujLNnz2LkyJGs49SJAwcOwNjYmIqgYkYoFOLMmTNYuXIl\nBAIBfHx84OzsDBkZarL8XtQaTwhhgR61CWGIZoSKj4KCAlhZWaFp06YIDw9v8EVQ4L8zCak9nkiT\n8vJyODk5oUOHDvD396/xTmhZWVn06NEDKSkpdZSwZng8Hnr37o1Vq1YhNTUV165dQ/fu3bFlyxZo\naGjAyckJe/fuxatXr1hHJUSkGvKhSUKhEH5+fliwYAHrKOT/CIVCnDhxAsbGxli1ahWWLVuG5ORk\njBw5koqgtUSt8YQQFuiRmxCGWrVqhcLCQggEAtZRpNrTp0/Rv39//Pjjj9i/f3+DbbX7HAcHB4SH\nh7OOQUidq6ysxOjRo9GsWTMEBwd/95tXY2NjJCcnizidaOjq6mL27Nm4ePEisrOzMWLECJw9exZ6\nenowNzfHpk2bkJmZyTomIbU2cuRI3Lp1C48fP2YdReTOnDkDFRUVDBw4kHUUqScQCBASEoLu3btj\n48aNWLt2LW7dugVHR0cqgIpIdXU17QglhNQ7egQnhKFGjRqhWbNmtFuHoXv37sHMzAxTpkyBn5+f\n1L2wNTc3R1paGl6+fMk6CiF1prq6GuPGjQOPx8OhQ4dqtftEnAuh/9SyZUtMnDgRJ0+eRF5eHn77\n7Tekp6ejf//+6Nq1KxYtWoSkpCQIhULWUQmpMUVFRbi6uiI4OJh1FJHz9fWFt7c3ze5mqLq6GgcP\nHkTXrl3xxx9/YPPmzUhKSoK9vT39exExao0nhLAgXe/4CRFDNCeUnYSEBAwePBgbNmzAvHnzWMdh\nonHjxrC0tMT58+dZRyGkTgiFQri5ueHdu3c4duxYrU/ylZRC6D8PKfUDAAAgAElEQVQpKCjA3t4e\nu3btwvPnz7F7925wHAc3Nzdoampi2rRpOH/+PCoqKlhHJeSbTZ06FcHBwaiurmYdRWQSExORm5uL\nESNGsI4ilaqqqrB3714YGBhg165d2LlzJxITE2FjY0MF0DpCrfGEEBaoEEoIYzQnlI1Tp05h5MiR\nOHToEFxdXVnHYYra40lDxXEcpk2bhqdPn+L06dMiOeCoW7duyMjIkNiioYyMDPr27Yt169YhLS0N\nly5dgp6eHtasWQMNDQ2MHj0aBw8eRGFhIeuohHxV9+7doaWl1aA+yPP19cXcuXOpMFTPKisrERQU\nhM6dO2P//v0IDAxEQkICLC0tqQBax6g1nhDCAhVCCWGMdoTWP39/f8yYMQNRUVGwsrJiHYe5oUOH\nIjo6GlVVVayjECIyHMdh9uzZSE1NRXh4OJSUlESyroKCAvT19XHv3j2RrMdap06d4O3tjStXruDR\no0ews7PD8ePH0b59ewwePBjbtm1rkHMYScPQkA5NSk9Px9WrV+Hm5sY6itSoqKjAf/7zH3Ts2BHH\njh3Dvn37EBcXh0GDBrGOJjWoNZ4QwgIVQglhjHaE1h+O47B06VJs3rwZly9fRs+ePVlHEgutW7dG\nx44dceXKFdZRCBEJjuOwaNEiXL58GZGRkWjatKlI15fE9vhvoa6uDnd3d4SFhSE3NxezZs3C3bt3\n0adPHxgZGWHZsmW4c+cOOI5jHZUQAMDYsWNx5coV5OTksI5Sa5s2bYKXl5fIPrQhX1ZeXo4dO3ZA\nX18fZ86cQUhICKKjozFgwADW0aQOtcYTQligQighjNGO0PpRXV0NDw8PREZGIjExEXp6eqwjiRVq\njycNyerVq3H27FlER0ejWbNmIl+/oRZC/0lJSQmOjo7Ys2cPcnNzsX37dpSUlGDs2LFo3749ZsyY\ngZiYGFRWVrKOSqSYsrIyxowZgz179rCOUiu5ubk4fvw4pk+fzjpKg1ZaWopt27ZBT08P0dHROHXq\nFCIiIvDjjz+yjia1qDWeEMICFUIJYYx2hNa90tJSODs7IycnBxcvXoS6ujrrSGKHCqGkofDz88OB\nAwcQGxuLVq1a1ck1pKEQ+k+ysrIYMGAA/Pz88OjRI0RGRqJdu3ZYunQpNDQ04OLigqNHj+Ldu3es\noxIpNHXqVAQFBUEoFLKO8t22b98OFxcXqKmpsY7SIJWUlGDTpk3Q09NDfHw8zp49izNnzqBPnz6s\no0k9ao0nhLBAhVBCGKMdoXXr9evXsLS0RLNmzXDmzBk0adKEdSSxZGxsjKKiImRkZLCOQsh327lz\nJ/78809cuHABrVu3rrPrGBkZ4a+//oJAIKiza4grHo+HLl26YOHChbh+/TrS0tJgYWGBffv2QVNT\nEzY2Nvjzzz/x999/s45KpESvXr3QsmVLxMTEsI7yXYqLi7Fr1y7MnTuXdZQGp6ioCBs2bICuri6S\nkpIQFRWFU6dO0WgkMUKFUEIIC1QIJYQxDQ0N2hFaR548eQIzMzOYm5tj3759kJeXZx1JbMnIyGDo\n0KE4d+4c6yiEfJfg4GBs2LABFy5cgJaWVp1eS1VVFerq6vTBAYA2bdrA09MTERERyMnJwdSpU3Ht\n2jUYGhqid+/eWLVqFVJTU2muKKlTknxoUlBQEAYNGgR9fX3WURqMt2/fYs2aNdDT00NKSgri4uJw\n7Ngx9OjRg3U08j+qq6tpRighpN5RIZQQxqg1vm6kpqbCzMwM06ZNw4YNGyAjQw93/4ba44mkOnLk\nCJYuXYrY2Fjo6OjUyzWlrT3+WzRt2hSjRo3CgQMHkJubC19fX7x+/RqOjo7Q09PDnDlzcOnSJVRX\nV7OOShoYV1dXXLhwQeJeT1VVVWHLli3w9vZmHaVBKCwsxIoVK6Cvr4/09HQkJCTg8OHD6Nq1K+to\n5AtoRyghhAWqDBDCmLq6OvLz82m3jAjFx8djyJAh8PPzw+zZs1nHkRhDhgxBUlISzfkjEuX06dOY\nM2cOoqKi0KlTp3q7LhVCv05OTg4WFhbYunUrsrKycPr0aTRv3hzz5s1D69atMXHiRJw8eRLFxcWs\no5IGQEVFBc7Ozti3bx/rKDVy7Ngx6OjowMTEhHUUifb69WssXboU+vr6ePz4Ma5evYr9+/fjhx9+\nYB2N/AsqhBJCWKBCKCGMKSkpQU5OjopPInLixAmMHj0aR44cwU8//cQ6jkRp0qQJ+vXrJ7Fz1oj0\niYiIwLRp0xAREYFu3brV67WpEPrteDweDA0N4ePjg9u3byM5ORmmpqYICAhA27ZtYW9vj127diE3\nN5d1VCLB3h+aJCkfLHMcB19fX9oNWgsvX77EwoUL0alTJ+Tm5uLmzZvYs2cPOnbsyDoa+UYCgYBa\n4wkh9Y4KoYSIATowSTR27tyJWbNmISoqCpaWlqzjSCRqjyeS4sKFC5g0aRLCwsKYHHzxvhAqKUUX\ncaKlpYXp06cjOjoaz549w4QJExAXFwcDAwP07dsX69evx4MHD+jPltRI3759IS8vj/j4eNZRvkls\nbCyqqqpgZ2fHOorEycvLg7e3Nzp37ow3b97gzp07CAwMhK6uLutopIaqq6tpRyghpN5RIZQQMUBz\nQmuH4zgsXrwY27Ztw+XLl2FsbMw6ksSyt7dHREQEhEIh6yiEfFFiYiJ++uknnDhxAn379mWSoU2b\nNmjUqBGdjl5Lqqqq+OmnnxASEoK8vDysXLkSz549g7W1NTp37gxvb29cuXIFAoGAdVQi5ng8Hjw9\nPbFr1y7WUb7Jxo0b4e3tTTPMa+D58+eYM2cODAwMUF5ejpSUFPj7+6N9+/aso5HvRK3xhBAW6JmX\nEDFAO0K/X1VVFaZMmYKYmBgkJibSboBa0tXVRcuWLXHr1i3WUQj5rJs3b8LZ2RmHDh3CwIEDmWah\n9njRkpeXh7W1NXbu3ImnT5/iyJEjUFRUxPTp09GmTRu4u7sjLCwMpaWlrKMSMTV+/HhERETg9evX\nrKN8VXJyMh48eABXV1fWUSTC33//jV9//fXDCJR79+5h+/bt0NLSYpyM1Ba1xhNCWKBCKCFigHaE\nfp+SkhI4OTkhNzcXcXFxUFNTYx2pQaD2eCKuUlJS4ODggN27d8Pa2pp1HCqE1iEej4devXph5cqV\nSElJQVJSEgwNDbF161a0bt0aTk5O2LNnD16+fMk6KhEjLVq0gIODAw4cOMA6ylf5+flh1qxZkJeX\nZx1FrD19+hReXl7o0aMHFBQUkJaWhi1btqBt27asoxERodZ4QggLVAglRAzQjtCae/XqFSwtLaGm\npoawsDA0adKEdaQGgwqhRBw9ePAAtra22L59O4YNG8Y6DgAqhNYnHR0dzJo1CxcvXsTjx48xcuRI\nnDt3Dvr6+hgwYAD8/PyQkZHBOiYRA1OnTkVgYKDYzph98uQJIiMj4enpyTqK2MrOzoanpyeMjY2h\nqqqK9PR0+Pr6onXr1qyjERGj1nhCCAtUCCVEDNCO0Jp5/PgxzMzMYGFhgT179kBOTo51pAalX79+\nePz4MXJyclhHIQQAkJmZCSsrK2zYsAFjxoxhHecDKoSy0aJFC0yYMAEnTpxAXl4eFi5ciIyMDJib\nm6NLly5YuHAhrl+/TrOOpZS5uTmqq6tx7do11lE+a8uWLXB3d4eqqirrKGInMzMT7u7u6N27NzQ0\nNPDo0SOsW7eOOn4aMGqNJ4SwQIVQQsQA7Qj9dikpKejfvz+mT5+OdevWgcfjsY7U4DRq1Ag2NjaI\niIhgHYUQPHnyBEOGDMHSpUsxceJE1nE+oqurizdv3oj9PMKGTEFBAUOHDkVAQABycnIQHBwMAHB3\nd0e7du3w888/IyIiAuXl5YyTkvrC4/Hg4eGBwMBA1lE+UVBQgP3792PWrFmso4iV9PR0TJw4EX37\n9oW2tjYyMzOxatUqtGzZknU0UseoNZ4QwgIVQgkRA7Qj9NtcvHgRVlZW2Lx5M2bOnMk6ToNG7fFE\nHDx//hyWlpaYPXs2fv75Z9ZxPiEjIwNDQ0PaFSomZGRk0LdvX6xbtw5paWlISEhAx44dsW7dOmho\naGDUqFE4cOAACgoKWEcldWzSpEk4ffo03rx5wzrKR/z9/TF8+HBoamqyjiIW0tLS4Orqiv79+6NT\np07g8/lYvnw5mjdvzjoaqSfUGk8IYYEKoYSIAdoR+u+OHTuGsWPHIiQkRKxaYxsqW1tbXLx4kXZR\nEWby8/NhaWmJKVOmYPbs2azjfBG1x4uvjh07Yv78+bh8+TIyMzNhb2+PkydPokOHDrCwsMDWrVuR\nnZ3NOiapA+rq6rC2tsbhw4dZR/mgvLwcO3bswPz581lHYS41NRVjxoyBhYUFevTogaysLCxZsoTG\nBUghao0nhLBAhVBCxADtCP267du3Y+7cuYiOjsbgwYNZx5EKLVu2RI8ePXDp0iXWUYgUKigogLW1\nNUaNGoWFCxeyjvNVVAiVDGpqanBzc0NoaChyc3Mxe/ZspKamwtTUFIaGhvDx8cHt27fF9oAdUnPi\ndmjSgQMH0LNnT3Tr1o11FGaSk5MxYsQIWFtbw8TEBHw+H7///juaNm3KOhphhFrjCSEsUCGUEDHQ\nvHlzlJaWoqKignUUscJxHBYuXIgdO3bgypUrMDIyYh1JqlB7PGHh7du3sLW1xZAhQ7By5UrWcf4V\nFUIlj5KSEhwdHREcHIwXL15g586dKCsrg4uLC7S1tTF9+nRER0ejsrKSdVRSC5aWlnj79i1u377N\nOgqEQiH8/Pzg7e3NOgoTN2/exPDhw2Fvbw9zc3NkZWVh/vz5aNKkCetohDFqjSeEsECFUELEAI/H\ng5qaGrXH/0NVVRXc3NwQFxeHK1euoEOHDqwjSR17e3ucO3dObHbTkIavpKQE9vb26N27N3x9fSXi\nMLQuXbrgyZMnKCkpYR2FfAdZWVn0798fvr6+SE9PR3R0NLS0tLBs2TJoaGjgp59+QkhICN6+fcs6\nKqkhGRkZTJkyRSwOTTpz5gxUVFQwcOBA1lHq1fXr1zF06NAPu0D5fD5mz54NJSUl1tGImKBCKCGE\nBSqEEiImNDQ0qD3+/5SUlMDR0REvX75EXFwc1NTUWEeSSt26dYNQKERaWhrrKEQKlJWVYfjw4ejY\nsSN27NghEUVQAJCTk4OBgQFSU1NZRyG1xOPxYGBggN9//x3Xrl1DWloaBg8ejAMHDkBLSwvW1tbY\nuXMnnj17xjoq+UZubm44duwYiouLmebw9fXFggULJOZxrbauXLkCa2trjB07FsOHD0dmZiZmzJgB\nRUVF1tGImKEZoYQQFqgQSoiYoAOT/uvly5ewsLCAhoYGQkNDoayszDqS1OLxeNQeT+pFZWUlRo0a\nBXV1dQQFBUFGRrJenlB7fMPUpk0beHp64ty5c3j+/Dl+/vlnJCUlwcjICL169cLKlSuRkpJCu+bF\nWNu2bTFw4ECEhIQwy5CYmIjc3FyMGDGCWYb6Eh8fj8GDB2PixIkYM2YMMjIyMG3aNDRu3Jh1NCKm\naEYoIYQFyXqnQUgDRgcmAdnZ2TAzM4OVlRWCg4MhJyfHOpLUe98eT0hdqa6uhouLC+Tl5bF//36J\nfENEhdCGr0mTJhg5ciT279+PvLw8+Pn5oaCgAE5OTtDV1cXs2bNx8eJFVFdXs45K/sf7Q5NY8fX1\nxdy5cyXyse1bcByHCxcuYODAgZgyZQomTJiA9PR0eHh4QF5ennU8IuaoNZ4QwgIVQgkRE9K+IzQ5\nORn9+/fHzJkzsWbNGqlpHxN3FhYWuHv3LgoKClhHIQ2QQCDApEmTUFpaipCQEIn98IMKodKlUaNG\nsLCwwNatW5GVlYWwsDC0bNkS3t7e0NDQwIQJE3DixAkUFRWxjkoA2Nra4vnz50zGVzx8+BBXr16F\nm5tbvV+7rnEch6ioKPTv3x9eXl7w8PDAw4cP4ebmJrGP5aT+UWs8IYQFKoQSIiakeUdoXFwcbGxs\nsG3bNsyYMYN1HPIPioqKGDRoECIjI1lHIQ2MUCjEzz//jBcvXuDUqVMS3TrZo0cPpKWloaqqinUU\nUs94PB569OiBpUuX4tatW0hJScGPP/6IwMBAtGvXDkOHDkVAQABevHjBOqrUkpWVhbu7+0e7Qk+e\nPImZM2fC3NwcqqqqkJGRwcSJEz97/ydPnkBGRuaLf7m6un7x2ps2bYKXl1eDOhyI4zicO3cOffv2\nxZw5czBjxgykpaVhwoQJVNAiNUat8YQQFujZihAxoaGhgbt377KOUe+OHj2KX3/9FceOHcOgQYNY\nxyGf8b49/mtv9gipCY7jMHPmTDx48ABRUVESf4BGkyZNoK2tjQcPHqBHjx6s4xCGNDU14eXlBS8v\nL7x9+xaRkZEIDQ3F77//js6dO8PR0RGOjo4wMDCgzod65O7ujp49e2Ljxo1QVFTE6tWrkZqaiiZN\nmkBTUxMPHz781zWMjIzg5OT0yfe7dev22Z/Pzc3FiRMn8OjRo1rnFwccx+HMmTNYuXIlqqqqsHTp\nUowcOVLiZjoT8UKt8YQQFqgQSoiYkMYdodu2bYOvry9iY2OpeCDG7O3tsWjRIlRXV9NuD1JrHMdh\nwYIFuH79Oi5cuIAmTZqwjiQSPXv2RHJyMj2WkQ9UVVUxduxYjB07FpWVlYiPj0dYWBhsbGygoKDw\noSjar18/KgTUsfbt28PExAQnTpzAhAkTsHXrVmhqakJPTw/x8fGwsLD41zWMjIzg4+Pzzdfcvn07\nXF1doaamVpvozAmFQpw+fRqrVq0Cj8eDj48PHB0dqQBKRIJa4wkhLNAzGCFiQppmhAqFQvz222/w\n9/dHYmIiFQ7EnKamJrS1tXHt2jXWUUgDsGLFCkRFRSEqKgqqqqqs44gMzQklXyMvLw8rKyvs2LED\nT58+xdGjR6GsrIxff/0VrVu3hpubG8LCwlBaWso6aoPl6emJXbt2AQAGDhwIPT29OrtWUVERAgIC\nMHfu3Dq7Rl0TCAQ4evQoevTogfXr12PVqlW4c+cOnJ2dqQhKRIZa4wkhLNCzGCFiQlp2hFZVVWHy\n5MlISEhAYmIi2rdvzzoS+QZ0ejwRhQ0bNuDo0aOIiYlBy5YtWccRKSqEkm/F4/HQs2dPrFixAnfv\n3sXNmzdhZGSEbdu2oXXr1nB0dERwcLDUfDhaXxwcHJCZmYkHDx581/2fP3+OXbt2Yd26ddi1axf+\n+uuvL/7s7t27YWFhUafF1rpSXV2NQ4cOoVu3btiyZQt8fX1x48YNDBs2jMY5EJGj1nhCCAs8juM4\n1iEIIf8tECopKaGioqLBftJeXFyMUaNGoVGjRh92wxDJcP36dXh4eODevXusoxAJ9ccff+CPP/5A\nfHw82rVrxzqOyL1+/Rq6urooLCxssI/hpO4VFBQgIiICYWFhiI6ORvfu3T+00Hfq1Il1PIm3cOFC\nVFZWYtOmTR++9741fvz48di/f/8n93ny5Al0dHQ+KQJyHIdBgwZh37590NLS+vD9qqoq6Ovr48SJ\nE+jTp0/d/TIi9r4AumbNGqirq2PZsmUYMmQIFT9JnWrXrh1u3LjRIF8XEELEF71SJ0RMyMnJQUVF\nBa9fv2YdpU7k5+fDwsIC7dq1Q2hoKBVBJUyfPn2Qn5+Px48fs45CJFBgYCA2bdqE2NjYBvtmp2XL\nllBVVUV2djbrKESCtWjRAuPHj8fx48eRl5eHxYsXIzMzEwMHDoSBgQEWLlyI69evQygUso4qkTw8\nPHDgwAFUVFR8832UlJTg4+OD27dvo7CwEIWFhYiPj8fgwYNx6dIlDBkyBGVlZR9+/tixY9DR0ZGY\nImhVVRWCg4PRuXNn7NmzBwEBAbh8+TKsrKyoCErqHLXGE0JYoEIoIWKkoc4JzcrKgpmZGWxtbREU\nFERD0SWQrKwshg4dSu3xpMYOHjyIFStWIDY2Fh06dGAdp05RezwRJQUFBdjZ2SEgIAA5OTnYu3cv\neDwepkyZgnbt2sHT0xPnzp1DeXk566gSQ09PD927d0doaOg330dNTQ3Lly+HkZERVFRUoKKigv79\n+yMqKgqmpqbIzMxEUFAQgP/uEvX19YW3t3dd/QoiU1lZiV27dqFjx444fPgwgoODcenSJVhYWFAB\nlNQbao0nhLBAhVBCxEhDnBN6584d9O/fH3PmzPlw4iiRTPb29ggPD2cdg0iQEydOwNvbG9HR0ejY\nsSPrOHWOCqGkrsjIyMDU1BRr167F/fv3cfnyZXTu3BkbNmyAhoYGRo4cif379zfYrhJRmjp1KgID\nA2u9jqysLDw8PMBxHBISEgAAsbGxqK6uhp2dXa3Xryvl5eX4888/oa+vj1OnTuHQoUOIjY3FwIED\nWUcjUogKoYQQFqgQSogYaWg7QmNjY2FjY4Pt27fDy8uLdRxSS9bW1rhy5QpKSkpYRyESIDw8HNOn\nT8f58+fRpUsX1nHqBRVCSX3R19fHvHnzkJCQgMzMTAwbNgynTp2Cjo4OLCwssHXrVhrT8AXOzs5I\nTU0Fn8+v9VpqamoA8OF5cePGjZg/f75YzgkuKyvDH3/8AX19fUREROD48eOIjIyEmZkZ62hEilVX\nV1OnGCGk3onfszQhUqwh7Qg9cuQIxo0bh5MnT2LkyJGs4xARUFVVhYmJCS5cuMA6ChFzMTExcHd3\nx9mzZ2FkZMQ6Tr2hQihhQU1NDZMnT0ZoaChyc3MxZ84c/PXXXzA1NUWPHj2wdOlS3Lp1C3Q+6n81\nbtwYEyZM+NDOXhvXrl0DAOjq6iI5ORkPHjyAq6trrdcVpZKSEmzevBl6enqIi4tDWFgYwsPDYWpq\nyjoaIbQjlBDCBBVCCREjDWVH6JYtW7BgwQLExsbC3NycdRwiQtQeT/5NQkICXF1dcfLkSZiYmLCO\nU6+0tLRQWVmJ3Nxc1lGIlFJSUsLw4cOxe/duvHjxAv7+/qioqMC4ceOgpaUFLy8vREdHo7KyknVU\npjw8PLB3715UVVX9688mJyd/toh84cIFbN26FTweD+PHj4efnx9mzZoFeXn5uohcY8XFxdi4cSP0\n9PRw9epVREREIDQ0FL169WIdjZAPqBBKCGGB9qETIkbU1dVx48YN1jG+m1AoxG+//Ybw8HAkJiZC\nW1ubdSQiYg4ODvDz8wPHcTTvlXwiKSkJo0aNwpEjRzBgwADWceodj8f7sCtUnGcEEukgKysLMzMz\nmJmZYePGjXj48CFCQ0OxbNkyPHz4EDY2NnB0dISdnR2aNWvGOm69evToEYRCIWxsbNC4cWMAwNWr\nV+Hm5gYAaNWqFXx9fQEAc+fORUZGBvr16wdNTU0AQGpqKuLi4sDj8bB69Wq0adMGkZGR+PPPP9n8\nQv/w7t077NixA1u3bsXgwYMRGxuLbt26sY5FyGcJBAJqjSeE1DseR30yhIiNsLAwBAUF4ezZs6yj\n1FhlZSXc3d2RlZWFs2fPomXLlqwjkTrSqVMnhISEoGfPnqyjEDGSnJwMW1tbBAcHw97ennUcZry9\nvdG8eXMsWrSIdRRCvig3Nxdnz55FWFgYEhISYGpqCkdHRzg6OkJLS4t1vDq3YsUKrFixAhzHfXae\nZ4cOHT7MEN2zZw9Onz6Ne/fu4dWrV6iqqoKGhgb69euH6dOnw8zMDLNnz4acnNyH4ikLb968wR9/\n/IHt27fDxsYGixcvhoGBAbM8hPyb9///CYVC+nCdEFKvqBBKiBi5fv06Zs2ahaSkJNZRaqSoqAij\nRo1C48aNERISAiUlJdaRSB2aM2cOWrRogaVLl7KOQsTE/fv3MWTIEOzYsUPqZwIfPnwYp0+fxvHj\nx1lHIeSbFBcXIzo6+sPsyPbt28PJyQmOjo7o0aNHgy1QlJaWQktLC8nJybXqYCkoKIC+vj5SU1M/\n7BitTwUFBdi2bRt27twJBwcHLFq0CJ06dar3HITUlEAggJycHIRCIesohBApQzNCCREjknhYUl5e\nHiwsLKCtrY1Tp05REVQKODg40JxQ8kFGRgasra3h5+cn9UVQgA5MIpKnSZMmGDFiBPbt24e8vDxs\n3rwZhYWFcHZ2ho6ODmbNmoW4uLhvmqcpSZSUlODi4oLg4OBarePv74/hw4fXexH01atXWLx4MTp2\n7IicnBwkJSVh7969VAQlEoPa4gkhrNCOUELESElJCdTU1FBSUiIROzD4fD5sbGwwbtw4LF++XCIy\nk9qrrKyEuro60tPToaGhwToOYejx48cYOHAgfHx8MGXKFNZxxIJAIICqqipycnKgqqrKOg4h343j\nONy7dw9hYWEICwtDVlYW7Ozs4OjoCFtbWzRt2pR1xFpLSUmBg4MDHj9+/F0HtpSXl0NHRwcxMTH1\nNoczPz8fmzZtQmBgIEaPHo2FCxeiQ4cO9XJtQkSptLQULVu2RFlZGesohBApQztCCREjysrK4PF4\nKC4uZh3lX92+fRsDBgzA/PnzsWLFCiqCShF5eXkMGTIE58+fZx2FMJSTkwNLS0vMnz+fiqD/ICsr\ni+7duyMlJYV1FEJqhcfjoXv37liyZAlu3ryJlJQUmJmZISgoCG3btoWdnR0CAgLw/Plz1lG/m6Gh\nIdq2bYvIyMjvuv+BAwfQs2fPeimC5ubmYt68efjhhx9Q/P/Yu/e4nu///+O3d0SlyWk6yDFDGDnT\nEBFSyrkihmHMJOeZw8ZkzEzmPOdjyvHtHEJEaKwcE3JIqTlMUim9e//+2Hd+O9g+6F2vd/W4Xi77\nQ73fz+f9PXn3ej9ez8fz+fw5kZGRLF++XIqgIt+SE+OFEEqRQqgQCti+fTs+Pj60bt0aMzMzDAwM\n6N+/PwDm5ub8+uuv/3jO6dOn6dy5M2XLlsXExIT69euzYMECRfbVOXz4MM7OzixevJhhw4bl+fxC\nedIeX7glJSXRrl07Pv30U0aOHKl0HL0j7fGiILK2tmb48OEEBwdz//59BgwYQGhoKHXq1KFZs2bM\nmjWLK1eukN+azYYMGcKKFSve+nnZ2dl8//33jB8/PhdS/TWbVxgAACAASURBVH/x8fGMGjWK2rVr\nk5WVxaVLl1i8eHGO9jUVQh9Ia7wQQilSCBVCATNnzmTx4sVERUVhbW39l9WUr9snVK1W4+DgQFhY\nGN27d2fkyJG8fPmS0aNH4+XllafZN23ahLe3N9u3b6dbt255OrfQH87Ozhw5coTMzEylo4g89vjx\nY5ycnPDy8mLChAlKx9FLUggVBZ2ZmRkeHh5s3ryZpKQk/Pz8ePDgAc7OznzwwQeMHTuWEydOoNFo\nlI76P3l6ehIaGsqDBw/e6nm7d++mZMmSODg45Eque/fuMWLECD788EOKFi3KlStXWLBgARUqVMiV\n+YTIa1lZWbIiVAihCCmECqEAf39/YmJiSE5OZsmSJX9ZPfH3FaEpKSkMGTKEokWLEhoayooVK5gz\nZw6RkZG0aNGCbdu2ERQUlCe5582bx6RJkwgJCaFVq1Z5MqfQT+bm5tSsWZOTJ08qHUXkoadPn9Kx\nY0ecnZ2ZNm2a0nH0VoMGDbhw4YLSMYTIE39sl7Jw4ULu3r3L1q1bMTU1xcfHBwsLCwYOHMiuXbtI\nTU1VOuprmZqa0qtXL9asWfNWz5s7dy4TJkzQ+dZAd+7c4dNPP8XOzg5TU1Oio6OZN28elpaWOp1H\nCKVJa7wQQilSCBVCAQ4ODtjY2Lz2e39fEbp161YePXqEl5cXDRo0ePX1YsWKMXPmTLRaLUuXLs3V\nvNnZ2YwdO5ZVq1Zx6tSpPDsQQOg3aY8vXJ4/f07nzp2xt7dn9uzZsi/wf6hbty43btzgxYsXSkcR\nIk+pVCoaNGjA9OnTiYyMJCIiggYNGrBw4UIsLS1xc3Nj1apVr90CSElDhgxh5cqVb7zd0KlTp0hM\nTKR79+46y3Dr1i0++eQTGjVqRLly5YiJiWHOnDmUL19eZ3MIoU+kNV4IoRQphAqhZ/6+IvTYsWOo\nVCo6duz4j8e2bt0aExMTTp8+zcuXL3MlT2ZmJt7e3pw9e5awsDAqVqyYK/OI/EcKoYVHeno6Xbp0\noU6dOvj7+0sR9H8wMjLigw8+4PLly0pHEUJRVapUwcfHh5CQEO7evYuHhwfBwcHUqFGDjz76iO++\n+47r168rHZPGjRtjZmZGSEjIGz1+7ty5jBkzRier2WJiYvj4449p1qwZ1tbW3LhxAz8/P8qVK5fj\nsYXQZ9IaL4RQihRChdAzf18R+scHhBo1avzjsUWKFKFq1apkZWURGxur8ywpKSm4uLiQlpbG4cOH\nKVOmjM7nEPmXnZ0daWlpxMTEKB1F5KKMjAy6detGhQoVWLZsGQYGcunwJmSfUCH+qnTp0vTt25eg\noCCSkpKYMmUKsbGxtG3bFltbW7744gvCw8MVOQRSpVL949CklJQU7t69S1xcHBkZGa++Hh0dzenT\npxk4cGCO5rx27Rre3t589NFHVK9enZs3bzJ9+nS51hKFhrTGCyGUIp9mhNAzf18RmpycDPx+MMHr\n/PH1p0+f6jRHUlISbdq0oVq1amzbtg1jY2Odji/yP5VKhYuLi6wKLcBevnyJh4cHpqamrF27Vj6w\nvAUphArx74oXL46zszPLli3j/v37rFu3jiJFijBkyBCsrKwYMmQIe/fuJT09Pc8y9enTh/3799Or\nlxs2NpaYm5elRYs6NG1qS6lS71Gvng2+vp8zdepUPvvsM0xMTN5pnsuXL+Pp6YmDgwN16tTh1q1b\nTJ06lVKlSun4FQmh36QQKoRQihRChdAzrzs1Pq/duHEDe3t73NzcWLZsmezfI/6VtMcXXBqNhn79\n+pGVlcXmzZvlfeAtSSFUiDdjYGBA06ZN8fPz4/Lly5w6dQpbW1vmzp2LhYUF3bt3Z926dTx+/DjX\nMkRERNC6dRNKlszA3HwPU6YksmfPSzZvTiUgIJVdu14ybFgsjx4tZ//+bYSHHyM+Pv6t5oiMjKRn\nz560b9+eRo0aERsby6RJkyhZsmQuvSoh9JvsESqEUIoUQoXQM39fEfrHis8/Vob+3R9f19VKgp9/\n/pnWrVszceJEvvrqK9kLUPyndu3aERER8a8/nyJ/ys7OZvDgwTx+/Jht27ZRrFgxpSPlO3Z2dly6\ndAmNRqN0FCHyFRsbG8aMGUNoaCi3bt3Czc2NXbt2UbVqVdq0acP8+fN1th2QVqvlq68m4+zsgJvb\nTTZtyqJnT6haFf68UK14cahdGwYPzmLnTqhQ4TT169dix44d/3OO8+fP07VrV5ydnbG3t+fWrVuM\nHz8eU1NTnbwGIfIr2SNUCKEUKYQKoWf+viK0Zs2aAK/dh1Gj0XD79m2KFi1KtWrVcjx3cHAwzs7O\nLF26lKFDh+Z4PFHwlShRgpYtW3Lo0CGlowgd0Wq1fP7559y8eZNdu3ZhZGSkdKR8yczMDHNzc9lD\nV4gcKFeuHAMGDGDnzp0kJSUxduxYrly5QosWLfjwww+ZMmUKERER77SvqFarxcdnGNu2+fPTT+m0\nbw9vcu+3WDHo3z8LP7/nDBvmzaZNG1/7uHPnzuHq6oqbmxuOjo7ExsYyZswYSpQo8dZZhSiIpDVe\nCKEUKYQKoWfKlClDSkoKmZmZADg6OqLVajl48OA/HhsaGkpaWhofffQRhoaGOZp3w4YN9O/fn507\nd9K1a9ccjSUKF2mPLzi0Wi1jx47l559/Zt++ffKBPYekPV4I3TE2NqZLly6sXLmShIQEli9fTmZm\nJt7e3lSsWJHhw4cTHBz8l4ON/suSJYs5dGgTc+ak8S7nE9WsCXPmpOPjM5Tz58+/+vrp06fp1KkT\nPXv2pHPnzty6dQsfHx/Za12Iv5HWeCGEUqQQKoSeMTAwoFy5cjx8+BCAnj17Uq5cObZs2fKXC+2M\njAymTJmCSqVi+PDh7zyfVqtl7ty5TJ48maNHj9KyZcscvwZRuLi4uHDgwAFpAS4Apk2bxtGjRzl4\n8KDsW6cDUggVIncUKVIEe3t7vvvuO65fv05ISAhVqlRh+vTpmJub4+HhwebNm//1IMnbt28zdepE\nJk9OJScd6lWrwrBh6fTr14sjR47Qvn17+vTpQ/fu3blx4wafffaZrKoX4l9Ia7wQQilyC0YIBajV\nanbt2gVAYmIi8PsKgoEDBwK/3yFNSkqiQoUKvPfee6xYsYJevXrRpk0bPD09KVOmDLt37yYmJoZe\nvXrRq1evd8qRnZ3NuHHjOHToEKdPn8ba2lo3L1AUKlWqVKF8+fJERETQvHlzpeOIdzRr1iy2b99O\naGgoZd5leZT4hwYNGvDDDz8oHUOIAq9WrVrUqlWLiRMnkpSUxJ49ewgICGDYsGE0bdoUd3d33N3d\nqVSpEgBTp06gW7cM/u+POdK+PezbdxcvL0/mzPmOfv365bhLR4jCQFrjhRBKkUKoEAqIjIxk/fr1\nr/6sUqm4ffs2t2/fBqB48eJ/OTDJ3d2d0NBQ/Pz82LFjBy9evKB69erMnz+fkSNHvlOGjIwMBgwY\nwP379zl58iSlS5fO2YsShdof7fFSCM2f/P39WbNmDSdOnOD9999XOk6B8ceKUK1WKwfPCZFHzM3N\nGTx4MIMHD+b58+ccOnQItVrN9OnTqVixIk5OTuzevZsNG3TTxaBSQd++2axdW5KBAwfKv3Uh3pC0\nxgshlCKt8UIo4KuvvkKj0fzrfz169PjLgUkALVq0YO/evTx+/JjU1FSioqLw8fF5pwvuZ8+e0blz\nZzIyMjh06JAUQUWOubq6sm/fPqVjiHewbNkyFixYQEhICJaWlkrHKVAsLS0xNDQkLi5O6ShCFEqm\npqZ0796ddevWkZiYiL+/P1FRUdSp8xIzM93N06gRPHmSJIejCfEWpDVeCKEUKYQKoYfMzc3/siJU\nlxITE3FwcKBGjRps3bpVNu8XOtG8eXPu3bvH/fv3lY4i3sK6devw8/PjyJEjr1pGhW7JPqFC6Iei\nRYvi4OBAtWrWNGyo1enYBgZga1vkL3u5CyH+m7TGCyGUIoVQIfRQ+fLl/7EiVBdiYmKwt7ene/fu\nLFmyRC4+hM4ULVqUTp06yarQfCQoKIhJkyZx+PBhbGxslI5TYEkhVAj9cunSBapV0/24lSs/5/Ll\ni7ofWIgCSlrjhRBKkUKoEHooN1aEnjt3DgcHB7788kumTp0qe1gJnZP2+PxDrVYzcuRIDh48SK1a\ntZSOU6A1bNhQCqFC6JG0tDRyoxnG2FhLamqK7gcWooCS1nghhFKkECqEHtL1itADBw7g4uLC8uXL\nGTx4sM7GFeLPOnbsyPHjx0lPT1c6ivgPwcHBDBkyhH379lGvXj2l4xR4siJUCP1iZFScjAzdj5uZ\nCUZGJrofWIgCSlrjhRBKkUKoEHpIlytC169fz4ABA1Cr1bi5uelkTCFep0yZMtjZ2XHs2DGlo4h/\ncfz4cfr168euXbto3Lix0nEKhapVq5KcnMzjx4+VjiKEAGxt63Pnju7HvXfPlNq16+p+YCEKKCmE\nCiGUIoVQIfSQLlaEarVa5syZw9SpUzl+/Dj29vY6SifEv5P2eP0VHh5Or1692LJli7wf5CEDAwPs\n7OxkVagQeqJZs1bExOh25aZWC7/8ks7t27dJTEzU6dhCFFRZWVmyR6gQQhFSCBVCD5UvX56HDx+S\nnZ39Ts/Pzs7G19eXjRs3cvr0aWxtbXWcUIjXc3FxYe/evWi1uj2RV+TM+fPncXd3Z8OGDTg6Oiod\np9CR9ngh9Ierqyvh4dmkpeluzMuXoVixkly7dg1bW1tatGjB7NmzuXbtmvw+FOJfyIpQIYRSpBAq\nhB4qVqwYpqam/Pbbb2/93IyMDLy8vIiMjOTkyZNUqFAhFxIK8Xq1a9fGwMCAy5cvKx1F/J/Lly/j\n4uLCTz/9RKdOnZSOUyhJIVQI/WFlZUWrVh+hy+aFnTuNGTduCoGBgSQlJTF9+nTi4uJwcnKiZs2a\njB8/nrCwMDQaje4mFSKfk0KoEEIpUggVQg+lpaVRsmRJ1Go1J06c4P79+2+0oiA5ORlnZ2eysrII\nDg6mVKlSeZBWiP9PpVLh6urK3r17lY4igOvXr9OxY0f8/f3p2rWr0nEKLSmECqEfsrKyWLRoEadO\nXWD9+qLo4lzK8HCIjS3JkCFDgd9vZnfo0IHFixcTFxdHQEAAxsbGjBgxAktLSwYNGoRarSZNl0tS\nhciHNBqNtMYLIRQhhVAh9MTjx4/5bu53VLOthlkZM+LT4vH51ge3IW58UPcDzMqZ4dHXg3Pnzr32\n+Q8ePMDBwQFbW1uCgoIwMjLK41cgxO9cXFxkn1A9EBsbi5OTEzNnzsTT01PpOIWara0td+/eJTU1\nVekoQhRaISEh2NnZsWvXLk6cOMHEiVOZPduEzMx3HzMxEfz9jVmzJgBTU9N/fF+lUtGoUSNmzJhB\nVFQUZ8+epX79+vj7+2NhYYG7uzurV6/W2QGZQuQnWVlZsiJUCKEIlVY2rhFCURqNhnnz5/H1jK+h\nBqTXS4cKwJ9vkGqBZDC4YoBxlDEN6jRg09pNVKpUCfh91VenTp0YPHgwX375JSqVSoFXIsTvXrx4\nQfny5YmNjaVcuXJKxymU4uLiaN26NePHj+ezzz5TOo4AGjduzMKFC2nRooXSUYQoVGJjYxk3bhxR\nUVHMmzcPd3d3VCoVGo0GL68e3L17mK++SsPkLc9Pio+HL74wYezYGfj6jn3rXE+ePGHfvn3s3r2b\nQ4cO8eGHH+Lu7o67uzs1atR46/GEyG9WrVrFqVOnWL16tdJRhBCFjKwIFUJBjx8/pmnLpsxYPoP0\ngemkd0mHyvy1CAqgAkpB9kfZpH6aypkiZ6hdvza7d+/m7NmzODg4MHXqVCZPnixFUKE4IyMjHB0d\nOXjwoNJRCqXExETatWvHyJEjpQiqR6Q9Xoi89fz5c7788kuaNm1KkyZNuHLlCl27dn11nVSkSBE2\nb95G/fq9GDrUhAsX3mxcrRbUavDxMebLL+e+UxEUoEyZMvTr14+tW7eSlJTE5MmTuXnz5qvuni++\n+ILw8PB3PjhTCH0nrfFCCKXIO48QCvntt99o3ro598reI7NP5pvfligCWS2zyKqaRS/vXhSnOJs3\nb8bV1TVX8wrxNv5oj/f29lY6SqHy6NEj2rdvT//+/RkzZozSccSfSCFUiLyRnZ3Npk2bmDRpEo6O\njkRFRf3rwZFFixblp5/Wsn9/b4YO7U/58i9wdU2lUSMwM/v/j9NqISkJTp1SsXevCQ8fvmDLliCd\nXXsZGRnh7OyMs7MzS5cuJSIiArVazZAhQ3j06BFdunTB3d2ddu3aYWxsrJM5hVCatMYLIZQirfFC\nKECr1eLs5syxx8fI7JD5+4rPd5EARpuNuPTLJapXr67TjELkRHx8PB9++CFJSUkYGhoqHadQ+O23\n32jXrh2dOnXCz89PVofrmfDwcEaOHMnPP/+sdBQhCqyIiAh8fHzQaDQsWLDgrbaiyMzMpF+/fvz8\n8wkePnyKqWkRypQpilYLCQkZFCtWnDZtHBgxYiznzp3j+PHjeXIw4M2bN1Gr1ajVaqKiomjXrh3u\n7u64urpStmzZXJ9fiNyycOFCrl+/zqJFi5SOIoQoZKQQKoQCAgICGDJuCKmDUnO8LtvgjAF2T+2I\nOBWBgYHsdiH0R6NGjfjhhx9wcHBQOkqBl5KSgpOTEy1atOCHH36QIqgeSk1N5f333yc5OVluDgih\nY4mJiUyaNIng4GBmzZpF//793/qaSKvVYmtry5o1a2jWrBmxsbE8evSIIkWKUKFCBaysrF49NjMz\nkzp16rB48WI6dOig65fzrx49esTevXtRq9WEhITQoEGDV/uK2tjY5FkOIXTB39+fO3fu4O/vr3QU\nIUQhI1UTIfJYdnY2YyaOIbVDzougANlNs4mJj+Hw4cM5H0wIHZLT4/NGWloarq6u2NnZSRFUj5Uo\nUYLKlStz9epVpaMIUWBkZGQwd+5c6tatS/ny5YmOjmbAgAHvdGM4MjKSjIwMmjdvjoGBAdWrV6d5\n8+Y0adLkL0VQgGLFivH9998zZswYsrKydPVy/qdy5coxYMAAdu7cSVJSEuPGjePq1avY29tTt25d\nJk+ezLlz52RfUZEvSGu8EEIpUggVIo8dPnyYVIPU3w9F0gUDeG73nDnz5+hoQCF0w9XVNU/aBguz\nFy9e0LVrVypXrsySJUukCKrnZJ9QIXRDq9Wyd+9e6taty4kTJwgPD2fOnDmULFnynccMCAjAy8vr\njd9H3dzcMDc3Z8WKFe88Z04YGxvTpUsXVq5cSUJCAj/99BNZWVn079+fihUrMmzYMA4cOEBGRoYi\n+YT4XzQajRRChRCKkEKoEHkscHsgKTVT3n1f0NepC2HHw+RiV+iVxo0b8/jxY2JjY5WOUiBlZmbS\nq1cvSpUqxerVq2VrjHxACqFC5Fx0dDTOzs6MHz+ehQsXsmfPHj744IMcjZmdnU1AQAB9+vR54+eo\nVCrmz5/P119/zdOnT3M0f04VKVIEe3t75syZQ3R0NEePHqVatWrMnDkTc3NzevXqxcaNG/ntt98U\nzSnEn8mp8UIIpcinJiHy2Kmzp+D1h5e+u+JgXN6YS5cu6XhgId6dgYEBnTt3lvb4XJCVlYW3tzcG\nBgZs2rRJPkjkE1IIFeLdPX36lDFjxtCqVSs6derExYsX6dSpk07GDgsLo3Tp0tStW/etnlevXj26\ndu3KN998o5MculKzZk0mTJjAqVOnuH79Op06dSIoKIjKlSvj6OjIggULuHPnjtIxRSEnrfFCCKVI\nIVSIPHYv9h68r/txte9ruX79uu4HFiIHpD1e97Kzsxk0aBDJyckEBgbKwTv5SIMGDYiKipL9+4R4\nCxqNhhUrVlCrVi2eP3/OlStX8PX11el73+bNm99qNeifzZgxg3Xr1hETE6OzPLpkbm7OJ598wu7d\nu3nw4AE+Pj5ERkbSpEkT6tevz7Rp0zh//jxyfq7Ia9IaL4RQihRChchjWZlZOjkk6e+yi2ZLa7zQ\nO05OTpw+fZrnz58rHaVA0Gq1DB8+nHv37rFz506MjIyUjiTeQtmyZTEzM5PtIoR4QydPnqRJkyZs\n2LCBAwcO8NNPP1G+fHmdzpGZmcm2bdvw9PR8p+ebm5szYcIExo8fr9NcuaFEiRJ07dqVNWvWkJiY\nyKJFi0hLS8PT05NKlSoxYsQIDh06RGZmptJRRSEghVAhhFKkECpEHituXBxy4frSINOAEiVK6H5g\nIXKgZMmSNGvWjCNHjigdJd/TarX4+vpy8eJF9uzZg4mJidKRxDuQ9ngh/re4uDi8vLzw9vZm4sSJ\nhIaG0qBBg1yZ6/Dhw9SsWZMqVaq88xijRo3i8uXL+ep3XZEiRWjVqhXff/89MTExBAcHY21tzbRp\n0zA3N8fT05OAgACSk5OVjioKKNkjVAihFCmECpHHqtesDkm6H1ebqH3rva2EyAvSHp9zWq2WL7/8\nkpMnT3LgwAHee+89pSOJdySFUCH+XVpaGjNmzKBBgwbUrFmTa9eu4eHh8cYnub+LnLTF/6F48eLM\nnTuX0aNHk5WVpaNkeUelUlG7dm0mTZrEmTNnuHr1Ko6OjmzcuJGKFSvi5OTEokWLiIuLUzqqKEBk\nj1AhhFKkECpEHmvVohUG93X8T+85pD9Jx9TUVLfjCqEDrq6u7Nu3T/ZFzAE/Pz/27NnDoUOHKFWq\nlNJxRA5IIVSIf9JqtQQFBWFra8uVK1c4f/48X3/9da6vfE9NTWXfvn306tUrx2N169aNsmXLsmrV\nKh0kU5alpSVDhw5l3759JCQkMGzYMM6dO4ednR2NGjVi+vTpREZGyr6iIkekNV4IoRQphAqRx/r3\n7Y/xZWPQYU3IINKAipUr0qBBA1q1asWiRYtITEzU3QRC5ED16tUxMzOT4s87mjdvHhs2bODIkSOU\nK1dO6Tgih6QQKsRfRUVF0bZtW2bNmsX69esJDAykcuXKeTL37t27adGihU72HVWpVMyfP5+vvvqq\nQLWTm5qa0qNHD9avX09SUhLz5s3j6dOndO/enapVqzJq1CiOHj3Ky5cvlY4q8hlpjRdCKEUKoULk\nsSZNmlDJqhJc1dGAmWAUaUTQpiAePHjAhAkTOHPmDLVq1aJdu3b89NNPPHr0SEeTCfFuXFxcpD3+\nHSxZsoTFixcTEhKChYWF0nGEDlSsWJGXL1/y4MEDpaMIoahHjx4xbNgwOnTogJeXF+fPn8fBwSFP\nM+iiLf7PGjRogKurK35+fjobU58ULVqUNm3aMH/+fG7dusWePXt4//33mThxIhYWFnh7e7N161ae\nPXumdFSRD0hrvBBCKVIIFUIByxcuxzjEGNJyPlbx0OJ0bNuRJk2aULx4cbp06cLGjRt58OABn332\nGUeOHMHGxoZOnTqxZs0anj59mvNJhXhLsk/o21uzZg2zZ88mJCQEa2trpeMIHVGpVLIqVBRqL1++\nZMGCBdja2mJkZER0dDSffvppnhdEHj9+zIkTJ+jatatOx505cyarV6/m5s2bOh1X36hUKj788EOm\nTJlCREQEFy9e5KOPPmLVqlVUqFABZ2dnli1bRkJCgtJRhZ6S1nghhFKkECqEAlq1asWAvgMw2WMC\nmhwMdBVMbpiwYumKf3zL2NiYHj16EBQURHx8PAMGDECtVlOpUiXc3NzYtGkTKSkpOZhciDfXsmVL\nbt68KVs2vKGAgACmTJnCkSNHqFq1qtJxhI5JIVQUVocPH8bOzo59+/YRGhqKv78/pUuXViTL9u3b\n6dixo84Pn7OwsGDcuHFMmDBBp+PquwoVKjB8+HAOHjxIfHw8AwcO5OTJk9StW5emTZvi5+fH5cuX\nZV9R8Yq0xgshlCKFUCEUsuCHBdhXscd4hzG8eIcBouC9I+8RcjCEsmXL/udDTU1N8fT0ZNeuXcTF\nxdGzZ082b96MtbU1PXv2ZOvWraSl6WB5qhD/wtDQECcnJ/bv3690FL23c+dORo8eTXBwMDVq1FA6\njsgFUggVhc2tW7dwd3dn+PDhfPvttwQHB1O7dm1FM+m6Lf7PfH19+eWXXzh27FiujK/vSpYsSe/e\nvdm0aRNJSUl8++23JCUl4eLiQvXq1RkzZgyhoaFkZWUpHVUoSFrjhRBKkUKoEAoxNDRkv3o/Hi08\nMFlpAjeAN7lJ/hyMdxljHWVN2LEwGjRo8FbzmpmZ0b9/f/bt20dsbCydOnXip59+wsrKCi8vL9Rq\nNRkZGe/0moT4L9Ie/78dOHCAYcOGsX//furWrat0HJFLpBAqCouUlBQmTZpEs2bNsLe358qVK7i5\nuaFSqRTNFRcXx8WLF3F2ds6V8Y2MjPjuu+8YPXo0Gk1OWn/yP0NDQ9q1a8ePP/7InTt32L59O2Zm\nZowePRoLCws+/vhjduzYwfPnz5WOKvKYtMYLIZQihVAhFGRoaMiaFWvYuWknVqesMF1jCueAJP7a\nMp8CXAeT3SYYLTViiOMQYi7HUK9evRzNX7ZsWQYPHszhw4eJiYmhdevW/PDDD68uTPfv309mZmaO\n5hDiD87OzoSEhEih/V8cPXqUjz/+GLVaTcOGDZWOI3JRjRo1SEpKKlAnSwvxZ9nZ2axfv55atWqR\nkJDAxYsXmThxIsWLF1c6GgCBgYF07949V/P07NmT9957jzVr1uTaHPmNSqXCzs6Or776igsXLnDh\nwgWaNGnC0qVLsbKywtXVlRUrVsg2OoWEtMYLIZSi0spGLULohezsbEJCQli6cilnzp3hYcJDihoV\nJTsrm6JFi1Knfh08unowcMBAypQpk6tZEhIS2Lp1K4GBgcTExNCtWzc8PDxo06aNXLCIHGnRogUz\nZszAyclJ6Sh65dSpU3Tr1o2tW7fm+anJQhktWrRg9uzZ8vctCpyzZ88yatQotFotP/74I82aNVM6\n0j80atSI7777jnbt2uXqPOfPn8fV1ZXr169TsmTJXJ0rv3v69CkHDhxArVYTHBxMrVq1cHd3x93d\nnVq1aim+iljonoeHB926dcPT01PpKEKIQkYKoULo8tIsFAAAIABJREFUqfT0dFJSUjA0NKRUqVKK\nXQDevXuXoKAgAgMDX+0v6uHhQcuWLTEwkEXl4u34+fnx66+/smDBAqWj6I2IiAhcXFzYuHEjHTp0\nUDqOyCOfffYZNWrUwNfXV+koQujEgwcPmDRpEocPH+bbb7/F29tbL68ToqOjadu2Lffv38+TttyB\nAwdibm7O7Nmzc32ugiIzM5Pjx4+jVqvZvXs3xsbGr4qiLVq0kHbqAuKPzxS9evVSOooQopDRv6sT\nIQTw+6nv5cuXp3Tp0oreBa9cuTLjx4/n559/5tSpU1SoUIHPP/+cihUr4uvrS3h4uJwAKt7YH/uE\nys/M7y5evIirqyurVq2SImghI/uEioIiIyODOXPm8OGHH2JpaUl0dDT9+/fXyyIoQEBAAJ6ennlW\nTPPz82PFihXExsbmyXwFQbFixejQoQOLFy/m3r17BAQEYGxszIgRI7C0tGTQoEGo1Wo56FNPbd++\nHR8fH1q3bo2ZmRkGBgb079//H4/78x6h2dnZrFy5EgcHB8qUKYOJiQk2NjZ4enpy8+bNvH4JQogC\nTj+vUIQQeql69ep8+eWXXLx4kSNHjlCqVCkGDRpElSpVmDBhAufPn5cCl/hP9erVIzMzk+vXrysd\nRXHR0dF06tSJRYsW0aVLF6XjiDzWsGFDKYSKfE2r1bJ7927q1KlDeHg4Z86c4dtvv+W9995TOtq/\n0mq1uXpa/OtYWVkxZswYJk6cmGdzFiQqlYpGjRoxY8YMoqKiOHv2LPXr12fBggVYWFjg7u7O6tWr\n+fXXX5WOKv7PzJkzWbx4MVFRUVhbW//rgo6srCyKFi1KamoqTk5ODB06lOfPnzNgwAB8fX1p2bIl\n586dIyYmJo9fgRCioJPWeCFEjmi1Wi5evEhgYCCBgYEYGBjg4eGBh4cHdevWlT2dxD8MGzaM6tWr\nM27cOKWjKObWrVu0adOGWbNm0a9fP6XjCAVkZGRQunRpnjx5gpGRkdJxhHgrV69eZfTo0cTFxbFg\nwYJ8s+9zREQEffr0ISYmJk+vT9LT07G1tWX9+vW0bt06z+Yt6J48ecL+/ftRq9UcPnyYunXrvmqh\nr1GjhtLxCq3Q0FCsra2xsbEhNDSUtm3b4u3tzfr16//yuM6dOzNixAg2b97Mli1bWL58OYMHD/7H\neHK6vBBC12RFqBAiR1QqFfXr12fWrFncvHmTgIAAXrx4gYuLC3Xq1GH69OlER0crHVPokT/a4wur\ne/fu0a5dO6ZOnSpF0EKsePHifPDBB1y+fFnpKEK8sd9++41Ro0bh4OCAi4sLUVFR+aYICr+3xXt5\neeX5TVpjY2PmzJmDr68vGo0mT+cuyMqUKYO3tzdbt24lMTGRyZMnv7rRaGtryxdffEF4eDjZ2dlK\nRy1UHBwcsLGx+Z+P02g03L59+9V2Fa8rggJSBBVC6JwUQoUQOqNSqWjcuDHff/89d+7cYeXKlTx5\n8gRHR0fs7Oz49ttvZY8sgaOjI+fPn+fp06dKR8lzCQkJtGvXDl9fX4YOHap0HKEw2SdU5BcajYZl\ny5ZRq1YtMjMzuXr1Kj4+PhgaGiod7Y1pNBq2bNmCl5eXIvP37t0bY2Pjf6yKE7phZGSEs7Mzy5Yt\n4/79+6xbt44iRYowZMgQrKysGDJkCHv37iU9PV3pqOL/aDQaQkNDUalUeHp68uzZMzZu3Mjs2bNZ\nsWIFt27dUjqiEKKAkkKoECJXGBgYYG9vz4IFC4iLi8Pf35979+7RvHlzmjRpwrx584iLi1M6plCA\niYkJrVu3Jjg4WOkoeerXX3+lffv2DBo0SE4KF4AUQkX+EBoaSqNGjQgICCA4OJilS5fy/vvvKx3r\nrYWGhmJhYYGtra0i86tUKvz9/Zk8eTIpKSmKZCgsDAwMaNq0KX5+fly+fJlTp05ha2vL3LlzsbCw\noHv37qxbt45Hjx4pHbVQy8rK4saNGwDcuXMHGxsbPv74YyZPnsywYcOoUaMGn3/+uZw/IITQOSmE\nCiFyXZEiRWjTpg1Lly4lISGBWbNmcfXqVezs7Pjoo4/48ccfefDggdIxRR4qbO3xT548oUOHDvTs\n2ZNJkyYpHUfoCSmECn129+5devfuTf/+/Zk8eTLHjx/Hzs5O6VjvLK8PSXqdJk2a0L59e2bPnq1o\njsLGxsaGMWPGEBoayq1bt3B3d0etVmNjY4ODgwM//PCDrD5UgEaj4enTp2i1WsaMGYOjoyPR0dGk\npKRw5MgRqlevztKlS/nmm2+UjiqEKGDksCQhhGIyMzM5fPgwgYGB7NmzBzs7Ozw8POjRo0e+XG0i\n3ty9e/do1KgRiYmJBX7vp2fPntG+fXtat27N3Llz5QAx8cqzZ8+wsrIiOTm5wP87EPlHWloac+bM\nYfHixfj4+DBu3DhMTEyUjpUjGRkZWFlZvTrFWkn379+nfv36nD9/nipVqiiapbBLT08nJCQEtVrN\nnj17KFeu3KvDlho3boyBgawZyqn/OizJ3t6e+Ph44uLiqFu3LlFRUX+5Rrp48SINGzbE1NSUR48e\nUbRo0byOL4QooOTdXQihmGLFiuHi4sL69et58OABPj4+HD9+nOrVq9OhQwdWr17Nb7/9pnRMkQsq\nVaqEpaUlZ8+eVTpKrkpNTcXFxYUmTZpIEVT8Q8mSJbGwsCAmJkbpKEKg1WoJDAzE1taW69evc+HC\nBaZNm5bvi6AABw8epG7duooXQQGsra0ZNWoUEydOVDpKoWdsbIyrqysrVqwgISGBFStWoNFo+Pjj\nj7G2tmbYsGEcOHCAjIwMpaMWSFlZWbz33nuoVCq6dOnyj2ukevXqUbVqVVJSUrh27ZpCKYUQBZEU\nQoUQesHIyIhu3bqxZcsWEhISGDx4MHv37qVy5cq4urqyYcMGnj17pnRMoUMFvT0+PT0dd3d3Pvjg\nAxYuXChFUPFa0h4v9MEvv/yCg4MDs2fPZuPGjWzZsoVKlSopHUtn9KEt/s/GjRtHeHg4YWFhSkcR\n/8fAwIAWLVowe/Zsrl27xvHjx7GxscHPzw9zc3N69erFxo0b5Qa9Dmk0mlerokuVKvXax5QuXRpA\nDrkSQuiUFEKFEHqnRIkS9O7dmx07dnD//n08PT0JCgqiYsWKdO/encDAQFJTU5WOKXLIxcWFffv2\nKR0jV2RmZtKzZ0/ef/99VqxYIe114l81aNCACxcuKB1DFFIPHz7k008/xdnZmX79+vHzzz/TqlUr\npWPpVEpKCgcPHqRnz55KR3nFxMSE2bNn4+vrS3Z2ttJxxGvUqFGD8ePHExYWRkxMDM7OzmzdupXK\nlSvj6OjIggULuHPnjtIx8zWNRkPz5s3RarVcvnz5H9/PzMx8dZiSbCMhhNAl+WQmhNBrJUuWxNvb\nmz179nDnzh1cXV1ZvXo1VlZWeHh4sHPnTl68eKF0TPEOmjdvTnx8PPfu3VM6ik5lZWXh5eVFsWLF\nWL9+vez9KP6TrAgVSnj58iX+/v7Url2bEiVKEB0dzZAhQwrk+9WuXbto3bo1ZcuWVTrKX3h5eWFo\naMjGjRuVjiL+h/LlyzNo0CDUajWJiYmMGjWKqKgomjZtSv369Zk2bRrnz5+X083fUlZWFh06dMDK\nyorAwEAiIiL+8v0ZM2aQnJyMo6Mj5cuXVyilEKIgksOShBD50sOHD9mxYwdbtmwhMjISV1dXPDw8\n6NChA8WKFVM6nnhD/fr1w97enuHDhysdRSc0Gg39+/fnyZMn7Nq1i+LFiysdSei5xMREateuzePH\nj2X7BJEngoOD8fX1pXLlysyfPx9bW1ulI+UqZ2dn+vfvj5eXl9JR/uHMmTP07NmT6OhoTE1NlY4j\n3pJGoyE8PBy1Wo1arSY9PR03Nzfc3d1p06ZNob0eVavV7Nq1C/j9d1xwcDDVqlV7tdq8XLlyzJ07\nF1tbW7Zv305CQgJdunRBq9XSvXt3KlSowNmzZwkLC8PCwoKTJ09iY2Oj5EsSQhQwUggVQuR7Dx48\nYNu2bQQGBnLt2jW6du2Kh4cHjo6OcsKkntuyZQsbN24sEHuFZmdnM3ToUGJjY9m3bx/GxsZKRxL5\nhKWlJWfOnKFy5cpKRxEF2I0bNxg7dizXrl1j/vz5uLi4FPji+8OHD6levToJCQmUKFFC6Tiv5e3t\nTbVq1ZgxY4bSUUQOaLVaoqOjXxVFo6Oj6dixI+7u7jg7O//rHpgF0fTp0//z57lKlSrcunWLGjVq\nsGfPHmrWrMmlS5f45ptvCA0NJTk5GQsLC1xdXZkyZQoWFhZ5mF4IURhIIVQIUaDExcURFBREYGAg\nd+7coUePHnh4eNCqVasC2fKX3/32229UqlSJpKSkfH0ysVarxcfHhwsXLhAcHCwre8Rb6dy5M0OH\nDqVr165KRxEF0LNnz/Dz82PVqlVMnDgRHx+fQrNafcmSJYSFhbF582alo/yruLg47Ozs+OWXXwrU\nAVWFXWJiInv27EGtVnPixAmaNWuGu7s7bm5u8vf8f6pVq8bhw4dltacQIs/JHqFCiAKlYsWKjB07\nlnPnznHmzBkqVaqEr68vFStWxMfHh9OnT8vBBHqkdOnSNGrUiKNHjyod5Z1ptVomTpzImTNn2L9/\nvxRBxVuTfUJFbsjOzmbNmjXUqlWLhw8fcvnyZcaPH19oiqCgf6fFv07FihUZOXIkX3zxhdJRhA5Z\nWFgwZMgQ9u7dS0JCAsOHDyciIoKGDRvSsGFDpk+fTmRkZKHeV1Sj0cgiBSGEImRFqBCiULh+/TqB\ngYFs2bKF58+f07t3bzw8PGjcuHGBbw3Ud3PnziU2NpalS5cqHeWdfP311+zcuZNjx45RpkwZpeOI\nfGjbtm2sX7+e3bt3Kx1FFBBnzpzBx8eHIkWK8OOPP9KkSROlI+W5O3fu0LhxYxISEvR+r8bU1FRq\n1qxJUFAQ9vb2SscRuSgrK4tTp069aqHXaDSv9hVt3bo1hoaGSkfMM9bW1oSHh1OxYkWlowghChkp\nhAohChWtVsvly5cJDAwkMDCQ7OxsPDw88PDwoF69elIUVcC1a9fo0KED9+7dy3f//+fMmcPatWsJ\nDQ2VE03FO7t16xZt2rQhLi5O6Sgin0tISGDixIkcO3aM2bNn06dPHwwMCmcD2OzZs7lz5w7Lli1T\nOsob2bBhA4sWLSI8PLzQ/p0VNlqtlitXrrwqit68eRNnZ2fc3d3p1KkTJUuWVDpirrK0tOTChQtY\nWloqHUUIUcjIb1khRKGiUqn48MMPmTlzJjExMWzdupWsrCzc3d2xtbXlq6++4urVq0rHLFRq1apF\nsWLFuHjxotJR3srChQtZsWIFR44ckSKoyJGqVavy7NkzHj16pHQUkU+9ePGCWbNmUa9ePSpVqkR0\ndDTe3t6FuqCWH9ri/6xv375otVq93s9U6JZKpaJu3bpMnjyZc+fOcenSJVq1asWaNWuwtramU6dO\nLF26lPj4eKWj5oqsrCxpjRdCKEJWhAohBL/flT979iyBgYEEBQVRtmzZVytFq1evrnS8Am/UqFE8\nfPiQcuXKERkZSVRUFCkpKXh7e7N+/fp/PD4rK4vFixcTFRXFL7/8wtWrV3n58iUrV65k0KBBuZ53\n5cqVzJw5k9DQUDnpW+iEg4MDU6ZMwcnJSekoIh/RarWo1WrGjh1LvXr1mDdvHtWqVVM6luIuX76M\ns7Mzd+/ezVfF4NOnT+Ph4UF0dLTennIv8sazZ88IDg5GrVazf/9+bGxscHd3x93dnbp16+a7DprX\nKVOmDDdu3KBs2bJKRxFCFDJSCBVCiL/Jzs4mLCyMwMBAtm3bRsWKFfHw8KB3795S9Molhw4dolu3\nbrx48QJTU1Osra2Jjo6mb9++ry2EJicnU7p0aVQqFebm5hQrVoy4uDhWrFiR64XQjRs38sUXX3D8\n+HEpkgud8fX1xcrKigkTJigdReQTV65cYdSoUSQmJuLv70/79u2VjqQ3Jk+eTGZmJnPnzlU6ylvz\n8vKiZs2afP3110pHEXri5cuXnDx58lULvYGBwauiaMuWLSlatKjSEd+JmZkZ9+7dw8zMTOkoQohC\nJv/cIhVCiDxiYGBA69atWbx4MfHx8cyZM4eYmBgaNWpEixYt8Pf3L7BtSkpxcHAAIDw8nOTkZJYs\nWfKfJ6mamJhw4MABEhISSEhIYODAgXmSc9u2bYwfP55Dhw5JEVTolJwcL97UkydPGDlyJG3btqVr\n165ERkZKEfRP/mgvz09t8X82e/ZsFi1axP3795WOIvSEoaEhjo6OLFiwgNu3b7Nz505Kly7N2LFj\nsbCwoH///mzfvp3nz58rHfWtSGu8EEIpUggVQoj/ULRoUdq1a8eKFSt48OAB06ZNIzIykg8//BAH\nBweWLFnCr7/+qnTMfK948eJ07NiR69evv9HjDQ0N6dixI+bm5rmc7P/bt28fI0aM4MCBA9SuXTvP\n5hWFgxRCxf+SlZXF0qVLsbW1RaPRcPXqVT7//PN8uxost5w5cwYjIyPs7OyUjvJOKleuzPDhw5k0\naZLSUYQeUqlU1K9fn2nTpnH+/Hl++eUXmjVrxvLly7GyssLFxYWffvqJBw8eKB31f9JoNFIIFUIo\nQgqhQgjxhgwNDXF2dmbt2rUkJCQwZswYwsLCqFGjBk5OTqxcuZInT54oHTPfcnFxYe/evUrHeK0j\nR44wcOBA9uzZk28/XAv9Zmtry7179/Ldih6RN44fP06jRo0ICgri8OHDLFmyhHLlyikdSy/9sRo0\nP++hOHHiRI4dO8bZs2eVjiL0XMWKFRkxYgSHDh0iLi6Ofv36cezYMWrXrk3z5s359ttvuXr16n92\n2ShFo9HIjRwhhCKkECqEEO/AyMgId3d3Nm/eTEJCAp9++ikHDx6katWqdO7cmXXr1pGcnKx0zHyl\nc+fOHDp0iJcvXyod5S9OnjxJnz592LFjB02bNlU6jiigDA0NqV27NhcvXlQ6itAjd+7coVevXgwc\nOJBp06Zx9OhR6tWrp3QsvZWVlUVQUBBeXl5KR8kRU1NT/Pz88PX11csCltBPZmZmeHp6EhAQQFJS\nEt988w3x8fF07NiRGjVqMG7cOE6ePIlGo1EkX3p6OkePHuW77+bi7T2UrKziDBvmy/Lly7lw4YL8\nrAsh8owUQoUQIodMTEzo2bMn27Zt4/79+3h7e7Njxw4qVqxI165dCQgIkFVeb8DS0pLq1asTFham\ndJRXzp49S48ePdi8eTMtW7ZUOo4o4KQ9XvwhNTWVadOm0bhxY+rXr8/Vq1fp0aNHvl7lmBeOHj1K\n5cqVC8Qezv369ePly5ds2bJF6SgiHypWrBhOTk4sWrSIe/fuERgYSIkSJRg5ciQWFhYMHDiQXbt2\nkZaWlutZ4uPj8fEZx/vvV6RbtylMnRrPpk0NgYWsXv0BY8acwcHBk0qVavPjjwvJyMjI9UxCiMJN\nCqFCCKFD7733Hn369EGtVnPv3j26du3K+vXrqVChAr1792b79u2kp6crHVNvubi4sG/fPqVjABAZ\nGYmbmxtr1qyRg0hEnpBCqNBqtQQEBGBra8utW7eIjIxkypQpGBsbKx0tX8jPhyT9nYGBAf7+/kyc\nODFPilWi4FKpVDRs2JDp06cTGRlJREQEDRo0YOHChVhYWODm5saqVat0vue9Vqtl1ao11Kxpx7Jl\n2aSmnuXZs9NkZvoDw4CBgC9paWt4/vw69+8vZ9KkYGrVasTPP/+s0yxCCPFnUggVQohcUqpUKQYM\nGMCBAweIjY3FycmJJUuWYGlpSd++fdm9e7fc9f4bV1dXvdgn9MqVKzg7O7NkyRJcXFyUjiMKCSmE\nFm7nz5+nVatWfP/99wQEBLBp0yasra2VjpVvpKeno1ar8fDwUDqKzrRs2ZLmzZszb948paOIAqRK\nlSr4+PgQEhLC3bt38fDwIDg4mBo1avDRRx/x3XffvfHhlf8mOzubTz4ZwahR80lNPcLLlz8ANv/x\nDBXQmrS0Pdy5M5nWrTsTGBiUowxCCPFvpBAqhBB5oGzZsgwZMoSQkBCio6Oxt7dn7ty5WFpaMnDg\nQA4ePKh3e2MqoWHDhiQnJ3P//n3FMty4cYMOHTrw/fff06NHD8VyiMKnXr16XLt2Td4LCplff/2V\nwYMH4+rqysCBAzl37hwfffSR0rHynX379tGoUSMsLS2VjqJTc+bMwd/fn/j4eKWjiAKodOnS9O3b\nl6CgIJKSkpg6dSq3b9/G0dGRWrVqMXHiRMLDw8nOzn6rcX18xhMYGEVqahhQ/y2eqQK8SE8/wsCB\nPuzfv/+t5hVCiDchhVAhhMhjFhYWjBgxgpMnT3Lx4kXq1avH119/jZWVFZ9++ilHjx5VbCN7pRkY\nGNC5c2fOnDmjyPx37tyhffv2zJgxg759+yqSQRReJUqUoHLlyly9elXpKCIPZGZmMm/ePOrUqUOp\nUqWIjo7mk08+oUiRIkpHy5cCAgLy/SFJr1O1alU+/fRTvvzyS6WjiAKuePHidOrUiaVLlxIXF8eG\nDRswNDRkyJAhWFlZMXjwYPbs2fM/t3g6dOgQa9ZsIy1tL1DyHdPUIz19K336fMKjR4/ecQwhhHg9\nKYQKIYSCrK2tGT16NGfOnCEiIgIbGxvGjRtHhQoV+PzzzwkLC3vru/D5naurK+Hh4Xk+b3x8PO3a\ntWP8+PF88skneT6/ECDt8YXFgQMHqFevHiEhIYSFhfH9999jZmamdKx86+nTpxw5coTu3bsrHSVX\nTJo0icOHDxMREaF0FFFIGBgY0KRJE2bOnMnly5c5deoUderUYd68eVhYWNCtWzfWrVv3jyLlixcv\n6Nt3CGlpK4HSOUzRivT0PgwfPjaH4wghxF+ptFqtVukQQggh/iomJoagoCC2bNnC06dP6d27Nx4e\nHjRt2rTAnhqsVqvZtWvXq1Nys7OzqVatGq1atQKgXLlyzJ0799Xj58yZQ3R0NPD7wUZRUVHY29vz\nwQcfAL/vrfamBc2kpCQcHBz45JNPGD9+vI5fmRBvbu7cudy/f58FCxYoHUXkgpiYGEaPHs3NmzeZ\nP38+nTt3VjpSgbBmzRp2797Nzp07lY6Sa1avXs3q1as5efJkgb0OEPnD48eP2bdvH7t27SIkJAQ7\nOzvc3d1xc3MjPDyczz7bwPPnh3Q02zOKF69MbOwVrKysdDSmEKKwk0KoEELouStXrhAYGEhgYCCZ\nmZl4eHjg4eGBnZ1dgfowNH36dGbMmAH8vsm+gcFfmxaqVKnCrVu3Xv25bdu2nDhx4l/H+/jjj1m9\nevX/nPfx48e0bduWHj168NVXX71jeiF048iRI8yYMeM/f7ZF/pOcnMw333zD2rVrmTRpEiNHjqRY\nsWJKxyownJycGDp0KL169VI6Sq7RaDQ0adKEL774gt69eysdRwjg90PKQkJCUKvV7Nmzh6dPs8nI\n+AnoqrM5jIyGM2mSNdOmTdbZmEKIwk0KoUIIkU9otVqioqLYsmULgYGBGBoa4unpiYeHB3Xq1FE6\nnk79+OOPREZGvlEhMyeSk5Np164d7dq1Y/bs2QWqsCzyp8ePH1OtWjV+++23f9wMEPlPdnY2a9eu\nZfLkyXTu3JlZs2Zhbm6udKwCJTExEVtbWxISEjA2NlY6Tq4KDQ3l448/5tq1awX+tYr8Jy0tjZIl\ny6LRPAF0+fO5l6ZNf+TsWV2tMhVCFHZyhS2EEPmESqXCzs6O2bNnExsby8aNG0lNTaVjx47UrVuX\nb775hpiYGKVj6oSLiwv79+/P1f1Rnz9/TufOnbG3t5ciqNAbZcuWpVSpUsTGxiodReTQ6dOnadq0\nKatWrWLPnj2sWrVKiqC5ICgoiC5duhSKwqCDgwONGzdm/vz5SkcR4h+uXLlCiRI10G0RFKARV66c\nR9ZvCSF0RQqhQgiRD6lUKpo2bcq8efO4d+8ey5Yt49dff6V169Y0bNiQOXPmcPv2baVjvjMbGxtK\nly7N+fPnc2X89PR03NzcqF27Nv7+/lIEFXpFDkzK3+7fv0/fvn3x8PBgzJgxhIWF0bhxY6VjFVib\nN2+mT58+SsfIM9999x0//PADDx48UDqKEH/x4MEDVKrKuTCyJenpz3j58mUujC2EKIykECqEEPmc\ngYEBLVu2ZOHChcTHxzNv3jxiY2Np2rQpzZo1Y/78+dy/f1/pmG/N1dWVvXv36nzcjIwMunfvjpWV\nFcuWLZP2Y6F3pBCaP7148QI/Pz/s7OyoVq0a165do0+fPnKjJRfdunWL2NhY2rVrp3SUPFOtWjUG\nDx7M5MmyX6LQL1qtltxctCkrQoUQuiKf/oQQogApUqQIbdu2Zfny5SQkJDBjxgwuXbpEvXr1aNWq\nFYsWLSIxMVHpmG8kNwqhL1++xNPTkxIlSrB27VqKFCmi0/GF0AUphOYvWq2WHTt2ULt2bX755Rci\nIiL45ptvMDU1VTpagRcQEEDv3r0xNDRUOkqe+vLLLzlw4ECudU0I8S7Kly8PJOTCyI8oVsxEDpgT\nQuiMHJYkhBCFQEZGBocOHSIwMJC9e/fSqFEjPDw86N69O+XKlVM63mu9fPmS8uXLc+XKFaysrHI8\nnkajwdvbm5SUFHbs2CEX1EJvxcXF0bhxYxITE2U1oZ67dOkSvr6+/PrrryxYsABHR0elIxUaWq2W\nOnXqsHLlSuzt7ZWOk+dWrFjBhg0bCA0NlfcJoRfS0tIwMytHVtZTQJfXWME0aDCbCxeO6XBMIURh\nJitChRCiEChevDhdunRh48aNPHjwgBEjRnDkyBFsbGzo1KkTa9eu5enTp0rH/AtDQ0M6duzI/v37\nczxWdnY2gwcP5tGjR2zbtk2KoEKvWVtbo9FoZA9APfb48WM+//xz2rdvT48ePfjll1+kCJrHLl68\nSFpaGi1atFA6iiIGDRpEcnIy27dvVzqKEACYmJhQrVpt4LhOxy1W7BBOTh/pdEwhROEmhVAhhChk\njI2N6d69O0FBQcTHxzNgwADUajWVKlXCzc3X1XlwAAAgAElEQVSNTZs2kZKSonRMQDft8Vqtls8/\n/5xbt26xa9cujIyMdJROiNyhUqmkPV5PZWVlsWjRImxtbVGpVFy7do3PPvuMokWLKh2t0Nm8eTNe\nXl6FdjVkkSJF8Pf3Z/z48bx48ULpOEIAMHr0EEqUWKbDEdMxMFjPsGGf6HBMIURhJ63xQgghAEhO\nTkatVhMYGEhYWBhOTk54eHjg4uKCiYmJIpkePXqEjY0NSUlJ71TA1Gq1jBs3jrCwsP/H3p3H1Zz+\n/x9/tpFKyFiyZGsnrbZG9rKbylBZBmNnaLGvJbuiwphhbNmakGLs+z6hooXKXrbsJO11fn98Z/p9\nmjEz0jlddc7zfrv5h3Ou9yNjlFfv93XhxIkT0NbWlkElkfRNnz4d2tramDt3rugU+sPp06fh7u6O\n2rVrIygoCC1atBCdpLAKCwvRuHFjHDp0CGZmZqJzhHJyckKbNm0wc+ZM0SlEyMjIQP36+khPjwDQ\nttTrqaouQufO0Th+PLz0cUREf+AdoUREBACoVq0avvvuOxw6dAj3799Hjx49sGHDBtSrVw9ubm7Y\nv38/cnJyyrTpq6++QosWLXDu3Lkvev/8+fNx+vRpHD16lENQqlB4R2j58eDBAzg7O2PUqFHw9fXF\nyZMnOQQV7NKlS6hWrZrCD0EBwM/PD/7+/hXmIESSb1paWvjll9XQ0BgBIKuUq8WhcuUgbNq0Whpp\nRERFOAglIqK/qVmzJkaNGoUTJ07g9u3b6NChAwICAlC3bl0MGzYMR44cQV5ensw7JBIJzMzMMHPm\nXLRq1Q01a+qhWrW6qFOnGbp0ccTChYtx9+7dT753yZIlCAsLw/Hjx1GjRg2ZtxJJEweh4mVkZGDO\nnDlo1aoVbGxscOvWLTg5OSnso9jlya5duzBo0CDRGeWCvr4+RowYgXnz5olOIQIADBw4EL17t0aV\nKgMBfOk30B+iSpW++OmnQDRs2FCaeUREfDSeiIg+39OnT7Fnzx6Ehobi9u3bcHJygouLCzp16iT1\nPfL2798PT8/5SEv7iKwsNwDtAJgCqAwgHUAs1NQuQUUlBNbWVvjpJ7+iu4MCAwPx448/4vz589DV\n1ZVqF1FZKCgoQLVq1fD48WNUr15ddI5CkUgk2LVrF2bMmIFOnTph+fLlqF+/vugs+kNeXh7q1auH\nq1evokmTJqJzyoX379/DyMgIR48ehYWFhegcIuTl5eGbb9xw4sQT5Of/CqBRCd59ClWqDMOyZbMw\nefJEWSUSkQLjIJSIiL5ISkoKdu/ejdDQUDx69AjffvstXFxc0L59eygrf/kDB+np6Rg+fDyOHbuK\nzMzVALrj3x9gyIaS0haoq8/HjBkeqF1bBytWrMC5c+egp6f3xR1Eotna2mLJkiXo1KmT6BSFERUV\nhcmTJyMvLw9BQUGwtbUVnUR/cfjwYSxatAiXL18WnVKurF+/HiEhIThz5gzvWqZyITQ0FOPHT0J2\ntgTZ2dMgkYwCoPMv70hG5cr+0NQ8ih07NqBnz55llUpECoaDUCIiKrW7d+9i9+7d+PXXX/H69WsM\nGDAArq6uaNOmTYn+Qfbu3TvY2trj/n1z5OSsBlCSQ5oeoVKl/lBRuYfY2EgYGBiU+OMgKk8mTpwI\nfX19eHp6ik6Re2lpaZg9ezaOHj2KxYsXY9iwYaX6hg7JzpAhQ9C2bVv88MMPolPKlfz8fFhZWWHB\nggVwcnISnUMK7v79+2jbti0OHz4MDQ0NzJ27BIcPH4SaWhd8/NgaEokJgEoA0qGicgMSyTGoq6di\nwoQxmDNnOp+EICKZ4iCUiIikKjExEaGhoQgNDUVWVhYGDhwIFxcXWFlZ/etQtLCwEG3bdkVsrBly\nc4MAfMkdLR9RpYoDJk7sDD+/RV/8MRCVBxs3bsT58+exbds20SlyKzc3F6tXr8ayZcuK9lnkwWrl\n18ePH1G/fn0kJyejTp06onPKnZMnT2Ls2LG4desWKleuLDqHFFROTg7at2+PIUOGwN3dvejnX758\niRMnTuD336MRG3sbubm5qFpVC7a2LZGYeBONGjWCn5+fwHIiUhQchBIRkUxIJBLExcUVDUWVlZXh\n4uICFxcXtGjR4m9D0ZUrA+HtHYaPH8+hdGf5PUeVKuY4c2Y/2rRpU6qPgUik6OhoDB8+HPHx8aJT\n5NKhQ4fg6ekJIyMjrFy5EoaGhqKT6D/8+uuv2Lp1K44ePSo6pdzq168f7OzsMG3aNNEppKDc3d2R\nmpqKffv2ffZTQdeuXcOQIUOQlJTErR2ISOY4CCUiIpmTSCSIjo4uGopqaWkVDUWNjY3x+vVr6OkZ\nIjPzCgB9KVwxBEZGK5GYeI1fUFOFlZOTg+rVq+PNmzeoUqWK6By5kZSUBC8vL9y/fx+BgYHo0aOH\n6CT6TN988w2cnZ0xbNgw0Snl1u3bt2Fra4ubN2/yrlkqc+Hh4fDy8kJMTAxq1Kjx2e+TSCTQ09PD\nsWPHYGpqKsNCIqLS3XJDRET0WZSUlGBjYwM/Pz88fPgQGzduxJs3b9ClSxdYWFhg8OChKCzsDekM\nQQHABY8fv8PVq1eltB5R2atcuTIMDQ2RkJAgOkUuvH//HlOmTIGdnR3s7e0RHx/PIWgF8ubNG5w9\ne5b7X/4HQ0NDfPfdd5g/f77oFFIwDx8+xNixY/Hrr7+WaAgK/N/XiU5OTggPD5dRHRHR/8dBKBER\nlSllZWXY2toiKCgIjx49QmBgIC5dikV29jhpXgVZWaPx009bpbgmUdmztLTE9evXRWdUaAUFBdi4\ncSOMjY3x4cMH3Lx5E56enlBTUxOdRiUQFhYGBwcH7uH6GebNm4eIiAjExcWJTiEFkZubCxcXF8yc\nOfOLtyVycnLCvn37pFxGRPR3HIQSEZEwKioqsLa2Rk7OOwCtpbp2YWFHXLx4RaprEpU1DkJL5+LF\ni2jVqhWCg4Nx6NAhbNiwAbVr1xadRV9g165dGDRokOiMCqFGjRrw9vaGl5cXuAsalYVZs2ahTp06\n8PT0/OI17OzskJqaipSUFCmWERH9HQehREQkVHx8PKpUaQ5AVcormyMl5Rby8/OlvC5R2eEg9Ms8\nevQIbm5uGDRoEKZPn47z58/DyspKdBZ9oSdPniA2NhY9e/YUnVJhjBkzBs+ePcNvv/0mOoXk3IED\nBxAWFoatW7eWal92VVVV9O3bFxEREVKsIyL6Ow5CiYhIqHfv3kFJqaYMVq4CJSU1ZGZmymBtorJh\nYWGB+Ph4FBQUiE6pELKysuDr6wtLS0sYGhoiMTERrq6uPDStggsNDYWjoyPU1dVFp1QYqqqqCAgI\nwJQpU5Cbmys6h+RUSkoKRo8ejZCQEOjo6JR6PWdnZz4eT0Qyx0EoEREJpaKiAkAWd21KIJHkQ1VV\n2neaEpUdbW1t6OrqIjk5WXRKuSaRSLB3716YmJggISEBUVFRWLBgATQ1NUWnkRTwsfgv4+DgACMj\nI6xdu1Z0CsmhvLw8uLq6YurUqWjXrp1U1uzWrRtiY2Px4sULqaxHRPQpHIQSEZFQTZo0QX7+HRms\n/BjKyuo4d+4cXr58KYP1icoGH4//d3FxcejSpQsWLlyIrVu3Yvfu3WjcuLHoLJKS5ORkPHnyBJ07\ndxadUiH5+/tj6dKl/DxIUjdnzhzo6OhgypQpUltTXV0d3bt3x4EDB6S2JhHRX3EQSkREQunr66Og\n4C2AV1JeORq1aunC398fBgYGaNy4Mb799lssW7YMJ0+exNu3b6V8PSLZ4CD00169eoXx48fD3t4e\nLi4uiI6ORqdOnURnkZSFhITAxcXlj6cHqKSMjY0xaNAgeHt7i04hOXLo0CH8+uuvCA4OhrKydEcK\nTk5OCA8Pl+qaRET/i4NQIiISSllZGR06dAMQJtV1NTT2wstrDE6dOoU3b97gxIkT6N+/P16+fImF\nCxdCT08P+vr6cHV1hb+/P86ePYv09HSpNhBJAwehxeXl5WHNmjUwNTWFmpoaEhMTMW7cOG6DIYck\nEgkfi5cCb29v7N27FwkJCaJTSA48evQII0eOxK5du/DVV19Jff1evXrhwoUL/JqMiGRGSSKRSERH\nEBGRYjt16hQcHT2RkXED0vke3XOoqxvj6dP7qFGjxidfUVBQgNu3byMqKqroR2xsLBo0aAAbG5ui\nHxYWFtDS0pJCE9GXSUtLg6mpKV6/fl106M+TJ08wb948HDt2DK9fv4auri4cHR3h7e2N6tWrCy6W\nnZMnT8Ld3R316tVDYGAgmjdvLjqJZCgqKgqurq64c+cOD7wqpTVr1uDAgQM4fvw4fy/pi+Xl5aFz\n587o06cPZs6cKbPr9O7dG0OHDoWrq6vMrkFEiouDUCIiEk4ikaBly3a4eXMYJJLxpV6vSpVBGDmy\nHtas8S/R+/Lz85GYmFhsOJqQkIAmTZoUG46am5ujSpUqpe4k+ly6urqIjIxEo0aNcP/+fbRr1w6v\nXr2Co6MjjIyMcPXqVZw+fRrGxsa4dOnSP34DoKK6d+8epkyZgvj4eKxatQr9+vXjMEcBTJkyBRoa\nGli4cKHolAovLy8PLVu2hJ+fH/r06SM6hyqoWbNm4caNGzh06JDUH4n/X5s2bcKxY8ewe/dumV2D\niBQXB6FERFQuJCYmwtraDllZFwEYl2KlX1Gv3nzcuXMDGhoape7Kzc3FzZs3iw1HExMTYWhoWGw4\namZmhsqVK5f6ekSf0qtXL4wZMwaOjo7o3r07Tp48iTVr1mDChAlFr5kyZQoCAgIwbtw4rFu3TmCt\n9GRkZGDJkiXYsGEDpk6dCg8PD6irq4vOojJQUFAAPT09nDx5EiYmJqJz5MKRI0fg4eGB+Ph4VKpU\nSXQOVTBHjx7F6NGjERMTg1q1asn0Wi9fvoSBgQHS0tL4dz4RSR0HoUREVG5s2RKMiRPnISvrJADD\nL1jhILS0vsf588dgaWkp7bwi2dnZiI+PLzYcvXPnDkxNTYsNR5s3bw41NTWZdZDimDNnDlRVVTFs\n2DDo6+ujSZMmuHfvXrHXZGRkQFdXFwDw4sWLCn3XcmFhIXbu3IlZs2aha9euWLp0KerVqyc6i8rQ\nmTNn4OXlxf1xpaxnz57o0aMH3N3dRadQBfLkyRPY2NggNDQUHTp0KJNrduzYEVOnTkXfvn3L5HpE\npDi4qzwREZUbI0YMQ35+Ptzd2yMrayWAIQA+5/HXHKipLUCVKptx4sRBmQ5BAUBdXR2tWrVCq1at\nin4uMzMTsbGxiIqKwoULFxAQEICHDx/CzMys2HDU2NiYh7pQiVlaWmLbtm3Q09MDADg4OPztNVpa\nWvj6669x4sQJREZGonPnzmWdKRVXr16Fu7s7CgsLsXfvXrRt21Z0EgnAQ5JkY+XKlejUqROGDBmC\nmjVris6hCiA/Px9ubm744YcfymwICgDOzs4IDw/nIJSIpI6nxhMRUbkyevRIXLx4FE2b+kFLqzOA\nfQDy/+HV6VBS+hGqqobQ1Y1AcvINtG7dugxr/z8NDQ20a9cOkyZNQnBwMG7evIm0tDT4+fmhWbNm\nRafWV69eHe3bt4eHhwd27NiBpKQkFBYWCmmmiuPPk+OTk5OhpKQEQ8NP3zFtYGAAALh9+3ZZ5knF\ns2fPMHz4cDg6OmLcuHH4/fffOQRVUDk5Odi3bx8PSpEBU1NTuLi4wMfHR3QKVRA+Pj5QV1fHrFmz\nyvS6jo6O+O2335Cf/09fAxIRfRnekkJEROWOlZUVEhOjEBYWhmXLViEpaTjU1S2Qm2uKwkJ1qKqm\nQ1X1BrKyktGtWy+MGhWA0aNH4/3796hbt67o/CJVq1aFnZ0d7Ozsin7u/fv3iImJQVRUFH777Td4\ne3vj5cuXsLKyKnbnaLNmzXgYDBVp0qQJ0tPTkZaWBgCoVq3aJ1/358+/e/euzNpKKycnB4GBgfDz\n88OoUaOQnJyMqlWris4igY4dO4bmzZujYcOGolPkko+PD0xMTDB+/HiYmpqKzqFy7MSJE9iyZQti\nYmJkejjSpzRq1AiNGjXChQsXKuwTDkRUPnEQSkRE5VKlSpXg5uYGNzc3vHnzBjExMUhOTkZubi60\ntLRgZjYGLVu2LDoQ6f79+5gyZQoOHjwouPzfVatWDZ07dy72Rf3r16+LhqN79uzBjBkzkJ6eDmtr\n62LD0UaNGnE4qqCUlZVhYWGB169fi06RGolEgoMHD8LLywumpqaIjIyEvr6+6CwqB/hYvGzVrFkT\nc+bMwZQpU3DkyBHROVROPXv2DMOGDcPOnTtRp04dIQ1OTk4IDw/nIJSIpIqHJRERkVzIzc1F8+bN\nsXbtWnTv3l10Tqm9ePEC0dHRRYcxXbt2Dbm5ucUGozY2Nqhfvz6HowrC09MTMTExuHjxIvz9/eHp\n6fm310yaNAnr1q3DunXrMHbsWAGVnycxMREeHh549OgRAgIC5OL/WZKODx8+oEGDBrh37x6++uor\n0TlyKy8vDy1atEBgYCB69uwpOofKmYKCAnTr1g2dOnWCt7e3sI7ExEQ4ODggNTWVX+sQkdRwj1Ai\nIpILlSpVwsqVK+Hp6Ym8vDzROaVWu3Zt9OzZE/PmzcP+/fvx9OlTxMXFYcKECVBWVsaGDRtgZWUF\nXV1d9OnTBz4+Pjh48GDRo9MkfywtLZGVlQWJRPKPe4DeuXMHAP5xD1HR3r17Bw8PD3To0AG9evVC\nbGwsh6BUzP79+2FnZ8chqIypqalh5cqV8PLykovPmSRdvr6+UFZWxty5c4V2mJiYQFNTE1FRUUI7\niEi+8I5QIiKSGxKJBA4ODujXrx8mTZokOkfmJBIJHj16VHTX6J8/NDQ0/nbnKIcKFV98fDz69euH\nlJQUNGnSBPfu3Sv26xkZGdDV1QXwf3cUV6lSRUTmJxUUFGDTpk2YP38+HB0dsXDhQtSqVUt0FpVD\nvXr1wpAhQ/hofBmQSCTo3r07+vbtqxCfM+nznDp1CkOHDkVMTEy52Hd99uzZkEgkWLp0qegUIpIT\nHIQSEZFcSUhIQJcuXZCYmIiaNWuKzilzEokEDx48KDYYjY6ORo0aNYoNRq2trVGjRg3RuVQCeXl5\nqFatGr7++mucPn0aQUFB+OGHH4p+3cvLC4GBgRg/fjx+/PFHgaXFnT9/Hu7u7qhatSpWr14NCwsL\n0UlUTr18+RL6+vp48uQJtLS0ROcohD8/ZyYlJUFHR0d0DgmWlpYGa2trbNu2DV27dhWdAwC4du0a\nhgwZgqSkJD4eT0RSwUEoERHJnYkTJ0JZWRlr1qwRnVIuFBYW4u7du8WGo9evX0edOnWKDUetrKyg\nra0tOpf+RatWrTBt2jS4u7vjxYsX6NevH0xMTBAZGYmzZ8/C2NgYly5dKhdD7tTUVEybNg2RkZHw\n8/PDgAED+I9Y+lc//fQTzp8/j5CQENEpCmXChAlQU1NDUFCQ6BQSqKCgAN27d4etrS18fX1F5xSR\nSCTQ09PDsWPHYGpqKjqHiOQAB6FERCR3Xr9+DRMTE5w5cwbNmzcXnVMuFRQUIDk5udhwNDY2Fg0b\nNiw2HLW0tISmpqboXPrDmDFj0LJlSzg5OWH+/Pk4evQoXr9+DV1dXTg7O2P+/PmoVq2a0MbMzEz4\n+flhzZo1+OGHHzB9+nRoaGgIbaKKwc7ODtOnT0ffvn1FpyiUly9fwtTUFBcuXICxsbHoHBJk4cKF\nOHXqFE6dOgUVFRXROcVMnjwZderUwZw5c0SnEJEc4CCUiIjk0urVq3Hw4EEcO3aMd6F9pvz8fNy6\ndavYcDQhIQFNmzYtNhw1NzcvV/tPKpKffvoJUVFR2LRpk+iUv5FIJNizZw+mTZuGdu3aYcWKFdDT\n0xOdRRVESkoKrK2t8fTpU1SqVEl0jsJZtWoVTp8+jYMHD4pOIQHOnj0LNzc3REdHo169eqJz/ubM\nmTOYOnUqoqOjRacQkRzgIJSIiORSXl4ezM3NsXz5ct5dVAq5ublISEgoNhxNSkqCoaFhseGomZkZ\nKleuLDpX7kVGRmLChAmIiYkRnVLMjRs34O7ujvT0dAQFBaFDhw6ik6iCWb58Oe7fv4/169eLTlFI\nubm5aN68OdauXYvu3buLzqEy9OLFC1hZWWHz5s1wcHAQnfNJ+fn50NXVRVRUFBo1aiQ6h4gqOA5C\niYhIbh09ehSTJ09GQkIC7zCSouzsbMTFxRUbjt69exfNmzcvNhw1NTWFmpqa6Fy5kpmZia+++grv\n3r0rF3+mX758iXnz5iEiIgK+vr4YOXJkuXukkioGCwsLBAYGolOnTqJTFNaBAwcwa9YsxMbGQlVV\nVXQOlYHCwkL07NkTNjY2WLx4seicf/X999/D3Nwc7u7uolOIqILjIJSIiORa79690aVLF0yZMkV0\nilzLzMzEjRs3ig1HU1JS0LJly2LDUWNjYw7KSsnU1BS7du0Sevp6Xl4e1q1bh8WLF2Pw4MGYP39+\nuTigiSqmmzdvonv37khJSeHfDwJJJBLY29vD2dkZEyZMEJ1DZWDJkiU4evQoTp8+Xe6H3wcPHoSf\nnx/OnTsnOoWIKjgOQomISK4lJyejffv2uHnzJmrXri06R6F8+PAB169fLzYcffbsGSwsLIoNRw0M\nDKCsrCw6t8IYPHgwunXrhhEjRgi5/vHjx+Hh4YGGDRsiMDAQJiYmQjpIfsydOxfZ2dnw9/cXnaLw\n4uLiYG9vj6SkJH5zQ85duHABAwYMQFRUFBo0aCA65z9lZ2ejbt26uH37Nr+eI6JS4SCUiIjknqen\nJzIzM7n3XDnw7t07xMTEFBuOvn79GlZWVsWGo02bNuUhV//A398fqampWL16dZle9+7du/Dy8sKt\nW7cQEBCAPn368L8RlZpEIkGzZs2wd+9eWFlZic4hAGPHjoWmpiZWrVolOoVk5OXLl7CyssKGDRvQ\ns2dP0TmfzcXFBfb29hg1apToFCKqwDgIJSIiuff27VuYmJjg6NGjQh8npk97/fo1oqOjiw1HMzIy\nYG1tXWw4qqenx8EbgFOnTsHHxwcXLlwok+t9+PABixYtwqZNmzB9+nS4u7vzYCySmsjISAwfPhyJ\niYn8/7ucePHiBUxNTXH58mUYGhqKziEpKywsRO/evWFubo5ly5aJzimRX3/9Fdu3b8ehQ4dEpxBR\nBcZBKBERKYSff/4Zv/76K86cOcN/bFcAz58/LzYcvXbtGvLz84sNRm1sbFCvXj2F++/55s0bNG7c\nGO/evZPplgKFhYXYvn07Zs+eDQcHByxZsgS6uroyux4pJnd3d+jo6MDb21t0Cv0PPz8/XLhwAQcO\nHBCdQlK2fPlyHDhwAGfPnq1wBxqmp6ejQYMGePz4MbS1tUXnEFEFxUEoEREphPz8fFhZWcHb2xv9\n+/cXnUNf4OnTp8XuGr127RpUVVX/NhytU6eO6FSZa9SoEU6ePAkDAwOZrH/lyhVMnjwZSkpKWL16\nNVq3bi2T65Biy8/PR4MGDXD+/HneeVjO5OTkwNTUFOvXr0e3bt1E55CUXLp0Cf3798e1a9fQsGFD\n0TlfpHfv3hg6dChcXV1FpxBRBcVBKBERKYzTp09j1KhRuHXrFtTV1UXnUClJJBI8evSo2HA0KioK\nmpqaxQaj1tbW+Oqrr0TnSpWjoyMGDRqEgQMHSnXdp0+fYubMmTh16hSWLVuGwYMH8yArkpkTJ05g\n9uzZuHbtmugU+oTw8HDMnz8f169fL/cnitN/e/36NSwtLbFu3Tr06dNHdM4X27hxI44fP47du3eL\nTiGiCoqDUCIiUihOTk5o3bo1Zs2aJTqFZEAikeDBgwfFBqPR0dHQ0dEpNhy1srKqkCci5+bmIiIi\nAqsWL0bao0fIzMtDQWEhdLS1YWlhgbbdumHwkCElvis2OzsbgYGB8Pf3x5gxYzBr1ixUrVpVRh8F\n0f8ZMWIEWrZsCU9PT9Ep9AkSiQRdunSBq6srxo4dKzqHSqGwsBD9+vWDsbEx/P39ReeUyosXL2Bo\naIi0tDR+U5uIvggHoUREpFDu3buHNm3aID4+nvsdKojCwkLcvXu32HD0+vXrqFu3brHhqKWlZbnd\ncyw/Px8Bfn5YtXw5jAsL4fThA2wANAOgAuAlgBgAp9XVsQ9A7549sWLtWtSrV+9f15VIJDhw4AC8\nvLxgZmaGlStXolmzZjL/eIiys7Ohq6uLmzdv/uefUxLnxo0b6NGjB5KTk1GtWjXROfSF/P39ERYW\nhvPnz1e4fUE/pWPHjpg6dSr69u0rOoWIKiAOQomISOHMmDEDL168wJYtW0SnkCAFBQVITk4uNhyN\njY2Fnp5eseGohYUFNDU1hbbeuXMHgx0dof3wIYIyM9H8P17/DoC/qio2qKtj9YYNcHVz++Trbt26\nBQ8PDzx58gSBgYGwt7eXejvRP9m3bx/Wrl2L06dPi06h/zB69GhUr14dfn5+olPoC0RGRuKbb77B\n1atX0ahRI9E5UhEUFITY2Fhs3rxZdAoRVUAchBIRkcJJT0+HsbEx9u/fj1atWonOoXIiLy8PiYmJ\nxYajCQkJaNasWbHhqLm5eZk9jpeQkACH9u0xOz0dEyUSKJXgvTEAnDU04LVwISZ7eRX9/Nu3b+Hj\n44OQkBDMmzcP48eP5/5/VOa+/fZb9OjRA6NGjRKdQv/h+fPnaN68OSIjI6Gvry86h0rgzZs3sLKy\nQlBQEL755hvROVKTkpICGxsbPHv2jJ+/iKjEOAglIiKFtHnzZmzatAkXL16EklJJxkukSHJzc5GQ\nkFBsOJqUlAQjI6Niw1EzMzNUqlRJqtd++fIlLI2NseLNGwz6wjVSAdhpaGDVtm1wdHTEL7/8Am9v\nb/Tv3x++vr5yd4gUVQzv37+Hnp4eHj58WCH36lVEy5Ytw5UrVxAeHi46hT6TRCKBo6MjmjZtioCA\nANE5UmdjYwM/Pz907txZdAoRVTAchBIRkUIqLCxEq1atMHXqVLj9w6PDRJ+SnZ2NuLi4YsPRu3fv\nonnz5sWGo6ampqXai82lb1/oHT8Ov9dqWogAACAASURBVNzcUvVeAdBHQwO1GzdGrVq1EBQUBHNz\n81KtSVQaW7duRUREBCIiIkSn0GfKzs6GiYkJNm3ahC5duojOoc8QEBCAkJAQXLx4UerfqCsPFi9e\njOfPn2P16tWiU4ioguEglIiIFNaFCxcwePBgJCUlQUNDQ3QOVWAfP35EbGxsseFoSkoKWrZsWWw4\namxsDBUVlf9c7+zZsxjdpw/iPn5EFSn0TQZwu2NHHDlzhndAk3AODg4YNWoUBg4cKDqFSmDv3r1Y\nuHAhYmJiPuvvMRLn6tWr6NOnD65cuYImTZqIzpGJxMREODg4IDU1lZ/XiKhEOAglIiKF5uLiAlNT\nU3h7e4tOITnz4cMHXL9+HdeuXSsajqalpcHCwqLYcNTAwADKysrF3tu/Rw/YHzuGcVJqSQNgoq6O\nB8+eoXr16lJalajknj9/DiMjIzx9+pTfgKpgJBIJOnbsiKFDh2L06NGic+gfvHv3DpaWlli1ahWc\nnJxE58iUsbExtm/fzv3eiahEOAglIiKFlpKSAisrK9y4cQMNGzYUnUNy7u3bt4iJiSl25+ifh1n8\nORg1NDREx7Zt8SQ3F1WleO0BmproERSEkSNHSnFVopJZs2YNrl69iu3bt4tOoS8QHR2NPn36IDk5\nGdra2qJz6C8kEgn69++PBg0aKMQj47NmzQIALF26VHAJEVUkHIQSEZHCmzdvHu7fv4+dO3eKTiEF\n9OrVK0RHRxcNRi9evAjdV68QJ+XrrAGQMHQo1m/bJuWViT5fu3btMH/+fPTs2VN0Cn2h77//HrVr\n18ayZctEp9BfrFmzBsHBwbh06RIqV64sOkfmrl27hiFDhiApKYmPxxPRZ+MglIiIFF5GRgaMjY2x\nZ88etGvXTnQOKbiAgADcmzkTa0t5SNJfXQQwxcgIV5KSpLou0ee6f/8+2rZtiydPnpTqIDES69mz\nZzAzM8PVq1fRtGlT0Tn0h6ioKPTs2RORkZFo1qyZ6JwyIZFIoKenh2PHjsHU1FR0DhFVEMr//RIi\nIiL5pqWlhWXLlsHd3R2FhYWic0jBvX//HjWlPAQFgJoA3n/4IPV1iT5XSEgIBgwYwCFoBaerqwsv\nLy9Mnz5ddAr94f3793BxccG6desUZggKAEpKSnByckJ4eLjoFCKqQDgIJSIiAjBo0CAoKytz3zoS\nTlVVFXkyeMQvD4AqT3omQSQSCXbt2gU3NzfRKSQFnp6eiIqKwrlz50SnKDyJRIJRo0ahR48eGDBg\ngOicMsdBKBGVFAehREREAJSVlREUFITZs2cjIyNDdA4psCZNmuCOpqbU170N8DFWEiY+Ph4ZGRmw\ntbUVnUJSUKVKFaxYsQKenp4oKCgQnaPQfvrpJ9y9excrV64UnSKEnZ0dUlJSkJKSIjqFiCoIDkKJ\niIj+0KZNG3Tp0oWnj5JQ1tbWiJLBFu7RKiqw7thR6usSfY4/7wZVVuY/P+TFgAEDoKGhgeDgYNEp\nCuv69evw9vbG7t27oa6uLjpHCFVVVfTt2xcRERGiU4ioguBXIkRERP9j2bJlWL9+PR48eCA6hRSU\noaEhoKGBKCmuWQhgr7o6uvOkbhKgsLAQISEhGDRokOgUkiIlJSUEBARg7ty5+MD9h8tceno6Bg4c\niDVr1sDAwEB0jlDOzs7Yt2+f6AwiqiA4CCUiIvof9evXh7u7Ow+BIGGUlZUxzsMDP1apIrU1jwOo\nqquLNm3aSG1Nos91+fJlVK1aFWZmZqJTSMpatWoFe3t7PklRxiQSCcaMGYOuXbvC1dVVdI5w3bp1\nQ2xsLF68eCE6hYgqAA5CiYiI/mLq1Km4evUqD4EgYUaPG4ejlSrhshTWygLgqamJOcuWQUkGhzAR\n/ZeQkBC4ubnxz5+cWrJkCZ+kKGMbNmxAYmIiAgICRKeUC+rq6ujevTsOHDggOoWIKgAOQomIiP7i\nz0MgPDw8eAgECaGjo4O1mzZhuIYG3pVyLU8AlXR14ejoKI00ohLJy8vDnj17eFq8HKtfvz48PDww\nY8YM0SkKITY2FnPnzsXu3btRRYpPDlR0PD2eiD4XB6FERESfMHDgQGhpaWHz5s2iU0hB9e/fH72H\nDUPPLxyGSgD4qKribMOG0KpdG3369MHbt2+lnUn0r06ePIlmzZqhadOmolNIhqZMmYLIyEhcvHhR\ndIpc+/DhAwYOHIjAwEAYGRmJzilXevXqhQsXLiA9PV10ChGVcxyEEhERfYKSkhKCgoIwf/58vH//\nXnQOKaiVa9fCdsQIWGto4GwJ3pcGoL+GBvY3aYJz167h7NmzMDExQatWrRAXFyejWqK/27VrFw9J\nUgAaGhpYvnw5PDw8UFhYKDpHLkkkEowbNw52dnYYPHiw6JxyR1tbG3Z2djh8+LDoFCIq5zgIJSIi\n+gdWVlbo1asXFi1aJDqFFJSysjJWrl2LwF9/xUBNTfRSUsIZ/N/dnp+SAmCWmhpaVqkC4/HjERkf\njzp16kBNTQ2rVq2Cr68vunbtil27dpXhR0GKKjMzE7/99hsGDhwoOoXKgKurK9TU1LB9+3bRKXJp\n06ZNiIuLw+rVq0WnlFtOTk48PZ6I/pOSRCL5p6+liYiIFF5aWhpatGiB33//HQYGBqJzSEFJJBJY\nWFigbZs2uHz8OJ6kpcFKXR3NcnOhLJHglZoaYiQSvJNI8N1332G8hwcMDQ0/uVZcXBycnJzQr18/\nrFixAmpqamX80ZCiCA0NxaZNm3D8+HHRKVRGrly5AmdnZyQnJ0NLS0t0jtyIj49Hly5dcP78eZiY\nmIjOKbdevHgBQ0NDpKWlQV1dXXQOEZVTHIQSERH9h+XLl+Py5cvYv3+/6BRSUBEREfD19UV0dDSU\nlJTw/PlzREdHIzU1FQUFBahRowYsLS1haGgIFRWV/1zv7du3GDx4MDIzMxEaGoo6deqUwUdBisbR\n0RGOjo4YPny46BQqQ0OHDkXjxo2xcOFC0SlyISMjA61atcKsWbPw3Xffic4p9zp27Ihp06ahT58+\nolOIqJziIJSIiOg/5OTkwNTUFOvXr0e3bt1E55CCKSwshJWVFXx9fdGvXz+prVtQUAAfHx8EBwdj\nz549aNOmjdTWJnr79i0aN26M1NRUVKtWTXQOlaHHjx/D3NwcMTExaNSokeicCk0ikWDYsGFQUVHB\nli1bROdUCEFBQYiNjeVhl0T0j7hHKBER0X+oXLky/P394eHhgfz8fNE5pGAiIiKgqqqKvn37SnVd\nFRUVLFy4EGvWrEHfvn3xyy+/SHV9UmxhYWGwt7fnEFQBNWjQAJMmTcLMmTNFp1R4W7duRXR0NNau\nXSs6pcJwdHTEb7/99o9frwUHB0NZWflff3DLGCL5xjtCiYiIPoNEIkG3bt3g7OyMiRMnis4hBVFY\nWAgLCwssWbJEpo/5JScnw8nJCe3bt8eaNWtQuXJlmV2LFEOXLl3www8/wNnZWXQKCfDx40cYGxsj\nNDQUtra2onMqpJs3b6JTp044e/YsmjdvLjqnQrGxsYGfnx86d+78t1+LjY39x62Ozp8/jzNnzqBP\nnz7cDolIjnEQSkRE9Jni4+PRrVs3JCYmQkdHR3QOKYC9e/dixYoVuHLlCpSUlGR6rQ8fPmDEiBF4\n9OgRwsLC0KBBA5lej+TXkydPYGZmhqdPn/LAEgW2Y8cOrF69GpGRkVBW5oOIJfHx40e0bt0aU6dO\nxYgRI0TnVDiLFy/G8+fPsXr16hK9z9bWFleuXMGBAwfQu3dvGdURkWj8jERERPSZzMzM4OzsjAUL\nFohOIQVQWFiIBQsWwMfHR+ZDUACoWrUq9uzZA2dnZ7Ru3Rrnzp2T+TVJPu3evRvffPMNh6AKbtCg\nQVBSUsLOnTtFp1Q4kyZNgrW1NQ8a+0JOTk4IDw9HSe75SkhIQGRkJOrXr49evXrJsI6IROMglIiI\nqAR8fX2xa9cuJCYmik4hObd3715oamqiZ8+eZXZNJSUlzJgxA8HBwXBxcUFgYGCJ/iFJBAC7du3C\noEGDRGeQYMrKyggMDMTs2bPx8eNH0TkVxvbt2/H7779j3bp1ZfJNMHlkYmICTU1NREVFffZ71q9f\nDyUlJYwaNYq/70Ryjo/GExERlVBAQACOHz+OI0eOiE4hOVVQUICWLVti5cqV6NGjh5CGhw8fwtnZ\nGSYmJtiwYQM0NTWFdFDFcvv2bXTo0AGPHz+Gqqqq6BwqBwYNGgRDQ0P4+PiITin3EhMT0aFDB5w+\nfRpmZmaicyq0WbNmAQCWLl36n6/Nzs5GvXr1kJGRgQcPHqB+/fqyziMigXhHKBERUQlNnDgR9+/f\nx+HDh0WnkJzas2cPtLW10b17d2ENjRs3xqVLl6CiogJbW1vcu3dPWAtVHCEhIXBxceEQlIosW7YM\na9aswaNHj0SnlGuZmZkYOHAgli5dyiGoFDg7O2Pfvn2f9VRDaGgo3r17h549e3IISqQAOAglIiIq\noUqVKmHVqlXw8vJCXl6e6BySMwUFBViwYAEWLFgg/PG8KlWqIDg4GGPGjIGtrS3vgqZ/JZFI+Fg8\n/Y2enh4mTpxYdIcefZq7uztatmyJkSNHik6RCzY2NsjMzPysrYw2bNgAJSUljB07tgzKiEg0DkKJ\niIi+QK9evdC4cWP8+OOPolNIzoSGhkJHRwf29vaiUwD8376hEydORFhYGEaNGoVFixahsLBQdBaV\nQ9evX0d+fj5at24tOoXKmenTp+Ps2bOIjIwUnVIu7dq1C+fPn8fPP/8s/Btg8kJJSano0KR/c+vW\nLfz+++9o0KBBme7JTUTicBBKRET0BZSUlBAQEIDFixfj5cuXonNIThQUFMDX17dc3A36V+3bt8e1\na9dw5MgRODs74/3796KTqJzZtWsX3Nzcyt2fXRJPS0sLS5YsgYeHBw9g+4vbt2/D3d0du3fvRtWq\nVUXnyJXPGYTykCQixcNBKBER0RcyMTHBoEGDMH/+fNEpJCdCQkJQq1YtdO3aVXTKJ9WrVw9nzpxB\n/fr10aZNG9y6dUt0EpUTBQUFCAkJ4WPx9I+GDBlS9OeE/k9WVhYGDhyIhQsXwtzcXHSO3LGzs0NK\nSgpSUlI++es5OTnYsWMHVFRU8P3335dxHRGJwkEoERFRKXh7e2Pfvn2Ii4sTnUIVXH5+frm9G/R/\nVapUCT/++CNmzpyJjh07IiwsTHQSlQMXLlxArVq1YGpqKjqFyillZWUEBARg5syZyMzMFJ1TLnh6\nesLY2Jh7U8qIqqoq+vbti4iIiE/++u7du/H27Vv06tWLhyQRKRAOQomIiEpBR0cH8+fPh6enJx/3\no1LZtWsXdHV10blzZ9Epn2X48OE4evQopkyZgpkzZ6KgoEB0EgnEQ5Loc7Rv3x7t2rWDv7+/6BTh\nQkNDcerUqaKDekg2/jw9/lP+/L0fM2ZMGVcRkUhKEv6rjYiIqFTy8/NhYWGBRYsWwdHRUXQOVUD5\n+fkwMTHBL7/8gk6dOonOKZGXL1/Czc0NysrKCAkJQc2aNUUnURnLzc1FvXr1EBMTAz09PdE5VM49\nfPgQ1tbWiIuLU9i78O7evQtbW1scO3YMlpaWonPkWnZ2NurWrYvbt2+jdu3aRT+flJQEU1NT6Onp\n4cGDBxxGEykQ3hFKRERUSqqqqggICMDUqVORk5MjOocqoB07dqBBgwYVbggKALVq1cLRo0dhYWEB\nGxsbxMTEiE6iMnbs2DGYmJhwCEqfpXHjxhg3bhxmz54tOkWI7OxsDBw4EN7e3hyClgF1dXU4ODjg\nwIEDxX7e2NgYhYWFePjwIYegRAqGg1AiIiIpsLe3h6mpKYKCgkSnUAWTl5eHhQsXYsGCBaJTvpiq\nqipWrFiBFStWoHv37ti2bZvoJCpDfCyeSmrmzJk4ceIErl27JjqlzE2dOhXNmjXDhAkTRKcoDGdn\n5/88PZ6IFAcfjSciIpKSO3fuoF27dkhISEDdunVF51AFsXnzZuzcuROnTp0SnSIVCQkJcHJyQo8e\nPbBy5UpUqlRJdBLJUEZGBurXr4979+7hq6++Ep1DFciWLVuwceNGXLx4UWHuyNu7dy9mzJiBmJgY\nVKtWTXSOwkhPT0eDBg3w+PFjaGtri84hIsF4RygREZGUGBgYYMSIEZgzZ47oFKog8vLysGjRogp9\nN+hftWjRAteuXcPDhw/RtWtXPHv2THQSydD+/fvRvn17DkGpxIYNG4asrCzs3r1bdEqZuHfvHiZM\nmIDQ0FAOQcuYtrY27OzscPjwYdEpRFQOcBBKREQkRXPnzsXhw4e5TyJ9luDgYDRr1gzt27cXnSJV\n1atXx/79+2Fvb49WrVrh8uXLopNIRkJCQuDm5iY6gyogZWVlBAYGYsaMGcjKyhKdI1M5OTlwcXHB\n3LlzYWNjIzpHITk5Of3j6fFEpFj4aDwREZGU/fLLL9i2bRvOnz+vMI/7Ucnl5ubC0NAQu3btgq2t\nregcmTl06BBGjBiBBQsWYNy4cfx/Qo68evUKzZo1w+PHj1G1alXROVRBDRgwABYWFnL9NIW7uzse\nPXqEsLAw/h0oyIsXL2BoaIi0tDSoq6uLziEigXhHKBERkZR9//33+PDhA/bs2SM6hcqxrVu3wsjI\nSK6HoADQu3dvXL58GevWrcP3338v93d+KZK9e/eiZ8+eHIJSqSxfvhyrVq3C06dPRafIRHh4OA4c\nOIBNmzZxCCpQ7dq1YW5ujpMnT4pOISLBOAglIiKSMhUVFQQFBWH69Okc+tAn5ebmYvHixXK1N+i/\n0dfXx++//46srCzY2dkhJSVFdBJJAU+LJ2lo2rQpRo8eLZd3hD548ABjx45FaGgoatSoITpH4Tk7\nO/PxeCLiIJSIiEgWOnbsCBsbG6xcuVJ0CpVDmzdvhqmpKdq2bSs6pcxoaWkV7SfZpk0bnDp1SnQS\nlUJqaipu3ryJHj16iE4hOTB79mwcPXoU0dHRolOkJjc3Fy4uLpg1axZat24tOocAODo64rfffkN+\nfr7oFCISiHuEEhERyciDBw9gY2ODuLg41K9fX3QOlRM5OTkwMDDA3r17FfYfx6dPn8bgwYPh5eWF\nqVOn8nHRCsjPzw+3b9/GL7/8IjqF5MTGjRsRHBwsN/tre3l54d69e4iIiJCLj0deWFtbw9/fH507\ndxadQkSCcBBKREQkQ3PmzEFqaiq2b98uOoXKiXXr1uHQoUM4dOiQ6BShUlNT0b9/fzRp0gSbN2+G\nlpaW6CQqAUtLS6xatYrDBJKagoICWFtbY86cORgwYIDonFI5cOAAJk+ejJiYGOjo6IjOof+xePFi\n3Lt3D3379kXs9et4//o1VNXU0MTQEDY2NrCwsEClSpVEZxKRDHEQSkREJEMZGRkwMjJCWFiYQj0G\nTZ+WnZ0NAwMD7Nu3D61atRKdI1x2djYmTpyIK1euIDw8HAYGBqKT6DPcunUL9vb2SE1NhYqKiugc\nkiNnzpzB999/j8TExAp7sndKSgpat26NiIgItGvXTnQO/UEikSAiIgJ+8+cjPiEBHbS1YZmRAZ3C\nQuQBuFOlCqLU1JAGYOTYsZjo4YF69eqJziYiGeAeoURERDKkpaWFJUuWwMPDA4WFhaJzSLCNGzfC\nwsKCQ9A/qKurY+PGjZg0aRK+/vprHDx4UHQSfYaQkBC4urpyCEpS17lzZ1haWiIwMFB0yhfJy8uD\nq6srpk2bxiFoOZKamooednbw/e47TEpIwCsAh9LTsaiwEF4AZgDYmJWFG+npOJeejoygIJgbGmLL\npk3gfWNE8od3hBIREclYYWEh2rZti8mTJ2PIkCGic0iQ7Oxs6OvrY//+/bC2thadU+5ERkZiwIAB\nGDlyJObPnw9lZX6/vjySSCTQ19fH7t27+eeYZOLevXto06YNEhISULdu3X99bVhYGM6dO4cbN24g\nNjYWHz58wJAhQ7Bt27a/vfbx48dYsmQJYmJikJKSgrdv30JHRwdNmjTB0KFDMXz48FLfhTpt2jQk\nJibiwIED/DusnLhy5Qq+cXDApMxMTM/Ph9pnvi8WwDBNTVh/8w02bNvGb/wQyREOQomIiMrA5cuX\n4eLigqSkJGhqaorOIQFWr16NU6dOYf/+/aJTyq20tDQMHDgQ2tra2LFjB6pXry46if7i6tWrGDJk\nCJKTk3kADMnM9OnT8ebNG2zcuPFfX2dpaYm4uDhoaWmhQYMGSEpKwuDBgz85CD137hwcHR3Rpk0b\nNG3aFDo6Onj9+jWOHDmC1NRUtG7dGufPn//i/SEPHTqE8ePH4/r166hZs+YXrUHSFR8fj662ttic\nkYE+X/D+DAD9NDRg8O23WB8cLO08IhKEg1AiIqIyMmjQIOjr68PX11d0CpWxrKws6Ovr4+DBg7C0\ntBSdU67l5eVh6tSpOHz4MMLDw9GiRQvRSfQ/PDw8UL16dfj4+IhOITn2/v17GBsb4/Dhw//6d+a5\nc+fQoEEDNGvWDOfOnUPnzp3/8Y7Q/Px8qKqq/u3nCwoKYG9vj3PnziE4OPiLntx49OgRWrVqhbCw\nMHz99dclfj9JX05ODmxMTDDlwQMML8U6GQBsNDSwKDgY3377rZTqiEgk3q9PRERURpYvX44ff/wR\nKSkpolOojK1fvx6tW7fmEPQzqKmpISgoCN7e3ujcuTNCQ0NFJ9EfCgoKEBoaCjc3N9EpJOeqVasG\nHx8feHp6/usejR07dkSzZs0+a81PDUEBQEVFBY6OjpBIJHjy5EmJW//cF9TDw4ND0HJk+aJFaPr8\nOYaVch0tAFsyM/HDyJF49+6dNNKISDAOQomIiMpIw4YNMXnyZEyfPl10CpWhzMxMLF++nHfQldCQ\nIUNw4sQJzJo1C1OnTkV+fr7oJIV35swZ1K9fH0ZGRqJTSAGMHDkSb968QXh4uEyvU1hYiEOHDkFJ\nSQkdO3Ys8fvnzZsHbW1tfm4vR7KysrAmMBArMzMhjQ082gHokp+P4C1bpLAaEYnGQSgREVEZmjZt\nGn7//XdcuHBBdAqVkZ9++gm2trYwNzcXnVLhWFhY4Nq1a4iPj4eDgwNevnwpOkmh7dq1C4MGDRKd\nQQpCVVUVAQEBmDZtGnJycqS27uvXr+Hj4wMfHx9MnDgRxsbGuHLlCtauXYu2bduWaK0jR45g586d\n2LZtGw9HKkfCwsJgA0BfimtOyMzEz6tWSXFFIhKFf1sTERGVIQ0NDSxfvhweHh4oLCwUnUMy9vHj\nR/j5+cHb21t0SoVVs2ZNHD58GO3atYONjQ2ioqJEJymk7OxsREREwMXFRXQKKZCuXbuiRYsWCAoK\nktqar169gq+vLxYuXIiff/4Z9+7dg6OjI+zt7Uu0zpMnTzBixAjs3LkTtWrVklofld6ZQ4fQLyND\nqmt+DeDFy5dIS0uT6rpEVPY4CCUiIipjrq6uUFdXx9atW0WnkIytW7cOdnZ2aNmypeiUCk1FRQWL\nFy9GYGAgevbsic2bN4tOUjhHjhyBubk56tevLzqFFIy/vz9WrFiB58+fS2U9IyMjFBYWIj8/Hykp\nKQgMDERERARat26NxMTEz1ojPz8fbm5umDRpEjp06CCVLpKe6CtXYC3lNZUAWFWujOjoaCmvTERl\njYNQIiKiMqakpITAwEDMnTsX6enponNIRjIyMuDv78+7QaXIyckJ58+fx4oVKzBu3DipPi5L/46P\nxZMoBgYGGDZsGObPny/VdZWUlNCgQQNMmjQJ69evx7t37z57L2cfHx+oq6tj1qxZUm0i6Xj84gWa\nyGDdJnl5ePz4sQxWJqKyxEEoERGRAK1atYKDgwOWLFkiOoVk5Mcff0SnTp3QokUL0SlyxcTEBFev\nXsXz58/RqVOnLzrlmUomPT0dx48fR//+/UWnkIKaN28eIiIiEBsbK5P1e/bsCQCIi4v7z9ceP34c\nW7duxY4dO7gvaDklkUikckjSXykD3NaISA7wb24iIiJBlixZgo0bN+LevXuiU0jKPnz4gJUrV/Ju\nUBnR1tZGWFgY+vbti9atW/PwMRkLDw9Hp06doKOjIzqFFFT16tXh4+MDT09PSCQSqa//511+2tra\n//q6p0+fYtiwYdixYwdq164t9Q4qvfz8fFTT0IB0NlIo7rmqKmrWrCmDlYmoLHEQSkREJEi9evXg\n5eWFqVOnik4hKVu7di26du0KU1NT0SlyS1lZGbNnz8bmzZvx7bffYvXq1TIZkBAQEhICNzc30Rmk\n4EaPHo0XL17gwIEDX/T+69evf/JuvoyMDLi7u0NJSQnOzs7/+P6CggIMHjwY48ePR6dOnb6ogaQr\nLy8PcXFx2LJlC3744Qe0a9cO1atXR2ZmJmJkcL3oggJYWVnJYGUiKktKEn7FSEREJEx2djZMTEyw\nadMmdOnSRXQOSUF6ejr09fVx7tw5mJiYiM5RCPfv34ezszNatmyJn3/+GRoaGqKT5Mbz589hZGSE\nJ0+eQFNTU3QOKbgTJ05gwoQJSEhIQOXKlbF//35EREQAANLS0nDs2DE0bdoUdnZ2AICvvvoKfn5+\nAP5vj+FLly7B1tYWenp60NDQwKNHj3DkyBG8f/8e9vb2OHDgACpVqvTJa3t7e+PixYs4fvw4VFRU\nyuYDpiK5ubm4desWoqOji34kJCRAT08PVlZWsLa2hrW1NSwtLbFl82bEzJ6N4KwsqV3/NgC7qlWR\n9v49lJRk8eA9EZUVDkKJiIgECwsLw4IFCxATEwNVVVXROVRKixcvxq1bt7Bz507RKQolMzMTo0eP\nxq1bt7Bv3z40aSKLozIUz9q1axEZGYkdO3aITiECAPTp0wedO3fGlClTsGDBAvj6+v7jaxs3bly0\n/cyRI0cQEhJStMdwZmYmdHR0YGFhgcGDB2PIkCH/uM6pU6cwdOhQxMTEoG7dulL/mKi4nJwcJCQk\nICYmpmjoefPmTTRp0gTW1tZFg08LCwtUrVr1b+9/9eoVDBo2xN3sbEjrQXavSpVQedIkLPX3l9KK\nRCQKB6FERESCSSQSdO7cGa6uAkwztgAAIABJREFUrhg3bpzoHCqF9+/fQ19fHxcvXoSRkZHoHIUj\nkUiwZs0aLF68GNu3b4eDg4PopArP1tYWc+fORa9evUSnEAEAkpKSYGdnh1u3bqFWrVoyv15aWhqs\nrKywfft2dO3aVebXUzTZ2dmIj49HdHR00eAzMTERzZo1+9vQsyR3pY90c0ONffvgn5tb6sZUAFZV\nqiDq1i00bty41OsRkVgchBIREZUDN27cQI8ePZCUlITq1auLzqEvtHDhQty+fRvbt28XnaLQzp8/\nD1dXV0yaNAkzZ87kY4xf6MGDB2jdujWePn0KNTU10TlERTw8PJCTk4OffvpJptcpKCiAg4MD2rdv\njwULFsj0WoogKysLcXFxxYaeycnJMDAwKHq03crKCubm5qXe4uTly5cw09dHeHo62pViHQmAHpqa\n6DBtGubwAEQiucBBKBERUTkxZswYaGlpYdWqVaJT6Au8e/cOBgYGuHTpEgwNDUXnKLzHjx/j22+/\nRb169bB169b/PA2a/m7p0qVITU2V+bCJqKTevHkDY2NjnDp1CmZmZjK7jq+vL86cOYOTJ09yX9AS\nyszMRGxsbNGj7TExMbhz5w6MjIyKhp7W1tYwMzNDlSpVZNKwf/9+THBzw9msLBh8wfslADwrVUKU\nqSnOXrvG7YuI5AQHoUREROXEixcvYGpqikuXLvGx6gpowYIFuH//PoKDg0Wn0B9ycnLg7u6Oc+fO\nITw8HMbGxqKTKhQzMzOsW7eu6OAZovJk7dq12L9/P44fPy6Tu77Pnj0LNzc3REdHo169elJfX558\n/PgRN27cKDb0vHfvHkxMTP429KxcuXKZtm3+5RfM8/DAtsxMlGRjg3QAkypXRmKzZjh28SJq1Kgh\nq0QiKmMchBIREZUj/v7+OHPmDA4dOiQ6hUrg3bt30NfXR2RkJPT19UXn0F9s2rQJs2bNwoYNG+Do\n6Cg6p0KIj49H79698fDhQygrK4vOIfqbvLw8mJubY8WKFejTp49U137+/Dmsra2xefNm7jX8Fx8+\nfCg29IyOjsbDhw/RvHnzYkPPFi1aoFKlSqJzAQDHjh3DqEGD/h97dx5VVb3/f/wFokziPM9iBs4D\nZgoKHE3NLEtNK0vNodIyh8q6ZTmVpjebb7PNds1rXivL9NvgPKOoOQAi4CyiEMoocPbvj/vt/C5f\n0Rg27APn+VirdY1zzvu8WN1anBfvvT+6NT1dz2Zny/86z70iaaWkv/n4aMCwYXr1nXcKPJAJQPlF\nEQoAgBO5cuWK2rdvr7feeku33nqr1XFQSLNnz9aJEyf06aefWh0F17Br1y7dfffdGjVqlObNm8dl\nrn/h2Wefld1u16JFi6yOAlzT2rVrNXXqVP3++++mlW52u1233nqrunfvrpdeesmUmeXVpUuXtHfv\n3nynt588eVIdOnRwHGIUFBSkdu3aOf19hFNTUzV/9mx9+vHH6uLmptC0NHUxDNWWlCMpRtIeT0+t\ncndXm3bt9NzLL+uWW26xODWA0kARCgCAk1m9erWefvppHThwwOk/WEBKSUlR69attXPnTrVq1crq\nOLiO8+fP65577pGnp6f++c9/qlatWlZHckp2u13+/v767rvv1KlTJ6vjANd12223qX///po2bZop\n8+bPn69169bpt99+c6l7Qv7xxx9XlZ5nzpxRx44d853e3qZNm3L9s0lmZqZ+/PFH7dq6Vfu2bVNq\naqo8PDzk37q1gsLC1L9/f7Vt29bqmABKEUUoAABOxjAMDRgwQLfffrumTJlidRz8hRdeeEFnzpzR\nxx9/bHUUFEJubq6eeeYZffvtt/r3v/9N0VeArVu36uGHH9bBgwdL5d6LgJmOHDmi0NBQHTlyRHXq\n1CnRrE2bNmnEiBHas2ePGjdubFJC55OcnHxV6ZmYmKhOnTrlO709MDDQpcpgAK6BIhQAACd06NAh\n2Ww2HTlyRLVr17Y6Dq4hOTlZrVu3VkREhFq2bGl1HBTB119/rccff1xvvPGG7r//fqvjOJXJkyer\nQYMGev75562OAhTKlClTZLfb9Y9//KPYM5KSktS1a1d99NFHFerWNBcvXsx3iNGePXt04cIFde7c\nOV/pGRAQwC1DALgEilAAAJzU5MmTJalEH+xQumbOnKnz58/ro48+sjoKiuHAgQMaOnSobr/9dr3y\nyivl+nJPs+Tk5Khx48bavn07t3pAuXHx4kW1adNG69evV7t27Yr8ervdrkGDBqlTp05auHBhKSQs\nG0lJSfkOMdq7d69SUlLUpUuXfAcZtW7dmkPQALgsilAAAJxUST/YoXRduHBBAQEB2rNnj1q0aGF1\nHBRTSkqKHnjgAaWlpelf//qX6tevb3UkS61du1Zz5szRjh07rI4CFMmbb76pNWvWaO3atUpPT9eP\nP/6obTu2aefenbp8+bIqV6msdoHtFNozVAMHDlSzZs0cr124cKFWr16tDRs2lJtfiCQmJuYrPffs\n2aPLly/nO8QoKChIrVq1ovQEgP9CEQoAgBN766239MMPP2jdunXcq8/JPPvss0pOTtYHH3xgdRSU\nkN1u19y5c/XJJ59oxYoV6tGjh9WRLDN69Gh169aN+xOj3MnJyVGbNm3UOrC1Nm7aKI/mHkqrnyaj\nviF5SsqTdEHyOe8je7RdwSHBWvTiImVlZenuu+/W7t271bRpU6u/jQKdOXMm3/089+zZo8zMzHyH\nGAUFBcnf35+fFQDgL1CEAgDgxHJychyX6g0ePNjqOPhfSUlJCgwMVGRkZL6tIpRvq1ev1vjx4/XS\nSy/p4YcftjpOmcvIyFCjRo0UFRWlBg0aWB0HKJJvvvlGY8aPUUZAhtRLUvXrPPmKpP2S1xYvechD\nX372pe66664ySnpthmHo9OnTV5WeOTk5V5WeLVq0oPQEgGKgCAUAwMmtW7dOkydP1sGDB+Xp6Wl1\nHEh65plndOnSJb333ntWR4HJYmJiNGTIEAUHB+vtt9+Wl5eX1ZHKzIoVK/Thhx/q559/tjoKUCTz\nF87XgtcXKOOODKkoS52XJY/vPBTSIkRrV68t03/fDcPQyZMn8x1itGfPHhmGke8Qo6CgIDVr1ozS\nEwBMQhEKAEA5cPvttys8PFxPPfWU1VFc3vnz5xUYGKj9+/c77WWUKJnLly9r3LhxOn78uFauXOky\n/5yHDBmiwYMHa+zYsVZHAQrtw48+1PTnpyvjgQypWjEG5Ene33srvHm4fvz2x1IpHA3D0PHjx686\nvb1SpUr57ufZtWtXNWnShNITAEoRRSgAAOVAdHS0QkJCdPjwYdWrV8/qOC5txowZysjI0DvvvGN1\nFJQiwzD0yiuv6PXXX9eyZcsUHh5udaRSlZKSohYtWujEiROqXv161xQDziMuLk4dunb4TwlatwSD\nciXfz331zovvaMyYMSXKZBiG4uPjryo9PT09ryo9GzVqROkJAGWMIhQAgHLiiSeeUFpamj788EOr\no7isxMREtWnTRgcOHFCTJk2sjoMy8Msvv+iBBx7Q008/renTp1teWqxcuVIbN27Uvn37tH//fl2+\nfFkPPPCAvvjiiwKfn5aWpvfee0/Lly9XQkKCsrOz1bRpU/Xr109PPvmk4x63H3/8sdasWaOVK1eW\n5bcDlEhYvzBtrbRVeT3zSj7srFT1X1V1Kv5UoX8ZYLfbFRcXl+9+nnv37pWvr+9VpWfDhg1LnhEA\nUGIUoQAAlBN//PGHAgMDtXbtWnXu3NnqOC7pySef1JUrV/T2229bHQVlKCEhQcOGDdONN96oJUuW\nyNfX17IsXbp00YEDB1S1alU1adJEUVFRuv/++wssQrOystS9e3cdPHhQbdq00S233CJPT0/t3r1b\nGzduVI0aNbRt2zYFBgaqb9++evTRRzVs2DALviug6GJiYtSpeydlPZ4leZgz0+dbHy0ct1CPP/74\nVY/Z7XYdPXo03/08IyMjVb169XyHGHXt2lX169c3JxAAwHQUoQAAlCPvv/++vv76a61fv97yzTRX\nc+7cObVt21YHDx5Uo0aNrI6DMpaZmalJkyZp7969WrVqlVq1amVJjo0bN6pJkyZq1aqVNm7cKJvN\nds2N0C+++EIPPvig+vXrp3Xr1uV7bM6cOZo3b57GjRunF198UW3bttWZM2fk7e1dVt8KUCJPPf2U\n3trxlnL65pg3NF7y3+mvmIMxiomJuar0rF27dr5DjLp27aq6dUtyTT4AoKy5Wx0AAAAU3oQJE5Sc\nnMzlqxZYtGiRRo0aRQnqory9vfXpp59q4sSJCg4O1po1ayzJERYWVugSNikpSZJ02223XfXYnXfe\n6XjO8uXLdeedd1KColz5eePPymlhYgkqSc2k+Nh4Va9eXbfffru+++471a9fX88//7zi4+MVHx+v\nb775Rs8995wGDBhACQoA5ZBJFxEAAICy4OHhoTfeeEPjx4/X7bffLi8vL6sjuYSzZ8/q888/16FD\nh6yOAgu5ubnp0UcfVadOnTRixAhNnDhRM2fOlLu7c+4W2Gw2ubm56aefftKUKVPybZGvXr1abm5u\n6tevn7744gu99NJLFiYFisYwDEUfjJbCTR5cSfJp5KMV76/QwIEDTR4OAHAGzvlTGwAAuKY+ffqo\nS5cueu2116yO4jIWLlyoMWPGcNgFJEkhISGKiIjQ2rVrNWTIEKWmplodqUBdu3bVkiVLtGvXLnXo\n0EHTpk3T008/rT59+mj+/PmaMmWK+vXrp+PHj6tPnz5WxwUKLScnRznZOZKP+bM9anjoypUr5g8G\nADgFNkIBACiHFi9erO7du+vBBx/kUu1Sdvr0aX355Zc6fPiw1VHgRBo2bKj169friSee0E033aRV\nq1apXbt2Vse6Sv/+/TVixAgtWbJER44ccXy9b9++uu+++7R8+XLdc8898vDgYwHKDzc3NxmGIRmS\nTL5dtmEY3IMbACowfuIBAKAc8vf314QJE/Tcc8/ps88+szpOhbZw4UKNHTtWDRo0sDoKnEyVKlX0\nj3/8Q59//rnCw8P17rvvavjw4VbHckhISFCPHj2UmZmp999/X4MHD5aPj4+2bt2qxx9/XL1791bd\nunW1YsUKq6MC12S323Xq1ClFR0crKipK0dHR/yn1K0m6JKm6yW/4h9S4cWOThwIAnAVFKAAA5dTM\nmTMVEBCg3bt366abbrI6ToV06tQpffXVV/k26YD/a8yYMerQoYOGDh2qiIgIzZ8/3yk2LOfMmaOk\npCS99dZbmjBhguPrAwYM0DfffKPOnTsrMTFRPXr0sDAl8B/p6emKiYnJV3hGRUXp6NGjqlatmgID\nAxUQEKDAwEDdfvvtSslIUeTZSHOL0CtS5vlMtW/f3sShAABnYv1PaAAAoFj8/Pz00ksvaerUqdq6\ndSuX8pWCl19+WePHj1f9+vWtjgIn17VrV0VEROjee+/VwIEDtWzZMtWpU8fSTHv27JEkhYeHX/VY\nx44d5enpqezsbP3xxx+qWbNmGaeDKzIM46rtzj//NykpSTfccIOj8Bw0aJCeeOIJBQQEqFq1alfN\n2hu5V0dWHVFWYJZ5AY9KHbp0kKenp3kzAQBOhSIUAIBy7MEHH9Q777yjZcuWaeTIkVbHqVBOnjyp\nZcuWKSoqyuooKCfq1KmjtWvX6vnnn1e3bt20cuVKBQUFWZanSpUqkqSkpKSrHsvKylJWVpbc3d0d\nzwPMkpGRUeB2Z0xMjPz8/PJtd952220KCAhQ8+bNValSpUK/x4TxE/TighelPpK8zcntd8BPM+bM\nMGcYAMApUYQCAFCOubu764033tDIkSN15513ytfX1+pIFcaCBQv00EMPqV69elZHQTni4eGhhQsX\nqlu3brr11lu1ePFijRkzxpIsffv2VWRkpBYsWKDg4OB8hef48eMlSd27d+e/GygWwzB0+vTpArc7\nz58/r1atWjkKz4EDB2ratGkKCAhQ9ermXMter1493X333VqxaYWyB2SXfGC05Jvhq6FDh5Z8FgDA\nabkZhmFYHQIAAJTMvffeq8DAQM2ZM8fqKBXC8ePH1bVrV0VHR1t+eTPKr0OHDmnIkCHq37+/Xnvt\nNVM2L7/77jt9++23kqRz585p3bp18vf3V+/evSX9Zyv1lVdekSRdvHhRwcHBio2NVfPmzXXrrbfK\n29tbW7du1c6dO1WlShVt3rxZ3bt3L3EuVFwZGRk6evRogdudvr6+jrLzzw3PgIAAtWjRokjbncWV\nkpKiVoGtlDIgRWpVgkHpkvfH3vrp3z8pLCzMtHwAAOdDEQoAQAVw4sQJdenSRfv27VPTpk2tjlPu\nPfLII6pVq5Zefvllq6OgnEtNTdXo0aN18eJFrVixQg0bNizRvLlz52revHnXfLxFixY6duyY4+8v\nXbqkRYsW6fvvv1dcXJzy8vLUoEEDnTt3TmvWrFGfPn1KlAcVg2EYOnPmjKKjo68qPBMTE+Xv75/v\ncvY/i88aNWpYHV3r16/XoCGDlDk8U2pSjAEZks/XPpp8/2QtWrDI9HwAAOdCEQoAQAUxa9YsxcbG\n6p///KfVUcq1hIQEBQUFKSYmRrVr17Y6DioAu92u+fPn64MPPtDy5csVEhJiaZ4ffvhBCxcu1JYt\nWyzNgbKXmZl5ze1Ob2/vfEXnf293eng49x3VfvzxR424f4Qye2fKCDKkwp4deFLy+dFHE0ZO0BuL\n3+DQQQBwARShAABUEOnp6QoMDNTy5csVHBxsdZxy68/7gs6fP9/qKKhg1qxZowcffFCzZ8/Wo48+\nalnpMnLkSPXq1UuPPvqoJe+P0mUYhs6dO3fVfTujoqJ09uzZa2531qxZ0+roJXLo0CENv3+4TmSc\nUPpN6dINktyv8eREyXOPp7yOeenjDz7WsGHDyjIqAMBCFKEAAFQgS5cu1ZtvvqmdO3fK3f1anwBx\nLfHx8erWrZuOHj2qWrVqWR0HFVBsbKyGDh2qrl276r333pO3t0nHXRdSenq6GjdurKNHj6pu3bpl\n+t4wV1ZWVoHbndHR0fLy8ipwu7Nly5ZOv91ZErm5uVq6dKkWvb5IJ06dUKWmlZRWK02GpyHlSd5/\neMvjnIc8sj00edJkTZk8hftAA4CLoQgFAKACsdvtCg4O1qRJkyw7qbo8Gz9+vBo1aqQXX3zR6iio\nwNLT0zVhwgTFxMTo3//+t5o3b15m771s2TJ98cUX+umnn8rsPVF8hmEoMTGxwO3OM2fOqGXLlgVu\nd/KLHOno0aOKiIjQ3n179celP+RZxVPt27RXUFCQOnfurMqVK1sdEQBgAYpQAAAqmJ07d2ro0KGK\nioqSn5+f1XHKjWPHjunmm2/W0aNHy/0lonB+hmHojTfe0KJFi7R06VLdcsstZfK+d9xxh0aMGKFR\no0aVyfuhcLKyshQbG5uv7Pzzz1WqVLnmdidlHgAARUMRCgBABTR69Gg1adJECxYssDpKuTF27Fg1\na9ZMc+fOtToKXMj69es1cuRITZ8+XTNmzCjV+4ZevHhR/v7+OnXqFL8ksYBhGDp//nyB252nT59W\nixYt8m11/vlnDm0DAMA8FKEAAFRAp0+fVqdOnbR79261bNnS6jhOLzY2Vj169FBsbKxq1KhhdRy4\nmJMnT2rYsGFq3ry5Pvnkk1IrKT/44AP99ttvWr58eanMx39kZ2dfc7uzUqVKCgwMvGq709/fn+1O\nAADKAEUoAAAV1EsvvaR9+/bpm2++sTqK0xszZoz8/f01e/Zsq6PARWVlZWny5Mnavn27Vq1apRtv\nvNH09wgLC9MTTzyhO++80/TZrsYwDCUlJRW43Xnq1Ck1b968wO1ODuYBAMBaFKEAAFRQmZmZatOm\njT7//HOFhYVZHcdpxcTEKCQkRLGxsapevbrVceDiPvzwQz3//PNasmSJBg8ebNrckydPqnPnzjpz\n5ow8PT1Nm1vRXblyRceOHSuw8HRzc7vmdmeVKlWsjg4AAApAEQoAQAX2r3/9SwsWLNCePXtUqVIl\nq+M4pVGjRunGG2/UCy+8YHUUQJK0Y8cODR8+XOPGjdPs2bPl7u5e4pmLFy9WVFSUlixZYkLCisUw\nDF24cKHAsvPkyZNq1qxZgYcV1alTp1Tv6QoAAMxHEQoAQAVmGIbCwsI0atQoPfTQQ1bHcTrR0dHq\n1auXjh07pmrVqlkdB3BITEzUiBEjVLVqVS1dulQ1a9b8y9ccP35cu3fv1r59B/THH2ny9q6igIAb\nFBQUpHHjxunVV19Vnz59yiC9c7py5Yri4uIKLDwNwyhwu7NVq1ZsdwIAUIFQhAIAUMHt3btXt912\nm6Kjo7n0+/+4//771bZtW82cOdPqKMBVcnJyNGPGDP3www9atWqVOnTocNVz8vLytHz5ci1a9K6O\nHj0qD4+blZbWSYZRQ1K2fH2PSNqljIwzmjXraU2Z8phq1apV5t9LWSpouzM6OlrHjx9X06ZNC9zu\nrFu3LtudAAC4AIpQAABcwPjx41WzZk0tXrzY6ihO48iRIwoLC1NsbCzboHBqS5cu1fTp0/X222/r\n3nvvdXw9JiZGI0aMVWysofT0GZLukORxjSl75eX1try81unTT9/VXXfdVRbRS01OTs41tzvz8vKu\nud3J/VEBAHBtFKEAALiAc+fOqX379tq+fbtat25tdRyncN9996ljx4569tlnrY4C/KV9+/Zp6NCh\nGjJkiBYtWqT169dryJCRysx8QXb7ZEmFvY/oZvn4jNXEicO1ePECp9+CvHjxYr6tzj//nJCQoCZN\nmhS43VmvXj2n/74AAIA1KEIBAHARf//737VlyxZ9//33Vkex3KFDh9SnTx/FxsbKz8/P6jhAoSQn\nJ2vkyJFKTExUdPQpZWauktSrGJMuyMenn6ZOvVMLFswxOWXR5eTkKD4+vsDtzpycnKuKzsDAQN1w\nww1sdwIAgCKjCAUAwEVkZ2erXbt2eu+999SvXz+r41jqnnvuUdeuXfXMM89YHQUokpSUFDVufKMy\nMz+TNKgEk87L27uL1q5dptDQUJPSXV9ycvJVRWd0dLTi4+PVuHHjArc769evz3YnAAAwDUUoAAAu\n5Ntvv9Xzzz+vffv2ycPjWvcSrNgOHjyovn376tixY6patarVcYAiGT9+sv75z2xlZX1kwrTv1bDh\nE0pIOGzayei5ubnX3O7Mzs6+5nanl5eXKe8PAABwPRShAAC4EMMwdMstt2jo0KF67LHHrI5jieHD\nh6t79+6aMWOG1VGAIrl48aIaN26l7OxYSXVMmVm1ah8tWfKI7rnnniK9LiUlpcDtzri4ODVq1Chf\n2fnnnxs0aMB2JwAAsBRFKAAALub3339X3759FRUVpVq1alkdp0wdOHBA/fv317Fjx+Tr62t1HKBI\nFi9+TbNm7Vdm5ucmTl2prl3f1p49G656JDc3VwkJCVeVndHR0crIyLjmdqe3t7eJ+QAAAMxDEQoA\ngAuaNGmSKleurLfeesvqKGVq2LBhCg4O1pNPPml1FKDIQkPv0ObNYyUNNXFqpjw8auu339YpLi7u\nqu3OBg0aFLjd2bBhQ7Y7AQBAuUMRCgCAC0pKSlLbtm21ceNGtW3b1uo4ZWLfvn0aOHCgjh07Jh8f\nH6vjAEVWo0ZDpabukNTc5MktFRjopa5du+bb7mzdujXbnQAAoEKhCAUAwEW98cYbWrt2rX766SeX\n2OwaMmSIQkNDNX36dKujAEWWm5urypWrSMqTZO6/r9Wq3anPPx+ru+66y9S5AAAAzsbd6gAAAMAa\njz32mBISErRmzRqro5S6yMhI7dy5UxMnTrQ6ClBs//mFRWn80sJddru9FOYCAAA4F4pQAABcVOXK\nlfXaa6/piSee0JUrV6yOU6rmzJmjZ555hst8UW55eHjI07OqpAumz3ZzS1Tt2rVNnwsAAOBsKEIB\nAHBht912m/z9/fXOO+9YHaXU7NmzRxEREXr44YetjgIUS0pKir799ltVrVpX0l6Tp+cqI+OAOnf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MmJF11123rGtDU/nHP/6Rrl27ZubMmU364J8pU6bkoIMOyqRJk9KlS5cmu24l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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -541,7 +555,15 @@ "\n", "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assingment_history)-1, step=1, value=0)\n", "w=widgets.interactive(step_func,iteration=iteration_slider)\n", - "display(w)" + "display(w)\n", + "\n", + "visualize_callback = make_visualize(iteration_slider)\n", + "\n", + "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "\n", + "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", + "display(a)" ] }, { @@ -651,7 +673,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.\n" + "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step.\n" ] }, { @@ -665,7 +687,7 @@ "data": { "image/png": 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cPrjP8fyJV2cNYjUiMlrp0I+IiIgD6jAl5vUf6hERcZMCU2LenAWl/OKxPzma2xMJs+ZH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\nRBxQYIqIiDigwBQREXFAgSkiIuLA/wGx9HtR0bJVGAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -678,7 +700,15 @@ "\n", "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assingment_history)-1, step=0, value=0)\n", "w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n", - "display(w)" + "display(w)\n", + "\n", + "visualize_callback = make_visualize(iteration_slider)\n", + "\n", + "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "\n", + "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", + "display(a)" ] }, { @@ -727,9 +757,9 @@ "outputs": [ { "data": { - "image/png": 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YB3QOXGmSlFUGpkLbgWKgAnimZ+wIkACCnu9TZMJyJjALuBPoGvBKJWngGZgK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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -739,7 +769,15 @@ "source": [ "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assingment_history)-1, step=0, value=0)\n", "w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n", - "display(w)" + "display(w)\n", + "\n", + "visualize_callback = make_visualize(iteration_slider)\n", + "\n", + "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "\n", + "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", + "display(a)" ] } ], @@ -763,305 +801,555 @@ }, "widgets": { "state": { - "017b94f5b593403faf39d77f2f1181e1": { + "00e6193e3c1241d092e88190018a393a": { + "views": [] + }, + "01ca8f81f7e54b94812e55443502b9a7": { + "views": [] + }, + "02df2f5698d4498f8329dbb86d1ecbd6": { + "views": [] + }, + 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output like book\n", - " fig = plt.matshow(grid, cmap=plt.cm.bwr)\n", + " fig = plt.imshow(grid, cmap=plt.cm.bwr, interpolation='nearest')\n", "\n", " plt.axis('off')\n", " fig.axes.get_xaxis().set_visible(False)\n", @@ -383,14 +384,28 @@ "\n", " plt.show()\n", " \n", - " return plot_grid_step" + " return plot_grid_step\n", + "\n", + "def make_visualize(slider):\n", + " ''' Takes an input a slider and returns \n", + " callback function for timer and animation\n", + " '''\n", + " \n", + " def visualize_callback(Visualize, time_step):\n", + " if Visualize is True:\n", + " for i in range(slider.min, slider.max + 1):\n", + " slider.value = i\n", + " time.sleep(time_step)\n", + " \n", + " return visualize_callback\n", + " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -423,7 +438,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -437,14 +452,20 @@ "iteration_slider = widgets.IntSlider(min=1, max=15, step=1, value=0)\n", "w=widgets.interactive(plot_grid_step,iteration=iteration_slider)\n", "display(w)\n", - " " + "\n", + "visualize_callback = make_visualize(iteration_slider)\n", + "\n", + "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", + "display(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Move the slider above to observe how the utility changes across iterations. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click." + "Move the slider above to observe how the utility changes across iterations. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step." ] } ], @@ -468,536 +489,2486 @@ }, "widgets": { "state": { - "00d75b759a1647a69706c9cf5b0e8a98": { + "001e6c8ed3fc4eeeb6ab7901992314dd": { + "views": [] + }, + "00f29880456846a8854ab515146ec55b": { + "views": [] + }, + "010f52f7cde545cba25593839002049b": { + "views": [] + }, + "01473ad99aa94acbaca856a7d980f2b9": { + "views": [] + }, + "021a4a4f35da484db5c37c5c8d0dbcc2": { + "views": [] + }, + "02229be5d3bc401fad55a0378977324a": { + "views": [] + }, + "022a5fdfc8e44fb09b21c4bd5b67a0db": { + "views": [ + { + "cell_index": 27 + } + ] + }, + "025c3b0250b94d4c8d9b33adfdba4c15": { + "views": [] + }, + "028f96abfed644b8b042be1e4b16014d": { + "views": [] + }, + "0303bad44d404a1b9ad2cc167e42fcb7": { + "views": [] + }, + "031d2d17f32347ec83c43798e05418fe": { + "views": [] + }, + "03de64f0c2fd43f1b3b5d84aa265aeb7": { + "views": [] + }, + "03fdd484675b42ad84448f64c459b0e0": { + "views": [] + }, + 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versions of ipywidgets (#241) --- csp.ipynb | 8 ++++---- mdp.ipynb | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 9b08fb9d2..09a24e468 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -482,7 +482,7 @@ " if Visualize is True:\n", " for i in range(slider.min, slider.max + 1):\n", " slider.value = i\n", - " time.sleep(time_step)\n", + " time.sleep(float(time_step))\n", " \n", " return visualize_callback\n", " " @@ -560,7 +560,7 @@ "visualize_callback = make_visualize(iteration_slider)\n", "\n", "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n", "\n", "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", "display(a)" @@ -705,7 +705,7 @@ "visualize_callback = make_visualize(iteration_slider)\n", "\n", "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n", "\n", "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", "display(a)" @@ -774,7 +774,7 @@ "visualize_callback = make_visualize(iteration_slider)\n", "\n", "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n", "\n", "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", "display(a)" diff --git a/mdp.ipynb b/mdp.ipynb index c4aa73ffb..909b874ca 100644 --- a/mdp.ipynb +++ b/mdp.ipynb @@ -395,7 +395,7 @@ " if Visualize is True:\n", " for i in range(slider.min, slider.max + 1):\n", " slider.value = i\n", - " time.sleep(time_step)\n", + " time.sleep(float(time_step))\n", " \n", " return visualize_callback\n", " " @@ -456,7 +456,7 @@ "visualize_callback = make_visualize(iteration_slider)\n", "\n", "visualize_button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "time_select = widgets.ToggleButtons(description='Extra Delay:',options=[0, 0.1, 0.2, 0.5, 0.7, 1.0])\n", + "time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n", "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", "display(a)" ] From 7bdeb78d35f462bad3dd5dd760e8991c93673f69 Mon Sep 17 00:00:00 2001 From: SnShine Date: Wed, 15 Jun 2016 18:55:56 +0530 Subject: [PATCH 322/513] adds legend to the plot and shows final path after completing search --- search.ipynb | 632 ++++++++++++++++++++++----------------------------- 1 file changed, 272 insertions(+), 360 deletions(-) diff --git a/search.ipynb b/search.ipynb index affda83e9..51b652341 100644 --- a/search.ipynb +++ b/search.ipynb @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Giurgiu': (375, 270), 'Arad': (91, 492), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Craiova': (253, 288), 'Mehadia': (168, 339), 'Neamt': (406, 537), 'Rimnicu': (233, 410), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Bucharest': (400, 327), 'Fagaras': (305, 449), 'Oradea': (131, 571), 'Sibiu': (207, 457), 'Drobeta': (165, 299), 'Lugoj': (165, 379), 'Zerind': (108, 531), 'Hirsova': (534, 350), 'Pitesti': (320, 368), 'Eforie': (562, 293)}\n" + "{'Lugoj': (165, 379), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Neamt': (406, 537), 'Eforie': (562, 293), 'Drobeta': (165, 299), 'Pitesti': (320, 368), 'Mehadia': (168, 339), 'Giurgiu': (375, 270), 'Hirsova': (534, 350), 'Vaslui': (509, 444), 'Craiova': (253, 288), 'Arad': (91, 492), 'Fagaras': (305, 449), 'Zerind': (108, 531), 'Sibiu': (207, 457), 'Rimnicu': (233, 410), 'Bucharest': (400, 327), 'Oradea': (131, 571), 'Iasi': (473, 506)}\n" ] } ], @@ -325,6 +325,7 @@ "%matplotlib inline\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", + "from matplotlib import lines\n", "\n", "from ipywidgets import interact\n", "import ipywidgets as widgets\n", @@ -364,6 +365,7 @@ " # node_colors to color nodes while exploring romania map\n", " node_colors[n] = \"white\"\n", "\n", + "# we'll save the initial node colors to a dict to use later\n", "initial_node_colors = dict(node_colors)\n", " \n", "# positions for node labels\n", @@ -401,7 +403,7 @@ "def show_map(node_colors):\n", " # set the size of the plot\n", " plt.figure(figsize=(18,13))\n", - " # draw the graph with locations from romania_locations\n", + " # draw the graph (both nodes and edges) with locations from romania_locations\n", " nx.draw(G, pos = romania_locations, node_color = [node_colors[node] for node in G.nodes()])\n", "\n", " # draw labels for nodes\n", @@ -411,7 +413,16 @@ "\n", " # add edge lables to the graph\n", " nx.draw_networkx_edge_labels(G, pos = romania_locations, edge_labels=edge_labels, font_size = 14)\n", - "\n", + " \n", + " # add a legend\n", + " white_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"white\")\n", + " orange_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"orange\")\n", + " red_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"red\")\n", + " gray_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"gray\")\n", + " plt.legend((white_circle, orange_circle, red_circle, gray_circle),\n", + " ('Un-explored', 'Frontier', 'Currently exploring', 'Explored'),\n", + " numpoints=1,prop={'size':16}, loc=(.8,.75))\n", + " \n", " # show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", " plt.show()" ] @@ -432,9 +443,9 @@ "outputs": [ { "data": { - "image/png": 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Ijo5WzZo1tWPHDmahAACQA+Lj4zVz5kzNmTNHL7/8skaNGiUPDw+jYwEAAJOj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\nJwAAAACwJD8BAAAAgCX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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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rYGRkJHYcIiIiKmEjRozA4cOHER8fL3YUIqrgWPwkIvoO2dnZ4NLJVJzK4rR3QRDg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\nj/Z+XLlyZY3rTz31VIYOHVo9a3T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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -473,16 +484,57 @@ "\n", "We add the colors to the nodes to have a nice visualisation when displaying. So, these are the different colors we are using in these visuals:\n", "* Un-explored nodes - white\n", - "* Frontier nodes - blue\n", + "* Frontier nodes - orange\n", "* Currently exploring node - red\n", "* Already explored nodes - gray\n", - "* Goal node - green" + "\n", + "Now, we will define some methods which we are gonna use in all the searching algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def final_path_colors(problem, solution):\n", + " \"returns a node_colors dict of the final path provided the problem and solution\"\n", + " \n", + " # get initial node colors\n", + " final_colors = dict(initial_node_colors)\n", + " # color all the nodes in solution and starting node to green\n", + " final_colors[problem.initial] = \"green\"\n", + " for node in solution:\n", + " final_colors[node] = \"green\" \n", + " return final_colors\n", + "\n", + "\n", + "def display_visual(all_node_colors):\n", + " def slider_callback(iteration):\n", + " show_map(all_node_colors[iteration])\n", + "\n", + " def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)\n", + "\n", + " slider = widgets.IntSlider(min=0, max=len(all_node_colors)-1, step=1, value=0)\n", + " w = widgets.interactive(slider_callback, iteration = slider)\n", + " display(w)\n", + "\n", + " button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", + " a = widgets.interactive(visualize_callback, Visualize = button)\n", + " display(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "## Breadth first tree search\n", "\n", "We have a working implementation in search module. But as we want to interact with the graph while it is searching, we need to modify the implementation. Here's the modified breadth first tree search.\n", @@ -492,18 +544,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ - "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)" + "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)\n", + "node_colors = dict(initial_node_colors)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -522,12 +575,9 @@ " \n", " frontier.append(Node(problem.initial))\n", " \n", - " # modify the color of frontier nodes to blue\n", - " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", - " for n in frontier_list:\n", - " node_colors[n.state] = \"blue\"\n", - " iterations += 1\n", - " all_node_colors.append(dict(node_colors))\n", + " node_colors[Node(problem.initial).state] = \"orange\"\n", + " iterations += 1\n", + " all_node_colors.append(dict(node_colors))\n", " \n", " while frontier:\n", " node = frontier.pop()\n", @@ -545,11 +595,9 @@ " return node\n", " \n", " frontier.extend(node.expand(problem))\n", - " \n", - " # modify the color of frontier nodes to blue\n", - " frontier_list = frontier.__dict__[\"A\"][frontier.__dict__[\"start\"]:] \n", - " for n in frontier_list:\n", - " node_colors[n.state] = \"blue\"\n", + " \n", + " for n in node.expand(problem):\n", + " node_colors[n.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", "\n", @@ -569,12 +617,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's call the `modified breadth_first_tree_search` with our problem statement. Print `iterations` to see how many intermediate steps we have got " + "Let's call the modified `breadth_first_tree_search` with our problem statement. Print `iterations` to see how many intermediate steps we have got " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -583,16 +631,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "86\n", - "86\n" + "['Sibiu', 'Fagaras']\n", + "27\n", + "28\n" ] } ], "source": [ - "breadth_first_tree_search(romania_problem).solution()\n", + "solution = breadth_first_tree_search(romania_problem).solution()\n", + "\n", + "all_node_colors.append(final_path_colors(romania_problem, solution))\n", "\n", - "print(len(all_node_colors))\n", - "print(iterations)" + "print(solution)\n", + "print(iterations)\n", + "print(len(all_node_colors))" ] }, { @@ -603,24 +655,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def slider_callback(iteration):\n", - " show_map(all_node_colors[iteration])\n", - "\n", - "def visualize_callback(Visualize):\n", - " if Visualize is True:\n", - " for i in range(slider.max + 1):\n", - " slider.value = i\n", - "# time.sleep(.5)" - ] - }, { "cell_type": "code", "execution_count": 17, @@ -630,9 +664,9 @@ "outputs": [ { "data": { - "image/png": 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KlMjTbMVJQkKCGjRooK1btzILBQCAApCSkqKZM2dqzpw5evrppzV27FhVrlzZ\n6FgAAMDkKD+BAtaqVSft2DFA0tM5HsPNrZWio0PVqVOnvAtWDL377rv68ssvtXHjRjY/AgAAAADA\nhO5lsUEAeeD06dOS7s/VGFbr/f87DnIjODhYCQkJWrlypdFRAAAAAABAPqD8BArY9etpkpxzNUZm\nprPS0tLyJlAx5uDgoLlz5+rVV19Vamqq0XEAAAAAAEAeo/wECpira2lJybkaw8EhRaVLl86bQMVc\n27Zt1aZNG7311ltGRwEAAH9z7do1oyMAAAAToPwECljLlk1kZ7cpFyOky2r99q53WMW/Cw8P18KF\nC3X06FGjowAAgP9Vp04dRUREKD093egoAACgCKP8BArYq68OkpPTQknWHI7wherVq60GDRrkZaxi\n7b777tOoUaP0yiuvGB0FAIBc69u3r+zs7DR16tRsx7du3So7OztdunTJoGR/Wbx4sdzc3P71uujo\naH3yySeqX7++li1bJqs1p787AQCA4ozyEyhgTZs2VbVqFSV9naP7XV3n6bXXBudtKOiVV17Rr7/+\nqjVr1hgdBQCAXLFYLHJ2dlZ4eLguXrx4yzmj2Wy2u8rRsmVLbd68We+//77mzp2rhg0batWqVbLZ\nbAWQEgAAmAXlJ2CAsLCxcnEZLOnedmy3t5+lcuXO64knnsifYMWYo6Oj3n33Xb3yyiusMQYAKPIC\nAgJUvXp1TZo06Y7XHDp0SF27dpW7u7sqVqyo3r17KyEhIev8nj171KlTJ5UvX16lS5fWgw8+qB07\ndmQbw87OTgsXLlT37t1VqlQp1atXT999953OnDmjzp07y9XVVY0bN9a+ffsk/TX7tH///kpNTZWd\nnZ3s7e3/MaMktWvXTj/++KOmTZumiRMnqnnz5tqwYQMlKAAAuCuUn4ABHn/8cY0ZEyIXl3aSjt3V\nPfb2s1SmzAx9993XcnR0zN+AxVSnTp3k5+enGTNmGB0FAIBcsbOz07Rp07Rw4UKdOHHilvN//PGH\n2rZtK39/f+3Zs0ebN29WamqqunXrlnXNlStX9Pzzz2v79u3avXu3GjdurMcee0xJSUnZxpo6dap6\n9+6tAwcOqFmzZnrmmWc0YMAADR48WPv27ZO3t7f69u0rSWrdurVmzZolFxcXJSQk6Ny5cxoxYsS/\nvh+LxaKuXbsqJiZGI0eO1NChQ9W2bVt9//33uftBAQAA07PY+MgUMMzcuQs0atR4ZWT0U3r6IEk1\n/s8VVkmjAaXqAAAgAElEQVRrVarUXJUrd1pbt65TtWrVDEhafJw4cULNmjVTTEyMqlatanQcAADu\nWb9+/XTx4kV9+eWXateunby8vBQVFaWtW7eqXbt2SkxM1KxZs/TTTz9p48aNWfclJSWpbNmy2rVr\nl5o2bXrLuDabTffdd5+mT5+u3r17S/qrZH3jjTc0ZcoUSVJsbKz8/Pw0c+ZMDR06VJKyva6np6cW\nL16sIUOG6PLlyzl+jxkZGVq6dKkmTpyoevXqaerUqXrggQdyPB4AADAvZn4CBgoJGaT9+39UixYx\ncnDwl5tbR5UsOUQODiPl4jJALi415ePzpubP76P4+BiKzwJQo0YNDRkyRMOHDzc6CgAAuRYWFqbo\n6Gjt3bs32/GYmBht3bpVbm5uWV9Vq1aVxWLRsWN/PZWSmJiooKAg1atXT2XKlJG7u7sSExN18uTJ\nbGP5+fll/blixYqSlG1jxpvHzp8/n2fvy8HBQX379tXhw4cVGBiowMBAPfnkk4qNjc2z1wAAAObg\nYHQAoLirXbu2kpMT9OWXnyk1NVVnz57VtWvXVKZMHTVtGqwmTZoYHbHYGTVqlHx8fLRp0yZ16NDB\n6DgAAORYs2bN1KNHD40cOVLjxo3LOp6ZmamuXbtqxowZt6ydebOsfP7555WYmKjZs2erWrVqKlmy\npNq1a6cbN25ku75EiRJZf765kdH/PWaz2ZSZmZnn78/R0VHBwcHq27ev5s+fr4CAAHXq1EmhoaGq\nVatWnr8eAAAoeig/AYNZLBb98ssvRsfA3zg7O2vWrFkaMmSI9u/fzxqrAIAi7c0335SPj4/Wr1+f\ndaxJkyaKjo5W1apVZW9vf9v7tm/frjlz5qhz586SlLVGZ078fXd3R0dHWa3WHI1zJy4uLhoxYoRe\nfPFFzZw5Uy1atNCTTz6pcePGqXLlynn6WgAAoGjhsXcAuI3AwEBVr15dc+bMMToKAAC5UqtWLQUF\nBWn27NlZxwYPHqyUlBT17NlTu3bt0okTJ7Rp0yYFBQUpNTVVklS3bl0tXbpUcXFx2r17t3r16qWS\nJUvmKMPfZ5dWr15d165d06ZNm3Tx4kWlpaXl7g3+jbu7uyZMmKDDhw+rTJky8vf317Bhw+75kfu8\nLmcBAIBxKD8B4DYsFotmz56tt956K8ezXAAAKCzGjRsnBweHrBmYlSpV0vbt22Vvb69HH31UDRo0\n0JAhQ+Tk5JRVcC5atEh//vmnmjZtqt69e+u///2vqlevnm3cv8/ovNtjrVq10ksvvaRevXqpQoUK\nCg8Pz8N3+peyZcsqLCxMsbGxysjIUP369TVmzJhbdqr/v86cOaOwsDA999xzeuONN3T9+vU8zwYA\nAAoWu70DwD94/fXXdfr0aS1ZssToKAAAIId+//13TZo0SevXr9epU6dkZ3frHJDMzEx1795dv/zy\ni3r37q3vv/9e8fHxmjNnjv7nf/5HNpvttsUuAAAo3Cg/AeAf/Pnnn6pfv76WL1+uNm3aGB0HAADk\nQkpKitzd3W9bYp48eVKPPPKIXnvtNfXr10+SNG3aNK1fv15ff/21XFxcCjouAADIAzz2DhRi/fr1\nU2BgYK7H8fPz06RJk/IgUfHj6uqq6dOnKyQkhPW/AAAo4kqXLn3H2Zve3t5q2rSp3N3ds45VqVJF\nx48f14EDByRJ165d07vvvlsgWQEAQN6g/ARyYevWrbKzs5O9vb3s7Oxu+Wrfvn2uxn/33Xe1dOnS\nPEqLnOrZs6c8PDz03nvvGR0FAADkg59++km9evVSXFycnn76aQUHB2vLli2aM2eOatasqfLly0uS\nDh8+rNdff12VKlXi9wIAAIoIHnsHciEjI0OXLl265fgXX3yhQYMG6bPPPlOPHj3ueVyr1Sp7e/u8\niCjpr5mfTz/9tMaPH59nYxY3Bw8eVLt27RQbG5v1HyAAAFD0Xb16VeXLl9fgwYPVvXt3JScna8SI\nESpdurS6du2q9u3bq2XLltnuiYyM1Lhx42SxWDRr1iw99dRTBqUHAAD/hpmfQC44ODioQoUK2b4u\nXryoESNGaMyYMVnF59mzZ/XMM8/I09NTnp6e6tq1q44ePZo1zsSJE+Xn56fFixerdu3acnJy0tWr\nV9W3b99sj70HBARo8ODBGjNmjMqXL6+KFStq5MiR2TIlJiaqW7ducnFxUY0aNbRo0aKC+WGYXIMG\nDdS7d2+NGTPG6CgAACAPRUVFyc/PT6NHj1br1q3VpUsXzZkzR6dPn1b//v2zik+bzSabzabMzEz1\n799fp06dUp8+fdSzZ08FBwcrNTXV4HcCAABuh/ITyEMpKSnq1q2b2rVrp4kTJ0qS0tLSFBAQoFKl\nSun777/Xjh075O3trQ4dOujatWtZ9544cULLly/XihUrtH//fpUsWfK2a1JFRUWpRIkS+umnnzRv\n3jzNmjVLn376adb5F154QcePH9eWLVu0evVqffzxx/r999/z/80XA6Ghofrqq68UHx9vdBQAAJBH\nrFarzp07p8uXL2cd8/b2lqenp/bs2ZN1zGKxZPvd7KuvvtLevXvl5+en7t27q1SpUgWaGwAA3B3K\nTyCP2Gw29erVSyVLlsy2Tufy5cslSR9++KF8fX1Vt25dLViwQH/++afWrFmTdV16erqWLl2qRo0a\nycfH546Pvfv4+Cg0NFS1a9fWU089pYCAAG3evFmSdOTIEa1fv14RERFq2bKlGjZsqMWLF+vq1av5\n+M6LjzJlymjfvn2qV6+eWDEEAABzaNu2rSpWrKiwsDCdPn1aBw4c0NKlS3Xq1Cndf//9kpQ141P6\na9mjzZs3q2/fvsrIyNCKFSvUsWNHI98CAAD4Bw5GBwDM4vXXX9fOnTu1e/fubJ/8x8TE6Pjx43Jz\nc8t2fVpamo4dO5b1feXKlVWuXLl/fR1/f/9s33t7e+v8+fOSpPj4eNnb26tZs2ZZ56tWrSpvb+8c\nvSfcqkKFCnfcJRYAABQ9999/vz766CMFBwerWbNmKlu2rG7cuKHXXntNderUyVqL/ea//2+//bYW\nLlyozp07a8aMGfL29pbNZuP3AwAACinKTyAPfPLJJ3rnnXf09ddfq2bNmtnOZWZmqnHjxvr0009v\nmS3o6emZ9ee7fVSqRIkS2b63WCxZMxH+fgz5415+tteuXZOTk1M+pgEAAHnBx8dH3333nQ4cOKCT\nJ0+qSZMmqlChgqT/vxHlhQsX9MEHH2jatGkaOHCgpk2bppIlS0ridy8AAAozyk8gl/bt26cBAwYo\nLCxMHTp0uOV8kyZN9Mknn6hs2bJyd3fP1yz333+/MjMztWvXrqzF+U+ePKmzZ8/m6+siu8zMTG3c\nuFExMTHq16+fvLy8jI4EAADugr+/f9ZTNjc/XHZ0dJQkvfzyy9q4caNCQ0MVEhKikiVLKjMzU3Z2\nrCQGAEBhxr/UQC5cvHhR3bt3V0BAgHr37q2EhIRbvp599llVrFhR3bp107Zt2/Tbb79p27ZtGjFi\nRLbH3vNC3bp11alTJwUFBWnHjh3at2+f+vXrJxcXlzx9HfwzOzs7ZWRkaPv27RoyZIjRcQAAQA7c\nLDVPnjypNm3aaM2aNZoyZYpGjBiR9WQHxScAAIUfMz+BXFi7dq1OnTqlU6dO3bKu5s21n6xWq7Zt\n26bXXntNPXv2VEpKiry9vRUQECAPD497er27eaRq8eLFGjhwoNq3b69y5cppwoQJSkxMvKfXQc7d\nuHFDjo6Oeuyxx3T27FkFBQXpm2++YSMEAACKqKpVq2r48OGqVKlS1pM1d5rxabPZlJGRccsyRQAA\nwDgWG1sWA0CuZWRkyMHhr8+Trl27phEjRmjJkiVq2rSpRo4cqc6dOxucEAAA5DebzaaGDRuqZ8+e\nGjp06C0bXgIAgILHcxoAkEPHjh3TkSNHJCmr+IyIiFD16tX1zTffaPLkyYqIiFCnTp2MjAkAAAqI\nxWLRypUrdejQIdWuXVvvvPOO0tLSjI4FAECxRvkJADm0bNkyPf7445KkPXv2qGXLlho1apR69uyp\nqKgoBQUFqWbNmuwACwBAMVKnTh1FRUVp06ZN2rZtm+rUqaOFCxfqxo0bRkcDAKBY4rF3AMghq9Wq\nsmXLqnr16jp+/LgefPBBDRo0SP/5z39uWc/1woULiomJYe1PAACKmV27dmns2LE6evSoQkND9eyz\nz8re3t7oWAAAFBuUnwCQC5988ol69+6tyZMn67nnnlPVqlVvuearr75SdHS0vvjiC0VFRemxxx4z\nICkAADDS1q1bNWbMGF26dEmTJk1Sjx492C0eAIACQPkJALnUsGFDNWjQQMuWLZP012YHFotF586d\n03vvvafVq1erRo0aSktL088//6zExESDEwMAACPYbDatX79eY8eOlSRNmTJFnTt3ZokcAADyER81\nAkAuRUZGKi4uTqdPn5akbP+Bsbe317FjxzRp0iStX79eXl5eGjVqlFFRAQCAgSwWix599FHt2bNH\nb7zxhoYPH64HH3xQW7duNToaAACmxcxPIA/dnPGH4uf48eMqV66cfv75ZwUEBGQdv3Tpkp599ln5\n+PhoxowZ2rJlizp27KhTp06pUqVKBiYGAABGs1qtioqKUmhoqGrVqqWpU6eqWbNmRscCAMBU7END\nQ0ONDgGYxd+Lz5tFKIVo8eDh4aGQkBDt2rVLgYGBslgsslgscnZ2VsmSJbVs2TIFBgbKz89P6enp\nKlWqlGrWrGl0bAAAYCA7Ozs1bNhQwcHBun79uoKDg7Vt2zb5+vqqYsWKRscDAMAUeOwdyAORkZF6\n8803sx27WXhSfBYfrVq10s6dO3X9+nVZLBZZrVZJ0vnz52W1WlW6dGlJ0uTJk9W+fXsjowIAgEKk\nRIkSCgoK0q+//qqHHnpIHTp0UO/evfXrr78aHQ0AgCKP8hPIAxMnTlTZsmWzvt+5c6dWrlypL7/8\nUrGxsbLZbMrMzDQwIQpC//79VaJECU2ZMkWJiYmyt7fXyZMnFRkZKQ8PDzk4OBgdEQAAFGLOzs56\n9dVXdfToUfn4+KhVq1YaMGCATp48aXQ0AACKLNb8BHIpJiZGrVu3VmJiotzc3BQaGqoFCxYoNTVV\nbm5uqlWrlsLDw9WqVSujo6IA7NmzRwMGDFCJEiVUqVIlxcTEqFq1aoqMjFS9evWyrktPT9e2bdtU\noUIF+fn5GZgYAAAUVklJSQoPD9d7772nZ599Vm+88Ya8vLyMjgUAQJHCzE8gl8LDw9WjRw+5ublp\n5cqVWrVqld544w39+eefWr16tZydndWtWzclJSUZHRUFoGnTpoqMjFSnTp107do1BQUFacaMGapb\nt67+/lnTuXPn9Pnnn2vUqFFKSUkxMDEAACisPDw89Oabb+rQoUOys7OTr6+vXn/9dV26dMnoaAAA\nFBnM/ARyqUKFCnrggQc0btw4jRgxQl26dNHYsWOzzh88eFA9evTQe++9l20XcBQP/7Th1Y4dOzRs\n2DBVrlxZ0dHRBZwMAAAUNadOndLkyZP1+eefa+jQoXrllVfk5uZmdCwAAAo1Zn4CuZCcnKyePXtK\nkgYNGqTjx4/roYceyjqfmZmpGjVqyM3NTZcvXzYqJgxw83Olm8Xn//2c6caNGzpy5IgOHz6sH374\ngRkcAADgX1WpUkXvv/++duzYocOHD6t27dqaMWOG0tLSjI4GAEChRfkJ5MLZs2c1d+5czZ49WwMH\nDtTzzz+f7dN3Ozs7xcbGKj4+Xl26dDEwKQrazdLz7Nmz2b6X/toQq0uXLurfv7+ee+457d+/X56e\nnobkBAAARU/t2rW1dOlSbd68Wdu3b1edOnW0YMEC3bhxw+hoAAAUOpSfQA6dPXtWDz/8sKKiolS3\nbl2FhIRoypQp8vX1zbomLi5O4eHhCgwMVIkSJQxMCyOcPXtWgwYN0v79+yVJp0+f1tChQ/XQQw8p\nPT1dO3fu1OzZs1WhQgWDkwIAgKKoQYMG+vzzz7V69Wp98cUXuv/++7V48WJZrVajowEAUGhQfgI5\nNH36dF24cEEDBgzQhAkTlJKSIkdHR9nb22dds3fvXp0/f16vvfaagUlhFG9vb6WmpiokJETvv/++\nWrZsqZUrVyoiIkJbt27VAw88YHREAABgAk2bNtX69ev10Ucf6YMPPlCDBg0UHR2tzMzMux4jJSVF\nc+fO1SOPPKLGjRurYcOGCggIUFhYmC5cuJCP6QEAyF9seATkkLu7u1atWqWDBw9q+vTpGjlypF5+\n+eVbrktLS5Ozs7MBCVEYJCYmqlq1arp27ZpGjhypN954Q6VLlzY6FgAAMCmbzaYNGzZo7NixyszM\n1OTJk9WlS5c7bsB47tw5TZw4UZ9++qk6duyoPn366L777pPFYlFCQoI+++wzrVq1So8//rgmTJig\nWrVqFfA7AgAgdyg/gRxYvXq1goKClJCQoOTkZE2bNk3h4eHq37+/pkyZoooVK8pqtcpiscjOjgnW\nxV14eLimT5+uY8eOydXV1eg4AACgGLDZbFq1apXGjRunMmXKaOrUqXr44YezXRMXF6dHH31UTz/9\ntF599VVVqlTptmNdunRJ8+fP17x587Rq1Sq1bNmyAN4BAAB5g/ITyIEHH3xQrVu3VlhYWNaxDz74\nQFOnTlWPHj00Y8YMA9OhMCpTpozGjRun4cOHGx0FAAAUI1arVcuXL1doaKhq1KihKVOmqEWLFjp1\n6pRat26tyZMnq2/fvnc11tq1a9W/f39t2bIl2zr3AAAUZpSfwD26cuWKPD09dfjwYdWsWVNWq1X2\n9vayWq364IMP9Oqrr+rhhx/W3LlzVaNGDaPjopDYv3+/zp8/r/bt2zMbGAAAFLj09HQtWrRIkydP\nVpMmTXT+/Hl1795do0ePvqdxlixZorfeekuxsbF3fJQeAIDChPITyIHk5GSVKVPmtudWrlypUaNG\nydfXV8uXL1epUqUKOB0AAABwe9euXdOECRMUERGhhIQElShR4p7ut9lsatiwoWbOnKn27dvnU0oA\nAPIO04+AHLhT8SlJTz75pN555x1duHCB4hMAAACFipOTk1JTUzVkyJB7Lj4lyWKxKDg4WPPnz8+H\ndAAA5D1mfgL5JCkpSR4eHkbHQCF1869eHhcDAAAFKTMzUx4eHjp06JDuu+++HI1x5coVVa5cWb/9\n9hu/7wIACj1mfgL5hF8E8U9sNpt69uypmJgYo6MAAIBi5PLly7LZbDkuPiXJzc1NXl5e+uOPP/Iw\nGQAA+YPyE8glJk8jJ+zs7NS5c2eFhIQoMzPT6DgAAKCYSEtLk7Ozc67HcXZ2VlpaWh4kAgAgf1F+\nArlgtVr1008/UYAiR/r166eMjAwtWbLE6CgAAKCYKF26tFJSUnL9+2tycrJKly6dR6kAAMg/lJ9A\nLmzcuFFDhw5l3UbkiJ2dnebNm6fXXntNKSkpRscBAADFgLOzs2rUqKEffvghx2McOXJEaWlpqlKl\nSh4mAwAgf1B+Arnw4Ycf6r///a/RMVCENWvWTF27dlVoaKjRUQAAQDFgsVg0aNCgXO3WvnDhQvXv\n31+Ojo55mAwAgPzBbu9ADiUmJqpOnTr6/fffeeQHuZKYmChfX19t2bJFDRo0MDoOAAAwueTkZNWo\nUUNxcXHy8vK6p3tTU1NVrVo17dmzR9WrV8+fgAAA5CFmfgI5tGTJEnXr1o3iE7lWvnx5TZgwQUOG\nDGH9WOD/sXef0VFW+9vHvzOThDRK6IhAgBBqIk2qoBAx0qUOIkVAUemCgNKbCNKLja7AgaFLRwki\nErq0P4QuISBJ6C2VJPO88DHrcIDQEu6EuT5rsWBm9t73dWeJzPxmFxERSXPZsmXjk08+oXXr1sTH\nxz92v6SkJDp27EiDBg1U+BQRkQxDxU+Rp2C327XkXVLVRx99xPXr11myZInRUURERMQBjBw5Ei8v\nL5o0acKdO3ce2T4+Pp7333+f8PBwvv/+++eQUEREJHWo+CnyFHbt2sXdu3epUaOG0VHkBeHk5MT0\n6dP57LPPHusDiIiIiMizsFgsLF68mHz58vHKK68wadIkrl+/fl+7O3fu8P333/PKK69w69YtNm7c\niKurqwGJRUREno72/BR5Ch988AHFihWjf//+RkeRF0zbtm0pUKAAo0ePNjqKiIiIOAC73U5wcDDf\nffcd69at46233iJ//vyYTCYiIyPZsGEDpUuXJiwsjNOnT+Ps7Gx0ZBERkSei4qfIE7p9+zYFCxZ8\nqg3iRR4lPDwcPz8/duzYga+vr9FxRERExIFcunSJjRs3cuXKFZKSksiRIwcBAQEUKFCA6tWr06VL\nF9q0aWN0TBERkSei4qfIE5o9ezZr1qxh1apVRkeRF9T48eMJCgpi/fr1mEwmo+OIiIiIiIiIZFja\n81PkCemgI0lrPXr0IDQ0lDVr1hgdRURERERERCRD08xPkScQEhLCm2++SVhYGE5OTkbHkRfYr7/+\nykcffcTRo0dxc3MzOo6IiIiIiIhIhqSZnyJPYPbs2bz//vsqfEqaq1OnDuXLl2fcuHFGRxERERER\nERHJsDTzU+QxxcfHU6BAAYKDg/Hx8TE6jjiAc+fOUb58ef7880+8vb2NjiMiIiIiIiKS4Wjmp8hj\nWrNmDSVLllThU56bQoUK8emnn9K7d2+jo4iIiIjcY/jw4fj7+xsdQ0RE5JE081PkMdWtW5f33nuP\nNm3aGB1FHEhsbCylS5fm22+/JTAw0Og4IiIikoF16NCBq1evsnr16mceKzo6mri4OLy8vFIhmYiI\nSNrRzE+Rx3D+/Hn27NlDs2bNjI4iDsbV1ZUpU6bQo0cP4uPjjY4jIiIiAoC7u7sKnyIikiGo+Cny\nGObNm4fVatWp22KIBg0aUKxYMaZMmWJ0FBEREXlB7Nu3j8DAQHLlykXWrFmpUaMGu3btuqfNDz/8\nQPHixXFzcyNXrlzUrVuXpKQk4J9l735+fkZEFxEReSIqfoo8QlJSEnPmzOGDDz4wOoo4sMmTJzN2\n7Fj+/vtvo6OIiIjIC+D27du0a9eO4OBg9u7dS7ly5ahfvz7Xr18H4M8//6Rbt24MHz6ckydPsmXL\nFt5+++17xjCZTEZEFxEReSJORgcQSS/u3LnDggUL2bRpO1ev3sDFxZmCBfPi51eMrFmzUr58eaMj\nigPz8fHho48+ol+/fixcuNDoOCIiIpLB1apV657HU6ZMYdmyZWzYsIHWrVsTFhaGp6cnDRs2xMPD\ngwIFCmimp4iIZEgqforDCw0NZfToCSxYsBCz+Q2iohoB2YF4TKazWCxTyZLFzrfffkfnzh/i5KS/\nNmKMAQMGULJkSbZt20bNmjWNjiMiIiIZ2OXLlxk0aBBbt24lMjKSxMREYmNjCQsLA6BOnToUKlQI\nb29vAgMDeeutt2jatCmenp4GJxcREXkyWvYuDm3Hjh288koV5s7NTEzMYaKiVgDvA42A5tjtfUlI\n+Itr1+bSt+8S6tRpzJ07d4wNLQ7Lw8ODCRMm0K1bNxISEoyOIyIiIhlYu3bt+PPPP5kyZQo7d+7k\n0KFD5M+fP/mARU9PT/bv38/SpUspVKgQY8aMoUSJEkRERBicXERE5Mmo+CkOa//+/bz1VmNu3ZpL\nQsJo4OWHtDQBtYiO/oWdO3Pz1ltNdOq2GKZ58+bkypWL7777zugoIiIikoEFBwfTvXt33n77bUqW\nLImHhwfh4eH3tDGbzbzxxht8+eWXHDp0iKioKNauXWtQYhERkaej4qc4pNjYWN56qzFRUT8AdR+z\nlzNxcbM4eNCNzz8fmpbxRB7KZDIxbdo0RowYwaVLl4yOIyIiIhmUr68vCxYs4NixY+zdu5d3332X\nTJkyJb++bt06pk6dysGDBwkLC2PhwoXcuXOHUqVKGZhaRETkyan4KQ5p6dKlxMWVApo+YU8LMTFT\nmTFjJtHR0WkRTeSRSpUqRbt27fjiiy+MjiIiIiIZ1Jw5c7hz5w4VK1akdevWdOrUCW9v7+TXs2XL\nxqpVq6hTpw4lS5Zk4sSJzJ49m2rVqhkXWkRE5CmY7Ha73egQIs+bn181jhzpDzR+qv6eng2ZOrUp\nHTp0SN1gIo/p1q1blChRgpUrV1K5cmWj44iIiIiIiIikS5r5KQ4nJCSEv/46D9R/6jHu3PmEiRNn\npV4okSeUJUsWxo4dS9euXUlMTDQ6joiIiIiIiEi6pOKnOJy//voLZ2d/wOkZRilLWNiZ1Iok8lTa\ntGmDq6src+bMMTqKiIiIiIiISLqk4qc4nDt37pCU5PGMo3gSG3snVfKIPC2TycT06dMZPHgw165d\nMzqOiIiIiIiISLqj4qc4nCxZsmA2337GUW7h5pYlVfKIPIuyZcvSrFkzhgwZYnQUERERkWS7d+82\nOoKIiAig4qc4oBIlShAX9ycQ+wyj7KBgwSKpFUnkmYwcOZKlS5dy8OBBo6OIiIiIADB48GCjI4iI\niAAqfooDKlKkCGXLlgWWPfUYzs4TCQs7Qvny5RkzZgxnz55NvYAiTyh79uyMHDmSbt26YbfbjY4j\nIiIiDu7u3bucOXOG33//3egoIiIiKn6KY+rfvwuZM3/7lL2P4uERRkREBBMmTCA0NJRKlSpRqVIl\nJkyYwPnz51M1q8jj6NSpE7GxsSxcuNDoKCIiIuLgnJ2dGTp0KIMGDdIXsyIiYjiTXf8aiQNKSEjA\nx8ef8+e7kZTU5Ql6xuDuHsDAgU0YMKDvPeNt2bIFm83GqlWrKF68OFarlRYtWvDSSy+l/g2IPMCu\nXbto1qwZx44dI0sW7UkrIiIixklMTKRMmTJMnjyZwMBAo+OIiIgDU/FTHNZff/1FhQqvcfPmSOz2\nTo/R4zbu7i0IDMzB8uULMJlMD2wVHx/P5s2bsdlsrF69Gn9/f6xWK82aNSNPnjypexMi/6Njx45k\nz56d8ePHGx1FREREHNzSpUv5+uuv2bNnz0PfO4uIiKQ1FT/FoZ08eZLXX6/LzZtViInpDlQG/veN\nWUdHdjQAACAASURBVDRgw8NjHE2aVGfu3O9wcnJ6rPHj4uLYtGkTNpuNdevWUaFCBaxWK02bNiVn\nzpypfDciEBkZSZkyZfj9998pVaqU0XFERETEgSUlJVG+fHmGDRvGO++8Y3QcERFxUCp+isO7fv06\nM2fOZuLE74iKysqdO42A7EA8zs6hWCyLqVy5Cv36daFu3bpP/a11TEwM69evZ8mSJWzcuJEqVapg\ntVpp0qQJXl5eqXpP4timTp3K6tWr+fXXXzXLQkRERAy1Zs0aBgwYwKFDhzCbdeSEiIg8fyp+ivx/\nSUlJ/PLLL/zxRzBbt+7gxo1rtGvXipYtW1K4cOFUvVZUVBRr167FZrMRFBREjRo1sFqtNGrUiKxZ\ns6bqtcTxJCQkUK5cOYYOHUrz5s2NjiMiIiIOzG63U7VqVXr16kWrVq2MjiMiIg5IxU8Rg926dYs1\na9Zgs9nYunUrtWvXxmq10rBhQzw9PY2OJxnU77//Trt27QgJCcHDw8PoOCIiIuLANm/eTNeuXTl6\n9Ohjbx8lIiKSWlT8FElHbty4wapVq1iyZAnBwcHUqVMHq9VK/fr1cXd3NzqeZDCtW7emaNGijBw5\n0ugoIiIi4sDsdju1atWiffv2dOjQweg4IiLiYFT8FEmnrl69ysqVK7HZbOzdu5e6devSsmVL6tat\ni6urq9HxJAP4+++/eeWVV9i1axc+Pj5GxxEREREHtn37dtq0acPJkydxcXExOo6IiDgQFT9FMoBL\nly6xYsUKbDYbBw8epEGDBlitVt566y29eZQUjR07lu3bt7NmzRqjo4iIiIiDq1u3Lg0bNqRLly5G\nRxEREQei4qdIBhMeHs6yZcuw2WyEhITQuHFjrFYrAQEBODs7Gx1P0pm4uDj8/f2ZMGECDRo0MDqO\niIiIOLB9+/bRuHFjTp8+jZubm9FxRETEQaj4KZJKGjZsSK5cuZgzZ85zu+aFCxdYunQpNpuNM2fO\n0KRJE6xWK6+//ro2k5dkmzZtomvXrhw5ckRbJoiIiIihmjZtymuvvUbv3r2NjiIiIg7CbHQAkbR2\n4MABnJycqFGjhtFRUt3LL7/Mp59+yq5du9i7dy/FihWjf//+5M+fny5duvD777+TmJhodEwxWGBg\nIH5+fkyYMMHoKCIiIuLghg8fztixY7l9+7bRUURExEGo+CkvvFmzZiXPejtx4kSKbRMSEp5TqtTn\n7e1N37592bdvH8HBwbz88sv07NmTAgUK0KNHD4KDg0lKSjI6phhk4sSJTJo0ibCwMKOjiIiIiAPz\n8/MjICCAqVOnGh1FREQchIqf8kKLjY3lP//5D507d6ZZs2bMmjUr+bVz585hNptZvHgxAQEBeHh4\nMGPGDK5du0br1q0pUKAA7u7ulClThnnz5t0zbkxMDO+//z6ZM2cmX758fPXVV8/5zlLm4+PDgAED\nOHjwIFu2bCFnzpx07tyZQoUK0adPH/bs2YN2vHAshQsXpnv37vTp08foKCIiIuLghg0bxuTJk7l+\n/brRUURExAGo+CkvtKVLl+Lt7U3p0qVp27YtP/30033LwAcMGEDXrl0JCQnhnXfeITY2lgoVKrB+\n/XpCQkLo1asXH3/8Mb/99ltynz59+hAUFMTKlSsJCgriwIEDbNu27Xnf3mMpUaIEQ4YM4ejRo2zY\nsAEPDw/atm1LkSJF6N+/P/v371ch1EH069ePffv2sXnzZqOjiIiIiAPz9fWlUaNGTJw40egoIiLi\nAHTgkbzQatWqRaNGjfj0008BKFKkCOPHj6dp06acO3eOwoULM3HiRHr16pXiOO+++y6ZM2dmxowZ\nREVFkSNHDubNm0erVq0AiIqK4uWXX6ZJkybP9cCjp2W32zl06BA2m40lS5ZgNpuxWq20bNkSPz8/\nTCaT0REljfz88898/vnnHDp0CBcXF6PjiIiIiIMKDQ2lQoUKHD9+nFy5chkdR0REXmCa+SkvrNOn\nT7N9+3befffd5Odat27N7Nmz72lXoUKFex4nJSXx5Zdf8sorr5AzZ04yZ87MypUrk/dKPHPmDHfv\n3qVKlSrJfTw8PPDz80vDu0ldJpOJsmXL8tVXX3H69GkWLVpEXFwcDRs2pFSpUgwbNoxjx44ZHVPS\nQKNGjfD29mbatGlGRxEREREH5u3tTatWrRg7dqzRUURE5AXnZHQAkbQya9YskpKSKFCgwH2v/f33\n38l/9vDwuOe1cePGMWnSJKZOnUqZMmXw9PTkiy++4PLly2me2Qgmk4mKFStSsWJFvv76a3bt2sWS\nJUt48803yZ49O1arFavVSrFixYyOKqnAZDIxZcoUqlWrRuvWrcmXL5/RkURERMRBDRw4kDJlytC7\nd29eeuklo+OIiMgLSjM/5YWUmJjITz/9xJgxYzh06NA9v/z9/Zk7d+5D+wYHB9OwYUNat26Nv78/\nRYoU4eTJk8mvFy1aFCcnJ3bt2pX8XFRUFEeOHEnTe3oeTCYTVatWZdKkSZw/f55vv/2WiIgIatSo\nQfny5RkzZgxnz541OqY8I19fXz788EP69+9vdBQRERFxYC+99BJdunTh6tWrRkcREZEXmGZ+ygtp\n7dq1XL16lQ8++AAvL697XrNarfzwww+0adPmgX19fX1ZsmQJwcHB5MiRg+nTp3P27NnkcTw8POjU\nqRP9+/cnZ86c5MuXj5EjR5KUlJTm9/U8mc1matSoQY0aNZgyZQrbtm3DZrNRqVIlChcunLxH6INm\n1kr6N3DgQEqWLMn27dt57bXXjI4jIiIiDmrkyJFGRxARkRecZn7KC2nOnDnUrl37vsInQIsWLQgN\nDWXz5s0PPNhn0KBBVKpUiXr16vHGG2/g6el5X6F0/Pjx1KpVi6ZNmxIQEICfnx81a9ZMs/sxmsVi\noVatWnz//feEh4czatQojh07RtmyZalWrRpTpkzh4sWLRseUJ+Dp6cm4cePo1q0biYmJRscRERER\nB2UymXTYpoiIpCmd9i4iTy0+Pp7Nmzdjs9lYvXo1/v7+tGzZkubNm5MnTx6j48kj2O12atWqRcuW\nLenSpYvRcURERERERERSnYqfIpIq4uLi2LRpEzabjXXr1lGhQgWsVitNmzYlZ86cTz1uUlIS8fHx\nuLq6pmJa+df//d//ERAQwNGjR8mVK5fRcURERETus3PnTtzd3fHz88Ns1uJFERF5Mip+ikiqi4mJ\nYf369SxZsoSNGzdSpUoVrFYrTZo0eeBWBCk5duwYU6ZMISIigtq1a9OpUyc8PDzSKLlj6tWrF9HR\n0cyYMcPoKCIiIiLJtm3bRseOHYmIiCBXrly88cYbfP311/rCVkREnoi+NhORVOfm5kazZs2w2Wxc\nvHiRjh07snbtWry9vWnQoAHz58/n5s2bjzXWzZs3yZ07NwULFqRXr15Mnz6dhISENL4DxzJs2DDW\nrFnD3r17jY4iIiIiAvzzHrBr1674+/uzd+9exo4dy82bN+nWrZvR0UREJIPRzE8ReW5u377N6tWr\nsdlsbN26ldq1a2Oz2ciUKdMj+65atYpPPvmExYsX8/rrrz+HtI5l3rx5fPfdd+zcuVPLyURERMQQ\nUVFRuLi44OzsTFBQEB07dmTJkiVUrlwZ+GdFUJUqVTh8+DCFChUyOK2IiGQU+oQrIs9N5syZee+9\n91i9ejVhYWG8++67uLi4pNgnPj4egEWLFlG6dGl8fX0f2O7KlSt89dVXLF68mKSkpFTP/qJr164d\nZrOZefPmGR1FREREHFBERAQLFizg1KlTABQuXJi///6bMmXKJLdxc3PDz8+PW7duGRVTREQyIBU/\nRR6iVatWLFq0yOgYL6xs2bJhtVoxmUwptvu3OPrrr7/y9ttvJ+/xlJSUxL8T19etW8fQoUMZOHAg\nffr0YdeuXWkb/gVkNpuZPn06AwYM4MaNG0bHEREREQfj4uLC+PHjOX/+PABFihShWrVqdOnShejo\naG7evMnIkSM5f/48+fPnNzitiIhkJCp+ijyEm5sbsbGxRsdwaImJiQCsXr0ak8lElSpVcHJyAv4p\n1plMJsaNG0e3bt1o1qwZr776Ko0bN6ZIkSL3jPP3338THBysGaGPUKFCBd555x2GDh1qdBQRERFx\nMNmzZ6dSpUp8++23xMTEAPDzzz9z4cIFatSoQYUKFThw4ABz5swhe/bsBqcVEZGMRMVPkYdwdXVN\nfuMlxpo3bx4VK1a8p6i5d+9eOnTowIoVK/jll1/w8/MjLCwMPz8/8ubNm9xu0qRJ1KtXj/bt2+Pu\n7k63bt24ffu2EbeRIXz55ZcsWrSIw4cPGx1FREREHMzEiRM5duwYzZo1Y+nSpSxZsoRixYpx7tw5\nXFxc6NKlCzVq1GDVqlWMGDGCCxcuGB1ZREQyABU/RR7C1dVVMz8NZLfbsVgs2O12fvvtt3uWvP/+\n+++0bduWqlWrsmPHDooVK8bs2bPJnj07/v7+yWOsXbuWgQMHEhAQwB9//MHatWvZvHkzv/zyi1G3\nle7lyJGD4cOH0717d3QenoiIiDxPefLkYe7cuRQtWpQePXowbdo0Tpw4QadOndi2bRsffPABLi4u\nXL16le3bt/PZZ58ZHVlERDIAJ6MDiKRXWvZunLt37zJ27Fjc3d1xdnbG1dWV6tWr4+zsTEJCAkeP\nHuXs2bP88MMPxMXF0b17dzZv3kzNmjUpXbo08M9S95EjR9KkSRMmTpwIQL58+ahUqRKTJ0+mWbNm\nRt5iuta5c2dmzJjB4sWLeffdd42OIyIiIg6kevXqVK9ena+//ppbt27h5OREjhw5AEhISMDJyYlO\nnTpRvXp1qlWrxtatW3njjTeMDS0iIumaZn6KPISWvRvHbDbj6enJmDFj6NmzJ5GRkaxZs4aLFy9i\nsVj44IMP2L17N2+//TY//PADzs7ObN++nVu3buHm5gbA/v37+fPPP+nfvz/wT0EV/tlM383NLfmx\n3M9isTB9+nT69u2rLQJERETEEG5ublgsluTCZ2JiIk5OTsl7wpcoUYKOHTvy3XffGRlTREQyABU/\nRR5CMz+NY7FY6NWrF5cuXeL8+fMMGzaMuXPn0rFjR65evYqLiwtly5blyy+/5MiRI3z88cdky5aN\nX375hd69ewP/LI3Pnz8//v7+2O12nJ2dAQgLC8Pb25v4+HgjbzHdq169OgEBAYwaNcroKCIiIuJg\nkpKSqFOnDmXKlKFXr16sW7eOW7duAf+8T/zX5cuXyZo1a3JBVERE5EFU/BR5CO35mT7kz5+fIUOG\ncOHCBRYsWEDOnDnva3Pw4EHeeecdDh8+zNdffw3Ajh07CAwMBEgudB48eJCrV69SqFAhPDw8nt9N\nZFBjx45l9uzZHD9+3OgoIiIi4kDMZjNVq1bl0qVLREdH06lTJypVqkT79u2ZP38+wcHBLF++nBUr\nVlC4cOF7CqIiIiL/S8VPkYfQsvf050GFz7/++ov9+/dTunRp8uXLl1zUvHLlCj4+PgA4Of2zvfHK\nlStxcXGhatWqADrQ5xHy5s3LwIED6dGjh35WIiIi8lwNHTqUTJky0b59e8LDwxkxYgTu7u6MGjWK\nVq1a0aZNGzp27MgXX3xhdFQREUnnTHZ9ohV5oAULFrBx40YWLFhgdBR5CLvdjslkIjQ0FGdnZ/Ln\nz4/dbichIYEePXqwf/9+goODcXJy4saNGxQvXpz333+fwYMH4+nped84cr+7d+9StmxZRo0aRZMm\nTYyOIyIiIg5k4MCB/Pzzzxw5cuSe5w8fPoyPjw/u7u6A3suJiEjKVPwUeYhly5axePFili1bZnQU\neQr79u2jXbt2+Pv74+vry9KlS3FyciIoKIjcuXPf09Zut/Ptt99y/fp1rFYrxYoVMyh1+rRlyxY6\nduxISEhI8ocMERERkefB1dWVefPm0apVq+TT3kVERJ6Elr2LPISWvWdcdrudihUrsmjRIlxdXdm2\nbRtdunTh559/Jnfu3CQlJd3Xp2zZskRGRlKzZk3Kly/PmDFjOHv2rAHp05/atWtTuXJlxo4da3QU\nERERcTDDhw9n8+bNACp8iojIU9HMT5GHCAoKYvTo0QQFBRkdRZ6jxMREtm3bhs1mY8WKFXh7e2O1\nWmnRogUFCxY0Op5hzp8/T7ly5dizZw9FihQxOo6IiIg4kBMnTuDr66ul7SIi8lQ081PkIXTau2Oy\nWCzUqlWL77//nosXL/Lll19y7NgxypUrR7Vq1ZgyZQoXL140OuZzV6BAAfr06UPv3r2NjiIiIiIO\npnjx4ip8iojIU1PxU+QhtOxdnJycqFOnDrNmzSI8PJxBgwYlnyz/+uuv88033xAZGWl0zOemd+/e\nHD16lA0bNhgdRUREREREROSxqPgp8hBubm6a+SnJXFxcqFevHj/++CMRERH06dOHHTt2ULx4cQIC\nApgxYwZXrlwxOmaaypQpE1OmTKFnz57ExcUZHUdEREQckN1uJykpSe9FRETksan4KfIQmvkpD5Mp\nUyYaNWrEwoULCQ8Pp2vXrgQFBVG0aFECAwOZM2cO169fNzpmmqhXrx4lSpRg0qRJRkcRERERB2Qy\nmejatStfffWV0VFERCSD0IFHIg9x8eJFKlSoQHh4uNFRJIOIiopi7dq12Gw2goKCqFGjBi1btqRx\n48ZkzZrV6Hip5syZM1SuXJmDBw/y8ssvGx1HREREHMxff/1FpUqVOHHiBDly5DA6joiIpHMqfoo8\nxPXr1ylSpMgLO4NP0tbt27dZvXo1NpuNrVu3Urt2baxWKw0bNsTT09PoeM9syJAhnDx5ksWLFxsd\nRURERBzQJ598QpYsWRg7dqzRUUREJJ1T8VPkIWJiYvDy8tK+n/LMbty4wapVq1iyZAnBwcHUqVMH\nq9VK/fr1cXd3NzreU4mOjqZUqVLMnTuXWrVqGR1HREREHMyFCxd45ZVXOHr0KHnz5jU6joiIpGMq\nfoo8RFJSEhaLhaSkJEwmk9Fx5AVx9epVVq5cic1mY+/evdStW5eWLVtSt25dXF1djY73RFasWMGQ\nIUM4cOAAzs7ORscRERERB/Ppp5+SmJjI1KlTjY4iIiLpmIqfIilwdXXlxo0bGa4oJRnDpUuXWLFi\nBTabjYMHD9KgQQOsVitvvfUWLi4uRsd7JLvdTmBgIPXq1aNXr15GxxEREREHExkZSalSpThw4AAF\nCxY0Oo6IiKRTKn6KpCBbtmycPXsWLy8vo6PICy48PJzly5djs9k4evQojRs3xmq1EhAQkK5nVR4/\nfpwaNWpw5MgR8uTJY3QcERERcTADBgzgypUrzJgxw+goIiKSTqn4KZKCvHnzcuDAAfLly2d0FHEg\nFy5cYOnSpdhsNk6fPk2TJk2wWq288cYbODk5GR3vPv369ePy5cvMnTvX6CgiIiLiYK5du4avry+7\ndu3Cx8fH6DgiIpIOqfgpkoLChQuzZcsWChcubHQUcVChoaHJhdDz58/TrFkzrFYrr732GhaLxeh4\nwD8n25csWZKlS5dStWpVo+OIiIiIgxkxYgSnTp1i/vz5RkcREZF0SMVPkRSULFmS5cuXU6pUKaOj\niHD69GmWLFnCkiVLuHTpEs2bN8dqtVK1alXMZrOh2RYuXMjEiRPZs2dPuinKioiIiGO4desWPj4+\nbN26Ve/bRUTkPsZ+WhZJ51xdXYmNjTU6hggAPj4+DBgwgIMHD7JlyxZy5sxJ586dKVSoEH369GH3\n7t0Y9X1W69atcXd3Z9asWYZcX0RERBxXlixZ6Nu3L0OHDjU6ioiIpEOa+SmSgmrVqjF+/HiqVatm\ndBSRhzp69Cg2mw2bzUZ8fDwtW7bEarVSrlw5TCbTc8tx6NAh3nrrLUJCQsiRI8dzu66IiIhIdHQ0\nPj4+rFu3jnLlyhkdR0RE0hHN/BRJgaurKzExMUbHEElR6dKlGTFiBMePH2flypWYzWZatGiBr68v\nAwcO5PDhw89lRugrr7xCy5YtGTRoUJpfS0REROS/ubu7M2DAAAYPHmx0FBERSWdU/BRJgZa9S0Zi\nMpkoW7YsX331FadPn2bRokXEx8fTsGFDSpUqxbBhwwgJCUnTDCNGjGDlypXs378/Ta8jIiIi8r8+\n/PBD/u///o+dO3caHUVERNIRFT9FUuDm5qbip2RIJpOJihUrMm7cOEJDQ5k7dy43b97krbfews/P\nj1GjRnHq1KlUv66Xlxdffvkl3bp1IykpKdXHFxEREXmYTJkyMXjwYK1CERGRe6j4KZICLXuXF4HJ\nZKJKlSpMmjSJsLAwvv32WyIjI6lZsybly5dnzJgx/PXXX6l2vQ4dOpCQkMD8+fNTbUwRERGRx9G+\nfXvCwsLYsmWL0VFERCSdUPFTJAVa9i4vGrPZTI0aNZg2bRoXLlxgwoQJhIaGUqVKFSpVqsT48eMJ\nCwt75mt88803fP7551y7do3169cTENCYfPl8yZo1L3nyFKVy5TrJy/JFREREUouzszPDhg1j8ODB\nz2XPcxERSf902rtICrp160aJEiXo1q2b0VFE0lRCQgK//fYbNpuNlStXUrx4caxWKy1atOCll156\n4vHsdjvVq9fk4METWCwFuHOnC/AakBmIAg6SOfP3mExH6dGjC0OHDsDJySmV70pEREQcUWJiIv7+\n/owfP566desaHUdERAymmZ8iKdCyd3EUTk5O1KlTh1mzZhEeHs6gQYPYv38/pUuX5vXXX+ebb74h\nMjLyscZKTEzk/fc/5tCh28TErOHOnX1AJ6A48BJQDGjB7dtB3Lr1GxMnbqdOncZER0en3Q2KiIiI\nw7BYLIwcOZJBgwZp9qeIiGjmp0hKNm3ahJubGzVr1jQ6iogh4uLi2LRpEzabjXXr1lGhQgWsVitN\nmzYlZ86cD+zTpcun/PjjfqKj1/LPTM9HuYura3tq1Ihmw4blWCyWVL0HERERcTx2u50KFSowaNAg\nmjZtanQcERExkIqfIin496+HyWQyOImI8WJiYtiwYQM2m42NGzdSpUoVrFYrTZo0wcvLC4CgoCAa\nNepMdPQ+wOsJRo/H3b02Eye246OPOqdJfhEREXEs69evp1+/fhw6dEhfroqIODAVP0VE5IlFRUWx\ndu1abDYbmzdvpkaNGlitVubNW8Zvv9UDPn6KUTdTuHAfzpw5qC8cRERE5JnZ7XZee+01unTpwnvv\nvWd0HBERMYiKnyIi8kxu377N6tWrmTdvHps37wAieLzl7v8rCQ+PkmzaNIfq1aunckoRERFxRL/9\n9hudO3cmJCQEZ2dno+OIiIgBdOCRiIg8k8yZM/Pee+9Rt25dXFxa83SFTwAz0dGdmD17YWrGExER\nEQdWq1YtChYsyE8//WR0FBERMYiKnyIikirCwsKJjy/2TGPY7T6EhoanUiIRERERGDVqFCNGjCAu\nLs7oKCIiYgAVP0Wewd27d0lISDA6hki6EB0dC2R6xlEy8ddfZ1m4cCFBQUEcOXKEK1eukJSUlBoR\nRURExAFVrVoVPz8/Zs6caXQUERExgJPRAUTSs02bNlGlShWyZs2a/Nx/nwA/b948kpKS+Oijj4yK\nKJJu5M7tBVx7xlGuYzIlsXbtWiIiIoiMjCQiIoI7d+6QK1cu8uTJQ968eVP83cvLSwcmiYiIyD1G\njBhBgwYN6NixI+7u7kbHERGR50gHHomkwGw2ExwcTNWqVR/4+syZM5kxYwbbt28nU6ZnnfEmkrGt\nX7+eVq2Gcvv23qcew939XUaPrkrPnj3ueT4+Pp5Lly7dUxB92O/R0dHkyZPnsQqlWbNmzfCFUrvd\nzsyZM9m2bRuurq4EBATQqlWrDH9fIiIiqa158+ZUqVKFzz77zOgoIiLyHKn4KZICDw8PFi1aRJUq\nVYiJiSE2NpaYmBhiYmKIi4tj9+7dfPHFF1y9ehUvLy+j44oYKjExkXz5fLh8eQnw6lOMEIGra0ki\nIkLvmW39pGJjY4mMjHxkkTQyMpL4+PjHKpLmzZsXT0/PdFdQjIqKokePHuzcuZPGjRsTERHByZMn\nadWqFd27dwfg6NGjjBw5kl27dmGxWGjXrh1Dhw41OLmIiMjzFxISQq1atTh16hRZsmQxOo6IiDwn\nKn6KpCBfvnxERkbi5uYG/LPU3Ww2Y7FYsFgseHh4AHDw4EEVP0WA0aPHMmrUUWJinvxEVYtlBK1b\nX+Cnn2akQbIHi46OfqxCaUREBHa7/b6i6MMKpf/+vyGtBQcHU7duXebOnUuzZs0A+O677xg6dChn\nzpzh4sWLBAQEUKlSJfr27cvJkyeZMWMGr7/+OqNHj34uGUVERNKTtm3b4uvry+DBg42OIiIiz4mK\nnyIpyJMnD23btuXNN9/EYrHg5OSEs7PzPb8nJibi7++Pk5O20BW5du0aJUqU58qVUdjtbZ6g5+94\nerbgzz+34+vrm2b5nsWdO3ceazZpREQEFovlsWaT5smTJ/nLlafx448/MmDAAE6fPo2LiwsWi4Vz\n587RoEEDevTogdlsZtiwYRw/fjy5IDtnzhyGDx/O/v37yZEjR2r9eERERDKE06dPU6VKFU6ePEn2\n7NmNjiMiIs+BqjUiKbBYLFSsWJG3337b6CgiGUL27Nn57bd1VKsWwO3b8djtHR+j1ybc3duyatWi\ndFv4BPD09MTT05OiRYum2M5ut3P79u0HFkb37dt33/Ourq4pzib19fXF19f3gUvus2bNSmxsLKtX\nr8ZqtQKwYcMGjh8/zq1bt7BYLGTLlg0PDw/i4+NxcXGhePHixMXFsX37dho3bpwmPysREZH0ysfH\nh6ZNmzJ+/HitghARcRAqfoqkoEOHDnh7ez/wNbvdnu72/xNJD0qXLs2ePb9Tq1Z9bt/+D3fudAEa\nce8/OXZgCxbLRDw9/2TdupVUr17dmMCpzGQykSVLFrJkyUKxYsVSbGu327l58+YDZ4/u2rWLiIgI\nateuTe/evR/Y/+2336Zjx4706NGD2bNnkzt3bi5cuEBiYiK5cuUiX758XLhwgYULF/Lee+9xfvrx\nXQAAIABJREFU+/Ztpk2bxuXLl4mOjk6L23cYiYmJhISEcPXqVeCfwn/p0qWxWCwGJxMRkUcZNGgQ\n5cqVo1evXuTOndvoOCIiksa07F3kGVy/fp27d++SM2dOzGaz0XFE0pW4uDhWrFjBmDHfcPp0KE5O\nlUlMzILZfAe7/TA5cjhz48bfrF79MzVr1jQ6boZ18+ZN/vjjD7Zv3558KNPKlSvp3r077du3Z/Dg\nwUyYMIHExERKlixJlixZiIyMZPTo0cn7hMrju3z5MjNnzWTyN5OJSYrBktkCJki8lYgrrvTs2pPO\nH3bWh2kRkXSuR48eODk5MXHiRKOjiIhIGlPxUyQFS5cupWjRopQvX/6e55OSkjCbzSxbtoy9e/fS\nvXt3Xn75ZYNSiqR/R44cSV6K7eHhQeHChXn11VeZNm0aW7ZsYdWqVUZHfGGMGDGCNWvWMGPGDMqV\nKwfArVu3OHbsGPny5WPWrFls3ryZr7/+mtdee+2evomJibRv3/6he5TmzJnTYWc22u12xo0fx5Dh\nQzCXNBNTLgby/0+ji+B6wBV7iJ0hg4bwRf8vtEJARCSdioiIoHTp0hw6dEjv40VEXnAqfoqkoEKF\nCjRs2JBhw4Y98PVdu3bRrVs3xo8fzxtvvPFcs4mIHDhwgISEhOQi5/Lly+natSt9+/alb9++ydtz\n/PfM9Bo1alCoUCGmTZuGl5fXPeMlJiaycOFCIiMjH7hn6fXr18mRI0eKBzj9++ccOXK8UDPie/Xp\nxUzbTKJbREO2RzS+Ce5L3Xm/yftMnzJdBVARkXSqf//+3Lp1i++++87oKCIikoa056dICrJly8aF\nCxc4fvw4UVFRxMTEEBMTQ3R0NPHx8fz9998cPHiQ8PBwo6OKiAOKjIxk8ODB3Lp1i1y5cnHjxg3a\ntm1Lt27dMJvNLF++HLPZzKuvvkpMTAxffPEFp0+fZty4cfcVPuGfQ97atWv30OslJCRw+fLl+4qi\nFy5c4M8//7zn+X8zPc6J99mzZ0/XBcIp06Ywc/FMottEg/tjdMgK0W2imTd/HoULFeazPp+leUYR\nEXly/fr1o3jx4vTr14/ChQsbHUdERNKIZn6KpKBdu3YsWLAAFxcXkpKSsFgsODk54eTkhLOzM5kz\nZ+bu3bvMmTOHN9980+i4IuJg4uLiOHnyJCdOnODq1av4+PgQEBCQ/LrNZmPo0KGcPXuWnDlzUrFi\nRfr27Xvfcve0EB8fz6VLlx44g/R/n4uKiiJ37tyPLJLmzZuXrFmzPtdCaVRUFLlfyk10+2jI8YSd\nr4HbXDci/44kc+bMaZJPRESezbBhwwgNDWXevHlGRxERkTSi4qdIClq2bEl0dDTjxo3DYrHcU/x0\ncnLCbDaTmJiIl5cXmTJlMjquiEjyUvf/Fhsby7Vr13B1dSV79uwGJXu42NjYhxZK//f3uLi45OX1\njyqUZs6c+ZkLpbNnz6bn5J5ENY96qv4eKzwY9/E4Pvnkk2fKISIiaePmzZv4+Pjwxx9/UKJECaPj\niIhIGlDxUyQF7du3B+DHH380OIlIxlGrVi38/PyYOnUqAIULF6Z79+707t37oX0ep40IQExMzGMV\nSSMjI0lISHis2aR58uTB09PzvmvZ7XaK+xXnVNlTUOwpA58B793e/HX8r3S9tF9ExJGNGTOGgwcP\nsnjxYqOjiIhIGtCenyIpaN26NXFxccmP/3tGVWJiIgBms1kfaMWhXLlyhSFDhrBhwwbCw8PJli0b\nfn5+fP755wQEBLBy5UqcnZ2faMx9+/bh4eGRRonlReLm5oa3tzfe3t6PbBsVFfXAwujhw4f59ddf\n73nebDbfN5s0W7Zs/HXqL2j2DIELw8UVF7l69So5c+Z8hoFERCStdO/eHR8fHw4fPoy/v7/RcURE\nJJWp+CmSgsDAwHse/3eR02KxPO84IulC06ZNiY2NZe7cuRQtWpRLly7x+++/c/XqVeCfg8KeVI4c\nT7qZosijeXh4UKRIEYoUKZJiO7vdzp07d+4rkh47dgyTqwme5dB6M7hkduH69esqfoqIpFMeHh58\n/vnnDB48mJ9//tnoOCIiksq07F3kERITEzl27BinT5/G29ubsmXLEhsby/79+4mOjqZMmTLkzZvX\n6Jgiz8XNmzfx8vJi8+bN1K5d+4FtHrTs/f333+f06dOsWrUKT09PPvvsM/r06ZPc53+XvZvNZpYt\nW0bTpk0f2kYkrZ0/f54S5UoQ3T36mcbx+MaD/9v9fzpJWEQkHYuNjaVYsWIsX76cSpUqGR1HRERS\n0bPMZRBxCGPHjsXf359WrVrRsGFD5s6di81mo379+rRo0YLPP/+cyMhIo2OKPBeenp54enqyevXq\ne7aEeJRJkyZRunRpDhw4wIgRIxgwYACrVq1Kw6Qizy5HjhzE34mH+GcY5C7E347X7GYRkXTO1dWV\nQYMGMXjwYA4cOEDnzp0pX748RYsWpXTp0gQGBrJgwYInev8jIiLpg4qfIinYtm0bCxcuZMyYMcTG\nxjJ58mQmTJjAzJkzmT59Oj/++CPHjh3jhx9+MDqqyHNhsVj48ccfWbBgAdmyZaNatWr07duXPXv2\npNivcuXKfP755/j4+PDhhx/Srl07Jk6c+JxSizwdd3d3Xnv9NTj6DIOEwKtVXyVLliyplktERNJG\nvnz5+PPPP2nYsCHe3t7MmDGDTZs2YbPZ+PDDD5k/fz4FCxZk4MCBxMbGGh1XREQek4qfIim4cOEC\nWbJkSV6e26xZMwIDA3FxceG9996jUaNGvPPOO+zevdvgpCLPT5MmTbh48SJr166lXr167Ny5kypV\nqjBmzJiH9qlatep9j0NCQtI6qsgz69erH5kPZ37q/pkPZ6Z/r/6pmEhERNLC5MmT6dKlC7NmzeLc\nuXMMGDCAihUr4uPjQ5kyZWjevDmbNm1i+/btnDhxgjp16nDt2jWjY4uIyGNQ8VMkBU5OTkRHR99z\nuJGzszN37txJfhwfH098/LOsiRTJeFxcXAgICGDQoEFs376dTp06MWzYMBISElJlfJPJxP9uSX33\n7t1UGVvkSQQGBuKe4A6nnqLzGXCJcqF+/fqpnktERFLPrFmzmD59Ojt27OCdd95J8WDTYsWKsWTJ\nEsqVK0fjxo01A1REJANQ8VMkBQUKFABg4cKFAOzatYudO3disViYNWsWy5cvZ8OGDdSqVcvImCKG\nK1myJAkJCQ/9ALBr1657Hu/cuZOSJUs+dLxcuXIRHh6e/DgyMvKexyLPi9lsxjbfhttaN3iS/wQj\nwW2NG7YFthQ/RIuIiLHOnj3L559/zvr16ylYsOBj9TGbzUyePJlcuXLx5ZdfpnFCERF5Vk5GBxBJ\nz8qWLUv9+vXp0KED8+bNIzQ0lLJly/Lhhx/y7rvv4urqyquvvsqHH35odFSR5+LatWu0aNGCjh07\n4u/vT+bMmdm7dy/jxo3jzTffxNPT84H9du3axdixY2nWrBm//fYbCxYs4D//+c9Dr1O7dm2++eYb\nqlatitlsZuDAgbi5uaXVbYmk6PXXX2f+7Pm069SO6MBoKMHDvz5OAk5CpvWZmDNjDgEBAc8xqYiI\nPKkffviB9u3b4+vr+0T9zGYzo0eP5o033mDw4MG4uLikUUIREXlWKn6KpMDNzY3hw4dTuXJlgoKC\naNy4MR9//DFOTk4cOnSIU6dOUbVqVVxdXY2OKvJceHp6UrVqVaZOncrp06eJi4sjf/78tGnThoED\nBwL/LFn/byaTid69e3P48GFGjRqFp6cnI0eOpEmTJve0+W8TJkzggw8+oFatWuTJk4evv/6a48eP\np/0NijxEs2bNyJMnDx0+6kD4tnCiX4nGXsYOHv+/QTSYjphwP+SOp5MnFk8LDeo3MDSziIikLC4u\njrlz57J9+/an6l+iRAlKly7NihUraNWqVSqnExGR1GKy/++maiIiIiLyQHa7nd27dzN+ynjWr1tP\nbNQ/Wz24urvydr23+aznZ1StWpUOHTrg6urK999/b3BiERF5mNWrVzN58mS2bNny1GMsXryY+fPn\ns27dulRMJiIiqUkzP0Ue07/fE/z3DDW73X7fjDUREXlxmUwmqlSpwrIqywCSD/lycrr3LdWUKVN4\n5ZVXWLdunQ48EhFJp/7+++8nXu7+v3x9fbl48WIqJRIRkbSg4qfIY3pQkVOFTxERx/a/Rc9/Zc2a\nldDQ0OcbRkREnkhsbOwzb1/l6upKTExMKiUSEZG0oNPeRURERERExOFkzZqV69evP9MYN27cIFu2\nbKmUSERE0oKKnyIiIiIiIuJwXn31VYKCgrh79+5Tj7Fx40YqVqyYiqlERCS1qfgp8ggJCQlayiIi\nIiIi8oLx8/OjcOHCrFmz5qn6x8fHM3PmTD755JNUTiYiIqlJxU+RR1i3bh2tWrUyOoaIiIiIiKSy\nLl26MH369OTDTZ/EypUrKV68OKVLl06DZCIiklpU/BR5BG1iLpI+hIaGkiNHDq5du2Z0FMkAOnTo\ngNlsxmKxYDabk/98+PBho6OJiEg60qxZM65cucLEiROfqN+ZM2fo1asXgwcPTqNkIiKSWlT8FHkE\nV1dXYmNjjY4h4vC8vb155513mDJlitFRJIOoU6cOERERyb/Cw8MpU6aMYXmeZU85ERFJGy4uLqxb\nt46pU6cybty4x5oBevToUQICAhg6dCgBAQHPIaWIiDwLFT9FHsHNzU3FT5F0YsCAAXzzzTfcuHHD\n6CiSAWTKlIlcuXKRO3fu5F9ms5kNGzZQo0YNvLy8yJEjB/Xq1ePkyZP39N2xYwflypXDzc2NypUr\ns3HjRsxmMzt27AD+2Q+6U6dOFClSBHd3d4oXL86ECRPuGaNt27Y0adKEr776ipdffhlvb28Afvrp\nJ1599VWyZMlC3rx5adWqFREREcn97t69S7du3XjppZdwdXWlUKFCmlkkIpKGChQowPbt25k/fz7V\nqlVjyZIlD/zC6siRI3Tt2pWaNWsyatQoPv74YwPSiojIk3IyOoBIeqdl7yLpR9GiRalfvz7Tpk1T\nMUieWnR0NJ999hl+fn5ERUUxYsQIGjVqxNGjR7FYLNy+fZtGjRrRoEEDFi1axPnz5+nVqxcmkyl5\njMTERAoVKsSyZcvImTMnu3btonPnzuTOnZu2bdsmtwsKCiJr1qz8+uuvybOJEhISGDVqFMWLF+fy\n5cv069eP1q1bs2XLFgAmTpzIunXrWLZsGQUKFODChQucOnXq+f6QREQcTIECBQgKCqJo0aJMnDiR\nXr16UatWLbJmzUpsbCwnTpzg7NmzdO7cmcOHD5M/f36jI4uIyGMy2Z9mZ2cRB3Ly5Enq16+vD54i\n6cSJEydo2bIl+/btw9nZ2eg4kk516NCBBQsW4OrqmvxczZo1Wbdu3X1tb926hZeXFzt37qRSpUp8\n8803DB8+nAsXLuDi4gLA/Pnzef/99/njjz+oVq3aA6/Zt29fjh49yvr164F/Zn4GBQURFhaGk9PD\nv28+cuQI/v7+REREkDt3brp27cqZM2fYuHHjs/wIRETkCY0cOZJTp07x008/ERISwv79+7lx4wZu\nbm689NJLvPnmm3rvISKSAWnmp8gjaNm7SPpSvHhxDh48aHQMyQBef/11Zs6cmTzj0s3NDYDTp08z\nZMgQdu/ezZUrV0hKSgIgLCyMSpUqceLECfz9/ZMLnwCVK1e+bx+4b775hnnz5nHu3DliYmK4e/cu\nPj4+97Tx8/O7r/C5b98+Ro4cyaFDh7h27RpJSUmYTCbCwsLInTs3HTp0IDAwkOLFixMYGEi9evUI\nDAy8Z+apiIikvv9eVVKqVClKlSplYBoREUkt2vNT5BG07F0k/TGZTCoEySO5u7tTuHBhihQpQpEi\nRciXLx8A9erV4/r168yaNYs9e/awf/9+TCYT8fHxjz32woUL6du3Lx988AG//PILhw4d4qOPPrpv\nDA8Pj3se37lzh7fffpusWbOycOFC9u3blzxT9N++FStW5Ny5c3z55ZckJCTQpk0b6tWr9yw/ChER\nERERh6WZnyKPoNPeRTKepKQkzGZ9vyf3u3TpEqdPn2bu3LlUr14dgD179iTP/gQoUaIENpuNu3fv\nJi9v3L179z0F9+DgYKpXr85HH32U/NzjbI8SEhLC9evX+eqrr5L3i3vQTGZPT0+aN29O8+bNadOm\nDa+99hqhoaHJhyaJiIiIiMjj0SdDkUfQsneRjCMpKYlly5ZhtVrp378/O3fuNDqSpDM5c+Yke/bs\nzJgxgzNnzrB161a6deuGxWJJbtO2bVsSExP58MMPOX78OL/++itjx44FSC6A+vr6sm/fPn755RdO\nnz7N8OHDk0+CT4m3tzcuLi5MnTqV0NBQ1q5dy7Bhw+5pM2HCBGw2GydOnODUqVP85z//IVu2bLz0\n0kup94MQEREREXEQKn6KPMK/e7XdvXvX4CQi8jD/Lhfev38//fr1w2KxsHfvXjp16sTNmzcNTifp\nidlsZsmSJezfvx8/Pz969uzJmDFj7jnAInPmzKxdu5bDhw9Trlw5vvjiC4YPH47dbk8+QKlLly40\nbdqUVq1aUblyZS5evMinn376yOvnzp2befPmsXz5ckqVKsXo0aOZNGnSPW08PT0ZO3Ysr776KpUq\nVSIkJIRNmzbdswepiIgYJzExEbPZzOrVq9O0j4iIpA6d9i7yGDw9PQkPDydz5sxGRxGR/xIdHc2g\nQYPYsGEDRYsWpUyZMoSHhzNv3jwAAgMD8fHx4dtvvzU2qGR4y5cvp1WrVly5coWsWbMaHUdERB6i\ncePGREVFsXnz5vteO3bsGKVLl+aXX37hzTfffOprJCYm4uzszKpVq2jUqNFj97t06RJeXl46MV5E\n5DnTzE+Rx6Cl7yLpj91up1WrVuzZs4fRo0dTvnx5NmzYQExMTPKBSD179uSPP/4gLi7O6LiSwcyb\nN4/g4GDOnTvHmjVr6NOnD02aNFHhU0QknevUqRNbt24lLCzsvtdmz56Nt7f3MxU+n0Xu3LlV+BQR\nMYCKnyKPQSe+i6Q/J0+e5NSpU7Rp04YmTZowYsQIJk6cyPLlywkNDSUqKorVq1eTK1cu/f2VJxYR\nEcF7771HiRIl6NmzJ40bN06eUSwiIulX/fr1yZ07N3Pnzr3n+f/H3r3HxZT/fwB/zRRdJXJZad1K\nKKLIvc39vsvii+iicguFXdcoikQIaxffKFHWumRbrG/4srLrGsJGqUSRiEgSaZrz+2O/5ifXojrN\n9Ho+Hvt47Jw558xreuRM8z7vz+cjk8kQHh4OV1dXAMCsWbPQrFkzaGtro0mTJpg3b16Raa7S0tIw\nePBgGBgYQEdHB+bm5oiIiHjna964cQNSqRRXrlxRbHtzmDuHvRMRiYervRMVA1d8J6p4dHV18fz5\nc9jY2Ci2WVtbo2nTphg/fjzu3r0LdXV12NvbQ19fX8SkpIzmzp2LuXPnih2DiIhKSE1NDU5OTggN\nDcXChQsV2/ft24esrCw4OzsDAKpXr45t27ahXr16uHr1KiZOnAhtbW14eXkBACZOnAiJRIITJ05A\nV1cXCQkJRRbHe9OrBfGIiKjiYecnUTFw2DtRxVO/fn2YmZlh9erVKCwsBPDPF5unT5/Cz88PHh4e\ncHFxgYuLC4B/VoInIiIi1efq6orU1NQi836GhISgT58+MDQ0BAAsWLAAHTp0QIMGDdC/f3/MmTMH\nO3bsUOyflpYGGxsbmJubo2HDhujbt+8Hh8tzKQ0iooqLnZ9ExcBh70QV08qVKzF8+HD06NEDbdq0\nwcmTJ/HNN9+gffv2aN++vWK//Px8aGhoiJiUiIiIyouJiQlsbW0REhKCXr164e7duzh06BB27dql\n2Gfnzp1Yt24dbty4gdzcXMhksiKdndOmTcPUqVNx4MAB9OzZE0OHDkWbNm3EeDtERPSZ2PlJVAzs\n/CSqmMzMzLBu3Tq0bNkSV65cQZs2beDj4wMAePjwIfbv34+RI0fCxcUFq1evRnx8vMiJiYiIqDy4\nuroiMjIS2dnZCA0NhYGBgWJl9r/++gv29vYYNGgQDhw4gEuXLsHX1xcvX75UHD9hwgTcvHkTY8eO\nxfXr19GxY0csXbr0na8llf7ztfr17s/X5w8lIiJxsfhJVAyc85Oo4urZsyd++uknHDhwAJs3b0ad\nOnUQEhKCr776CkOHDsXjx49RUFCALVu2YNSoUZDJZGJHJvqoBw8ewNDQECdOnBA7ChGRUho+fDg0\nNTURFhaGLVu2wMnJSdHZeerUKTRq1Ahz585F27ZtYWxsjJs3b751jvr162P8+PHYuXMnvL29ERQU\n9M7Xql27NgAgIyNDsS02NrYM3hUREX0KFj+JioHD3okqtsLCQujo6ODOnTvo1asXJk2ahK+++grX\nr1/Hf/7zH+zcuRPnzp2DhoYGlixZInZcoo+qXbs2goKC4OTkhJycHLHjEBEpHU1NTdjZ2WHRokVI\nSUlRzAEOAKampkhLS8Mvv/yClJQU/Pjjj9i9e3eR4z08PHD48GHcvHkTsbGxOHToEMzNzd/5Wrq6\numjXrh2WLVuG+Ph4/PXXX5gzZw4XQSIiqiBY/CQqBg57J6rYXnVy/PDDD3j48CH++9//YuPGjWjS\npAmAf1Zg1dTURNu2bXH9+nUxoxIV26BBg9C7d2/MmDFD7ChEREpp3LhxyM7ORpcuXdCsWTPF9iFD\nhmDGjBmYNm0aLC0tceLECfj6+hY5trCwEFOnToW5uTn69++PL7/8EiEhIYrn3yxsbt26FTKZDNbW\n1pg6dSr8/PzeysNiKBGROCQCl6Uj+qixY8eiW7duGDt2rNhRiOg90tPT0atXL4wePRpeXl6K1d1f\nzcP19OlTtGjRAnPmzIG7u7uYUYmKLTc3F61bt0ZgYCAGDx4sdhwiIiIiIqXDzk+iYuCwd6KKLz8/\nH7m5ubCzswPwT9FTKpUiLy8Pu3btQo8ePVCnTh2MGjVK5KRExaerq4tt27Zh0qRJuH//vthxiIiI\niIiUDoufRMXAYe9EFV+TJk1Qv359+Pr6IikpCc+fP0dYWBg8PDywatUqGBkZYe3atYpFCYiURZcu\nXeDs7Izx48eDA3aIiIiIiEqGxU+iYuBq70TKYcOGDUhLS0OHDh1Qq1YtBAYG4saNGxgwYADWrl0L\nGxsbsSMSfZJFixbh9u3bReabIyIiIiKij1MXOwCRMuCwdyLlYGlpiYMHD+Lo0aPQ0NBAYWEhWrdu\nDUNDQ7GjEX2WqlWrIiwsDN27d0f37t0Vi3kREREREdGHsfhJVAxaWlp4+PCh2DGIqBi0tbXx9ddf\nix2DqNS1bNkS8+bNg6OjI6Kjo6GmpiZ2JCIiIiKiCo/D3omKgcPeiYioIpg+fTqqVq2KFStWiB2F\niIiIiEgpsPhJVAwc9k5ERBWBVCpFaGgoAgMDcenSJbHjEBFVaA8ePICBgQHS0tLEjkJERCJi8ZOo\nGLjaO5FyEwSBq2STymjQoAFWrlwJBwcHfjYREX3AypUrMXLkSDRo0EDsKEREJCIWP4mKgcPeiZSX\nIAjYvXs3oqKixI5CVGocHBzQrFkzLFiwQOwoREQV0oMHD7Bp0ybMmzdP7ChERCQyFj+JioHD3omU\nl0QigUQiwaJFi9j9SSpDIpFg48aN2LFjB44fPy52HCKiCmfFihUYNWoUvvzyS7GjEBGRyFj8JCoG\nDnsnUm7Dhg1Dbm4uDh8+LHYUolJTq1YtbNq0CWPHjsWTJ0/EjkNEVGFkZmZi8+bN7PokIiIALH4S\nFQs7P4mUm1QqxYIFC+Dj48PuT1IpAwYMQL9+/TBt2jSxoxARVRgrVqyAnZ0duz6JiAgAi59ExcI5\nP4mU34gRI5CVlYVjx46JHYWoVK1cuRInT57E3r17xY5CRCS6zMxMBAcHs+uTiIgUWPwkKgYOeydS\nfmpqaliwYAF8fX3FjkJUqnR1dREWFobJkyfj3r17YschIhJVQEAARo8eDSMjI7GjEBFRBcHiJ1Ex\ncNg7kWqws7NDeno6oqOjxY5CVKo6duyI8ePHY9y4cZzagYgqrfv37yMkJIRdn0REVASLn0TFwGHv\nRKpBXV0d8+fPZ/cnqSRvb29kZGRg06ZNYkchIhJFQEAAxowZg/r164sdhYiIKhCJwPYAoo969OgR\nTExM8OjRI7GjENFnKigogKmpKcLCwtC1a1ex4xCVqmvXruGrr77CmTNnYGJiInYcIqJyc+/ePZiZ\nmeHvv/9m8ZOIiIpg5ydRMXDYO5HqqFKlCjw9PbF48WKxoxCVOjMzM3h5ecHR0REymUzsOERE5SYg\nIAD29vYsfBIR0VvY+UlUDHK5HOrq6igsLIREIhE7DhF9ppcvX6Jp06bYuXMnOnbsKHYcolIll8vR\np08f9OjRA56enmLHISIqc6+6PuPi4mBoaCh2HCIiqmBY/CQqJg0NDeTk5EBDQ0PsKERUCjZs2IAD\nBw7g999/FzsKUam7ffs22rZti6ioKFhZWYkdh4ioTH333XcoLCzE2rVrxY5CREQVEIufRMVUvXp1\npKamQl9fX+woRFQK8vPzYWxsjMjISLRr107sOESlbvv27Vi6dCnOnz8PLS0tseMQEZWJjIwMmJub\n4+rVq6hXr57YcYiIqALinJ9ExcQV34lUi4aGBubMmcO5P0lljR49Gi1btuTQdyJSaQEBAXB0dGTh\nk4iI3oudn0TF1KhRIxw/fhyNGjUSOwoRlZLnz5/D2NgYv//+OywtLcWOQ1TqHj16BAsLC2zbtg09\nevQQOw4RUali1ycRERUHOz+JiokrvhOpHi0tLcyaNQtLliwROwpRmahZsyY2b94MZ2dnZGdnix2H\niKhULV++HE5OTix8EhHRB7Hzk6iY2rRpgy1btrA7jEjF5OXloUmTJjhy5AhatWoldhyiMjFlyhTk\n5OQgLCxM7ChERKXi7t27aNmyJa5du4YvvvhC7DhERFSBsfOTqJi0tLQ45yeRCtLW1sYIMrUwAAAg\nAElEQVT333/P7k9SaQEBATh79ix2794tdhQiolKxfPlyjB07loVPIiL6KHWxAxApCw57J1Jdbm5u\nMDY2xrVr12BmZiZ2HKJSp6Ojg7CwMHzzzTfo2rUrh4gSkVJLT09HWFgYrl27JnYUIiJSAuz8JCom\nrvZOpLp0dXUxY8YMdn+SSuvQoQMmTZoEFxcXcNYjIlJmy5cvh7OzM7s+iYioWFj8JComDnsnUm1T\npkzBkSNHkJCQIHYUojKzYMECPHz4EBs3bhQ7ChHRJ0lPT0d4eDhmz54tdhQiIlISLH4SFROHvROp\ntmrVqmHatGlYunSp2FGIykyVKlUQFhYGb29vJCUliR2HiKjEli1bBhcXF9StW1fsKEREpCQ45ydR\nMXHYO5Hqc3d3h7GxMZKTk2FiYiJ2HKIy0bx5c3h7e8PBwQF//fUX1NX55yARKYc7d+5g+/btHKVB\nREQlws5PomLisHci1Ve9enVMnTqV3Z+k8qZMmQI9PT34+/uLHYWIqNiWLVsGV1dX1KlTR+woRESk\nRHirn6iYOOydqHKYNm0aTExMcPPmTTRu3FjsOERlQiqVYsuWLbC0tET//v3Rrl07sSMREX3Q7du3\n8fPPP7Prk4iISoydn0TFxGHvRJVDjRo14Obmxo44Unn169fHDz/8AAcHB97cI6IKb9myZRg3bhy7\nPomIqMRY/CQqJg57J6o8ZsyYgT179iA1NVXsKERlatSoUWjTpg3mzp0rdhQiove6ffs2duzYgZkz\nZ4odhYiIlBCLn0TF8OLFC7x48QJ3797F/fv3UVhYKHYkIipDBgYGmDBhApYvXw4AkMvlyMzMRFJS\nEm7fvs0uOVIpP/30E/bu3YsjR46IHYWI6J38/f0xfvx4dn0SEdEnkQiCIIgdgqiiunDhAlatWo+9\ne3dDLtcEoAE1tRfQ1KyKqVMnwM1tPAwNDcWOSURlIDMzE6amppgwwQ1btuxAbm4u1NX1IZe/gEz2\nBAMHDsbMmZPRqVMnSCQSseMSfZYjR47AxcUFV65cQY0aNcSOQ0SkkJaWBktLSyQkJKB27dpixyEi\nIiXE4ifRO6SmpuKbb0bjxo27eP58EuRyFwCv/7H1NzQ0NkAi+QXDhw/H5s3roKGhIVZcIiplMpkM\nHh6zERS0CcC3KCycBqDta3s8hkQSCm3tDTA01MX+/TvQrFkzkdISlQ4PDw88fPgQP//8s9hRiIgU\n3NzcUL16dSxbtkzsKEREpKRY/CR6w7Vr19C1a2/k5MxEYaEHALUP7J0DLS0XtGyZhePHf4e2tnZ5\nxSSiMvLy5Uv07z8MZ84UIC/vZwA1P7C3HBJJMHR1vXDs2AGumE1KLS8vD1ZWVvDx8cHIkSPFjkNE\nhNTUVFhZWeH69euoVauW2HGIiEhJsfhJ9JqMjAy0bt0JDx8uhiA4FPOoQmhqjsVXX+XiP/+JgFTK\nqXSJlJUgCBg1yhn79z/G8+d7AFQp5pG/QV/fDRcvnkTjxo3LMiJRmYqJicGgQYNw8eJF1K9fX+w4\nRFTJTZo0CTVq1IC/v7/YUYiISImx+En0mvHj3REaWhUy2aoSHvkSOjrW2LXLHwMGDCiTbERU9k6d\nOoU+fRzw7NkVADolOlYqXYwhQxIRERFWNuGIyomvry9OnjyJqKgozmdLRKJh1ycREZUWFj+J/ic3\nNxd16jTA8+dXABh9whlCYGu7F8ePHyjtaERUToYOtUdkpBUE4btPOPoRNDWNkZaWyAUZSKnJZDJ0\n6dIFjo6OmDJlithxiKiSmjhxIgwMDLB06VKxoxARkZLj+Fyi/wkP3w6ptBs+rfAJAKNw9uwZ3Lx5\ns/RCEVG5yczMxMGDByAIYz/xDDUhkXyLTZtCSjMWUblTV1dHWFgYFi5ciOvXr4sdh4gqodTUVOzZ\nswfff/+92FGIiEgFsPhJ9D87dhzAs2ejP+MM2pBIBuPgwYOllomIys9///tfVKnSAx9e4OjDnj8f\ngx079pdeKCKRmJqawtfXFw4ODigoKBA7DhFVMn5+fpg0aRIMDAzEjkJERCqAxU+i/3n4MAtAvc86\nx4sX9fDo0aPSCURE5SorKwsFBZ93DQC+wOPHvAaQanBzc0PNmjXh5+cndhQiqkRu3bqFiIgIfPfd\np0xBQ0RE9DYWP4mIiIjoLRKJBCEhIdiwYQPOnTsndhwiqiT8/Pzg5ubGrk8iIio16mIHIKooatUy\nAJDxWefQ1MxAzZpWpROIiMqVgYEBqlTJQH7+55zlHmrU+PRh80QVjaGhIdatWwcHBwfExsZCW1tb\n7EhEpMJu3ryJvXv3IikpSewoRESkQtj5SfQ/dnaDoKPz82ecIQ+C8BsGDBhQapmIqPz06tULBQXH\nAHz6sHUtre2ws/u69EIRVQAjRoyAtbU1Zs+eLXYUIlJxfn5+mDx5MmrW5I1EIiIqPRJBEASxQxBV\nBLm5uahTpwGeP7+CT1vxPQSGhgE4d+4o6tevX9rxiKgcDB1qj8hIKwjCp8wz9ghVqjTC7dtJqFu3\nbqlnIxJTdnY2LCwssGnTJvTt21fsOESkglJSUtC+fXskJiay+ElERKWKnZ9E/6Orqwt7+zFQV1/9\nCUe/hLb2GrRv3wKtWrXClClTkJaWVuoZiahszZw5GdraPwF4VuJjpdIfoaNTDQMHDsTRo0dLPxyR\niPT19bFlyxa4urpyYT8iKhPs+iQiorLC4ifRa3x956NGjQhIJNtKcFQhNDVd0bWrMSIiIpCQkIBq\n1arB0tISEyZMwM2bN8ssLxGVrk6dOmHgQBtoaY0GUFCCIyOhp7cR58+fwKxZszBhwgT069cPly9f\nLquoROWuZ8+eGD58ONzc3MCBQ0RUmlJSUvDbb79hxowZYkchIiIVxOIn0Wu++OILHD9+EPr686Cm\nFgig8CNH5EBLawRatbqDX3/dDqlUijp16mDZsmVITExE3bp10a5dOzg7O3PidiIlIJFIEBYWhM6d\nBWhrDwKQ9ZEj5JBINkFPbxKOHNkHY2NjjBw5EvHx8Rg4cCD69OkDBwcHpKamlkd8ojLn7++Pv//+\nGzt27BA7ChGpkCVLlmDKlCmoUaOG2FGIiEgFsfhJ9AYzMzPExp6CuXkEtLWNIZUuA5D5xl5/Q0PD\nDZqajTB8eC38+WfUWyvgGhgYYPHixbhx4wYaN26Mzp07w97eHvHx8eX2Xoio5KpWrYqoqL1wcjKH\npqYJtLRcAVx4Y69HkEgCoaPTDCYmG3DuXDTatWtX5Bzu7u5ISkpCo0aNYGlpie+//x5ZWR8rphJV\nbFpaWggPD8f06dNx+/ZtseMQkQq4ceMG9u3bh+nTp4sdhYiIVBSLn0Tv0LBhQ1y+fBInTkRg1Khk\naGiYQEurHnR1TaCpWRs1avTH7Nn1cONGHLZt+zc0NDTeey59fX14e3vjxo0bMDc3R7du3TBy5Ej8\n/fff5fiOiKgk1NXVsX59INLSErFggSlq1RoGDQ0D6OqaQF29NtTUjPDtt7E4cmQbrl+/gGbNmr3z\nPHp6eli8eDGuXr2KZ8+eoXnz5li+fDmeP39ezu+IqPRYWVnBw8MDzs7OkMvlYschIiW3ZMkSTJ06\nlV2fRERUZrjaO1Ex5Ofn4+HDh8jLy0P16tVhYGAANTW1TzpXbm4uNm7ciFWrVqFTp07w8vKCpaVl\nKScmotIkl8uRlZWF7Oxs7Nq1CykpKQgODi7xeRISEuDp6YmYmBj4+vrC0dHxk68lRGKSyWSwsbGB\nnZ0dPDw8xI5DREoqOTkZHTt2RHJyMvT19cWOQ0REKorFTyIiIiIqseTkZHTq1AknTpxAixYtxI5D\nREpo3bp1yMrKwqJFi8SOQkREKozFTyIiIiL6JP/+97+xadMmnD59GlWqVBE7DhEpkVdfQwVBgFTK\n2diIiKjs8FOGiIiIiD7JhAkTULduXSxevFjsKESkZCQSCSQSCQufRERU5tj5SURERESfLCMjA5aW\nloiMjETHjh3FjkNEREREVARvs5FKkUql2Lt372edY+vWrdDT0yulRERUUTRu3BiBgYFl/jq8hlBl\nU69ePfz0009wcHDAs2fPxI5DRERERFQEOz9JKUilUkgkErzr11UikcDJyQkhISHIzMxEjRo1Pmve\nsfz8fDx9+hS1atX6nMhEVI6cnZ2xdetWxfA5Q0NDDBw4EEuXLlWsHpuVlQUdHR1oamqWaRZeQ6iy\ncnJygra2NjZs2CB2FCKqYARBgEQiETsGERFVUix+klLIzMxU/P/+/fsxYcIE3Lt3T1EM1dLSQrVq\n1cSKV+oKCgq4cARRCTg7O+Pu3bsIDw9HQUEBrl27BhcXF9jY2GD79u1ixytV/AJJFdWTJ09gYWGB\njRs3on///mLHIaIKSC6Xc45PIiIqd/zkIaVQp04dxX+vurhq166t2Paq8Pn6sPfU1FRIpVLs3LkT\n3bp1g7a2NqysrPD333/j6tWr6NKlC3R1dWFjY4PU1FTFa23durVIIfXOnTsYMmQIDAwMoKOjAzMz\nM+zatUvxfFxcHHr37g1tbW0YGBjA2dkZOTk5iufPnz+Pvn37onbt2qhevTpsbGxw5syZIu9PKpVi\n/fr1GDZsGHR1dTF//nzI5XKMGzcOTZo0gba2NkxNTbFixYrS/+ESqQgNDQ3Url0bhoaG6NWrF0aM\nGIHDhw8rnn9z2LtUKsXGjRsxZMgQ6OjooFmzZjh+/DjS09PRr18/6OrqwtLSErGxsYpjXl0fjh07\nhlatWkFXVxc9evT44DUEAA4ePIiOHTtCW1sbtWrVwuDBg/Hy5ct35gKA7t27w8PD453vs2PHjoiO\njv70HxRRGalevTpCQ0Mxbtw4PHz4UOw4RCSywsJCnD17FlOmTIGnpyeePn3KwicREYmCnz6k8hYt\nWoR58+bh0qVL0NfXh52dHTw8PODv74+YmBi8ePHirSLD611Vbm5ueP78OaKjo3Ht2jWsWbNGUYDN\ny8tD3759oaenh/PnzyMyMhKnTp2Cq6ur4vinT5/C0dERJ0+eRExMDCwtLTFw4EA8fvy4yGv6+vpi\n4MCBiIuLw5QpUyCXy2FkZIQ9e/YgISEBS5cuhb+/P7Zs2fLO9xkeHg6ZTFZaPzYipZaSkoKoqKiP\ndlD7+flh9OjRuHLlCqytrTFq1CiMGzcOU6ZMwaVLl2BoaAhnZ+cix+Tn52PZsmUIDQ3FmTNnkJ2d\njUmTJhXZ5/VrSFRUFAYPHoy+ffvi4sWLOHHiBLp37w65XP5J783d3R1OTk4YNGgQ4uLiPukcRGWl\ne/fuGDVqFNzc3N45VQ0RVR6rVq3C+PHjce7cOURERKBp06Y4ffq02LGIiKgyEoiUzJ49ewSpVPrO\n5yQSiRARESEIgiDcunVLkEgkwqZNmxTPHzhwQJBIJEJkZKRiW2hoqFCtWrX3PrawsBB8fX3f+XpB\nQUGCvr6+8OzZM8W248ePCxKJRLhx48Y7j5HL5UK9evWE7du3F8k9bdq0D71tQRAEYe7cuULv3r3f\n+ZyNjY1gYmIihISECC9fvvzouYhUydixYwV1dXVBV1dX0NLSEiQSiSCVSoW1a9cq9mnUqJGwatUq\nxWOJRCLMnz9f8TguLk6QSCTCmjVrFNuOHz8uSKVSISsrSxCEf64PUqlUSEpKUuyzfft2QVNTU/H4\nzWtIly5dhNGjR783+5u5BEEQunXrJri7u7/3mBcvXgiBgYFC7dq1BWdnZ+H27dvv3ZeovD1//lww\nNzcXwsLCxI5CRCLJyckRqlWrJuzfv1/IysoSsrKyhB49egiTJ08WBEEQCgoKRE5IRESVCTs/SeW1\natVK8f9169aFRCJBy5Yti2x79uwZXrx48c7jp02bhsWLF6Nz587w8vLCxYsXFc8lJCTAwsIC2tra\nim2dO3eGVCrFtWvXAAAPHjzAxIkT0axZM+jr60NPTw8PHjxAWlpakddp27btW6+9ceNGWFtbK4b2\nr169+q3jXjlx4gQ2b96M8PBwmJqaIigoSDGslqgysLW1xZUrVxATEwMPDw8MGDAA7u7uHzzmzesD\ngLeuD0DReYc1NDRgYmKieGxoaIiXL18iOzv7na8RGxuLHj16lPwNfYCGhgZmzJiBxMRE1K1bFxYW\nFpgzZ857MxCVJ01NTYSFheG7775772cWEam21atXo0OHDhg0aBBq1qyJmjVrYu7cudi3bx8ePnwI\ndXV1AP9MFfP639ZERERlgcVPUnmvD3t9NRT1XdveNwTVxcUFt27dgouLC5KSktC5c2f4+vp+9HVf\nndfR0REXLlzA2rVrcfr0aVy+fBn169d/qzCpo6NT5PHOnTsxY8YMuLi44PDhw7h8+TImT578wYKm\nra0tjh49ivDwcOzduxcmJib46aef3lvYfR+ZTIbLly/jyZMnJTqOSEza2tpo3LgxzM3NsWbNGjx7\n9uyj/1aLc30QBKHI9eHVF7Y3j/vUYexSqfSt4cEFBQXFOlZfXx/+/v64cuUKHj58CFNTU6xatarE\n/+aJSpulpSVmzJiBsWPHfvK/DSJSToWFhUhNTYWpqaliSqbCwkJ07doV1atXx+7duwEAd+/ehbOz\nMxfxIyKiMsfiJ1ExGBoaYty4cfjll1/g6+uLoKAgAECLFi3w999/49mzZ4p9T548CUEQYGZmpnjs\n7u6Ofv36oUWLFtDR0UFGRsZHX/PkyZPo2LEj3Nzc0KZNGzRp0gTJycnFytulSxdERUVhz549iIqK\ngrGxMdasWYO8vLxiHX/16lUEBASga9euGDduHLKysop1HFFFsnDhQixfvhz37t37rPN87pcyS0tL\nHD169L3P165du8g14cWLF0hISCjRaxgZGSE4OBh//PEHoqOj0bx5c4SFhbHoRKKaPXs28vPzsXbt\nWrGjEFE5UlNTw4gRI9CsWTPFDUM1NTVoaWmhW7duOHjwIABgwYIFsLW1haWlpZhxiYioEmDxkyqd\nNzusPmb69Ok4dOgQbt68iUuXLiEqKgrm5uYAgDFjxkBbWxuOjo6Ii4vDiRMnMGnSJAwbNgyNGzcG\nAJiamiI8PBzx8fGIiYmBnZ0dNDQ0Pvq6pqamuHjxIqKiopCcnIzFixfjxIkTJcrevn177N+/H/v3\n78eJEydgbGyMlStXfrQg0qBBAzg6OmLKlCkICQnB+vXrkZ+fX6LXJhKbra0tzMzMsGTJks86T3Gu\nGR/aZ/78+di9eze8vLwQHx+Pq1evYs2aNYruzB49emD79u2Ijo7G1atX4erqisLCwk/Kam5ujn37\n9iEsLAzr16+HlZUVDh06xIVnSBRqamrYtm0bli5diqtXr4odh4jKUc+ePeHm5gag6Gekvb094uLi\ncO3aNfz8889YtWqVWBGJiKgSYfGTVMqbHVrv6tgqaReXXC6Hh4cHzM3N0bdvX3zxxRcIDQ0FAGhp\naeHQoUPIyclBhw4d8O2336JLly4IDg5WHL9lyxbk5uaiXbt2GD16NFxdXdGoUaOPZpo4cSJGjBiB\nMWPGoH379khLS8PMmTNLlP0VKysr7N27F4cOHYKamtpHfwY1atRA3759cf/+fZiamqJv375FCrac\nS5SUxffff4/g4GDcvn37k68PxblmfGif/v3749dff0VUVBSsrKzQvXt3HD9+HFLpPx/B8+bNQ48e\nPTBkyBD069cPNjY2n90FY2Njg1OnTsHb2xseHh7o1asXLly48FnnJPoUxsbGWLp0Kezt7fnZQVQJ\nvJp7Wl1dHVWqVIEgCIrPyPz8fLRr1w5GRkZo164devToASsrKzHjEhFRJSER2A5CVOm8/ofo+54r\nLCxEvXr1MG7cOMyfP18xJ+mtW7ewc+dO5ObmwtHREU2bNi3P6ERUQgUFBQgODoavry9sbW3h5+eH\nJk2aiB2LKhFBEPDNN9/AwsICfn5+YschojLy9OlTuLq6ol+/fujWrdt7P2smT56MjRs3Ii4uTjFN\nFBERUVli5ydRJfShLrVXw20DAgKgqamJIUOGFFmMKTs7G9nZ2bh8+TKaNWuGVatWcV5BogqsSpUq\nmDRpEhITE9GiRQtYW1tj2rRpePDggdjRqJKQSCTYvHkzgoODcerUKbHjEFEZCQsLw549e7Bu3TrM\nmjULYWFhuHXrFgBg06ZNir8xfX19ERERwcInERGVG3Z+EtE7ffHFF3BycoKXlxd0dXWLPCcIAs6e\nPYvOnTsjNDQU9vb2iiG8RFSxZWZmYvHixdixYwdmzJiB6dOnF7nBQVRWfv31V8yaNQuXLl1663OF\niJTfhQsXMHnyZIwZMwYHDx5EXFwcunfvDh0dHWzbtg3p6emoUaMGgA+PQiIiIiptrFYQkcKrDs6V\nK1dCXV0dQ4YMeesLamFhISQSiWIxlYEDB75V+MzNzS23zERUMnXq1MG6detw5swZXLlyBaampggK\nCoJMJhM7Gqm4b7/9FjY2Nvj+++/FjkJEZaBt27bo2rUrnjx5gqioKPz444/IyMhASEgIjI2Ncfjw\nYdy4cQNAyefgJyIi+hzs/CQiCIKA//73v9DV1UWnTp3w5ZdfYuTIkVi4cCGqVav21t35mzdvomnT\nptiyZQscHBwU55BIJEhKSsKmTZuQl5cHe3t7dOzYUay3RUTFEBMTg9mzZ+PevXvw9/fH4MGD+aWU\nykxOTg5at26NdevWYdCgQWLHIaJSdufOHTg4OCA4OBhNmjTBrl27MGHCBLRs2RK3bt2ClZUVtm/f\njmrVqokdlYiIKhF2fhIRBEHAH3/8gS5duqBJkybIzc3F4MGDFX+YviqEvOoMXbJkCczMzNCvXz/F\nOV7t8+zZM1SrVg337t1D586d4ePjU87vhohKwtraGseOHcOqVavg5eWFrl274uTJk2LHIhWlp6eH\nrVu3YsGCBew2JlIxhYWFMDIyQsOGDbFw4UIAwKxZs+Dj44O//voLq1atQrt27Vj4JCKicsfOTyJS\nSElJgb+/P4KDg9GxY0esXbsWbdu2LTKs/fbt22jSpAmCgoLg7Oz8zvPI5XIcPXoU/fr1w4EDB9C/\nf//yegtE9BkKCwsRHh4OLy8vWFlZwd/fHy1atBA7FqkguVwOiUTCLmMiFfH6KKEbN27Aw8MDRkZG\n+PXXX3H58mXUq1dP5IRERFSZsfOTiBSaNGmCTZs2ITU1FY0aNcL69eshl8uRnZ2N/Px8AICfnx9M\nTU0xYMCAt45/dS/l1cq+7du3Z+GTVNqTJ0+gq6sLVbmPqKamBicnJ1y/fh1dunTBV199hQkTJuDu\n3btiRyMVI5VKP1j4fPHiBfz8/LBr165yTEVEJZWXlweg6CghY2NjdO3aFSEhIfD09FQUPl+NICIi\nIipvLH4S0Vu+/PJL/Pzzz/j3v/8NNTU1+Pn5wcbGBlu3bkV4eDi+//571K1b963jXv3hGxMTg717\n92L+/PnlHZ2oXFWvXh06OjrIyMgQO0qp0tLSwqxZs3D9+nVUr14drVq1woIFC5CTkyN2NKok7ty5\ng/T0dHh7e+PAgQNixyGid8jJyYG3tzeOHj2K7OxsAFCMFho7diyCg4MxduxYAP/cIH9zgUwiIqLy\nwk8gInqvqlWrQiKRwNPTE8bGxpg4cSLy8vIgCAIKCgreeYxcLsfatWvRunVrLmZBlULTpk2RlJQk\ndowyUbNmTaxYsQKxsbG4c+cOmjZtih9++AEvX74s9jlUpSuWyo8gCDAxMUFgYCAmTJiA8ePHK7rL\niKji8PT0RGBgIMaOHQtPT09ER0criqD16tWDo6Mj9PX1kZ+fzykuiIhIVCx+EtFH1ahRAzt27EBm\nZiamT5+O8ePHw8PDA48fP35r38uXL2P37t3s+qRKw9TUFImJiWLHKFMNGjRAaGgojhw5gqioKDRv\n3hw7duwo1hDGly9f4uHDhzh9+nQ5JCVlJghCkUWQqlatiunTp8PY2BibNm0SMRkRvSk3NxenTp3C\nxo0bMX/+fERFReFf//oXPD09cfz4cTx69AgAEB8fj4kTJ+Lp06ciJyYiosqMxU8iKjY9PT0EBgYi\nJycHQ4cOhZ6eHgAgLS1NMSfomjVrYGZmhm+//VbMqETlRpU7P99kYWGBgwcPIjg4GIGBgWjfvj1u\n3rz5wWMmTJiAr776CpMnT8aXX37JIhYVIZfLkZ6ejoKCAkgkEqirqys6xKRSKaRSKXJzc6Grqyty\nUiJ63Z07d9C2bVvUrVsXkyZNQkpKChYvXoyoqCiMGDECXl5eiI6OhoeHBzIzM7nCOxERiUpd7ABE\npHx0dXXRu3dvAP/M97R06VJER0dj9OjRiIiIwLZt20ROSFR+mjZtiu3bt4sdo1x1794dZ8+eRURE\nBL788sv37rdmzRr8+uuvWLlyJXr37o0TJ05gyZIlaNCgAfr27VuOiakiKigoQMOGDXHv3j3Y2NhA\nS0sLbdu2haWlJerVq4eaNWti69atuHLlCho1aiR2XCJ6jampKebMmYNatWoptk2cOBETJ07Exo0b\nERAQgJ9//hlPnjzBtWvXRExKREQESAROxkVEn0kmk2Hu3LkICQlBdnY2Nm7cCDs7O97lp0rhypUr\nsLOzw9WrV8WOIgpBEN47l5u5uTn69euHVatWKbZNmjQJ9+/fx6+//grgn6kyWrduXS5ZqeIJDAzE\nzJkzsXfvXpw/fx5nz57FkydPcPv2bbx8+RJ6enrw9PTE+PHjxY5KRB8hk8mgrv7/vTXNmjWDtbU1\nwsPDRUxFRETEzk8iKgXq6upYuXIlVqxYAX9/f0yaNAmxsbFYvny5Ymj8K4IgIC8vD9ra2pz8nlSC\niYkJUlJSIJfLK+VKtu/7d/zy5Us0bdr0rRXiBUGApqYmgH8Kx5aWlujevTs2bNgAU1PTMs9LFct3\n332Hbdu24eDBgwgKClIU03Nzc3Hr1i00b968yO9YamoqAKBhw4ZiRSai93hV+JTL5YiJiUFSUhIi\nIyNFTkVERMQ5P4moFL1aGV4ul8PNzQ06Ojrv3G/cuHHo3Lkz/vOf/3AlaFJ62jPd+/gAACAASURB\nVNraMDAwwO3bt8WOUqFUrVoVtra22LVrF3bu3Am5XI7IyEicPHkS1apVg1wuh4WFBe7cuYOGDRui\nRYsWGDVq1DsXUiPVtm/fPmzduhV79uyBRCJBYWEhdHV10bJlS6irq0NNTQ0A8PDhQ4SHh2POnDlI\nSUkROTURvY9UKsWzZ88we/ZstGjRQuw4RERELH4SUdmwsLBQfGF9nUQiQXh4OKZPn45Zs2ahffv2\n2LdvH4ugpNQqw4rvJfHq3/OMGTOwYsUKuLu7o2PHjpg5cyauXbuG3r17QyqVQiaTwdDQECEhIYiL\ni8OjR49gYGCAoKAgkd8BlacGDRogICAArq6uyMnJeednBwDUqlULNjY2kEgkGD58eDmnJKKS6N69\nO5YuXSp2DCIiIgAsfhKRCNTU1DBy5EhcuXIF8+bNg7e3NywtLREREQG5XC52PKISq0wrvn+MTCbD\n0aNHkZGRAeCf1d4zMzMxZcoUmJubo0uXLvjXv/4F4J9rgUwmA/BPB23btm0hkUiQnp6u2E6Vw7Rp\n0zBnzhxcv379nc8XFhYCALp06QKpVIpLly7h8OHD5RmRiN5BEIR33sCWSCSVcioYIiKqmPiJRESi\nkUqlGDp0KGJjY7F48WIsW7YMFhYW+OWXXxRfdImUAYuf/y8rKws7duyAj48Pnjx5guzsbLx8+RK7\nd+9Geno65s6dC+CfOUElEgnU1dWRmZmJoUOHYufOndi+fTt8fHyKLJpBlcO8efNgbW1dZNurooqa\nmhpiYmLQunVrHD9+HFu2bEH79u3FiElE/xMbG4thw4Zx9A4REVV4LH4SkegkEgm+/vprnDt3DitX\nrsQPP/wAc3NzhIeHs/uLlAKHvf+/unXrws3NDWfOnIGZmRkGDx4MIyMj3LlzB4sWLcLAgQMB/P/C\nGHv27EH//v2Rn5+P4OBgjBo1Ssz4JKJXCxslJiYqOodfbVu8eDE6deoEY2NjHDp0CI6OjtDX1xct\nKxEBPj4+sLW1ZYcnERFVeBKBt+qIqIIRBAHHjh2Dj48P7t69i/nz58Pe3h5VqlQROxrRO8XHx2Pw\n4MEsgL4hKioKN27cgJmZGSwtLYsUq/Lz83HgwAFMnDgR1tbW2Lhxo2IF71crflPltGHDBgQHByMm\nJgY3btyAo6Mjrl69Ch8fH4wdO7bI75FcLmfhhUgEsbGxGDRoEJKTk6GlpSV2HCIiog9i8ZOIKrTo\n6Gj4+voiJSUF8+bNg5OTEzQ0NMSORVREfn4+qlevjqdPn7JI/x6FhYVFFrKZO3cugoODMXToUHh5\necHIyIiFLFKoWbMmWrZsicuXL6N169ZYsWIF2rVr997FkHJzc6Grq1vOKYkqr8GDB6Nnz57w8PAQ\nOwoREdFH8RsGEVVotra2OHr0KMLDw7F37140bdoUP/30E168eCF2NCIFDQ0NGBoa4tatW2JHqbBe\nFa3S0tIwZMgQ/Pjjjxg3bhz+/e9/w8jICABY+CSFgwcP4q+//sLAgQMRGRmJDh06vLPwmZubix9/\n/BEBAQH8XCAqJxcvXsT58+cxfvx4saMQEREVC79lEJFS6NKlC6KiorBnzx5ERUXB2NgYa9asQV5e\nntjRiABw0aPiMjQ0hImJCbZu3YolS5YAABc4o7d07NgR3333HY4ePfrB3w9dXV0YGBjgzz//ZCGG\nqJwsWrQIc+fO5XB3IiJSGix+EpFSad++Pfbv34/9+/fjxIkTaNKkCVasWIHc3Fyxo1ElZ2pqyuJn\nMairq2PlypUYNmyYopPvfUOZBUFATk5OecajCmTlypVo2bIljh8//sH9hg0bhoEDB2L79u3Yv39/\n+YQjqqQuXLiAixcv8mYDEREpFRY/iUgpWVlZYe/evThy5AjOnz8PY2NjLF26lIUSEk3Tpk254FEZ\n6N+/PwYNGoS4uDixo5AIIiIi0K1bt/c+//jxY/j7+8Pb2xuDBw9G27Ztyy8cUSX0qutTU1NT7ChE\nRETFxuInESm1Vq1aYefOnTh+/DiuXbsGY2Nj+Pr6Ijs7W+xoVMlw2Hvpk0gkOHbsGHr27IkePXrA\nxcUFd+7cETsWlSN9fX3Url0bz549w7Nnz4o8d/HiRXz99ddYsWIFAgMD8euvv8LQ0FCkpESq7/z5\n84iNjcW4cePEjkJERFQiLH4SkUpo0aIFwsPDcerUKdy8eRMmJibw8vJCVlaW2NGokjA1NWXnZxnQ\n0NDAjBkzkJiYiC+++AKtW7fGnDlzeIOjktm1axfmzZsHmUyGvLw8rFmzBra2tpBKpbh48SImTZok\ndkQilbdo0SLMmzePXZ9ERKR0JIIgCGKHICIqbSkpKVi2bBkiIiIwfvx4fPfdd6hTp47YsUiFyWQy\n6OrqIjs7m18My1B6ejoWLlyIffv2Yc6cOZgyZQp/3pVARkYG6tevD09PT1y9ehW///47vL294enp\nCamU9/KJylpMTAyGDh2KpKQkXnOJiEjp8K9FIlJJTZo0QVBQEGJjY/H06VM0b94c33//PTIyMsSO\nRipKXV0dDRs2REpKithRVFr9+vWxefNm/PHHH4iOjkbz5s0RFhYGuVwudjQqQ/Xq1UNISAiWLl2K\n+Ph4nD59GgsWLGDhk6icsOuTiIiUGTs/iahSSE9PR0BAAMLCwmBvb4/Zs2fDyMioROd48eIF9uzZ\ng2PHjuHRo0eoWrUq6tevjzFjxqBdu3ZllJyUyddffw1XV1cMGTJE7CiVxp9//onZs2fj+fPnWL58\nOfr06QOJRCJ2LCojI0eOxK1bt3Dy5Emoq6uLHYeoUjh37hyGDRuG5ORkaGhoiB2HiIioxHi7nIgq\nhfr162Pt2rW4du0aqlatCgsLC7i5uSE1NfWjx969exezZs2CoaEh/P39cf/+fairq6OgoACXL1/G\ngAED0Lp1a4SGhqKwsLAc3g1VVFz0qPzZ2Njg1KlT8Pb2hoeHB3r16oULFy6IHYvKSEhICK5evYq9\ne/eKHYWo0njV9cnCJxERKSt2fhJRpfTgwQMEBgYiKCgI3377LebNmwdjY+O39rt48SL69+8PExMT\ntG3bFgYGBm/tI5fLkZycjNOnT8Pc3Bw7d+6EtrZ2ebwNqmA2bNiA2NhYBAUFiR2lUiooKEBwcDB8\nfX1ha2sLPz8/NGnSROxYVMri4+Mhk8nQqlUrsaMQqbyzZ89i+PDh7PokIiKlxs5PIqqUateuDX9/\nfyQmJsLQ0BAdOnSAk5NTkdW64+Li0KtXL3Tr1g19+vR5Z+ETAKRSKUxNTTFmzBikp6dj8ODBkMlk\n5fVWqALhiu/iqlKlCiZNmoTExES0aNEC1tbWmDZtGh48eCB2NCpFLVq0YOGTqJwsWrQInp6eLHwS\nEZFSY/GTiCo1AwMD+Pr6Ijk5GSYmJujSpQtGjx6NS5cuoX///ujRowfMzMyKdS51dXUMGjQId+7c\ngbe3dxknp4qIw94rBl1dXXh7eyM+Ph5yuRwtWrSAn58fnj17JnY0KkMczERUus6cOYOrV6/CxcVF\n7ChERESfhcVPIiIA+vr68PLywo0bN2BhYQFbW1tIpdISdxepqamhT58+2LBhA54/f15GaamiMjIy\nwuPHj5Gbmyt2FAJQp04drFu3DmfOnMGVK1dgamqKoKAgdmarIEEQEBkZyXmXiUoRuz6JiEhVsPhJ\nRPQaPT09zJ07F82aNUOHDh0+6Rw1a9ZE/fr1sWvXrlJORxWdVCqFsbExkpOTxY5CrzExMcHOnTsR\nGRmJHTt2oFWrVoiMjGSnoAoRBAHr1q1DQECA2FGIVMLp06cRHx/Prk8iIlIJLH4SEb0hMTERycnJ\naN68+Sefw8LCAj/++GMppiJlwaHvFZe1tTWOHTuGVatWwcvLC127dsXJkyfFjkWlQCqVIjQ0FIGB\ngYiNjRU7DpHSe9X1WbVqVbGjEBERfTYWP4mI3pCcnAxDQ0Ooqal98jnq1auHlJSUUkxFysLU1JTF\nzwpMIpFgwIABuHTpEiZMmAA7Ozt8++23SEhIEDsafaYGDRogMDAQ9vb2ePHihdhxiJTWqVOnkJCQ\nAGdnZ7GjEBERlQoWP4mI3pCbm/vZnQ4aGhrIy8srpUSkTJo2bcoV35WAmpoanJyccP36dXTu3Bk2\nNjaYOHEiMjIyxI5Gn8He3h5mZmaYP3++2FGIlNaiRYswf/58dn0SEZHKYPGTiOgN1apVw8uXLz/r\nHPn5+dDR0SmlRKRMOOxduWhpaWHWrFm4fv069PT00LJlSyxYsAA5OTliR6NPIJFIsHHjRvzyyy/4\n448/xI5DpHROnjyJxMREjB07VuwoREREpYbFTyKiN5iamuLOnTuftSJ0eno6TExMSjEVKQtTU1N2\nfiqhmjVrYsWKFYiNjcWdO3dgamqKH3744bNvhFD5MzAwwObNmzF27Fg8efJE7DhESsXHx4ddn0RE\npHJY/CQieoOxsTFatWqF+Pj4Tz7H5cuX4e7uXoqpSFnUrVsXL168QHZ2tthR6BM0aNAAoaGhOHz4\nMKKiotCiRQv88ssvkMvlYkejEujfvz8GDBgADw8PsaMQKY2TJ08iKSkJTk5OYkchIiIqVSx+EhG9\nw4wZM3D58uVPOvbhw4fIzMzE8OHDSzkVKQOJRMKh7yrAwsICBw8exObNm7Fq1Sq0b98eR48eFTsW\nlcDKlStx6tQpREREiB2FSClwrk8iIlJVLH4SEb3DN998A5lMhosXL5boOJlMhkOHDsHd3R0aGhpl\nlI4qOg59Vx3du3fH2bNnMWvWLEyYMAH9+vX75BsjVL50dHQQFhaGKVOmcCEroo/466+/kJyczK5P\nIiJSSSx+EhG9g7q6Og4dOoSTJ0/i77//LtYxBQUF+O2332BqagovL68yTkgVGTs/VYtUKsXIkSMR\nHx+PQYMGoW/fvnB0dERqaqrY0egjOnbsiPHjx8PV1RWCIIgdh6jCWrRoERYsWIAqVaqIHYWIiKjU\nsfhJRPQepqamiI6OxunTp/H777/j3r1779xPJpMhLi4OYWFhaN68OSIiIqCmplbOaakiYfFTNVWt\nWhVTp05FYmIiGjVqBCsrK8ycOROPHj0SOxp9gLe3NzIzMxEUFCR2FKIK6c8//0RKSgocHR3FjkJE\nRFQmJAJvgxMRfdCDBw+wfv16rF+/Hnp6emjUqBG0tbVRWFiIJ0+e4OrVq2jevDlmzJiBYcOGQSrl\nfaXK7syZM3B3d0dMTIzYUagMZWRkwMfHBxEREZg5cyY8PDygpaUldix6h/j4eNjY2OD06dNo2rSp\n2HGIKpSePXtizJgxcHFxETsKERFRmWDxk4iomGQyGfbt24fo6Gikp6fj0KFDmD59Ouzs7GBmZiZ2\nPKpAsrKyYGxsjMePH0MikYgdh8rY9evX4enpiZiYGPj4+MDR0ZHd3xXQDz/8gB07duDPP/+Eurq6\n2HGIKoQTJ07A2dkZCQkJHPJOREQqi8VPIiKiMlCzZk1cv34dtWvXFjsKlZPTp09j9uzZyM7OxrJl\nyzBgwAAWvysQuVyOPn36oHv37pg/f77YcYgqhB49esDBwQHOzs5iRyEiIiozHJtJRERUBrjie+XT\nqVMnnDhxAn5+fpg1a5ZipXiqGKRSKUJDQ7F27VpcuHBB7DhEoouOjkZaWhocHBzEjkJERFSmWPwk\nIiIqA1z0qHKSSCT45ptvcOXKFdjb22PYsGH417/+xd+FCsLIyAhr1qyBg4MDnj9/LnYcIlG9WuGd\n00AQEZGqY/GTiIioDLD4Wbmpq6tj3LhxSExMhJWVFTp16oQpU6bg/v37Yker9Ozs7NCqVSvMmzdP\n7ChEojl+/Dhu374Ne3t7saMQERGVORY/iYiIygCHvRMAaGtrY968eUhISEDVqlVhZmYGHx8f5Obm\nFvscd+/eha+vL/r164eOHTviq6++wsiRIxEZGQmZTFaG6VWTRCLBhg0bsGfPHhw9elTsOESiWLRo\nEby8vNj1SURElQKLn0REIvDx8YGFhYXYMagMsfOTXlerVi2sXr0a58+fR2JiIpo2bYr169ejoKDg\nvcdcvnwZI0aMgLm5OTIyMuDu7o7Vq1dj8eLF6Nu3LwICAtC4cWP4+fnhxYsX5fhulF/NmjURHBwM\nZ2dnZGdnix2HqFz98ccfSE9Px5gxY8SOQkREVC642jsRVTrOzs7IysrCvn37RMuQl5eH/Px81KhR\nQ7QMVLZycnJgaGiIp0+fcsVvesvFixcxZ84cpKamYunSpRg2bFiR35N9+/bB1dUVCxYsgLOzM/T0\n9N55ntjYWCxcuBDZ2dn47bffeE0poalTpyI7Oxvh4eFiRyEqF4IgoFu3bnB1dYWjo6PYcYiIiMoF\nOz+JiESgra3NIoWK09PTg66uLu7evSt2FKqArKyscOTIEfz000/w8/NTrBQPAEePHsX48eNx8OBB\nTJs27b2FTwCwtLREZGQk2rRpg0GDBnERnxIKCAhATEwMdu3aJXYUonLxxx9/ICMjA6NHjxY7ChER\nUblh8ZOI6DVSqRR79+4tsq1x48YIDAxUPE5KSoKtrS20tLRgbm6OQ4cOoVq1ati2bZtin7i4OPTu\n3Rva2towMDCAs7MzcnJyFM/7+PigVatWZf+GSFQc+k4f07t3b1y4cAHu7u5wcnJCv379MGLECOza\ntQvW1tbFOodUKsWaNWtgZGQELy+vMk6sWrS1tREWFgZ3d3feqCCVJwgC5/okIqJKicVPIqISEAQB\nQ4YMQdWqVXHu3DmEhIRg4cKFePnypWKfvLw89O3bF3p6ejh//jwiIyNx6tQpuLq6FjkXh0KrPi56\nRMUhlUoxZswYJCQkQEdHBx06dICtrW2JzxEQEIAtW7bg2bNnZZRUNbVv3x5ubm5wcXEBZ4MiVXbs\n2DHcu3cPdnZ2YkchIiIqVyx+EhGVwOHDh5GUlISwsDC0atUKHTp0wOrVq4ssWrJ9+3bk5eUhLCwM\nZmZmsLGxQVBQECIiIpCSkiJieipv7PykkqhatSoSEhIwa9asTzq+YcOG6Nq1K3bs2FHKyVTf/Pnz\nkZWVhQ0bNogdhahMvOr69Pb2ZtcnERFVOix+EhGVwPXr12FoaIgvvvhCsc3a2hpS6f9fThMSEmBh\nYQFtbW3Fts6dO0MqleLatWvlmpfExeInlcT58+chk8nQrVu3Tz7HxIkTsWXLltILVUlUqVIF4eHh\n8Pb2Zrc2qaSjR48iMzMTo0aNEjsKERFRuWPxk4joNRKJ5K1hj693dZbG+any4LB3Kom0tDSYm5t/\n1nXC3NwcaWlppZiq8mjWrBkWLVoEBwcHyGQyseMQlRp2fRIRUWXH4icR0Wtq166NjIwMxeP79/+P\nvfsOr/H+/zj+PCeRjSDUjkRFYhPEqj2KotRMSK3Uqi02TWJWjaB2EXukiNolBI0tIVZKZaBmjUTI\nPvfvj/6cb1PaJpHkTuT9uK5zXdzn/nzu1x2Rk/M+n/Eoxd/t7e25f/8+Dx8+1B87f/48Op1O/3cH\nBweuXLmSYt29wMBAFEXBwcEhk+9AZCdly5YlPDyc5ORktaOIHODVq1cpRoynh7m5Oa9fv86gRLnP\n4MGDsbS0ZObMmWpHESLDHDlyhD/++ENGfQohhMi1pPgphMiVoqOjuXz5copHZGQkTZs2ZcmSJVy8\neJHg4GD69OmDqampvl2LFi2ws7PD1dWVkJAQzpw5w+jRo8mTJ49+tJaLiwtmZma4urpy9epVTpw4\nwcCBA/niiy+wtbVV65aFCszMzLCysuLu3btqRxE5gKWlJVFRUe/VR1RUFPnz58+gRLmPVqtlzZo1\nfP/995w/f17tOEK8t7+O+jQwMFA7jhBCCKEKKX4KIXKlkydPUqNGjRQPd3d35s+fj42NDU2aNKFr\n1664ublRpEgRfTuNRoOfnx8JCQk4OTnRp08fJk2aBICJiQkApqamHDp0iOjoaJycnOjYsSP169dn\n9erVqtyrUJdMfRepVblyZc6cOUNsbGy6+zh27BhVq1bNwFS5T4kSJVi8eDG9evWSUbQixzty5AjP\nnj2jW7duakcRQgghVKNR/r64nRBCiDS5fPky1atX5+LFi1SvXj1VbSZOnEhAQACnTp3K5HRCbQMH\nDqRy5coMGTJE7SgiB2jdujU9evTA1dU1zW0VRaFGjRp8++23tGzZMhPS5S7Ozs4UKlSIxYsXqx1F\niHRRFIX69eszdOhQevTooXYcIYQQQjUy8lMIIdLIz8+Pw4cPExERwbFjx+jTpw/Vq1dPdeHz9u3b\n+Pv7U6lSpUxOKrID2fFdpMXgwYNZsmTJWxuvpcaZM2eIjIyUae8ZZMmSJezevZvDhw+rHUWIdDl8\n+DAvXryga9euakcRQgghVCXFTyGESKOXL1/y9ddfU7FiRXr16kXFihU5ePBgqtpGRUVRsWJFTExM\nmDJlSiYnFdmBTHsXadGmTRsSEhL47rvv0tTu+fPn9OvXj88//5yOHTvSu3fvFJu1ibQrUKAAa9as\noW/fvjx79kztOEKkiaIofPPNN7LWpxBCCIFMexdCCCEyVWhoKO3atZPRnyLV7t27p5+qOnr0aP1m\nav/k0aNHfPbZZ3zyySfMnz+f6OhoZs6cyQ8//MDo0aMZOXKkfk1ikXbDhg3jyZMnbNmyRe0oQqTa\noUOHGDlyJFeuXJHipxBCiFxPRn4KIYQQmcjW1pa7d++SmJiodhSRQ5QsWZKlS5fi5eVF69atOXDg\nADqd7q3znjx5wuzZs3F0dKRt27bMmzcPgHz58jF79mzOnj3LuXPnqFChAjt37kzXVHoBs2fP5tKl\nS1L8FDnGm1Gf33zzjRQ+hRBCCGTkpxBCCJHpypYty4EDB7Czs1M7isgBoqOjcXR0ZOrUqSQlJbFk\nyRKeP39OmzZtKFiwIPHx8YSFhXH48GE6derE4MGDcXR0/Mf+/P39GTFiBFZWVnh7e8tu8Olw4cIF\n2rRpQ1BQECVLllQ7jhD/6uDBg4wePZqQkBApfgohhBBI8VMIIYTIdJ9++ilDhw6lbdu2akcR2Zyi\nKPTo0QNLS0uWL1+uP37u3DlOnTrFixcvMDY2pmjRonTo0IGCBQumqt+kpCRWrVqFh4cHHTt2ZNq0\naRQuXDizbuODNG3aNE6ePMnBgwfRamXylMieFEWhTp06jB49WjY6EkIIIf6fFD+FEEKITDZs2DBs\nbGwYOXKk2lGEEOmUlJREgwYNcHFxYejQoWrHEeKdDhw4gLu7OyEhIVKkF0IIIf6fvCIKIUQmiYuL\nY/78+WrHENlAuXLlZMMjIXI4Q0ND1q9fj6enJ6GhoWrHEeItf13rUwqfQgghxP/Iq6IQQmSQvw+k\nT0xMZMyYMbx8+VKlRCK7kOKnEB8GOzs7pk2bRq9evWQTM5HtHDhwgNjYWL744gu1owghhBDZihQ/\nhRAinXbu3Mmvv/5KVFQUABqNBoDk5GSSk5MxMzPD2NiYFy9eqBlTZAN2dnbcvHlT7RhCiAwwcOBA\nrKysmD59utpRhNCTUZ9CCCHEP5M1P4UQIp0cHBy4c+cOzZs359NPP6VSpUpUqlSJAgUK6M8pUKAA\nx44do1q1aiomFWpLSkrCwsKCFy9eYGJionYcIVIlKSkJQ0NDtWNkS/fv36d69er89NNPODk5qR1H\nCPbt28f48eO5fPmyFD+FEEKIv5FXRiGESKcTJ06wePFiXr9+jYeHB66urnTr1o2JEyeyb98+AAoW\nLMjjx49VTirUZmhoSJkyZbh9+7baUUQ2EhkZiVarJSgoKFteu3r16vj7+2dhqpyjePHifP/99/Tq\n1YtXr16pHUfkcoqi4OHhIaM+hRBCiH8gr45CCJFOhQsXpm/fvhw+fJhLly4xduxYLC0t2bNnD25u\nbjRo0IDw8HBiY2PVjiqyAZn6njv16dMHrVaLgYEBRkZGlC1bFnd3d16/fk3p0qV5+PChfmT48ePH\n0Wq1PHv2LEMzNGnShGHDhqU49vdrv4unpydubm507NhRCvfv0KVLF5ycnBg7dqzaUUQut2/fPuLj\n4+nUqZPaUYQQQohsSYqfQgjxnpKSkihWrBiDBg1i+/bt7N69m9mzZ+Po6EiJEiVISkpSO6LIBmTT\no9yrRYsWPHz4kPDwcGbMmMHSpUsZO3YsGo2GIkWK6EdqKYqCRqN5a/O0zPD3a79Lp06duH79OrVr\n18bJyYlx48YRHR2d6dlyksWLF7Nnzx4OHjyodhSRS8moTyGEEOK/ySukEEK8p7+uiZeQkICtrS2u\nrq4sXLiQo0eP0qRJExXTiexCip+5l7GxMYULF6ZEiRJ0796dnj174ufnl2LqeWRkJE2bNgX+HFVu\nYGBA37599X3MmTOHjz/+GDMzM6pWrcqmTZtSXMPLy4syZcpgYmJCsWLF6N27N/DnyNPjx4+zZMkS\n/QjUO3fupHrKvYmJCRMmTCAkJIRHjx5hb2/PmjVr0Ol0GftFyqEsLS3x8fGhf//+PH36VO04Ihfa\nu3cviYmJdOzYUe0oQgghRLYlq9gLIcR7unfvHmfOnOHixYvcvXuX169fkydPHurWrctXX32FmZmZ\nfkSXyL3s7OzYsmWL2jFENmBsbEx8fHyKY6VLl2bHjh107tyZGzduUKBAAUxNTQGYNGkSO3fuZNmy\nZdjZ2XH69Gnc3NwoWLAgrVu3ZseOHcybN49t27ZRqVIlHj9+zJkzZwBYuHAhN2/exMHBgVmzZqEo\nCoULF+bOnTtp+plUvHhxfHx8OH/+PMOHD2fp0qV4e3vToEGDjPvC5FBNmzalS5cuDBo0iG3btsnP\nepFlZNSnEEIIkTpS/BRCiPfwyy+/MHLkSCIiIihZsiRFixbFwsKC169fs3jxYg4ePMjChQspX768\n2lGFymTkpwA4d+4cmzdvpmXLlimOazQaChYsCPw58vPNn1+/fs2CBQs4fPgw9evXB8Da2pqzZ8+y\nZMkSWrduzZ07dyhevDgtWrTAwMCAkiVLUqNGDQDy5cuHkZERZmZmFC5cj2rt9AAAIABJREFUOMU1\n0zO9vlatWgQGBrJlyxZ69OhBgwYN+PbbbyldunSa+/qQzJw5E0dHRzZv3oyLi4vacUQusWfPHpKT\nk/n888/VjiKEEEJka/IRoRBCpNNvv/2Gu7s7BQsW5MSJEwQHB3PgwAF8fX3ZtWsXK1asICkpiYUL\nF6odVWQDJUqU4MWLF8TExKgdRWSxAwcOkDdvXkxNTalfvz5NmjRh0aJFqWp7/fp14uLi+PTTT8mb\nN6/+sXz5csLCwoA/N96JjY2lTJky9O/fnx9//JGEhIRMux+NRoOzszOhoaHY2dlRvXp1vvnmm1y9\n67mpqSkbN25k5MiR3L17V+04IheQUZ9CCCFE6skrpRBCpFNYWBhPnjxhx44dODg4oNPpSE5OJjk5\nGUNDQ5o3b0737t0JDAxUO6rIBrRaLa9evcLc3FztKCKLNWrUiJCQEG7evElcXBy+vr5YWVmlqu2b\ntTX37t3L5cuX9Y9r165x6NAhAEqWLMnNmzdZuXIl+fPnZ8yYMTg6OhIbG5tp9wRgbm6Op6cnwcHB\n+qn1mzdvzpINm7KjGjVqMHz4cHr37i1roopM99NPP6Eoioz6FEIIIVJBip9CCJFO+fPn5+XLl7x8\n+RJAv5mIgYGB/pzAwECKFSumVkSRzWg0GlkPMBcyMzPDxsaGUqVKpfj58HdGRkYAJCcn649VqFAB\nY2NjIiIisLW1TfEoVapUiratW7dm3rx5nDt3jmvXruk/eDEyMkrRZ0YrXbo0W7ZsYfPmzcybN48G\nDRpw/vz5TLtedjZu3DhiY2NZvHix2lHEB+yvoz7lNUUIIYT4b7LmpxBCpJOtrS0ODg7079+fyZMn\nkydPHnQ6HdHR0URERLBz506Cg4PZtWuX2lGFEDmAtbU1Go2Gffv28dlnn2FqaoqFhQVjxoxhzJgx\n6HQ6GjZsSExMDGfOnMHAwID+/fuzbt06kpKScHJywsLCgq1bt2JkZES5cuUAKFOmDOfOnSMyMhIL\nCwsKFSqUKfnfFD19fHzo0KEDLVu2ZNasWbnqAyBDQ0PWr19PnTp1aNGiBRUqVFA7kvgA7d69G4AO\nHTqonEQIIYTIGWTkpxBCpFPhwoVZtmwZ9+/fp3379gwePJjhw4czYcIEVqxYgVarZc2aNdSpU0ft\nqEKIbOqvo7aKFy+Op6cnkyZNomjRogwdOhSAadOm4eHhwbx586hUqRItW7Zk586d2NjYAGBpacnq\n1atp2LAhlStXZteuXezatQtra2sAxowZg5GRERUqVKBIkSLcuXPnrWtnFK1WS9++fQkNDaVo0aJU\nrlyZWbNmERcXl+HXyq4+/vhjZs6cSa9evTJ17VWROymKgqenJx4eHjLqUwghhEgljZJbF2YSQogM\n9Msvv3DlyhXi4+PJnz8/pUuXpnLlyhQpUkTtaEIIoZrbt28zZswYLl++zNy5c+nYsWOuKNgoikK7\ndu2oVq0a06dPVzuO+IDs2rWLadOmcfHixVzxf0kIIYTICFL8FEKI96QoirwBERkiLi4OnU6HmZmZ\n2lGEyFD+/v6MGDECKysrvL29qVq1qtqRMt3Dhw+pVq0au3btom7dumrHER8AnU5HjRo18PLyon37\n9mrHEUIIIXIMWfNTCCHe05vC598/S5KCqEirNWvW8OTJEyZPnvyvG+MIkdM0a9aM4OBgVq5cScuW\nLenYsSPTpk2jcOHCakfLNEWLFmXp0qW4uroSHByMhYWF2pFEDhEWFsaNGzeIjo7G3NwcW1tbKlWq\nhJ+fHwYGBrRr107tiCIbe/36NWfOnOHp06cAFCpUiLp162JqaqpyMiGEUI+M/BRCCCGyyOrVq2nQ\noAHlypXTF8v/WuTcu3cvEyZMYOfOnfrNaoT40Dx//hxPT082bdrExIkTGTJkiH6n+w/Rl19+iamp\nKcuXL1c7isjGkpKS2LdvH0uXLiU4OJiaNWuSN29eXr16xZUrVyhatCj3799nwYIFdO7cWe24Ihu6\ndesWy5cvZ926ddjb21O0aFEUReHBgwfcunWLPn36MGDAAMqWLat2VCGEyHKy4ZEQQgiRRcaPH8+x\nY8fQarUYGBjoC5/R0dFcvXqV8PBwrl27xqVLl1ROKkTmKVCgAN7e3pw4cYJDhw5RuXJl9u/fr3as\nTLNo0SIOHjz4Qd+jeD/h4eFUq1aN2bNn06tXL+7evcv+/fvZtm0be/fuJSwsjClTplC2bFmGDx/O\n+fPn1Y4sshGdToe7uzsNGjTAyMiICxcu8Msvv/Djjz+yY8cOTp06xZkzZwCoU6cOEydORKfTqZxa\nCCGyloz8FEIIIbJIhw4diImJoXHjxoSEhHDr1i3u379PTEwMBgYGfPTRR5ibmzNz5kzatm2rdlwh\nMp2iKOzfv59Ro0Zha2vL/PnzcXBwSHX7xMRE8uTJk4kJM0ZAQADOzs6EhIRgZWWldhyRjfz22280\natSI8ePHM3To0P88/6effqJfv37s2LGDhg0bZkFCkZ3pdDr69OlDeHg4fn5+FCxY8F/P/+OPP2jf\nvj0VKlRg1apVskSTECLXkJGfQgjxnhRF4d69e2+t+SnE39WrV49jx47x008/ER8fT8OGDRk/fjzr\n1q1j79697N69Gz8/Pxo1aqR2VJEOCQkJODk5MW/ePLWj5BgajYa2bdty5coVWrZsScOGDRkxYgTP\nnz//z7ZvCqcDBgxg06ZNWZA2/Ro3boyzszMDBgyQ1wqhFxUVRevWrfnmm29SVfgEaN++PVu2bKFL\nly7cvn07kxNmDzExMYwYMYIyZcpgZmZGgwYNuHDhgv75V69eMXToUEqVKoWZmRn29vZ4e3urmDjr\neHl5cevWLQ4dOvSfhU8AKysrDh8+zOXLl5k1a1YWJBRCiOxBRn4KIUQGsLCw4MGDB+TNm1ftKCIb\n27ZtG4MHD+bMmTMULFgQY2NjzMzM0Grls8gPwZgxY/j111/56aefZDRNOj158oQpU6awa9cuLl68\nSIkSJf7xa5mYmIivry9nz55lzZo1ODo64uvrm203UYqLi6NWrVq4u7vj6uqqdhyRDSxYsICzZ8+y\ndevWNLedOnUqT548YdmyZZmQLHvp1q0bV69eZfny5ZQoUYINGzawYMECbty4QbFixfjqq684evQo\na9asoUyZMpw4cYL+/fuzevVqXFxc1I6faZ4/f46trS3Xr1+nWLFiaWp79+5dqlatSkREBPny5cuk\nhEIIkX1I8VMIITJAqVKlCAwMpHTp0mpHEdnY1atXadmyJTdv3nxr52edTodGo5GiWQ61d+9ehgwZ\nQlBQEIUKFVI7To7366+/Ymdnl6r/DzqdjsqVK2NjY8PixYuxsbHJgoTpc+nSJVq0aMGFCxewtrZW\nO45QkU6nw97eHh8fH+rVq5fm9vfv36dixYpERkZ+0MWruLg48ubNy65du/jss8/0x2vWrEmbNm3w\n8vKicuXKdO7cmW+++Ub/fOPGjalSpQqLFi1SI3aWWLBgAUFBQWzYsCFd7bt06UKTJk0YPHhwBicT\nQojsR4aaCCFEBihQoECqpmmK3M3BwYFJkyah0+mIiYnB19eXK1euoCgKWq1WCp851N27d+nXrx9b\ntmyRwmcGKV++/H+ek5CQAICPjw8PHjzg66+/1hc+s+tmHtWqVWP06NH07t0722YUWcPf3x8zMzPq\n1q2brvbFixenRYsWrF+/PoOTZS9JSUkkJydjbGyc4ripqSm//PILAA0aNGDPnj3cu3cPgFOnTnH5\n8mVat26d5XmziqIoLFu27L0Kl4MHD2bp0qWyFIcQIleQ4qcQQmQAKX6K1DAwMGDIkCHky5ePuLg4\nZsyYwSeffMKgQYMICQnRnydFkZwjMTGR7t27M2rUqHSN3hL/7N8+DNDpdBgZGZGUlMSkSZPo2bMn\nTk5O+ufj4uK4evUqq1evxs/PLyvippq7uzuJiYm5Zk1C8W6BgYG0a9fuvT70ateuHYGBgRmYKvux\nsLCgbt26TJ8+nfv376PT6di4cSOnT5/mwYMHACxatIgqVapQunRpjIyMaNKkCd9+++0HXfx8/Pgx\nz549o06dOunuo3HjxkRGRhIVFZWByYQQInuS4qcQQmQAKX6K1HpT2DQ3N+fFixd8++23VKxYkc6d\nOzNmzBhOnTola4DmIFOmTCF//vy4u7urHSVXefP/aPz48ZiZmeHi4kKBAgX0zw8dOpRWrVqxePFi\nhgwZQu3atQkLC1MrbgoGBgasX7+eWbNmcfXqVbXjCJU8f/48VRvU/JuCBQvy4sWLDEqUfW3cuBGt\nVkvJkiUxMTHh+++/x9nZWf9auWjRIk6fPs3evXsJCgpiwYIFjB49mp9//lnl5JnnzffP+xTPNRoN\nBQsWlN9fhRC5gry7EkKIDCDFT5FaGo0GnU6HsbExpUqV4smTJwwdOpRTp05hYGDA0qVLmT59OqGh\noWpHFf/h4MGDbNq0iXXr1knBOgvpdDoMDQ0JDw9n+fLlDBw4kMqVKwN/TgX19PTE19eXWbNmceTI\nEa5du4apqWm6NpXJLLa2tsyaNYuePXvqp++L3MXIyOi9/+0TEhI4deqUfr3onPz4t6+FjY0Nx44d\n49WrV9y9e5czZ86QkJCAra0tcXFxTJw4ke+++442bdpQqVIlBg8eTPfu3Zk7d+5bfel0OpYsWaL6\n/b7vw8HBgWfPnr3X98+b76G/LykghBAfIvlNXQghMkCBAgUy5JdQ8eHTaDRotVq0Wi2Ojo5cu3YN\n+PMNSL9+/ShSpAhTp07Fy8tL5aTi3/z+++/06dOHTZs2ZdvdxT9EISEh3Lp1C4Dhw4dTtWpV2rdv\nj5mZGQCnT59m1qxZfPvtt7i6umJlZYWlpSWNGjXCx8eH5ORkNeOn0K9fP0qXLo2Hh4faUYQKihYt\nSnh4+Hv1ER4eTrdu3VAUJcc/jIyM/vN+TU1N+eijj3j+/DmHDh3i888/JzExkcTExLc+gDIwMHjn\nEjJarZYhQ4aofr/v+4iOjiYuLo5Xr16l+/snKiqKqKio9x6BLIQQOYGh2gGEEOJDINOGRGq9fPkS\nX19fHjx4wMmTJ/n111+xt7fn5cuXABQpUoRmzZpRtGhRlZOKf5KUlISzszNDhgyhYcOGasfJNd6s\n9Td37ly6detGQEAAq1atoly5cvpz5syZQ7Vq1Rg0aFCKthEREZQpUwYDAwMAYmJi2LdvH6VKlVJt\nrVaNRsOqVauoVq0abdu2pX79+qrkEOro3LkzNWrUYN68eZibm6e5vaIorF69mu+//z4T0mUvP//8\nMzqdDnt7e27dusXYsWOpUKECvXv3xsDAgEaNGjF+/HjMzc2xtrYmICCA9evXv3Pk54cib968NGvW\njC1bttC/f/909bFhwwY+++wzTExMMjidEEJkP1L8FEKIDFCgQAHu37+vdgyRA0RFRTFx4kTKlSuH\nsbExOp2Or776inz58lG0aFGsrKzInz8/VlZWakcV/8DT0xMjIyMmTJigdpRcRavVMmfOHGrXrs2U\nKVOIiYlJ8XM3PDycPXv2sGfPHgCSk5MxMDDg2rVr3Lt3D0dHR/2x4OBgDh48yNmzZ8mfPz8+Pj6p\n2mE+o3300UcsW7YMV1dXLl26RN68ebM8g8h6kZGRLFiwQF/QHzBgQJr7OHHiBDqdjsaNG2d8wGwm\nKiqKCRMm8Pvvv1OwYEE6d+7M9OnT9R9mbNu2jQkTJtCzZ0+ePXuGtbU1M2bMeK+d0HOCwYMHM378\nePr165fmtT8VRWHp0qUsXbo0k9IJIUT2olEURVE7hBBC5HSbN29mz549bNmyRe0oIgcIDAykUKFC\nPHr0iObNm/Py5UsZeZFDHDlyhC+//JKgoCA++ugjtePkajNnzsTT05NRo0Yxa9Ysli9fzqJFizh8\n+DAlSpTQn+fl5YWfnx/Tpk2jbdu2+uM3b97k4sWLuLi4MGvWLMaNG6fGbQDQt29fDAwMWLVqlWoZ\nROa7fPky3333HQcOHKB///5Ur16db775hnPnzpE/f/5U95OUlESrVq34/PPPGTp0aCYmFtmZTqej\nfPnyfPfdd3z++edpartt2za8vLy4evXqe22aJIQQOYWs+SmEEBlANjwSaVG/fn3s7e355JNPuHbt\n2jsLn+9aq0yo68GDB7i6urJhwwYpfGYDEydO5I8//qB169YAlChRggcPHhAbG6s/Z+/evRw5coQa\nNWroC59v1v20s7Pj1KlT2Nraqj5CzNvbmyNHjuhHrYoPh6IoHD16lE8//ZQ2bdpQtWpVwsLC+Pbb\nb+nWrRvNmzfniy++4PXr16nqLzk5mYEDB5InTx4GDhyYyelFdqbVatm4cSNubm6cOnUq1e2OHz/O\n119/zYYNG6TwKYTINaT4KYQQGUCKnyIt3hQ2tVotdnZ23Lx5k0OHDrFr1y62bNnC7du3ZffwbCY5\nORkXFxe++uormjZtqnYc8f/y5s2rX3fV3t4eGxsb/Pz8uHfvHgEBAQwdOhQrKytGjBgB/G8qPMDZ\ns2dZuXIlHh4eqk83z5cvH+vWrWPAgAE8efJE1SwiYyQnJ+Pr60vt2rUZMmQIXbt2JSwsDHd3d/0o\nT41Gw8KFCylRogSNGzcmJCTkX/sMDw+nU6dOhIWF4evrS548ebLiVkQ25uTkxMaNG+nQoQM//PAD\n8fHx/3huXFwcy5cvp0uXLmzdupUaNWpkYVIhhFCXTHsXQogM8Ouvv9KuXTtu3rypdhSRQ8TFxbFs\n2TKWLFnCvXv3SEhIAKB8+fJYWVnxxRdf6As2Qn1eXl4cO3aMI0eO6ItnIvvZvXs3AwYMwNTUlMTE\nRGrVqsXs2bPfWs8zPj6ejh07Eh0dzS+//KJS2reNHTuWW7dusXPnThmRlUPFxsbi4+PD3LlzKVas\nGGPHjuWzzz771w+0FEXB29ubuXPnYmNjw+DBg2nQoAH58+cnJiaGS5cusWzZMk6fPo2bmxteXl6p\n2h1d5B7BwcG4u7tz9epV+vXrR48ePShWrBiKovDgwQM2bNjAihUrqF27NvPmzaNKlSpqRxZCiCwl\nxU8hhMgAjx8/pmLFijJiR6Ta999/z5w5c2jbti3lypUjICCA2NhYhg8fzt27d9m4cSMuLi6qT8cV\nEBAQQI8ePbh48SLFixdXO45IhSNHjmBnZ0epUqX0RURFUfR/9vX1pXv37gQGBlKnTh01o6YQHx9P\nrVq1GDVqFL1791Y7jkiDp0+fsnTpUr7//nvq1q2Lu7s79evXT1MfiYmJ7Nmzh+XLl3Pjxg2ioqKw\nsLDAxsaGfv360b17d8zMzDLpDsSHIDQ0lOXLl7N3716ePXsGQKFChWjXrh0nT57E3d2drl27qpxS\nCCGynhQ/hRAiAyQmJmJmZkZCQoKM1hH/6fbt23Tv3p0OHTowZswYTExMiIuLw9vbG39/fw4fPszS\npUtZvHgxN27cUDturvb48WNq1KjBmjVraNmypdpxRBrpdDq0Wi3x8fHExcWRP39+nj59yieffELt\n2rXx8fFRO+JbQkJCaNasGefPn6dMmTJqxxH/ISIiggULFrBhwwY6derE6NGjcXBwUDuWEG/ZtWsX\n3333XZrWBxVCiA+FFD+FECKDWFhY8ODBA9XXjhPZX2RkJNWqVePu3btYWFjojx85coS+ffty584d\nfv31V2rVqkV0dLSKSXM3nU5H69atqVmzJjNmzFA7jngPx48fZ9KkSbRr147ExETmzp3L1atXKVmy\npNrR3um7775jz549HDt2TJZZEEIIIYR4T7KbghBCZBDZ9EiklrW1NYaGhgQGBqY47uvrS7169UhK\nSiIqKgpLS0uePn2qUkoxe/ZsYmNj8fT0VDuKeE+NGjXiyy+/ZPbs2UydOpU2bdpk28InwKhRowCY\nP3++ykmEEEIIIXI+GfkphBAZpEqVKqxfv55q1aqpHUXkADNnzmTlypXUqVMHW1tbgoODCQgIwM/P\nj1atWhEZGUlkZCROTk4YGxurHTfXOXnyJF26dOHChQvZukgm0s7LywsPDw9at26Nj48PhQsXVjvS\nO4WHh1O7dm38/f1lcxIhhBBCiPdg4OHh4aF2CCGEyMkSEhLYu3cv+/fv58mTJ9y/f5+EhARKliwp\n63+Kf1SvXj1MTEwIDw/nxo0bFCxYkKVLl9KkSRMALC0t9SNERdb6448/aNmyJT/88AOOjo5qxxEZ\nrFGjRvTu3Zv79+9ja2tLkSJFUjyvKArx8fG8fPkSU1NTlVL+OZugcOHCjB07lr59+8rPAiGEEEKI\ndJKRn0IIkU537tzh++9XsGLFahTFnlev7IB8GBu/RKs9RuHCJowdO5hevXqmWNdRiL+KiooiMTER\nKysrtaMI/lzns127dlSsWJE5c+aoHUeoQFEUli9fjoeHBx4eHri5ualWeFQUhY4dO1K+fHm+/fZb\nVTLkZIqipOtDyKdPn7JkyRKmTp2aCan+2bp16xg6dGiWrvV8/PhxmjZtypMnTyhYsGCWXVekTmRk\nJDY2Nly4cIEaNWqoHUcIIXIsWfNTCCHSYcuWrdjb12Dhwhiio4/x8mUAOt1KdLq5xMau4NWrUCIi\n5uPufghb20pcv35d7cgim8qfP78UPrORefPm8fz5c9ngKBfTaDQMGjSIn3/+me3bt1O9enX8/f1V\ny7Jy5UrWr1/PyZMnVcmQU7169SrNhc+IiAiGDx9OuXLluHPnzj+e16RJE4YNG/bW8XXr1r3Xpofd\nu3cnLCws3e3To379+jx48EAKnyro06cP7du3f+v4xYsX0Wq13Llzh9KlS/Pw4UNZUkkIId6TFD+F\nECKNVq9eS//+Y4mNPUpCwkLA4R1naYHmvHq1iz/+mEadOk24du1aFicVQqTF6dOnmTt3Llu3biVP\nnjxqxxEqq1q1KkePHsXT0xM3Nzc6duzI7du3szxHkSJFWLlyJa6urlk6IjCnun37Nl26dKFs2bIE\nBwenqs2lS5dwcXHB0dERU1NTrl69yg8//JCu6/9TwTUxMfE/2xobG2f5h2GGhoZvLf0g1Pfm+0ij\n0VCkSBG02n9+256UlJRVsYQQIseS4qcQQqRBYGAgQ4eO5/Xrw0DqNqBQlF7ExMynSZO2REVFZW5A\nIUS6PHv2jB49erBq1SpKly6tdhyRTWg0Gjp16sT169epXbs2Tk5OjB8/npcvX2Zpjnbt2tG8eXNG\njhyZpdfNSa5evUqzZs1wcHAgPj6eQ4cOUb169X9to9PpaNWqFW3btqVatWqEhYUxe/Zsihcv/t55\n+vTpQ7t27ZgzZw6lSpWiVKlSrFu3Dq1Wi4GBAVqtVv/o27cvAD4+Pm+NHN2/fz916tTBzMwMKysr\nOnToQEJCAvBnQXXcuHGUKlUKc3NznJyc+Pnnn/Vtjx8/jlar5ejRo9SpUwdzc3Nq1aqVoij85pxn\nz5699z2LjBcZGYlWqyUoKAj437/XgQMHcHJywsTEhJ9//pl79+7RoUMHChUqhLm5ORUqVGD79u36\nfq5evUqLFi0wMzOjUKFC9OnTR/9hyuHDhzE2Nub58+cprj1x4kT9iNNnz57h7OxMqVKlMDMzo1Kl\nSvj4+GTNF0EIITKAFD+FECINJk2aRWzsTKB8mtopiguvXjmxbt36zAkmhEg3RVHo06cPnTp1eucU\nRCFMTEyYMGECISEhPHz4kPLly7N27Vp0Ol2WZZg/fz4BAQHs3r07y66ZU9y5cwdXV1euXr3KnTt3\n+Omnn6hatep/ttNoNMyYMYOwsDDc3d3Jnz9/huY6fvw4V65c4dChQ/j7+9O9e3cePnzIgwcPePjw\nIYcOHcLY2JjGjRvr8/x15OjBgwfp0KEDrVq1IigoiBMnTtCkSRP9913v3r05efIkW7du5dq1a3z5\n5Ze0b9+eK1eupMgxceJE5syZQ3BwMIUKFaJnz55vfR1E9vH3LTne9e8zfvx4ZsyYQWhoKLVr12bw\n4MHExcVx/Phxrl+/jre3N5aWlgC8fv2aVq1akS9fPi5cuICfnx+nTp2iX79+ADRr1ozChQvj6+ub\n4hpbtmyhV69eAMTFxeHo6Mj+/fu5fv06I0aMYODAgRw7diwzvgRCCJHxFCGEEKkSFhammJgUUuCV\nAko6HseVkiXtFZ1Op/atiGwkLi5OiYmJUTtGrrZgwQKlVq1aSnx8vNpRRA5x9uxZpW7duoqjo6Py\nyy+/ZNl1f/nlF6Vo0aLKw4cPs+ya2dXfvwaTJk1SmjVrply/fl0JDAxU3NzcFA8PD+XHH3/M8Gs3\nbtxYGTp06FvHfXx8lLx58yqKoii9e/dWihQpoiQmJr6zj0ePHillypRRRo0a9c72iqIo9evXV5yd\nnd/Z/vbt24pWq1Xu3r2b4vjnn3+uDBkyRFEURQkICFA0Go1y+PBh/fOBgYGKVqtVfv/9d/05Wq1W\nefr0aWpuXWSg3r17K4aGhoqFhUWKh5mZmaLVapXIyEglIiJC0Wg0ysWLFxVF+d+/6a5du1L0VaVK\nFcXLy+ud11m5cqViaWmpvHr1Sn/sTT+3b99WFEVRRo0apTRs2FD//MmTJxVDQ0P998m7dO/eXXFz\nc0v3/QshRFaSkZ9CCJFKS5asRKdzBczS2cMnvHhhIJ+SixTGjh3LihUr1I6Ra50/f56ZM2eybds2\njIyM1I4jcojatWsTGBjIqFGj6N69Oz169PjXDXIySv369enduzdubm5vjQ7LLWbOnEnFihXp0qUL\nY8eO1Y9y/PTTT3n58iX16tWjZ8+eKIrCzz//TJcuXZg2bRovXrzI8qyVKlXC0NDwreOJiYl06tSJ\nihUrMnfu3H9sHxwcTNOmTd/5XFBQEIqiUKFCBfLmzat/7N+/P8XatBqNhsqVK+v/Xrx4cRRF4fHj\nx+9xZyKjNGrUiJCQEC5fvqx/bN68+V/baDQaHB0dUxwbPnw406ZNo169ekyZMkU/TR4gNDSUKlWq\nYGb2v99f69Wrh1ar1W/I2bNnTwIDA7l79y4AmzdvplGjRvolIHTMEf1IAAAgAElEQVQ6HTNmzKBq\n1apYWVmRN29edu3alSU/94QQIiNI8VMIIVLpl1+CSEho/h49aEhIaJHqDRhE7lCuXDlu3bqldoxc\n6cWLF3Tr1o3ly5djY2OjdhyRw2g0GpydnQkNDcXOzo7q1avj4eHB69evM/W6np6e3LlzhzVr1mTq\ndbKbO3fu0KJFC3bs2MH48eNp06YNBw8eZPHixQA0aNCAFi1a8NVXX+Hv78/KlSsJDAzE29ubtWvX\ncuLEiQzLki9fvneu4f3ixYsUU+fNzc3f2f6rr74iKiqKrVu3pnvKuU6nQ6vVcuHChRSFsxs3brz1\nvfHXDdzeXC8rl2wQ/8zMzAwbGxtsbW31j5IlS/5nu79/b/Xt25eIiAj69u3LrVu3qFevHl5eXv/Z\nz5vvh+rVq1O+fHk2b95MUlISvr6++invAN999x0LFixg3LhxHD16lMuXL6dYf1YIIbI7KX4KIUQq\n/flGx/K9+khIyM+LF7LpkfgfKX6qQ1EU+vXrR9u2benUqZPacUQOZm5ujqenJ0FBQYSGhmJvb8+W\nLVsybWSmkZERGzduZPz48YSFhWXKNbKjU6dOcevWLfbs2UOvXr0YP3485cuXJzExkdjYWAD69+/P\n8OHDsbGx0Rd1hg0bRkJCgn6EW0YoX758ipF1b1y8eJHy5f99TfC5c+eyf/9+9u3bh4WFxb+eW716\ndfz9/f/xOUVRePDgQYrCma2tLcWKFUv9zYgPRvHixenfvz9bt27Fy8uLlStXAuDg4MCVK1d49eqV\n/tzAwEAURcHBwUF/rGfPnmzatImDBw/y+vVrvvjiixTnt2vXDmdnZ6pUqYKtrS03b97MupsTQoj3\nJMVPIYRIJRMTUyD2vfowMIjFzMw0YwKJD4KdnZ28gVDBkiVLiIiI+Ncpp0KkhbW1NVu3bmXz5s3M\nnTuXBg0acOHChUy5VqVKlRg/fjyurq4kJydnyjWym4iICEqVKqUvdMKf08fbtGmDqemfr6tlypTR\nT9NVFAWdTkdiYiIAT58+zbAsgwYNIiwsjGHDhhESEsLNmzdZsGAB27ZtY+zYsf/Y7siRI0yaNIml\nS5dibGzMo0ePePTokX7X7b+bNGkSvr6+TJkyhRs3bnDt2jW8vb2Ji4ujXLlyODs707t3b3bs2EF4\neDgXL15k3rx5+Pn56ftITRE+ty6hkJ3927/Ju54bMWIEhw4dIjw8nEuXLnHw4EEqVqwIgIuLC2Zm\nZvpNwU6cOMHAgQP54osvsLW11ffh4uLCtWvXmDJlCu3atUtRnLezs8Pf35/AwEBCQ0P5+uuvCQ8P\nz8A7FkKIzCXFTyGESCUbm5JA6Hv1YWoamqrpTCL3KF26NE+ePEnxhl5krqCgILy8vNi2bRvGxsZq\nxxEfmAYNGnD+/Hn69etH+/bt6dOnDw8ePMjw64wcOZI8efLkmgJ+586diYmJoX///gwYMIB8+fJx\n6tQpxo8fz8CBA/n1119TnK/RaNBqtaxfv55ChQrRv3//DMtiY2PDiRMnuHXrFq1atcLJyYnt27fz\n448/0rJly39sFxgYSFJSEl27dqV48eL6x4gRI955fuvWrdm1axcHDx6kRo0aNGnShICAALTaP9/C\n+fj40KdPH8aNG4eDgwPt2rXj5MmTWFtbp/g6/N3fj8lu79nPX/9NUvPvpdPpGDZsGBUrVqRVq1YU\nLVoUHx8fAExNTTl06BDR0dE4OTnRsWNH6tevz+rVq1P0Ubp0aRo0aEBISEiKKe8AkydPpnbt2rRp\n04bGjRtjYWFBz549M+huhRAi82kU+ahPCCFS5ciRI3TsOJqYmEtAet4o3MPUtAqPHkWSN2/ejI4n\ncjAHBwd8fX2pVKmS2lE+eNHR0dSoUYOZM2fStWtXteOID1x0dDQzZsxg9erVjB49mpEjR2JiYpJh\n/UdGRlKzZk0OHz5MtWrVMqzf7CoiIoKffvqJ77//Hg8PD1q3bs2BAwdYvXo1pqam7N27l9jYWDZv\n3oyhoSHr16/n2rVrjBs3jmHDhqHVaqXQJ4QQQuRCMvJTCCFSqWnTpuTLFwecSld7Q8NVODs7S+FT\nvEWmvmcNRVFwc3OjefPmUvgUWSJfvnx8++23nDlzhrNnz1KhQgV27dqVYdOMra2tmTdvHr169SIu\nLi5D+szOypQpw/Xr16lTpw7Ozs4UKFAAZ2dn2rZty507d3j8+DGmpqaEh4cza9YsKleuzPXr1xk5\nciQGBgZS+BRCCCFyKSl+CiFEKmm1WsaO/RozswlAWne3DCNPnuWMGjU4M6KJHE42PcoaK1euJDQ0\nlAULFqgdReQyH3/8MX5+fqxatYqpU6fSrFkzQkJCMqTvXr16YWdnx+TJkzOkv+xMURSCgoKoW7du\niuPnzp2jRIkS+jUKx40bx40bN/D29qZgwYJqRBVCCCFENiLFTyGESIOvvx5MgwaFMDHpReoLoPcw\nM2vN7NlTqVChQmbGEzmUFD8z3+XLl5k8eTLbt2/Xb44iRFZr1qwZwcHBdO7cmRYtWjBo0CCePHny\nXn1qNBpWrFjB5s2bCQgIyJig2cTfR8hqNBr69OnDypUrWbhwIWFhYXzzzTdcunSJnj17YmZmBkDe\nvHlllKcQQggh9KT4KYQQaWBgYICf32Y++SQeM7NWwPl/OTsJ2IGZWT2mTHFj2LAhWZRS5DQy7T1z\nvXz5kq5du+Lt7U358uXVjiNyOUNDQwYPHkxoaCjGxsZUqFABb29v/a7k6WFlZcWqVavo3bs3UVFR\nGZg26ymKgr+/Py1btuTGjRtvFUD79+9PuXLlWLZsGc2bN2ffvn0sWLAAFxcXlRILIYQQIruTDY+E\nECIdkpOTmT9/IXPnfk9sbCFevhwAVATMgSgMDI5hbLyScuVsmDlzAm3atFE5scjO7t27R61atTJl\nR+jcTlEUvv76a+Lj4/nhhx/UjiPEW27cuMHIkSOJiIhg/vz57/V6MWDAAOLj4/W7POckSUlJ7Nix\ngzlz5hAXF4e7uzvOzs4YGRm98/xff/0VrVZLuXLlsjipEEIIIXIaKX4KIcR7SE5O5tChQyxevJYT\nJwIxNzenSJGPqF27CiNGDKRKlSpqRxQ5gE6nI2/evDx8+FA2xMpgiqKg0+lITEzM0F22hchIiqKw\nf/9+Ro0aRdmyZZk/fz729vZp7icmJoZq1aoxZ84cOnXqlAlJM97r169Zu3Yt8+bNo2TJkowdO5Y2\nbdqg1coENSGEEEJkDCl+CiGEENlA1apVWbt2LTVq1FA7ygdHURRZ/0/kCAkJCSxZsoSZM2fi4uLC\nN998Q4ECBdLUx+nTp+nYsSOXLl2iaNGimZT0/T19+pQlS5awZMkS6tWrx9ixY9/ayEgIkfX8/f0Z\nPnw4V65ckddOIcQHQz5SFUIIIbIB2fQo88ibN5FTGBkZMXLkSK5fv05cXBz29vYsW7aMpKSkVPdR\nt25d+vfvT//+/d9aLzM7iIiIYNiwYZQrV467d+9y/Phxdu3aJYVPIbKJpk2botFo8Pf3VzuKEEJk\nGCl+CiGEENmAnZ2dFD+FEAAULlyY5cuX8/PPP7N9+3Zq1KjB0aNHU91+6tSp3L9/n1WrVmViyrQJ\nDg7G2dmZmjVrYm5uzrVr11i1alW6pvcLITKPRqNhxIgReHt7qx1FCCEyjEx7F0IIIbKBtWvXcuzY\nMdavX692lBzlt99+4/r16xQoUABbW1tKlCihdiQhMpSiKOzcuRN3d3eqVq3K3LlzKVu27H+2u379\nOg0bNuTMmTN8/PHHWZD0bW92bp8zZw7Xr19n5MiRuLm5kS9fPlXyCCFSJzY2ljJlynDy5Ens7OzU\njiOEEO9NRn4KIYQQ2YBMe0+7gIAAOnXqxMCBA/n8889ZuXJliufl813xIdBoNHzxxRdcv36d2rVr\n4+TkxPjx43n58uW/tqtQoQKTJ0/G1dU1TdPmM0JSUhJbt27F0dGR4cOH4+LiQlhYGKNHj5bCpxA5\ngKmpKV999RWLFi1SO4oQQmQIKX4KIUQaaLVadu7cmeH9zps3DxsbG/3fPT09Zaf4XMbOzo6bN2+q\nHSPHeP36Nd26daNz585cuXKFadOmsWzZMp49ewZAfHy8rPUpPigmJiZMmDCBkJAQHj58SPny5Vm7\ndi06ne4f2wwbNgxTU1PmzJmTJRlfv37NkiVLsLOzY+nSpXh5eXHlyhW+/PJLjIyMsiSDECJjDBo0\niM2bN/P8+XO1owghxHuT4qcQ4oPWu3dvtFotbm5ubz03btw4tFot7du3VyHZ2/5aqHF3d+f48eMq\nphFZrXDhwiQlJemLd+Lffffdd1SpUoWpU6dSqFAh3NzcKFeuHMOHD8fJyYnBgwdz9uxZtWMKkeGK\nFy+Oj48Pfn5+rFq1itq1axMYGPjOc7VaLWvXrsXb25vg4GD98WvXrrFo0SI8PT2ZPn06K1as4MGD\nB+nO9Mcff+Dp6YmNjQ3+/v5s2rSJEydO8Nlnn6HVytsNIXKi4sWL07ZtW1avXq12FCGEeG/y24gQ\n4oOm0WgoXbo027dvJzY2Vn88OTmZDRs2YG1trWK6f2ZmZkaBAgXUjiGykEajkanvaWBqakp8fDxP\nnjwBYPr06Vy9epXKlSvTvHlzfvvtN1auXJni/70QH5I3Rc9Ro0bRvXt3evTowZ07d946r3Tp0syf\nPx8XFxc2btxI48aNadGiBTdu3CA5OZnY2FgCAwOpUKECXbt2JSAgINVLRoSHhzN06FDs7Oy4d+8e\nJ06cYOfOnbJzuxAfiBEjRrB48eIsXzpDCCEymhQ/hRAfvMqVK1OuXDm2b9+uP7Zv3z5MTU1p3Lhx\ninPXrl1LxYoVMTU1xd7eHm9v77feBD59+pSuXbtiYWFB2bJl2bRpU4rnJ0yYgL29PWZmZtjY2DBu\n3DgSEhJSnDNnzhyKFStGvnz56N27NzExMSme9/T0pHLlyvq/X7hwgVatWlG4cGHy58/PJ598wpkz\nZ97nyyKyIZn6nnpWVlYEBwczbtw4Bg0axLRp09ixYwdjx45lxowZuLi4sGnTpncWg4T4UGg0Gpyd\nnQkNDcXOzo4aNWrg4eHB69evU5zXunVroqOjWbhwIUOGDCEyMpJly5bh5eXFjBkzWL9+PZGRkTRq\n1Ag3NzcGDBjwr8WO4OBgevToQa1atbCwsNDv3F6+fPnMvmUhRBZydHSkdOnS+Pn5qR1FCCHeixQ/\nhRAfPI1GQ79+/VJM21mzZg19+vRJcd6qVauYPHky06dPJzQ0lHnz5jFnzhyWLVuW4rxp06bRsWNH\nQkJC6NatG3379uXevXv65y0sLPDx8SE0NJRly5axbds2ZsyYoX9++/btTJkyhWnTphEUFISdnR3z\n589/Z+43Xr58iaurK4GBgZw/f57q1avTtm1bWYfpAyMjP1Ovb9++TJs2jWfPnmFtbU3lypWxt7cn\nOTkZgHr16lGhQgUZ+SlyBXNzczw9Pbl48SKhoaHY29uzZcsWFEXhxYsXNGnShK5du3L27Fm6dOlC\nnjx53uojX758DBkyhKCgIO7evYuLi0uK9UQVReHIkSO0bNmSdu3aUbNmTcLCwpg1axbFihXLytsV\nQmShESNGsHDhQrVjCCHEe9EoshWqEOID1qdPH54+fcr69espXrw4V65cwdzcHBsbG27dusWUKVN4\n+vQpP/30E9bW1sycORMXFxd9+4ULF7Jy5UquXbsG/Ll+2sSJE5k+fTrw5/T5fPnysWrVKpydnd+Z\nYcWKFcybN08/oq9+/fpUrlyZ5cuX689p0aIFt2/fJiwsDPhz5OeOHTsICQl5Z5+KolCiRAnmzp37\nj9cVOc/GjRvZt28fW7ZsUTtKtpSYmEhUVBRWVlb6Y8nJyTx+/JhPP/2UHTt28PHHHwN/btQQHBws\nI6RFrnTy5ElGjBiBiYkJBgYGVKlShcWLF6d6E7C4uDhatmxJs2bNmDRpEj/++CNz5swhPj6esWPH\n0qNHD9nASIhcIikpiY8//pgff/yRmjVrqh1HCCHSxVDtAEIIkRUsLS3p2LEjq1evxtLSksaNG1Oy\nZEn983/88Qd3795lwIABDBw4UH88KSnprTeLf52ObmBgQOHChXn8+LH+2I8//sjChQv57bffiImJ\nITk5OcXomRs3bry1AVPdunW5ffv2P+Z/8uQJkydPJiAggEePHpGcnExcXJxM6f3A2NnZsWDBArVj\nZEubN29m9+7dHDhwgM6dO7Nw4ULy5s2LgYEBRYsWxcrKirp169KlSxcePnzIuXPnOHXqlNqxhVDF\nJ598wrlz55g2bRpLlizh6NGjqS58wp87y2/YsIEqVaqwZs0arK2t8fLyok2bNrKBkRC5jKGhIUOH\nDmXhwoVs2LBB7ThCCJEuUvwUQuQaffv25csvv8TCwkI/cvONN8XJFStW/OdGDX+fLqjRaPTtz5w5\nQ48ePfD09KRVq1ZYWlqye/du3N3d3yu7q6srT548YeHChVhbW2NsbEzTpk3fWktU5Gxvpr0ripKm\nQsWH7tSpUwwdOhQ3Nzfmzp3L119/jZ2dHePHjwf+/D+4e/dupk6dyuHDh2nRogWjRo2idOnSKicX\nQj0GBgbcv3+f4cOHY2iY9l/5ra2tcXJywtHRkVmzZmVCQiFETtGvXz9sbW25f/8+xYsXVzuOEEKk\nmRQ/hRC5RrNmzTAyMuLZs2d06NAhxXNFihShePHi/PbbbymmvafVqVOnKFmyJBMnTtQfi4iISHGO\ng4MDZ86coXfv3vpjp0+f/td+AwMDWbx4MZ9++ikAjx494sGDB+nOKbKnAgUKYGRkxOPHj/noo4/U\njpMtJCUl4erqysiRI5k8eTIADx8+JCkpidmzZ2NpaUnZsmVp0aIF8+fPJzY2FlNTU5VTC6G+6Oho\nfH19uXHjRrr7GD16NBMnTpTipxC5nKWlJS4uLixbtoxp06apHUcIIdJMip9CiFzlypUrKIryzs0e\nPD09GTZsGPnz56dNmzYkJiYSFBTE77//rh9h9l/s7Oz4/fff2bx5M3Xr1uXgwYNs3bo1xTnDhw/n\nyy+/pGbNmjRu3BhfX1/OnTtHoUKF/rXfjRs3Urt2bWJiYhg3bhzGxsZpu3mRI7zZ8V2Kn39auXIl\nDg4ODBo0SH/syJEjREZGYmNjw/379ylQoAAfffQRVapUkcKnEP/v9u3bWFtbU7Ro0XT30aRJE/3r\npoxGFyJ3GzFiBKdPn5afB0KIHEkW7RFC5Crm5uZYWFi887l+/fqxZs0aNm7cSLVq1WjYsCGrVq3C\n1tZWf867ftn767HPPvsMd3d3Ro4cSdWqVfH393/rE/KuXbvi4eHB5MmTqVGjBteuXWP06NH/mnvt\n2rXExMRQs2ZNnJ2d6devH2XKlEnDnYucQnZ8T8nJyQlnZ2fy5s0LwKJFiwgKCsLPz4+AgAAuXLhA\neHg4a9euVTmpENlLVFQU+fLle68+jIyMMDAwIDY2NoNSCSFyqrJly+Li4iKFTyFEjiS7vQshhBDZ\nyPTp03n16pVMM/2LxMRE8uTJQ1JSEvv376dIkSLUqVMHnU6HVqulZ8+elC1bFk9PT7WjCpFtnDt3\njsGDB3PhwoV095GcnIyRkRGJiYmy0ZEQQgghciz5LUYIIYTIRt5Me8/tXrx4of/zm81aDA0N+eyz\nz6hTpw4AWq2W2NhY/o+9O4+qOX/8B/6890Z7KRWF0oqhLMk6GHvWiWZCDJV9HcYyfAwjS2bGFhFG\nCsPYM8puhslYk5KloiJLKkuhReu9vz/83O80RPu77n0+zukc99738uzOjLk9ey337t1DrVq1BMlJ\nVFXVr18f9+/fL9OozaioKJiYmLD4JCIiomqNn2SIiIiqEE57B2bMmAEvLy/cu3cPwNulJd5NVPl3\nCSOTyfD999/j5cuXmDFjhiBZiaoqExMTODg4YP/+/aW+xubNm+Hu7l6OqYhIUaWnp+PEiRMIDQ1F\nRkaG0HGIiArhtHciIqIqJCMjA0ZGRsjIyFDK0Vbbtm2Dh4cH1NXV0bt3b8yaNQsODg7vbVJ2+/Zt\neHt748SJE/jrr79gY2MjUGKiqisoKAheXl64fPlyic9NT0+HmZkZbty4gfr161dAOiJSFM+fP8eQ\nIUOQmpqKpKQk9OnTh2txE1GVonw/VREREVVhWlpaqFWrFhITE4WOUunS0tJw4MABLFu2DCdOnMCt\nW7cwevRo7N+/H2lpaYWObdCgAVq0aIFff/2VxSdREfr164fnz59j7969JT530aJF6NGjB4tPInqP\nVCpFUFAQ+vbti8WLF+PUqVNISUnBqlWrEBgYiMuXL8Pf31/omEREcipCByAiIqLC3k19b9CggdBR\nKpVYLEavXr1gYWGBTp06ISoqCq6urpg4cSKmTJkCDw8PWFpaIjMzE4GBgXB3d4eGhobQsYmqLIlE\ngoMHD6Jnz57Q0dFBnz59PnmOTCbDL7/8gqNHj+LixYuVkJKIqptRo0bh6tWrGDFiBC5cuICdO3ei\nT58+6NatGwBg/PjxWL9+PTw8PAROSkT0Fkd+EhERVTHKuumRrq4uxo0bh/79+wN4u8HRvn37sGzZ\nMqxduxbTp0/HuXPnMH78eKxbt47FJ1ExNG/eHIcPH4a7uzs8PT3x9OnTIo+9e/cu3N3dsXPnTpw+\nfRr6+vqVmJSIqoM7d+4gNDQUY8eOxQ8//IDjx49jypQp2Ldvn/yY2rVrQ11d/aN/3xARVSaO/CQi\nIqpilHnTIzU1NfmfCwoKIJFIMGXKFHz++ecYMWIEBgwYgMzMTERGRgqYkqh6ad++PS5cuAAvLy+Y\nm5tjwIABGDp0KAwNDVFQUIBHjx5h27ZtiIyMhIeHB86fPw9dXV2hYxNRFZSXl4eCggK4uLjInxsy\nZAjmzJmDyZMnw9DQEH/88Qfatm0LIyMjyGQyiEQiARMTEbH8JCIiqnKsra1x/vx5oWMITiKRQCaT\nQSaToUWLFti+fTscHBywY8cONG3aVOh4RNWKpaUlFi1ahMDAQLRo0QJbtmxBamoqVFRUYGhoCDc3\nN3z11VdQVVUVOioRVWHNmjWDSCRCcHAwJk2aBAAICQmBpaUlTE1NcfToUTRo0ACjRo0CABafRFQl\ncLd3IiKiKub27dtwdnZGTEyM0FGqjLS0NLRr1w7W1tY4cuSI0HGIiIiUlr+/P7y9vdG1a1e0bt0a\ne/fuRd26deHn54ekpCTo6upyaRoiqlJYfhIRlcC7abjvcCoPVYTs7GzUqlULGRkZUFHhJA0AePHi\nBXx8fLBo0SKhoxARESk9b29v/Pbbb3j16hVq164NX19f2Nvby19PTk5G3bp1BUxIRPR/WH4SEZVR\ndnY2srKyoKWlhZo1awodhxSEmZkZzp49CwsLC6GjVJrs7GyoqqoW+QsF/rKBiIio6nj27BlevXoF\nKysrAG9naQQGBmLDhg1QV1eHnp4enJyc8NVXX6FWrVoCpyUiZcbd3omIiik3NxcLFy5Efn6+/Lm9\ne/di0qRJmDp1KhYvXowHDx4ImJAUibLt+J6UlAQLCwskJSUVeQyLTyIioqrDwMAAVlZWyMnJgaen\nJ6ytrTF27FikpaVh2LBhaNmyJfbv3w83NzehoxKRkuPITyKiYnr06BEaNWqEzMxMFBQUYPv27Zgy\nZQratWsHbW1thIaGQlVVFdeuXYOBgYHQcamamzRpEpo0aYKpU6cKHaXCFRQUoGfPnujcuTOntRMR\nEVUjMpkMP/74I/z9/dG+fXvo6+vj6dOnkEqlOHz4MB48eID27dvD19cXTk5OQsclIiXFkZ9ERMX0\n/PlzSCQSiEQiPHjwAOvWrcPcuXNx9uxZBAUF4ebNmzA2NsaKFSuEjkoKwNraGrGxsULHqBRLly4F\nACxYsEDgJESKxdPTE7a2tkLHICIFFh4ejpUrV2LGjBnw9fXF5s2bsWnTJjx//hxLly6FmZkZvvnm\nG6xevVroqESkxFh+EhEV0/Pnz1G7dm0AkI/+nD59OoC3I9cMDQ0xatQoXLp0SciYpCCUZdr72bNn\nsXnzZuzatavQZmJEis7d3R1isVj+ZWhoiAEDBuDOnTvlep+qulxESEgIxGIxUlNThY5CRGUQGhqK\nLl26YPr06TA0NAQA1KlTB127dkVcXBwAoEePHmjTpg2ysrKEjEpESozlJxFRMb18+RKPHz/GgQMH\n8Ouvv6JGjRryHyrflTZ5eXnIyckRMiYpCGUY+fn06VOMGDEC27dvh7GxsdBxiCpdz549kZKSguTk\nZJw+fRpv3rzB4MGDhY71SXl5eWW+xrsNzLgCF1H1VrduXdy6davQ59+7d+/Cz88PTZo0AQA4ODhg\n4cKF0NDQEComESk5lp9ERMWkrq6OOnXqYP369Thz5gyMjY3x6NEj+etZWVmIjo5Wqt25qeKYm5sj\nMTERubm5QkepEFKpFN988w3c3NzQs2dPoeMQCUJVVRWGhoYwMjJCixYtMGPGDMTExCAnJwcPHjyA\nWCxGeHh4oXPEYjECAwPlj5OSkjB8+HAYGBhAU1MTrVq1QkhISKFz9u7dCysrK+jo6GDQoEGFRluG\nhYWhd+/eMDQ0hK6uLjp16oTLly+/d09fX184OztDS0sL8+fPBwBERUWhf//+0NHRQZ06deDq6oqU\nlBT5ebdu3UKPHj2gq6sLbW1ttGzZEiEhIXjw4AG6desGADA0NIREIoGHh0f5vKlEVKkGDRoELS0t\nfP/999i0aRO2bNmC+fPno1GjRnBxcQEA1KpVCzo6OgInJSJlpiJ0ACKi6qJXr174559/kJKSgtTU\nVEgkEtSqVUv++p07d5CcnIw+ffoImJIURY0aNdCgQQPcu3cPjRs3FjpOufvpp5/w5s0beHp6Ch2F\nqEpIT0/Hnj17YGdnB1VVVQCfnrKelZWFzp07o27duggKCoKJiQlu3rxZ6Jj79+9j3759OHz4MDIy\nMjBkyBDMnz8fGzdulN935MiR8PHxAQCsX78e/fr1Q1xcHPIUEPAAACAASURBVPT09OTXWbx4Mby8\nvLBq1SqIRCIkJyejS5cuGDt2LFavXo3c3FzMnz8fX375pbw8dXV1RYsWLRAWFgaJRIKbN29CTU0N\npqamOHjwIL766itER0dDT08P6urq5fZeElHl2r59O3x8fPDTTz9BV1cXBgYG+P7772Fubi50NCIi\nACw/iYiK7dy5c8jIyHhvp8p3U/datmyJQ4cOCZSOFNG7qe+KVn7+888/WLduHcLCwqCiwo8ipLyO\nHz8ObW1tAG/XkjY1NcWxY8fkr39qSviuXbvw9OlThIaGyovKhg0bFjqmoKAA27dvh5aWFgBg3Lhx\n2LZtm/z1rl27Fjp+7dq1OHDgAI4fPw5XV1f580OHDi00OvPHH39EixYt4OXlJX9u27ZtqF27NsLC\nwtC6dWs8ePAAs2fPhrW1NQAUmhmhr68P4O3Iz3d/JqLqqU2bNti+fbt8gEDTpk2FjkREVAinvRMR\nFVNgYCAGDx6MPn36YNu2bXjx4gWAqruZBFV/irjp0fPnz+Hq6oqAgADUr19f6DhEgurSpQtu3LiB\nyMhIXL16Fd27d0fPnj2RmJhYrPOvX78OOzu7QiM0/8vMzExefAKAiYkJnj59Kn/87NkzjB8/Ho0a\nNZJPTX327BkePnxY6Dr29vaFHl+7dg0hISHQ1taWf5mamkIkEiE+Ph4A8N1332H06NHo3r07vLy8\nyn0zJyKqOsRiMYyNjVl8ElGVxPKTiKiYoqKi0Lt3b2hra2PBggVwc3PDzp07i/1DKlFJKdqmR1Kp\nFCNHjoSrqyuXhyACoKGhAXNzc1hYWMDe3h5btmzB69ev8euvv0Isfvsx/d+jP/Pz80t8jxo1ahR6\nLBKJIJVK5Y9HjhyJa9euYe3atbh06RIiIyNRr16999Yb1tTULPRYKpWif//+8vL23VdsbCz69+8P\n4O3o0OjoaAwaNAgXL16EnZ1doVGnRERERJWB5ScRUTGlpKTA3d0dO3bsgJeXF/Ly8jB37ly4ublh\n3759hUbSEJUHRSs/V61ahZcvX2Lp0qVCRyGqskQiEd68eQNDQ0MAbzc0eiciIqLQsS1btsSNGzcK\nbWBUUhcuXMDUqVPh6OiIJk2aQFNTs9A9i9KqVSvcvn0bpqamsLCwKPT176LU0tISU6ZMwZEjRzB6\n9Gj4+fkBAGrWrAng7bR8IlI8n1q2g4ioMrH8JCIqpvT0dKipqUFNTQ3ffPMNjh07hrVr18p3qR04\ncCACAgKQk5MjdFRSEIo07f3SpUtYuXIl9uzZ895INCJllZOTg5SUFKSkpCAmJgZTp05FVlYWBgwY\nADU1NbRr1w4///wzoqKicPHiRcyePbvQUiuurq4wMjLCl19+ifPnz+P+/fsIDg5+b7f3j7GxscHO\nnTsRHR2Nq1evYtiwYfINlz5m8uTJePXqFVxcXBAaGor79+/jzz//xPjx45GZmYns7GxMmTJFvrv7\nlStXcP78efmUWDMzM4hEIhw9ehTPnz9HZmZmyd9AIqqSZDIZzpw5U6rR6kREFYHlJxFRMWVkZMhH\n4uTn50MsFsPZ2RknTpzA8ePHUb9+fYwePbpYI2aIiqNBgwZ4/vw5srKyhI5SJqmpqRg2bBi2bNkC\nU1NToeMQVRl//vknTExMYGJignbt2uHatWs4cOAAOnXqBAAICAgA8HYzkYkTJ2LZsmWFztfQ0EBI\nSAjq16+PgQMHwtbWFosWLSrRWtQBAQHIyMhA69at4erqitGjR7+3adKHrmdsbIwLFy5AIpGgT58+\naNasGaZOnQo1NTWoqqpCIpEgLS0N7u7uaNy4MZydndGxY0esWrUKwNu1Rz09PTF//nzUrVsXU6dO\nLclbR0RVmEgkwsKFCxEUFCR0FCIiAIBIxvHoRETFoqqqiuvXr6NJkyby56RSKUQikfwHw5s3b6JJ\nkybcwZrKzWeffYa9e/fC1tZW6CilIpPJ4OTkBEtLS6xevVroOERERFQJ9u/fj/Xr15doJDoRUUXh\nyE8iomJKTk5Go0aNCj0nFoshEokgk8kglUpha2vL4pPKVXWf+u7t7Y3k5GT89NNPQkchIiKiSjJo\n0CAkJCQgPDxc6ChERCw/iYiKS09PT7777n+JRKIiXyMqi+q86VFoaCiWL1+OPXv2yDc3ISIiIsWn\noqKCKVOmYO3atUJHISJi+UlERFSVVdfy8+XLlxgyZAg2bdoEc3NzoeMQERFRJRszZgyCg4ORnJws\ndBQiUnIsP4mIyiA/Px9cOpkqUnWc9i6TyTB69Gj0798fgwcPFjoOERERCUBPTw/Dhg3Dxo0bhY5C\nREqO5ScRURnY2NggPj5e6BikwKrjyM8NGzYgISEBK1euFDoKERERCWjatGnYtGkTsrOzhY5CREqM\n5ScRURmkpaVBX19f6BikwExMTJCeno7Xr18LHaVYwsPDsXjxYuzduxeqqqpCxyEiIiIBNWrUCPb2\n9ti9e7fQUYhIibH8JCIqJalUivT0dOjq6godhRSYSCSqNqM/X79+DRcXF6xfvx5WVlZCxyFSKsuX\nL8fYsWOFjkFE9J7p06fD29ubS0URkWBYfhIRldKrV6+gpaUFiUQidBRScNWh/JTJZBg7dix69uwJ\nFxcXoeMQKRWpVIqtW7dizJgxQkchInpPz549kZeXh7///lvoKESkpFh+EhGVUlpaGvT09ISOQUrA\n2tq6ym96tHnzZty5cwdr1qwROgqR0gkJCYG6ujratGkjdBQioveIRCL56E8iIiGw/CQiKiWWn1RZ\nbGxsqvTIz8jISCxYsAD79u2Dmpqa0HGIlI6fnx/GjBkDkUgkdBQiog8aMWIELl68iLi4OKGjEJES\nYvlJRFRKLD+pslTlae/p6elwcXGBt7c3bGxshI5DpHRSU1Nx5MgRjBgxQugoRERF0tDQwNixY+Hj\n4yN0FCJSQiw/iYhKieUnVRYbG5sqOe1dJpNh4sSJ6NSpE4YPHy50HCKltGvXLvTt2xe1a9cWOgoR\n0UdNmjQJv/32G169eiV0FCJSMiw/iYhKieUnVRYDAwNIpVK8ePFC6CiF+Pv7IzIyEuvWrRM6CpFS\nkslk8invRERVXf369eHo6Ah/f3+hoxCRkmH5SURUSiw/qbKIRKIqN/X91q1bmDt3Lvbt2wcNDQ2h\n4xAppWvXriE9PR1du3YVOgoRUbFMnz4dPj4+KCgoEDoKESkRlp9ERKXE8pMqU1Wa+p6ZmQkXFxes\nXLkSTZo0EToOkdLy8/PD6NGjIRbzIz0RVQ9t2rRB3bp1ERwcLHQUIlIi/KRERFRKqamp0NfXFzoG\nKYmqNPJzypQpaNOmDUaNGiV0FCKllZmZiX379sHNzU3oKEREJTJ9+nR4e3sLHYOIlAjLTyKiUuLI\nT6pMVaX83LFjBy5fvoz169cLHYVIqe3fvx8dO3ZEvXr1hI5CRFQigwcPxr179xARESF0FCJSEiw/\niYhKieUnVaaqMO09OjoaM2fOxL59+6ClpSVoFiJlx42OiKi6UlFRwZQpU7B27VqhoxCRklAROgAR\nUXXF8pMq07uRnzKZDCKRqNLvn5WVBRcXFyxfvhy2traVfn8i+j/R0dGIj49H3759hY5CRFQqY8aM\ngZWVFZKTk1G3bl2h4xCRguPITyKiUmL5SZWpVq1aUFNTQ0pKiiD3//bbb2FnZ4fRo0cLcn8i+j9b\nt26Fm5sbatSoIXQUIqJS0dfXx9ChQ7Fp0yahoxCREhDJZDKZ0CGIiKojPT09xMfHc9MjqjQdO3bE\n8uXL0blz50q97++//w5PT0+EhYVBW1u7Uu9NRIXJZDLk5eUhJyeH/z0SUbUWExODL774AgkJCVBT\nUxM6DhEpMI78JCIqBalUivT0dOjq6godhZSIEJse3b17F99++y327t3LooWoChCJRKhZsyb/eySi\naq9x48Zo2bIl9uzZI3QUIlJwLD+JiErgzZs3CA8PR3BwMNTU1BAfHw8OoKfKUtnlZ3Z2NlxcXLB4\n8WK0aNGi0u5LREREymH69Onw9vbm52kiqlAsP4mIiiEuLg6zZs2Cqakp3N3dsXr1apibm6Nbt26w\nt7eHn58fMjMzhY5JCq6yd3z/7rvvYGNjgwkTJlTaPYmIiEh59OrVC7m5uQgJCRE6ChEpMJafREQf\nkZubi7Fjx6J9+/aQSCS4cuUKIiMjERISgps3b+Lhw4fw8vJCUFAQzMzMEBQUJHRkUmCVOfJz3759\nOHXqFLZs2SLI7vJERESk+EQiEb799lt4e3sLHYWIFBg3PCIiKkJubi6+/PJLqKioYPfu3dDS0vro\n8aGhoXBycsJPP/2EkSNHVlJKUiYZGRkwMjJCRkYGxOKK+/1lfHw82rdvj+PHj8Pe3r7C7kNERESU\nlZUFMzMzXL58GZaWlkLHISIFxPKTiKgIHh4eePHiBQ4ePAgVFZVinfNu18pdu3ahe/fuFZyQlFG9\nevVw6dIlmJqaVsj1c3Jy0KFDB7i5uWHq1KkVcg8i+rh3/+/Jz8+HTCaDra0tOnfuLHQsIqIKM2/e\nPLx584YjQImoQrD8JCL6gJs3b8LR0RGxsbHQ0NAo0bmHDh2Cl5cXrl69WkHpSJl98cUXWLBgQYWV\n69OmTUNiYiIOHDjA6e5EAjh27Bi8vLwQFRUFDQ0N1KtXD3l5eWjQoAG+/vprODk5fXImAhFRdfP4\n8WPY2dkhISEBOjo6QschIgXDNT+JiD7A19cX48aNK3HxCQADBw7E8+fPWX5ShajITY8OHTqE4OBg\nbN26lcUnkUDmzp0Le3t7xMbG4vHjx1izZg1cXV0hFouxatUqbNq0SeiIRETlrn79+ujduzf8/f2F\njkJECogjP4mI/uP169cwMzPD7du3YWJiUqpr/Pzzz4iOjsa2bdvKNxwpvRUrViApKQmrV68u1+sm\nJCSgTZs2CA4ORtu2bcv12kRUPI8fP0br1q1x+fJlNGzYsNBrT548QUBAABYsWICAgACMGjVKmJBE\nRBXkypUrGDZsGGJjYyGRSISOQ0QKhCM/iYj+IywsDLa2tqUuPgHA2dkZZ8+eLcdURG9VxI7vubm5\nGDJkCObOncvik0hAMpkMderUwcaNG+WPCwoKIJPJYGJigvnz52PcuHH466+/kJubK3BaIqLy1bZt\nW9SpUwdHjhwROgoRKRiWn0RE/5GamgoDA4MyXcPQ0BBpaWnllIjo/1TEtPd58+ahTp06mDFjRrle\nl4hKpkGDBhg6dCgOHjyI3377DTKZDBKJpNAyFFZWVrh9+zZq1qwpYFIioooxffp0bnpEROWO5ScR\n0X+oqKigoKCgTNfIz88HAPz5559ISEgo8/WI3rGwsMCDBw/k/46VVXBwMA4cOIBt27ZxnU8iAb1b\niWr8+PEYOHAgxowZgyZNmmDlypWIiYlBbGws9u3bhx07dmDIkCECpyUiqhiDBw9GXFwcrl+/LnQU\nIlIgXPOTiOg/Lly4gClTpiAiIqLU17h+/Tp69+6Npk2bIi4uDk+fPkXDhg1hZWX13peZmRlq1KhR\njt8BKbqGDRvir7/+gqWlZZmu8/DhQzg4OODQoUPo0KFDOaUjotJKS0tDRkYGpFIpXr16hYMHD+L3\n33/HvXv3YG5ujlevXuHrr7+Gt7c3R34SkcL6+eefERMTg4CAAKGjEJGCYPlJRPQf+fn5MDc3x5Ej\nR9C8efNSXWP69OnQ1NTEsmXLAABv3rzB/fv3ERcX997XkydPUL9+/Q8Wo+bm5lBVVS3Pb48UQK9e\nvTBjxgz06dOn1NfIy8tDly5d4OTkhDlz5pRjOiIqqdevX8PPzw+LFy+GsbExCgoKYGhoiO7du2Pw\n4MFQV1dHeHg4mjdvjiZNmnCUNhEptNTUVFhZWSE6Ohp16tQROg4RKQCWn0REH7BkyRIkJiZi06ZN\nJT43MzMTpqamCA8Ph5mZ2SePz83NRUJCwgeL0YcPH6JOnTofLEYtLS2hoaFRmm+PqrnJkyejUaNG\nmDZtWqmvMXfuXNy4cQNHjhyBWMxVcIiENHfuXPz999+YOXMmDAwMsH79ehw6dAj29vZQV1fHihUr\nuBkZESmVCRMmQFtbG/r6+jh37hzS0tJQs2ZN1KlTBy4uLnBycuLMKSIqNpafREQfkJSUhM8++wzh\n4eEwNzcv0bk///wzLly4gKCgoDLnyM/Px8OHDxEfH/9eMXrv3j3o6+sXWYzq6OiU+f6lkZWVhf37\n9+PGjRvQ0tKCo6MjHBwcoKKiIkgeReTt7Y34+Hj4+PiU6vzjx49j3LhxCA8Ph6GhYTmnI6KSatCg\nATZs2ICBAwcCeDvqydXVFZ06dUJISAju3buHo0ePolGjRgInJSKqeFFRUfj+++/x119/YdiwYXBy\nckLt2rWRl5eHhIQE+Pv7IzY2FmPHjsWcOXOgqakpdGQiquL4kygR0QcYGxtjyZIl6NOnD0JCQoo9\n5SYwMBBr167F+fPnyyWHiooKLCwsYGFhgZ49exZ6TSqVIjExsVAhumfPHvmftbS0iixG9fX1yyXf\nhzx//hxXrlxBVlYW1qxZg7CwMAQEBMDIyAgAcOXKFZw+fRrZ2dmwsrJC+/btYWNjU2gap0wm47TO\nj7CxscHx48dLdW5iYiLc3d2xb98+Fp9EVcC9e/dgaGgIbW1t+XP6+vqIiIjA+vXrMX/+fDRt2hTB\nwcFo1KgR/34kIoV2+vRpDB8+HLNnz8aOHTugp6dX6PUuXbpg1KhRuHXrFjw9PdGtWzcEBwfLP2cS\nEX0IR34SEX3EkiVLsG3bNuzZswcODg5FHpeTkwNfX1+sWLECwcHBsLe3r8SU75PJZEhOTv7gVPq4\nuDhIJJIPFqNWVlYwNDQs0w/WBQUFePLkCRo0aICWLVuie/fuWLJkCdTV1QEAI0eORFpaGlRVVfH4\n8WNkZWVhyZIl+PLLLwG8LXXFYjFSU1Px5MkT1K1bFwYGBuXyviiK2NhY9O7dG/fu3SvRefn5+ejW\nrRt69+6N+fPnV1A6IioumUwGmUwGZ2dnqKmpwd/fH5mZmfj999+xZMkSPH36FCKRCHPnzsXdu3ex\nd+9eTvMkIoV18eJFODk54eDBg+jUqdMnj5fJZPjf//6HU6dOISQkBFpaWpWQkoiqI5afRESf8Ntv\nv+GHH36AiYkJJk2ahIEDB0JHRwcFBQV48OABtm7diq1bt8LOzg6bN2+GhYWF0JE/SiaT4cWLF0UW\no7m5uUUWo8bGxiUqRo2MjDBv3jx8++238nUlY2NjoampCRMTE8hkMsycORPbtm3D9evXYWpqCuDt\ndKeFCxciLCwMKSkpaNmyJXbs2AErK6sKeU+qm7y8PGhpaeH169cl2hDrhx9+QGhoKE6cOMF1Pomq\nkN9//x3jx4+Hvr4+dHR08Pr1a3h6esLNzQ0AMGfOHERFReHIkSPCBiUiqiBv3ryBpaUlAgIC0Lt3\n72KfJ5PJMHr0aNSsWbNUa/UTkXJg+UlEVAwFBQU4duwYNmzYgPPnzyM7OxsAYGBggGHDhmHChAkK\nsxZbWlraB9cYjYuLQ3p6OiwtLbF///73pqr/V3p6OurWrYuAgAC4uLgUedyLFy9gZGSEK1euoHXr\n1gCAdu3aIS8vD5s3b0a9evXg4eGB7OxsHDt2TD6CVNnZ2Njg8OHDaNKkSbGOP336NNzc3BAeHs6d\nU4mqoLS0NGzduhXJyckYNWoUbG1tAQB37txBly5dsGnTJjg5OQmckoioYmzfvh179+7FsWPHSnxu\nSkoKGjVqhPv37783TZ6ICOCan0RExSKRSDBgwAAMGDAAwNuRdxKJRCFHz+np6aF169byIvLf0tPT\nER8fDzMzsyKLz3fr0SUkJEAsFn9wDaZ/r1n3xx9/QFVVFdbW1gCA8+fPIzQ0FDdu3ECzZs0AAKtX\nr0bTpk1x//59fPbZZ+X1rVZr1tbWiI2NLVb5mZSUhFGjRmHXrl0sPomqKD09PcyaNavQc+np6Th/\n/jy6devG4pOIFJqvry8WLFhQqnPr1KmDvn37Yvv27Zg+fXo5JyMiRaB4P7UTEVWCGjVqKGTx+Sna\n2tpo0aIF1NTUijxGKpUCAKKjo6Gjo/Pe5kpSqVRefG7btg2enp6YOXMmdHV1kZ2djVOnTsHU1BTN\nmjVDfn4+AEBHRwfGxsa4efNmBX1n1Y+NjQ3u3r37yeMKCgowfPhwjBs3Dl27dq2EZERUXrS1tdG/\nf3+sXr1a6ChERBUmKioKSUlJ6NOnT6mvMWHCBAQEBJRjKiJSJBz5SUREFSIqKgpGRkaoVasWgLej\nPaVSKSQSCTIyMrBw4UL88ccfmDp1KmbPng0AyM3NRXR0tHwU6LsiNSUlBQYGBnj9+rX8Wsq+27G1\ntTUiIyM/edzSpUsBoNSjKYhIWBytTUSK7uHDh2jcuDEkEkmpr9G0aVM8evSoHFMRkSJh+UlEROVG\nJpPh5cuXqF27NmJjY9GwYUPo6uoCgLz4vH79Or799lukp6dj8+bN6NmzZ6Ey8+nTp/Kp7e+WpX74\n8CEkEgnXcfoXa2trHDhw4KPHnD17Fps3b8a1a9fK9AMFEVUO/mKHiJRRVlYWNDQ0ynQNDQ0NZGZm\nllMiIlI0LD+JiKjcJCYmolevXsjOzkZCQgLMzc2xadMmdOnSBe3atcOOHTuwatUqdO7cGV5eXtDW\n1gYAiEQiyGQy6OjoICsrC1paWgAgL+wiIyOhrq4Oc3Nz+fHvyGQyrFmzBllZWfJd6S0tLRW+KNXQ\n0EBkZCT8/f2hqqoKExMTdOrUCSoqb//XnpKSghEjRmD79u0wNjYWOC0RFUdoaCgcHByUclkVIlJe\nurq68tk9pfXq1Sv5bCMiov9i+UlEVALu7u548eIFgoKChI5SJdWrVw979uxBREQEkpKScO3aNWze\nvBlXr17F2rVrMWPGDKSlpcHY2BjLly9Ho0aNYGNjg+bNm0NNTQ0ikQhNmjTBxYsXkZiYiHr16gF4\nuymSg4MDbGxsPnhfAwMDxMTEIDAwUL4zfc2aNeVF6LtS9N2XgYFBtRxdJZVKcfLkSfj6+uLSpUto\n3rw5zp07h5ycHMTGxuLp06cYP348PDw8MGrUKLi7u6Nnz55CxyaiYkhMTISjoyMePXok/wUQEZEy\naNq0Ka5fv4709HT5L8ZL6uzZs7CzsyvnZESkKESyd3MKiYgUgLu7O7Zv3w6RSCSfJt20aVN89dVX\nGDdunHxUXFmuX9by88GDBzA3N0dYWBhatWpVpjzVzd27dxEbG4t//vkHN2/eRFxcHB48eIDVq1dj\nwoQJEIvFiIyMhKurK3r16gVHR0ds2bIFZ8+exd9//w1bW9ti3Ucmk+HZs2eIi4tDfHy8vBB995Wf\nn/9eIfruq27dulWyGH3+/DmcnJyQlZWFyZMnY9iwYe9NEQsPD8fGjRuxd+9emJiY4NatW2X+d56I\nKoeXlxcePHiAzZs3Cx2FiKjSff311+jWrRsmTpxYqvM7deqEGTNmYPDgweWcjIgUActPIlIo7u7u\nePLkCXbu3In8/Hw8e/YMZ86cwbJly2BlZYUzZ85AXV39vfPy8vJQo0aNYl2/rOVnQkICLC0tcfXq\nVaUrP4vy33XuDh8+jJUrVyIuLg4ODg5YvHgxWrRoUW73S01N/WApGhcXh8zMzA+OFrWyskK9evUE\nmY767NkzdOrUCYMHD8bSpUs/meHmzZvo27cvfvjhB4wfP76SUhJRaUmlUlhbW2PPnj1wcHAQOg4R\nUaU7e/Yspk6dips3b5b4l9A3btxA3759kZCQwF/6EtEHsfwkIoVSVDl5+/ZttGrVCv/73//w448/\nwtzcHG5ubnj48CECAwPRq1cv7N27Fzdv3sR3332HCxcuQF1dHQMHDsTatWuho6NT6Ppt27aFj48P\nMjMz8fXXX2Pjxo1QVVWV3++XX37Br7/+iidPnsDa2hpz5szB8OHDAQBisVi+xiUAfPHFFzhz5gzC\nwsIwf/58hIeHIzc3F3Z2dlixYgXatWtXSe8eAcDr16+LLEZTU1Nhbm7+wWLU1NS0Qj5wFxQUoFOn\nTvjiiy/g5eVV7PPi4uLQqVMn7Nixg1Pfiaq4M2fOYMaMGbh+/XqVHHlORFTRZDIZPv/8c3Tv3h2L\nFy8u9nnp6eno3Lkz3N3dMW3atApMSETVGX8tQkRKoWnTpnB0dMTBgwfx448/AgDWrFmDH374Adeu\nXYNMJkNWVhYcHR3Rrl07hIWF4cWLFxgzZgxGjx6N/fv3y6/1999/Q11dHWfOnEFiYiLc3d3x/fff\nw9vbGwAwf/58BAYGYuPGjbCxscGlS5cwduxY6Ovro0+fPggNDUWbNm1w6tQp2NnZoWbNmgDefngb\nOXIkfHx8AADr169Hv379EBcXp/Cb91QlOjo6aNmyJVq2bPnea1lZWbh37568DL1x44Z8ndHk5GSY\nmpp+sBht2LCh/J9zSR0/fhx5eXlYtmxZic6zsrKCj48PFi1axPKTqIrz8/PDmDFjWHwSkdISiUQ4\ndOgQOnTogBo1auCHH3745N+Jqamp+PLLL9GmTRtMnTq1kpISUXXEkZ9EpFA+Ni193rx58PHxQUZG\nBszNzWFnZ4fDhw/LX9+yZQvmzJmDxMRE+VqKISEh6Nq1K+Li4mBhYQF3d3ccPnwYiYmJ8unzu3bt\nwpgxY5CamgqZTAYDAwOcPn0aHTt2lF97xowZiI2NxZEjR4q95qdMJkO9evWwcuVKuLq6ltdbRBUk\nJycH9+/f/+CI0cePH8PExOS9UtTS0hIWFhYfXIrhnb59+2LIkCEYNWpUiTPl5+ejYcOGOHr0KJo3\nb16Wb4+IKsiLFy9gaWmJe/fuQV9fX+g4RESCSkpKQv/+/aGnp4dp06ahX79+kEgkhY5JTU1FQEAA\n1q1bBxcXF/z888+CLEtERNUHR34SkdL477qSrVu3LvR6TEwM7OzsCm0i06FDB4jFYkRFRcHCwgIA\nYGdnV6isat++PXJzcxEfH4/s7GxkZ2fD0dGx0LXzU5XbswAAGdNJREFU8/Nhbm7+0XzPnj3DDz/8\ngL///hspKSkoKChAdnY2Hj58WOrvmSqPqqoqGjdujMaNG7/3Wl5eHh48eCAvQ+Pj43H27FnExcXh\n/v37MDQ0/OCIUbFYjKtXr+LgwYOlyqSiooLx48fD19eXm6gQVVG7du1Cv379WHwSEQEwNjbGxYsX\nsX//fvz000+YOnUqBgwYAH19feTl5SEhIQEnTpzAgAEDsHfvXi4PRUTFwvKTiJTGvwtMANDU1Cz2\nuZ+advNuEL1UKgUAHDlyBA0aNCh0zKc2VBo5ciSePXuGtWvXwszMDKqqqujWrRtyc3OLnZOqpho1\nasgLzf8qKCjA48ePC40UvXz5MuLi4nDnzh1069btoyNDP6Vfv37w8PAoS3wiqiAymQxbtmzBunXr\nhI5CRFRlqKqqYsSIERgxYgQiIiJw7tw5pKWlQVtbG927d4ePjw8MDAyEjklE1QjLTyJSCrdu3cKJ\nEyewcOHCIo9p0qQJAgICkJmZKS9GL1y4AJlMhiZNmsiPu3nzJt68eSMvpC5dugRVVVVYWlqioKAA\nqqqqSEhIQJcuXT54n3drPxYUFBR6/sKFC/Dx8ZGPGk1JSUFSUlLpv2mqFiQSCczMzGBmZobu3bsX\nes3X1xcRERFlur6enh5evnxZpmsQUcW4evUq3rx5U+T/L4iIlF1R67ATEZUEF8YgIoWTk5MjLw5v\n3LiB1atXo2vXrnBwcMDMmTOLPG/48OHQ0NDAyJEjcevWLZw7dw4TJkyAs7NzoRGj+fn58PDwQFRU\nFE6fPo158+Zh3LhxUFdXh5aWFmbNmoVZs2YhICAA8fHxiIyMxObNm+Hn5wcAMDIygrq6Ok6ePImn\nT5/i9evXAAAbGxvs3LkT0dHRuHr1KoYNG1ZoB3lSPurq6sjLyyvTNXJycvjvEVEV5efnBw8PD65V\nR0RERFSB+EmLiBTOn3/+CRMTE5iZmaFHjx44cuQIFi9ejJCQEPlozQ9NY39XSL5+/Rpt27bFoEGD\n0LFjR2zdurXQcV26dEHTpk3RtWtXODs7o0ePHvj555/lry9ZsgSLFi3CqlWr0KxZM/Tq1QuBgYHy\nNT8lEgl8fHzg5+eHevXqwcnJCQDg7++PjIwMtG7dGq6urhg9ejQaNmxYQe8SVQfGxsaIi4sr0zXi\n4uJQt27dckpEROUlIyMD+/fvh5ubm9BRiIiIiBQad3snIiKqonJzc2FmZoYzZ84UWnqhJJycnNC3\nb1+MGzeunNMRUVn4+/vjjz/+QFBQkNBRiIiIiBQaR34SERFVUTVr1sSYMWOwcePGUp3/8OFDnDt3\nDq6uruWcjIjKys/PD2PGjBE6BhEREZHCY/lJRERUhY0bNw67du3C3bt3S3SeTCbDjz/+iG+++QZa\nWloVlI6ISuP27dtISEhA3759hY5CRCSolJQU9OrVC1paWpBIJGW6lru7OwYOHFhOyYhIkbD8JCIi\nqsIaNGiAn376CX379sWjR4+KdY5MJoOnpyciIiKwdOnSCk5IRCW1detWuLm5QUVFRegoREQVyt3d\nHWKxGBKJBGKxWP7VoUMHAMCKFSuQnJyMGzduICkpqUz3WrduHXbu3FkesYlIwfATFxERURU3duxY\npKeno0OHDti0aRP69OlT5O7Qjx8/xsKFCxEeHo7jx49DW1u7ktMS0cfk5ORg586duHjxotBRiIgq\nRc+ePbFz5078e7uRmjVrAgDi4+Nhb28PCwuLUl+/oKAAEomEn3mIqEgc+UlERFQNfPfdd9iwYQMW\nLFgAa2trrFy5Erdu3UJiYiLi4+Nx8uRJODs7w9bWFhoaGjh37hyMjY2Fjk1E/xEUFIRmzZrByspK\n6ChERJVCVVUVhoaGMDIykn/VqlUL5ubmCAoKwvbt2yGRSODh4QEAePToEQYNGgQdHR3o6OjA2dkZ\niYmJ8ut5enrC1tYW27dvh5WVFdTU1JCVlQU3N7f3pr3/8ssvsLKygoaGBpo3b45du3ZV6vdORFUD\nR34SERFVEwMHDsSAAQMQGhoKX19fbN26FS9fvoSamhpMTEwwYsQIbNu2jSMfiKowbnRERPRWWFgY\nhg0bhtq1a2PdunVQU1ODTCbDwIEDoampiZCQEMhkMkyePBmDBg1CaGio/Nz79+9j9+7dOHDgAGrW\nrAlVVVWIRKJC158/fz4CAwOxceNG2NjY4NKlSxg7diz09fXRp0+fyv52iUhALD+JiIiqEZFIhLZt\n26Jt27ZCRyGiEkpISMC1a9dw+PBhoaMQEVWa/y7DIxKJMHnyZCxfvhyqqqpQV1eHoaEhAOD06dO4\ndesW7t27hwYNGgAAfv/9d1hZWeHMmTPo1q0bACAvLw87d+6EgYHBB++ZlZWFNWvW4PTp0+jYsSMA\nwMzMDFeuXMGGDRtYfhIpGZafRERERESVICAgAK6urlBTUxM6ChFRpenSpQu2bNlSaM3PWrVqffDY\nmJgYmJiYyItPADA3N4eJiQmioqLk5Wf9+vWLLD4BICoqCtnZ2XB0dCz0fH5+PszNzcvy7RBRNcTy\nk4iIiIioghUUFMDf3x9Hjx4VOgoRUaXS0NAol8Lx39PaNTU1P3qsVCoFABw5cqRQkQoANWrUKHMW\nIqpeWH4SEREREVWwU6dOwdjYGHZ2dkJHISKqspo0aYInT57g4cOHMDU1BQDcu3cPT548QdOmTYt9\nnc8++wyqqqpISEhAly5dKiouEVUTLD+JiIiIiCoYNzoiImWVk5ODlJSUQs9JJJIPTlvv0aMHbG1t\nMXz4cHh7e0Mmk2HatGlo3bo1vvjii2LfU0tLC7NmzcKsWbMglUrRuXNnZGRk4PLly5BIJPz7mEjJ\niIUOQERERKXj6enJUWRE1UBKSgr++usvDB06VOgoRESV7s8//4SJiYn8y9jYGK1atSry+KCgIBga\nGqJbt27o3r07TExMcOjQoRLfd8mSJVi0aBFWrVqFZs2aoVevXggMDOSan0RKSCT796rDREREVO6e\nPn2KZcuW4ejRo3j8+DEMDQ1hZ2eHKVOmlGm30aysLOTk5EBPT68c0xJReVuxYgWio6Ph7+8vdBQi\nIiIipcPyk4iIqAI9ePAAHTp0gK6uLpYsWQI7OztIpVL8+eefWLFiBRISEt47Jy8vj4vxEykImUyG\nxo0bw9/fHx07dhQ6DhEREZHS4bR3IiKiCjRx4kSIxWJcu3YNzs7OsLa2RqNGjTB58mTcuHEDACAW\ni+Hr6wtnZ2doaWlh/vz5kEqlGDNmDCwsLKChoQEbGxusWLGi0LU9PT1ha2srfyyTybBkyRKYmppC\nTU0NdnZ2CAoKkr/esWNHzJ49u9A10tPToaGhgT/++AMAsGvXLrRp0wY6OjqoU6cOXFxc8OTJk4p6\ne4gU3vnz5yEWi9GhQwehoxAREREpJZafREREFSQtLQ0nT57ElClToK6u/t7rOjo68j8vXrwY/fr1\nw61btzB58mRIpVLUr18fBw4cQExMDLy8vLB8+XIEBAQUuoZIJJL/2dvbG6tWrcKKFStw69YtDBo0\nCIMHD5aXrCNGjMCePXsKnX/gwAGoq6ujX79+AN6OOl28eDFu3LiBo0eP4sWLF3B1dS2394RI2bzb\n6Ojf/60SERERUeXhtHciIqIKcvXqVbRt2xaHDh3Cl19+WeRxYrEY06ZNg7e390evN2/ePFy7dg2n\nTp0C8Hbk58GDB+XlZv369TFx4kTMnz9ffk7Xrl3RoEED7NixA6mpqTA2NsaJEyfQtWtXAEDPnj1h\naWmJTZs2ffCeMTEx+Oyzz/D48WOYmJiU6PsnUnYvX75Ew4YNcffuXRgZGQkdh4iIiEgpceQnERFR\nBSnJ7xft7e3fe27Tpk1wcHCAkZERtLW1sWbNGjx8+PCD56enp+PJkyfvTa39/PPPERUVBQDQ19eH\no6Mjdu3aBQB48uQJzp49i2+++UZ+fHh4OJycnNCwYUPo6OjAwcEBIpGoyPsSUdF2796Nnj17svgk\nIiIiEhDLTyIiogpibW0NkUiE6OjoTx6rqalZ6PHevXsxY8YMeHh44NSpU4iMjMSkSZOQm5tb4hz/\nnm47YsQIHDx4ELm5udizZw9MTU3lm7BkZWXB0dERWlpa2LlzJ8LCwnDixAnIZLJS3ZdI2b2b8k5E\nREREwmH5SUREVEH09PTQu3dvrF+/HllZWe+9/urVqyLPvXDhAtq1a4eJEyeiRYsWsLCwQFxcXJHH\na2trw8TEBBcuXCj0/Pnz5/HZZ5/JHw8cOBAAEBwcjN9//73Qep4xMTF48eIFli1bhs8//xw2NjZI\nSUnhWoVEpRAREYHnz5+jR48eQkchIiIiUmosP4mIiCrQhg0bIJPJ0Lp1axw4cAB3797FnTt3sHHj\nRjRv3rzI82xsbBAeHo4TJ04gLi4OS5Yswblz5z56r9mzZ2PlypXYs2cPYmNjsXDhQpw/f77QDu+q\nqqoYPHgwli5dioiICIwYMUL+mqmpKVRVVeHj44P79+/j6NGjWLhwYdnfBCIltHXrVnh4eEAikQgd\nhYiIiEipqQgdgIiISJGZm5sjPDwcXl5emDt3LhITE1G7dm00a9ZMvsHRh0ZWjh8/HpGRkRg+fDhk\nMhmcnZ0xa9Ys+Pv7F3mvadOmISMjA99//z1SUlLQqFEjBAYGolmzZoWOGzFiBLZt24ZWrVqhcePG\n8ucNDAywfft2/O9//4Ovry/s7OywZs0aODo6ltO7QaQc3rx5g927dyMiIkLoKERERERKj7u9ExER\nERGVo507d2LXrl04fvy40FGIiIiIlB6nvRMRERERlSNudERERERUdXDkJxERERFRObl79y46deqE\nR48eoWbNmkLHISIiIlJ6XPOTiIiIiKgE8vPzceTIEWzevBk3b97Eq1evoKmpiYYNG6JWrVoYOnQo\ni08iIiKiKoLT3omIiIiIikEmk2H9+vWwsLDAL7/8guHDh+PixYt4/PgxIiIi4OnpCalUih07duC7\n775Ddna20JGJiIiIlB6nvRMRERERfYJUKsWECRMQFhaGrVu3omXLlkUe++jRI8ycORNPnjzBkSNH\nUKtWrUpMSkRERET/xvKTiIiIiOgTZs6ciatXr+LYsWPQ0tL65PFSqRRTp05FVFQUTpw4AVVV1UpI\nSURERET/xWnvREREREQf8c8//yAwMBCHDx8uVvEJAGKxGOvWrYOGhgbWrVtXwQmJiIiIqCgc+UlE\nRERE9BFDhw5Fhw4dMG3atBKfGxoaiqFDhyIuLg5iMccdEBEREVU2fgIjIiIiIipCcnIyTp48iZEj\nR5bqfAcHB+jr6+PkyZPlnIyIiIiIioPlJxERERFREQIDAzFw4MBSb1okEokwevRo7N69u5yTERER\nEVFxsPwkIiIiIipCcnIyzM3Ny3QNc3NzJCcnl1MiIiIiIioJlp9EREREREXIzc1FzZo1y3SNmjVr\nIjc3t5wSEREREVFJsPwkIiIiIiqCnp4eUlNTy3SN1NTUUk+bJyIiIqKyYflJRERERFSEjh07Ijg4\nGDKZrNTXCA4Oxueff16OqYiIiIiouFh+EhEREREVoWPHjlBVVcWZM2dKdf7z588RFBQEd3f3ck5G\nRERERMXB8pOIiIiIqAgikQiTJk3CunXrSnX+li1b4OTkhNq1a5dzMiIiIiIqDpGsLHN4iIiIiIgU\nXEZGBtq0aYPx48fj22+/LfZ5586dw1dffYVz586hcePGFZiQiIiIiIqiInQAIiIiIqKqTEtLC8eO\nHUPnzp2Rl5eHmTNnQiQSffSc48ePY+TIkdi9ezeLTyIiIiIBceQnEREREVExPH78GAMGDECNGjUw\nadIkDBkyBOrq6vLXpVIpTp48CV9fX4SFheHgwYPo0KGDgImJiIiIiOUnEREREVExFRQU4MSJE/D1\n9UVoaCjs7e2hq6uLzMxM3L59G/r6+pg8eTKGDh0KDQ0NoeMSERERKT2Wn0REREREpZCQkICoqCi8\nfv0ampqaMDMzg62t7SenxBMRERFR5WH5SURERERERERERApJLHQAIiIiIiIiIiIioorA8pOIiIiI\niIiIiIgUEstPIiIiIiIiIiIiUkgsP4mIiIiI/j9zc3OsXr26Uu4VEhICiUSC1NTUSrkfERERkTLi\nhkdEREREpBSePn2K5cuX4+jRo3j06BF0dXVhZWWFoUOHwt3dHZqamnjx4gU0NTWhpqZW4Xny8/OR\nmpoKIyOjCr8XERERkbJSEToAEREREVFFe/DgATp06IBatWph2bJlsLW1hbq6Om7fvg0/Pz8YGBhg\n6NChqF27dpnvlZeXhxo1anzyOBUVFRafRERERBWM096JiIiISOFNmDABKioquHbtGr7++ms0btwY\nZmZm6Nu3LwIDAzF06FAA7097F4vFCAwMLHStDx3j6+sLZ2dnaGlpYf78+QCAo0ePonHjxlBXV0e3\nbt2wb98+iMViPHz4EMDbae9isVg+7X3btm3Q1tYudK//HkNEREREJcPyk4iIiIgUWmpqKk6dOoUp\nU6ZU2HT2xYsXo1+/frh16xYmT56MR48ewdnZGQMGDMCNGzcwZcoUzJkzByKRqNB5/34sEonee/2/\nxxARERFRybD8JCIiIiKFFhcXB5lMBhsbm0LPN2jQANra2tDW1sakSZPKdI+hQ4fCw8MDDRs2hJmZ\nGTZu3AhLS0usWLEC1tbWGDx4MMaPH1+mexARERFRybH8JCIiIiKldP78eURGRqJNmzbIzs4u07Xs\n7e0LPY6JiYGDg0Oh59q2bVumexARERFRybH8JCIiIiKFZmVlBZFIhJiYmELPm5mZwcLCAhoaGkWe\nKxKJIJPJCj2Xl5f33nGampplzikWi4t1LyIiIiIqPpafRERERKTQ9PX10atXL6xfvx6ZmZklOtfQ\n0BBJSUnyxykpKYUeF6Vx48YICwsr9NyVK1c+ea+srCxkZGTIn4uIiChRXiIiIiIqjOUnERERESk8\nX19fSKVStG7dGnv27EF0dDRiY2Oxe/duREZGQkVF5YPndevWDRs2bMC1a9cQEREBd3d3qKurf/J+\nEyZMQHx8PGbPno27d+8iMDAQv/76K4DCGxj9e6Rn27ZtoampiXnz5iE+Ph4HDx7Exo0by/idExER\nESk3lp9EREREpPDMzc0REREBR0dHLFy4EK1atYK9vT28vb0xefJkrFmzBsD7O6uvWrUKFhYW6Nq1\nK1xcXDB27FgYGRkVOuZDu7Gbmpri4MGDCA4ORosWLbB27Vr8+OOPAFBox/l/n6unp4ddu3bh9OnT\nsLOzg5+fH5YuXVpu7wERERGRMhLJ/ruwEBERERERlbu1a9di0aJFSEtLEzoKERERkdL48PweIiIi\nIiIqE19fXzg4OMDQ0BCXLl3C0qVL4e7uLnQsIiIiIqXC8pOIiIiIqALExcXBy8sLqampqF+/PiZN\nmoQFCxYIHYuIiIhIqXDaOxERERERERERESkkbnj0/9qxAxkAAACAQf7W9/gKIwAAAABgSX4CAAAA\nAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAALMlPAAAAAGBJfgIA\nAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAAAAAsyU8AAAAAYEl+\nAgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJAAAAACzJTwAAAABg\nSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAALMlPAAAA\nAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAAAAAsyU8A\nAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJAAAAACzJ\nTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl+QkAAAAA\nLMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAAgCX5CQAA\nAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEAAACAJfkJ\nAAAAACzJTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/AQAAAIAl\n+QkAAAAALMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACwJD8BAAAA\ngCX5CQAAAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAAALAkPwEA\nAACAJfkJAAAAACzJTwAAAABgSX4CAAAAAEvyEwAAAABYkp8AAAAAwJL8BAAAAACW5CcAAAAAsCQ/\nAQAAAIAl+QkAAAAALMlPAAAAAGBJfgIAAAAAS/ITAAAAAFiSnwAAAADAkvwEAAAAAJbkJwAAAACw\nJD8BAAAAgCX5CQAAAAAsyU8AAAAAYEl+AgAAAABL8hMAAAAAWJKfAAAAAMCS/AQAAAAAluQnAAAA\nALAkPwEAAACAJfkJAAA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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nsAOsCRtjAIXz4YcfVgeHzz//fK6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -640,13 +674,7 @@ } ], "source": [ - "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", - "w = widgets.interactive(slider_callback, iteration = slider)\n", - "display(w)\n", - "\n", - "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "a = widgets.interactive(visualize_callback, Visualize = button)\n", - "display(a)" + "display_visual(all_node_colors)" ] }, { @@ -668,8 +696,8 @@ }, "outputs": [], "source": [ - "node_colors = dict(initial_node_colors)\n", - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", + "node_colors = dict(initial_node_colors)" ] }, { @@ -705,7 +733,7 @@ " frontier.append(node)\n", " \n", " # modify the color of frontier nodes to blue\n", - " node_colors[node.state] = \"blue\"\n", + " node_colors[node.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", @@ -727,7 +755,7 @@ " return child\n", " frontier.append(child)\n", "\n", - " node_colors[child.state] = \"blue\"\n", + " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", @@ -748,48 +776,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "24\n", + "['Sibiu', 'Fagaras', 'Bucharest']\n", + "23\n", "24\n" ] } ], "source": [ - "breadth_first_search(romania_problem).solution()\n", + "solution = breadth_first_search(romania_problem).solution()\n", "\n", - "print(len(all_node_colors))\n", - "print(iterations)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def slider_callback(iteration):\n", - " show_map(all_node_colors[iteration])\n", + "all_node_colors.append(final_path_colors(romania_problem, solution))\n", "\n", - "def visualize_callback(Visualize):\n", - " if Visualize is True:\n", - " for i in range(slider.max + 1):\n", - " slider.value = i\n", - "# time.sleep(.5)" + "print(solution)\n", + "print(iterations)\n", + "print(len(all_node_colors))" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nAAAAluQnAAAAALAkPwE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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nzj777Bx//PEpLy9P06ZNc+aZZ+b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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -797,13 +811,7 @@ } ], "source": [ - "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", - "w = widgets.interactive(slider_callback, iteration = slider)\n", - "display(w)\n", - "\n", - "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "a = widgets.interactive(visualize_callback, Visualize = button)\n", - "display(a)" + "display_visual(all_node_colors)" ] }, { @@ -817,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -829,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -866,7 +874,7 @@ " frontier = PriorityQueue(min, f)\n", " frontier.append(node)\n", " \n", - " node_colors[node.state] = \"blue\"\n", + " node_colors[node.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", @@ -888,7 +896,7 @@ " for child in node.expand(problem):\n", " if child.state not in explored and child not in frontier:\n", " frontier.append(child)\n", - " node_colors[child.state] = \"blue\"\n", + " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " elif child in frontier:\n", @@ -896,7 +904,7 @@ " if f(child) < f(incumbent):\n", " del frontier[incumbent]\n", " frontier.append(child)\n", - " node_colors[child.state] = \"blue\"\n", + " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", "\n", @@ -912,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -921,48 +929,34 @@ "name": "stdout", "output_type": "stream", "text": [ + "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']\n", "41\n", - "41\n" + "42\n" ] } ], "source": [ - "uniform_cost_search(romania_problem).solution()\n", + "solution = uniform_cost_search(romania_problem).solution()\n", "\n", - "print(len(all_node_colors))\n", - "print(iterations)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def slider_callback(iteration):\n", - " show_map(all_node_colors[iteration])\n", + "all_node_colors.append(final_path_colors(romania_problem, solution))\n", "\n", - "def visualize_callback(Visualize):\n", - " if Visualize is True:\n", - " for i in range(slider.max + 1):\n", - " slider.value = i\n", - "# time.sleep(.5)" + "print(solution)\n", + "print(iterations)\n", + "print(len(all_node_colors))" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nAAAAWJKfAAAAAMCS/AQ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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -970,13 +964,7 @@ } ], "source": [ - "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", - "w = widgets.interactive(slider_callback, iteration = slider)\n", - "display(w)\n", - "\n", - "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "a = widgets.interactive(visualize_callback, Visualize = button)\n", - "display(a)" + "display_visual(all_node_colors)" ] }, { @@ -990,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -1002,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": { "collapsed": true }, @@ -1039,7 +1027,7 @@ " frontier = PriorityQueue(min, f)\n", " frontier.append(node)\n", " \n", - " node_colors[node.state] = \"blue\"\n", + " node_colors[node.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " \n", @@ -1061,7 +1049,7 @@ " for child in node.expand(problem):\n", " if child.state not in explored and child not in frontier:\n", " frontier.append(child)\n", - " node_colors[child.state] = \"blue\"\n", + " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", " elif child in frontier:\n", @@ -1069,7 +1057,7 @@ " if f(child) < f(incumbent):\n", " del frontier[incumbent]\n", " frontier.append(child)\n", - " node_colors[child.state] = \"blue\"\n", + " node_colors[child.state] = \"orange\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", "\n", @@ -1088,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -1097,48 +1085,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "41\n", - "41\n" + "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']\n", + "24\n", + "25\n" ] } ], "source": [ - "uniform_cost_search(romania_problem).solution()\n", + "solution = astar_search(romania_problem).solution()\n", "\n", - "print(len(all_node_colors))\n", - "print(iterations)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def slider_callback(iteration):\n", - " show_map(all_node_colors[iteration])\n", + "all_node_colors.append(final_path_colors(romania_problem, solution))\n", "\n", - "def visualize_callback(Visualize):\n", - " if Visualize is True:\n", - " for i in range(slider.max + 1):\n", - " slider.value = i\n", - "# time.sleep(.5)" + "print(solution)\n", + "print(iterations)\n", + "print(len(all_node_colors))" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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pOv/PP//UN998owsXLihfvnyZnC7vuHLlimrWrKkdO3YwCwUAgGwQGxur6dOn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\nAAAAWJKfAAAAAMCS/AQ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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1146,13 +1120,7 @@ } ], "source": [ - "slider = widgets.IntSlider(min=0, max=iterations-1, step=1, value=0)\n", - "w = widgets.interactive(slider_callback, iteration = slider)\n", - "display(w)\n", - "\n", - "button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - "a = widgets.interactive(visualize_callback, Visualize = button)\n", - "display(a)" + "display_visual(all_node_colors)" ] }, { @@ -1185,413 +1153,357 @@ }, "widgets": { "state": { - "01c5cebb63c540dd959a164ec54b7506": { - "views": [] - }, - "03620569743d4d2e942bdfda14684624": { - "views": [] - }, - "0386e04d7d17499d81fa0d07dcd3b6ea": { - "views": [] - }, - "03c57ee34df6417b92be5cd7a0f1a045": { - "views": [] + "05b6ffb7f1e8468a91bf39e09d6ceada": { + "views": [ + { + "cell_index": 44 + } + ] }, - "057cacf5c97a442ba2b4f2e14252975a": { + "07e1c465e75e46958607250feeb85ddf": { "views": [] }, - "06cbbf6363eb434e92cc337fe827d1dd": { - "views": [] + 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318 deletions(-) diff --git a/search.ipynb b/search.ipynb index 51b652341..034a8874f 100644 --- a/search.ipynb +++ b/search.ipynb @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Lugoj': (165, 379), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Neamt': (406, 537), 'Eforie': (562, 293), 'Drobeta': (165, 299), 'Pitesti': (320, 368), 'Mehadia': (168, 339), 'Giurgiu': (375, 270), 'Hirsova': (534, 350), 'Vaslui': (509, 444), 'Craiova': (253, 288), 'Arad': (91, 492), 'Fagaras': (305, 449), 'Zerind': (108, 531), 'Sibiu': (207, 457), 'Rimnicu': (233, 410), 'Bucharest': (400, 327), 'Oradea': (131, 571), 'Iasi': (473, 506)}\n" + "{'Arad': (91, 492), 'Oradea': (131, 571), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Pitesti': (320, 368), 'Bucharest': (400, 327), 'Zerind': (108, 531), 'Rimnicu': (233, 410), 'Fagaras': (305, 449), 'Drobeta': (165, 299), 'Lugoj': (165, 379), 'Mehadia': (168, 339), 'Sibiu': (207, 457), 'Hirsova': (534, 350), 'Craiova': (253, 288), 'Eforie': (562, 293), 'Urziceni': (456, 350), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Timisoara': (94, 410)}\n" ] } ], @@ -445,7 +445,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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rYGRkJHYcIiIiKmEjRozA4cOHER8fL3YUIqrgWPwkIvoO2dnZ4NLJVJzK4rR3QRDg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\nj/Z+XLlyZY3rTz31VIYOHVo9a3T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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -495,7 +495,7 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -511,23 +511,79 @@ " return final_colors\n", "\n", "\n", - "def display_visual(all_node_colors):\n", - " def slider_callback(iteration):\n", - " show_map(all_node_colors[iteration])\n", + "def display_visual(user_input, algorithm, problem=None):\n", + " if user_input == False:\n", + " def slider_callback(iteration):\n", + " # don't show graph for the first time running the cell calling this function\n", + " try:\n", + " show_map(all_node_colors[iteration])\n", + " except:\n", + " pass\n", + " def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " button.value = False\n", + " \n", + " global all_node_colors\n", + " \n", + " iterations, all_node_colors, node = algorithm(problem)\n", + " solution = node.solution()\n", + " all_node_colors.append(final_path_colors(problem, solution))\n", + " \n", + " slider.max = len(all_node_colors) - 1\n", + " \n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + " # time.sleep(.5)\n", + " \n", + " slider = widgets.IntSlider(min=0, max=1, step=1, value=0)\n", + " slider_visual = widgets.interactive(slider_callback, iteration = slider)\n", + " display(slider_visual)\n", "\n", - " def visualize_callback(Visualize):\n", - " if Visualize is True:\n", - " for i in range(slider.max + 1):\n", - " slider.value = i\n", - "# time.sleep(.5)\n", + " button = widgets.ToggleButton(value = False)\n", + " button_visual = widgets.interactive(visualize_callback, Visualize = button)\n", + " display(button_visual)\n", + " \n", + " if user_input == True: \n", + " node_colors = dict(initial_node_colors)\n", + " \n", + " def slider_callback(iteration):\n", + " # don't show graph for the first time running the cell calling this function\n", + " try:\n", + " show_map(all_node_colors[iteration])\n", + " except:\n", + " pass\n", + " def visualize_callback(Visualize):\n", + " if Visualize is True:\n", + " button.value = False\n", + " \n", + " problem = GraphProblem(start_dropdown.value, end_dropdown.value, romania_map)\n", + " global all_node_colors\n", + " \n", + " iterations, all_node_colors, node = algorithm(problem)\n", + " solution = node.solution()\n", + " all_node_colors.append(final_path_colors(problem, solution))\n", "\n", - " slider = widgets.IntSlider(min=0, max=len(all_node_colors)-1, step=1, value=0)\n", - " w = widgets.interactive(slider_callback, iteration = slider)\n", - " display(w)\n", + " slider.max = len(all_node_colors) - 1\n", + " \n", + " for i in range(slider.max + 1):\n", + " slider.value = i\n", + "# time.sleep(.5)\n", + " \n", + " \n", + " start_dropdown = widgets.Dropdown(description = \"Start city: \", options = sorted(list(node_colors.keys())), value = \"Arad\")\n", + " display(start_dropdown)\n", "\n", - " button = widgets.ToggleButton(desctiption = \"Visualize\", value = False)\n", - " a = widgets.interactive(visualize_callback, Visualize = button)\n", - " display(a)" + " end_dropdown = widgets.Dropdown(description = \"Goal city: \", options = sorted(list(node_colors.keys())), value = \"Fagaras\")\n", + " display(end_dropdown)\n", + " \n", + " button = widgets.ToggleButton(value = False)\n", + " button_visual = widgets.interactive(visualize_callback, Visualize = button)\n", + " display(button_visual)\n", + " \n", + " slider = widgets.IntSlider(min=0, max=1, step=1, value=0)\n", + " slider_visual = widgets.interactive(slider_callback, iteration = slider)\n", + " display(slider_visual)\n", + " " ] }, { @@ -549,18 +605,6 @@ "collapsed": false }, "outputs": [], - "source": [ - "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)\n", - "node_colors = dict(initial_node_colors)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], "source": [ "def tree_search(problem, frontier):\n", " \"\"\"Search through the successors of a problem to find a goal.\n", @@ -568,10 +612,9 @@ " Don't worry about repeated paths to a state. [Figure 3.7]\"\"\"\n", " \n", " # we use these two variables at the time of visualisations\n", - " global iterations\n", " iterations = 0\n", - " global all_node_colors\n", " all_node_colors = []\n", + " node_colors = dict(initial_node_colors)\n", " \n", " frontier.append(Node(problem.initial))\n", " \n", @@ -592,7 +635,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " frontier.extend(node.expand(problem))\n", " \n", @@ -610,7 +653,8 @@ "\n", "def breadth_first_tree_search(problem):\n", " \"Search the shallowest nodes in the search tree first.\"\n", - " return tree_search(problem, FIFOQueue())" + " iterations, all_node_colors, node = tree_search(problem, FIFOQueue())\n", + " return(iterations, all_node_colors, node)" ] }, { @@ -620,53 +664,50 @@ "Let's call the modified `breadth_first_tree_search` with our problem statement. Print `iterations` to see how many intermediate steps we have got " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback function which are called when we interact with slider and the button.\n", + "\n" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Sibiu', 'Fagaras']\n", - "27\n", - "28\n" - ] + "data": { + "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nsAOsCRtjAIXz4YcfVgeHzz//fK6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "solution = breadth_first_tree_search(romania_problem).solution()\n", - "\n", - "all_node_colors.append(final_path_colors(romania_problem, solution))\n", - "\n", - "print(solution)\n", - "print(iterations)\n", - "print(len(all_node_colors))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback function which are called when we interact with slider and the button.\n", - "\n" + "all_node_colors = []\n", + "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)\n", + "display_visual(user_input = False, algorithm = breadth_first_tree_search, problem = romania_problem)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nsAOsCRtjAIXz4YcfVgeHzz//fK6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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nHUWLFsXLy4uwsLCXPsbNmzfx9vambt26VKxYkWrVq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7aNypUrA1CgQAEOHjzI3LlzqVev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\n050WLlyIsLAwpKSkoG3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -674,7 +715,8 @@ } ], "source": [ - "display_visual(all_node_colors)" + "all_node_colors = []\n", + "display_visual(user_input = True, algorithm = breadth_first_tree_search)" ] }, { @@ -690,19 +732,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", - "node_colors = dict(initial_node_colors)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -712,10 +742,9 @@ " \"[Figure 3.11]\"\n", " \n", " # we use these two variables at the time of visualisations\n", - " global iterations\n", " iterations = 0\n", - " global all_node_colors\n", " all_node_colors = []\n", + " node_colors = dict(initial_node_colors)\n", " \n", " node = Node(problem.initial)\n", " \n", @@ -727,7 +756,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " frontier = FIFOQueue()\n", " frontier.append(node)\n", @@ -752,7 +781,7 @@ " node_colors[child.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return child\n", + " return(iterations, all_node_colors, child)\n", " frontier.append(child)\n", "\n", " node_colors[child.state] = \"orange\"\n", @@ -767,43 +796,40 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Sibiu', 'Fagaras', 'Bucharest']\n", - "23\n", - "24\n" - ] + "data": { + "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nPZS99957HVYFAMCqEn4CAMBK7LXXXikrK8u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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "solution = breadth_first_search(romania_problem).solution()\n", - "\n", - "all_node_colors.append(final_path_colors(romania_problem, solution))\n", - "\n", - "print(solution)\n", - "print(iterations)\n", - "print(len(all_node_colors))" + "all_node_colors = []\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", + "display_visual(user_input = False, algorithm = breadth_first_search, problem = romania_problem)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nPZS99957HVYFAMCqEn4CAMBK7LXXXikrK8u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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\ny5133lnjvoMOOii77bZbunfvnj5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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -811,7 +837,8 @@ } ], "source": [ - "display_visual(all_node_colors)" + "all_node_colors = []\n", + "display_visual(user_input = True, algorithm = breadth_first_search)" ] }, { @@ -825,19 +852,7 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "node_colors = dict(initial_node_colors)\n", - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -853,10 +868,9 @@ " a best first search you can examine the f values of the path returned.\"\"\"\n", " \n", " # we use these two variables at the time of visualisations\n", - " global iterations\n", " iterations = 0\n", - " global all_node_colors\n", " all_node_colors = []\n", + " node_colors = dict(initial_node_colors)\n", " \n", " f = memoize(f, 'f')\n", " node = Node(problem.initial)\n", @@ -869,7 +883,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " frontier = PriorityQueue(min, f)\n", " frontier.append(node)\n", @@ -890,7 +904,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " explored.add(node.state)\n", " for child in node.expand(problem):\n", @@ -915,48 +929,46 @@ "\n", "def uniform_cost_search(problem):\n", " \"[Figure 3.14]\"\n", - " return best_first_graph_search(problem, lambda node: node.path_cost)" + " iterations, all_node_colors, node = best_first_graph_search(problem, lambda node: node.path_cost)\n", + " return(iterations, all_node_colors, node)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']\n", - "41\n", - "42\n" - ] + "data": { + "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "solution = uniform_cost_search(romania_problem).solution()\n", - "\n", - "all_node_colors.append(final_path_colors(romania_problem, solution))\n", - "\n", - "print(solution)\n", - "print(iterations)\n", - "print(len(all_node_colors))" + "all_node_colors = []\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", + "display_visual(user_input = False, algorithm = uniform_cost_search, problem = romania_problem)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nzUb+Vb06aVi+YYctAZvX6NGjc/z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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -964,7 +976,8 @@ } ], "source": [ - "display_visual(all_node_colors)" + "all_node_colors = []\n", + "display_visual(user_input = True, algorithm = uniform_cost_search)" ] }, { @@ -978,19 +991,7 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "node_colors = dict(initial_node_colors)\n", - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -1006,10 +1007,9 @@ " a best first search you can examine the f values of the path returned.\"\"\"\n", " \n", " # we use these two variables at the time of visualisations\n", - " global iterations\n", " iterations = 0\n", - " global all_node_colors\n", " all_node_colors = []\n", + " node_colors = dict(initial_node_colors)\n", " \n", " f = memoize(f, 'f')\n", " node = Node(problem.initial)\n", @@ -1022,7 +1022,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " frontier = PriorityQueue(min, f)\n", " frontier.append(node)\n", @@ -1043,7 +1043,7 @@ " node_colors[node.state] = \"green\"\n", " iterations += 1\n", " all_node_colors.append(dict(node_colors))\n", - " return node\n", + " return(iterations, all_node_colors, node)\n", " \n", " explored.add(node.state)\n", " for child in node.expand(problem):\n", @@ -1071,48 +1071,47 @@ " You need to specify the h function when you call astar_search, or\n", " else in your Problem subclass.\"\"\"\n", " h = memoize(h or problem.h, 'h')\n", - " return best_first_graph_search(problem, lambda n: n.path_cost + h(n))" + " iterations, all_node_colors, node = best_first_graph_search(problem, lambda n: n.path_cost + h(n))\n", + " return(iterations, all_node_colors, node)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest']\n", - "24\n", - "25\n" - ] + "data": { + "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "solution = astar_search(romania_problem).solution()\n", - "\n", - "all_node_colors.append(final_path_colors(romania_problem, solution))\n", - "\n", - "print(solution)\n", - "print(iterations)\n", - "print(len(all_node_colors))" + "all_node_colors = []\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", + "display_visual(user_input = False, algorithm = astar_search, problem = romania_problem)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": false }, "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nzUb+Vb06aVi+YYctAZvX6NGjc/z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"views": [ + { + "cell_index": 44 + } + ] + }, + "ff2784288a024ab3879aff54c2927a6d": { + "views": [] + }, + "ff3d7c7d2f00430381ccebf11ed43061": { + "views": [ + { + "cell_index": 44 + } + ] + }, + "ff7c494fcd594f1f9ee597a9e627f981": { + "views": [] + }, + "ff90139706b145eb8e276e932e66a8cd": { + "views": [] + }, + "ffadb2ad36fa418981e017132de6d62f": { + "views": [ + { + "cell_index": 44 + } + ] + }, + "ffc226886b594ff39e6783a0e19a4c1e": { + "views": [] } }, "version": "1.1.1" From c541d31e89c7dd3746a21dd2cc53159a935055e1 Mon Sep 17 00:00:00 2001 From: SnShine Date: Sun, 19 Jun 2016 19:34:20 +0530 Subject: [PATCH 324/513] users can select searching algorithm to search on romania map --- search.ipynb | 3634 +++++++------------------------------------------- 1 file changed, 459 insertions(+), 3175 deletions(-) diff --git a/search.ipynb b/search.ipynb index 034a8874f..5446e9711 100644 --- a/search.ipynb +++ b/search.ipynb @@ -298,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Arad': (91, 492), 'Oradea': (131, 571), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Pitesti': (320, 368), 'Bucharest': (400, 327), 'Zerind': (108, 531), 'Rimnicu': (233, 410), 'Fagaras': (305, 449), 'Drobeta': (165, 299), 'Lugoj': (165, 379), 'Mehadia': (168, 339), 'Sibiu': (207, 457), 'Hirsova': (534, 350), 'Craiova': (253, 288), 'Eforie': (562, 293), 'Urziceni': (456, 350), 'Vaslui': (509, 444), 'Iasi': (473, 506), 'Timisoara': (94, 410)}\n" + "{'Rimnicu': (233, 410), 'Hirsova': (534, 350), 'Arad': (91, 492), 'Vaslui': (509, 444), 'Oradea': (131, 571), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Fagaras': (305, 449), 'Bucharest': (400, 327), 'Sibiu': (207, 457), 'Urziceni': (456, 350), 'Lugoj': (165, 379), 'Craiova': (253, 288), 'Zerind': (108, 531), 'Iasi': (473, 506), 'Mehadia': (168, 339), 'Pitesti': (320, 368), 'Timisoara': (94, 410), 'Drobeta': (165, 299), 'Eforie': (562, 293)}\n" ] } ], @@ -445,7 +445,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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rYGRkJHYcIiIiKmEjRozA4cOHER8fL3YUIqrgWPwkIvoO2dnZ4NLJVJzK4rR3QRDg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\nj/Z+XLlyZY3rTz31VIYOHVo9a3T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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -511,7 +511,7 @@ " return final_colors\n", "\n", "\n", - "def display_visual(user_input, algorithm, problem=None):\n", + "def display_visual(user_input, algorithm=None, problem=None):\n", " if user_input == False:\n", " def slider_callback(iteration):\n", " # don't show graph for the first time running the cell calling this function\n", @@ -543,8 +543,12 @@ " button_visual = widgets.interactive(visualize_callback, Visualize = button)\n", " display(button_visual)\n", " \n", - " if user_input == True: \n", + " if user_input == True:\n", " node_colors = dict(initial_node_colors)\n", + " if algorithm == None:\n", + " algorithms = {\"Breadth First Tree Search\": breadth_first_tree_search, \"Breadth First Search\": breadth_first_search, \"Uniform Cost Search\": uniform_cost_search, \"A-star Search\": astar_search}\n", + " algo_dropdown = widgets.Dropdown(description = \"Search algorithm: \", options = sorted(list(algorithms.keys())), value = \"Breadth First Tree Search\")\n", + " display(algo_dropdown)\n", " \n", " def slider_callback(iteration):\n", " # don't show graph for the first time running the cell calling this function\n", @@ -552,6 +556,7 @@ " show_map(all_node_colors[iteration])\n", " except:\n", " pass\n", + " \n", " def visualize_callback(Visualize):\n", " if Visualize is True:\n", " button.value = False\n", @@ -559,7 +564,13 @@ " problem = GraphProblem(start_dropdown.value, end_dropdown.value, romania_map)\n", " global all_node_colors\n", " \n", - " iterations, all_node_colors, node = algorithm(problem)\n", + " if algorithm == None:\n", + " user_algorithm = algorithms[algo_dropdown.value]\n", + " \n", + "# print(user_algorithm)\n", + "# print(problem)\n", + " \n", + " iterations, all_node_colors, node = user_algorithm(problem)\n", " solution = node.solution()\n", " all_node_colors.append(final_path_colors(problem, solution))\n", "\n", @@ -568,8 +579,7 @@ " for i in range(slider.max + 1):\n", " slider.value = i\n", "# time.sleep(.5)\n", - " \n", - " \n", + " \n", " start_dropdown = widgets.Dropdown(description = \"Start city: \", options = sorted(list(node_colors.keys())), value = \"Arad\")\n", " display(start_dropdown)\n", "\n", @@ -683,7 +693,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg\noCC2bduGh4cHPj4+dOjQgQIFCqTyUYgk5efnR+7cuZk+fbrRKSIiImLlgoOD+fTTTzly5Mhz3zuL\niIikNQ0/xaqdP3+ehm81JCpvFDHVY6Ao8H/flz0BwsDlqAvtmrRj5dKV2NnZvdD+4+Li+PbbbwkK\nCmLnzp3UqFEDHx8f2rdvT968eVP7cES4efMmbm5u7N+/n0qVKhmdIyIiIlbMbDZTvXp1AgICaNu2\nrdE5IiJipTT8FKsXGRnJ0mVLmTlvJtE20TxyfQROQALYP7TH9owtderUYfig4TRr1izZn1rHxMTw\n9ddfs2HDBnbt2kXdunXx8fGhXbt25MqVK3UPSqza3Llz2bZtG7t379YqCxERETHU9u3bGTlyJCdP\nnsTGRrecEBGR9Kfhp8j/Yzab+e677/jx4I/8cPAH7t+7T/d3utOpUydKliyZqt8rOjqaHTt2EBQU\nxN69e2nQoAE+Pj60bt2aHDlypOr3EusTHx9PtWrVGDduHG+//bbROSIiImLFLBYL9erVY9CgQXTu\n3NnoHBERsUIafooY7MGDB2zfvp2goCB++OEHGjVqhI+PD61atSJr1qxG50kmtX//frp3786ZM2dw\ncXExOkdERESs2J49e+jXrx9hYWEvfPkoERGR1KLhp0gGcv/+fbZu3cqGDRsICQmhcePG+Pj40KJF\nC5ydnY3Ok0zG19eX0qVLM3HiRKNTRERExIpZLBY8PT1599136dmzp9E5IiJiZTT8FMmg7t69y5Yt\nWwgKCuLo0aM0a9aMTp060axZMxwdHY3Ok0zgjz/+oEqVKhw6dIgyZcoYnSMiIiJW7ODBg3Tt2pXz\n58/j4OBgdI6IiFgRDT9FMoFbt26xefNmgoKCOHHiBC1btsTHx4cmTZrozaP8o8DAQA4ePMj27duN\nThEREREr16xZM1q1aoW/v7/RKSIiYkU0/BTJZK5fv87GjRsJCgrizJkztGnTBh8fH7y8vLC3tzc6\nTzKYuLg4PDw8mDFjBi1btjQ6R0RERKzYsWPHaNOmDRcuXMDJycnoHBERsRIafoqkklatWpEvXz5W\nrFiRbt/z6tWrBAcHExQUxMWLF2nXrh0+Pj40bNhQF5OXRN9++y39+vXj9OnTumSCiIiIGKp9+/a8\n/vrrDB482OgUERGxEjZGB4iktePHj2NnZ0eDBg2MTkl1RYsW5cMPP+TQoUMcPXqUsmXLMmLECIoU\nKYK/vz/79+8nISHB6EwxmLe3N+7u7syYMcPoFBEREbFy48ePJzAwkIcPHxqdIiIiVkLDT3nlLVu2\nLHHV27lz5/5x2/j4+HSqSn2urq4MGzaMY8eOERISQtGiRRk4cCDFihXjgw8+ICQkBLPZbHSmGGTm\nzJnMmjWL8PBwo1NERETEirm7u+Pl5cXcuXONThERESuh4ae80mJjY1m7di3vv/8+HTp0YNmyZYnP\n/f7779jY2LB+/Xq8vLxwcXFhyZIl3Lt3D19fX4oVK4azszNubm6sWrUqyX5jYmLo0aMH2bJlo1Ch\nQnzyySfpfGT/rEyZMowcOZITJ06wb98+8ubNy/vvv0+JEiUYMmQIR44cQVe8sC4lS5ZkwIABDBky\nxOgUERERsXIBAQHMnj2byMhIo1NERMQKaPgpr7Tg4GBcXV2pXLky3bp144svvnjmNPCRI0fSr18/\nzpw5Q9u2bYmNjaVGjRp8/fXXnDlzhkGDBvGf//yH77//PvE1Q4YMYe/evWzZsoW9e/dy/PhxDhw4\nkN6H90IqVKjA2LFjCQsL45tvvsHFxYVu3bpRqlQpRowYQWhoqAahVmL48OEcO3aMPXv2GJ0iIiIi\nVqxcuXK0bt2amTNnGp0iIiJWQDc8kleap6cnrVu35sMPPwSgVKlSTJ8+nfbt2/P7779TsmRJZs6c\nyaBBg/5xP126dCFbtmwsWbKE6Oho8uTJw6pVq+jcuTMA0dHRFC1alHbt2qXrDY+Sy2KxcPLkSYKC\ngtiwYQM2Njb4+PjQqVMn3N3dMZlMRidKGvnqq6/46KOPOHnyJA4ODkbniIiIiJW6cuUKNWrU4Ndf\nfyVfvnxG54iIyCtMKz/llXXhwgUOHjxIly5dEh/z9fVl+fLlSbarUaNGkq/NZjOTJ0+mSpUq5M2b\nl2zZsrFly5bEayVevHiRp0+fUrdu3cTXuLi44O7unoZHk7pMJhNVq1blk08+4cKFC6xbt464uDha\ntWpFpUqVCAgI4OzZs0ZnShpo3bo1rq6uzJs3z+gUERERsWKurq507tyZwMBAo1NEROQVZ2d0gEha\nWbZsGWazmWLFij3z3B9//JH4excXlyTPTZs2jVmzZjF37lzc3NzImjUrH3/8Mbdv307zZiOYTCZq\n1qxJzZo1+fTTTzl06BAbNmzgrbfeInfu3Pj4+ODj40PZsmWNTpVUYDKZmDNnDvXr18fX15dChQoZ\nnSQiIiJWatSoUbi5uTF48GAKFy5sdI6IiLyitPJTXkkJCQl88cUXTJ06lZMnTyb55eHhwcqVK5/7\n2pCQEFq1aoWvry8eHh6UKlWK8+fPJz5funRp7OzsOHToUOJj0dHRnD59Ok2PKT2YTCbq1avHrFmz\niIiIYMGCBdy4cYMGDRpQvXp1pk6dyuXLl43OlBQqV64c7733HiNGjDA6RURERKxY4cKF8ff35+7d\nu0aniIjIK0wrP+WVtGPHDu7evUvv3r3JlStXkud8fHxYvHgxXbt2/dvXlitXjg0bNhASEkKePHmY\nP38+ly9fTtyPi4sLvXr1YsSIEeTNm5dChQoxceJEzGZzmh9XerKxsaFBgwY0aNCAOXPmcODAAYKC\ngqhduzYlS5ZMvEbo362slYxv1KhRVKxYkYMHD/L6668bnSMiIiJWauLEiUYniIjIK04rP+WVtGLF\nCho1avTM4BOgY8eOXLlyhT179vztjX1Gjx5N7dq1ad68OW+++SZZs2Z9ZlA6ffp0PD09ad++PV5e\nXri7u/PGG2+k2fEYzdbWFk9PTxYtWsT169eZNGkSZ8+epWrVqtSvX585c+Zw7do1ozPlJWTNmpVp\n06bRv39/EhISjM4RERERK2UymXSzTRERSVO627uIJNuTJ0/Ys2cPQUFBbNu2DQ8PDzp16sTbb79N\ngQIFjM6Tf2GxWPD09KRTp074+/sbnSMiIiIiIiKS6jT8FJFUERcXx7fffktQUBA7d+6kRo0a+Pj4\n0L59e/LmzZvs/ZrNZp48eYKjo2Mq1spf/vvf/+Ll5UVYWBj58uUzOkdERETkGT///DPOzs64u7tj\nY6OTF0VE5OVo+CkiqS4mJoavv/6aDRs2sGvXLurWrYuPjw/t2rX720sR/JOzZ88yZ84cbty4QaNG\njejVqxcuLi5pVG6dBg0axOPHj1myZInRKSIiIiKJDhw4gJ+fHzdu3CBfvny8+eabfPrpp/rAVkRE\nXoo+NhORVOfk5ESHDh0ICgri2rVr+Pn5sWPHDlxdXWnZsiVffvklUVFRL7SvqKgo8ufPT/HixRk0\naBDz588nPj4+jY/AugQEBLB9+3aOHj1qdIqIiIgI8Od7wH79+uHh4cHRo0cJDAwkKiqK/v37G50m\nIiKZjFZ+iki6efjwIdu2bSMoKIgffviBRo0aERQURJYsWf71tVu3bqVv376sX7+ehg0bpkOtdVm1\nahULFy7k559/1ulkIiIiYojo6GgcHBywt7dn7969+Pn5sWHDBurUqQP8eUZQ3bp1OXXqFCVKlDC4\nVkREMgv9hCsi6SZbtmy88847bNu2jfDwcLp06YKDg8M/vubJkycArFu3jsqVK1OuXLm/3e7OnTt8\n8sknrF+/HrPZnOrtr7ru3btjY2PDqlWrjE4RERERK3Tjxg1Wr17Nb7/9BkDJkiX5448/cHNzS9zG\nyckJd3d3Hjx4YFSmiIhkQhp+ijxH586dWbdundEZr6ycOXPi4+ODyWT6x+3+Go7u3r2bpk2bJl7j\nyWw289fC9Z07dzJu3DhGjRrFkCFDOHToUNrGv4JsbGyYP38+I0eO5P79+0bniIiIiJVxcHBg+vTp\nREREAFCqVCnq16+Pv78/jx8/JioqiokTJxIREUGRIkUMrhURkcxEw0+R53ByciI2NtboDKuWkJAA\nwLZt2zCZTNStWxc7Ozvgz2GdyWRi2rRp9O/fnw4dOlCrVi3atGlDqVKlkuznjz/+ICQkRCtC/0WN\nGjVo27Yt48aNMzpFRERErEzu3LmpXbs2CxYsICYmBoCvvvqKq1ev0qBBA2rUqMHx48dZsWIFuXPn\nNrhWREQyEw0/RZ7D0dEx8Y2XGGvVqlXUrFkzyVDz6NGj9OzZk82bN/Pdd9/h7u5OeHg47u7uFCxY\nMHG7WbNm0bx5c959912cnZ3p378/Dx8+NOIwMoXJkyezbt06Tp06ZXSKiIiIWJmZM2dy9uxZOnTo\nQHBwMBs2bKBs2bL8/vvvODg44O/vT4MGDdi6dSsTJkzg6tWrRieLiEgmoOGnyHM4Ojpq5aeBLBYL\ntra2WCwWvv/++ySnvO/fv59u3bpRr149fvrpJ8qWLcvy5cvJnTs3Hh4eifvYsWMHo0aNwsvLix9/\n/JEdO3awZ88evvvuO6MOK8PLkycP48ePZ8CAAeh+eCIiIpKeChQowMqVKyldujQffPAB8+bN49y5\nc/Tq1YsDBw7Qu3dvHBwcuHv3LgcPHmTo0KFGJ4uISCZgZ3SASEal096N8/TpUwIDA3F2dsbe3h5H\nR0dee+017O3tiY+PJywsjMuXL7N48WLi4uIYMGAAe/bs4Y033qBy5crAn6e6T5w4kXbt2jFz5kwA\nChUqRO3atZk9ezYdOnQw8hAztPfff58lS5awfv16unTpYnSOiIiIWJHXXnuN1157jU8//ZQHDx5g\nZ2dHnjx5AIiPj8fOzo5evXrx2muvUb9+fX744QfefPNNY6NFRCRD08pPkefQae/GsbGxIWvWrEyd\nOpWBAwdy8+ZNtm/fzrVr17C1taV3794cPnyYpk2bsnjxYuzt7Tl48CAPHjzAyckJgNDQUH755RdG\njBgB/DlQhT8vpu/k5JT4tTzL1taW+fPnM2zYMF0iQERERAzh5OSEra1t4uAzISEBOzu7xGvCV6hQ\nAT8/PxYuXGhkpoiIZAIafoo8h1Z+GsfW1pZBgwZx69YtIiIiCAgIYOXKlfj5+XH37l0cHByoWrUq\nkydP5vTp0/znP/8hZ86cfPfddwwePBj489T4IkWK4OHhgcViwd7eHoDw8HBcXV158uSJkYeY4b32\n2mt4eXkxadIko1NERETEypjNZho3boybmxuDBg1i586dPHjwAPjzfeJfbt++TY4cORIHoiIiIn9H\nw0+R59A1PzOGIkWKMHbsWK5evcrq1avJmzfvM9ucOHGCtm3bcurUKT799FMAfvrpJ7y9vQESB50n\nTpzg7t27lChRAhcXl/Q7iEwqMDCQ5cuX8+uvvxqdIiIiIlbExsaGevXqcevWLR4/fkyvXr2oXbs2\n7777Ll9++SUhISFs2rSJzZs3U7JkySQDURERkf9Lw0+R59Bp7xnP3w0+L126RGhoKJUrV6ZQoUKJ\nQ807d+5QpkwZAOzs/ry88ZYtW3BwcKBevXoAuqHPvyhYsCCjRo3igw8+0J+ViIiIpKtx48aRJUsW\n3n33Xa5fv86ECRNwdnZm0qRJdO7cma5du+Ln58fHH39sdKqIiGRwJot+ohX5W6tXr2bXrl2sXr3a\n6BR5DovFgslk4sqVK9jb21OkSBEsFgvx8fF88MEHhIaGEhISgp2dHffv36d8+fL06NGDMWPGkDVr\n1mf2I896+vQpVatWZdKkSbRr187oHBEREbEio0aN4quvvuL06dNJHj916hRlypTB2dkZ0Hs5ERH5\nZxp+ijzHxo0bWb9+PRs3bjQ6RZLh2LFjdO/eHQ8PD8qVK0dwcDB2dnbs3buX/PnzJ9nWYrGwYMEC\nIiMj8fHxoWzZsgZVZ0z79u3Dz8+PM2fOJP6QISIiIpIeHB0dWbVqFZ07d06827uIiMjL0GnvIs+h\n094zL4vFQs2aNVm3bh2Ojo4cOHAAf39/vvrqK/Lnz4/ZbH7mNVWrVuXmzZu88cYbVK9enalTp3L5\n8mUD6jOeRo0aUadOHQIDA41OERERESszfvx49uzZA6DBp4iIJItWfoo8x969e5kyZQp79+41OkXS\nUUJCAgcOHCAoKIjNmzfj6uqKj48PHTt2pHjx4kbnGSYiIoJq1apx5MgRSpUqZXSOiIiIWJFz585R\nrlw5ndouIiLJopWfIs+hu71bJ1tbWzw9PVm0aBHXrl1j8uTJnD17lmrVqlG/fn3mzJnDtWvXjM5M\nd8WKFWPIkCEMHjzY6BQRERGxMuXLl9fgU0REkk3DT5Hn0GnvYmdnR+PGjVm2bBnXr19n9OjRiXeW\nb9iwIZ999hk3b940OjPdDB48mLCwML755hujU0REREREREReiIafIs/h5OSklZ+SyMHBgebNm/P5\n559z48YNhgwZwk8//UT58uXx8vJiyZIl3Llzx+jMNJUlSxbmzJnDwIEDiYuLMzpHRERErJDFYsFs\nNuu9iIiIvDANP0WeQys/5XmyZMlC69atWbNmDdevX6dfv37s3buX0qVL4+3tzYoVK4iMjDQ6M000\nb96cChUqMGvWLKNTRERExAqZTCb69evHJ598YnSKiIhkErrhkchzXLt2jRo1anD9+nWjUySTiI6O\nZseOHQQFBbF3714aNGhAp06daNOmDTly5DA6L9VcvHiROnXqcOLECYoWLWp0joiIiFiZS5cuUbt2\nbc6dO0eePHmMzhERkQxOw0+R54iMjKRUqVKv7Ao+SVsPHz5k27ZtBAUF8cMPP9CoUSN8fHxo1aoV\nWbNmNTovxcaOHcv58+dZv3690SkiIiJihfr27Uv27NkJDAw0OkVERDI4DT9FniMmJoZcuXLpup+S\nYvfv32fr1q1s2LCBkJAQGjdujI+PDy1atMDZ2dnovGR5/PgxlSpVYuXKlXh6ehqdIyIiIlbm6tWr\nVKlShbCwMAoWLGh0joiIZGAafoo8h9lsxtbWFrPZjMlkMjpHXhF3795ly5YtBAUFcfToUZo1a0an\nTp1o1qwZjo6ORue9lM2bNzN27FiOHz+Ovb290TkiIiJiZT788EMSEhKYO3eu0SkiIpKBafgp8g8c\nHR25f/9+phtKSeZw69YtNm/eTFBQECdOnKBly5b4+PjQpEkTHBwcjM77VxaLBW9vb5o3b86gQYOM\nzhERERErc/PmTSpVqsTx48cpXry40TkiIpJBafgp8g9y5szJ5cuXyZUrl9Ep8oq7fv06mzZtIigo\niLCwMNq0aYOPjw9eXl4ZelXlr7/+SoMGDTh9+jQFChQwOkdERESszMiRI7lz5w5LliwxOkVERDIo\nDT9F/kHBggU5fvw4hQoVMjpFrMjVq1cJDg4mKCiICxcu0K5dO/4/9u48Lub8jwP4a2bSnUqXYm2p\nLYpWznKf69y1jlWOUMh97brJkfsKax0rFGltyFpCzsWyznWEpEIlOlSu7prm98f+zEOOTJr6drye\nj4cHM/P9fL+v6aGpec/78/k4Ozujbdu2UFFRETree6ZNm4Znz57B19dX6ChERERUyaSmpsLa2hqX\nLl2ClZWV0HGIiKgMYvGTqBAWFhY4ffo0LCwshI5ClVR0dLS8EPr48WP06dMHzs7OaNmyJSQSidDx\nAPy3s33dunWxd+9eODk5CR2HiIiIKhkvLy9ERkbC399f6ChERFQGsfhJVIi6desiKCgItra2Qkch\nQlRUFPbs2YM9e/YgKSkJffv2hbOzM5ycnCAWiwXNFhAQAG9vb1y5cqXMFGWJiIiocnj16hWsrKxw\n5swZ/t5ORETvEfbdMlEZp66ujqysLKFjEAEArKysMGvWLNy8eROnT5+GoaEhPDw88OWXX+Knn37C\n5cuXIdTnWQMGDICmpia2bt0qyPWJiIio8qpatSqmTp2KefPmCR2FiIjKIHZ+EhWiefPmWLVqFZo3\nby50FKKPunv3LgIDAxEYGIicnBz069cPzs7OcHBwgEgkKrUct27dwjfffIOwsDAYGBiU2nWJiIiI\nMjIyYGVlhcOHD8PBwUHoOEREVIaw85OoEOrq6sjMzBQ6BlGh7Ozs4OXlhfDwcPzxxx8Qi8X44Ycf\nYG1tjdmzZyM0NLRUOkK//vpr9OvXD3PmzCnxaxERERG9TVNTE7NmzYKnp6fQUYiIqIxh8ZOoEJz2\nTuWJSCRCgwYNsHTpUkRFRWH37t3IycnBt99+C1tbW8yfPx9hYWElmsHLywt//PEHrl+/XqLXISIi\nInrXiBEjcPv2bVy8eFHoKEREVIaw+ElUCA0NDRY/qVwSiURo3LgxVq5ciejoaPj6+uLly5f45ptv\nUL9+fSxatAiRkZFKv66+vj4WL16McePGIT8/X+nnJyIiIvoYNTU1eHp6chYKEREVwOInUSE47Z0q\nApFIBEdHR6xZswaxsbHYuHEjEhMT0bp1azRs2BDLli3Dw4cPlXY9Nzc35OXlwd/fX2nnJCIiIlLE\nkCFDEBsbi9OnTwsdhYiIyggWP4kKwWnvVNGIxWK0atUK69evR1xcHFavXo3o6Gg4OjqiadOmWLVq\nFWJjY4t9jQ0bNmDGjBlITU3FkSNH0KFrB5iam0LXQBcmX5igWetm8mn5RERERMpSpUoVzJ8/H56e\nnqWy5jkREZV93O2dqBDjxo1DnTp1MG7cOKGjEJWovLw8/PXXXwgMDMQff/wBGxsbODs744cffoCZ\nmVmRzyeTydCiZQvcvHsTEj0J0r5OA2oBUAWQCyAB0AnVgShZhAljJ2Ce5zyoqKgo+2kRERFRJSSV\nSmFvb49Vq1aha9euQschIiKBsfhJVIgpU6bAxMQEU6dOFToKUanJycnByZMnERgYiIMHD8Le3h79\n+vVD3759YWJi8snxUqkU7h7u2HdiHzI6ZwA1AIg+cvAzQPOUJpp+0RSHDxyGpqamUp8LERERVU77\n9+/H4sWLce3aNYhEH/tFhIiIKgMWP4kKcezYMWhoaKB169ZCRyESRHZ2No4dO4bAwEAcPnwYjRo1\ngrOzM3r37g1DQ8MPjhkzfgx2hOxAxg8ZgJoCF5EC6sHqaGXaCkcPHoVEIlHukyAiIqJKRyaToVGj\nRpgzZw569+4tdBwiIhIQi59EhXjz7cFPi4mAzMxMHD16FIGBgQgJCYGjoyOcnZ3Rq1cv6OvrAwBO\nnTqF7wZ8hwy3DECjCCfPAzR3a8J7qjdGjhxZMk+AiIiIKpUjR45g2rRpuHXrFj9cJSKqxFj8JCKi\nIktPT0dwcDACAwNx8uRJtGrVCs7OzvD7zQ9/qfwFNPmMkz4ALK5a4EHYA37gQERERMUmk8nQsmVL\njBkzBgMHDhQ6DhERCYTFTyIiKpbXr1/j4MGD8PPzw8mzJ4EpUGy6+7vyAS0fLRzbewwtWrRQdkwi\nIiKqhP766y94eHggLCwMVapUEToOEREJQCx0ACIiKt90dHQwcOBAdO3aFaoOqp9X+AQAMZBRLwPb\ndmxTaj4iIiKqvNq1a4datWph586dQkchIiKBsPhJRERKERsXi5yqOcU6h0xfhui4aOUEIiIiIgKw\naNEieHl5ITs7W+goREQkABY/iYohNzcXeXl5QscgKhMyMjMAlWKeRAV4+PAhAgICcOrUKdy5cwfJ\nycnIz89XSkYiIiKqfJycnFC/fn34+PgIHYWIiARQ3LepRBXasWPH4OjoCF1dXfl9b++vM0MKAAAg\nAElEQVQA7+fnh/z8fO5OTQTA2NAYuFfMk2QCIogQHByMhIQEJCYmIiEhAWlpaTAyMoKJiQmqV69e\n6N/6+vrcMImIiIgK8PLyQo8ePeDu7g5NTU2h4xARUSli8ZOoEF27dsWFCxfg5OQkv+/dosrWrVsx\ndOhQqKl97kKHRBVDc6fm0Nmlg9d4/dnn0IzWxKTRkzBx4sQC9+fk5CApKalAQTQxMREPHz7ExYsX\nC9yfkZEBExMThQqlurq65b5QKpPJ4OPjg3PnzkFdXR0dOnSAi4tLuX9eREREytSwYUM0b94cGzdu\nxJQpU4SOQ0REpYi7vRMVQktLC7t374ajoyMyMzORlZWFzMxMZGZmIjs7G5cvX8bMmTORkpICfX19\noeMSCUoqlcL0S1M86/YMqPEZJ3gNqP+qjoS4hALd1kWVlZWFxMTEAkXSj/2dk5OjUJG0evXq0NbW\nLnMFxfT0dEyYMAEXL15Ez549kZCQgIiICLi4uGD8+PEAgLt372LhwoW4dOkSJBIJBg8ejHnz5gmc\nnIiIqPSFhYWhXbt2iIyMRNWqVYWOQ0REpYTFT6JCmJqaIjExERoaGgD+6/oUi8WQSCSQSCTQ0tIC\nANy8eZPFTyIAS5YuwaKgRcj8NrPIYyXnJBhQawB2+pbebqwZGRkKFUoTEhIgk8neK4p+rFD65rWh\npF24cAFdu3aFr68v+vTpAwDYtGkT5s2bhwcPHuDp06fo0KEDmjZtiqlTpyIiIgJbtmxBmzZtsGTJ\nklLJSEREVJa4urrC2toanp6eQkchIqJSwuInUSFMTEzg6uqKjh07QiKRQEVFBVWqVCnwt1Qqhb29\nPVRUuIoEUWpqKurUr4Nkx2TI7Ivw4yUa0D6gjX8v/wtra+sSy1ccaWlpCnWTJiQkQCKRKNRNamJi\nIv9w5XPs2LEDs2bNQlRUFFRVVSGRSBATE4MePXpgwoQJEIvFmD9/PsLDw+UF2e3bt2PBggW4fv06\nDAwMlPXlISIiKheioqLg6OiIiIgIVKtWTeg4RERUClitISqERCJB48aN0aVLF6GjEJUL1apVw1/H\n/0LzNs3xWvoaMgcFCqBRgGawJg7sO1BmC58AoK2tDW1tbVhaWhZ6nEwmw+vXrz9YGL127dp796ur\nqxfaTWptbQ1ra+sPTrnX1dVFVlYWDh48CGdnZwDA0aNHER4ejlevXkEikUBPTw9aWlrIycmBqqoq\nbGxskJ2djfPnz6Nnz54l8rUiIiIqq6ysrNC7d2+sWrWKsyCIiCoJFj+JCuHm5gZzc/MPPiaTycrc\n+n9EZYGdnR2uXLiCdt+0w+v7r5FmnwbYAJC8dZAMwCNAckkC7RRtHA4+jBYtWgiUWLlEIhGqVq2K\nqlWr4quvvir0WJlMhpcvX36we/TSpUtISEhA+/bt8eOPP35wfJcuXeDu7o4JEyZg27ZtMDY2Rlxc\nHKRSKYyMjGBqaoq4uDgEBARg4MCBeP36NdavX49nz54hIyOjJJ5+pSGVShEWFoaUlBQA/xX+7ezs\nIJFIPjGSiIiENmfOHDg4OGDSpEkwNjYWOg4REZUwTnsnKobnz58jNzcXhoaGEIvFQschKlOys7Ox\nf/9+LPNehqiHUVCppQKpqhTiXDFkCTIYaBvgxbMXOPjnQbRu3VrouOXWy5cv8ffff+P8+fPyTZn+\n+OMPjB8/HkOGDIGnpydWr14NqVSKunXromrVqkhMTMSSJUvk64SS4p49ewafrT5Yu2EtMvMzIdGR\nACJA+koKdahj4tiJ8BjhwTfTRERl3IQJE6CiogJvb2+hoxARUQlj8ZOoEHv37oWlpSUaNmxY4P78\n/HyIxWLs27cPV69exfjx41GzZk2BUhKVfXfu3JFPxdbS0oKFhQWaNGmC9evX4/Tp0zhw4IDQESsM\nLy8vHDp0CFu2bIGDgwMA4NWrV7h37x5MTU2xdetWnDx5EitWrEDLli0LjJVKpRgyZMhH1yg1NDSs\ntJ2NMpkMK1etxNwFcyGuK0amQyZQ452DngLqN9QhC5Nh7py5mDl9JmcIEBGVUQkJCbCzs8OtW7f4\nezwRUQXH4idRIRo1aoRvv/0W8+fP/+Djly5dwrhx47Bq1Sq0bdu2VLMREd24cQN5eXnyImdQUBDG\njh2LqVOnYurUqfLlOd7uTG/VqhW+/PJLrF+/Hvr6+gXOJ5VKERAQgMTExA+uWfr8+XMYGBgUuoHT\nm38bGBhUqI74ST9Ngk+gDzJ+yAD0PnHwS0BzryaG9hqKX9b9wgIoEVEZNX36dLx69QqbNm0SOgoR\nEZUgrvlJVAg9PT3ExcUhPDwc6enpyMzMRGZmJjIyMpCTk4MnT57g5s2biI+PFzoqEVVCiYmJ8PT0\nxKtXr2BkZIQXL17A1dUV48aNg1gsRlBQEMRiMZo0aYLMzEzMnDkTUVFRWLly5XuFT+C/Td4GDx78\n0evl5eXh2bNn7xVF4+Li8O+//xa4/00mRXa8r1atWpkuEK5bvw4+v/sgY1AGoKnAAF0gY1AG/Pz9\nYPGlBab8NKXEMxIRUdFNmzYNNjY2mDZtGiwsLISOQ0REJYSdn0SFGDx4MHbt2gVVVVXk5+dDIpFA\nRUUFKioqqFKlCnR0dJCbm4vt27ejY8eOQsclokomOzsbERERuH//PlJSUmBlZYUOHTrIHw8MDMS8\nefPw6NEjGBoaonHjxpg6dep7091LQk5ODpKSkj7YQfrufenp6TA2Nv5kkbR69erQ1dUt1UJpeno6\njM2MkTEkAzAo4uBUQMNXA4lPEqGjo1Mi+YiIqHjmz5+P6Oho+Pn5CR2FiIhKCIufRIXo168fMjIy\nsHLlSkgkkgLFTxUVFYjFYkilUujr60NNTU3ouERE8qnub8vKykJqairU1dVRrVo1gZJ9XFZW1kcL\npe/+nZ2dLZ9e/6lCqY6OTrELpdu2bcPEtROR3jf9s8Zr7dfCylErMXr06GLlICKikvHy5UtYWVnh\n77//Rp06dYSOQ0REJYDFT6JCDBkyBACwY8cOgZMQlR/t2rVD/fr18fPPPwMALCwsMH78ePz4448f\nHaPIMUQAkJmZqVCRNDExEXl5eQp1k5qYmEBbW/u9a8lkMtjUt0Fkg0jgq88M/AAwv2yOh+EPy/TU\nfiKiymzZsmW4efMmfv/9d6GjEBFRCeCan0SFGDBgALKzs+W33+6okkqlAACxWMw3tFSpJCcnY+7c\nuTh69Cji4+Ohp6eH+vXrY8aMGejQoQP++OMPVKlSpUjnvHbtGrS0tEooMVUkGhoaMDc3h7m5+SeP\nTU9P/2BhNDQ0FCdOnChwv1gsfq+bVE9PDw8jHwJ9ihHYAni6/ylSUlJgaGhYjBMREVFJGT9+PKys\nrBAaGgp7e3uh4xARkZKx+ElUiM6dOxe4/XaRUyKRlHYcojKhd+/eyMrKgq+vLywtLZGUlISzZ88i\nJSUFwH8bhRWVgUFRF1Mk+jQtLS3Url0btWvXLvQ4mUyGtLS094qk9+7dg0hdBBRn03oxoKqjiufP\nn7P4SURURmlpaWHGjBnw9PTEn3/+KXQcIiJSMk57J/oEqVSKe/fuISoqCubm5mjQoAGysrJw/fp1\nZGRkoF69eqhevbrQMYlKxcuXL6Gvr4+TJ0+iffv2HzzmQ9Pehw4diqioKBw4cADa2tqYMmUKfvrp\nJ/mYd6e9i8Vi7Nu3D7179/7oMUQl7fHjx6jjUAcZ4zOKdR6tDVq4ffk2dxImIirDsrKy8NVXXyEo\nKAhNmzYVOg4RESlRcXoZiCqF5cuXw97eHi4uLvj222/h6+uLwMBAdO/eHT/88ANmzJiBxMREoWMS\nlQptbW1oa2vj4MGDBZaE+JQ1a9bAzs4ON27cgJeXF2bNmoUDBw6UYFKi4jMwMEBOWg6QU4yT5AI5\nr3PY3UxEVMapq6tjzpw58PT0xI0bN+Dh4YGGDRvC0tISdnZ26Ny5M3bt2lWk33+IiKhsYPGTqBDn\nzp1DQEAAli1bhqysLKxduxarV6+Gj48PfvnlF+zYsQP37t3Dr7/+KnRUolIhkUiwY8cO7Nq1C3p6\nemjevDmmTp2KK1euFDquWbNmmDFjBqysrDBixAgMHjwY3t7epZSa6PNoamqiZZuWwN1inCQMaOLU\nBFWrVlVaLiIiKhmmpqb4999/8e2338Lc3BxbtmzBsWPHEBgYiBEjRsDf3x+1atXC7NmzkZWVJXRc\nIiJSEIufRIWIi4tD1apV5dNz+/Tpg86dO0NVVRUDBw7Ed999h++//x6XL18WOClR6enVqxeePn2K\n4OBgdOvWDRcvXoSjoyOWLVv20TFOTk7v3Q4LCyvpqETFNm3SNOiE6nz2eJ1QHUyfNF2JiYiIqCSs\nXbsWY8aMwdatWxETE4NZs2ahcePGsLKyQr169dC3b18cO3YM58+fx/3799GpUyekpqYKHZuIiBTA\n4idRIVRUVJCRkVFgc6MqVaogLS1NfjsnJwc5OcWZE0lU/qiqqqJDhw6YM2cOzp8/j2HDhmH+/PnI\ny8tTyvlFIhHeXZI6NzdXKecmKorOnTtDM08TiPyMwQ8A1XRVdO/eXem5iIhIebZu3YpffvkF//zz\nD77//vtCNzb96quvsGfPHjg4OKBnz57sACUiKgdY/CQqxBdffAEACAgIAABcunQJFy9ehEQiwdat\nWxEUFISjR4+iXbt2QsYkElzdunWRl5f30TcAly5dKnD74sWLqFu37kfPZ2RkhPj4ePntxMTEAreJ\nSotYLEagfyA0gjWAovwXTAQ0DmkgcFdgoW+iiYhIWI8ePcKMGTNw5MgR1KpVS6ExYrEYa9euhZGR\nERYvXlzCCYmIqLhUhA5AVJY1aNAA3bt3h5ubG/z8/BAdHY0GDRpgxIgR6N+/P9TV1dGkSROMGDFC\n6KhEpSI1NRU//PAD3N3dYW9vDx0dHVy9ehUrV65Ex44doa2t/cFxly5dwvLly9GnTx/89ddf2LVr\nF3777bePXqd9+/bYsGEDnJycIBaLMXv2bGhoaJTU0yIqVJs2beC/zR+Dhw1GRucMoA4+/vFxPoAI\nQO2IGrZv2Y4OHTqUYlIiIiqqX3/9FUOGDIG1tXWRxonFYixZsgRt27aFp6cnVFVVSyghEREVF4uf\nRIXQ0NDAggUL0KxZM5w6dQo9e/bEqFGjoKKiglu3biEyMhJOTk5QV1cXOipRqdDW1oaTkxN+/vln\nREVFITs7GzVq1MCgQYMwe/ZsAP9NWX+bSCTCjz/+iNDQUCxatAja2tpYuHAhevXqVeCYt61evRrD\nhw9Hu3btYGJighUrViA8PLzknyDRR/Tp0wcmJiZwG+mG+HPxyPg6A7J6MkDr/wdkAKI7Imje0oS2\nijYk2hL06N5D0MxERFS47Oxs+Pr64vz58581vk6dOrCzs8P+/fvh4uKi5HRERKQsItm7i6oRERER\n0QfJZDJcvnwZq9atwpHDR5CV/t9SD+qa6ujSrQumTJwCJycnuLm5QV1dHZs3bxY4MRERfczBgwex\ndu1anD59+rPP8fvvv8Pf3x+HDx9WYjIiIlImdn4SKejN5wRvd6jJZLL3OtaIiKjiEolEcHR0xD7H\nfQAg3+RLRaXgr1Tr1q3D119/jcOHD3PDIyKiMurJkydFnu7+Lmtrazx9+lRJiYiIqCSw+EmkoA8V\nOVn4JCKq3N4ter6hq6uL6Ojo0g1DRERFkpWVVezlq9TV1ZGZmamkREREVBK42zsRERERERFVOrq6\nunj+/HmxzvHixQvo6ekpKREREZUEFj+JiIiIiIio0mnSpAlOnTqF3Nzczz5HSEgIGjdurMRURESk\nbCx+En1CXl4ep7IQEREREVUw9evXh4WFBQ4dOvRZ43NycuDj44PRo0crORkRESkTi59En3D48GG4\nuLgIHYOIiIiIiJRszJgx+OWXX+SbmxbFH3/8ARsbG9jZ2ZVAMiIiUhYWP4k+gYuYE5UN0dHRMDAw\nQGpqqtBRqBxwc3ODWCyGRCKBWCyW/zs0NFToaEREVIb06dMHycnJ8Pb2LtK4Bw8eYNKkSfD09Cyh\nZEREpCwsfhJ9grq6OrKysoSOQVTpmZub4/vvv8e6deuEjkLlRKdOnZCQkCD/Ex8fj3r16gmWpzhr\nyhERUclQVVXF4cOH8fPPP2PlypUKdYDevXsXHTp0wLx589ChQ4dSSElERMXB4ifRJ2hoaLD4SVRG\nzJo1Cxs2bMCLFy+EjkLlgJqaGoyMjGBsbCz/IxaLcfToUbRq1Qr6+vowMDBAt27dEBERUWDsP//8\nAwcHB2hoaKBZs2YICQmBWCzGP//8A+C/9aCHDRuG2rVrQ1NTEzY2Nli9enWBc7i6uqJXr15YunQp\natasCXNzcwDAzp070aRJE1StWhXVq1eHi4sLEhIS5ONyc3Mxbtw4mJmZQV1dHV9++SU7i4iIStAX\nX3yB8+fPw9/fH82bN8eePXs++IHVnTt3MHbsWLRu3RqLFi3CqFGjBEhLRERFpSJ0AKKyjtPeicoO\nS0tLdO/eHevXr2cxiD5bRkYGpkyZgvr16yM9PR1eXl747rvvcPfuXUgkErx+/RrfffcdevTogd27\nd+Px48eYNGkSRCKR/BxSqRRffvkl9u3bB0NDQ1y6dAkeHh4wNjaGq6ur/LhTp05BV1cXJ06ckHcT\n5eXlYdGiRbCxscGzZ88wbdo0DBgwAKdPnwYAeHt74/Dhw9i3bx+++OILxMXFITIysnS/SERElcwX\nX3yBU6dOwdLSEt7e3pg0aRLatWsHXV1dZGVl4f79+3j06BE8PDwQGhqKGjVqCB2ZiIgUJJJ9zsrO\nRJVIREQEunfvzjeeRGXE/fv30a9fP1y7dg1VqlQROg6VUW5ubti1axfU1dXl97Vu3RqHDx9+79hX\nr15BX18fFy9eRNOmTbFhwwYsWLAAcXFxUFVVBQD4+/tj6NCh+Pvvv9G8efMPXnPq1Km4e/cujhw5\nAuC/zs9Tp04hNjYWKiof/7z5zp07sLe3R0JCAoyNjTF27Fg8ePAAISEhxfkSEBFRES1cuBCRkZHY\nuXMnwsLCcP36dbx48QIaGhowMzNDx44d+bsHEVE5xM5Pok/gtHeissXGxgY3b94UOgaVA23atIGP\nj4+841JDQwMAEBUVhblz5+Ly5ctITk5Gfn4+ACA2NhZNmzbF/fv3YW9vLy98AkCzZs3eWwduw4YN\n8PPzQ0xMDDIzM5GbmwsrK6sCx9SvX/+9wue1a9ewcOFC3Lp1C6mpqcjPz4dIJEJsbCyMjY3h5uaG\nzp07w8bGBp07d0a3bt3QuXPnAp2nRESkfG/PKrG1tYWtra2AaYiISFm45ifRJ3DaO1HZIxKJWAii\nT9LU1ISFhQVq166N2rVrw9TUFADQrVs3PH/+HFu3bsWVK1dw/fp1iEQi5OTkKHzugIAATJ06FcOH\nD8fx48dx69YtjBw58r1zaGlpFbidlpaGLl26QFdXFwEBAbh27Zq8U/TN2MaNGyMmJgaLFy9GXl4e\nBg0ahG7duhXnS0FEREREVGmx85PoE7jbO1H5k5+fD7GYn+/R+5KSkhAVFQVfX1+0aNECAHDlyhV5\n9ycA1KlTB4GBgcjNzZVPb7x8+XKBgvuFCxfQokULjBw5Un6fIsujhIWF4fnz51i6dKl8vbgPdTJr\na2ujb9++6Nu3LwYNGoSWLVsiOjpavmkSEREREREphu8MiT6B096Jyo/8/Hzs27cPzs7OmD59Oi5e\nvCh0JCpjDA0NUa1aNWzZsgUPHjzAmTNnMG7cOEgkEvkxrq6ukEqlGDFiBMLDw3HixAksX74cAOQF\nUGtra1y7dg3Hjx9HVFQUFixYIN8JvjDm5uZQVVXFzz//jOjoaAQHB2P+/PkFjlm9ejUCAwNx//59\nREZG4rfffoOenh7MzMyU94UgIiIiIqokWPwk+oQ3a7Xl5uYKnISIPubNdOHr169j2rRpkEgkuHr1\nKoYNG4aXL18KnI7KErFYjD179uD69euoX78+Jk6ciGXLlhXYwEJHRwfBwcEIDQ2Fg4MDZs6ciQUL\nFkAmk8k3UBozZgx69+4NFxcXNGvWDE+fPsXkyZM/eX1jY2P4+fkhKCgItra2WLJkCdasWVPgGG1t\nbSxfvhxNmjRB06ZNERYWhmPHjhVYg5SIiIQjlUohFotx8ODBEh1DRETKwd3eiRSgra2N+Ph46Ojo\nCB2FiN6SkZGBOXPm4OjRo7C0tES9evUQHx8PPz8/AEDnzp1hZWWFjRs3ChuUyr2goCC4uLggOTkZ\nurq6QschIqKP6NmzJ9LT03Hy5Mn3Hrt37x7s7Oxw/PhxdOzY8bOvIZVKUaVKFRw4cADfffedwuOS\nkpKgr6/PHeOJiEoZOz+JFMCp70Rlj0wmg4uLC65cuYIlS5agYcOGOHr0KDIzM+UbIk2cOBF///03\nsrOzhY5L5Yyfnx8uXLiAmJgYHDp0CD/99BN69erFwicRURk3bNgwnDlzBrGxse89tm3bNpibmxer\n8FkcxsbGLHwSEQmAxU8iBXDHd6KyJyIiApGRkRg0aBB69eoFLy8veHt7IygoCNHR0UhPT8fBgwdh\nZGTE718qsoSEBAwcOBB16tTBxIkT0bNnT3lHMRERlV3du3eHsbExfH19C9yfl5eHXbt2YdiwYQCA\nqVOnwsbGBpqamqhduzZmzpxZYJmr2NhY9OzZEwYGBtDS0oKdnR2CgoI+eM0HDx5ALBYjNDRUft+7\n09w57Z2ISDjc7Z1IAdzxnajs0dbWRmZmJlq1aiW/r0mTJvjqq68wYsQIPH36FCoqKhg0aBD09PQE\nTErl0YwZMzBjxgyhYxARURFJJBIMGTIEfn5+mDdvnvz+gwcPIiUlBW5ubgAAXV1d7Ny5E6amprh7\n9y5GjhwJTU1NeHp6AgBGjhwJkUiEc+fOQVtbG+Hh4QU2x3vXmw3xiIio7GHnJ5ECOO2dqOypUaMG\nbG1tsWbNGkilUgD/vbF5/fo1Fi9ejAkTJsDd3R3u7u4A/tsJnoiIiCq+YcOGISYmpsC6n9u3b8c3\n33wDMzMzAMCcOXPQrFkz1KpVC127dsX06dOxe/du+fGxsbFo1aoV7Ozs8OWXX6Jz586FTpfnVhpE\nRGUXOz+JFMBp70Rl06pVq9C3b1+0b98eDRo0wIULF/Ddd9+hadOmaNq0qfy47OxsqKmpCZiUiIiI\nSouVlRXatGmD7du3o2PHjnj69CmOHTuGPXv2yI8JDAzE+vXr8eDBA6SlpSEvL69AZ+fEiRMxbtw4\nBAcHo0OHDujduzcaNGggxNMhIqJiYucnkQLY+UlUNtna2mL9+vWoV68eQkND0aBBAyxYsAAAkJyc\njEOHDsHZ2Rnu7u5Ys2YN7t27J3BiIiIiKg3Dhg3DgQMH8OLFC/j5+cHAwEC+M/v58+cxaNAg9OjR\nA8HBwbh58ya8vLyQk5MjH+/h4YFHjx5h6NChuH//PhwdHbFkyZIPXkss/u9t9dvdn2+vH0pERMJi\n8ZNIAVzzk6js6tChAzZs2IDg4GBs3boVxsbG2L59O1q3bo3evXvj+fPnyM3Nha+vL1xcXJCXlyd0\nZKJPevbsGczMzHDu3DmhoxARlUt9+/aFuro6/P394evriyFDhsg7O//55x+Ym5tjxowZaNSoESwt\nLfHo0aP3zlGjRg2MGDECgYGBmDt3LrZs2fLBaxkZGQEA4uPj5ffduHGjBJ4VERF9DhY/iRTAae9E\nZZtUKoWWlhbi4uLQsWNHjBo1Cq1bt8b9+/dx9OhRBAYG4sqVK1BTU8OiRYuEjkv0SUZGRtiyZQuG\nDBmCV69eCR2HiKjcUVdXR//+/TF//nw8fPhQvgY4AFhbWyM2Nha///47Hj58iF9++QV79+4tMH7C\nhAk4fvw4Hj16hBs3buDYsWOws7P74LW0tbXRuHFjLFu2DPfu3cP58+cxffp0boJERFRGsPhJpABO\neycq2950cvz8889ITk7GyZMnsXnzZtSuXRvAfzuwqquro1GjRrh//76QUYkU1qNHD3Tq1AmTJ08W\nOgoRUbk0fPhwvHjxAi1atICNjY38/u+//x6TJ0/GxIkT4eDggHPnzsHLy6vAWKlUinHjxsHOzg5d\nu3bFF198ge3bt8sff7ewuWPHDuTl5aFJkyYYN24cFi9e/F4eFkOJiIQhknFbOqJPGjp0KNq2bYuh\nQ4cKHYWIPuLJkyfo2LEjBgwYAE9PT/nu7m/W4Xr9+jXq1q2L6dOnY/z48UJGJVJYWloavv76a3h7\ne6Nnz55CxyEiIiIiKnfY+UmkAE57Jyr7srOzkZaWhv79+wP4r+gpFouRkZGBPXv2oH379jA2NoaL\ni4vASYkUp62tjZ07d2LUqFFITEwUOg4RERERUbnD4ieRAjjtnajsq127NmrUqAEvLy9ERkYiMzMT\n/v7+mDBhAlavXo2aNWti3bp18k0JiMqLFi1awM3NDSNGjAAn7BARERERFQ2Ln0QK4G7vROXDpk2b\nEBsbi2bNmsHQ0BDe3t548OABunXrhnXr1qFVq1ZCRyT6LPPnz8fjx48LrDdHRERERESfpiJ0AKLy\ngNPeicoHBwcHHDlyBKdOnYKamhqkUim+/vprmJmZCR2NqFhUVVXh7++Pdu3aoV27dvLNvIiIiIiI\nqHAsfhIpQENDA8nJyULHICIFaGpq4ttvvxU6BpHS1atXDzNnzsTgwYNx9uxZSCQSoSMREREREZV5\nnPZOpABOeyciorJg0qRJUFVVxcqVK4WOQkRERERULrD4SaQATnsnIqKyQCwWw8/PD97e3rh586bQ\ncYiIyrRnz57BwMAAsbGxQkchIiIBsfhJpADu9k5UvslkMu6STRVGrVq1sGrVKri6uvJnExFRIVat\nWgVnZ2fUqlVL6ChERCQgFj+JFMBp70Tll0wmw969exESEiJ0FCKlcXV1hY2NDebMmSN0FCKiMunZ\ns2fw8fHBzJkzhY5CREQCY/GTSAGc9k5UfolEIohEIsyfP5/dn1RhiEQibN68GVYttPYAACAASURB\nVLt378aZM2eEjkNEVOasXLkSLi4u+OKLL4SOQkREAmPxk0gBnPZOVL716dMHaWlpOH78uNBRiJTG\n0NAQPj4+GDp0KF6+fCl0HCKiMiMpKQlbt25l1ycREQFg8ZNIIez8JCrfxGIx5syZgwULFrD7kyqU\nbt26oUuXLpg4caLQUYiIyoyVK1eif//+7PokIiIALH4SKYRrfhKVf/369UNKSgpOnz4tdBQipVq1\nahUuXLiA/fv3Cx2FiEhwSUlJ2LZtG7s+iYhIjsVPIgVw2jtR+SeRSDBnzhx4eXkJHYVIqbS1teHv\n748xY8YgISFB6DhERIJasWIFBgwYgJo1awodhYiIyggWP4kUwGnvRBVD//798eTJE5w9e1boKERK\n5ejoiBEjRmD48OFc2oGIKq3ExERs376dXZ9ERFQAi59ECuC0d6KKQUVFBbNnz2b3J1VIc+fORXx8\nPHx8fISOQkQkiBUrVmDgwIGoUaOG0FGIiKgMEcnYHkD0SampqbCyskJqaqrQUYiomHJzc2FtbQ1/\nf3+0bNlS6DhEShUWFobWrVvj0qVLsLKyEjoOEVGpSUhIgK2tLW7fvs3iJxERFcDOTyIFcNo7UcVR\npUoVzJo1CwsXLhQ6CpHS2drawtPTE4MHD0ZeXp7QcYiISs2KFSswaNAgFj6JiOg97PwkUkB+fj5U\nVFQglUohEomEjkNExZSTk4OvvvoKgYGBcHR0FDoOkVLl5+fjm2++Qfv27TFr1iyh4xARlbg3XZ93\n7tyBmZmZ0HGIiKiMYfGTSEFqamp49eoV1NTUhI5CREqwadMmBAcH4/Dhw0JHIVK6x48fo1GjRggJ\nCUHDhg2FjkNEVKJ+/PFHSKVSrFu3TugoRERUBrH4SaQgXV1dxMTEQE9PT+goRKQE2dnZsLS0xIED\nB9C4cWOh4xApXUBAAJYsWYJr165BQ0ND6DhERCUiPj4ednZ2uHv3LkxNTYWOQ0REZRDX/CRSEHd8\nJ6pY1NTUMH36dK79SRXWgAEDUK9ePU59J6IKbcWKFRg8eDALn0RE9FHs/CRSkLm5Oc6cOQNzc3Oh\noxCRkmRmZsLS0hKHDx+Gg4OD0HGIlC41NRX29vbYuXMn2rdvL3QcIiKlYtcnEREpgp2fRAriju9E\nFY+GhgamTp2KRYsWCR2FqERUq1YNW7duhZubG168eCF0HCIipVq+fDmGDBnCwicRERWKnZ9ECmrQ\noAF8fX3ZHUZUwWRkZKB27do4ceIE6tevL3QcohIxduxYvHr1Cv7+/kJHISJSiqdPn6JevXoICwtD\n9erVhY5DRERlGDs/iRSkoaHBNT+JKiBNTU389NNP7P6kCm3FihW4fPky9u7dK3QUIiKlWL58OYYO\nHcrCJxERfZKK0AGIygtOeyequEaPHg1LS0uEhYXB1tZW6DhESqelpQV/f3989913aNmyJaeIElG5\n9uTJE/j7+yMsLEzoKEREVA6w85NIQdztnaji0tbWxuTJk9n9SRVas2bNMGrUKLi7u4OrHhFRebZ8\n+XK4ubmx65OIiBTC4ieRgjjtnahiGzt2LE6cOIHw8HChoxCVmDlz5iA5ORmbN28WOgoR0Wd58uQJ\ndu3ahWnTpgkdhYiIygkWP4kUxGnvRBWbjo4OJk6ciCVLlggdhajEVKlSBf7+/pg7dy4iIyOFjkNE\nVGTLli2Du7s7TExMhI5CRETlBNf8JFIQp70TVXzjx4+HpaUloqKiYGVlJXQcohJRp04dzJ07F66u\nrjh//jxUVPjrIBGVD3FxcQgICOAsDSIiKhJ2fhIpiNPeiSo+XV1djBs3jt2fVOGNHTsWVatWxdKl\nS4WOQkSksGXLlmHYsGEwNjYWOgoREZUj/KifSEGc9k5UOUycOBFWVlZ49OgRLCwshI5DVCLEYjF8\nfX3h4OCArl27onHjxkJHIiIq1OPHj/Hbb7+x65OIiIqMnZ9ECuK0d6LKQV9fH6NHj2ZHHFV4NWrU\nwM8//wxXV1d+uEdEZd6yZcswfPhwdn0SEVGRsfhJpCBOeyeqPCZPnox9+/YhJiZG6ChEJcrFxQUN\nGjTAjBkzhI5CRPRRjx8/xu7duzFlyhShoxARUTnE4ieRArKyspCVlYWnT58iMTERUqlU6EhEVIIM\nDAzg4eGB5cuXAwDy8/ORlJSEyMhIPH78mF1yVKFs2LAB+/fvx4kTJ4SOQkT0QUuXLsWIESPY9UlE\nRJ9FJJPJZEKHICqr/v33X2zcuBF79+6Furo61NTUkJWVBVVVVXh4eGDEiBEwMzMTOiYRlYCkpCRY\nW1tj9OjR2L17N9LS0qCnp4esrCy8fPkSPXv2xJgxY+Dk5ASRSCR0XKJiOXHiBNzd3REaGgp9fX2h\n4xARycXGxsLBwQHh4eEwMjISOg4REZVD7Pwk+oCYmBi0aNECffv2hbW1NR48eICkpCQ8fvwYz549\nQ0hICBITE1GvXj14eHggOztb6MhEpER5eXlYtmwZpFIpnjx5gqCgICQnJyMqKgpxcXGIjY1Fo0aN\nMHToUDRq1Aj3798XOjJRsXTq1Am9evXC2LFjhY5CRFTAm65PFj6JiOhzsfOT6B1hYWHo1KkTpkyZ\nggkTJkAikXz02FevXsHd3R0pKSk4fPgwNDU1SzEpEZWEnJwc9OnTB7m5ufjtt99QrVq1jx6bn5+P\nbdu2wdPTE8HBwdwxm8q1jIwMNGzYEAsWLICzs7PQcYiIEBMTg4YNG+L+/fswNDQUOg4REZVTLH4S\nvSU+Ph5OTk5YuHAhXF1dFRojlUoxdOhQpKWlISgoCGIxG6qJyiuZTAY3Nzc8f/4c+/btQ5UqVRQa\n9+eff2L06NG4cOECLCwsSjglUcm5evUqevTogevXr6NGjRpCxyGiSm7UqFHQ19fH0qVLhY5CRETl\nGIufRG8ZP348VFVVsXr16iKNy8nJQZMmTbB06VJ069athNIRUUn7559/4OrqitDQUGhpaRVp7MKF\nCxEREQF/f/8SSkdUOry8vHDhwgWEhIRwPVsiEgy7PomISFlY/CT6v7S0NNSqVQuhoaGoWbNmkcdv\n374d+/fvR3BwcAmkI6LSMGjQIDRs2BA//vhjkcempqbC0tISERERXJeMyrW8vDy0aNECgwcP5hqg\nRCSYkSNHwsDAAEuWLBE6ChERlXMsfhL936+//opjx45h//79nzU+IyMDtWrVwtWrVzntlagcerO7\n+8OHDwtd57Mw7u7usLGxwfTp05Wcjqh0RUREoHnz5rhw4QJsbGyEjkNElcybrs+IiAgYGBgIHYeI\niMo5Lk5I9H/BwcEYMGDAZ4/X1NREz549ceTIESWmIqLScvLkSbRv3/6zC58AMHDgQBw6dEiJqYiE\nYW1tDS8vL7i6uiI3N1foOERUySxevBijRo1i4ZOIiJSCxU+i/0tJSYGpqWmxzmFqaorU1FQlJSKi\n0qSM14Dq1avzNYAqjNGjR6NatWpYvHix0FGIqBKJjo5GUFDQZy1BQ0RE9CEsfhIRERHRe0QiEbZv\n345NmzbhypUrQschokpi8eLFGD16NLs+iYhIaVj8JPo/AwMDxMfHF+sc8fHxxZoyS0TCUcZrQEJC\nAl8DqEIxMzPD+vXr4erqioyMDKHjEFEF9+jRI+zfv59dn0REpFQsfhL9X48ePfDbb7999viMjAz8\n+eef6NatmxJTEVFp6dixI06fPl2saesBAQH49ttvlZiKSHj9+vVDkyZNMG3aNKGjEFEFt3jxYowZ\nM4YfJBIRkVJxt3ei/0tLS0OtWrUQGhqKmjVrFnn89u3bsWLFCpw6dQo1atQogYREVNIGDRqEhg0b\nflbHSWpqKszNzREZGQkTE5MSSEcknBcvXsDe3h4+Pj7o3Lmz0HGIqAJ6+PAhmjZtioiICBY/iYhI\nqdj5SfR/2traGDhwINasWVPksTk5OVi7di3q1q2L+vXrY+zYsYiNjS2BlERUksaMGYMNGzYgPT29\nyGN/+eUX6OjooHv37jh16lQJpCMSjp6eHnx9fTFs2DBu6kVEJYJdn0REVFJY/CR6y+zZsxEUFISd\nO3cqPEYqlWLYsGGwtLREUFAQwsPDoaOjAwcHB3h4eODRo0clmJiIlMnJyQmtWrXCgAEDkJubq/C4\nAwcOYPPmzTh37hymTp0KDw8PdOnSBbdu3SrBtESlq0OHDujbty9Gjx4NThwiImV6+PAh/vzzT0ye\nPFnoKEREVAGx+En0lurVq+PIkSOYOXMmvL29IZVKCz3+1atX6NevH+Li4hAQEACxWAxjY2MsW7YM\nERERMDExQePGjeHm5obIyMhSehZE9LlEIhG2bNkCmUyGHj16ICUlpdDj8/Pz4ePjg1GjRuHgwYOw\ntLSEs7Mz7t27h+7du+Obb76Bq6srYmJiSukZEJWspUuX4vbt29i9e7fQUYioAlm0aBHGjh0LfX19\noaMQEVEFxOIn0TtsbW3xzz//ICgoCJaWlli2bBmSkpIKHHP79m2MHj0a5ubmMDQ0REhICDQ1NQsc\nY2BggIULF+LBgwewsLBA8+bNMWjQINy7d680nw4RFZGqqir2798POzs7WFlZYdiwYfj3338LHJOa\nmgpvb2/Y2Nhg06ZNOHv2LBo3blzgHOPHj0dkZCTMzc3h4OCAn3766ZPFVKKyTkNDA7t27cKkSZPw\n+PFjoeMQUQXw4MEDHDx4EJMmTRI6ChERVVAsfhJ9wJdffokLFy4gKCgIUVFRsLKygqmpKaysrGBk\nZISuXbvC1NQUd+7cwa+//go1NbWPnktPTw9z587FgwcPYGdnh7Zt28LZ2Rm3b98uxWdEREWhoqIC\nb29vREREwNraGn369IGBgYH8NaBmzZq4ceMGdu7ciX///Rc2NjYfPE/VqlWxcOFC3L17F+np6ahT\npw6WL1+OzMzMUn5GRMrTsGFDTJgwAW5ubsjPzxc6DhGVc4sWLcK4cePY9UlERCWGu70TKSA7OxvJ\nycnIyMiArq4uDAwMIJFIPutcaWlp2Lx5M1avXg0nJyd4enrCwcFByYmJSJny8/ORkpKCFy9eYM+e\nPXj48CG2bdtW5POEh4dj1qxZuHr1Kry8vDB48ODPfi0hElJeXh5atWqF/v37Y8KECULHIaJyKioq\nCo6OjoiKioKenp7QcYiIqIJi8ZOIiIiIiiwqKgpOTk44d+4c6tatK3QcIiqH1q9fj5SUFMyfP1/o\nKEREVIGx+ElEREREn+XXX3+Fj48PLl68iCpVqggdh4jKkTdvQ2UyGcRirsZGREQlhz9liIiIiOiz\neHh4wMTEBAsXLhQ6ChGVMyKRCCKRiIVPIiIqcez8JCIiIqLPFh8fDwcHBxw4cACOjo5CxyEiIiIi\nKoAfs1GFIhaLsX///mKdY8eOHahataqSEhFRWWFhYQFvb+8Svw5fQ6iyMTU1xYYNG+Dq6or09HSh\n4xARERERFcDOTyoXxGIxRCIRPvTfVSQSYciQIdi+fTuSkpKgr69frHXHsrOz8fr1axgaGhYnMhGV\nIjc3N+zYsUM+fc7MzAzdu3fHkiVL5LvHpqSkQEtLC+rq6iWaha8hVFkNGTIEmpqa2LRpk9BRiKiM\nkclkEIlEQscgIqJKisVPKheSkpLk/z506BA8PDyQkJAgL4ZqaGhAR0dHqHhKl5uby40jiIrAzc0N\nT58+xa5du5Cbm4uwsDC4u7ujVatWCAgIEDqeUvENJJVVL1++hL29PTZv3oyuXbsKHYeIyqD8/Hyu\n8UlERKWOP3moXDA2Npb/edPFZWRkJL/vTeHz7WnvMTExEIvFCAwMRNu2baGpqYmGDRvi9u3buHv3\nLlq0aAFtbW20atUKMTEx8mvt2LGjQCE1Li4O33//PQwMDKClpQVbW1vs2bNH/vidO3fQqVMnaGpq\nwsDAAG5ubnj16pX88WvXrqFz584wMjKCrq4uWrVqhUuXLhV4fmKxGBs3bkSfPn2gra2N2bNnIz8/\nH8OHD0ft2rWhqakJa2trrFy5UvlfXKIKQk1NDUZGRjAzM0PHjh3Rr18/HD9+XP74u9PexWIxNm/e\njO+//x5aWlqwsbHBmTNn8OTJE3Tp0gXa2tpwcHDAjRs35GPevD6cPn0a9evXh7a2Ntq3b1/oawgA\nHDlyBI6OjtDU1IShoSF69uyJnJycD+YCgHbt2mHChAkffJ6Ojo44e/bs53+hiEqIrq4u/Pz8MHz4\ncCQnJwsdh4gEJpVKcfnyZYwdOxazZs3C69evWfgkIiJB8KcPVXjz58/HzJkzcfPmTejp6aF///6Y\nMGECli5diqtXryIrK+u9IsPbXVWjR49GZmYmzp49i7CwMKxdu1ZegM3IyEDnzp1RtWpVXLt2DQcO\nHMA///yDYcOGyce/fv0agwcPxoULF3D16lU4ODige/fueP78eYFrenl5oXv37rhz5w7Gjh2L/Px8\n1KxZE/v27UN4eDiWLFmCpUuXwtfX94PPc9euXcjLy1PWl42oXHv48CFCQkI+2UG9ePFiDBgwAKGh\noWjSpAlcXFwwfPhwjB07Fjdv3oSZmRnc3NwKjMnOzsayZcvg5+eHS5cu4cWLFxg1alSBY95+DQkJ\nCUHPnj3RuXNnXL9+HefOnUO7du2Qn5//Wc9t/PjxGDJkCHr06IE7d+581jmISkq7du3g4uKC0aNH\nf3CpGiKqPFavXo0RI0bgypUrCAoKwldffYWLFy8KHYuIiCojGVE5s2/fPplYLP7gYyKRSBYUFCST\nyWSy6OhomUgkkvn4+MgfDw4OlolEItmBAwfk9/n5+cl0dHQ+etve3l7m5eX1wett2bJFpqenJ0tP\nT5ffd+bMGZlIJJI9ePDgg2Py8/NlpqamsoCAgAK5J06cWNjTlslkMtmMGTNknTp1+uBjrVq1kllZ\nWcm2b98uy8nJ+eS5iCqSoUOHylRUVGTa2toyDQ0NmUgkkonFYtm6devkx5ibm8tWr14tvy0SiWSz\nZ8+W375z545MJBLJ1q5dK7/vzJkzMrFYLEtJSZHJZP+9PojFYllkZKT8mICAAJm6urr89ruvIS1a\ntJANGDDgo9nfzSWTyWRt27aVjR8//qNjsrKyZN7e3jIjIyOZm5ub7PHjxx89lqi0ZWZmyuzs7GT+\n/v5CRyEigbx69Uqmo6MjO3TokCwlJUWWkpIia9++vWzMmDEymUwmy83NFTghERFVJuz8pAqvfv36\n8n+bmJhAJBKhXr16Be5LT09HVlbWB8dPnDgRCxcuRPPmzeHp6Ynr16/LHwsPD4e9vT00NTXl9zVv\n3hxisRhhYWEAgGfPnmHkyJGwsbGBnp4eqlatimfPniE2NrbAdRo1avTetTdv3owmTZrIp/avWbPm\nvXFvnDt3Dlu3bsWuXbtgbW2NLVu2yKfVElUGbdq0QWhoKK5evYoJEyagW7duGD9+fKFj3n19APDe\n6wNQcN1hNTU1WFlZyW+bmZkhJycHL168+OA1bty4gfbt2xf9CRVCTU0NkydPRkREBExMTGBvb4/p\n06d/NANRaVJXV4e/vz9+/PHHj/7MIqKKbc2aNWjWrBl69OiBatWqoVq1apgxYwYOHjyI5ORkqKio\nAPhvqZi3f7cmIiIqCSx+UoX39rTXN1NRP3Tfx6aguru7Izo6Gu7u7oiMjETz5s3h5eX1yeu+Oe/g\nwYPx77//Yt26dbh48SJu3bqFGjVqvFeY1NLSKnA7MDAQkydPhru7O44fP45bt25hzJgxhRY027Rp\ng1OnTmHXrl3Yv38/rKyssGHDho8Wdj8mLy8Pt27dwsuXL4s0jkhImpqasLCwgJ2dHdauXYv09PRP\nfq8q8vogk8kKvD68ecP27rjPncYuFovfmx6cm5ur0Fg9PT0sXboUoaGhSE5OhrW1NVavXl3k73ki\nZXNwcMDkyZMxdOjQz/7eIKLySSqVIiYmBtbW1vIlmaRSKVq2bAldXV3s3bsXAPD06VO4ublxEz8i\nIipxLH4SKcDMzAzDhw/H77//Di8vL2zZsgUAULduXdy+fRvp6enyYy9cuACZTAZbW1v57fHjx6NL\nly6oW7cutLS0EB8f/8lrXrhwAY6Ojhg9ejQaNGiA2rVrIyoqSqG8LVq0QEhICPbt24eQkBBYWlpi\n7dq1yMjIUGj83bt3sWLFCrRs2RLDhw9HSkqKQuOIypJ58+Zh+fLlSEhIKNZ5ivumzMHBAadOnfro\n40ZGRgVeE7KyshAeHl6ka9SsWRPbtm3DX3/9hbNnz6JOnTrw9/dn0YkENW3aNGRnZ2PdunVCRyGi\nUiSRSNCvXz/Y2NjIPzCUSCTQ0NBA27ZtceTIEQDAnDlz0KZNGzg4OAgZl4iIKgEWP6nSebfD6lMm\nTZqEY8eO4dGjR7h58yZCQkJgZ2cHABg4cCA0NTUxePBg3LlzB+fOncOoUaPQp08fWFhYAACsra2x\na9cu3Lt3D1evXkX//v2hpqb2yetaW1vj+vXrCAkJQVRUFBYuXIhz584VKXvTpk1x6NAhHDp0COfO\nnYOlpSVWrVr1yYJIrVq1MHjwYIwdOxbbt2/Hxo0bkZ2dXaRrEwmtTZs2sLW1xaJFi4p1HkVeMwo7\nZvbs2di7dy88PT1x79493L17F2vXrpV3Z7Zv3x4BAQE4e/Ys7t69i2HDhkEqlX5WVjs7Oxw8eBD+\n/v7YuHEjGjZsiGPHjnHjGRKERCLBzp07/8fencdTlf9/AH/dS0SopFJpI4rSQmnTPu37vitpL+0q\ntKBFG+0yNYyitGJaNaW0kFIpo4WKFqVlKiRZ7/39Mb/ud0w1Q+Fc7uv5eNzHY7r3nON1DPe47/P+\nfD5YvXo17ty5I3QcIipGXbp0wbRp0wDkvUaOGTMGMTExuHv3Lvbt2wc3NzehIhIRkQJh8ZNKlX92\naH2tY6ugXVwSiQSzZs1Cw4YN0b17d+jq6sLHxwcAoKamhtOnTyM1NRUtW7bEwIED0bZtW3h5ecn2\n//XXX5GWlobmzZtj1KhRsLGxQZ06df4z05QpUzBs2DCMHj0aFhYWePr0KRYsWFCg7J+ZmZkhICAA\np0+fhpKS0n9+DypWrIju3bvj1atXMDIyQvfu3fMUbDmXKJUU8+fPh5eXF549e/bd7w/5ec/4t216\n9uyJwMBABAcHw8zMDJ06dUJoaCjE4r8uwfb29ujcuTMGDBiAHj16oF27dj/cBdOuXTuEh4dj2bJl\nmDVrFn766SfcuHHjh45J9D0MDAywevVqjBkzhtcOIgXwee5pZWVllClTBlKpVHaNzMzMRPPmzaGn\np4fmzZujc+fOMDMzEzIuEREpCJGU7SBECufvf4h+67Xc3FxUq1YNEydOhKOjo2xO0sePH+PAgQNI\nS0uDlZUVDA0NizM6ERVQdnY2vLy84OLigg4dOmDVqlXQ19cXOhYpEKlUin79+qFx48ZYtWqV0HGI\nqIh8+PABNjY26NGjBzp27PjNa8306dPh6emJmJgY2TRRRERERYmdn0QK6N+61D4Pt123bh3Kli2L\nAQMG5FmMKTk5GcnJybh9+zbq168PNzc3zitIJMfKlCmDqVOnIi4uDsbGxmjRogVmz56NN2/eCB2N\nFIRIJMIvv/wCLy8vhIeHCx2HiIqIr68vDh8+jK1bt8LOzg6+vr54/PgxAGDXrl2yvzFdXFxw5MgR\nFj6JiKjYsPOTiL5KV1cX48aNw9KlS6GhoZHnNalUiqtXr6JNmzbw8fHBmDFjZEN4iUi+vX79GitW\nrIC/vz/mzp2LOXPm5LnBQVRUAgMDYWdnh1u3bn1xXSGiku/GjRuYPn06Ro8ejZMnTyImJgadOnVC\nuXLlsGfPHjx//hwVK1YE8O+jkIiIiAobqxVEJPO5g3PDhg1QVlbGgAEDvviAmpubC5FIJFtMpXfv\n3l8UPtPS0ootMxEVTJUqVbB161ZEREQgOjoaRkZG2LlzJ3JycoSORqXcwIED0a5dO8yfP1/oKERU\nBMzNzWFpaYmUlBQEBwdj27ZtSEpKgre3NwwMDPD777/j0aNHAAo+Bz8REdGPYOcnEUEqleLs2bPQ\n0NBA69atUbNmTQwfPhzLly+HpqbmF3fnExISYGhoiF9//RVjx46VHUMkEuHBgwfYtWsX0tPTMWbM\nGLRq1Uqo0yKifIiMjMTChQvx8uVLuLq6on///vxQSkUmNTUVTZo0wdatW9GnTx+h4xBRIUtMTMTY\nsWPh5eUFfX19HDx4EJMnT0ajRo3w+PFjmJmZYe/evdDU1BQ6KhERKRB2fhIRpFIpzp8/j7Zt20Jf\nXx9paWno37+/7A/Tz4WQz52hK1euhImJCXr06CE7xudtPn78CE1NTbx8+RJt2rSBs7NzMZ8NERVE\nixYtcO7cObi5uWHp0qWwtLREWFiY0LGolNLS0sLu3buxZMkSdhsTlTK5ubnQ09ND7dq1sXz5cgCA\nnZ0dnJ2dcfnyZbi5uaF58+YsfBIRUbFj5ycRycTHx8PV1RVeXl5o1aoVNm/eDHNz8zzD2p89ewZ9\nfX3s3LkT1tbWXz2ORCJBSEgIevTogePHj6Nnz57FdQpE9ANyc3Ph5+eHpUuXwszMDK6urjA2NhY6\nFpVCEokEIpGIXcZEpcTfRwk9evQIs2bNgp6eHgIDA3H79m1Uq1ZN4IRERKTI2PlJRDL6+vrYtWsX\nnjx5gjp16sDDwwMSiQTJycnIzMwEAKxatQpGRkbo1avXF/t/vpfyeWVfCwsLFj6pVEtJSYGGhgZK\ny31EJSUljBs3DrGxsWjbti3at2+PyZMn48WLF0JHo1JGLBb/a+EzIyMDq1atwsGDB4sxFREVVHp6\nOoC8o4QMDAxgaWkJb29vODg4yAqfn0cQERERFTcWP4noCzVr1sS+ffvw888/Q0lJCatWrUK7du2w\ne/du+Pn5Yf78+ahateoX+33+wzcyMhIBAQFwdHQs7uhExap8+fIoV64ckpKShI5SqNTU1GBnZ4fY\n2FiUL18epqamWLJkCVJTU4WORgoiMTERz58/x7Jly3D8+HGh4xDRV6Smbtl+WwAAIABJREFUpmLZ\nsmUICQlBcnIyAMhGC40fPx5eXl4YP348gL9ukP9zgUwiIqLiwisQEX2TiooKRCIRHBwcYGBggClT\npiA9PR1SqRTZ2dlf3UcikWDz5s1o0qQJF7MghWBoaIgHDx4IHaNIaGtrY/369YiKikJiYiIMDQ2x\nZcsWZGVl5fsYpaUrloqPVCpFvXr14O7ujsmTJ2PSpEmy7jIikh8ODg5wd3fH+PHj4eDggAsXLsiK\noNWqVYOVlRUqVKiAzMxMTnFBRESCYvGTiP5TxYoV4e/vj9evX2POnDmYNGkSZs2ahffv33+x7e3b\nt3Ho0CF2fZLCMDIyQlxcnNAxilStWrXg4+ODM2fOIDg4GA0aNIC/v3++hjBmZWXhzz//xJUrV4oh\nKZVkUqk0zyJIKioqmDNnDgwMDLBr1y4BkxHRP6WlpSE8PByenp5wdHREcHAwhg4dCgcHB4SGhuLd\nu3cAgHv37mHKlCn48OGDwImJiEiRsfhJRPmmpaUFd3d3pKamYtCgQdDS0gIAPH36VDYn6KZNm2Bi\nYoKBAwcKGZWo2JTmzs9/aty4MU6ePAkvLy+4u7vDwsICCQkJ/7rP5MmT0b59e0yfPh01a9ZkEYvy\nkEgkeP78ObKzsyESiaCsrCzrEBOLxRCLxUhLS4OGhobASYno7xITE2Fubo6qVati6tSpiI+Px4oV\nKxAcHIxhw4Zh6dKluHDhAmbNmoXXr19zhXciIhKUstABiKjk0dDQQNeuXQH8Nd/T6tWrceHCBYwa\nNQpHjhzBnj17BE5IVHwMDQ2xd+9eoWMUq06dOuHq1as4cuQIatas+c3tNm3ahMDAQGzYsAFdu3bF\nxYsXsXLlStSqVQvdu3cvxsQkj7Kzs1G7dm28fPkS7dq1g5qaGszNzdGsWTNUq1YN2tra2L17N6Kj\no1GnTh2h4xLR3xgZGWHRokXQ0dGRPTdlyhRMmTIFnp6eWLduHfbt24eUlBTcvXtXwKRERESASMrJ\nuIjoB+Xk5GDx4sXw9vZGcnIyPD09MXLkSN7lJ4UQHR2NkSNH4s6dO0JHEYRUKv3mXG4NGzZEjx49\n4ObmJntu6tSpePXqFQIDAwH8NVVGkyZNiiUryR93d3csWLAAAQEBuH79Oq5evYqUlBQ8e/YMWVlZ\n0NLSgoODAyZNmiR0VCL6Dzk5OVBW/l9vTf369dGiRQv4+fkJmIqIiIidn0RUCJSVlbFhwwasX78e\nrq6umDp1KqKiorB27VrZ0PjPpFIp0tPToa6uzsnvqVSoV68e4uPjIZFIFHIl22/9HmdlZcHQ0PCL\nFeKlUinKli0L4K/CcbNmzdCpUyfs2LEDRkZGRZ6X5Mu8efOwZ88enDx5Ejt37pQV09PS0vD48WM0\naNAgz8/YkydPAAC1a9cWKjIRfcPnwqdEIkFkZCQePHiAoKAggVMRERFxzk8iKkSfV4aXSCSYNm0a\nypUr99XtJk6ciDZt2uDUqVNcCZpKPHV1dVSqVAnPnj0TOopcUVFRQYcOHXDw4EEcOHAAEokEQUFB\nCAsLg6amJiQSCRo3bozExETUrl0bxsbGGDFixFcXUqPS7ejRo9i9ezcOHz4MkUiE3NxcaGhooFGj\nRlBWVoaSkhIA4M8//4Sfnx8WLVqE+Ph4gVMT0beIxWJ8/PgRCxcuhLGxsdBxiIiIWPwkoqLRuHFj\n2QfWvxOJRPDz88OcOXNgZ2cHCwsLHD16lEVQKtEUYcX3gvj8+zx37lysX78etra2aNWqFRYsWIC7\nd++ia9euEIvFyMnJQfXq1eHt7Y2YmBi8e/cOlSpVws6dOwU+AypOtWrVwrp162BjY4PU1NSvXjsA\nQEdHB+3atYNIJMKQIUOKOSURFUSnTp2wevVqoWMQEREBYPGTiASgpKSE4cOHIzo6Gvb29li2bBma\nNWuGI0eOQCKRCB2PqMAUacX3/5KTk4OQkBAkJSUB+Gu199evX2PGjBlo2LAh2rZti6FDhwL4670g\nJycHwF8dtObm5hCJRHj+/LnseVIMs2fPxqJFixAbG/vV13NzcwEAbdu2hVgsxq1bt/D7778XZ0Qi\n+gqpVPrVG9gikUghp4IhIiL5xCsSEQlGLBZj0KBBiIqKwooVK7BmzRo0btwY+/fvl33QJSoJWPz8\nn7dv38Lf3x/Ozs5ISUlBcnIysrKycOjQITx//hyLFy8G8NecoCKRCMrKynj9+jUGDRqEAwcOYO/e\nvXB2ds6zaAYpBnt7e7Ro0SLPc5+LKkpKSoiMjESTJk0QGhqKX3/9FRYWFkLEJKL/FxUVhcGDB3P0\nDhERyT0WP4lIcCKRCH379sW1a9ewYcMGbNmyBQ0bNoSfnx+7v6hE4LD3/6latSqmTZuGiIgImJiY\noH///tDT00NiYiKcnJzQu3dvAP9bGOPw4cPo2bMnMjMz4eXlhREjRggZnwT0eWGjuLg4Wefw5+dW\nrFiB1q1bw8DAAKdPn4aVlRUqVKggWFYiApydndGhQwd2eBIRkdwTSXmrjojkjFQqxblz5+Ds7IwX\nL17A0dERY8aMQZkyZYSORvRV9+7dQ//+/VkA/Yfg4GA8evQIJiYmaNasWZ5iVWZmJo4fP44pU6ag\nRYsW8PT0lK3g/XnFb1JMO3bsgJeXFyIjI/Ho0SNYWVnhzp07cHZ2xvjx4/P8HEkkEhZeiAQQFRWF\nPn364OHDh1BTUxM6DhER0b9i8ZOI5NqFCxfg4uKC+Ph42NvbY9y4cVBVVRU6FlEemZmZKF++PD58\n+MAi/Tfk5ubmWchm8eLF8PLywqBBg7B06VLo6emxkEUy2traaNSoEW7fvo0mTZpg/fr1aN68+TcX\nQ0pLS4OGhkYxpyRSXP3790eXLl0wa9YsoaMQERH9J37CICK51qFDB4SEhMDPzw8BAQEwNDTE9u3b\nkZGRIXQ0IhlVVVVUr14djx8/FjqK3PpctHr69CkGDBiAbdu2YeLEifj555+hp6cHACx8kszJkydx\n+fJl9O7dG0FBQWjZsuVXC59paWnYtm0b1q1bx+sCUTG5efMmrl+/jkmTJgkdhYiIKF/4KYOISoS2\nbdsiODgYhw8fRnBwMAwMDLBp0yakp6cLHY0IABc9yq/q1aujXr162L17N1auXAkAXOCMvtCqVSvM\nmzcPISEh//rzoaGhgUqVKuHSpUssxBAVEycnJyxevJjD3YmIqMRg8ZOIShQLCwscO3YMx44dw8WL\nF6Gvr4/169cjLS1N6Gik4IyMjFj8zAdlZWVs2LABgwcPlnXyfWsos1QqRWpqanHGIzmyYcMGNGrU\nCKGhof+63eDBg9G7d2/s3bsXx44dK55wRArqxo0buHnzJm82EBFRicLiJxGVSGZmZggICMCZM2dw\n/fp1GBgYYPXq1SyUkGAMDQ254FER6NmzJ/r06YOYmBiho5AAjhw5go4dO37z9ffv38PV1RXLli1D\n//79YW5uXnzhiBTQ567PsmXLCh2FiIgo31j8JKISzdTUFAcOHEBoaCju3r0LAwMDuLi4IDk5Weho\npGA47L3wiUQinDt3Dl26dEHnzp0xYcIEJCYmCh2LilGFChVQuXJlfPz4ER8/fszz2s2bN9G3b1+s\nX78e7u7uCAwMRPXq1QVKSlT6Xb9+HVFRUZg4caLQUYiIiAqExU8iKhWMjY3h5+eH8PBwJCQkoF69\neli6dCnevn0rdDRSEEZGRuz8LAKqqqqYO3cu4uLioKuriyZNmmDRokW8waFgDh48CHt7e+Tk5CA9\nPR2bNm1Chw4dIBaLcfPmTUydOlXoiESlnpOTE+zt7dn1SUREJY5IKpVKhQ5BRFTY4uPjsWbNGhw5\ncgSTJk3CvHnzUKVKFaFjUSmWk5MDDQ0NJCcn84NhEXr+/DmWL1+Oo0ePYtGiRZgxYwa/3wogKSkJ\nNWrUgIODA+7cuYMTJ05g2bJlcHBwgFjMe/lERS0yMhKDBg3CgwcP+J5LREQlDv9aJKJSSV9fHzt3\n7kRUVBQ+fPiABg0aYP78+UhKShI6GpVSysrKqF27NuLj44WOUqrVqFEDv/zyC86fP48LFy6gQYMG\n8PX1hUQiEToaFaFq1arB29sbq1evxr1793DlyhUsWbKEhU+iYsKuTyIiKsnY+UlECuH58+dYt24d\nfH19MWbMGCxcuBB6enoFOkZGRgYOHz6MS5cuITk5GWXKlIGuri5GjBiB5s2bF1FyKkn69u0LGxsb\nDBgwQOgoCuPSpUtYuHAhPn36hLVr16Jbt24QiURCx6IiMnz4cDx+/BhhYWFQVlYWOg6RQrh27RoG\nDx6Mhw8fQlVVVeg4REREBcbb5USkEGrUqIHNmzfj7t27UFFRQePGjTFt2jQ8efLkP/d98eIFFi9e\njFq1asHPzw9NmjTBwIED0a1bN2hqamLo0KGwsLCAj48PcnNzi+FsSF5x0aPi165dO4SHh2PZsmWY\nNWsWfvrpJ9y4cUPoWFREvL29cefOHQQEBAgdhUhhfO76ZOGTiIhKKnZ+EpFCevPmDdzd3bFz504M\nHDgQ9vb2MDAw+GK7mzdvol+/fhg8eDBmzpwJQ0PDL7bJzc1FcHAwVq5ciWrVqsHPzw/q6urFcRok\nZ3bs2IGoqCjs3LlT6CgKKTs7G15eXnBxcUGHDh2watUq6OvrCx2LCtm9e/eQk5MDU1NToaMQlXpX\nr17FkCFD2PVJREQlGjs/iUghVa5cGa6uroiLi0P16tXRsmVLjBs3Ls9q3TExMejRowe2bNmCzZs3\nf7XwCQBKSkro3bs3QkNDUbZsWQwZMgQ5OTnFdSokR7jiu7DKlCmDqVOnIi4uDsbGxmjRogVmz56N\nN2/eCB2NCpGxsTELn0TFxMnJCQ4ODix8EhFRicbiJxEptEqVKsHFxQUPHz5EvXr10LZtW4waNQq3\nbt1Cv379sHHjRgwaNChfx1JVVcXu3bshkUjg7OxcxMlJHnHYu3zQ0NDAsmXLcO/ePUgkEhgbG2PV\nqlX4+PGj0NGoCHEwE1HhioiIwJ07dzBhwgShoxAREf0QDnsnIvqb1NRUeHh4wNXVFSYmJrhy5UqB\nj/Ho0SO0atUKT58+hZqaWhGkJHklkUigoaGB169fQ0NDQ+g49P8ePnwIR0dHXL58GcuXL8eECRO4\nWE4pI5VKERQUhH79+kFJSUnoOESlQo8ePTBgwABMnTpV6ChEREQ/hJ2fRER/o6WlhcWLF6Nx48aY\nP3/+dx3DwMAALVq0wMGDBws5Hck7sVgMAwMDPHz4UOgo9Df16tXDgQMHEBQUBH9/f5iamiIoKIid\ngqWIVCrF1q1bsW7dOqGjEJUKV65cwb1799j1SUREpQKLn0RE/xAXF4dHjx6hf//+332MadOmYdeu\nXYWYikoKDn2XXy1atMC5c+fg5uaGpUuXwtLSEmFhYULHokIgFovh4+MDd3d3REVFCR2HqMT7PNen\nioqK0FGIiIh+GIufRET/8PDhQzRu3BhlypT57mOYm5uz+09BGRkZsfgpx0QiEXr16oVbt25h8uTJ\nGDlyJAYOHIj79+8LHY1+UK1ateDu7o4xY8YgIyND6DhEJVZ4eDju378Pa2troaMQEREVChY/iYj+\nIS0tDZqamj90DE1NTXz48KGQElFJYmhoyBXfSwAlJSWMGzcOsbGxaNOmDdq1a4cpU6YgKSlJ6Gj0\nA8aMGQMTExM4OjoKHYWoxHJycoKjoyO7PomIqNRg8ZOI6B8Ko3D54cMHaGlpFVIiKkk47L1kUVNT\ng52dHWJjY6GlpYVGjRphyZIlSE1NFToafQeRSARPT0/s378f58+fFzoOUYkTFhaGuLg4jB8/Xugo\nREREhYbFTyKifzAyMkJUVBQyMzO/+xhXr16FkZFRIaaiksLIyIidnyWQtrY21q9fj6ioKCQmJsLI\nyAhbtmxBVlaW0NGogCpVqoRffvkF48ePR0pKitBxiEoUZ2dndn0SEVGpw+InEdE/GBgYoFGjRggI\nCPjuY3h4eGDy5MmFmIpKiqpVqyIjIwPJyclCR6HvUKtWLfj4+OD3339HcHAwjI2NsX//fkgkEqGj\nUQH07NkTvXr1wqxZs4SOQlRihIWF4cGDBxg3bpzQUYiIiAoVi59ERF8xY8YMeHh4fNe+sbGxiI6O\nxpAhQwo5FZUEIpGIQ99LgcaNG+PkyZP45Zdf4ObmBgsLC4SEhAgdiwpgw4YNCA8Px5EjR4SOQlQi\ncK5PIiIqrVj8JCL6in79+uHVq1fw8vIq0H6ZmZmYOnUqZs6cCVVV1SJKR/KOQ99Lj06dOuHq1auw\ns7PD5MmT0aNHD9y+fVvoWJQP5cqVg6+vL2bMmMGFrIj+w+XLl/Hw4UN2fRIRUanE4icR0VcoKyvj\n+PHjcHR0xN69e/O1z6dPnzBixAhUqFABDg4ORZyQ5Bk7P0sXsViM4cOH4969e+jTpw+6d+8OKysr\nPHnyROho9B9atWqFSZMmwcbGBlKpVOg4RHLLyckJS5YsQZkyZYSOQkREVOhY/CQi+gYjIyOEhITA\n0dEREydO/Ga3V1ZWFg4cOIA2bdpAXV0d+/fvh5KSUjGnJXnC4mfppKKigpkzZyIuLg516tSBmZkZ\nFixYgHfv3gkdjf7FsmXL8Pr1a+zcuVPoKERy6dKlS4iPj4eVlZXQUYiIiIqESMrb4ERE/+rNmzfw\n9PTEzz//jDp16qBfv36oVKkSsrKykJCQAF9fXzRo0ADTp0/H4MGDIRbzvpKii4iIgK2tLSIjI4WO\nQkUoKSkJzs7OOHLkCBYsWIBZs2ZBTU1N6Fj0Fffu3UO7du1w5coVGBoaCh2HSK506dIFo0ePxoQJ\nE4SOQkREVCRY/CQiyqecnBwcPXoUly9fRlJSEk6fPg1bW1sMHz4cJiYmQscjOfL27VsYGBjg/fv3\nEIlEQsehIhYbGwsHBwdERkbC2dkZVlZW7P6WQ1u2bIG/vz8uXboEZWVloeMQyYWLFy/C2toa9+/f\n55B3IiIqtVj8JCIiKgLa2tqIjY1F5cqVhY5CxeTKlStYuHAhkpOTsWbNGvTq1YvFbzkikUjQrVs3\ndOrUCY6OjkLHIZILnTt3xtixY2FtbS10FCIioiLDsZlERERFgCu+K57WrVvj4sWLWLVqFezs7GQr\nxZN8EIvF8PHxwebNm3Hjxg2h4xAJ7sKFC3j69CnGjh0rdBQiIqIixeInERFREeCiR4pJJBKhX79+\niI6OxpgxYzB48GAMHTqUPwtyQk9PD5s2bcLYsWPx6dMnoeMQCerzCu+cBoKIiEo7Fj+JiIiKAIuf\nik1ZWRkTJ05EXFwczMzM0Lp1a8yYMQOvXr0SOprCGzlyJExNTWFvby90FCLBhIaG4tmzZxgzZozQ\nUYiIiIoci59ERERFgMPeCQDU1dVhb2+P+/fvQ0VFBSYmJnB2dkZaWlq+j/HixQu4uLigR48eaNWq\nFdq3b4/hw4cjKCgIOTk5RZi+dBKJRNixYwcOHz6MkJAQoeMQCcLJyQlLly5l1ycRESkEFj+JiATg\n7OyMxo0bCx2DihA7P+nvdHR0sHHjRly/fh1xcXEwNDSEh4cHsrOzv7nP7du3MWzYMDRs2BBJSUmw\ntbXFxo0bsWLFCnTv3h3r1q1D3bp1sWrVKmRkZBTj2ZR82tra8PLygrW1NZKTk4WOQ1Sszp8/j+fP\nn2P06NFCRyEiIioWXO2diBSOtbU13r59i6NHjwqWIT09HZmZmahYsaJgGahopaamonr16vjw4QNX\n/KYv3Lx5E4sWLcKTJ0+wevVqDB48OM/PydGjR2FjY4MlS5bA2toaWlpaXz1OVFQUli9fjuTkZPz2\n2298TymgmTNnIjk5GX5+fkJHISoWUqkUHTt2hI2NDaysrISOQ0REVCzY+UlEJAB1dXUWKUo5LS0t\naGho4MWLF0JHITlkZmaGM2fOYPv27Vi1apVspXgACAkJwaRJk3Dy5EnMnj37m4VPAGjWrBmCgoLQ\ntGlT9OnTh4v4FNC6desQGRmJgwcPCh2FqFicP38eSUlJGDVqlNBRiIiIig2Ln0REfyMWixEQEJDn\nubp168Ld3V327wcPHqBDhw5QU1NDw4YNcfr0aWhqamLPnj2ybWJiYtC1a1eoq6ujUqVKsLa2Rmpq\nqux1Z2dnmJqaFv0JkaA49J3+S9euXXHjxg3Y2tpi3Lhx6NGjB4YNG4aDBw+iRYsW+TqGWCzGpk2b\noKenh6VLlxZx4tJFXV0dvr6+sLW15Y0KKvWkUinn+iQiIoXE4icRUQFIpVIMGDAAKioquHbtGry9\nvbF8+XJkZWXJtklPT0f37t2hpaWF69evIygoCOHh4bCxsclzLA6FLv246BHlh1gsxujRo3H//n2U\nK1cOLVu2RIcOHQp8jHXr1uHXX3/Fx48fiyhp6WRhYYFp06ZhwoQJ4GxQVJqdO3cOL1++xMiRI4WO\nQkREVKxY/CQiKoDff/8dDx48gK+vL0xNTdGyZUts3Lgxz6Ile/fuRXp6Onx9fWFiYoJ27dph586d\nOHLkCOLj4wVMT8WNnZ9UECoqKrh//z7s7Oy+a//atWvD0tIS/v7+hZys9HN0dMTbt2+xY8cOoaMQ\nFYnPXZ/Lli1j1ycRESkcFj+JiAogNjYW1atXh66uruy5Fi1aQCz+39vp/fv30bhxY6irq8uea9Om\nDcRiMe7evVuseUlYLH5SQVy/fh05OTno2LHjdx9jypQp+PXXXwsvlIIoU6YM/Pz8sGzZMnZrU6kU\nEhKC169fY8SIEUJHISIiKnYsfhIR/Y1IJPpi2OPfuzoL4/ikODjsnQri6dOnaNiw4Q+9TzRs2BBP\nnz4txFSKo379+nBycsLYsWORk5MjdByiQsOuTyIiUnQsfhIR/U3lypWRlJQk+/erV6/y/LtBgwZ4\n8eIFXr58KXsuMjISEolE9m9jY2P88ccfeebdCwsLg1QqhbGxcRGfAckTAwMDJCQkIDc3V+goVAJ8\n/PgxT8f49yhXrhzS09MLKZHimT59OipUqIDVq1cLHYWo0Jw9exZ//vknuz6JiEhhsfhJRAopNTUV\nt2/fzvN48uQJOnfujO3bt+PGjRuIioqCtbU11NTUZPt17doVRkZGsLKyQnR0NCIiIjB//nyUKVNG\n1q01evRoqKurw8rKCjExMbh48SKmTp2KwYMHQ19fX6hTJgGoq6tDR0cHz549EzoKlQAVKlRASkrK\nDx0jJSUF5cuXL6REikcsFsPb2xvbtm1DZGSk0HGIftjfuz6VlJSEjkNERCQIFj+JSCFdunQJZmZm\neR52dnZwd3dH3bp10alTJwwbNgyTJk1ClSpVZPuJRCIEBQUhKysLLVu2hLW1NRwdHQEAZcuWBQCo\nqanh9OnTSE1NRcuWLTFw4EC0bdsWXl5egpwrCYtD3ym/TE1NERERgU+fPn33Mc6fP48mTZoUYirF\nU6NGDWzduhVjx45lFy2VeGfPnsW7d+8wfPhwoaMQEREJRiT95+R2RERUILdv30azZs1w48YNNGvW\nLF/7ODg4IDQ0FOHh4UWcjoQ2depUmJqaYsaMGUJHoRKgZ8+eGDlyJKysrAq8r1QqhZmZGdauXYtu\n3boVQTrFMmrUKFSqVAlbt24VOgrRd5FKpWjbti1sbW0xcuRIoeMQEREJhp2fREQFFBQUhDNnzuDx\n48c4f/48rK2t0axZs3wXPh89eoSQkBA0atSoiJOSPOCK71QQ06dPx/bt279YeC0/IiIi8OTJEw57\nLyTbt2/Hb7/9hjNnzggdhei7nDlzBsnJyRg2bJjQUYiIiATF4icRUQF9+PABM2fORMOGDTF27Fg0\nbNgQwcHB+do3JSUFDRs2RNmyZbF06dIiTkrygMPeqSB69eqFrKwsrF+/vkD7vX//HjY2NhgwYAAG\nDhyI8ePH51msjQquYsWK8Pb2xoQJE/Du3Tuh4xAViFQqxfLlyznXJxERETjsnYiIqEjdv38fffv2\nZfcn5VtiYqJsqOr8+fNli6l9y6tXr9CnTx+0a9cO7u7uSE1NxerVq/HLL79g/vz5mDt3rmxOYiq4\nWbNm4c2bN/D39xc6ClG+nT59GnPnzsUff/zB4icRESk8dn4SEREVIX19fTx79gzZ2dlCR6ESQk9P\nDx4eHnBxcUHPnj1x6tQpSCSSL7Z78+YN1qxZA3Nzc/Tu3Rtubm4AAC0tLaxZswZXr17FtWvXYGJi\ngoCAgO8aSk/AmjVrcOvWLRY/qcT43PW5fPlyFj6JiIjAzk8iIqIiZ2BggFOnTsHIyEjoKFQCpKam\nwtzcHMuWLUNOTg62b9+O9+/fo1evXtDW1kZmZibi4+Nx5swZDBo0CNOnT4e5ufk3jxcSEoI5c+ZA\nR0cHmzZt4mrw3+H69evo1asXbt68CT09PaHjEP2r4OBgzJ8/H9HR0Sx+EhERgcVPIiKiItejRw/Y\n2tqid+/eQkchOSeVSjFy5EhUqFABnp6esuevXbuG8PBwJCcnQ1VVFbq6uujfvz+0tbXzddycnBzs\n2rULTk5OGDhwIFasWIHKlSsX1WmUSitWrMClS5cQHBwMsZiDp0g+SaVStGrVCvPnz+dCR0RERP+P\nxU8iIqIiNmvWLNStWxdz584VOgoRfaecnBxYWlpi9OjRsLW1FToO0VedOnUKdnZ2iI6OZpGeiIjo\n//GKSERURDIyMuDu7i50DJIDhoaGXPCIqIRTVlbGnj174OzsjPv37wsdh+gLf5/rk4VPIiKi/+FV\nkYiokPyzkT47OxsLFizAhw8fBEpE8oLFT6LSwcjICCtWrMDYsWO5iBnJnVOnTuHTp08YPHiw0FGI\niIjkCoufRETfKSAgALGxsUhJSQEAiEQiAEBubi5yc3Ohrq4OVVVVJCcnCxmT5ICRkRHi4uKEjkFE\nhWDq1KnQ0dHBypUrhY5CJMOuTyIiom/jnJ9ERN/J2NgYT58+xU8//YQePXqgUaNGaNSoESpWrCjb\npmLFijh//jyaNm0qYFISWk5ODjQ0NJCcnIyyZcsKHYcoX3JycqBt0R/vAAAgAElEQVSsrCx0DLn0\n4sULNGvWDEePHkXLli2FjkOEEydOYPHixbh9+zaLn0RERP/AKyMR0Xe6ePEitm7divT0dDg5OcHK\nygrDhw+Hg4MDTpw4AQDQ1tbG69evBU5KQlNWVkadOnXw6NEjoaOQHHny5AnEYjFu3rwpl1+7WbNm\nCAkJKcZUJUf16tWxbds2jB07Fh8/fhQ6Dik4qVQKJycndn0SERF9A6+ORETfqXLlypgwYQLOnDmD\nW7duYeHChahQoQKOHTuGSZMmwdLSEgkJCfj06ZPQUUkOcOi7YrK2toZYLIaSkhJUVFRgYGAAOzs7\npKeno1atWnj58qWsM/zChQsQi8V49+5doWbo1KkTZs2alee5f37tr3F2dsakSZMwcOBAFu6/YujQ\noWjZsiUWLlwodBRScCdOnEBmZiYGDRokdBQiIiK5xOInEdEPysnJQbVq1TBt2jQcPHgQv/32G9as\nWQNzc3PUqFEDOTk5QkckOcBFjxRX165d8fLlSyQkJGDVqlXw8PDAwoULIRKJUKVKFVmnllQqhUgk\n+mLxtKLwz6/9NYMGDcLdu3dhYWGBli1bYtGiRUhNTS3ybCXJ1q1bcezYMQQHBwsdhRQUuz6JiIj+\nG6+QREQ/6O9z4mVlZUFfXx9WVlbYvHkzzp07h06dOgmYjuQFi5+KS1VVFZUrV0aNGjUwYsQIjBkz\nBkFBQXmGnj958gSdO3cG8FdXuZKSEiZMmCA7xrp161CvXj2oq6ujSZMm2Lt3b56v4eLigjp16qBs\n2bKoVq0axo8fD+CvztMLFy5g+/btsg7Up0+f5nvIfdmyZWFvb4/o6Gi8evUKDRo0gLe3NyQSSeF+\nk0qoChUqwMfHBxMnTsTbt2+FjkMK6Pjx48jOzsbAgQOFjkJERCS3OIs9EdEPSkxMREREBG7cuIFn\nz54hPT0dZcqUQevWrTF58mSoq6vLOrpIcRkZGcHf31/oGCQHVFVVkZmZmee5WrVq4ciRIxgyZAju\n3buHihUrQk1NDQDg6OiIgIAA7NixA0ZGRrhy5QomTZoEbW1t9OzZE0eOHIGbmxsOHDiARo0a4fXr\n14iIiAAAbN68GXFxcTA2NoarqyukUikqV66Mp0+fFug9qXr16vDx8UFkZCRmz54NDw8PbNq0CZaW\nloX3jSmhOnfujKFDh2LatGk4cOAA3+up2LDrk4iIKH9Y/CQi+gGXL1/G3Llz8fjxY+jp6UFXVxca\nGhpIT0/H1q1bERwcjM2bN6N+/fpCRyWBsfOTAODatWvYt28funXrlud5kUgEbW1tAH91fn7+7/T0\ndGzcuBFnzpxB27ZtAQC1a9fG1atXsX37dvTs2RNPnz5F9erV0bVrVygpKUFPTw9mZmYAAC0tLaio\nqEBdXR2VK1fO8zW/Z3h9ixYtEBYWBn9/f4wcORKWlpZYu3YtatWqVeBjlSarV6+Gubk59u3bh9Gj\nRwsdhxTEsWPHkJubiwEDBggdhYiISK7xFiER0Xd6+PAh7OzsoK2tjYsXLyIqKgqnTp3CoUOHEBgY\niJ9//hk5OTnYvHmz0FFJDtSoUQPJyclIS0sTOgoVs1OnTkFTUxNqampo27YtOnXqhC1btuRr37t3\n7yIjIwM9evSApqam7OHp6Yn4+HgAfy288+nTJ9SpUwcTJ07E4cOHkZWVVWTnIxKJMGrUKNy/fx9G\nRkZo1qwZli9frtCrnqupqcHPzw9z587Fs2fPhI5DCoBdn0RERPnHKyUR0XeKj4/HmzdvcOTIERgb\nG0MikSA3Nxe5ublQVlbGTz/9hBEjRiAsLEzoqCQHxGIxPn78iHLlygkdhYpZhw4dEB0djbi4OGRk\nZODQoUPQ0dHJ176f59Y8fvw4bt++LXvcuXMHp0+fBgDo6ekhLi4OO3fuRPny5bFgwQKYm5vj06dP\nRXZOAFCuXDk4OzsjKipKNrR+3759xbJgkzwyMzPD7NmzMX78eM6JSkXu6NGjkEql7PokIiLKBxY/\niYi+U/ny5fHhwwd8+PABAGSLiSgpKcm2CQsLQ7Vq1YSKSHJGJBJxPkAFpK6ujrp166JmzZp53h/+\nSUVFBQCQm5sre87ExASqqqp4/Pgx9PX18zxq1qyZZ9+ePXvCzc0N165dw507d2Q3XlRUVPIcs7DV\nqlUL/v7+2LdvH9zc3GBpaYnIyMgi+3rybNGiRfj06RO2bt0qdBQqxf7e9clrChER0X/jnJ9ERN9J\nX18fxsbGmDhxIpYsWYIyZcpAIpEgNTUVjx8/RkBAAKKiohAYGCh0VCIqAWrXrg2RSIQTJ06gT58+\nUFNTg4aGBhYsWIAFCxZAIpGgffv2SEtLQ0REBJSUlDBx4kTs3r0bOTk5aNmyJTQ0NLB//36oqKjA\n0NAQAFCnTh1cu3YNT548gYaGBipVqlQk+T8XPX18fNC/f39069YNrq6uCnUDSFlZGXv27EGrVq3Q\ntWtXmJiYCB2JSqHffvsNANC/f3+BkxAREZUM7PwkIvpOlStXxo4dO/DixQv069cP06dPx+zZs2Fv\nb4+ff/4ZYrEY3t7eaNWqldBRiUhO/b1rq3r16nB2doajoyN0dXVha2sLAFixYgWcnJzg5uaGRo0a\noVu3bggICEDdunUBABUqVICXlxfat28PU1NTBAYGIjAwELVr1wYALFiwACoqKjAxMUGVKlXw9OnT\nL752YRGLxZgwYQLu378PXV1dmJqawtXVFRkZGYX+teRVvXr1sHr1aowdO7ZI514lxSSVSuHs7Awn\nJyd2fRIREeWTSKqoEzMRERWiy5cv448//kBmZibKly+PWrVqwdTUFFWqVBE6GhGRYB49eoQFCxbg\n9u3b2LBhAwYOHKgQBRupVIq+ffuiadOmWLlypdBxqBQJDAzEihUrcOPGDYX4XSIiIioMLH4SEf0g\nqVTKDyBUKDIyMiCRSKCuri50FKJCFRISgjlz5kBHRwebNm1CkyZNhI5U5F6+fImmTZsiMDAQrVu3\nFjoOlQISiQRmZmZwcXFBv379hI5DRERUYnDOTyKiH/S58PnPe0ksiFJBeXt7482bN1iyZMm/LoxD\nVNJ06dIFUVFR2LlzJ7p164aBAwdixYoVqFy5stDRioyuri48PDxgZWWFqKgoaGhoCB2JSoj4+Hjc\nu3cPqampKFeuHPT19dGoUSMEBQVBSUkJffv2FToiybH09HRERETg7du3AIBKlSqhdevWUFNTEzgZ\nEZFw2PlJRERUTLy8vGBpaQlDQ0NZsfzvRc7jx4/D3t4eAQEBssVqiEqb9+/fw9nZGXv37oWDgwNm\nzJghW+m+NBo3bhzU1NTg6ekpdBSSYzk5OThx4gQ8PDwQFRWF5s2bQ1NTEx8/fsQff/wBXV1dvHjx\nAhs3bsSQIUOEjkty6MGDB/D09MTu3bvRoEED6OrqQiqVIikpCQ8ePIC1tTWmTJkCAwMDoaMSERU7\nLnhERERUTBYvXozz589DLBZDSUlJVvhMTU1FTEwMEhIScOfOHdy6dUvgpERFp2LFiti0aRMuXryI\n06dPw9TUFCdPnhQ6VpHZsmULgoODS/U50o9JSEhA06ZNsWbNGowdOxbPnj3DyZMnceDAARw/fhzx\n8fFYunQpDAwMMHv2bERGRgodmeSIRCKBnZ0dLC0toaKiguvXr+Py5cs4fPgwjhw5gvDwcERERAAA\nWrVqBQcHB0gkEoFTExEVL3Z+EhERFZP+/fsjLS0NHTt2RHR0NB48eIAXL14gLS0NSkpKqFq1KsqV\nK4fVq1ejd+/eQsclKnJSqRQnT57EvHnzoK+vD3d3dxgbG+d7/+zsbJQpU6YIExaO0NBQjBo1CtHR\n0dDR0RE6DsmRhw8fokOHDli8eDFsbW3/c/ujR4/CxsYGR44cQfv27YshIckziUQCa2trJCQkICgo\nCNra2v+6/Z9//ol+/frBxMQEu3bt4hRNRKQw2PlJRPSDpFIpEhMTv5jzk+if2rRpg/Pnz+Po0aPI\nzMxE+/btsXjxYuzevRvHjx/Hb7/9hqCgIHTo0EHoqPQdsrKy0LJlS7i5uQkdpcQQiUTo3bs3/vjj\nD3Tr1g3t27fHnDlz8P79+//c93PhdMqUKdi7d28xpP1+HTt2xKhRozBlyhReK0gmJSUFPXv2xPLl\ny/NV+ASAfv36wd/fH0OHDsWjR4+KOKF8SEtLw5w5c1CnTh2oq6vD0tIS169fl73+8eNH2NraombN\nmlBXV0eDBg2wadMmARMXHxcXFzx48ACnT5/+z8InAOjo6ODMmTO4ffs2XF1diyEhEZF8YOcnEVEh\n0NDQQFJSEjQ1NYWOQnLswIEDmD59OiIiIqCtrQ1VVVWoq6tDLOa9yNJgwYIFiI2NxdGjR9lN853e\nvHmDpUuXIjAwEDdu3ECNGjW++b3Mzs7GoUOHcPXqVXh7e8Pc3ByHDh2S20WUMjIy0KJFC9jZ2cHK\nykroOCQHNm7ciKtXr2L//v0F3nfZsmV48+YNduzYUQTJ5Mvw4cMRExMDT09P1KhRA76+vti4cSPu\n3buHatWqYfLkyTh37hy8vb1Rp04dXLx4ERMnToSXlxdGjx4tdPwi8/79e+jr6+Pu3buoVq1agfZ9\n9uwZmjRpgsePH0NLS6uIEhIRyQ8WP4mICkHNmjURFhaGWrVqCR2F5FhMTAy6deuGuLi4L1Z+lkgk\nEIlELJqVUMePH8eMGTNw8+ZNVKpUSeg4JV5sbCyMjIzy9fsgkUhgamqKunXrYuvWrahbt24xJPw+\nt27dQteuXXH9+nXUrl1b6DgkIIlEggYNGsDHxwdt2rQp8P4vXrxAw4YN8eTJk1JdvMrIyICmpiYC\nAwPRp08f2fPNmzdHr1694OLiAlNTUwwZMgTLly+Xvd6xY0c0btwYW7ZsESJ2sdi4cSNu3rwJX1/f\n79p/6NCh6NSpE6ZPn17IyYiI5A9bTYiICkHFihXzNUyTFJuxsTEcHR0hkUiQlpaGQ4cO4Y8//oBU\nKoVYLGbhs4R69uwZbGxs4O/vz8JnIalfv/5/bpOVlQUA8PHxQVJSEmbOnCkrfMrrYh5NmzbF/Pnz\nMX78eLnNSMUjJCQE6urqaN269XftX716dXTt2hV79uwp5GTyJScnB7m5uVBVVc3zvJqaGi5fvgwA\nsLS0xLFjx5CYmAgACA8Px+3bt9GzZ89iz1tcpFIpduzY8UOFy+nTp8PDw4NTcRCRQmDxk4ioELD4\nSfmhpKSEGTNmQEtLCxkZGVi1ahXatWuHadOmITo6WrYdiyIlR3Z2NkaMGIF58+Z9V/cWfdu/3QyQ\nSCRQUVFBTk4OHB0dMWbMGLRs2VL2ekZGBmJiYuDl5YWgoKDiiJtvdnZ2yM7OVpg5CenrwsLC0Ldv\n3x+66dW3b1+EhYUVYir5o6GhgdatW2PlypV48eIFJBIJ/Pz8cOXKFSQlJQEAtmzZgsaNG6NWrVpQ\nUVFBp06dsHbt2lJd/Hz9+jXevXuHVq1affcxOnbsiCdPniAlJaUQkxERyScWP4mICgGLn5Rfnwub\n5cqVQ3JyMtauXYuGDRtiyJAhWLBgAcLDwzkHaAmydOlSlC9fHnZ2dkJHUSiff48WL14MdXV1jB49\nGhUrVpS9bmtri+7du2Pr1q2YMWMGLCwsEB8fL1TcPJSUlLBnzx64uroiJiZG6DgkkPfv3+drgZp/\no62tjeTk5EJKJL/8/PwgFouhp6eHsmXLYtu2bRg1apTsWrllyxZcuXIFx48fx82bN7Fx40bMnz8f\nv//+u8DJi87nn58fKZ6LRCJoa2vz71ciUgj8dEVEVAhY/KT8EolEkEgkUFVVRc2aNfHmzRvY2toi\nPDwcSkpK8PDwwMqVK3H//n2ho9J/CA4Oxt69e7F7924WrIuRRCKBsrIyEhIS4OnpialTp8LU1BTA\nX0NBnZ2dcejQIbi6uuLs2bO4c+cO1NTUvmtRmaKir68PV1dXjBkzRjZ8nxSLiorKD/+/z8rKQnh4\nuGy+6JL8+LfvRd26dXH+/Hl8/PgRz549Q0REBLKysqCvr4+MjAw4ODhg/fr16NWrFxo1aoTp06dj\nxIgR2LBhwxfHkkgk2L59u+Dn+6MPY2NjvHv37od+fj7/DP1zSgEiotKIf6kTERWCihUrFsofoVT6\niUQiiMViiMVimJub486dOwD++gBiY2ODKlWqYNmyZXBxcRE4Kf2b58+fw9raGnv37pXb1cVLo+jo\naDx48AAAMHv2bDRp0gT9+vWDuro6AODKlStwdXXF2rVrYWVlBR0dHVSoUAEdOnSAj48PcnNzhYyf\nh42NDWrVqgUnJyeho5AAdHV1kZCQ8EPHSEhIwPDhwyGVSkv8Q0VF5T/PV01NDVWrVsX79+9x+vRp\nDBgwANnZ2cjOzv7iBpSSktJXp5ARi8WYMWOG4Of7o4/U1FRkZGTg48eP3/3zk5KSgpSUlB/uQCYi\nKgmUhQ5ARFQacNgQ5deHDx9w6NAhJCUl4dKlS4iNjUWDBg3w4cMHAECVKlXQpUsX6OrqCpyUviUn\nJwejRo3CjBkz0L59e6HjKIzPc/1t2LABw4cPR2hoKHbt2gVDQ0PZNuvWrUPTpk0xbdq0PPs+fvwY\nderUgZKSEgAgLS0NJ06cQM2aNQWbq1UkEmHXrl1o2rQpevfujbZt2wqSg4QxZMgQmJmZwc3NDeXK\nlSvw/lKpFF5eXti2bVsRpJMvv//+OyQSCRo0aIAHDx5g4cKFMDExwfjx46GkpIQOHTpg8eLFKFeu\nHGrXro3Q0FDs2bPnq52fpYWmpia6dOkCf39/TJw48buO4evriz59+qBs2bKFnI6ISP6w+ElEVAgq\nVqyIFy9eCB2DSoCUlBQ4ODjA0NAQqqqqkEgkmDx5MrS0tKCrqwsdHR2UL18eOjo6Qkelb3B2doaK\nigrs7e2FjqJQxGIx1q1bBwsLCyxduhRpaWl53ncTEhJw7NgxHDt2DACQm5sLJSUl3LlzB4mJiTA3\nN5c9FxUVheDgYFy9ehXly5eHj49PvlaYL2xVq1bFjh07YGVlhVu3bkFTU7PYM1Dxe/LkCTZu3Cgr\n6E+ZMqXAx7h48SIkEgk6duxY+AHlTEpKCuzt7fH8+XNoa2tjyJAhWLlypexmxoEDB2Bvb48xY8bg\n3bt3qF27NlatWvVDK6GXBNOnT8fixYthY2NT4Lk/pVIpPDw84OHhUUTpiIjki0gqlUqFDkFEVNLt\n27cPx44dg7+/v9BRqAQICwtDpUqV8OrVK/z000/48OEDOy9KiLNnz2LcuHG4efMmqlatKnQchbZ6\n9Wo4Oztj3rx5cHV1haenJ7Zs2YIzZ86gRo0asu1cXFwQFBSEFStWoHfv3rLn4+LicOPGDYwePRqu\nrq5YtGiREKcBAJgwYQKUlJSwa9cuwTJQ0bt9+zbWr1+PU6dOYeLEiWjWrBmWL1+Oa9euoXz58vk+\nTk5ODrp3744BAwbA1ta2CBOTPJNIJKhfvz7Wr1+PAQMGFGjfAwcOwMXFBTExMT+0aBIRUUnBOT+J\niAoBFzyigmjbti0aNGiAdu3a4c6dO18tfH5trjISVlJSEqysrODr68vCpxxwcHDAn3/+iZ49ewIA\natSogaSkJHz69Em2zfHjx3H27FmYmZnJCp+f5/00MjJCeHg49PX1Be8Q27RpE86ePSvrWqXSQyqV\n4ty5c+jRowd69eqFJk2aID4+HmvXrsXw4cPx008/YfDgwUhPT8/X8XJzczF16lSUKVMGU6dOLeL0\nJM/EYjH8/PwwadIkhIeH53u/CxcuYObMmfD19WXhk4gUBoufRESFgMVPKojPhU2xWAwjIyPExcXh\n9OnTCAwMhL+/Px49esTVw+VMbm4uRo8ejcmTJ6Nz585Cx6H/p6mpKZt3tUGDBqhbty6CgoKQmJiI\n0NBQ2NraQkdHB3PmzAHwv6HwAHD16lXs3LkTTk5Ogg8319LSwu7duzFlyhS8efNG0CxUOHJzc3Ho\n0CFYWFhgxowZGDZsGOLj42FnZyfr8hSJRNi8eTNq1KiBjh07Ijo6+l+PmZCQgEGDBiE+Ph6HDh1C\nmTJliuNUSI61bNkSfn5+6N+/P3755RdkZmZ+c9uMjAx4enpi6NCh2L9/P8zMzIoxKRGRsDjsnYio\nEMTGxqJv376Ii4sTOgqVEBkZGdixYwe2b9+OxMREZGVlAQDq168PHR0dDB48WFawIeG5uLjg/Pnz\nOHv2rKx4RvLnt99+w5QpU6Cmpobs7Gy0aNECa9as+WI+z8zMTAwcOBCpqam4fPmyQGm/tHDhQjx4\n8AABAQHsyCqhPn36BB8fH2zYsAHVqlXDwoUL0adPn3+9oSWVSrFp0yZs2LABdevWxfTp02FpaYny\n5csjLS0Nt27dwo4dO3DlyhVMmjQJLi4u+VodnRRHVFQU7OzsEBMTAxsbG4wcORLVqlWDVCpFUlIS\nfH198fPPP8PCwgJubm5o3Lix0JGJiIoVi59ERIXg9evXaNiwITt2KN+2bduGdevWoXfv3jA0NERo\naCg+ffqE2bNn49mzZ/Dz88Po0aMFH45LQGhoKEaOHIkbN26gevXqQsehfDh79iyMjIxQs2ZNWRFR\nKpXK/vvQoUMYMWIEwsLC0KpVKyGj5pGZmYkWLVpg3rx5GD9+vNBxqADevn0LDw8PbNu2Da1bt4ad\nnR3atm1boGNkZ2fj2LFj8PT0xL1795CSkgINDQ3UrVsXNjY2GDFiBNTV1YvoDKg0uH//Pjw9PXH8\n+HG8e/cOAFCpUiX07dsXly5dgp2dHYYNGyZwSiKi4sfiJxFRIcjOzoa6ujqysrLYrUP/6dGjRxgx\nYgT69++PBQsWoGzZssjIyMCmTZsQEhKCM2fOwMPDA1u3bsW9e/eEjqvQXr9+DTMzM3h7e6Nbt25C\nx6ECkkgkEIvFyMzMREZGBsqXL4+3b9+iXbt2sLCwgI+Pj9ARvxAdHY0uXbogMjISderUEToO/YfH\nj/+PvTsPqzH//wf+PKdoT6ksSWmXJUt2Y6xp7NsI2VpkG0v4ImMZiRhrjb1Q1iH7bhBCliR7ZWiz\nlqWktNf9+8PP+UxjmUp1tzwf13WuS/d9v9/38yQ653XeSwxWrVqF7du3o3///pg2bRosLCzEjkX0\nmYMHD2LZsmUFWh+UiKi8YPGTiKiIqKqq4uXLl6KvHUelX2xsLBo3boynT59CVVVVdvzs2bNwdHTE\nkydP8PDhQzRv3hzv378XMWnFlpubi27duqFZs2ZYtGiR2HHoOwQGBmL27Nno1asXsrKysHz5cty/\nfx96enpiR/uiZcuW4ejRozh//jyXWSAiIiL6TtxNgYioiHDTI8ovAwMDyMvLIygoKM/xvXv3ok2b\nNsjOzkZSUhI0NDTw9u1bkVLSkiVLkJaWBjc3N7Gj0Hdq3749Ro4ciSVLlmDevHno3r17qS18AsDU\nqVMBACtXrhQ5CREREVHZx5GfRERFxNLSEtu2bUPjxo3FjkJlgIeHB7y9vdGqVSsYGRnh1q1buHDh\nAg4dOgQbGxvExsYiNjYWLVu2hIKCgthxK5xLly5h4MCBCAkJKdVFMiq4BQsWYP78+ejWrRv8/Pyg\no6MjdqQvio6ORosWLRAQEMDNSYiIiIi+g9z8+fPnix2CiKgsy8zMxLFjx3DixAm8fv0aL168QGZm\nJvT09Lj+J31VmzZtoKioiOjoaISHh6Nq1apYt24dOnbsCADQ0NCQjRClkvXmzRt07doVmzZtgpWV\nldhxqIi1b98e9vb2ePHiBYyMjFCtWrU85wVBQEZGBpKTk6GkpCRSyo+zCXR0dDBjxgw4Ojry/wIi\nIiKiQuLITyKiQnry5Ak2btyIzZs3o27dujAzM4O6ujqSk5Nx/vx5KCoqYvz48Rg2bFiedR2J/ikp\nKQlZWVnQ1tYWOwrh4zqfvXr1Qv369bF06VKx45AIBEHAhg0bMH/+fMyfPx/Ozs6iFR4FQUC/fv1g\nbm6O33//XZQMZZkgCIX6EPLt27dYu3Yt5s2bVwypvm7r1q2YOHFiia71HBgYiE6dOuH169eoWrVq\nid2X8ic2NhaGhoYICQlB06ZNxY5DRFRmcc1PIqJC2L17N5o2bYqUlBScP38eFy5cgLe3N5YvX46N\nGzciIiICK1euxF9//YUGDRogLCxM7MhUSlWpUoWFz1JkxYoVSExM5AZHFZhEIsG4ceNw+vRp+Pv7\no0mTJggICBAti7e3N7Zt24ZLly6JkqGs+vDhQ4ELnzExMZg8eTJMTU3x5MmTr17XsWNHTJo06bPj\nW7du/a5NDwcPHoyoqKhCty+Mtm3b4uXLlyx8isDBwQG9e/f+7PjNmzchlUrx5MkT6OvrIy4ujksq\nERF9JxY/iYgKyNfXFzNmzMC5c+fg5eUFCwuLz66RSqXo0qULDh48CHd3d3Ts2BEPHjwQIS0R5dfV\nq1exfPly7N69G5UqVRI7DomsUaNGOHfuHNzc3ODs7Ix+/fohMjKyxHNUq1YN3t7eGDFiRImOCCyr\nIiMjMXDgQBgbG+PWrVv5anP79m0MHToUVlZWUFJSwv3797Fp06ZC3f9rBdesrKz/bKugoFDiH4bJ\ny8t/tvQDie/Tz5FEIkG1atUglX79bXt2dnZJxSIiKrNY/CQiKoCgoCC4urrizJkz+d6AYvjw4Vi5\nciV69OiBpKSkYk5IRIWRkJCAIUOGwMfHB/r6+mLHoVJCIpGgf//+CAsLQ4sWLdCyZUu4uroiOTm5\nRHP06tULXbp0wZQpU0r0vmXJ/fv30blzZ1hYWCAjIwN//fUXmjRp8s02ubm5sLGxQY8ePdC4cWNE\nRUVhyZIl0NXV/e48Dg4O6NWrF5YuXYratWujdu3a2Lp1K6RSKeTk5CCVSmUPR0dHAICfn99nI0dP\nnDiBVq1aQVlZGdra2ujTpw8yMzMBfCyozpw5E7Vr14aKigpatmyJ06dPy9oGBgZCKpXi3LlzaNWq\nFVRUVNC8efM8ReFP1yQkJHz3c6aiFxsbC6lUitDQUAD/+2zcndwAACAASURBVPs6efIkWrZsCUVF\nRZw+fRrPnj1Dnz59oKWlBRUVFdSrVw/+/v6yfu7fvw9ra2soKytDS0sLDg4Osg9Tzpw5AwUFBSQm\nJua596+//iobcZqQkAA7OzvUrl0bysrKaNCgAfz8/Ermm0BEVARY/CQiKoDFixfDw8MD5ubmBWo3\ndOhQtGzZEtu2bSumZERUWIIgwMHBAf379//iFEQiRUVFzJo1C3fv3kVcXBzMzc3h6+uL3NzcEsuw\ncuVKXLhwAYcPHy6xe5YVT548wYgRI3D//n08efIER44cQaNGjf6znUQiwaJFixAVFYXp06ejSpUq\nRZorMDAQ9+7dw19//YWAgAAMHjwYcXFxePnyJeLi4vDXX39BQUEBHTp0kOX558jRU6dOoU+fPrCx\nsUFoaCguXryIjh07yn7u7O3tcenSJezevRsPHjzAyJEj0bt3b9y7dy9Pjl9//RVLly7FrVu3oKWl\nhWHDhn32faDS499bcnzp78fV1RWLFi1CREQEWrRogfHjxyM9PR2BgYEICwuDp6cnNDQ0AACpqamw\nsbGBuro6QkJCcOjQIVy5cgVOTk4AgM6dO0NHRwd79+7Nc48///wTw4cPBwCkp6fDysoKJ06cQFhY\nGFxcXDB27FicP3++OL4FRERFTyAionyJiooStLS0hA8fPhSqfWBgoFC3bl0hNze3iJNRWZaeni6k\npKSIHaNCW7VqldC8eXMhIyND7ChURly/fl1o3bq1YGVlJVy+fLnE7nv58mWhRo0aQlxcXInds7T6\n9/dg9uzZQufOnYWwsDAhKChIcHZ2FubPny/s27evyO/doUMHYeLEiZ8d9/PzE9TU1ARBEAR7e3uh\nWrVqQlZW1hf7iI+PF+rUqSNMnTr1i+0FQRDatm0r2NnZfbF9ZGSkIJVKhadPn+Y53rdvX+GXX34R\nBEEQLly4IEgkEuHMmTOy80FBQYJUKhWeP38uu0YqlQpv377Nz1OnImRvby/Iy8sLqqqqeR7KysqC\nVCoVYmNjhZiYGEEikQg3b94UBOF/f6cHDx7M05elpaWwYMGCL97H29tb0NDQyPP69VM/kZGRgiAI\nwtSpU4Uff/xRdv7SpUuCvLy87OfkSwYPHiw4OzsX+vkTEZUkjvwkIsqnT2uuKSsrF6p9u3btICcn\nx0/JKY8ZM2Zg48aNYseosG7cuAEPDw/s2bMHlStXFjsOlREtWrRAUFAQpk6disGDB2PIkCHf3CCn\nqLRt2xb29vZwdnb+bHRYReHh4YH69etj4MCBmDFjhmyU408//YTk5GS0adMGw4YNgyAIOH36NAYO\nHAh3d3e8e/euxLM2aNAA8vLynx3PyspC//79Ub9+fSxfvvyr7W/duoVOnTp98VxoaCgEQUC9evWg\npqYme5w4cSLP2rQSiQQNGzaUfa2rqwtBEPDq1avveGZUVNq3b4+7d+/izp07sseuXbu+2UYikcDK\nyirPscmTJ8Pd3R1t2rTB3LlzZdPkASAiIgKWlpZ5Xr+2adMGUqlUtiHnsGHDEBQUhKdPnwIAdu3a\nhfbt28uWgMjNzcWiRYvQqFEjaGtrQ01NDQcPHiyR//eIiIoCi59ERPkUGhqKLl26FLq9RCKBtbV1\nvjdgoIrB1NQUjx49EjtGhfTu3TsMGjQIGzZsgKGhodhxqIyRSCSws7NDREQEzMzM0KRJE8yfPx+p\nqanFel83Nzc8efIEW7ZsKdb7lDZPnjyBtbU19u/fD1dXV3Tv3h2nTp3C6tWrAQA//PADrK2tMXr0\naAQEBMDb2xtBQUHw9PSEr68vLl68WGRZ1NXVv7iG97t37/JMnVdRUfli+9GjRyMpKQm7d+8u9JTz\n3NxcSKVShISE5CmchYeHf/az8c8N3D7drySXbKCvU1ZWhqGhIYyMjGQPPT29/2z3758tR0dHxMTE\nwNHREY8ePUKbNm2wYMGC/+zn089DkyZNYG5ujl27diE7Oxt79+6VTXkHgGXLlmHVqlWYOXMmzp07\nhzt37uRZf5aIqLRj8ZOIKJ+SkpJk6ycVVpUqVbjpEeXB4qc4BEGAk5MTevTogf79+4sdh8owFRUV\nuLm5ITQ0FBEREahbty7+/PPPYhuZWblyZezYsQOurq6IiooqlnuURleuXMGjR49w9OhRDB8+HK6u\nrjA3N0dWVhbS0tIAAKNGjcLkyZNhaGgoK+pMmjQJmZmZshFuRcHc3DzPyLpPbt68+Z9rgi9fvhwn\nTpzA8ePHoaqq+s1rmzRpgoCAgK+eEwQBL1++zFM4MzIyQs2aNfP/ZKjc0NXVxahRo7B7924sWLAA\n3t7eAAALCwvcu3cPHz58kF0bFBQEQRBgYWEhOzZs2DDs3LkTp06dQmpqKgYMGJDn+l69esHOzg6W\nlpYwMjLC33//XXJPjojoO7H4SUSUT0pKSrI3WIWVlpYGJSWlIkpE5YGZmRnfQIhg7dq1iImJ+eaU\nU6KCMDAwwO7du7Fr1y4sX74cP/zwA0JCQorlXg0aNICrqytGjBiBnJycYrlHaRMTE4PatWvn+T2c\nlZWF7t27y36v1qlTRzZNVxAE5ObmIisrCwDw9u3bIssybtw4REVFYdKkSbh79y7+/vtvrFq1Cnv2\n7MGMGTO+2u7s2bOYPXs21q1bBwUFBcTHxyM+Pl626/a/zZ49G3v37sXcuXMRHh6OBw8ewNPTE+np\n6TA1NYWdnR3s7e2xf/9+REdH4+bNm1ixYgUOHTok6yM/RfiKuoRCafatv5MvnXNxccFff/2F6Oho\n3L59G6dOnUL9+vUBfNx0U1lZWbYp2MWLFzF27FgMGDAARkZGsj6GDh2KBw8eYO7cuejVq1ee4ryZ\nmRkCAgIQFBSEiIgITJgwAdHR0UX4jImIiheLn0RE+aSnp4eIiIjv6iMiIiJf05mo4tDX18fr16+/\nu7BO+RcaGooFCxZgz549UFBQEDsOlTM//PADbty4AScnJ/Tu3RsODg54+fJlkd9nypQpqFSpUoUp\n4P/8889ISUnBqFGjMGbMGKirq+PKlStwdXXF2LFj8fDhwzzXSyQSSKVSbNu2DVpaWhg1alSRZTE0\nNMTFixfx6NEj2NjYoGXLlvD398e+ffvQtWvXr7YLCgpCdnY2bG1toaurK3u4uLh88fpu3brh4MGD\nOHXqFJo2bYqOHTviwoULkEo/voXz8/ODg4MDZs6cCQsLC/Tq1QuXLl2CgYFBnu/Dv/37GHd7L33+\n+XeSn7+v3NxcTJo0CfXr14eNjQ1q1KgBPz8/AB8/vP/rr7/w/v17tGzZEv369UPbtm2xefPmPH3o\n6+vjhx9+wN27d/NMeQeAOXPmoEWLFujevTs6dOgAVVVVDBs2rIieLRFR8ZMI/KiPiChfzp49i2nT\npuH27duFeqPw7NkzWFpaIjY2FmpqasWQkMoqCwsL7N27Fw0aNBA7Srn3/v17NG3aFB4eHrC1tRU7\nDpVz79+/x6JFi7B582ZMmzYNU6ZMgaKiYpH1Hxsbi2bNmuHMmTNo3LhxkfVbWsXExODIkSNYs2YN\n5s+fj27duuHkyZPYvHkzlJSUcOzYMaSlpWHXrl2Ql5fHtm3b8ODBA8ycOROTJk2CVCploY+IiKgC\n4shPIqJ86tSpE9LT03HlypVCtffx8YGdnR0Ln/QZTn0vGYIgwNnZGV26dGHhk0qEuro6fv/9d1y7\ndg3Xr19HvXr1cPDgwSKbZmxgYIAVK1Zg+PDhSE9PL5I+S7M6deogLCwMrVq1gp2dHTQ1NWFnZ4ce\nPXrgyZMnePXqFZSUlBAdHY3FixejYcOGCAsLw5QpUyAnJ8fCJxERUQXF4icRUT5JpVJMmDABs2bN\nKvDullFRUdiwYQPGjx9fTOmoLOOmRyXD29sbERERWLVqldhRqIIxMTHBoUOH4OPjg3nz5qFz5864\ne/dukfQ9fPhwmJmZYc6cOUXSX2kmCAJCQ0PRunXrPMeDg4NRq1Yt2RqFM2fORHh4ODw9PVG1alUx\nohIREVEpwuInEVEBjB8/HlpaWhg+fHi+C6DPnj1Dt27dMG/ePNSrV6+YE1JZxOJn8btz5w7mzJkD\nf39/bjpGouncuTNu3bqFn3/+GdbW1hg3bhxev379XX1KJBJs3LgRu3btwoULF4omaCnx7xGyEokE\nDg4O8Pb2hpeXF6KiovDbb7/h9u3bGDZsGJSVlQEAampqHOVJREREMix+EhEVgJycHHbt2oWMjAzY\n2Njgxo0bX702Ozsb+/fvR5s2beDs7IxffvmlBJNSWcJp78UrOTkZtra28PT0hLm5udhxqIKTl5fH\n+PHjERERAQUFBdSrVw+enp6yXckLQ1tbGz4+PrC3t0dSUlIRpi15giAgICAAXbt2RXh4+GcF0FGj\nRsHU1BTr169Hly5dcPz4caxatQpDhw4VKTERERGVdtzwiIioEHJycuDl5YU1a9ZAS0sLY8aMQf36\n9aGiooKkpCScP38e3t7eMDQ0xKxZs9C9e3exI1Mp9uzZMzRv3rxYdoSu6ARBwIQJE5CRkYFNmzaJ\nHYfoM+Hh4ZgyZQpiYmKwcuXK7/p9MWbMGGRkZMh2eS5LPn1guHTpUqSnp2P69Omws7ND5cqVv3j9\nw4cPIZVKYWpqWsJJiYiIqKxh8ZOI6Dvk5OTgr7/+gq+vL4KCgqCiooLq1avD0tISY8eOhaWlpdgR\nqQzIzc2Fmpoa4uLiuCFWERMEAbm5ucjKyirSXbaJipIgCDhx4gSmTp0KY2NjrFy5EnXr1i1wPykp\nKWjcuDGWLl2K/v37F0PSopeamgpfX1+sWLECenp6mDFjBrp37w6plBPUiIiIqGiw+ElERFQKNGrU\nCL6+vmjatKnYUcodQRC4/h+VCZmZmVi7di08PDwwdOhQ/Pbbb9DU1CxQH1evXkW/fv1w+/Zt1KhR\no5iSfr+3b99i7dq1WLt2Ldq0aYMZM2Z8tpEREZW8gIAATJ48Gffu3ePvTiIqN/iRKhERUSnATY+K\nD9+8UVlRuXJlTJkyBWFhYUhPT0fdunWxfv16ZGdn57uP1q1bY9SoURg1atRn62WWBjExMZg0aRJM\nTU3x9OlTBAYG4uDBgyx8EpUSnTp1gkQiQUBAgNhRiIiKDIufREREpYCZmRmLn0QEANDR0cGGDRtw\n+vRp+Pv7o2nTpjh37ly+28+bNw8vXryAj49PMaYsmFu3bsHOzg7NmjWDiooKHjx4AB8fn0JN7yei\n4iORSODi4gJPT0+xoxARFRlOeyciIioFfH19cf78eWzbtk3sKGXK48ePERYWBk1NTRgZGaFWrVpi\nRyIqUoIg4MCBA5g+fToaNWqE5cuXw9jY+D/bhYWF4ccff8S1a9dgYmJSAkk/92nn9qVLlyIsLAxT\npkyBs7Mz1NXVRclDRPmTlpaGOnXq4NKlSzAzMxM7DhHRd+PITyIiolKA094L7sKFC+jfvz/Gjh2L\nvn37wtvbO895fr5L5YFEIsGAAQMQFhaGFi1aoGXLlnB1dUVycvI329WrVw9z5szBiBEjCjRtvihk\nZ2dj9+7dsLKywuTJkzF06FBERUVh2rRpLHwSlQFKSkoYPXo0/vjjD7GjEBEVCRY/iYgKQCqV4sCB\nA0Xe74oVK2BoaCj72s3NjTvFVzBmZmb4+++/xY5RZqSmpmLQoEH4+eefce/ePbi7u2P9+vVISEgA\nAGRkZHCtTypXFBUVMWvWLNy9exdxcXEwNzeHr68vcnNzv9pm0qRJUFJSwtKlS0skY2pqKtauXQsz\nMzOsW7cOCxYswL179zBy5EhUrly5RDIQUdEYN24cdu3ahcTERLGjEBF9NxY/iahcs7e3h1QqhbOz\n82fnZs6cCalUit69e4uQ7HP/LNRMnz4dgYGBIqahkqajo4Ps7GxZ8Y6+bdmyZbC0tMS8efOgpaUF\nZ2dnmJqaYvLkyWjZsiXGjx+P69evix2TqMjp6urCz88Phw4dgo+PD1q0aIGgoKAvXiuVSuHr6wtP\nT0/cunVLdvzBgwf4448/4ObmhoULF2Ljxo14+fJloTO9efMGbm5uMDQ0REBAAHbu3ImLFy+iZ8+e\nkEr5doOoLNLV1UWPHj2wefNmsaMQEX03vhohonJNIpFAX18f/v7+SEtLkx3PycnB9u3bYWBgIGK6\nr1NWVoampqbYMagESSQSTn0vACUlJWRkZOD169cAgIULF+L+/fto2LAhunTpgsePH8Pb2zvPv3ui\n8uRT0XPq1KkYPHgwhgwZgidPnnx2nb6+PlauXImhQ4dix44d6NChA6ytrREeHo6cnBykpaUhKCgI\n9erVg62tLS5cuJDvJSOio6MxceJEmJmZ4dmzZ7h48SIOHDjAnduJygkXFxesXr26xJfOICIqaix+\nElG517BhQ5iamsLf31927Pjx41BSUkKHDh3yXOvr64v69etDSUkJdevWhaen52dvAt++fQtbW1uo\nqqrC2NgYO3fuzHN+1qxZqFu3LpSVlWFoaIiZM2ciMzMzzzVLly5FzZo1oa6uDnt7e6SkpOQ57+bm\nhoYNG8q+DgkJgY2NDXR0dFClShW0a9cO165d+55vC5VCnPqef9ra2rh16xZmzpyJcePGwd3dHfv3\n78eMGTOwaNEiDB06FDt37vxiMYiovJBIJLCzs0NERATMzMzQtGlTzJ8/H6mpqXmu69atG96/fw8v\nLy/88ssviI2Nxfr167FgwQIsWrQI27ZtQ2xsLNq3bw9nZ2eMGTPmm8WOW7duYciQIWjevDlUVVVl\nO7ebm5sX91MmohJkZWUFfX19HDp0SOwoRETfhcVPIir3JBIJnJyc8kzb2bJlCxwcHPJc5+Pjgzlz\n5mDhwoWIiIjAihUrsHTpUqxfvz7Pde7u7ujXrx/u3r2LQYMGwdHREc+ePZOdV1VVhZ+fHyIiIrB+\n/Xrs2bMHixYtkp339/fH3Llz4e7ujtDQUJiZmWHlypVfzP1JcnIyRowYgaCgINy4cQNNmjRBjx49\nuA5TOcORn/nn6OgId3d3JCQkwMDAAA0bNkTdunWRk5MDAGjTpg3q1avHkZ9UIaioqMDNzQ03b95E\nREQE6tatiz///BOCIODdu3fo2LEjbG1tcf36dQwcOBCVKlX6rA91dXX88ssvCA0NxdOnTzF06NA8\n64kKgoCzZ8+ia9eu6NWrF5o1a4aoqCgsXrwYNWvWLMmnS0QlyMXFBV5eXmLHICL6LhKBW6ESUTnm\n4OCAt2/fYtu2bdDV1cW9e/egoqICQ0NDPHr0CHPnzsXbt29x5MgRGBgYwMPDA0OHDpW19/Lygre3\nNx48eADg4/ppv/76KxYuXAjg4/R5dXV1+Pj4wM7O7osZNm7ciBUrVshG9LVt2xYNGzbEhg0bZNdY\nW1sjMjISUVFRAD6O/Ny/fz/u3r37xT4FQUCtWrWwfPnyr96Xyp4dO3bg+PHj+PPPP8WOUiplZWUh\nKSkJ2trasmM5OTl49eoVfvrpJ+zfvx8mJiYAPm7UcOvWLY6Qpgrp0qVLcHFxgaKiIuTk5GBpaYnV\nq1fnexOw9PR0dO3aFZ07d8bs2bOxb98+LF26FBkZGZgxYwaGDBnCDYyIKojs7GyYmJhg3759aNas\nmdhxiIgKRV7sAEREJUFDQwP9+vXD5s2boaGhgQ4dOkBPT092/s2bN3j69CnGjBmDsWPHyo5nZ2d/\n9mbxn9PR5eTkoKOjg1evXsmO7du3D15eXnj8+DFSUlKQk5OTZ/RMeHj4ZxswtW7dGpGRkV/N//r1\na8yZMwcXLlxAfHw8cnJykJ6ezim95YyZmRlWrVoldoxSadeuXTh8+DBOnjyJn3/+GV5eXlBTU4Oc\nnBxq1KgBbW1ttG7dGgMHDkRcXByCg4Nx5coVsWMTiaJdu3YIDg6Gu7s71q5di3PnzuW78Al83Fl+\n+/btsLS0xJYtW2BgYIAFCxage/fu3MCIqIKRl5fHxIkT4eXlhe3bt4sdh4ioUFj8JKIKw9HRESNH\njoSqqqps5OYnn4qTGzdu/M+NGv49XVAikcjaX7t2DUOGDIGbmxtsbGygoaGBw4cPY/r06d+VfcSI\nEXj9+jW8vLxgYGAABQUFdOrU6bO1RKls+zTtXRCEAhUqyrsrV65g4sSJcHZ2xvLlyzFhwgSYmZnB\n1dUVwMd/g4cPH8a8efNw5swZWFtbY+rUqdDX1xc5OZF45OTk8OLFC0yePBny8gV/yW9gYICWLVvC\nysoKixcvLoaERFRWODk5wcjICC9evICurq7YcYiICozFTyKqMDp37ozKlSsjISEBffr0yXOuWrVq\n0NXVxePHj/NMey+oK1euQE9PD7/++qvsWExMTJ5rLCwscO3aNdjb28uOXb169Zv9BgUFYfXq1fjp\np58AAPHx8Xj58mWhc1LppKmpicqVK+PVq1eoXr262HFKhezsbIwYMQJTpkzBnDlzAABxcXHIzs7G\nkiVLoKGhAWNjY1hbW2PlypVIS0uDkpKSyKmJxPf+/Xvs3bsX4eHhhe5j2rRp+PXXX1n8JKrgNDQ0\nMHToUKxfvx7u7u5ixyEiKjAWP4moQrl37x4EQfjiZg9ubm6YNGkSqlSpgu7duyMrKwuhoaF4/vy5\nbITZfzEzM8Pz58+xa9cutG7dGqdOncLu3bvzXDN58mSMHDkSzZo1Q4cOHbB3714EBwdDS0vrm/3u\n2LEDLVq0QEpKCmbOnAkFBYWCPXkqEz7t+M7i50fe3t6wsLDAuHHjZMfOnj2L2NhYGBoa4sWLF9DU\n1ET16tVhaWnJwifR/xcZGQkDAwPUqFGj0H107NhR9nuTo9GJKjYXFxdcvXqV/x8QUZnERXuIqEJR\nUVGBqqrqF885OTlhy5Yt2LFjBxo3bowff/wRPj4+MDIykl3zpRd7/zzWs2dPTJ8+HVOmTEGjRo0Q\nEBDw2Sfktra2mD9/PubMmYOmTZviwYMHmDZt2jdz+/r6IiUlBc2aNYOdnR2cnJxQp06dAjxzKiu4\n43teLVu2hJ2dHdTU1AAAf/zxB0JDQ3Ho0CFcuHABISEhiI6Ohq+vr8hJiUqXpKQkqKurf1cflStX\nhpycHNLS0oooFRGVVcbGxhg6dCgLn0RUJnG3dyIiolJk4cKF+PDhA6eZ/kNWVhYqVaqE7OxsnDhx\nAtWqVUOrVq2Qm5sLqVSKYcOGwdjYGG5ubmJHJSo1goODMX78eISEhBS6j5ycHFSuXBlZWVnc6IiI\niIjKLL6KISIiKkU+TXuv6N69eyf786fNWuTl5dGzZ0+0atUKACCVSpGWloaoqChoaGiIkpOotNLT\n00N0dPR3jdoMCwuDrq4uC59ERERUpvGVDBERUSnCae/AlClT4OHhgaioKAAfl5b4NFHln0UYQRAw\nc+ZMvHv3DlOmTBElK1Fppauri+bNm2Pv3r2F7mPjxo1wcHAowlREVF4lJyfj1KlTCA4ORkpKithx\niIjy4LR3IiKiUiQlJQXVqlVDSkpKhRxt5efnB0dHRygpKcHGxgb/93//h+bNm3+2SdmDBw/g6emJ\nU6dOISAgAGZmZiIlJiq9jhw5Ag8PD1y7dq3AbZOTk2FgYIC7d+9CT0+vGNIRUXnx5s0bDBo0CAkJ\nCXj58iW6devGtbiJqFSpeO+qiIiISjFVVVVoaGjg+fPnYkcpcYmJidi3bx8WLVqEU6dO4f79+3By\ncsLevXuRmJiY59ratWujcePG8Pb2ZuGT6Ct69OiBN2/eYM+ePQVuO3/+fHTp0oWFTyL6TG5uLo4c\nOYLu3btjwYIFOH36NOLj47FixQocOHAA165dw5YtW8SOSUQkIy92ACIiIsrr09T32rVrix2lREml\nUnTt2hVGRkZo164dwsLCYGdnh3HjxmHChAlwdHSEsbExPnz4gAMHDsDBwQHKyspixyYqteTk5LB/\n/35YW1tDXV0d3bp1+882giBg6dKlOH78OK5cuVICKYmorBk5ciRu3LiBYcOGISgoCDt27EC3bt3Q\nqVMnAMCYMWOwZs0aODo6ipyUiOgjjvwkIiIqZSrqpkdVqlTB6NGj0bNnTwAfNzjy9/fHokWL4OXl\nBRcXF1y8eBFjxozBH3/8wcInUT40atQIhw8fhoODA9zc3PDq1auvXvv333/DwcEBO3bswJkzZ1C1\natUSTEpEZcHDhw8RHBwMZ2dnzJkzBydPnsSECRPg7+8vu0ZLSwtKSkrf/P+GiKgkceQnERFRKVOR\nNz1SVFSU/TknJwdycnKYMGECfvjhBwwbNgy9evXChw8fcOfOHRFTEpUtrVu3RlBQEDw8PGBoaIhe\nvXph8ODB0NHRQU5ODp4+fQo/Pz/cuXMHjo6OuHz5MqpUqSJ2bCIqhbKyspCTkwNbW1vZsUGDBmHG\njBn45ZdfoKOjg0OHDqFly5aoVq0aBEGARCIRMTEREYufREREpY6pqSkuX74sdgzRycnJQRAECIKA\nxo0bY+vWrWjevDm2bduG+vXrix2PqEwxNjbG/PnzceDAATRu3Bg+Pj5ISEiAvLw8dHR0YG9vj59/\n/hkKCgpiRyWiUqxBgwaQSCQ4evQoxo8fDwAIDAyEsbEx9PX1cfz4cdSuXRsjR44EABY+iahU4G7v\nREREpcyDBw8wYMAAREREiB2l1EhMTESrVq1gamqKY8eOiR2HiIiowtqyZQs8PT3RsWNHNGvWDHv2\n7EGNGjWwadMmvHz5ElWqVOHSNERUqrD4SURUAJ+m4X7CqTxUHNLT06GhoYGUlBTIy3OSBgC8ffsW\nq1evxvz588WOQkREVOF5enpi+/btSEpKgpaWFtatWwcrKyvZ+bi4ONSoUUPEhERE/8PiJxHRd0pP\nT0dqaipUVVVRuXJlseNQOWFgYIDz58/DyMhI7CglJj09HQoKCl/9QIEfNhAREZUer1+/RlJSEkxM\nTAB8nKVx4MABrF27FkpKStDU1ETfvn3x888/Q0NDQ+S0RFSRcbd3IqJ8yszMxLx585CdnS07tmfP\nHowfPx4TJ07EggULEBsbK2JCKk8q2o7vL1++hJGRj0mrnQAAIABJREFUEV6+fPnVa1j4JCIiKj20\ntbVhYmKCjIwMuLm5wdTUFM7OzkhMTMSQIUPQpEkT7N27F/b29mJHJaIKjiM/iYjy6enTpzA3N8eH\nDx+Qk5ODrVu3YsKECWjVqhXU1NQQHBwMBQUF3Lx5E9ra2mLHpTJu/PjxsLCwwMSJE8WOUuxycnJg\nbW2NH3/8kdPaiYiIyhBBEPDbb79hy5YtaN26NapWrYpXr14hNzcXhw8fRmxsLFq3bo1169ahb9++\nYsclogqKIz+JiPLpzZs3kJOTg0QiQWxsLP744w+4urri/PnzOHLkCO7du4eaNWti2bJlYkelcsDU\n1BSPHj0SO0aJWLhwIQBg7ty5IichKl/c3NzQsGFDsWMQUTkWGhqK5cuXY8qUKVi3bh02btyIDRs2\n4M2bN1i4cCEMDAwwfPhwrFy5UuyoRFSBsfhJRJRPb968gZaWFgDIRn+6uLgA+DhyTUdHByNHjsTV\nq1fFjEnlREWZ9n7+/Hls3LgRO3fuzLOZGFF55+DgAKlUKnvo6OigV69eePjwYZHep7QuFxEYGAip\nVIqEhASxoxDRdwgODkb79u3h4uICHR0dAED16tXRsWNHPH78GADQpUsXtGjRAqmpqWJGJaIKjMVP\nIqJ8evfuHZ49e4Z9+/bB29sblSpVkr2p/FS0ycrKQkZGhpgxqZyoCCM/X716hWHDhmHr1q2oWbOm\n2HGISpy1tTXi4+MRFxeHM2fOIC0tDf379xc71n/Kysr67j4+bWDGFbiIyrYaNWrg/v37eV7//v33\n39i0aRMsLCwAAM2bN8e8efOgrKwsVkwiquBY/CQiyiclJSVUr14da9aswblz51CzZk08ffpUdj41\nNRXh4eEVanduKj6GhoZ4/vw5MjMzxY5SLHJzczF8+HDY29vD2tpa7DhEolBQUICOjg6qVauGxo0b\nY8qUKYiIiEBGRgZiY2MhlUoRGhqap41UKsWBAwdkX798+RJDhw6FtrY2VFRU0LRpUwQGBuZps2fP\nHpiYmEBdXR39+vXLM9oyJCQENjY20NHRQZUqVdCuXTtcu3bts3uuW7cOAwYMgKqqKmbPng0ACAsL\nQ8+ePaGuro7q1avDzs4O8fHxsnb3799Hly5dUKVKFaipqaFJkyYIDAxEbGwsOnXqBADQ0dGBnJwc\nHB0di+abSkQlql+/flBVVcXMmTOxYcMG+Pj4YPbs2TA3N4etrS0AQENDA+rq6iInJaKKTF7sAERE\nZUXXrl1x6dIlxMfHIyEhAXJyctDQ0JCdf/jwIeLi4tCtWzcRU1J5UalSJdSuXRtRUVGoW7eu2HGK\n3JIlS5CWlgY3NzexoxCVCsnJydi9ezcsLS2hoKAA4L+nrKempuLHH39EjRo1cOTIEejq6uLevXt5\nromOjoa/vz8OHz6MlJQUDBo0CLNnz8b69etl9x0xYgRWr14NAFizZg169OiBx48fQ1NTU9bPggUL\n4OHhgRUrVkAikSAuLg7t27eHs7MzVq5ciczMTMyePRt9+vSRFU/t7OzQuHFjhISEQE5ODvfu3YOi\noiL09fWxf/9+/PzzzwgPD4empiaUlJSK7HtJRCVr69atWL16NZYsWYIqVapAW1sbM2fOhKGhodjR\niIgAsPhJRJRvFy9eREpKymc7VX6autekSRMcPHhQpHRUHn2a+l7eip+XLl3CH3/8gZCQEMjL86UI\nVVwnT56EmpoagI9rSevr6+PEiROy8/81JXznzp149eoVgoODZYXKOnXq5LkmJycHW7duhaqqKgBg\n9OjR8PPzk53v2LFjnuu9vLywb98+nDx5EnZ2drLjgwcPzjM687fffkPjxo3h4eEhO+bn5wctLS2E\nhISgWbNmiI2NxfTp02FqagoAeWZGVK1aFcDHkZ+f/kxEZVOLFi2wdetW2QCB+vXrix2JiCgPTnsn\nIsqnAwcOoH///ujWrRv8/Pzw9u1bAKV3Mwkq+8rjpkdv3ryBnZ0dfH19oaenJ3YcIlG1b98ed+/e\nxZ07d3Djxg107twZ1tbWeP78eb7a3759G5aWlnlGaP6bgYGBrPAJALq6unj16pXs69evX2PMmDEw\nNzeXTU19/fo1njx5kqcfKyurPF/fvHkTgYGBUFNTkz309fUhkUgQGRkJAJg6dSqcnJzQuXNneHh4\nFPlmTkRUekilUtSsWZOFTyIqlVj8JCLKp7CwMNjY2EBNTQ1z586Fvb09duzYke83qUQFVd42PcrN\nzcWIESNgZ2fH5SGIACgrK8PQ0BBGRkawsrKCj48P3r9/D29vb0ilH1+m/3P0Z3Z2doHvUalSpTxf\nSyQS5Obmyr4eMWIEbt68CS8vL1y9ehV37txBrVq1PltvWEVFJc/Xubm56Nmzp6x4++nx6NEj9OzZ\nE8DH0aHh4eHo168frly5AktLyzyjTomIiIhKAoufRET5FB8fDwcHB2zbtg0eHh7IysqCq6sr7O3t\n4e/vn2ckDVFRKG/FzxUrVuDdu3dYuHCh2FGISi2JRIK0tDTo6OgA+Lih0Se3bt3Kc22TJk1w9+7d\nPBsYFVRQUBAmTpyIn376CRYWFlBRUclzz69p2rQpHjx4AH19fRgZGeV5/LNQamxsjAkTJuDYsWNw\ncnLCpk2bAACVK1cG8HFaPhGVP/+1bAcRUUli8ZOIKJ+Sk5OhqKgIRUVFDB8+HCdOnICXl5dsl9re\nvXvD19cXGRkZYkelcqI8TXu/evUqli9fjt27d382Eo2oosrIyEB8fDzi4+MRERGBiRMnIjU1Fb16\n9YKioiJatWqF33//HWFhYbhy5QqmT5+eZ6kVOzs7VKtWDX369MHly5cRHR2No0ePfrbb+7eYmZlh\nx44dCA8Px40bNzBkyBDZhkvf8ssvvyApKQm2trYIDg5GdHQ0zp49izFjxuDDhw9IT0/HhAkTZLu7\nX79+HZcvX5ZNiTUwMIBEIsHx48fx5s0bfPjwoeDfQCIqlQRBwLlz5wo1Wp2IqDiw+ElElE8pKSmy\nkTjZ2dmQSqUYMGAATp06hZMnT0JPTw9OTk75GjFDlB+1a9fGmzdvkJqaKnaU75KQkIAhQ4bAx8cH\n+vr6YschKjXOnj0LXV1d6OrqolWrVrh58yb27duHdu3aAQB8fX0BfNxMZNy4cVi0aFGe9srKyggM\nDISenh569+6Nhg0bYv78+QVai9rX1xcpKSlo1qwZ7Ozs4OTk9NmmSV/qr2bNmggKCoKcnBy6deuG\nBg0aYOLEiVBUVISCggLk5OSQmJgIBwcH1K1bFwMGDEDbtm2xYsUKAB/XHnVzc8Ps2bNRo0YNTJw4\nsSDfOiIqxSQSCebNm4cjR46IHYWICAAgETgenYgoXxQUFHD79m1YWFjIjuXm5kIikcjeGN67dw8W\nFhbcwZqKTL169bBnzx40bNhQ7CiFIggC+vbtC2NjY6xcuVLsOERERFQC9u7dizVr1hRoJDoRUXHh\nyE8ionyKi4uDubl5nmNSqRQSiQSCICA3NxcNGzZk4ZOKVFmf+u7p6Ym4uDgsWbJE7ChERERUQvr1\n64eYmBiEhoaKHYWIiMVPIqL80tTUlO2++28SieSr54i+R1ne9Cg4OBiLFy/G7t27ZZubEBERUfkn\nLy+PCRMmwMvLS+woREQsfhIREZVmZbX4+e7dOwwaNAgbNmyAoaGh2HGIiIiohI0aNQpHjx5FXFyc\n2FGIqIJj8ZOI6DtkZ2eDSydTcSqL094FQYCTkxN69uyJ/v37ix2HiIiIRKCpqYkhQ4Zg/fr1Ykch\nogqOxU8iou9gZmaGyMhIsWNQOVYWR36uXbsWMTExWL58udhRiIiISESTJk3Chg0bkJ6eLnYUIqrA\nWPwkIvoOiYmJqFq1qtgxqBzT1dVFcnIy3r9/L3aUfAkNDcWCBQuwZ88eKCgoiB2HiIiIRGRubg4r\nKyv8+eefYkchogqMxU8iokLKzc1FcnIyqlSpInYUKsckEkmZGf35/v172NraYs2aNTAxMRE7DlGF\nsnjxYjg7O4sdg4joMy4uLvD09ORSUUQkGhY/iYgKKSkpCaqqqpCTkxM7CpVzZaH4KQgCnJ2dYW1t\nDVtbW7HjEFUoubm52Lx5M0aNGiV2FCKiz1hbWyMrKwsXLlwQOwoRVVAsfhIRFVJiYiI0NTXFjkEV\ngKmpaanf9Gjjxo14+PAhVq1aJXYUogonMDAQSkpKaNGihdhRiIg+I5FIZKM/iYjEwOInEVEhsfhJ\nJcXMzKxUj/y8c+cO5s6dC39/fygqKoodh6jC2bRpE0aNGgWJRCJ2FCKiLxo2bBiuXLmCx48fix2F\niCogFj+JiAqJxU8qKaV52ntycjJsbW3h6ekJMzMzseMQVTgJCQk4duwYhg0bJnYUIqKvUlZWhrOz\nM1avXi12FCKqgFj8JCIqJBY/qaSYmZmVymnvgiBg3LhxaNeuHYYOHSp2HKIKaefOnejevTu0tLTE\njkJE9E3jx4/H9u3bkZSUJHYUIqpgWPwkIiokFj+ppGhrayM3Nxdv374VO0oeW7ZswZ07d/DHH3+I\nHYWoQhIEQTblnYiotNPT08NPP/2ELVu2iB2FiCoYFj+JiAqJxU8qKRKJpNRNfb9//z5cXV3h7+8P\nZWVlseMQVUg3b95EcnIyOnbsKHYUIqJ8cXFxwerVq5GTkyN2FCKqQFj8JCIqJBY/qSSVpqnvHz58\ngK2tLZYvXw4LCwux4xBVWJs2bYKTkxOkUr6kJ6KyoUWLFqhRowaOHj0qdhQiqkD4SomIqJASEhJQ\ntWpVsWNQBVGaRn5OmDABLVq0wMiRI8WOQlRhffjwAf7+/rC3txc7ChFRgbi4uMDT01PsGERUgbD4\nSURUSBz5SSWptBQ/t23bhmvXrmHNmjViRyGq0Pbu3Yu2bduiVq1aYkchIiqQ/v37IyoqCrdu3RI7\nChFVECx+EhEVEoufVJJKw7T38PBwTJs2Df7+/lBVVRU1C1FFx42OiKiskpeXx4QJE+Dl5SV2FCKq\nIOTFDkBEVFax+Ekl6dPIT0EQIJFISvz+qampsLW1xeLFi9GwYcMSvz8R/U94eDgiIyPRvXt3saMQ\nERXKqFGjYGJigri4ONSoUUPsOERUznHkJxFRIbH4SSVJQ0MDioqKiI+PF+X+kydPhqWlJZycnES5\nPxH9z+bNm2Fvb49KlSqJHYWIqFCqVq2KwYMHY8OGDWJHIaIKQCIIgiB2CCKiskhTUxORkZHc9IhK\nTNu2bbF48WL8+OOPJXrfXbt2wc3NDSEhIVBTUyvRexNRXoIgICsrCxkZGfz3SERlWkREBDp06ICY\nmBgoKiqKHYeIyjGO/CQiKoTc3FwkJyejSpUqYkehCkSMTY/+/vtvTJ48GXv27GGhhagUkEgkqFy5\nMv89ElGZV7duXTRp0gS7d+8WOwoRlXMsfhIRFUBaWhpCQ0Nx9OhRKCoqIjIyEhxATyWlpIuf6enp\nsLW1xYIFC9C4ceMSuy8RERFVDC4uLvD09OTraSIqVix+EhHlw+PHj/F///d/0NfXh4ODA1auXAlD\nQ0N06tQJVlZW2LRpEz58+CB2TCrnSnrH96lTp8LMzAxjx44tsXsSERFRxdG1a1dkZmYiMDBQ7ChE\nVI6x+ElE9A2ZmZlwdnZG69atIScnh+vXr+POnTsIDAzEvXv38OTJE3h4eODIkSMwMDDAkSNHxI5M\n5VhJjvz09/fH6dOn4ePjI8ru8kRERFT+SSQSTJ48GZ6enmJHIaJyjBseERF9RWZmJvr06QN5eXn8\n+eefUFVV/eb1wcHB6Nu3L5YsWYIRI0aUUEqqSFJSUlCtWjWkpKRAKi2+zy8jIyPRunVrnDx5ElZW\nVsV2HyIiIqLU1FQYGBjg2rVrMDY2FjsOEZVDLH4SEX2Fo6Mj3r59i/3790NeXj5fbT7tWrlz5050\n7ty5mBNSRVSrVi1cvXoV+vr6xdJ/RkYG2rRpA3t7e0ycOLFY7kFE3/bpd092djYEQUDDhg3x448/\nih2LiKjYzJo1C2lpaRwBSkTFgsVPIqIvuHfvHn766Sc8evQIysrKBWp78OBBeHh44MaNG8WUjiqy\nDh06YO7cucVWXJ80aRKeP3+Offv2cbo7kQhOnDgBDw8PhIWFQVlZGbVq1UJWVhZq166NgQMHom/f\nvv85E4GIqKx59uwZLC0tERMTA3V1dbHjEFE5wzU/iYi+YN26dRg9enSBC58A0Lt3b7x584bFTyoW\nxbnp0cGDB3H06FFs3ryZhU8ikbi6usLKygqPHj3Cs2fPsGrVKtjZ2UEqlWLFihXYsGGD2BGJiIqc\nnp4ebGxssGXLFrGjEFE5xJGfRET/8v79exgYGODBgwfQ1dUtVB+///47wsPD4efnV7ThqMJbtmwZ\nXr58iZUrVxZpvzExMWjRogWOHj2Kli1bFmnfRJQ/z549Q7NmzXDt2jXUqVMnz7kXL17A19cXc+fO\nha+vL0aOHClOSCKiYnL9+nUMGTIEjx49gpycnNhxiKgc4chPIqJ/CQkJQcOGDQtd+ASAAQMG4Pz5\n80WYiuij4tjxPTMzE4MGDYKrqysLn0QiEgQB1atXx/r162Vf5+TkQBAE6OrqYvbs2Rg9ejQCAgKQ\nmZkpcloioqLVsmVLVK9eHceOHRM7ChGVMyx+EhH9S0JCArS1tb+rDx0dHSQmJhZRIqL/KY5p77Nm\nzUL16tUxZcqUIu2XiAqmdu3aGDx4MPbv34/t27dDEATIycnlWYbCxMQEDx48QOXKlUVMSkRUPFxc\nXLjpEREVORY/iYj+RV5eHjk5Od/VR3Z2NgDg7NmziImJ+e7+iD4xMjJCbGys7Gfsex09ehT79u2D\nn58f1/kkEtGnlajGjBmD3r17Y9SoUbCwsMDy5csRERGBR48ewd/fH9u2bcOgQYNETktEVDz69++P\nx48f4/bt22JHIaJyhGt+EhH9S1BQECZMmIBbt24Vuo/bt2/DxsYG9evXx+PHj/Hq1SvUqVMHJiYm\nnz0MDAxQqVKlInwGVN7VqVMHAQEBMDY2/q5+njx5gubNm+PgwYNo06ZNEaUjosJKTExESkoKcnNz\nkZSUhP3792PXrl2IioqCoaEhkpKSMHDgQHh6enLkJxGVW7///jsiIiLg6+srdhQiKidY/CQi+pfs\n7GwYGhri2LFjaNSoUaH6cHFxgYqKChYtWgQASEtLQ3R0NB4/fvzZ48WLF9DT0/tiYdTQ0BAKCgpF\n+fSoHOjatSumTJmCbt26FbqPrKwstG/fHn379sWMGTOKMB0RFdT79++xadMmLFiwADVr1kROTg50\ndHTQuXNn9O/fH0pKSggNDUWjRo1gYWHBUdpEVK4lJCTAxMQE4eHhqF69uthxiKgcYPGTiOgL3N3d\n8fz5c2zYsKHAbT98+AB9fX2EhobCwMDgP6/PzMxETEzMFwujT548QfXq1b9YGDU2NoaysnJhnh6V\ncb/88gvMzc0xadKkQvfh6uqKu3fv4tixY5BKuQoOkZhcXV1x4cIFTJs2Ddra2lizZg0OHjwIKysr\nKCkpYdmyZdyMjIgqlLFjx0JNTQ1Vq1bFxYsXkZiYiMqVK6N69eqwtbVF3759OXOKiPKNxU8ioi94\n+fIl6tWrh9DQUBgaGhao7e+//46goCAcOXLku3NkZ2fjyZMniIyM/KwwGhUVhapVq361MKqurv7d\n9y+M1NRU7N27F3fv3oWqqip++uknNG/eHPLy8qLkKY88PT0RGRmJ1atXF6r9yZMnMXr0aISGhkJH\nR6eI0xFRQdWuXRtr165F7969AXwc9WRnZ4d27dohMDAQUVFROH78OMzNzUVOSkRU/MLCwjBz5kwE\nBARgyJAh6Nu3L7S0tJCVlYWYmBhs2bIFjx49grOzM2bMmAEVFRWxIxNRKcd3okREX1CzZk24u7uj\nW7duCAwMzPeUmwMHDsDLywuXL18ukhzy8vIwMjKCkZERrK2t85zLzc3F8+fP8xREd+/eLfuzqqrq\nVwujVatWLZJ8X/LmzRtcv34dqampWLVqFUJCQuDr64tq1aoBAK5fv44zZ84gPT0dJiYmaN26NczM\nzPJM4xQEgdM6v8HMzAwnT54sVNvnz5/DwcEB/v7+LHwSlQJRUVHQ0dGBmpqa7FjVqlVx69YtrFmz\nBrNnz0b9+vVx9OhRmJub8/9HIirXzpw5g6FDh2L69OnYtm0bNDU185xv3749Ro4cifv378PNzQ2d\nOnXC0aNHZa8ziYi+hCM/iYi+wd3dHX5+fti9ezeaN2/+1esyMjKwbt06LFu2DEePHoWVlVUJpvyc\nIAiIi4v74lT6x48fQ05O7ouFURMTE+jo6HzXG+ucnBy8ePECtWvXRpMmTdC5c2e4u7tDSUkJADBi\nxAgkJiZCQUEBz549Q2pqKtzd3dGnTx8AH4u6UqkUCQkJePHiBWrUqAFtbe0i+b6UF48ePYKNjQ2i\noqIK1C47OxudOnWCjY0NZs+eXUzpiCi/BEGAIAgYMGAAFBUVsWXLFnz48AG7du2Cu7s7Xr16BYlE\nAldXV/z999/Ys2cPp3kSUbl15coV9O3bF/v370e7du3+83pBEPDrr7/i9OnTCAwMhKqqagmkJKKy\niMVPIqL/sH37dsyZMwe6uroYP348evfuDXV1deTk5CA2NhabN2/G5s2bYWlpiY0bN8LIyEjsyN8k\nCALevn371cJoZmbmVwujNWvWLFBhtFq1apg1axYmT54sW1fy0aNHUFFRga6uLgRBwLRp0+Dn54fb\nt29DX18fwMfpTvPmzUNISAji4+PRpEkTbNu2DSYmJsXyPSlrsrKyoKqqivfv3xdoQ6w5c+YgODgY\np06d4jqfRKXIrl27MGbMGFStWhXq6up4//493NzcYG9vDwCYMWMGwsLCcOzYMXGDEhEVk7S0NBgb\nG8PX1xc2Njb5bicIApycnFC5cuVCrdVPRBUDi59ERPmQk5ODEydOYO3atbh8+TLS09MBANra2hgy\nZAjGjh1bbtZiS0xM/OIao48fP0ZycjKMjY2xd+/ez6aq/1tycjJq1KgBX19f2NrafvW6t2/folq1\narh+/TqaNWsGAGjVqhWysrKwceNG1KpVC46OjkhPT8eJEydkI0grOjMzMxw+fBgWFhb5uv7MmTOw\nt7dHaGgod04lKoUSExOxefNmxMXFYeTIkWjYsCEA4OHDh2jfvj02bNiAvn37ipySiKh4bN26FXv2\n7MGJEycK3DY+Ph7m5uaIjo7+bJo8ERHANT+JiPJFTk4OvXr1Qq9evQB8HHknJydXLkfPaWpqolmz\nZrJC5D8lJycjMjISBgYGXy18flqPLiYmBlKp9ItrMP1zzbpDhw5BQUEBpqamAIDLly8jODgYd+/e\nRYMGDQAAK1euRP369REdHY169eoV1VMt00xNTfHo/7V359FWlnX/+N/nIBxmFZECFThMYQqaivjg\nlDg8iGkqDaRkQs6oPabW1zTnocIRFDRnF6Q+CSVKgvZgkkMJSAgi4UERBEUTTZGQ4ZzfH/08y5Oi\nzAdvXq+1zlrse1/XdX/2FmHz3tfw0kurFX6+/vrr+cEPfpARI0YIPmETtfXWW+ecc86pce3999/P\nk08+mZ49ewo+gUIbOnRofv7zn69V3y996Uvp3bt37r777vzP//zPeq4MKILi/asdYCOoW7duIYPP\nz9OkSZPsuuuuqV+//irbVFZWJklefPHFNG3a9BOHK1VWVlYHn3fddVcuueSSnH322dlyyy2zdOnS\nPProo2ndunV23nnnrFixIknStGnTtGzZMtOmTdtAr+yLp1OnTpk1a9bntlu5cmWOPfbYnHTSSTng\ngAM2QmXA+tKkSZN84xvfyLXXXlvbpQBsMDNmzMjrr7+eQw89dK3HOOWUU3LnnXeux6qAIjHzE4AN\nYsaMGWnRokW22mqrJP+e7VlZWZk6depk8eLFufDCC/P73/8+Z5xxRs4999wkybJly/Liiy9WzwL9\nKEhduHBhmjdvnvfee696rM39tOOOHTtm6tSpn9vu8ssvT5K1nk0B1C6ztYGimzt3bjp37pw6deqs\n9Rg77bRT5s2btx6rAopE+AnAelNVVZV3330322yzTV566aW0bds2W265ZZJUB59/+9vf8qMf/Sjv\nv/9+brnllhx88ME1wsw333yzemn7R9tSz507N3Xq1LGP08d07NgxDzzwwGe2efzxx3PLLbdk8uTJ\n6/QPCmDj8MUOsDlasmRJGjZsuE5jNGzYMB988MF6qggoGuEnAOvN/Pnzc8ghh2Tp0qWZM2dOysvL\nc/PNN2f//ffPXnvtlXvuuSfXXHNN9ttvv1x55ZVp0qRJkqSkpCRVVVVp2rRplixZksaNGydJdWA3\nderUNGjQIOXl5dXtP1JVVZXrrrsuS5YsqT6Vvn379oUPShs2bJipU6fmjjvuSFlZWVq1apV99903\nW2zx77/aFy5cmH79+uXuu+9Oy5Yta7laYHU8++yz6dat22a5rQqw+dpyyy2rV/esrX/+85/Vq40A\n/pPwE2AN9O/fP2+//XZGjx5d26Vskrbbbrvcd999mTJlSl5//fVMnjw5t9xySyZOnJgbbrghZ511\nVt555520bNkyV111Vb7yla+kU6dO2WWXXVK/fv2UlJRkxx13zNNPP5358+dnu+22S/LvQ5G6deuW\nTp06fep9mzdvnpkzZ2bUqFHVJ9PXq1evOgj9KBT96Kd58+ZfyNlVlZWVGTduXIYOHZpnnnkmu+yy\nSyZMmJAPP/wwL730Ut58882cfPLJGTBgQH7wgx+kf//+Ofjgg2u7bGA1zJ8/P7169cq8efOqvwAC\n2BzstNNO+dvf/pb333+/+ovxNfX444+na9eu67kyoChKqj5aUwhQAP3798/dd9+dkpKS6mXSO+20\nU771rW/lpJNOqp4Vty7jr2v4+eqrr6a8vDyTJk3Kbrvttk71fNHMmjUrL730Uv785z9n2rRpqaio\nyKuvvpprr702p5xySkpLSzN16tQcc8wxOeSvmCxYAAAf70lEQVSQQ9KrV6/ceuutefzxx/OnP/0p\nXbp0Wa37VFVV5a233kpFRUVmz55dHYh+9LNixYpPBKIf/Xz5y1/eJIPRf/zjHznyyCOzZMmSDBw4\nMN/73vc+sUTsueeey7Bhw3L//fenVatWmT59+jr/ngc2jiuvvDKvvvpqbrnlltouBWCj+/a3v52e\nPXvm1FNPXav+++67b84666wcffTR67kyoAiEn0Ch9O/fPwsWLMjw4cOzYsWKvPXWWxk/fnyuuOKK\ndOjQIePHj0+DBg0+0W/58uWpW7fuao2/ruHnnDlz0r59+0ycOHGzCz9X5T/3uXvwwQdz9dVXp6Ki\nIt26dcull16aXXfddb3db9GiRZ8ailZUVOSDDz741NmiHTp0yHbbbVcry1Hfeuut7Lvvvjn66KNz\n+eWXf24N06ZNS+/evXPBBRfk5JNP3khVAmursrIyHTt2zH333Zdu3brVdjkAG93jjz+eM844I9Om\nTVvjL6Gff/759O7dO3PmzPGlL/CphJ9AoawqnHzhhRey22675Wc/+1kuuuiilJeX5/jjj8/cuXMz\natSoHHLIIbn//vszbdq0/PjHP85TTz2VBg0a5IgjjsgNN9yQpk2b1hi/e/fuGTJkSD744IN8+9vf\nzrBhw1JWVlZ9v1/96lf59a9/nQULFqRjx475yU9+kmOPPTZJUlpaWr3HZZJ8/etfz/jx4zNp0qSc\nf/75ee6557Js2bJ07do1gwYNyl577bWR3j2S5L333ltlMLpo0aKUl5d/ajDaunXrDfKBe+XKldl3\n333z9a9/PVdeeeVq96uoqMi+++6be+65x9J32MSNHz8+Z511Vv72t79tkjPPATa0qqqq7LPPPjnw\nwANz6aWXrna/999/P/vtt1/69++fM888cwNWCHyR+VoE2CzstNNO6dWrV0aOHJmLLrooSXLdddfl\nggsuyOTJk1NVVZUlS5akV69e2WuvvTJp0qS8/fbbOeGEE/LDH/4wv/3tb6vH+tOf/pQGDRpk/Pjx\nmT9/fvr375+f/vSnuf7665Mk559/fkaNGpVhw4alU6dOeeaZZ3LiiSemWbNmOfTQQ/Pss89mzz33\nzKOPPpquXbumXr16Sf794e24447LkCFDkiQ33nhjDjvssFRUVBT+8J5NSdOmTfO1r30tX/va1z7x\n3JIlS/Lyyy9Xh6HPP/989T6jb7zxRlq3bv2pwWjbtm2r/zuvqUceeSTLly/PFVdcsUb9OnTokCFD\nhuTiiy8WfsIm7rbbbssJJ5wg+AQ2WyUlJfnd736XHj16pG7durngggs+98/ERYsW5Zvf/Gb23HPP\nnHHGGRupUuCLyMxPoFA+a1n6eeedlyFDhmTx4sUpLy9P165d8+CDD1Y/f+utt+YnP/lJ5s+fX72X\n4hNPPJEDDjggFRUVadeuXfr3758HH3ww8+fPr14+P2LEiJxwwglZtGhRqqqq0rx58zz22GPZe++9\nq8c+66yz8tJLL+Xhhx9e7T0/q6qqst122+Xqq6/OMcccs77eIjaQDz/8MK+88sqnzhh97bXX0qpV\nq0+Eou3bt0+7du0+dSuGj/Tu3Tvf/e5384Mf/GCNa1qxYkXatm2bMWPGZJdddlmXlwdsIG+//Xba\nt2+fl19+Oc2aNavtcgBq1euvv55vfOMb2XrrrXPmmWfmsMMOS506dWq0WbRoUe68884MHjw43/nO\nd/LLX/6yVrYlAr44zPwENhv/ua/kHnvsUeP5mTNnpmvXrjUOkenRo0dKS0szY8aMtGvXLknStWvX\nGmHVf/3Xf2XZsmWZPXt2li5dmqVLl6ZXr141xl6xYkXKy8s/s7633norF1xwQf70pz9l4cKFWbly\nZZYuXZq5c+eu9Wtm4ykrK0vnzp3TuXPnTzy3fPnyvPrqq9Vh6OzZs/P444+noqIir7zySrbddttP\nnTFaWlqaiRMnZuTIkWtV0xZbbJGTTz45Q4cOdYgKbKJGjBiRww47TPAJkKRly5Z5+umn89vf/ja/\n+MUvcsYZZ+Twww9Ps2bNsnz58syZMydjx47N4Ycfnvvvv9/2UMBqEX4Cm42PB5hJ0qhRo9Xu+3nL\nbj6aRF9ZWZkkefjhh7PDDjvUaPN5Byodd9xxeeutt3LDDTekTZs2KSsrS8+ePbNs2bLVrpNNU926\ndasDzf+0cuXKvPbaazVmiv7lL39JRUVF/v73v6dnz56fOTP08xx22GEZMGDAupQPbCBVVVW59dZb\nM3jw4NouBWCTUVZWln79+qVfv36ZMmVKJkyYkHfeeSdNmjTJgQcemCFDhqR58+a1XSbwBSL8BDYL\n06dPz9ixY3PhhReuss2OO+6YO++8Mx988EF1MPrUU0+lqqoqO+64Y3W7adOm5V//+ld1IPXMM8+k\nrKws7du3z8qVK1NWVpY5c+Zk//33/9T7fLT348qVK2tcf+qppzJkyJDqWaMLFy7M66+/vvYvmi+E\nOnXqpE2bNmnTpk0OPPDAGs8NHTo0U6ZMWafxt95667z77rvrNAawYUycODH/+te/Vvn3BcDmblX7\nsAOsCRtjAIXz4YcfVgeHzz//fK6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -696,29 +706,6 @@ "display_visual(user_input = False, algorithm = breadth_first_tree_search, problem = romania_problem)" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nHUWLFsXLy4uwsLCXPsbNmzfx9vambt26VKxYkWrVq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7aNypUrA1CgQAEOHjzI3LlzqVev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\n050WLlyIsLAwpKSkoG3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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "all_node_colors = []\n", - "display_visual(user_input = True, algorithm = breadth_first_tree_search)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -732,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -796,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -805,7 +792,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nPZS99957HVYFAMCqEn4CAMBK7LXXXikrK8u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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -818,29 +805,6 @@ "display_visual(user_input = False, algorithm = breadth_first_search, problem = romania_problem)" ] }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\ny5133lnjvoMOOii77bZbunfvnj5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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "all_node_colors = []\n", - "display_visual(user_input = True, algorithm = breadth_first_search)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -852,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -935,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -944,7 +908,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -957,29 +921,6 @@ "display_visual(user_input = False, algorithm = uniform_cost_search, problem = romania_problem)" ] }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nzUb+Vb06aVi+YYctAZvX6NGjc/z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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "all_node_colors = []\n", - "display_visual(user_input = True, algorithm = uniform_cost_search)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -991,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -1077,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -1086,7 +1027,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1101,7 +1042,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": { "collapsed": false, "scrolled": false @@ -1109,9 +1050,9 @@ "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nnK9+9at5+OGH061bt81YIQAAa7NNfRcAAAC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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nvXqVfPnyUbt2bWxsbHB2dqZs2bIAZM6cGTs7OxwcHMiZM+cTX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/zDzs7uP8/X3t6e3Llz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\nv49gAzVs2LAMGDDAXnUAAOuQT1p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"ffadb2ad36fa418981e017132de6d62f": { + "ff18eaa36cbc48fda40695d7463b9464": { "views": [ { - "cell_index": 44 + "cell_index": 43 } ] }, - "ffc226886b594ff39e6783a0e19a4c1e": { + "ffb052b8caab4eb0a948a97a20be107f": { "views": [] } }, From ba9dc7249321bd80f699b9ba85727bda031a058b Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sun, 19 Jun 2016 21:46:16 +0530 Subject: [PATCH 325/513] Implemented Passive ADP Agent --- rl.py | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 54 insertions(+), 3 deletions(-) diff --git a/rl.py b/rl.py index 079456284..c819d0fc3 100644 --- a/rl.py +++ b/rl.py @@ -3,16 +3,67 @@ from collections import defaultdict from utils import argmax +from mdp import MDP, policy_evaluation -import agents import random -class PassiveADPAgent(agents.Agent): +class PassiveADPAgent: """Passive (non-learning) agent that uses adaptive dynamic programming on a given MDP and policy. [Figure 21.2]""" - NotImplemented + + class ModelMDP(MDP): + """ Class for implementing modifed Version of input MDP with + an editable transition model P and a custom function T. """ + def __init__(self, init, actlist, terminals, gamma, states): + super().__init__(init, actlist, terminals, gamma) + nested_dict = lambda: defaultdict(nested_dict) + # StackOverflow:whats-the-best-way-to-initialize-a-dict-of-dicts-in-python + self.P = nested_dict() + + def T(self, s, a): + """Returns a list of tuples with probabilities for states + based on the learnt model P. """ + return [(prob, res) for (res, prob) in self.P[(s, a)].items()] + + def __init__(self, pi, mdp): + self.pi = pi + self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist, + mdp.terminals, mdp.gamma, mdp.states) + self.U = {} + self.Nsa = defaultdict(int) + self.Ns1_sa = defaultdict(int) + self.s = None + self.a = None + + def __call__(self, percept): + s1, r1 = percept + self.mdp.states.add(s1) # Model keeps track of visited states. + R, P, mdp, pi = self.mdp.reward, self.mdp.P, self.mdp, self.pi + s, a, Nsa, Ns1_sa, U = self.s, self.a, self.Nsa, self.Ns1_sa, self.U + + if s1 not in R: # Reward is only available for visted state. + U[s1] = R[s1] = r1 + if s is not None: + Nsa[(s, a)] += 1 + Ns1_sa[(s1, s, a)] += 1 + # for each t such that Ns′|sa [t, s, a] is nonzero + for t in [res for (res, state, act), freq in Ns1_sa.items() + if (state, act) == (s, a) and freq != 0]: + P[(s, a)][t] = Ns1_sa[(t, s, a)] / Nsa[(s, a)] + + U = policy_evaluation(pi, U, mdp) + if s1 in mdp.terminals: + self.s = self.a = None + else: + self.s, self.a = s1, self.pi[s1] + return self.a + + def update_state(self, percept): + ''' To be overridden in most cases. The default case + assumes th percept to be of type (state, reward)''' + return percept class PassiveTDAgent: From 671fc2052ec5a396a0c311689d778178d6ed0a29 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 20 Jun 2016 19:28:59 +0530 Subject: [PATCH 326/513] Remove redundant initialization --- csp.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/csp.py b/csp.py index d696a787c..902fd58a0 100644 --- a/csp.py +++ b/csp.py @@ -372,8 +372,6 @@ def parse_neighbors(neighbors, variables=[]): True """ dic = defaultdict(list) - for var in variables: - dic[var] = [] specs = [spec.split(':') for spec in neighbors.split(';')] for (A, Aneighbors) in specs: A = A.strip() From 2c458ae549ae16c76769209496b6b2fdc3cb7305 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 20 Jun 2016 20:33:58 +0530 Subject: [PATCH 327/513] Style: address pep8 warnings. --- tests/test_csp.py | 4 +- tests/test_games.py | 2 +- tests/test_learning.py | 5 ++- tests/test_logic.py | 94 +++++++++++++++++++++++---------------- tests/test_mdp.py | 1 + tests/test_nlp.py | 5 ++- tests/test_planning.py | 14 +++--- tests/test_probability.py | 1 + tests/test_search.py | 9 +++- tests/test_text.py | 7 +-- tests/test_utils.py | 52 ++++++++++++---------- 11 files changed, 116 insertions(+), 78 deletions(-) diff --git a/tests/test_csp.py b/tests/test_csp.py index f22383f82..358d6fe07 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -1,6 +1,7 @@ import pytest from csp import * #noqa + def test_backtracking_search(): assert (backtracking_search(australia) is not None) == True assert (backtracking_search(australia, select_unassigned_variable=mrv) is not None) == True @@ -12,14 +13,15 @@ def test_backtracking_search(): assert (backtracking_search(usa, select_unassigned_variable=mrv, order_domain_values=lcv, inference=mac) is not None) == True + def test_universal_dict(): d = UniversalDict(42) assert d['life'] == 42 + def test_parse_neighbours(): assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} - if __name__ == "__main__": pytest.main() diff --git a/tests/test_games.py b/tests/test_games.py index 5603270cd..fc8733dc9 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -18,7 +18,7 @@ def gen_state(to_move='X', x_positions=[], o_positions=[], h=3, v=3, k=3): and how many consecutive X's or O's required to win, return the corresponding game state""" - moves = set([(x, y) for x in range(1, h+1) for y in range(1, v+1)]) \ + moves = set([(x, y) for x in range(1, h + 1) for y in range(1, v + 1)]) \ - set(x_positions) - set(o_positions) moves = list(moves) board = {} diff --git a/tests/test_learning.py b/tests/test_learning.py index 882e00a1d..31fb671bc 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,13 +1,14 @@ import pytest from learning import parse_csv, weighted_mode, weighted_replicate + def test_parse_csv(): assert parse_csv('1, 2, 3 \n 0, 2, na') == [[1, 2, 3], [0, 2, 'na']] def test_weighted_mode(): - assert weighted_mode('abbaa', [1,2,3,1,2]) == 'b' + assert weighted_mode('abbaa', [1, 2, 3, 1, 2]) == 'b' def test_weighted_replicate(): - assert weighted_replicate('ABC', [1,2,1], 4) == ['A', 'B', 'B', 'C'] + assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] diff --git a/tests/test_logic.py b/tests/test_logic.py index 4cca74b51..6de49101d 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -9,9 +9,11 @@ def test_expr(): assert (expr_handle_infix_ops('P & Q ==> R & ~S') == "P & Q |'==>'| R & ~S") + def test_extend(): assert extend({x: 1}, y, 2) == {x: 1, y: 2} + def test_PropKB(): kb = PropKB() assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 @@ -33,44 +35,44 @@ def test_KB_wumpus(): # TODO: Let's just use P11, P12, ... = symbols('P11, P12, ...') P = {} B = {} - P[1,1] = Symbol("P[1,1]") - P[1,2] = Symbol("P[1,2]") - P[2,1] = Symbol("P[2,1]") - P[2,2] = Symbol("P[2,2]") - P[3,1] = Symbol("P[3,1]") - B[1,1] = Symbol("B[1,1]") - B[2,1] = Symbol("B[2,1]") - - kb_wumpus.tell(~P[1,1]) - kb_wumpus.tell(B[1,1] |'<=>'| ((P[1,2] | P[2,1]))) - kb_wumpus.tell(B[2,1] |'<=>'| ((P[1,1] | P[2,2] | P[3,1]))) - kb_wumpus.tell(~B[1,1]) - kb_wumpus.tell(B[2,1]) + P[1, 1] = Symbol("P[1,1]") + P[1, 2] = Symbol("P[1,2]") + P[2, 1] = Symbol("P[2,1]") + P[2, 2] = Symbol("P[2,2]") + P[3, 1] = Symbol("P[3,1]") + B[1, 1] = Symbol("B[1,1]") + B[2, 1] = Symbol("B[2,1]") + + kb_wumpus.tell(~P[1, 1]) + kb_wumpus.tell(B[1, 1] | '<=>' | ((P[1, 2] | P[2, 1]))) + kb_wumpus.tell(B[2, 1] | '<=>' | ((P[1, 1] | P[2, 2] | P[3, 1]))) + kb_wumpus.tell(~B[1, 1]) + kb_wumpus.tell(B[2, 1]) # Statement: There is no pit in [1,1]. - assert kb_wumpus.ask(~P[1,1]) == {} + assert kb_wumpus.ask(~P[1, 1]) == {} # Statement: There is no pit in [1,2]. - assert kb_wumpus.ask(~P[1,2]) == {} + assert kb_wumpus.ask(~P[1, 2]) == {} # Statement: There is a pit in [2,2]. - assert kb_wumpus.ask(P[2,2]) == False + assert kb_wumpus.ask(P[2, 2]) == False # Statement: There is a pit in [3,1]. - assert kb_wumpus.ask(P[3,1]) == False + assert kb_wumpus.ask(P[3, 1]) == False # Statement: Neither [1,2] nor [2,1] contains a pit. - assert kb_wumpus.ask(~P[1,2] & ~P[2,1]) == {} + assert kb_wumpus.ask(~P[1, 2] & ~P[2, 1]) == {} # Statement: There is a pit in either [2,2] or [3,1]. - assert kb_wumpus.ask(P[2,2] | P[3,1]) == {} + assert kb_wumpus.ask(P[2, 2] | P[3, 1]) == {} def test_definite_clause(): - assert is_definite_clause(expr('A & B & C & D ==> E')) - assert is_definite_clause(expr('Farmer(Mac)')) + assert is_definite_clause(expr('A & B & C & D ==> E')) + assert is_definite_clause(expr('Farmer(Mac)')) assert not is_definite_clause(expr('~Farmer(Mac)')) - assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) + assert is_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) assert not is_definite_clause(expr('(Farmer(f) & ~Rabbit(r)) ==> Hates(f, r)')) assert not is_definite_clause(expr('(Farmer(f) | Rabbit(r)) ==> Hates(f, r)')) @@ -79,12 +81,13 @@ def test_pl_true(): assert pl_true(P, {}) is None assert pl_true(P, {P: False}) is False assert pl_true(P | Q, {P: True}) is True - assert pl_true((A|B)&(C|D), {A: False, B: True, D: True}) is True - assert pl_true((A&B)&(C|D), {A: False, B: True, D: True}) is False - assert pl_true((A&B)|(A&C), {A: False, B: True, C: True}) is False - assert pl_true((A|B)&(C|D), {A: True, D: False}) is None + assert pl_true((A | B) & (C | D), {A: False, B: True, D: True}) is True + assert pl_true((A & B) & (C | D), {A: False, B: True, D: True}) is False + assert pl_true((A & B) | (A & C), {A: False, B: True, C: True}) is False + assert pl_true((A | B) & (C | D), {A: True, D: False}) is None assert pl_true(P | P, {}) is None + def test_tt_true(): assert tt_true(P | ~P) assert tt_true('~~P <=> P') @@ -103,48 +106,56 @@ def test_tt_true(): assert tt_true('(A & (B | C)) <=> ((A & B) | (A & C))') assert tt_true('(A | (B & C)) <=> ((A | B) & (A | C))') + def test_dpll(): assert (dpll_satisfiable(A & ~B & C & (A | ~D) & (~E | ~D) & (C | ~D) & (~A | ~F) & (E | ~F) & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) == {B: False, C: True, A: True, F: False, D: True, E: False}) - assert dpll_satisfiable(A&~B) == {A: True, B: False} - assert dpll_satisfiable(P&~P) == False + assert dpll_satisfiable(A & ~B) == {A: True, B: False} + assert dpll_satisfiable(P & ~P) == False def test_unify(): assert unify(x, x, {}) == {} assert unify(x, 3, {}) == {x: 3} + def test_pl_fc_entails(): assert pl_fc_entails(horn_clauses_KB, expr('Q')) assert not pl_fc_entails(horn_clauses_KB, expr('SomethingSilly')) + def test_tt_entails(): assert tt_entails(P & Q, Q) assert not tt_entails(P | Q, Q) assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) + def test_eliminate_implications(): assert repr(eliminate_implications('A ==> (~B <== C)')) == '((~B | ~C) | ~A)' assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' assert repr(eliminate_implications(A & B | C & ~D)) == '((A & B) | (C & ~D))' + def test_dissociate(): assert dissociate('&', [A & B]) == [A, B] assert dissociate('|', [A, B, C & D, P | Q]) == [A, B, C & D, P, Q] assert dissociate('&', [A, B, C & D, P | Q]) == [A, B, C, D, P | Q] + def test_associate(): assert (repr(associate('&', [(A & B), (B | C), (B & C)])) == '(A & B & (B | C) & B & C)') assert (repr(associate('|', [A | (B | (C | (A & B)))])) == '(A | B | C | (A & B))') + def test_move_not_inwards(): assert repr(move_not_inwards(~(A | B))) == '(~A & ~B)' assert repr(move_not_inwards(~(A & B))) == '(~A | ~B)' assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' + def test_to_cnf(): assert (repr(to_cnf(wumpus_world_inference & ~expr('~P12'))) == "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") @@ -154,12 +165,14 @@ def test_to_cnf(): assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' assert repr(to_cnf("A | (B | (C | (D & E)))")) == '((D | A | B | C) & (E | A | B | C))' + def test_standardize_variables(): e = expr('F(a, b, c) & G(c, A, 23)') assert len(variables(standardize_variables(e))) == 3 #assert variables(e).intersection(variables(standardize_variables(e))) == {} assert is_variable(standardize_variables(expr('x'))) + def test_fol_bc_ask(): def test_ask(query, kb=None): q = expr(query) @@ -167,23 +180,25 @@ def test_ask(query, kb=None): answers = fol_bc_ask(kb or test_kb, q) return sorted( [dict((x, v) for x, v in list(a.items()) if x in test_variables) - for a in answers], key=repr) + for a in answers], key=repr) assert repr(test_ask('Farmer(x)')) == '[{x: Mac}]' assert repr(test_ask('Human(x)')) == '[{x: Mac}, {x: MrsMac}]' assert repr(test_ask('Rabbit(x)')) == '[{x: MrsRabbit}, {x: Pete}]' assert repr(test_ask('Criminal(x)', crime_kb)) == '[{x: West}]' + def test_d(): - assert d(x*x - x, x) == 2*x - 1 + assert d(x * x - x, x) == 2 * x - 1 + def test_WalkSAT(): - def check_SAT(clauses, single_solution = {}): + def check_SAT(clauses, single_solution={}): # Make sure the solution is correct if it is returned by WalkSat # Sometimes WalkSat may run out of flips before finding a solution soln = WalkSAT(clauses) if soln: assert all(pl_true(x, soln) for x in clauses) - if single_solution: #Cross check the solution if only one exists + if single_solution: # Cross check the solution if only one exists assert all(pl_true(x, single_solution) for x in clauses) assert soln == single_solution # Test WalkSat for problems with solution @@ -195,18 +210,19 @@ def check_SAT(clauses, single_solution = {}): assert WalkSAT([A | B, ~A, ~(B | C), C | D, P | Q], 0.5, 100) is None assert WalkSAT([A | B, B & C, C | D, D & A, P, ~P], 0.5, 100) is None + def test_SAT_plan(): - transition = {'A':{'Left': 'A', 'Right': 'B'}, - 'B':{'Left': 'A', 'Right': 'C'}, - 'C':{'Left': 'B', 'Right': 'C'}} + transition = {'A': {'Left': 'A', 'Right': 'B'}, + 'B': {'Left': 'A', 'Right': 'C'}, + 'C': {'Left': 'B', 'Right': 'C'}} assert SAT_plan('A', transition, 'C', 2) is None assert SAT_plan('A', transition, 'B', 3) == ['Right'] assert SAT_plan('C', transition, 'A', 3) == ['Left', 'Left'] - transition = {(0, 0):{'Right': (0, 1), 'Down': (1, 0)}, - (0, 1):{'Left': (1, 0), 'Down': (1, 1)}, - (1, 0):{'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, - (1, 1):{'Left': (1, 0), 'Up': (0, 1)}} + transition = {(0, 0): {'Right': (0, 1), 'Down': (1, 0)}, + (0, 1): {'Left': (1, 0), 'Down': (1, 1)}, + (1, 0): {'Right': (1, 0), 'Up': (1, 0), 'Left': (1, 0), 'Down': (1, 0)}, + (1, 1): {'Left': (1, 0), 'Up': (0, 1)}} assert SAT_plan((0, 0), transition, (1, 1), 4) == ['Right', 'Down'] diff --git a/tests/test_mdp.py b/tests/test_mdp.py index c4e6ed590..de0de064f 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -1,6 +1,7 @@ import pytest from mdp import * # noqa + def test_value_iteration(): assert value_iteration(sequential_decision_environment, .01) == {(3, 2): 1.0, (3, 1): -1.0, (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, diff --git a/tests/test_nlp.py b/tests/test_nlp.py index 87d11965e..4e7bebeae 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -1,9 +1,10 @@ import pytest from nlp import * + def test_rules(): - assert Rules(A = "B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} + assert Rules(A="B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} def test_lexicon(): - assert Lexicon(Art = "the | a | an") == {'Art': ['the', 'a', 'an']} + assert Lexicon(Art="the | a | an") == {'Art': ['the', 'a', 'an']} diff --git a/tests/test_planning.py b/tests/test_planning.py index aed4812ea..e90601a6f 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -2,6 +2,7 @@ from utils import expr from logic import FolKB + def test_action(): precond = [[expr("P(x)"), expr("Q(y, z)")] ,[expr("Q(x)")]] @@ -18,15 +19,16 @@ def test_action(): assert test_kb.ask(expr("Q(B, C)")) is not False assert not a.check_precond(test_kb, args) + def test_air_cargo(): p = air_cargo() assert p.goal_test() is False - solution =[expr("Load(C1 , P1, SFO)"), - expr("Fly(P1, SFO, JFK)"), - expr("Unload(C1, P1, JFK)"), - expr("Load(C2, P2, JFK)"), - expr("Fly(P2, JFK, SFO)"), - expr("Unload (C2, P2, SFO)")] + solution = [expr("Load(C1 , P1, SFO)"), + expr("Fly(P1, SFO, JFK)"), + expr("Unload(C1, P1, JFK)"), + expr("Load(C2, P2, JFK)"), + expr("Fly(P2, JFK, SFO)"), + expr("Unload (C2, P2, SFO)")] for action in solution: p.act(action) diff --git a/tests/test_probability.py b/tests/test_probability.py index 5aa472bc8..c280fdbe0 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -119,6 +119,7 @@ def test_forward_backward(): assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + def test_fixed_lag_smoothing(): umbrella_evidence = [T, F, T, F, T] e_t = F diff --git a/tests/test_search.py b/tests/test_search.py index e4eb8436f..87c1fd211 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -23,9 +23,11 @@ def test_depth_first_graph_search(): solution = depth_first_graph_search(romania_problem).solution() assert solution[-1] == 'Bucharest' + def test_iterative_deepening_search(): assert iterative_deepening_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + def test_depth_limited_search(): solution_3 = depth_limited_search(romania_problem, 3).solution() assert solution_3[-1] == 'Bucharest' @@ -33,12 +35,15 @@ def test_depth_limited_search(): solution_50 = depth_limited_search(romania_problem).solution() assert solution_50[-1] == 'Bucharest' + def test_astar_search(): assert astar_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + def test_recursive_best_first_search(): assert recursive_best_first_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + def test_BoggleFinder(): board = list('SARTELNID') """ @@ -50,6 +55,7 @@ def test_BoggleFinder(): f = BoggleFinder(board) assert len(f) == 206 + def test_and_or_graph_search(): def run_plan(state, problem, plan): if problem.goal_test(state): @@ -61,6 +67,7 @@ def run_plan(state, problem, plan): plan = and_or_graph_search(vacumm_world) assert run_plan('State_1', vacumm_world, plan) + def test_LRTAStarAgent(): my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_3') == 'Right' @@ -104,7 +111,7 @@ def test_LRTAStarAgent(): >>> boggle_hill_climbing(list('ABCDEFGHI'), verbose=False) (['E', 'P', 'R', 'D', 'O', 'A', 'G', 'S', 'T'], 123) -""" +""" if __name__ == '__main__': pytest.main() diff --git a/tests/test_text.py b/tests/test_text.py index df7103fd7..62e314951 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -30,6 +30,7 @@ def test_shift_decoding(): assert msg == 'This is a secret message.' + def test_rot13_encoding(): code = rot13('Hello, world!') @@ -52,7 +53,7 @@ def test_counting_probability_distribution(): ps = [D[n] for n in '123456'] - assert 1/7 <= min(ps) <= max(ps) <= 1/5 + assert 1 / 7 <= min(ps) <= max(ps) <= 1 / 5 def test_ngram_models(): @@ -179,9 +180,9 @@ def test_canonicalize(): def test_translate(): text = 'orange apple lemon ' - func = lambda x: ('s ' + x) if x==' ' else x + func = lambda x: ('s ' + x) if x ==' ' else x - assert translate(text, func) == 'oranges apples lemons ' + assert translate(text, func) == 'oranges apples lemons ' def test_bigrams(): diff --git a/tests/test_utils.py b/tests/test_utils.py index cc063847b..18e83485b 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -44,7 +44,6 @@ def test_argminmax(): assert argmax(['one', 'to', 'three'], key=len) == 'three' - def test_histogram(): assert histogram([1, 2, 4, 2, 4, 5, 7, 9, 2, 1]) == [(1, 2), (2, 3), (4, 2), (5, 1), @@ -60,29 +59,33 @@ def test_histogram(): def test_dotproduct(): assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230 + def test_element_wise_product(): assert element_wise_product([1, 2, 5], [7, 10, 0]) == [7, 20, 0] assert element_wise_product([1, 6, 3, 0], [9, 12, 0, 0]) == [9, 72, 0, 0] + def test_matrix_multiplication(): assert matrix_multiplication([[1, 2, 3], [2, 3, 4]], [[3, 4], [1, 2], - [1, 0]]) == [[8, 8],[13, 14]] + [1, 0]]) == [[8, 8], [13, 14]] assert matrix_multiplication([[1, 2, 3], [2, 3, 4]], [[3, 4, 8, 1], [1, 2, 5, 0], [1, 0, 0, 3]], - [[1,2], - [3,4], - [5,6], - [1,2]]) == [[132, 176], [224, 296]] + [[1, 2], + [3, 4], + [5, 6], + [1, 2]]) == [[132, 176], [224, 296]] + + def test_vector_to_diagonal(): - assert vector_to_diagonal([1, 2, 3]) == [[1, 0, 0], [0, 2, 0], [0, 0, 3]] - assert vector_to_diagonal([0, 3, 6]) == [[0, 0, 0], [0, 3, 0], [0, 0, 6]] + assert vector_to_diagonal([1, 2, 3]) == [[1, 0, 0], [0, 2, 0], [0, 0, 3]] + assert vector_to_diagonal([0, 3, 6]) == [[0, 0, 0], [0, 3, 0], [0, 0, 6]] def test_vector_add(): @@ -92,24 +95,27 @@ def test_vector_add(): def test_scalar_vector_product(): assert scalar_vector_product(2, [1, 2, 3]) == [2, 4, 6] + def test_scalar_matrix_product(): - assert rounder(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] - assert rounder(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] + assert rounder(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] + assert rounder(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] def test_inverse_matrix(): - assert rounder(inverse_matrix([[1, 0], [0, 1]])) == [[1, 0], [0, 1]] - assert rounder(inverse_matrix([[2, 1], [4, 3]])) == [[1.5, -0.5], [-2.0, 1.0]] - assert rounder(inverse_matrix([[4, 7], [2, 6]])) == [[0.6, -0.7], [-0.2, 0.4]] + assert rounder(inverse_matrix([[1, 0], [0, 1]])) == [[1, 0], [0, 1]] + assert rounder(inverse_matrix([[2, 1], [4, 3]])) == [[1.5, -0.5], [-2.0, 1.0]] + assert rounder(inverse_matrix([[4, 7], [2, 6]])) == [[0.6, -0.7], [-0.2, 0.4]] + def test_rounder(): - assert rounder(5.3330000300330) == 5.3330 - assert rounder(10.234566) == 10.2346 - assert rounder([1.234566, 0.555555, 6.010101]) == [1.2346, 0.5556, 6.0101] - assert rounder([[1.234566, 0.555555, 6.010101], + assert rounder(5.3330000300330) == 5.3330 + assert rounder(10.234566) == 10.2346 + assert rounder([1.234566, 0.555555, 6.010101]) == [1.2346, 0.5556, 6.0101] + assert rounder([[1.234566, 0.555555, 6.010101], [10.505050, 12.121212, 6.030303]]) == [[1.2346, 0.5556, 6.0101], [10.5051, 12.1212, 6.0303]] + def test_num_or_str(): assert num_or_str('42') == 42 assert num_or_str(' 42x ') == '42x' @@ -134,22 +140,22 @@ def test_step(): assert step(0) == 1 assert step(-1) == step(-0.5) == 0 - + def test_Expr(): A, B, C = symbols('A, B, C') assert symbols('A, B, C') == (Symbol('A'), Symbol('B'), Symbol('C')) assert A.op == repr(A) == 'A' assert arity(A) == 0 and A.args == () - + b = Expr('+', A, 1) assert arity(b) == 2 and b.op == '+' and b.args == (A, 1) - + u = Expr('-', b) assert arity(u) == 1 and u.op == '-' and u.args == (b,) - + assert (b ** u) == (b ** u) assert (b ** u) != (u ** b) - + assert A + b * C ** 2 == A + (b * (C ** 2)) ex = C + 1 / (A % 1) @@ -157,7 +163,7 @@ def test_Expr(): assert A in subexpressions(ex) assert B not in subexpressions(ex) - + def test_expr(): P, Q, x, y, z, GP = symbols('P, Q, x, y, z, GP') assert (expr(y + 2 * x) From 0dbb1f61063669d1a171b11d3e3d505b0af2e8b4 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Mon, 20 Jun 2016 21:20:49 +0530 Subject: [PATCH 328/513] Style: address pep8 warnings in main code. --- agents.py | 77 ++++++++++++++++++++++++++------------------------ canvas.py | 8 +++--- csp.py | 4 +-- games.py | 16 +++++------ learning.py | 12 ++++---- logic.py | 32 ++++++++++----------- planning.py | 18 ++++++------ probability.py | 6 ++-- rl.py | 4 +-- search.py | 36 ++++++++++++----------- text.py | 14 ++++----- utils.py | 44 +++++++++++++++++++++++------ 12 files changed, 150 insertions(+), 121 deletions(-) diff --git a/agents.py b/agents.py index 274630d91..cd5f0b865 100644 --- a/agents.py +++ b/agents.py @@ -362,7 +362,7 @@ def __add__(self, heading): }.get(heading, None) def move_forward(self, from_location): - x,y = from_location + x, y = from_location if self.direction == self.R: return (x+1, y) elif self.direction == self.L: @@ -389,11 +389,12 @@ def __init__(self, width=10, height=10): self.width = width self.height = height self.observers = [] - #Sets iteration start and end (no walls). - self.x_start,self.y_start = (0,0) - self.x_end,self.y_end = (self.width, self.height) + # Sets iteration start and end (no walls). + self.x_start, self.y_start = (0, 0) + self.x_end, self.y_end = (self.width, self.height) perceptible_distance = 1 + def things_near(self, location, radius=None): "Return all things within radius of location." if radius is None: @@ -447,7 +448,7 @@ def move_to(self, thing, destination): # for obs in self.observers: # obs.thing_added(thing) - def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items = False): + def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): '''Adds things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class''' @@ -462,7 +463,7 @@ def is_inbounds(self, location): x,y = location return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end) - def random_location_inbounds(self, exclude = None): + def random_location_inbounds(self, exclude=None): '''Returns a random location that is inbounds (within walls if we have walls)''' location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) if exclude is not None: @@ -486,14 +487,14 @@ def add_walls(self): '''Put walls around the entire perimeter of the grid.''' for x in range(self.width): self.add_thing(Wall(), (x, 0)) - self.add_thing(Wall(), (x, self.height-1)) + self.add_thing(Wall(), (x, self.height - 1)) for y in range(self.height): self.add_thing(Wall(), (0, y)) - self.add_thing(Wall(), (self.width-1, y)) + self.add_thing(Wall(), (self.width - 1, y)) - #Updates iteration start and end (with walls). - self.x_start,self.y_start = (1,1) - self.x_end,self.y_end = (self.width-1, self.height-1) + # Updates iteration start and end (with walls). + self.x_start, self.y_start = (1, 1) + self.x_end, self.y_end = (self.width - 1, self.height - 1) def add_observer(self, observer): """Adds an observer to the list of observers. @@ -662,6 +663,7 @@ class Wumpus(Agent): class Stench(Thing): pass + class Explorer(Agent): holding = [] has_arrow = True @@ -674,8 +676,9 @@ def can_grab(self, thing): class WumpusEnvironment(XYEnvironment): - pit_probability = 0.2 #Probability to spawn a pit in a location. (From Chapter 7.2) - #Room should be 4x4 grid of rooms. The extra 2 for walls + pit_probability = 0.2 # Probability to spawn a pit in a location. (From Chapter 7.2) + # Room should be 4x4 grid of rooms. The extra 2 for walls + def __init__(self, agent_program, width=6, height=6): super(WumpusEnvironment, self).__init__(width, height) self.init_world(agent_program) @@ -690,14 +693,14 @@ def init_world(self, program): for x in range(self.x_start, self.x_end): for y in range(self.y_start, self.y_end): if random.random() < self.pit_probability: - self.add_thing(Pit(), (x,y), True) - self.add_thing(Breeze(), (x - 1,y), True) - self.add_thing(Breeze(), (x,y - 1), True) - self.add_thing(Breeze(), (x + 1,y), True) - self.add_thing(Breeze(), (x,y + 1), True) + self.add_thing(Pit(), (x, y), True) + self.add_thing(Breeze(), (x - 1, y), True) + self.add_thing(Breeze(), (x, y - 1), True) + self.add_thing(Breeze(), (x + 1, y), True) + self.add_thing(Breeze(), (x, y + 1), True) "WUMPUS" - w_x, w_y = self.random_location_inbounds(exclude = (1,1)) + w_x, w_y = self.random_location_inbounds(exclude=(1, 1)) self.add_thing(Wumpus(lambda x: ""), (w_x, w_y), True) self.add_thing(Stench(), (w_x - 1, w_y), True) self.add_thing(Stench(), (w_x + 1, w_y), True) @@ -705,32 +708,31 @@ def init_world(self, program): self.add_thing(Stench(), (w_x, w_y + 1), True) "GOLD" - self.add_thing(Gold(), self.random_location_inbounds(exclude = (1,1)), True) + self.add_thing(Gold(), self.random_location_inbounds(exclude=(1, 1)), True) #self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier "AGENT" - self.add_thing(Explorer(program), (1,1), True) + self.add_thing(Explorer(program), (1, 1), True) - def get_world(self, show_walls = True): + def get_world(self, show_walls=True): '''returns the items in the world''' result = [] - x_start,y_start = (0,0) if show_walls else (1,1) - x_end,y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) + x_start, y_start = (0, 0) if show_walls else (1, 1) + x_end, y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) for x in range(x_start, x_end): row = [] for y in range(y_start, y_end): - row.append(self.list_things_at((x,y))) + row.append(self.list_things_at((x, y))) result.append(row) return result - def percepts_from(self, agent, location, tclass = Thing): + def percepts_from(self, agent, location, tclass=Thing): '''Returns percepts from a given location, and replaces some items with percepts from chapter 7.''' thing_percepts = { Gold: Glitter(), Wall: Bump(), Wumpus: Stench(), - Pit: Breeze() - } + Pit: Breeze()} '''Agents don't need to get their percepts''' thing_percepts[agent.__class__] = None @@ -740,19 +742,19 @@ def percepts_from(self, agent, location, tclass = Thing): result = [thing_percepts.get(thing.__class__, thing) for thing in self.things - if thing.location == location and isinstance(thing, tclass)] + if thing.location == location and isinstance(thing, tclass)] return result if len(result) else [None] def percept(self, agent): '''Returns things in adjacent (not diagonal) cells of the agent. Result format: [Left, Right, Up, Down, Center / Current location]''' - x,y = agent.location + x, y = agent.location result = [] - result.append(self.percepts_from(agent, (x - 1,y))) - result.append(self.percepts_from(agent, (x + 1,y))) - result.append(self.percepts_from(agent, (x,y - 1))) - result.append(self.percepts_from(agent, (x,y + 1))) - result.append(self.percepts_from(agent, (x,y))) + result.append(self.percepts_from(agent, (x - 1, y))) + result.append(self.percepts_from(agent, (x + 1, y))) + result.append(self.percepts_from(agent, (x, y - 1))) + result.append(self.percepts_from(agent, (x, y + 1))) + result.append(self.percepts_from(agent, (x, y))) '''The wumpus gives out a a loud scream once it's killed.''' wumpus = [thing for thing in self.things if isinstance(thing, Wumpus)] @@ -781,14 +783,14 @@ def execute_action(self, agent, action): agent.performance -= 1 elif action == 'Grab': things = [thing for thing in self.list_things_at(agent.location) - if agent.can_grab(thing)] + if agent.can_grab(thing)] if len(things): print("Grabbing", things[0].__class__.__name__) if len(things): agent.holding.append(things[0]) agent.performance -= 1 elif action == 'Climb': - if agent.location == (1,1): #Agent can only climb out of (1,1) + if agent.location == (1, 1): # Agent can only climb out of (1,1) agent.performance += 1000 if Gold() in agent.holding else 0 self.delete_thing(agent) elif action == 'Shoot': @@ -831,6 +833,7 @@ def is_done(self): #Almost done. Arrow needs to be implemented # ______________________________________________________________________________ + def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000): """See how well each of several agents do in n instances of an environment. Pass in a factory (constructor) for environments, and several for agents. diff --git a/canvas.py b/canvas.py index 8133babfd..4ad780380 100644 --- a/canvas.py +++ b/canvas.py @@ -36,7 +36,7 @@ def mouse_move(self, x, y): def execute(self, exec_str): "Stores the command to be exectued to a list which is used later during update()" if not isinstance(exec_str, str): - print("Invalid execution argument:",exec_str) + print("Invalid execution argument:", exec_str) self.alert("Recieved invalid execution command format") prefix = "{0}_canvas_object.".format(self.id) self.exec_list.append(prefix + exec_str + ';') @@ -98,14 +98,14 @@ def font(self, font): "Changes the font of text" self.execute('font("{0}")'.format(font)) - def text(self, txt, x, y, fill = True): + def text(self, txt, x, y, fill=True): "Display a text at (x, y)" if fill: self.execute('fill_text("{0}", {1}, {2})'.format(txt, x, y)) else: self.execute('stroke_text("{0}", {1}, {2})'.format(txt, x, y)) - def text_n(self, txt, xn, yn, fill = True): + def text_n(self, txt, xn, yn, fill=True): "Similar to text(), but with normalized coordinates" x = round(xn * self.width) y = round(yn * self.height) @@ -117,6 +117,6 @@ def alert(self, message): def update(self): "Execute the JS code to execute the commands queued by execute()" - exec_code = "" + exec_code = "" self.exec_list = [] display(HTML(exec_code)) diff --git a/csp.py b/csp.py index 902fd58a0..f300cb816 100644 --- a/csp.py +++ b/csp.py @@ -481,7 +481,7 @@ def display(self, assignment): for var in range(n): if assignment.get(var, '') == val: ch = 'Q' - elif (var+val) % 2 == 0: + elif (var + val) % 2 == 0: ch = '.' else: ch = '-' @@ -492,7 +492,7 @@ def display(self, assignment): ch = '*' else: ch = ' ' - print(str(self.nconflicts(var, val, assignment))+ch, end=' ') + print(str(self.nconflicts(var, val, assignment)) + ch, end=' ') print() # ______________________________________________________________________________ diff --git a/games.py b/games.py index 2fb78ecd3..90604bf69 100644 --- a/games.py +++ b/games.py @@ -96,7 +96,7 @@ def max_value(state, alpha, beta, depth): v = -infinity for a in game.actions(state): v = max(v, min_value(game.result(state, a), - alpha, beta, depth+1)) + alpha, beta, depth + 1)) if v >= beta: return v alpha = max(alpha, v) @@ -108,7 +108,7 @@ def min_value(state, alpha, beta, depth): v = infinity for a in game.actions(state): v = min(v, max_value(game.result(state, a), - alpha, beta, depth+1)) + alpha, beta, depth + 1)) if v <= alpha: return v beta = min(beta, v) @@ -245,8 +245,8 @@ def __init__(self, h=3, v=3, k=3): self.h = h self.v = v self.k = k - moves = [(x, y) for x in range(1, h+1) - for y in range(1, v+1)] + moves = [(x, y) for x in range(1, h + 1) + for y in range(1, v + 1)] self.initial = GameState(to_move='X', utility=0, board={}, moves=moves) def actions(self, state): @@ -274,8 +274,8 @@ def terminal_test(self, state): def display(self, state): board = state.board - for x in range(1, self.h+1): - for y in range(1, self.v+1): + for x in range(1, self.h + 1): + for y in range(1, self.v + 1): print(board.get((x, y), '.'), end=' ') print() @@ -315,7 +315,7 @@ def __init__(self, h=7, v=6, k=4): def actions(self, state): return [(x, y) for (x, y) in state.moves - if y == 1 or (x, y-1) in state.board] + if y == 1 or (x, y - 1) in state.board] class Canvas_TicTacToe(Canvas): @@ -374,7 +374,7 @@ def draw_board(self): if utility == 0: self.text_n('Game Draw!', 0.1, 0.1) else: - self.text_n('Player {} wins!'.format(1 if utility>0 else 2), 0.1, 0.1) + self.text_n('Player {} wins!'.format(1 if utility > 0 else 2), 0.1, 0.1) else: # Print which player's turn it is self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1) diff --git a/learning.py b/learning.py index ca953ae0a..963f2dc44 100644 --- a/learning.py +++ b/learning.py @@ -555,7 +555,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): o_units = len(o_nodes) err = [t_val[i] - o_nodes[i].value for i in range(o_units)] - delta[-1] = [(o_nodes[i].value)*(1 - o_nodes[i].value) * + delta[-1] = [(o_nodes[i].value) * (1 - o_nodes[i].value) * (err[i]) for i in range(o_units)] # Backward pass @@ -620,7 +620,7 @@ def predict(example): def Linearlearner(dataset, learning_rate=0.01, epochs=100): """Define with learner = Linearlearner(data); infer with learner(x).""" idx_i = dataset.inputs - idx_t = dataset.target # As of now, dataset.target gives only one index. + idx_t = dataset.target # As of now, dataset.target gives only one index. examples = dataset.examples # X transpose @@ -794,7 +794,7 @@ def cross_validation(learner, size, dataset, k=10, trials=1): k=10, trials=1) trial_errT += errT trial_errV += errV - return trial_errT/trials, trial_errV/trials + return trial_errT / trials, trial_errV / trials else: fold_errT = 0 fold_errV = 0 @@ -802,15 +802,15 @@ def cross_validation(learner, size, dataset, k=10, trials=1): examples = dataset.examples for fold in range(k): random.shuffle(dataset.examples) - train_data, val_data = train_and_test(dataset, fold * (n/k), - (fold + 1) * (n/k)) + train_data, val_data = train_and_test(dataset, fold * (n / k), + (fold + 1) * (n / k)) dataset.examples = train_data h = learner(dataset, size) fold_errT += test(h, dataset, train_data) fold_errV += test(h, dataset, val_data) # Reverting back to original once test is completed dataset.examples = examples - return fold_errT/k, fold_errV/k + return fold_errT / k, fold_errV / k def cross_validation_wrapper(learner, dataset, k=10, trials=1): diff --git a/logic.py b/logic.py index a4346a5ed..8b5e8bf8e 100644 --- a/logic.py +++ b/logic.py @@ -280,7 +280,7 @@ def pl_true(exp, model={}): return None if op == '<=>': return pt == qt - elif op == '^': # xor or 'not equivalent' + elif op == '^': # xor or 'not equivalent' return pt != qt else: raise ValueError("illegal operator in logic expression" + str(exp)) @@ -722,7 +722,7 @@ def translate_to_SAT(init, transition, goal, time): clauses.append(associate('|', [state_sym[s, t] for s in states])) for s in states: - for s_ in states[states.index(s)+1:]: + for s_ in states[states.index(s) + 1:]: # for each pair of states s, s_ only one is possible at time t clauses.append((~state_sym[s, t]) | (~state_sym[s_, t])) @@ -745,7 +745,7 @@ def translate_to_SAT(init, transition, goal, time): def extract_solution(model): true_transitions = [t for t in action_sym if model[action_sym[t]]] # Sort transitions based on time, which is the 3rd element of the tuple - true_transitions.sort(key = lambda x: x[2]) + true_transitions.sort(key=lambda x: x[2]) return [action for s, action, time in true_transitions] # Body of SAT_plan algorithm @@ -904,18 +904,18 @@ def fetch_rules_for_goal(self, goal): test_kb = FolKB( map(expr, ['Farmer(Mac)', - 'Rabbit(Pete)', - 'Mother(MrsMac, Mac)', - 'Mother(MrsRabbit, Pete)', - '(Rabbit(r) & Farmer(f)) ==> Hates(f, r)', - '(Mother(m, c)) ==> Loves(m, c)', - '(Mother(m, r) & Rabbit(r)) ==> Rabbit(m)', - '(Farmer(f)) ==> Human(f)', - # Note that this order of conjuncts - # would result in infinite recursion: - # '(Human(h) & Mother(m, h)) ==> Human(m)' - '(Mother(m, h) & Human(h)) ==> Human(m)' - ])) + 'Rabbit(Pete)', + 'Mother(MrsMac, Mac)', + 'Mother(MrsRabbit, Pete)', + '(Rabbit(r) & Farmer(f)) ==> Hates(f, r)', + '(Mother(m, c)) ==> Loves(m, c)', + '(Mother(m, r) & Rabbit(r)) ==> Rabbit(m)', + '(Farmer(f)) ==> Human(f)', + # Note that this order of conjuncts + # would result in infinite recursion: + # '(Human(h) & Mother(m, h)) ==> Human(m)' + '(Mother(m, h) & Human(h)) ==> Human(m)' + ])) crime_kb = FolKB( map(expr, @@ -982,7 +982,7 @@ def diff(y, x): elif op == '*': return u * diff(v, x) + v * diff(u, x) elif op == '/': - return (v*diff(u, x) - u*diff(v, x)) / (v * v) + return (v * diff(u, x) - u * diff(v, x)) / (v * v) elif op == '**' and isnumber(x.op): return (v * u ** (v - 1) * diff(u, x)) elif op == '**': diff --git a/planning.py b/planning.py index 9e52c839e..60247a7bc 100644 --- a/planning.py +++ b/planning.py @@ -4,6 +4,7 @@ from utils import Expr, expr, first from logic import FolKB + class PDLL: """ PDLL used to deine a search problem @@ -47,7 +48,7 @@ class Action: eat = Action(expr("Eat(person, food)"), [precond_pos, precond_neg], [effect_add, effect_rem]) """ - def __init__(self,action , precond, effect): + def __init__(self, action, precond, effect): self.name = action.op self.args = action.args self.precond_pos = precond[0] @@ -60,16 +61,16 @@ def __call__(self, kb, args): def substitute(self, e, args): """Replaces variables in expression with their respective Propostional symbol""" - new_args = [args[i] for x in e.args for i in range(len(self.args)) if self.args[i]==x] + new_args = [args[i] for x in e.args for i in range(len(self.args)) if self.args[i] == x] return Expr(e.op, *new_args) def check_precond(self, kb, args): """Checks if the precondition is satisfied in the current state""" - #check for positive clauses + # check for positive clauses for clause in self.precond_pos: if self.substitute(clause, args) not in kb.clauses: return False - #check for negative clauses + # check for negative clauses for clause in self.precond_neg: if self.substitute(clause, args) in kb.clauses: return False @@ -77,13 +78,13 @@ def check_precond(self, kb, args): def act(self, kb, args): """Executes the action on the state's kb""" - #check if the preconditions are satisfied + # check if the preconditions are satisfied if not self.check_precond(kb, args): raise Exception("Action pre-conditions not satisfied") - #remove negative literals + # remove negative literals for clause in self.effect_rem: kb.retract(self.substitute(clause, args)) - #add positive literals + # add positive literals for clause in self.effect_add: kb.tell(self.substitute(clause, args)) @@ -99,7 +100,7 @@ def air_cargo(): expr('Plane(P2)'), expr('Airport(JFK)'), expr('Airport(SFO)')] - + def goal_test(kb): required = [expr('At(C1 , JFK)'), expr('At(C2 ,SFO)')] for q in required: @@ -131,4 +132,3 @@ def goal_test(kb): fly = Action(expr("Fly(p, f, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [load, unload, fly], goal_test) - diff --git a/probability.py b/probability.py index 98add0b2b..944ea0ba5 100644 --- a/probability.py +++ b/probability.py @@ -632,16 +632,16 @@ def particle_filtering(e, N, HMM): for i in range(N): if s[i] == 'A': # P(U|A)*P(A) - w_i = HMM.sensor_dist(e)[0]*dist[0] + w_i = HMM.sensor_dist(e)[0] * dist[0] if s[i] == 'B': # P(U|B)*P(B) - w_i = HMM.sensor_dist(e)[1]*dist[1] + w_i = HMM.sensor_dist(e)[1] * dist[1] w[i] = w_i w_tot += w_i # Normalize all the weights for i in range(N): - w[i] = w[i]/w_tot + w[i] = w[i] / w_tot # Limit weights to 4 digits for i in range(N): diff --git a/rl.py b/rl.py index c819d0fc3..97bb313a0 100644 --- a/rl.py +++ b/rl.py @@ -30,7 +30,7 @@ def T(self, s, a): def __init__(self, pi, mdp): self.pi = pi self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist, - mdp.terminals, mdp.gamma, mdp.states) + mdp.terminals, mdp.gamma, mdp.states) self.U = {} self.Nsa = defaultdict(int) self.Ns1_sa = defaultdict(int) @@ -50,7 +50,7 @@ def __call__(self, percept): Ns1_sa[(s1, s, a)] += 1 # for each t such that Ns′|sa [t, s, a] is nonzero for t in [res for (res, state, act), freq in Ns1_sa.items() - if (state, act) == (s, a) and freq != 0]: + if (state, act) == (s, a) and freq != 0]: P[(s, a)][t] = Ns1_sa[(t, s, a)] / Nsa[(s, a)] U = policy_evaluation(pi, U, mdp) diff --git a/search.py b/search.py index 1124a66c2..12a723662 100644 --- a/search.py +++ b/search.py @@ -283,7 +283,7 @@ def recursive_dls(node, problem, limit): else: cutoff_occurred = False for child in node.expand(problem): - result = recursive_dls(child, problem, limit-1) + result = recursive_dls(child, problem, limit - 1) if result == 'cutoff': cutoff_occurred = True elif result is not None: @@ -384,7 +384,7 @@ def simulated_annealing(problem, schedule=exp_schedule()): return current next = random.choice(neighbors) delta_e = problem.value(next.state) - problem.value(current.state) - if delta_e > 0 or probability(math.exp(delta_e/T)): + if delta_e > 0 or probability(math.exp(delta_e / T)): current = next @@ -471,6 +471,7 @@ def update_state(self, percept): # ______________________________________________________________________________ + class OnlineSearchProblem(Problem): """ A problem which is solved by an agent executing @@ -757,20 +758,20 @@ def distance_to_node(n): One-dimensional state space Graph """ one_dim_state_space = Graph(dict( - State_1 = dict(Right = 'State_2'), - State_2 = dict(Right = 'State_3', Left = 'State_1'), - State_3 = dict(Right = 'State_4', Left = 'State_2'), - State_4 = dict(Right = 'State_5', Left = 'State_3'), - State_5 = dict(Right = 'State_6', Left = 'State_4'), - State_6 = dict(Left = 'State_5') + State_1=dict(Right='State_2'), + State_2=dict(Right='State_3', Left='State_1'), + State_3=dict(Right='State_4', Left='State_2'), + State_4=dict(Right='State_5', Left='State_3'), + State_5=dict(Right='State_6', Left='State_4'), + State_6=dict(Left='State_5') )) one_dim_state_space.least_costs = dict( - State_1 = 8, - State_2 = 9, - State_3 = 2, - State_4 = 2, - State_5 = 4, - State_6 = 3) + State_1=8, + State_2=9, + State_3=2, + State_4=2, + State_5=4, + State_6=3) """ [Figure 6.1] Principal states and territories of Australia @@ -812,6 +813,7 @@ def h(self, node): else: return infinity + class GraphProblemStochastic(GraphProblem): """ A version of GraphProblem where an action can lead to @@ -871,8 +873,8 @@ def conflict(self, row1, col1, row2, col2): "Would putting two queens in (row1, col1) and (row2, col2) conflict?" return (row1 == row2 or # same row col1 == col2 or # same column - row1-col1 == row2-col2 or # same \ diagonal - row1+col1 == row2+col2) # same / diagonal + row1 - col1 == row2 - col2 or # same \ diagonal + row1 + col1 == row2 + col2) # same / diagonal def goal_test(self, state): "Check if all columns filled, no conflicts." @@ -896,7 +898,7 @@ def goal_test(self, state): def random_boggle(n=4): """Return a random Boggle board of size n x n. We represent a board as a linear list of letters.""" - cubes = [cubes16[i % 16] for i in range(n*n)] + cubes = [cubes16[i % 16] for i in range(n * n)] random.shuffle(cubes) return list(map(random.choice, cubes)) diff --git a/text.py b/text.py index 6763031b4..39bbb921f 100644 --- a/text.py +++ b/text.py @@ -54,9 +54,9 @@ def add_sequence(self, words): """Add each of the tuple words[i:i+n], using a sliding window. Prefix some copies of the empty word, '', to make the start work.""" n = self.n - words = ['', ] * (n-1) + words - for i in range(len(words)-n): - self.add(tuple(words[i:i+n])) + words = ['', ] * (n - 1) + words + for i in range(len(words) - n): + self.add(tuple(words[i:i + n])) def samples(self, nwords): """Build up a random sample of text nwords words long, using @@ -92,7 +92,7 @@ def viterbi_segment(text, P): words[i] = w # Now recover the sequence of best words sequence = [] - i = len(words)-1 + i = len(words) - 1 while i > 0: sequence[0:0] = [words[i]] i = i - len(words[i]) @@ -198,6 +198,7 @@ def __init__(self, title, url, nwords): self.url = url self.nwords = nwords + def words(text, reg=re.compile('[a-z0-9]+')): """Return a list of the words in text, ignoring punctuation and converting everything to lowercase (to canonicalize). @@ -276,7 +277,7 @@ def bigrams(text): >>> bigrams(['this', 'is', 'a', 'test']) [['this', 'is'], ['is', 'a'], ['a', 'test']] """ - return [text[i:i+2] for i in range(len(text) - 1)] + return [text[i:i + 2] for i in range(len(text) - 1)] # Decoding a Shift (or Caesar) Cipher @@ -369,6 +370,3 @@ def actions(self, state): def goal_test(self, state): "We're done when we get all 26 letters assigned." return len(state) >= 26 - - - diff --git a/utils.py b/utils.py index 81b01748a..9b7c47707 100644 --- a/utils.py +++ b/utils.py @@ -18,6 +18,7 @@ def sequence(iterable): return (iterable if isinstance(iterable, collections.abc.Sequence) else tuple(iterable)) + def removeall(item, seq): """Return a copy of seq (or string) with all occurences of item removed.""" if isinstance(seq, str): @@ -25,14 +26,17 @@ def removeall(item, seq): else: return [x for x in seq if x != item] -def unique(seq): # TODO: replace with set + +def unique(seq): # TODO: replace with set """Remove duplicate elements from seq. Assumes hashable elements.""" return list(set(seq)) + def count(seq): """Count the number of items in sequence that are interpreted as true.""" return sum(bool(x) for x in seq) + def product(numbers): """Return the product of the numbers, e.g. product([2, 3, 10]) == 60""" result = 1 @@ -40,6 +44,7 @@ def product(numbers): result *= x return result + def first(iterable, default=None): "Return the first element of an iterable or the next element of a generator; or default." try: @@ -49,6 +54,7 @@ def first(iterable, default=None): except TypeError: return next(iterable, default) + def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) @@ -61,14 +67,17 @@ def is_in(elt, seq): argmin = min argmax = max + def argmin_random_tie(seq, key=identity): """Return a minimum element of seq; break ties at random.""" return argmin(shuffled(seq), key=key) + def argmax_random_tie(seq, key=identity): "Return an element with highest fn(seq[i]) score; break ties at random." return argmax(shuffled(seq), key=key) + def shuffled(iterable): "Randomly shuffle a copy of iterable." items = list(iterable) @@ -137,6 +146,7 @@ def _mat_mult(X_M, Y_M): return result + def vector_to_diagonal(v): """Converts a vector to a diagonal matrix with vector elements as the diagonal elements of the matrix""" @@ -146,18 +156,22 @@ def vector_to_diagonal(v): return diag_matrix + def vector_add(a, b): """Component-wise addition of two vectors.""" return tuple(map(operator.add, a, b)) + def scalar_vector_product(X, Y): """Return vector as a product of a scalar and a vector""" - return [X*y for y in Y] + return [X * y for y in Y] + def scalar_matrix_product(X, Y): return [scalar_vector_product(X, y) for y in Y] + def inverse_matrix(X): """Inverse a given square matrix of size 2x2""" assert len(X) == 2 @@ -191,6 +205,7 @@ def weighted_sampler(seq, weights): return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))] + def rounder(numbers, d=4): "Round a single number, or sequence of numbers, to d decimal places." if isinstance(numbers, (int, float)): @@ -199,6 +214,7 @@ def rounder(numbers, d=4): constructor = type(numbers) # Can be list, set, tuple, etc. return constructor(rounder(n, d) for n in numbers) + def num_or_str(x): """The argument is a string; convert to a number if possible, or strip it. @@ -211,6 +227,7 @@ def num_or_str(x): except ValueError: return str(x).strip() + def normalize(numbers): """Multiply each number by a constant such that the sum is 1.0""" total = float(sum(numbers)) @@ -236,7 +253,7 @@ def step(x): except ImportError: def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): "Return true if numbers a and b are close to each other." - return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) + return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) # ______________________________________________________________________________ # Misc Functions @@ -274,6 +291,7 @@ def name(obj): getattr(getattr(obj, '__class__', 0), '__name__', 0) or str(obj)) + def isnumber(x): "Is x a number?" return hasattr(x, '__int__') @@ -356,8 +374,8 @@ def __matmul__(self, rhs): return Expr('@', self, rhs) def __or__(self, rhs): "Allow both P | Q, and P |'==>'| Q." - if isinstance(rhs, Expression) : - return Expr('|', self, rhs) + if isinstance(rhs, Expression): + return Expr('|', self, rhs) else: return PartialExpr(rhs, self) @@ -394,7 +412,7 @@ def __eq__(self, other): def __hash__(self): return hash(self.op) ^ hash(self.args) def __repr__(self): - op = self.op + op = self.op args = [str(arg) for arg in self.args] if op.isidentifier(): # f(x) or f(x, y) return '{}({})'.format(op, ', '.join(args)) if args else op @@ -407,17 +425,20 @@ def __repr__(self): # An 'Expression' is either an Expr or a Number. # Symbol is not an explicit type; it is any Expr with 0 args. -Number = (int, float, complex) +Number = (int, float, complex) Expression = (Expr, Number) + def Symbol(name): "A Symbol is just an Expr with no args." return Expr(name) + def symbols(names): "Return a tuple of Symbols; names is a comma/whitespace delimited str." return tuple(Symbol(name) for name in names.replace(',', ' ').split()) + def subexpressions(x): "Yield the subexpressions of an Expression (including x itself)." yield x @@ -425,21 +446,24 @@ def subexpressions(x): for arg in x.args: yield from subexpressions(arg) + def arity(expression): "The number of sub-expressions in this expression." if isinstance(expression, Expr): return len(expression.args) - else: # expression is a number + else: # expression is a number return 0 # For operators that are not defined in Python, we allow new InfixOps: + class PartialExpr: """Given 'P |'==>'| Q, first form PartialExpr('==>', P), then combine with Q.""" def __init__(self, op, lhs): self.op, self.lhs = op, lhs def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) def __repr__(self): return "PartialExpr('{}', {})".format(self.op, self.lhs) + def expr(x): """Shortcut to create an Expression. x is a str in which: - identifiers are automatically defined as Symbols. @@ -455,6 +479,7 @@ def expr(x): infix_ops = '==> <== <=>'.split() + def expr_handle_infix_ops(x): """Given a str, return a new str with ==> replaced by |'==>'|, etc. >>> expr_handle_infix_ops('P ==> Q') @@ -464,6 +489,7 @@ def expr_handle_infix_ops(x): x = x.replace(op, '|' + repr(op) + '|') return x + class defaultkeydict(collections.defaultdict): """Like defaultdict, but the default_factory is a function of the key. >>> d = defaultkeydict(len); d['four'] @@ -529,7 +555,7 @@ def extend(self, items): def pop(self): e = self.A[self.start] self.start += 1 - if self.start > 5 and self.start > len(self.A)/2: + if self.start > 5 and self.start > len(self.A) / 2: self.A = self.A[self.start:] self.start = 0 return e From 08c86c807d72cf55279bf9ca2527328c1d1ceb2e Mon Sep 17 00:00:00 2001 From: SnShine Date: Tue, 21 Jun 2016 14:18:23 +0530 Subject: [PATCH 329/513] adds intro and removes views' output in search notebook --- search.ipynb | 1186 +++----------------------------------------------- 1 file changed, 51 insertions(+), 1135 deletions(-) diff --git a/search.ipynb b/search.ipynb index 5446e9711..9a9d49826 100644 --- a/search.ipynb +++ b/search.ipynb @@ -6,56 +6,87 @@ "collapsed": true }, "source": [ - "# The search.py module" + "# Solving problems by Searching\n", + "\n", + "This notebook serves as supporting material for topics covered in **Chapter 3 - Solving Problems by Searching** and **Chapter 4 - Beyond Classical Search** from the book *Artificial Intelligence: A Modern Approach.* This notebook uses implementations from [search.py](https://github.com/aimacode/aima-python/blob/master/search.py) module. Let's start by importing everything from search module." ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ - "Introduction\n", - "============\n", - "\n", - "Hello!\n", - "In this IPython notebook, we'll study different kinds of search techniques used in [ search.py ]( https://github.com/aimacode/aima-python/blob/master/search.py ) and try to get an intuition of what search algorithms are best suited for various problems. We first explore uninformed search algorithms and later get our hands on heuristic search strategies.\n", - "\n", - "The code in this IPython notebook, and the entire `aima-python` repository is intended to work with Python 3 (specifically, Python 3.4). So if you happen to be working on Python 2, you should switch to Python 3. For more help on how to install python3, or if you are having other problems, you can always have a look the `intro` IPython notebook. \n", - "\n", - "Now that you have all that sorted out, let's get started!" + "from search import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Uninformed Search Strategies" + "## Review\n", + "\n", + "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular navigation problem / route finding problem. First, we will start the problem solving by precicly defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", + "\n", + "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information aboout the problem other than its definition.\n", + "* Examples:\n", + " 1. Breadth First Search\n", + " 2. Depth First Search\n", + " 3. Depth Limited Search\n", + " 4. Iterative Deepening Search\n", + "\n", + "\n", + "* **Informed search algorithms**: These type of algorithms leverage any information (hueristics, path cost) on the problem to search through the search space to find the solution efficiently.\n", + "* Examples:\n", + " 1. Best First Search\n", + " 2. Uniform Cost Search\n", + " 3. A\\* Search\n", + " 4. Recursive Best First Search\n", + "\n", + "*In the end of this notebook, you can see how different searching algorithms solves the route finding problem defined on romania map.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Uninformed Search strategies are called `blind search`. In such search strategies, the only information we have about any state is generated by checking if a piece of data, or any of its successors, matches our `goal state` or not. THAT'S IT. NOTHING MORE. (Well ....not really. See the `value` method defined in the following section).\n", + "## Problem\n", "\n", - "First let's formulate the problem we intend to solve. So let's import everything from our module." + "Let's see how we define a Problem. Run the next cell to see how abstract class `Problem` is defined in the search module." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "from search import *" + "%psource Problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The search and other modules of the repository make use of several imports from the utils module. We will point the useful ones out if they are required to follow the material below. Don't worry. You don't need to read utils.py in order to understand search algorithms.\n", + "sdc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uninformed Search Strategies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", " \n", "The `Problem` class is an abstract class on which we define our problems(*duh*).\n", "Again, if you are confused about what `abstract class` means have a look at the `Intro` notebook.\n", @@ -925,7 +956,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## A* search\n", + "## A* search\n", "\n", "Let's change all the node_colors to starting position and define a different problem statement." ] @@ -1094,1122 +1125,7 @@ "version": "3.5.1" }, "widgets": { - "state": { - "00047d7c78734f529da7a72c8d8f089a": { - "views": [] - }, - "005958e8932245a480c9ac89f2a9864a": { - "views": [ - { - "cell_index": 43 - } - ] - }, - "02e92e1759b548babcaf598128415a01": { - "views": [] - }, - "042d4aa9ad8a4221ab693932649bdb47": { - 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"execution_count": 1, + "execution_count": null, "metadata": { "collapsed": true }, @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false }, @@ -60,22 +60,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "['R', 'G', 'B']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s = UniversalDict(['R','G','B'])\n", "s[5]" @@ -90,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": true }, @@ -108,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": true }, @@ -126,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": true }, @@ -137,28 +126,71 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "(,\n", - " ,\n", - " )" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "australia, usa, france" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NQueens\n", + "\n", + "The N-queens puzzle is the problem of placing N chess queens on a N×N chessboard so that no two queens threaten each other. Here N is a natural number. Like the graph coloring, problem NQueens is also implemented in the csp module. The **NQueensCSP** class inherits from the **CSP** class. It makes some modifications in the methods to suit the particular problem. The queens are assumed to be placed one per column, from left to right. That means position (x, y) represents (var, val) in the CSP. The constraint that needs to be passed on the CSP is defined in the **queen_constraint** function. The constraint is satisfied (true) if A, B are really the same variable, or if they are not in the same row, down diagonal, or up diagonal. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource queen_constraint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **NQueensCSP** method implements methods that support solving the problem via **min_conflicts** which is one of the techniques for solving CSPs. Because **min_conflicts** hill climbs the number of conflicts to solve the CSP **assign** and **unassign** are modified to record conflicts. More details about the structures **rows**, **downs**, **ups** which help in recording conflicts are explained in the docstring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource NQueensCSP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The _ ___init___ _ method takes only one parameter **n** the size of the problem. To create an instance we just pass the required n into the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "eight_queens = NQueensCSP(8)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -170,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": true }, @@ -201,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": true }, @@ -221,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": true }, @@ -261,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": true }, @@ -272,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, @@ -292,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": true }, @@ -303,42 +335,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 'R',\n", - " 1: 'R',\n", - " 2: 'R',\n", - " 3: 'R',\n", - " 4: 'G',\n", - " 5: 'R',\n", - " 6: 'G',\n", - " 7: 'R',\n", - " 8: 'B',\n", - " 9: 'R',\n", - " 10: 'G',\n", - " 11: 'B',\n", - " 12: 'G',\n", - " 13: 'G',\n", - " 14: 'Y',\n", - " 15: 'Y',\n", - " 16: 'B',\n", - " 17: 'B',\n", - " 18: 'B',\n", - " 19: 'G',\n", - " 20: 'B'}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result # A dictonary of assingments." ] @@ -352,22 +353,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "21" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "coloring_problem1.nassigns" ] @@ -381,22 +371,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "21" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(coloring_problem1.assingment_history)" ] @@ -412,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": true }, @@ -434,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": true }, @@ -497,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": true }, @@ -515,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": true }, @@ -533,22 +512,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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MmJF11123rGtDU/nHP/6Rrl27ZubMmU364J8pU6bkoIMOyqRJk9KlS5cmu24l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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display\n", @@ -577,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": true }, @@ -647,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "collapsed": true }, @@ -660,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": true }, @@ -678,22 +646,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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iIiIOKDBFREQcUGCKiIg4oMAUERFx4CsfXLBtV+tQ1eFY6ayB+ebsgaa1ciYW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\nRBxQYIqIiDigwBQREXFAgSkiIuLA/wGx9HtR0bJVGAAAAABJRU5ErkJggg==\n", 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Visualization\n", + "Now let us explore the optional keyword arguments that the **backtracking_search** function takes. These optional arguments help speed up the assignment further. Along with these, we will also point out to methods in the CSP class that help make this work. \n", + "\n", + "The first of these is **select_unassigned_variable**. It takes in a function that helps in deciding the order in which variables will be selected for assignment. We use a heuristic called Most Restricted Variable which is implemented by the function **mrv**. The idea behind **mrv** is to choose the variable with the fewest legal values left in its domain. The intuition behind selecting the **mrv** or the most constrained variable is that it allows us to encounter failure quickly before going too deep into a tree if we have selected a wrong step before. The **mrv** implementation makes use of another function **num_legal_values** to sort out the variables by a number of legal values left in its domain. This function, in turn, calls the **nconflicts** method of the **CSP** to return such values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource mrv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource num_legal_values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource CSP.nconflicts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another ordering related parameter **order_domain_values** governs the value ordering. Here we select the Least Constraining Value which is implemented by the function **lcv**. The idea is to select the value which rules out the fewest values in the remaining variables. The intuition behind selecting the **lcv** is that it leaves a lot of freedom to assign values later. The idea behind selecting the mrc and lcv makes sense because we need to do all variables but for values, we might better try the ones that are likely. So for vars, we face the hard ones first.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource lcv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the third parameter **inference** can make use of one of the two techniques called Arc Consistency or Forward Checking. The details of these methods can be found in the **Section 6.3.2** of the book. In short the idea of inference is to detect the possible failure before it occurs and to look ahead to not make mistakes. **mac** and **forward_checking** implement these two techniques. The **CSP** methods **support_pruning**, **suppose**, **prune**, **choices**, **infer_assignment** and **restore** help in using these techniques. You can know more about these by looking up the source code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us compare the performance with these parameters enabled vs the default parameters. We will use the Graph Coloring problem instance usa for comparison. We will call the instances **solve_simple** and **solve_parameters** and solve them using backtracking and compare the number of assignments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "solve_simple = copy.deepcopy(usa)\n", + "solve_parameters = copy.deepcopy(usa)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "backtracking_search(solve_simple)\n", + "backtracking_search(solve_parameters, order_domain_values=lcv, select_unassigned_variable=mrv, inference=mac )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "solve_simple.nassigns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "solve_parameters.nassigns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph Coloring Visualization\n", "\n", "Next, we define some functions to create the visualisation from the assingment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n" ] From af475483fe84d23448dca92d91640b46819e3e99 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 01:10:59 +0530 Subject: [PATCH 332/513] Fixed Typo in Docstring --- probability.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/probability.py b/probability.py index 944ea0ba5..247145b6c 100644 --- a/probability.py +++ b/probability.py @@ -118,7 +118,7 @@ def __repr__(self): def event_values(event, variables): - """Return a tuple of the values of variables variables in event. + """Return a tuple of the values of variables in event. >>> event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) (8, 10) >>> event_values ((1, 2), ['C', 'A']) From dfa50c2b0afc82210370f50bc0a4e8bd551e3549 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 01:32:01 +0530 Subject: [PATCH 333/513] Added Tests for enumerate_joint --- tests/test_probability.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tests/test_probability.py b/tests/test_probability.py index c280fdbe0..801fd2fba 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -65,6 +65,16 @@ def test_event_values(): assert event_values((1, 2), ['C', 'A']) == (1, 2) +def test_enumerate_joint(): + P = JointProbDist(['X', 'Y']) + P[0, 0] = 0.25 + P[0, 1] = 0.5 + P[1, 1] = P[2, 1] = 0.125 + assert enumerate_joint(['Y'], dict(X=0), P) == 0.75 + assert enumerate_joint(['X'], dict(Y=2), P) == 0 + assert enumerate_joint(['X'], dict(Y=1), P) == 0.75 + + def test_enumerate_joint_ask(): P = JointProbDist(['X', 'Y']) P[0, 0] = 0.25 From b86d845e6ed14a0123c6061ed4395a856ebec5de Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 02:33:25 +0530 Subject: [PATCH 334/513] Tests for Bayesnode.sample --- tests/test_probability.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/tests/test_probability.py b/tests/test_probability.py index 801fd2fba..dce6c23b4 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -10,13 +10,6 @@ def tests(): assert cpt.p(True, event) == 0.95 event = {'Burglary': False, 'Earthquake': True} assert cpt.p(False, event) == 0.71 - # assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375 - # assert BoolCPT(0.75).p(False, [], {}) == 0.25 - # cpt = BoolCPT({True: 0.2, False: 0.7}) - # assert cpt.rand(['A'], {'A': True}) in [True, False] - # cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3, - # (False, True): 0.5, (False, False): 0.7}) - # assert cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False] # #enumeration_ask('Earthquake', {}, burglary) s = {'A': True, 'B': False, 'C': True, 'D': False} @@ -87,6 +80,15 @@ def test_enumerate_joint_ask(): def test_bayesnode_p(): bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625}) assert bn.p(False, {'Burglary': False, 'Earthquake': True}) == 0.375 + assert BayesNode('W', '', 0.75).p(False, {'Random': True}) == 0.25 + + +def test_bayesnode_sample(): + X = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625}) + assert X.sample({'Burglary': False, 'Earthquake': True}) in [True, False] + Z = BayesNode('Z', 'P Q', {(True, True): 0.2, (True, False): 0.3, + (False, True): 0.5, (False, False): 0.7}) + assert Z.sample({'P': True, 'Q': False}) in [True, False] def test_enumeration_ask(): From 6a28b538076a1fcfbf1caf57260e807d2481ebb8 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 02:42:32 +0530 Subject: [PATCH 335/513] Added PassiveADPAgent to Index --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 88098c25c..43040f0d7 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 19.3 | Version-Space-Learning | | | 19.8 | Minimal-Consistent-Det | | | 19.12 | FOIL | | -| 21.2 | Passive-ADP-Agent | | | +| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | | 22.1 | HITS | | | From d25b37a4e7e043f04b6cd48cf17b0faa9a1078c2 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 22:13:00 +0530 Subject: [PATCH 336/513] Introduction & Examples for ProbDist --- probability.ipynb | 145 ++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 141 insertions(+), 4 deletions(-) diff --git a/probability.ipynb b/probability.ipynb index 446fc11fb..08f1a9612 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -1,14 +1,36 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Probability \n", + "\n", + "This IPy notebook acts as supporting material for **Chapter 13 Quantifying Uncertainty**, **Chapter 14 Probabilistic Reasoning** and **Chapter 15 Probabilistic Reasoning over Time** of the book* Artificial Intelligence: A Modern Approach*. This notebook makes use of the implementations in probability.py module. Let us import everything from the probability module. It might be helpful to view the source of some of our implementations. Please refer to the Introductory IPy file for more details on how to do so." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "import probability" + "from probability import *" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Probability Distribution\n", + "\n", + "Let us begin by specifying discrete probability distributions. The class **ProbDist** defines a discrete probability distribution. We name our random variable and then assign probabilities to the different values of the random variable. Assigning probabilities to the values works similar to that of using a dictionary with keys being the Value and we assign to it the probability. This is possible because of the magic methods **_ _getitem_ _** and **_ _setitem_ _** which store the probabilities in the prob dict of the object. You can keep the source window open alongside while playing with the rest of the code to get a better understanding." ] }, { @@ -18,7 +40,122 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "%psource ProbDist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = ProbDist('Flip')\n", + "p['H'], p['T'] = 0.25, 0.75\n", + "p['T']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first parameter of the constructor **varname** has a default value of '?'. So if the name is not passed it defaults to ?. The keyword argument **freqs** can be a dictionary of values of random variable:probability. These are then normalized such that the probability values sum upto 1 using the **normalize** method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = ProbDist(freqs={'low': 125, 'medium': 375, 'high': 500})\n", + "p.varname\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "(p['low'], p['medium'], p['high'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Besides the **prob** and **varname** the object also separately keeps track of all the values of the distribution in a list called **values**. Every time a new value is assigned a probability it is appended to this list, This is done inside the **_ _setitem_ _** method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The distribution by default is not normalized if values are added incremently. We can still force normalization by invoking the **normalize** method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = ProbDist('Y')\n", + "p['Cat'] = 50\n", + "p['Dog'] = 114\n", + "p['Mice'] = 64\n", + "(p['Cat'], p['Dog'], p['Mice'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p.normalize()\n", + "(p['Cat'], p['Dog'], p['Mice'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to display the approximate values upto decimals using the **show_approx** method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p.show_approx()" + ] } ], "metadata": { @@ -37,7 +174,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From e6100d48a89b67c9be1954cb2c3ddf71a067f289 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 22:36:41 +0530 Subject: [PATCH 337/513] Explained JointProbDist --- probability.ipynb | 107 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) diff --git a/probability.ipynb b/probability.ipynb index 08f1a9612..0aee76adb 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -156,6 +156,113 @@ "source": [ "p.show_approx()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Joint Probability Distribution\n", + "\n", + "The helper function **event_values** returns a tuple of the values of variables in event. An event is specified by a dict where the keys are the names of variables and the corresponding values are the value of the variable. Variables are specified with a list. The ordering of the returned tuple is same as those of the variables.\n", + "\n", + "\n", + "Alternatively if the event is specified by a list or tuple of equal length of the variables. Then the events tuple is returned as it is." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "event = {'A': 10, 'B': 9, 'C': 8}\n", + "variables = ['C', 'A']\n", + "event_values (event, variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "_A probability model is completely determined by the joint distribution for all of the random variables._ (**Section 13.3**) The probability module implements these as the class **JointProbDist** which inherits from the **ProbDist** class. This class specifies a discrete probability distribute over a set of variables. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource JointProbDist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Values for a Joint Distribution is a an ordered tuple in which each item corresponds to the value associate with a particular variable. For Joint Distribution of X, Y where X, Y take integer values this can be something like (18, 19).\n", + "\n", + "To specify a Joint distribution we first need an ordered list of variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "variables = ['X', 'Y']\n", + "j = JointProbDist(variables)\n", + "j" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like the **ProbDist** class **JointProbDist** also employes magic methods to assign probability to different values.\n", + "The probability can be assigned in either of the two formats for all possible values of the distribution. The **event_values** call inside **_ _getitem_ _** and **_ _setitem_ _** does the required processing to make this work." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "j[1,1] = 0.2\n", + "j[dict(X=0, Y=1)] = 0.5\n", + "\n", + "(j[1,1], j[0,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to list all the values for a particular variable using the **values** method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "j.values('X')" + ] } ], "metadata": { From d81be44c4aa3c22c61319560011d7eeccb23d711 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 30 Jun 2016 22:41:37 +0530 Subject: [PATCH 338/513] Added __repr__ to ProbDist --- probability.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/probability.py b/probability.py index 247145b6c..ed3aa5243 100644 --- a/probability.py +++ b/probability.py @@ -79,6 +79,9 @@ def show_approx(self, numfmt='%.3g'): return ', '.join([('%s: ' + numfmt) % (v, p) for (v, p) in sorted(self.prob.items())]) + def __repr__(self): + return "P(%s)" % self.varname + class JointProbDist(ProbDist): """A discrete probability distribute over a set of variables. From 671dea6c5421ef929cdea9a98548b6f22fb0a9d9 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Fri, 1 Jul 2016 01:40:27 +0530 Subject: [PATCH 339/513] Added section for inference by full joint distributions --- probability.ipynb | 162 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 162 insertions(+) diff --git a/probability.ipynb b/probability.ipynb index 0aee76adb..6668fdbf2 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -263,6 +263,168 @@ "source": [ "j.values('X')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference Using Full Joint Distributions\n", + "\n", + "In this section we use Full Joint Distributions to calculate the posterior distribution given some evidence. We represent evidence by using a python dictionary with variables as dict keys and dict values representing the values.\n", + "\n", + "This is illustrated in **Section 13.3** of the book. The functions **enumerate_joint** and **enumerate_joint_ask** implement this functionality. Under the hood they implement **Equation 13.9** from the book.\n", + "\n", + "$$\\textbf{P}(X | \\textbf{e}) = α \\textbf{P}(X, \\textbf{e}) = α \\sum_{y} \\textbf{P}(X, \\textbf{e}, \\textbf{y})$$\n", + "\n", + "Here **α** is the normalizing factor. **X** is our query variable and **e** is the evidence. According to the equation we enumerate on the remaining variables **y** (not in evidence or query variable) i.e. all possible combinations of **y**\n", + "\n", + "We will be using the same example as the book. Let us create the full joint distribution from **Figure 13.3**. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "full_joint = JointProbDist(['Cavity', 'Toothache', 'Catch'])\n", + "full_joint[dict(Cavity=True, Toothache=True, Catch=True)] = 0.108\n", + "full_joint[dict(Cavity=True, Toothache=True, Catch=False)] = 0.012\n", + "full_joint[dict(Cavity=True, Toothache=False, Catch=True)] = 0.016\n", + "full_joint[dict(Cavity=True, Toothache=False, Catch=False)] = 0.064\n", + "full_joint[dict(Cavity=False, Toothache=True, Catch=True)] = 0.072\n", + "full_joint[dict(Cavity=False, Toothache=False, Catch=True)] = 0.144\n", + "full_joint[dict(Cavity=False, Toothache=True, Catch=False)] = 0.008\n", + "full_joint[dict(Cavity=False, Toothache=False, Catch=False)] = 0.576" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us now look at the **enumerate_joint** function returns the sum of those entries in P consistent with e,provided variables is P's remaining variables (the ones not in e). Here, P refers to the full joint distribution. The function uses a recursive call in its implementation. The first parameter **variables** refers to remaining variables. The function in each recursive call keeps on variable constant while varying others." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource enumerate_joint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us assume we want to find **P(Toothache=True)**. This can be obtained by marginalization (**Equation 13.6**). We can use **enumerate_joint** to solve for this by taking Cavity=True as our evidence. **enumerate_joint** will return the sum of probabilities consistent with evidence i.e. Marginal Probability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "evidence = dict(Toothache=True)\n", + "variables = ['Cavity', 'Catch'] # variables not part of evidence\n", + "ans1 = enumerate_joint(variables, evidence, full_joint)\n", + "ans1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can verify that result from the row in our definition. We can use the same function to find more complex probabilities like **P(Cavity=True and Toothache=True)** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "evidence = dict(Cavity=True, Toothache=True)\n", + "variables = ['Catch'] # variables not part of evidence\n", + "ans2 = enumerate_joint(variables, evidence, full_joint)\n", + "ans2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Being able to find sum of probabilities satisfying given evidence allows us to compute conditional probabilities like **P(Cavity=True | Toothache=True)** as we can rewrite this as $$P(Cavity=True | Toothache = True) = \\frac{P(Cavity=True \\ and \\ Toothache=True)}{P(Toothache=True)}$$\n", + "\n", + "We have already calculated both the numerator and denominator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ans2/ans1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We might be interested in the probability distribution of a particular variable conditioned on some evidence. This can involve doing calculations like above for each possible value of the variable. This has been implemented slightly differently using normalization in the function **enumerate_joint_ask** which returns a probability distribution over the values of the variable **X**, given the {var:val} observations **e**, in the **JointProbDist P**. The implementation of this function calls **enumerate_joint** for each value of the query variable and passes **extended evidence** with the new evidence having **X = xi**. This is followed by normalization of the obtained distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource enumerate_joint_ask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us find **P(Cavity | Toothache=True)** using **enumerate_joint_ask**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "query_variable = 'Cavity'\n", + "evidence = dict(Toothache=True)\n", + "ans = enumerate_joint_ask(query_variable, evidence, full_joint)\n", + "(ans[True], ans[False])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can verify that the first value is the same as we obtained earlier by manual calculation." + ] } ], "metadata": { From 7bb9bf1af20eb6ac3b78531214252b0484eba7a6 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 1 Jul 2016 13:42:22 +0530 Subject: [PATCH 340/513] removes unnecessary definitions/examples from search notebook --- search.ipynb | 251 ++++++++++++--------------------------------------- 1 file changed, 58 insertions(+), 193 deletions(-) diff --git a/search.ipynb b/search.ipynb index 9a9d49826..ea0b15bae 100644 --- a/search.ipynb +++ b/search.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -45,7 +45,7 @@ " 3. A\\* Search\n", " 4. Recursive Best First Search\n", "\n", - "*In the end of this notebook, you can see how different searching algorithms solves the route finding problem defined on romania map.*" + "*Don't miss the visualisations of these algorithms solving route-finding problem defined on romania map at the end of this notebook.*" ] }, { @@ -72,30 +72,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "sdc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Uninformed Search Strategies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \n", - "The `Problem` class is an abstract class on which we define our problems(*duh*).\n", - "Again, if you are confused about what `abstract class` means have a look at the `Intro` notebook.\n", "The `Problem` class has six methods.\n", - "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. In this and all of the below methods `self` refers to the object itself--the object whose method is called. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins his task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", - "* `actions(self, state)` : This method returns all the possible actions our agent can make in state `state`.\n", + "\n", + "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", + "\n", + "\n", + "* `actions(self, state)` : This method returns all the possible actions agent can execute in the given state `state`.\n", + "\n", + "\n", "* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n", + "\n", + "\n", "* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else, of course, `False` is returned.\n", + "\n", + "\n", "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n", + "\n", + "\n", "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test." ] }, @@ -103,212 +96,92 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now the above abstract class acts as a parent class, and there is another named called `GraphProblem` in our module. It creates a graph problem from an instance of the `Graph` class. To create a graph, simply type `graph = Graph(dict(...))`. The dictionary must contain nodes of the graph as keys, so make sure they are `hashable`. If you don't know what that means just use strings or numbers. Each node contains the adjacent nodes as keys and the edge length as its value. Each dictionary then should correspond to another dictionary in the graph. The `Graph` class creates a directed(edges allow only one way traffic) by default. If you want to make an undirected graph, use `UndirectedGraph` instead, but make sure to mention any edge in only one of its nodes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you didn't understand the above paragraph, `Fret not!`. Just think of the below code as a magicical method to create a simple undirected graph. I'll explain what it is about later." + "We will use the abstract class `Problem` to define out real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "museum_graph = UndirectedGraph(dict(\n", - " Start = dict(Dog = 3, Cat = 9, Mouse = 4),\n", - " Dog = dict(Bear = 7),\n", - " Cat = dict(Monkey = 9, Fish = 8, Penguin = 3),\n", - " Mouse = dict(Penguin = 2),\n", - " Bear = dict(Monkey = 7),\n", - " Monkey = dict(Giraffe = 11, Fish = 6),\n", - " Fish = dict(Giraffe = 8),\n", - " Penguin = dict(Parrot = 4, Elephant = 6),\n", - " Giraffe = dict(Hen = 5),\n", - " Parrot = dict(Hen = 10),\n", - " Elephant = dict(Hen = 9)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Imagine we are in a museum showcasing statues of various animals. To navigate through the museum there are paths between some statues and the entrance. We define the entrance and the statues as nodes in our graph with the path connecting them as edges. The cost/weight of an edge specifies is its length. So `Start = dict(Dog = 3, Cat = 9, Mouse = 4)` means that there are paths from `Start` to `Dog`, `Cat` and `Mouse` with path costs 3, 9 and 4 respectively. \n", - "\n", - "Here's an image below to better understand our graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Breadth First Search" + "%psource GraphProblem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In Breadth First Search, the `shallowest` unexpanded node is chosen for expansion. That means that all nodes of a given depth must be expanded before any node of the next depth level. This search strategy accomplishes this by using a `FIFO` meaning 'First In First Out' queue. Anything that gets in the queue first also gets out first just like the checkout queue in a supermarket. To use the algorithm, first we need to define our problem. Say we want to find the statue of `Monkey` and we start from the entrance which is the `Start` state. We'll define our problem using the `GraphProblem` class." + "Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "monkey_problem = GraphProblem('Start', 'Monkey', museum_graph)" + "romania_map = UndirectedGraph(dict(\n", + " Arad=dict(Zerind=75, Sibiu=140, Timisoara=118),\n", + " Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211),\n", + " Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138),\n", + " Drobeta=dict(Mehadia=75),\n", + " Eforie=dict(Hirsova=86),\n", + " Fagaras=dict(Sibiu=99),\n", + " Hirsova=dict(Urziceni=98),\n", + " Iasi=dict(Vaslui=92, Neamt=87),\n", + " Lugoj=dict(Timisoara=111, Mehadia=70),\n", + " Oradea=dict(Zerind=71, Sibiu=151),\n", + " Pitesti=dict(Rimnicu=97),\n", + " Rimnicu=dict(Sibiu=80),\n", + " Urziceni=dict(Vaslui=142)))\n", + "\n", + "romania_map.locations = dict(\n", + " Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288),\n", + " Drobeta=(165, 299), Eforie=(562, 293), Fagaras=(305, 449),\n", + " Giurgiu=(375, 270), Hirsova=(534, 350), Iasi=(473, 506),\n", + " Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537),\n", + " Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410),\n", + " Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350),\n", + " Vaslui=(509, 444), Zerind=(108, 531))" ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's find the solution for our problem using the `breadth_first_search` method. Note that it returns a `Node` from which we can find the solution by looking at the path that was taken to reach there." - ] - }, - { - "cell_type": "code", - "execution_count": 4, "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Cat', 'Monkey']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bfs_node = breadth_first_search(monkey_problem)\n", - "bfs_node.solution()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We get the output as `['Cat', 'Monkey']`. That is because first the nodes `Dog`, `Cat` and `Mouse` are added to the `FIFO` queue in `some` order when we are expanding the `Start` node. Now when we start expanding nodes in the next level, only the `Cat` node gets us to `Monkey`. Note that during a breadth first search, the goal test is done when the node is being added to the queue." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Uniform-cost Search" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In Uniform-cost Search, we expand the node with the lowest path cost (the cost to reach that node from the start) instead of expanding the shallowest node. Rather than a `FIFO` queue, we use something called a `priority queue` which selects the element with the highest `priority` of all elements in the queue. For our problem, the shortest path between animals has the higher priority; the shortest path has the lowest path cost. Whenever we need to enqueue a node already in the queue, we will update its path cost if the newer path is better. This is a very important step, and it means that the path cost to a node may keep getting better until it is selected for expansion. This is the reason that we do a goal check only when a node is selected for expanion." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false + "collapsed": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Dog', 'Bear', 'Monkey']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "ucs_node = uniform_cost_search(monkey_problem)\n", - "ucs_node.solution()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We got the path`['Dog', 'Bear', 'Monkey']` instead of `['Cat', 'Monkey']`. Why? The path cost is lower! We can also see the path cost with the path_cost attribute. Let's compare the path cost of the Breadth first search solution and Uniform cost search solution" + "It is pretty straight forward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n", + "\n", + "And `romania_map.locations` contains the positions of each of the nodes. We will use the straight line distance (which is different from the one provided in `romania_map`) between two cities in algorithms like A\\*-search and Recursive Best First Search.\n", + "\n", + "**Define a problem:**\n", + "Hmm... say we want to start exploring from **Arad** and try to find **Bucharest** in our romania_map. So, this is how we do it." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "(18, 17)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "bfs_node.path_cost, ucs_node.path_cost" + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "We were right! \n", - "\n", - "The path cost through the `Cat` statue is indeed more than the path cost through `Dog` even though the former passes through two roads compared to the three roads in the `ucs_node` solution." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, "source": [ "# Romania map visualisations\n", "\n", - "Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem to reach 'Bucharest' starting from 'Arad'. This is how the problem is defined:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)" + "Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem named `romania_problem`." ] }, { @@ -320,19 +193,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Rimnicu': (233, 410), 'Hirsova': (534, 350), 'Arad': (91, 492), 'Vaslui': (509, 444), 'Oradea': (131, 571), 'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Fagaras': (305, 449), 'Bucharest': (400, 327), 'Sibiu': (207, 457), 'Urziceni': (456, 350), 'Lugoj': (165, 379), 'Craiova': (253, 288), 'Zerind': (108, 531), 'Iasi': (473, 506), 'Mehadia': (168, 339), 'Pitesti': (320, 368), 'Timisoara': (94, 410), 'Drobeta': (165, 299), 'Eforie': (562, 293)}\n" - ] - } - ], + "outputs": [], "source": [ "romania_locations = romania_map.locations\n", "print(romania_locations)" From 6f8ed98072a1c559955bf38671cb0f8af34fea5b Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Fri, 1 Jul 2016 14:04:09 +0530 Subject: [PATCH 341/513] Fix Description mismatch with code. --- probability.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/probability.ipynb b/probability.ipynb index 6668fdbf2..ff33047c2 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -322,7 +322,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us assume we want to find **P(Toothache=True)**. This can be obtained by marginalization (**Equation 13.6**). We can use **enumerate_joint** to solve for this by taking Cavity=True as our evidence. **enumerate_joint** will return the sum of probabilities consistent with evidence i.e. Marginal Probability." + "Let us assume we want to find **P(Toothache=True)**. This can be obtained by marginalization (**Equation 13.6**). We can use **enumerate_joint** to solve for this by taking Toothache=True as our evidence. **enumerate_joint** will return the sum of probabilities consistent with evidence i.e. Marginal Probability." ] }, { @@ -343,7 +343,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You can verify that result from the row in our definition. We can use the same function to find more complex probabilities like **P(Cavity=True and Toothache=True)** " + "You can verify the result from our definition of the full joint distribution. We can use the same function to find more complex probabilities like **P(Cavity=True and Toothache=True)** " ] }, { From 65f50c78171964673d209b00cbc0dad614007d95 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 1 Jul 2016 14:16:56 +0530 Subject: [PATCH 342/513] commits after running all visualisations --- search.ipynb | 511 ++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 460 insertions(+), 51 deletions(-) diff --git a/search.ipynb b/search.ipynb index ea0b15bae..77bbc91bf 100644 --- a/search.ipynb +++ b/search.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -179,7 +179,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Romania map visualisations\n", + "# Romania map visualisation\n", "\n", "Let's have a visualisation of Romania map [Figure 3.2] from the book and see how different searching algorithms perform / how frontier expands in each search algorithm for a simple problem named `romania_problem`." ] @@ -193,11 +193,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Drobeta': (165, 299), 'Timisoara': (94, 410), 'Pitesti': (320, 368), 'Bucharest': (400, 327), 'Craiova': (253, 288), 'Oradea': (131, 571), 'Iasi': (473, 506), 'Urziceni': (456, 350), 'Sibiu': (207, 457), 'Fagaras': (305, 449), 'Lugoj': (165, 379), 'Rimnicu': (233, 410), 'Vaslui': (509, 444), 'Eforie': (562, 293), 'Hirsova': (534, 350), 'Mehadia': (168, 339), 'Arad': (91, 492), 'Zerind': (108, 531)}\n" + ] + } + ], "source": [ "romania_locations = romania_map.locations\n", "print(romania_locations)" @@ -212,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -238,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -290,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -332,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -341,7 +349,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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rYGRkJHYcIiIiKmEjRozA4cOHER8fL3YUIqrgWPwkIvoO2dnZ4NLJVJzK4rR3QRDg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\nj/Z+XLlyZY3rTz31VIYOHVo9a3T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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -363,20 +371,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Searching algorithms\n", + "## Searching algorithms visualisations\n", "\n", "In this section, we have visualisations of the following searching algorithms:\n", "\n", - "1. breadth_first_tree_search\n", - "2. depth_first_tree_search\n", - "3. depth_first_graph_search\n", - "4. breadth_first_search\n", - "5. best_first_graph_search\n", - "6. uniform_cost_search\n", - "7. depth_limited_search\n", - "8. iterative_deepening_search\n", - "9. astar_search\n", - "10. recursive_best_first_search\n", + "1. Breadth First Tree Search - Implemented\n", + "2. Depth First Tree Search\n", + "3. Depth First Graph Search\n", + "4. Breadth First Search - Implemented\n", + "5. Best First Graph Search\n", + "6. Uniform Cost Search - Implemented\n", + "7. Depth Limited Search\n", + "8. Iterative Deepening Search\n", + "9. A\\*-Search - Implemented\n", + "10. Recursive Best First Search\n", "\n", "We add the colors to the nodes to have a nice visualisation when displaying. So, these are the different colors we are using in these visuals:\n", "* Un-explored nodes - white\n", @@ -384,12 +392,12 @@ "* Currently exploring node - red\n", "* Already explored nodes - gray\n", "\n", - "Now, we will define some methods which we are gonna use in all the searching algorithms." + "Now, we will define some helper methods to display interactive buttons ans sliders when visualising search algorithms." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -500,13 +508,12 @@ "## Breadth first tree search\n", "\n", "We have a working implementation in search module. But as we want to interact with the graph while it is searching, we need to modify the implementation. Here's the modified breadth first tree search.\n", - "\n", - "Let's define a problem statement:" + "\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -563,13 +570,6 @@ " return(iterations, all_node_colors, node)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's call the modified `breadth_first_tree_search` with our problem statement. Print `iterations` to see how many intermediate steps we have got " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -580,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -589,7 +589,7 @@ "data": { "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nsAOsCRtjAIXz4YcfVgeHzz//fK6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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nzj777Bx//PEpLy9P06ZNc+aZZ+b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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -938,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { "collapsed": false, "scrolled": false @@ -946,9 +946,9 @@ "outputs": [ { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nvXqVfPnyUbt2bWxsbHB2dqZs2bIAZM6cGTs7OxwcHMiZM+cTX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/zDzs7uP8/X3t6e3Llz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\nPDE//vGPV7rf+PHj8+1vfzvjx49Pt27d1mG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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nzUb+Vb06aVi+YYctAZvX6NGjc/z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gWuKB7_pQCs%CU@{S>b6`A^?B#vMT?9rA@;A2g>(L0RR91 literal 0 HcmV?d00001 diff --git a/probability.ipynb b/probability.ipynb index ff33047c2..08598db83 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -425,6 +425,190 @@ "source": [ "You can verify that the first value is the same as we obtained earlier by manual calculation." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian Networks\n", + "\n", + "A Bayesian network is a representation of the joint probability distribution encoding a collection of conditional independence statements.\n", + "\n", + "A Bayes Network is implemented as the class **BayesNet**. It consisits of a collection of nodes implemented by the class **BayesNode**. The implementation in the above mentioned classes focuses only on boolean variables. Each node is associated with a variable and it contains a **conditional probabilty table (cpt)**. The **cpt** represents the probability distribution of the variable conditioned on its parents **P(X | parents)**.\n", + "\n", + "Let us dive into the **BayesNode** implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource BayesNode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The constructor takes in the name of **variable**, **parents** and **cpt**. Here **variable** is a the name of the variable like 'Earthquake'. **parents** should a list or space separate string with variable names of parents. The conditional probability table is a dict {(v1, v2, ...): p, ...}, the distribution P(X=true | parent1=v1, parent2=v2, ...) = p. Here the keys are combination of boolean values that the parents take. The length and order of the values in keys should be same as the supplied **parent** list/string. In all cases the probability of X being false is left implicit, since it follows from P(X=true).\n", + "\n", + "The example below where we implement the network shown in **Figure 14.3** of the book will make this more clear.\n", + "\n", + "\n", + "\n", + "The alarm node can be made as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "alarm_node = BayesNode('Alarm', ['Burglary', 'Earthquake'], \n", + " {(True, True): 0.95,(True, False): 0.94, (False, True): 0.29, (False, False): 0.001})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to avoid using a tuple when there is only a single parent. So an alternative format for the **cpt** is" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "john_node = BayesNode('JohnCalls', ['Alarm'], {True: 0.90, False: 0.05})\n", + "mary_node = BayesNode('MaryCalls', 'Alarm', {(True, ): 0.70, (False, ): 0.01}) # Using string for parents.\n", + "# Equvivalant to john_node definition. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The general format used for the alarm node always holds. For nodes with no parents we can also use. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "burglary_node = BayesNode('Burglary', '', 0.001)\n", + "earthquake_node = BayesNode('Earthquake', '', 0.002)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to use the node for lookup function using the **p** method. The method takes in two arguments **value** and **event**. Event must be a dict of the type {variable:values, ..} The value corresponds to the value of the variable we are interested in (False or True).The method returns the conditional probability **P(X=value | parents=parent_values)**, where parent_values are the values of parents in event. (event must assign each parent a value.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "john_node.p(False, {'Alarm': True, 'Burglary': True}) # P(JohnCalls=False | Alarm=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With all the information about nodes present it is possible to construct a Bayes Network using **BayesNet**. The **BayesNet** class does not take in nodes as input but instead takes a list of **node_specs**. An entry in **node_specs** is a tuple of the parameters we use to construct a **BayesNode** namely **(X, parents, cpt)**. **node_specs** must be ordered with parents before children." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource BayesNet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The constructor of **BayesNet** takes each item in **node_specs** and adds a **BayesNode** to its **nodes** object variable by calling the **add** method. **add** in turn adds node to the net. Its parents must already be in the net, and its variable must not. Thus add allows us to grow a **BayesNet** given its parents are already present.\n", + "\n", + "**burglary** global is an instance of **BayesNet** corresponding to the above example.\n", + "\n", + " T, F = True, False\n", + "\n", + " burglary = BayesNet([\n", + " ('Burglary', '', 0.001),\n", + " ('Earthquake', '', 0.002),\n", + " ('Alarm', 'Burglary Earthquake',\n", + " {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001}),\n", + " ('JohnCalls', 'Alarm', {T: 0.90, F: 0.05}),\n", + " ('MaryCalls', 'Alarm', {T: 0.70, F: 0.01})\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "burglary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**BayesNet** method **variable_node** allows to reach **BayesNode** instances inside a Bayes Net. It is possible to modify the **cpt** of the nodes directly using this method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "type(burglary.variable_node('Alarm'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "burglary.variable_node('Alarm').cpt" + ] } ], "metadata": { From a121360ce740cf410691d0d38215c16c23beccf1 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 7 Jul 2016 03:23:28 +0530 Subject: [PATCH 344/513] BayesNet Enumeration --- probability.ipynb | 69 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/probability.ipynb b/probability.ipynb index 08598db83..b6443416c 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -609,6 +609,75 @@ "source": [ "burglary.variable_node('Alarm').cpt" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exact Inference in Bayesian Networks\n", + "\n", + "A Bayes Network is a more compact representation of the full joint distribution and like full joint distributions allows us to do inference i.e. answer questions about probability distributions of random variables given some evidence.\n", + "\n", + "Exact algorithms don't scale well for larger networks. Approximate algorithms are explained in the next section.\n", + "\n", + "### Inference by Enumeration\n", + "\n", + "We apply techniques similar to those used for **enumerate_joint_ask** and **enumerate_joint** to draw inference from Bayesian Networks. **enumeration_ask** and **enumerate_all** implement the algorithm described in **Figure 14.9** of the book." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource enumerate_all" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**enumerate__all** recursively evaluates a general form of the **Equation 14.4** in the book.\n", + "\n", + "$$\\textbf{P}(X | \\textbf{e}) = α \\textbf{P}(X, \\textbf{e}) = α \\sum_{y} \\textbf{P}(X, \\textbf{e}, \\textbf{y})$$ \n", + "\n", + "such that **P(X, e, y)** is written in the form of product of conditional probabilities **P(variable | parents(variable))** from the Bayesian Network.\n", + "\n", + "**enumeration_ask** calls **enumerate_all** on each value of query variable **X** and finally normalizes them. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource enumeration_ask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us solve the problem of finding out **P(Burglary=True | JohnCalls=True, MaryCalls=True)** using the **burglary** network.**enumeration_ask** takes three arguments **X** = variable name, **e** = Evidence (in form a dict like previously explained), **bn** = The Bayes Net to do inference on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ans_dist = enumeration_ask('Burglary', {'JohnCalls': True, 'MaryCalls': True}, burglary)\n", + "ans_dist[True]" + ] } ], "metadata": { From 915d55fcf03bb35970b13a18d3176aa6dfdd7759 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 7 Jul 2016 20:35:20 +0530 Subject: [PATCH 345/513] Added Section on Variable Elimination --- probability.ipynb | 251 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 251 insertions(+) diff --git a/probability.ipynb b/probability.ipynb index b6443416c..3afee2fa4 100644 --- a/probability.ipynb +++ b/probability.ipynb @@ -678,6 +678,257 @@ "ans_dist = enumeration_ask('Burglary', {'JohnCalls': True, 'MaryCalls': True}, burglary)\n", "ans_dist[True]" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable Elimination\n", + "\n", + "The enumeration algorithm can be improved substantially by eliminating repeated calculations. In enumeration we join the joint of all hidden variables. This is of exponential size for the number of hidden variables. Variable elimination employes interleaving join and marginalization.\n", + "\n", + "Before we look into the implementation of Variable Elimination we must first familiarize ourselves with Factors. \n", + "\n", + "In general we call a multidimensional array of type P(Y1 ... Yn | X1 ... Xm) a factor where some of Xs and Ys maybe assigned values. Factors are implemented in the probability module as the class **Factor**. They take as input **variables** and **cpt**. \n", + "\n", + "\n", + "#### Helper Functions\n", + "\n", + "There are certain helper functions that help creating the **cpt** for the Factor given the evidence. Let us explore them one by one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource make_factor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**make_factor** is used to create the **cpt** and **variables** that will be passed to the constructor of **Factor**. We use **make_factor** for each variable. It takes in the arguments **var** the particular variable, **e** the evidence we want to do inference on, **bn** the bayes network.\n", + "\n", + "Here **variables** for each node refers to a list consisting of the variable itself and the parents minus any variables that are part of the evidence. This is created by finding the **node.parents** and filtering out those that are not part of the evidence.\n", + "\n", + "The **cpt** created is the one similar to the original **cpt** of the node with only rows that agree with the evidence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource all_events" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **all_events** function is a recursive generator function which yields a key for the orignal **cpt** which is part of the node. This works by extending evidence related to the node, thus all the output from **all_events** only includes events that support the evidence. Given **all_events** is a generator function one such event is returned on every call. \n", + "\n", + "We can try this out using the example on **Page 524** of the book. We will make **f**5(A) = P(m | A)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f5 = make_factor('MaryCalls', {'JohnCalls': True, 'MaryCalls': True}, burglary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f5.cpt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f5.variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here **f5.cpt** False key gives probability for **P(MaryCalls=True | Alarm = False)**. Due to our representation where we only store probabilities for only in cases where the node variable is True this is the same as the **cpt** of the BayesNode. Let us try a somewhat different example from the book where evidence is that the Alarm = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "new_factor = make_factor('MaryCalls', {'Alarm': True}, burglary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_factor.cpt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the **cpt** is for **P(MaryCalls | Alarm = True)**. Therefore the probabilities for True and False sum up to one. Note the difference between both the cases. Again the only rows included are those consistent with the evidence.\n", + "\n", + "#### Operations on Factors\n", + "\n", + "We are interested in two kinds of operations on factors. **Pointwise Product** which is used to created joint distributions and **Summing Out** which is used for marginalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource Factor.pointwise_product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Factor.pointwise_product** implements a method of creating a joint via combining two factors. We take the union of **variables** of both the factors and then generate the **cpt** for the new factor using **all_events** function. Note that the given we have eliminated rows that are not consistent with the evidence. Pointwise product assigns new probabilities by multiplying rows similar to that in a database join." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource pointwise_product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pointwise_product** extends this operation to more than two operands where it is done sequentially in pairs of two." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource Factor.sum_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Factor.sum_out** makes a factor eliminating a variable by summing over its values. Again **events_all** is used to generate combinations for the rest of the variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource sum_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**sum_out** uses both **Factor.sum_out** and **pointwise_product** to finally eliminate a particular variable from all factors by summing over its values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Elimination Ask\n", + "\n", + "The algorithm described in **Figure 14.11** of the book is implemented by the function **elimination_ask**. We use this for inference. The key idea is that we eliminate the hidden variables by interleaving joining and marginalization. It takes in 3 arguments **X** the query variable, **e** the evidence variable and **bn** the Bayes network. \n", + "\n", + "The algorithm creates factors out of Bayes Nodes in reverse order and eliminates hidden variables using **sum_out**. Finally it takes a point wise product of all factors and normalizes. Let us finally solve the problem of inferring \n", + "\n", + "**P(Burglary=True | JohnCalls=True, MaryCalls=True)** using variable elimination." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource elimination_ask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "elimination_ask('Burglary', dict(JohnCalls=True, MaryCalls=True), burglary).show_approx()" + ] } ], "metadata": { From 551ce42c45fe47124fa72658c31874b9d1087f9c Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sat, 9 Jul 2016 23:48:24 +0530 Subject: [PATCH 346/513] adds intro section to learning notebook --- learning.ipynb | 52 +++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 49 insertions(+), 3 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index 4798f2914..73b743d19 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -1,14 +1,56 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": false }, + "source": [ + "# Learning\n", + "\n", + "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from learning module." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "import learning" + "from learning import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review\n", + "\n", + "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n", + "\n", + "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n", + "\n", + "There are three types of feedback that determine the three main types of learning:\n", + "\n", + "* **Supervised Learning**:\n", + "\n", + "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n", + "\n", + "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the aggent. The agent then learns a function that maps from an input image to one of those strings.\n", + "\n", + "* **Unsupervised Learning**:\n", + "\n", + "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n", + "\n", + "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n", + "\n", + "* **Reinforcement Learning**:\n", + "\n", + "In Reinforcement Learning the agent from a series of reinforcements—rewards or punishments.\n", + "\n", + "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." ] }, { @@ -38,6 +80,10 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" + }, + "widgets": { + "state": {}, + "version": "1.1.1" } }, "nbformat": 4, From f6ec5e0c66dc732eacbf14d71ea5362f1a5a65e7 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 10 Jul 2016 10:05:35 -0700 Subject: [PATCH 347/513] Experimental new version of probability.ipynb --- Probability-4e.ipynb | 1359 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1359 insertions(+) create mode 100644 Probability-4e.ipynb diff --git a/Probability-4e.ipynb b/Probability-4e.ipynb new file mode 100644 index 000000000..bd6e0acaa --- /dev/null +++ b/Probability-4e.ipynb @@ -0,0 +1,1359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "import itertools" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Bayesian Networks\n", + "\n", + "A Bayesian network, or Bayes net for short, is a data structure to represent a joint probability distribution, and do inference on it. For example, here is a network with five nodes, each with its conditional probability table, and with arrows from parent to child variables. The story, from Judea Pearl, is that Judea has a burglar alarm, and it can be triggered by either a burglary or an earthquake. If the alarm sounds, one or both of Judea's neighbors, John and Mary, might call him to let him know.\n", + "\n", + "

    \n", + "\n", + "This topic of Bayes nets can be confusing, because there are many different concepts to keep track of:\n", + "\n", + "* `BayesNet`: A graph, where each node represents a variable, and is pointed to by zero or more *parents*. (See diagram above.)\n", + "\n", + "* `Variable`: A random variable; the ovals in the diagram above. We will only allow variables with a finite discrete domain of possible values; in the diagram all the variables are Boolean, meaning their domain is the set $\\{t, f\\}$. The value of a variable depends on the value of the parents, in a probabilistic way specified by the variable's conditional probability table. Given the parents, the variable is independent of all the other variables. For example, if I know whether *Alarm* is true or false, then I know the probability of *JohnCalls*, and evidence about the other variables won't give me any more information about *JohnCalls*.\n", + "\n", + "* `ProbDist`: A probability distribution enumerates each possible value in the domain of a variable,\n", + "and the probability of that value. For example, `{True: 0.95, False: 0.05}` is a probability distribution for a Boolean variable.\n", + "\n", + "* `CPTable`: A conditional probability table is a a mapping, `{tuple: ProbDist, ...}`, where each tuple lists the values of each of the parent variables, in order, and the probability distribution says what the possible outcomes are for the variable, given those values of the parents. For example, for the variable *Alarm*, the top row of the `CPTable` says \"*t, t*, .95\", which means that when *Burglary* is true and *Earthquake* is true, the probability of *Alarm* being true is .95. Think of this row entry as an abbreviation that makes sense for Boolean variables, but to accomodate non-Boolean variables, we will represent this in the more general format: `{(True, True): {True: 0.95, False: 0.05}}`.\n", + "\n", + "* `Evidence`: A mapping, `{Variable: value, ...}`, which denotes which variables we have observed known values for.\n", + "\n", + "We will introduce implementations of these concepts:\n", + "\n", + "# `BayesNet`\n", + "\n", + "A `BayesNet` is a graph of variables, where each variable is specified by a triple of `(name, parentnames, cpt)`, where the name is a string, the `parentnames` is a sequence of strings, and the CPT is in a format we will explain soon." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class BayesNet(object):\n", + " \"Bayesian network: a graph with an ordered list of variables.\"\n", + " \n", + " def __init__(self): \n", + " self.variables = [] # List of variables, in parent-first topological order\n", + " self.lookup = {} # Mapping of {variable_name: variable} pairs\n", + " \n", + " def add(self, name, parentnames, cpt):\n", + " \"Add a new Variable to the BayesNet. Parentnames must already have been added.\"\n", + " parents = [self.lookup[name] for name in parentnames]\n", + " var = Variable(name, parents, cpt)\n", + " self.variables.append(var)\n", + " self.lookup[name] = var\n", + " return self" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# `Variable` \n", + "\n", + "The `Variable` data structure holds a name, a list of parents (which are actual variables, not names), and a conditional probability table. The order of the parent variables is important, because you will have to use the same order in the CPT. For convenience, we also store the* domain* of the variable: the set of possible values (all our variables are discrete). " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class Variable(object):\n", + " \"A discrete random variable in a BayesNet.\"\n", + " \n", + " def __init__(self, name, parents, cpt):\n", + " \"A variable has a name, list of parent variables, and a CPT.\"\n", + " self.name = name\n", + " self.parents = parents\n", + " self.cpt = CPT(cpt, parents)\n", + " self.domain = set(v for row in self.cpt for v in self.cpt[row])\n", + " \n", + " def P(self, evidence):\n", + " \"The full probability distribution for P(variable | evidence).\"\n", + " return self.cpt[tuple(evidence[var] for var in self.parents)]\n", + "\n", + " def __repr__(self): return self.name" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# `ProbDist` and `Evidence`\n", + "\n", + "A `ProbDist` is a mapping of `{outcome: probability}` for every outcome of a random variable. You can give it the same arguments that you would give to the `dict` constructor. As a shortcut for Boolean random variables, you can say `ProbDist(0.2)` instead of `ProbDist({False: 0.8, True: 0.2})`.\n", + "\n", + "`Evidence` is just a dict of `{variable: value}` pairs, describing the exact values for a set of variables." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class ProbDist(dict):\n", + " \"A Probability Distribution; an {outcome: probability} mapping.\"\n", + " def __init__(self, mapping=(), **kwargs):\n", + " if isinstance(mapping, float):\n", + " mapping = {True: mapping, False: 1 - mapping}\n", + " self.update(mapping, **kwargs)\n", + " total = sum(self.values())\n", + " normalize(self)\n", + " \n", + "def normalize(dic):\n", + " \"Make sum to values of dic sum to 1.0; assert no negative values.\"\n", + " total = sum(dic.values())\n", + " for key in dic:\n", + " dic[key] = dic[key] / total\n", + " assert dic[key] >= 0\n", + " \n", + "class Evidence(dict): pass" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'heads': 0.6, 'tails': 0.4}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# An example ProbDist\n", + "ProbDist(heads=6, tails=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.75, True: 0.25}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A Boolean ProbDist\n", + "ProbDist(0.25) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# `CPT`: Conditional Probability Table\n", + "\n", + "A `CPT` is a mapping from tuples of parent values to probability distributions. Every possible tuple must be represented in the table. We allow shortcuts for the case of `CPT`s with zeron or one parent." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "class CPT(dict):\n", + " \"\"\"A mapping of {row: ProbDist, ...} where each row is a tuple\n", + " of possible values of the parent variables.\"\"\"\n", + " \n", + " def __init__(self, data, parents=None):\n", + " \"\"\"Provides two shortcuts for writing a Conditional Probability Table. \n", + " With no parents, CPT(dist) => CPT({(): dist}).\n", + " With one parent, CPT({val: dist,...}) => CPT({(val,): dist,...}).\"\"\"\n", + " def Tuple(row): return row if isinstance(row, tuple) else (row,)\n", + " if not parents and (not isinstance(data, dict) or set(data.keys()) != {()}):\n", + " data = {(): data}\n", + " for row in data:\n", + " self[Tuple(row)] = ProbDist(data[row])\n", + " if parents:\n", + " assert set(self) == set(expected_tuples(parents)), (\n", + " \"CPT must handle all possibile tuples of parent values\")\n", + "\n", + "def expected_tuples(parents):\n", + " \"The set of tuples of one value from each parent (in order).\"\n", + " return set(itertools.product(*[p.domain for p in parents]))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{(): {False: 0.75, True: 0.25}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# An example of a CPT with no parents, and thus one row with an empty tuple\n", + "CPT({(): 0.25})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# An Example Bayes Net\n", + "\n", + "Now we are ready to define the network from the burglary alarm scenario:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "T = True\n", + "F = False\n", + "\n", + "alarm_net = (BayesNet()\n", + " .add('Burglary', [], 0.001)\n", + " .add('Earthquake', [], 0.002)\n", + " .add('Alarm', ['Burglary', 'Earthquake'],\n", + " {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001})\n", + " .add('JohnCalls', ['Alarm'], {T: 0.90, F: 0.05})\n", + " .add('MaryCalls', ['Alarm'], {T: 0.70, F:0.01}))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "globals().update(alarm_net.lookup)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{(False, False): {False: 0.999, True: 0.001},\n", + " (False, True): {False: 0.71, True: 0.29},\n", + " (True, False): {False: 0.06000000000000005, True: 0.94},\n", + " (True, True): {False: 0.050000000000000044, True: 0.95}}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Alarm.cpt" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.999, True: 0.001}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Alarm.P({Burglary:False, Earthquake:False})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.001" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Alarm.P({Burglary:False, Earthquake:False})[True]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Inference in Bayes Nets" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def enumeration_ask(X, e, bn):\n", + " \"Given evidence e, ask what the probability distribution is for X in bn.\"\n", + " assert X not in e, \"Query variable must be distinct from evidence\"\n", + " Q = {}\n", + " for xi in X.domain:\n", + " Q[xi] = enumerate_all_vars(bn.variables, extend(e, X, xi), bn)\n", + " return ProbDist(Q)\n", + "\n", + "def enumerate_all_vars(vars, e, bn):\n", + " \"\"\"Return the sum of those entries in P(vars | e_{others})\n", + " consistent with e, where P is the joint distribution represented\n", + " by bn, and e_{others} means e restricted to bn's other variables\n", + " (the ones other than vars). Parents must precede children in vars.\"\"\"\n", + " if not vars:\n", + " return 1.0\n", + " Y, rest = vars[0], vars[1:]\n", + " if Y in e:\n", + " y = e[Y]\n", + " return P(Y, e, y) * enumerate_all_vars(rest, e, bn)\n", + " else:\n", + " return sum(P(Y, e, y) * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", + " for y in Y.domain)\n", + " \n", + "def extend(dic, var, val):\n", + " \"\"\"Return a copy of the dict, with {var: val} added.\"\"\"\n", + " dic2 = dic.copy()\n", + " dic2[var] = val\n", + " return dic2" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.7158281646356071, True: 0.2841718353643929}" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {JohnCalls:T, MaryCalls:T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.9438825459610851, True: 0.056117454038914924}" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {MaryCalls:T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.8499098822502404, True: 0.15009011774975956}" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Alarm, {MaryCalls:T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.9641190847135443, True: 0.03588091528645573}" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Earthquake, {MaryCalls:T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.7029390000000001, True: 0.29706099999999996}" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(JohnCalls, {Earthquake:T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "def enumeration_ask(X, e, bn):\n", + " \"Given evidence e, ask what the probability distribution is for X in bn.\"\n", + " assert X not in e, \"Query variable must be distinct from evidence\"\n", + " Q = {}\n", + " for xi in X.domain:\n", + " Q[xi] = enumerate_all_vars(bn.variables, extend(e, X, xi), bn)\n", + " return ProbDist(Q)\n", + "\n", + "def enumerate_all_vars(vars, e, bn):\n", + " \"\"\"Return the sum of those entries in P(vars | e_{others})\n", + " consistent with e, where P is the joint distribution represented\n", + " by bn, and e_{others} means e restricted to bn's other variables\n", + " (the ones other than vars). Parents must precede children in vars.\"\"\"\n", + " if not vars:\n", + " return 1.0\n", + " Y, rest = vars[0], vars[1:]\n", + " if Y in e:\n", + " y = e[Y]\n", + " return P(Y, e, y) * enumerate_all_vars(rest, e, bn)\n", + " else:\n", + " return sum(P(Y, e, y) * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", + " for y in Y.domain)\n", + " \n", + "def extend(dic, var, val):\n", + " \"\"\"Return a copy of the dict, with {var: val} added.\"\"\"\n", + " dic2 = dic.copy()\n", + " dic2[var] = val\n", + " return dic2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Full Joint ???" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "({(False, False, False, False, False): 0.9367427006189999,\n", + " (False, False, False, False, True): 0.009462047481,\n", + " (False, False, False, True, False): 0.049302247401000004,\n", + " (False, False, False, True, True): 0.0004980024990000001,\n", + " (False, False, True, False, False): 2.9910059999999997e-05,\n", + " (False, False, True, False, True): 6.979013999999998e-05,\n", + " (False, False, True, True, False): 0.00026919054,\n", + " (False, False, True, True, True): 0.0006281112599999999,\n", + " (False, True, False, False, False): 0.00133417449,\n", + " (False, True, False, False, True): 1.3476510000000001e-05,\n", + " (False, True, False, True, False): 7.021971e-05,\n", + " (False, True, False, True, True): 7.0929e-07,\n", + " (False, True, True, False, False): 1.73826e-05,\n", + " (False, True, True, False, True): 4.055939999999999e-05,\n", + " (False, True, True, True, False): 0.00015644340000000003,\n", + " (False, True, True, True, True): 0.0003650346,\n", + " (True, False, False, False, False): 5.631714000000005e-05,\n", + " (True, False, False, False, True): 5.688600000000004e-07,\n", + " (True, False, False, True, False): 2.9640600000000024e-06,\n", + " (True, False, False, True, True): 2.994000000000003e-08,\n", + " (True, False, True, False, False): 2.8143599999999996e-05,\n", + " (True, False, True, False, True): 6.566839999999998e-05,\n", + " (True, False, True, True, False): 0.00025329240000000004,\n", + " (True, False, True, True, True): 0.0005910156,\n", + " (True, True, False, False, False): 9.405000000000008e-08,\n", + " (True, True, False, False, True): 9.500000000000009e-10,\n", + " (True, True, False, True, False): 4.950000000000005e-09,\n", + " (True, True, False, True, True): 5.0000000000000054e-11,\n", + " (True, True, True, False, False): 5.699999999999999e-08,\n", + " (True, True, True, False, True): 1.3299999999999993e-07,\n", + " (True, True, True, True, False): 5.130000000000001e-07,\n", + " (True, True, True, True, True): 1.197e-06},\n", + " 32,\n", + " 0.9999999999999999)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def full_joint(net):\n", + " rows = itertools.product(*[var.domain for var in net.variables])\n", + " return {row: joint_probability(row, net)\n", + " for row in rows}\n", + "\n", + "def joint_probability(row, net):\n", + " evidence = dict(zip(net.variables, row))\n", + " def Pvar(var): \n", + " return var.cpt[tuple(evidence[v] for v in var.parents)][evidence[var]]\n", + " return prod(Pvar(v) for v in net.variables)\n", + " \n", + "def prod(numbers):\n", + " product = 1\n", + " for x in numbers:\n", + " product *= x\n", + " return product\n", + "\n", + "j = full_joint(alarm_net)\n", + "j, len(j), sum(j.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "({(False, False, False, True, True): 0.23895323731595236,\n", + " (False, False, True, True, True): 0.3013824614795795,\n", + " (False, True, False, True, True): 0.0003403339180750413,\n", + " (False, True, True, True, True): 0.17515213192200013,\n", + " (True, False, False, True, True): 1.4365911696438334e-05,\n", + " (True, False, True, True, True): 0.2835830968876924,\n", + " (True, True, False, True, True): 2.399116849772601e-08,\n", + " (True, True, True, True, True): 0.00057434857383556},\n", + " 8,\n", + " 1.0)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def joint_distribution(net, evidence={}):\n", + " \"Given a Bayes net and some evidence variables, return the joint distribution over all variables.\"\n", + " values = [({evidence[var]} if var in evidence else var.domain)\n", + " for var in net.variables]\n", + " return ProbDist({row: joint_probability(row, net)\n", + " for row in itertools.product(*values)})\n", + "\n", + "def joint_probability(row, net):\n", + " evidence = dict(zip(net.variables, row))\n", + " def Pvar(var): \n", + " return var.cpt[tuple(evidence[v] for v in var.parents)][evidence[var]]\n", + " return prod(Pvar(v) for v in net.variables)\n", + " \n", + "def prod(numbers):\n", + " product = 1\n", + " for x in numbers:\n", + " product *= x\n", + " return product\n", + "\n", + "j = joint_distribution(alarm_net, {JohnCalls:True, MaryCalls:True})\n", + "j, len(j), sum(j.values())" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Burglary, Earthquake, Alarm, JohnCalls, MaryCalls]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alarm_net.variables" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'tests pass'" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def tests():\n", + " ProbDist({'heads': 1, 'tails': 1}) == ProbDist(heads=2, tails=2) == {'heads': 0.5, 'tails': 0.5}\n", + " ProbDist(0.2) == ProbDist({False: 0.8, True: 0.2})\n", + " \n", + " CPT(0.2, []) == CPT({(): {False: 0.8, True: 0.2}}, [])\n", + " \n", + " return 'tests pass'\n", + " \n", + "tests()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "The entries in a `CPTable` are all of the form `{(parent_value, ...): ProbDist}`. You could create such a table yourself, but we provide the function `CPT` to make it slightly easier. We provide functions to verify CPTs and ProbDists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "button": false, + "collapsed": true, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "The one method, `P`, gives the probability distribution for the variable, given evidence that specifies the values of all the parents.\n", + "(If you don't know the values for all the parents, later we will see that `enumeration_ask` can still give you an answer.)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.7, True: 0.3}" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ProbDist(.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "T = True \n", + "F = False\n", + "\n", + "def CPT(data, \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "Now name the variables and ask for **P**(*Alarm* | *Burglary*=*f*, *Earthquake*=*t*):" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.71, True: 0.29}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Alarm.P({Burglary:F, Earthquake:T})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Asia\n", + "https://www.norsys.com/tutorials/netica/secA/tut_A1.htm\n", + " \n", + "Asia = (BayesNet()\n", + " .add('VisitAsia', [], 0.01)\n", + " .add('Smoker', [], 0.30)\n", + " .add('TB', ['VisitAsia'], {T: " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "button": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "source": [ + "# Flu Net" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "sick = (BayesNet()\n", + " .add('Vaccinated', [], {(): 0.35})\n", + " .add('Flu', ['Vaccinated'], {T: 0.075, F: 0.45})\n", + " .add('Fever', ['Flu'], {T: 0.75, F: 0.25})\n", + " .add('Headache', ['Flu'], {T: 0.7, F: 0.4}))" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.6, True: 0.39999999999999997}" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "globals().update(sick)\n", + "\n", + "enumeration_ask(Headache, {Flu: False}, sick)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.386842105263158, True: 0.613157894736842}" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Headache, {Vaccinated: False, Fever: True}, sick)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.7158281646356071, True: 0.2841718353643929}" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {JohnCalls: True, MaryCalls: True}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.9999098156062451, True: 9.018439375484353e-05}" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {JohnCalls: False, MaryCalls: False}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.993123753926579, True: 0.0068762460734210235}" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {JohnCalls: False, MaryCalls: True}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{False: 0.9948701418665987, True: 0.005129858133401302}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enumeration_ask(Burglary, {JohnCalls: True, MaryCalls: False}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [], + "source": [ + "# Not executable yet\n", + "weather = (BayesNet()\n", + " .add('Yesterday', [], {(): {'rain': 0.2, 'sun': 0.8}})\n", + " .add('Pressure', [], {(): {'lo': 0.3, 'hi': 0.7}})\n", + " .add('Today', ['Yesterday', 'Pressure'], \n", + " {('rain', 'lo'): {'rain': 0.7, 'sun': 0.3},\n", + " ('rain', 'hi'): {'rain': 0.5, 'sun': 0.5},\n", + " ('sun', 'lo'): {'rain': 0.2, 'sun': 0.8},\n", + " ('sun', 'hi'): {'rain': 0.1, 'sun': 0.9}}))\n", + " \n", + "globals().update(weather)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "button": false, + "collapsed": false, + "deletable": true, + "new_sheet": false, + "run_control": { + "read_only": false + } + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "True", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0menumeration_ask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYesterday\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mToday\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'rain'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweather\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36menumeration_ask\u001b[0;34m(X, e, bn)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menumerate_all_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mProbDist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36menumerate_all_vars\u001b[0;34m(vars, e, bn)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mY\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0menumerate_all_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36menumerate_all_vars\u001b[0;34m(vars, e, bn)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n\u001b[0;32m---> 22\u001b[0;31m for y in (True, False))\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n\u001b[0;32m---> 22\u001b[0;31m for y in (True, False))\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: True" + ] + } + ], + "source": [ + "enumeration_ask(Yesterday, {Today: 'rain'}, weather)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 195e388a294885eb5b9aaaf5dbc1dbeade072fb7 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 12 Jul 2016 20:07:39 +0530 Subject: [PATCH 348/513] gets latest updates (adds MNIST data) from aima-data submodule --- aima-data | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aima-data b/aima-data index 1ad2ae2d3..a21fc108f 160000 --- a/aima-data +++ b/aima-data @@ -1 +1 @@ -Subproject commit 1ad2ae2d378f658d8f0ff8f4d2202b66b675397f +Subproject commit a21fc108f52ad551344e947b0eb97df82f8d2b2b From cb0895a781150ade32cb4ccab56a80c5dc23d2ce Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani 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z=;&7$Ifk4Iy?Kq<_2Y{jVPe9t_y%5rV;F6Ul2^Q0*>#x5M9M-?p4|E*sK=K^$UYcD+db}J2L0g z!(V#idWO`I7k3cuPf6ieN1R?#3A+oJ7;D?;XwGV)Jw+}`wDx;tHF1r?*e?xx!;I!J z6CM4&nj`bvirEuUQ}(Ha#Q1_11Ep4wMerv20_%2USs9Or8acTUYn3-QGFE zaz?ToplJ{Hs<6FO6Urk*L!Q?9`!Ce7vu<=Mxj$9YnUH#Bz)ORPy;G^7S|B+oU{63< z3wx44#4d?{A*6?z-%RSIkW6UE%WBsV5@_kfk+)4=Ift|d%Po*D|5!&?E6>e+fT@U9 zkzs8XmP@O6Joq%Ye;mzQ-x*1D zfF_Mv1qF;yjlSsCnrmvnozL7WWxXxwg7|crii | parents(Xi)** i.e. the probability distribution from which the value is sampled is conditioned on the values already assigned to the variable's parents. This can be thought of as a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource prior_sample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function **prior_sample** implements the algorithm described in **Figure 14.13** of the book. Nodes are sampled in the topological order. The old value of the event is passed as evidence for parent values. We will use the Bayesian Network in **Figure 14.12** to try out the **prior_sample**\n", + "\n", + "\n", + "\n", + "We store the samples on the observations. Let us find **P(Rain=True)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 1000\n", + "all_observations = [prior_sample(sprinkler) for x in range(N)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we filter to get the observations where Rain = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rain_true = [observation for observation in all_observations if observation['Rain'] == True]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can find **P(Rain=True)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "answer = len(rain_true) / N\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To evaluate a conditional distribution. We can use a two-step filtering process. We first separate out the variables that are consistent with the evidence. Then for each value of query variable, we can find probabilities. For example to find **P(Cloudy=True | Rain=True)**. We have already filtered out the values consistent with our evidence in **rain_true**. Now we apply a second filtering step on **rain_true** to find **P(Rain=True and Cloudy=True)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rain_and_cloudy = [observation for observation in rain_true if observation['Cloudy'] == True]\n", + "answer = len(rain_and_cloudy) / len(rain_true)\n", + "print(answer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rejection Sampling\n", + "\n", + "Rejection Sampling is based on an idea similar to what we did just now. First, it generates samples from the prior distribution specified by the network. Then, it rejects all those that do not match the evidence. The function **rejection_sampling** implements the algorithm described by **Figure 14.14**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource rejection_sampling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function keeps counts of each of the possible values of the Query variable and increases the count when we see an observation consistent with the evidence. It takes in input parameters **X** - The Query Variable, **e** - evidence, **bn** - Bayes net and **N** - number of prior samples to generate.\n", + "\n", + "**consistent_with** is used to check consistency." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource consistent_with" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To answer **P(Cloudy=True | Rain=True)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = rejection_sampling('Cloudy', dict(Rain=True), sprinkler, 1000)\n", + "p[True]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Likelihood Weighting\n", + "\n", + "Rejection sampling tends to reject a lot of samples if our evidence consists of a large number of variables. Likelihood Weighting solves this by fixing the evidence (i.e. not sampling it) and then using weights to make sure that our overall sampling is still consistent.\n", + "\n", + "The pseudocode in **Figure 14.15** is implemented as **likelihood_weighting** and **weighted_sample**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource weighted_sample" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**weighted_sample** samples an event from Bayesian Network that's consistent with the evidence **e** and returns the event and its weight, the likelihood that the event accords to the evidence. It takes in two parameters **bn** the Bayesian Network and **e** the evidence.\n", + "\n", + "The weight is obtained by multiplying **P(xi | parents(xi))** for each node in evidence. We set the values of **event = evidence** at the start of the function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "weighted_sample(sprinkler, dict(Rain=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource likelihood_weighting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**likelihood_weighting** implements the algorithm to solve our inference problem. The code is similar to **rejection_sampling** but instead of adding one for each sample we add the weight obtained from **weighted_sampling**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "likelihood_weighting('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gibbs Sampling\n", + "\n", + "In likelihood sampling, it is possible to obtain low weights in cases where the evidence variables reside at the bottom of the Bayesian Network. This can happen because influence only propagates downwards in likelihood sampling.\n", + "\n", + "Gibbs Sampling solves this. The implementation of **Figure 14.16** is provided in the function **gibbs_ask** " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource gibbs_ask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In **gibbs_ask** we initialize the non-evidence variables to random values. And then select non-evidence variables and sample it from **P(Variable | value in the current state of all remaining vars) ** repeatedly sample. In practice, we speed this up by using **markov_blanket_sample** instead. This works because terms not involving the variable get canceled in the calculation. The arguments for **gibbs_ask** are similar to **likelihood_weighting**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gibbs_ask('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()" + ] } ], "metadata": { @@ -948,6 +1241,10 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" + }, + "widgets": { + "state": {}, + "version": "1.1.1" } }, "nbformat": 4, From 935822e314342b2784cd6ae1ee2bdf3ef16aca43 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Wed, 13 Jul 2016 12:17:49 +0530 Subject: [PATCH 350/513] adds method to load MNIST data in learning notebook --- learning.ipynb | 139 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) diff --git a/learning.ipynb b/learning.ipynb index 73b743d19..b9b9ed7e3 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -53,6 +53,145 @@ "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical Machine Learning Task\n", + "\n", + "### MNIST hand-written digits calssification\n", + "\n", + "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n", + "\n", + "The dataset has **60,000 training images** each of size 28x28 pixels with labels and **10,000 testing images** of size 28x28 pixels with labels.\n", + "\n", + "In this section, we will use this database to compare performances of these different learning algorithms:\n", + "* kNN (k-Nearest Neighbour) classifier\n", + "* Single-hidden-layer Neural Network classifier\n", + "* SVMs (Support Vector Machines)\n", + "\n", + "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n", + "\n", + "Let's start by loading MNIST data into numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os, struct\n", + "import array\n", + "import numpy as np\n", + "\n", + "def load_MNIST(path=\"aima-data/MNIST\"):\n", + " \"helper function to load MNIST data\"\n", + " train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n", + " train_lbl_file = open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\")\n", + " test_img_file = open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\")\n", + " test_lbl_file = open(os.path.join(path, 't10k-labels-idx1-ubyte'), \"rb\")\n", + " \n", + " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", + " tr_img = array.array(\"B\", train_img_file.read())\n", + " train_img_file.close() \n", + " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", + " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", + " train_lbl_file.close()\n", + " \n", + " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", + " te_img = array.array(\"B\", test_img_file.read())\n", + " test_img_file.close()\n", + " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", + " te_lbl = array.array(\"b\", test_lbl_file.read())\n", + " test_lbl_file.close()\n", + "\n", + "# print(len(tr_img), len(tr_lbl), tr_size)\n", + "# print(len(te_img), len(te_lbl), te_size)\n", + " \n", + " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.uint8)\n", + " train_lbl = np.zeros((tr_size,), dtype=np.int8)\n", + " for i in range(tr_size):\n", + " train_img[i] = np.array(tr_img[i*tr_rows*tr_cols : (i+1)*tr_rows*tr_cols]).reshape((tr_rows*te_cols))\n", + " train_lbl[i] = tr_lbl[i]\n", + " \n", + " test_img = np.zeros((te_size, te_rows*te_cols), dtype=np.uint8)\n", + " test_lbl = np.zeros((te_size,), dtype=np.int8)\n", + " for i in range(te_size):\n", + " test_img[i] = np.array(te_img[i*te_rows*te_cols : (i+1)*te_rows*te_cols]).reshape((te_rows*te_cols))\n", + " test_lbl[i] = te_lbl[i]\n", + " \n", + " return(train_img, train_lbl, test_img, test_lbl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are gonna use to train & classify hand-written digits in various learning approaches." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train_img, train_lbl, test_img, test_lbl = load_MNIST()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n", + "\n", + "Each 28x28 pixel image is flattened to 784x1 array and we should have 60,000 of them in training data. Similarly we should have 10,000 of those 784x1 arrays in testing data. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training images size: (60000, 784)\n", + "Training labels size: (60000,)\n", + "Testing images size: (10000, 784)\n", + "Training labels size: (10000,)\n" + ] + } + ], + "source": [ + "print(\"Training images size:\", train_img.shape)\n", + "print(\"Training labels size:\", train_lbl.shape)\n", + "print(\"Testing images size:\", test_img.shape)\n", + "print(\"Training labels size:\", test_lbl.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize some of the images from training & testing datasets." + ] + }, { "cell_type": "code", "execution_count": null, From 51f39e520213597fe7cdac2e33e922c730d28de2 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Wed, 13 Jul 2016 12:38:44 +0530 Subject: [PATCH 351/513] adds visualisations of MNIST handwritten digits --- learning.ipynb | 120 ++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 113 insertions(+), 7 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index b9b9ed7e3..7699016e4 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -58,15 +58,17 @@ "metadata": { "collapsed": true }, - "source": [] + "source": [ + "## Explanations of learning module goes here" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Practical Machine Learning Task\n", + "# Practical Machine Learning Task\n", "\n", - "### MNIST hand-written digits calssification\n", + "## MNIST hand-written digits calssification\n", "\n", "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n", "\n", @@ -84,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -93,7 +95,22 @@ "import os, struct\n", "import array\n", "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ "def load_MNIST(path=\"aima-data/MNIST\"):\n", " \"helper function to load MNIST data\"\n", " train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n", @@ -142,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -162,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -189,7 +206,96 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visualize some of the images from training & testing datasets." + "Let's visualize some random images from training & testing datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "classes = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n", + "num_classes = len(classes)\n", + "\n", + "def show_MNIST(dataset, samples=8):\n", + " if dataset == \"training\":\n", + " labels = train_lbl\n", + " images = train_img\n", + " elif dataset == \"testing\":\n", + " labels = test_lbl\n", + " images = test_img\n", + " else:\n", + " raise ValueError(\"dataset must be 'testing' or 'training'!\")\n", + " \n", + " for y, cls in enumerate(classes):\n", + " idxs = np.nonzero([i == y for i in labels])\n", + " idxs = np.random.choice(idxs[0], samples, replace=False)\n", + " for i , idx in enumerate(idxs):\n", + " plt_idx = i * num_classes + y + 1\n", + " plt.subplot(samples, num_classes, plt_idx)\n", + " plt.imshow(images[idx].reshape((28, 28)))\n", + " plt.axis(\"off\")\n", + " if i == 0:\n", + " plt.title(cls)\n", + "\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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C4o8x3scHiT9GPU8dEn2M8T4+SPwx6nnqkOhjVOVJURRFURQlDHTxpCiKoiiK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\nDPUcGTBgA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bn51KlT5rRc7HcBCLfvOGFgTR0gqt7NBV8aTXGX+jpthmaOkQcDnjx5UvHc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\nERERC5o8iYiIiFj4D4tzQZGGWbuPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# takes 5-10 secs. to execute the cell\n", + "show_MNIST(\"training\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CBWNir+INOQ9t2rShbdu2Qe/98ssvAGzcuDHudsWDEiVKAHDRRRcBMHnyZPP6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Zk0JK5Xrx4A55xzDuDZucgpPdauaMeOHe46o6KVq666yiWYx3LVVVe5HprJxpQnwzAMwzCMAIRC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\n/JJvJfpHK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# takes 5-10 secs. to execute the cell\n", + "show_MNIST(\"testing\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## kNN classifier" ] }, { From 6eeea3f153ad4dbc62a1f1ddebbb7b3cfd79dc40 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 15 Jul 2016 12:43:27 +0530 Subject: [PATCH 352/513] adds visuals og average images from MNIST dataset --- learning.ipynb | 141 ++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 127 insertions(+), 14 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index 7699016e4..550c96edd 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -68,7 +68,7 @@ "source": [ "# Practical Machine Learning Task\n", "\n", - "## MNIST hand-written digits calssification\n", + "## MNIST handwritten digits calssification\n", "\n", "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n", "\n", @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -206,12 +206,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's visualize some random images from training & testing datasets." + "To get a better understanding of the dataset, let's visualize some random images for each class from training & testing datasets." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -247,16 +247,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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C4o8x3scHiT9GPU8dEn2M8T4+SPwx6nnqkOhjVOVJURRFURQlDHTxpCiKoiiK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\njLNnzzb0O9McI5OGW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KhN9OZr179wacQfCPP/4IQPXq1fP0nPHsY6tWrQA44YQTAHjvvfcAmDNnDvXr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\n//jjj/aPP/5oN2vWzG7WrFm6+6Wt8l7NnTvXzp8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GqPvoj+zZs/PHH38Ador/U089ZdTG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\nS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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -270,16 +270,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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CBWNir+INOQ9t2rShbdu2Qe/98ssvAGzcuDHudsWDEiVKAHDRRRcBMHnyZPP6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Zk0JK5Xrx4A55xzDuDZucgpPdauaMeOHe46o6KVq666yiWYx3LVVVe5HprJxpQnwzAMwzCMAIRC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\nIbzq5LpLly5Jkq5du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TQ1ajEvMNpEePHsaYrnXr1oDbGTzekWR3iB+VLJANGzaY3e4DDzwAZN50Twzr8ufPb8zP\n/KY4Cc2bN0/lcn/w4EHTd8rvSF5d4cKFTSJxuIqTIC7IgG/nnzIHS3qCJRLXXHNNMvNZcE0Tk5KS\nKFmyZNDfq1q1aoYKR6wRlenpp58G3DwncPOVAknZ923VqlU8/vjjgJuTF4i8hzjId+7cOd18zFhw\n/PhxU2Aj19JPP/0UIGjhRvv27Y26JEnzH374IeAU3WSlVD/S9O/fH3ALhoLxwgsvmHtAeu8hVgRS\n4ALuvTMSqhOo8qQoiqIoihIWvlCeMkLizBn1V5IqGakgePnllwH45JNPjCoViKghUsLoVwJ3RZnt\nReQlgwYNMnH6zCpOKY3hAO67776sDy7GHDt2jLPPPhvImkFbLAilijFctmzZwjfffBPx91VCo3r1\n6qkqzMSY+I477qBZs2ZBf+/w4cOmRDwSPUMjgZjSXnXVVUDwyjqpimvXrp3J7ZLH7r77bpNTk17V\n3aBBgwD4448/IjPwLHDy5EmjrojJpZidPvvss8awV6rnpkyZYhQpqWh/5513YjrmjJB78x133JHm\na6R118KFC2nTpk2y5woVKmTas4i6Fslq9LSIi8WT3GRCTZaWG/Rff/0FOIuolEmSAHXr1gXc8vdg\n4UA/IAmRx44d8703TlpkJtEyW7ZspjmrJP2JbD558mTfLz5+//134yUmC+AiRYqYMIkkfHodCkgL\nCX9nBknYPHjwIOCWGs+cOTNVcqdfGTx4MOD0OUvJqVOnjOePNDiOl3mlRDoxpFe6nzdvXtMT7IYb\nbgCcBsjg+gbFGtksy3U+pTM8OIsmgM8//9w8Vq5cOQDjwZYWn3zyCQCzZs3K+mAjxMmTJ80CSTak\nsuh44IEHzMLiggsuAJyxy0Iqkv0JI4n4aAWGXVMi59ivv/5q7oehIuE6ue5GCg3bKYqiKIqihIGv\nlSdxCw3sRZQZpIt7SiR8cu655wL+U54k2e3iiy8GYM+ePUZ6TmRESn/llVeSJRqD4zALrtGbn/n5\n559Nb62BAwcC8L///c+Y20nipqgyfiPwvJOk2ZSUKFEiVainZMmSJhFXjE9ll/zDDz9EY6gRIWXh\ngSRWr1+/nt9//z3Zc9mzZzc9z2RnL4muCxcujPJIw0f67kmILjNISETSHMTCwSvlSVRdMVR87LHH\nTAhILDWC2YJICKhs2bLphuvEsDlUE8pYI2q+HHezZs0y1xbh66+/NqqNJJiLGhxPiI2B/AwHCXOK\nUhopVHlSFEVRFEUJA18rTxLblf5mmaVXr16RGE7MKV++PODugKWtRaIiPeFWrVoFQOnSpY0xqiSr\n+rXMPSMkf2bevHm8/fbbgNMrC6BevXqA27/PLwSWe4u6Irt2oWPHjiZnQdi7dy8ff/wx4KqmguSn\n+BHJ6ZGfGSE5a9OmTQNcJfHiiy/2XWGHJHkXLFgwVUsKUdwGDx7MnDlzALeXmNgTFChQwFi/iPor\n869fv36a6n4skNynlD0mUyJGraICp8fzzz/PjBkzsj64GCC5XoEFHjL2kydPGqVQvtNatWrFeITp\ns3r1asCxTgDnuJIiALleiOoZqHLKv3Pnzm16GaZkw4YNUUuQ9/XiSRpYyk117969Yf2+SLgi68Yb\nEk4UZDHpV8SR+uGHHwbcapDt27ebRY8shqRyslixYubklgRO6TG2dOlS8935VToPl2XLlplqMwlJ\nSnK13wgMaaS8MUnIYMOGDSYMMGzYMMDxR9q1axfghA3ATYqfOXNmdAcdQyQ8Jz5sy5YtA6BVq1bG\nk84vyHcZrM+khHoCq5lThjD379/PQw89BDjpA4Dx/qpRo4ani6dQqFKliik6keKNYF5QckOOh6bd\n0hj4pZdeAuDQoUPmPJ03bx7gzFGqEuV+KNdXvzjnnzhxAnDDaqNGjTK+jzt27ACCL57kfiMb00Dk\n+L3xxhuj5gOoYTtFURRFUZQw8LXyJDucYM6p6XHeeecB8P777wNuYnhKpBTVr07CsvqWHdLSpUu9\nHE66FC9e3KgKtWvXTvZcYAm0eKzIXEqVKmXkZHGvlkTcYcOGxaWjekYcOnQo2f9l5//WW295MZw0\nufPOOwF31xeIfI/i8p4SUYslwVPOxcw6lPsZ2Q2L8vTEE0+YsIkfvIEyomLFikDGPnqC3y1CAhG/\nn+eee86EqwKVt5QqnKjfonj4EelVJ6quRGSaN2/OV199ley1tm0bZX/69OmAq5ROmTIlFsPNFDK3\n9GjcuDHghvsCkeR4KViJBqo8KYqiKIqihIGvlSfpsySxzZQ79kCSkpLo3bs34O6YRYFKC0mC3bdv\nX5bHGg1k9y67o5Tl0n5AXGuHDBkSUhmp5EG1aNEi1XP33HMPAK+99lpYY5Bu2dLzye9IDkJa//cL\nkoibGcSs75xzzgFcVSaREUXmyiuvpFq1akB8KE+STN6wYUO+++67DF8vSeXbt2+P6rgigSQSZ2QH\nIgbM8VCQIrnAYiop19K0zlex/JHcxIzui36nQoUKgKMmpkSMamNRmKLKk6IoiqIoShj4Wnlq2LAh\n4OaEbNy4MdVrZNfeu3dvLrnkkjTfS8r8AysMUpbs+g2JTa9btw5wrOn9Rrdu3YDQzct++uknwJ1T\nYJ8iafsglTsZlXuLBcVtt90GuDsyrxHTyIsvvpgtW7YAGHPTffv2pergLqXGZ511lq9L+cNBKrMk\nX9ErI8VYkJSUlOwnuC1A/IK0phg0aFCqNiZiYPrcc8+ZfEPJOUlPzY1F/7DMIteVjFpyiJovVijh\n5td6gdwXpeVYegqxZVncf//9gJu76Gej2owoXry4aZcTWI0uubHSqiZl7lc08MXi6ccffzSJp+Kq\nHUgk+rmNHj0agKFDh2b5vWKNJNn6Odk2WNnvihUrAOf7k39LCa3wyiuvmIT9Vq1aJfs5YsQIcxNO\nyVlnnWWcY/0mtYvzceCxJiHn48ePp1o8iT9QoiycypYtyxlnnAG4IaG0EsvjGSn5Fjf1Dh06AM73\n6TcH/AkTJgDwxhtvGPdtsRqQm2rdunX57LPPAPeckp5w33//vfm9MmXKAO6GyY/hO/FSS89BfMeO\nHcZFPx4WTeB4/vXo0QNwk6KD0ahRI8BpfizXU/GRixf/qmDkzZuXKlWqpHpcrCVi2YfQv1sHRVEU\nRVEUH+IL5ennn3/m+uuvB9xwTiSZO3eu6e8TT4iaE0zV8QsSogrWEVuMFMUgMRgLFiwwRpiyexDz\ntwceeIAGDRoAcN999wGYhNZatWqZBMi01CmvkHBrILK7DwwVy99HVNFEoWHDhqbYQexG4gFx8pfC\nBbGOCFbCf8EFF5iEVSmZlrD68OHDfVu8cOjQIcaMGQO46pIU2kjxB2DOO/kZaDciSOK4Xyw28ubN\na4pNQrlmDhs2zKQRxAs5cuQwit9dd90FuL1A69WrZ66dUqhh27Yp5RfX/3hE3NMlVOkHVHlSFEVR\nFEUJAyu9mHBEPsCyQvoAKTcX4y5pXREJatasmekkOdu2M9zChDrHcDj33HNN6bP0lJL2ApEmozlG\nY37BEEsK+e7HjRtnHpOdpCR4lilThuHDhwPu3yc9YjlHaYMQmCQt4w8836SNTSRaQXh1nAbjl19+\nMbmLdevWBWDJkiVZft9oz1FyKURRqVq1KuDs9qXUXRKRmzZtaq5Zkksiyk1W+tp5eS527NiR5s2b\nA6nb8ViWZdpciNIkydjhWr1Ea46dOnVKpTwFu7+98cYbgJskHmmieZzmyZPHXANFgZKWVwUKFCBP\nnjyAm/BfqVIlo+xnxXokJbG+3ohqJipvILt37zaPR7IwJcPj1C+LJ0FCHr179zaScbiIZC5y5fjx\n4zl58mSm3svLm5KEpaSRZTBfi0jgl8VTSlq1asWgQYMAt/+bhIP+/PNP01g3lMbRsZyjhDB79uxp\njmcZq23bvP7664BbGRKJZFU/LJ4kMXXGjBmmyEO81CJR7BDtOZYtWxZwG1NLiDVbtmypXNa3bt1q\nqjy//fZbIH0fulDx67kYSaI1x507d5pChWCLJ1lQtGzZEnBd8iNNtI9TuS9KpbP0USxQoIAJPUu1\n2alTp8x9JJLE+noj4X8JUYJbfX/jjTdGdGEoZDRHDdspiqIoiqKEge+UJyF//vwmiVy6tYv6sHr1\n6nQtB8TzQcqks4KXO3pRXUSKjMR8ghFPu13xOPnmm2/C2lHF0xwzgx+Upzp16gBOGOuyyy4DXLuK\nSBCrOYo7uJTyX3rppcaB+r333gMcFSO9QojMkujHKURvjr/++qvx/kmpPO3fv9+owNH2APLDuRht\n/KA8SaFNepYNWUGVJ0VRFEVRlAjiW+XJL+guIv7nB4k/Rz1OHRJ9jvE+P4jeHKtWrWrUCClUkPvb\nXXfdxcSJEzPztmGjx6lDJOf4wgsvAE4O5aOPPgrA9OnTgeiZR6vypCiKoiiKEkFUecoA3UXE//wg\n8eeox6lDos8x3ucHiT9HPU4dEn2OqjwpiqIoiqKEgS6eFEVRFEVRwiDqYTtFURRFUZREQpUnRVEU\nRVGUMNDFk6IoiqIoShjo4klRFEVRFCUMdPGkKIqiKIoSBrp4UhRFURRFCQNdPCmKoiiKooSBLp4U\nRVEURVHCQBdPiqIoiqIoYaCLJ0VRFEVRlDBIivYHJHpzQEj8Ocb7/CDx56jHqUOizzHe5weJP0c9\nTh0SfY6qPCmKoiiKooSBLp4URVEURVHCQBdPiqIoiqIoYRD1nCdFCUa5cuUA6Nu3L+3btwdg3Lhx\nALz11lsAnDhxgnXr1nkzQEVRFEVJA1WeFEVRFEVRwsCy7egmxMcq475+/fosXLgQgFOnTiV7bsaM\nGbz00ksAfPnll2G9r5+qCsqXL8+HH34IwAUXXABAtmzO+vfnn3/muuuuA2Dz5s1hvW8sql8KFSoE\nwD333ANA9+7dAVeBOv05Mh4Ajh8/znvvvQfAvffeC8C///6bqc+PZYVP0aJFAejWrRvnn38+ANOn\nTwdg0aJFHD9+PFIfZYjmcVq4cGGefvppAAYMGADAP//8k5m3onjx4uzcuROAihUrAvDbb7+F9Lt+\nOhejhZ8r0eRaky9fPgDatm0LQKNGjbjllluSvVbUZFGRA/HzHCOBHqcOiT5HVZ4URVEURVHCIO6V\np/LlywPw3XffUbhwYcBVLgIRxaJDhw4AzJs3L6T399MKe8mSJVx++eUpPxtw5tyiRQsAPvnkk7De\nN9o7wTx58tCoUSMAXnzxRQBy5coFwEUXXWReV69ePQAGDRoEYFQbgEsuuQSANWvWZGoMsdztNmzY\nEIDPPvss1XOzZs3i+++/B9xduSgvKRXTcIjmcdqgQQM+//xzAM4991wgfHVTqFOnjlF/K1euDMSn\n8lSpUqU0n2vXrh05cuQA3GMhV65czJ07F4Aff/wRwKiqgfhZlalWrRoAK1euzPC1Y8aMAaBXr16p\nnvPzHCOBn47TjBAV8eDBg2H9XjzNMbNkNMe4TRiXRdNDDz0EuGGhtJDn5fWLFy8O+4DxipYtWwJw\n8cUXp/maFStW8N1338VqSGHx5ptvmjCj3FR69uwJwK5du8zrZsyYAbjhgTfffNM816RJEyDzi6dY\n8ueffwKwb98+s0jcuHEjAM2bNzffpywS58yZA8AzzzzDN998A8DJkydjOeSg5M+fH4DRo0dz2WWX\nAbBnz55MvZcskmfPnh2ZwcWApCTn8li3bl1at24NQPXq1QGoXbt22O8nf4Nt27YBwRdPfqNkyZIA\nDBkyhDp16iR7To7z8ePHs2XLFgB69OgBwMyZM2M4SiUcSpcuDcCDDz5orqvbt28HYP/+/dx9990A\n7Nixw5sBBuHMM88EYNOmTeaaKixZssQs6OW4W716NYBJEYgGGrZTFEVRFEUJg7hSniREVa9ePd5/\n/30gY8UpJRIaGjlyJHfeeWdkBxglypYtC0DevHnTfM2uXbvYvXt3rIYUErLDqVmzJqVKlQLg5ptv\nBtLfmW7YsAGAw4cPkydPHgBuuOEGwPne/M4vv/wCOIpFzpw5ATfUUbVqVR5++GHACe8AJtzaokUL\nXn31VcBNqPeSpk2bAk5xgnyXmVU3zzvvPCD889UL5Dt74oknAMz3FSqrV682SuOmTZsAR+leu3Yt\n4BR3+J2TQjcHAAAgAElEQVRWrVoBmEKBypUrc/ToUcBNeejWrRsAf/31l/m9KVOmxHCUyfnwww8p\nUKAAgAmRTp06FXCuJaJYiLINbsGKRCHkGrp///64iUxkhKhL8t0UL14cgOzZs5sUF0mRsCyL66+/\nHnCVVy+R72f58uWAc26mTMspXrw4jRs3Btxj8tdffwWgf//+fPzxx1EZmypPiqIoiqIoYRBXCeOj\nRo0CnHyZYOOWZOTevXsD7gp73rx5JtlRfu/XX39NlpCcFl4mxtWvXx+AL774QsYS7LMBZ6clO4Zw\niVYC59VXXw3A/PnzTa5SenlbKdmxYwfFihVL9tj9998PYKwnQiXUOVaoUCHkBObMIjtfKXCYNGkS\n4Oa2Bb4mVKJxnD7zzDMA3H333Sbnaf369WGNS5C5vf/++xw7dgxwd7uizmRErM5FKbN/4403zGOT\nJ08G4H//+x8AXbp0Mc+JyiH5e4cOHeLIkSOZ+myvk6lFYRR1SfK0jh49St++fQH3OptZojXH7du3\nm9yYlNfKjRs3mvwtUbPTu/c9/PDDjBgxIjPD8EUytVw/atWqxYIFCwC3IEXsbjZt2mTOZ/mbjB49\nmoEDBwIwdOjQNN8/2nOU8YtxcqASv2/fPsBVmQKjGKKYDh48GHCUK7l2SUQgVOI6YVwWP82aNQPg\n1ltvTfUakVZ79+5tDgpBLmZNmjRJlfyWL18+IwlmtnIomuTLl88sAkNBQgJ+xLbtTCWQ2rad6gKX\nlYq0UIj2wgncOcixKwtgcBMdvURCrF27dgWcEEZmF03BWLx4MRD6oilWnH322QAMGzYs2eM//vij\n8SeT0FU8hI8zg5ynF154IeCGLBcuXMiyZcs8G1cotGvXjn79+gGuD554quXKlcscd4EVynIM7t27\nF3AXx/GObFZmzJhhKs0l9WHRokXmdRKak3vgH3/8ke6iKVZIdXXK9IU9e/YYP0Mprgnkgw8+SPZz\n8ODBXHXVVUD4i6eM0LCdoiiKoihKGPhaeRLFSZJog/HTTz8BrqwejGCJ1MWKFaNu3bqAP5WnkiVL\nhhSGk5CPlL37iUAPp99//z3s3z98+LD5tyTZvv3221kfWJSR0IEUJ4C7S9qyZYvxCBKFQ3aEBw8e\n9MXOVwopJGQqpfVZQfzV/IwovRK6EtasWWMUp0Rm9uzZVK1aFXB36SlVOD/z5ZdfGg8xURFPnDgB\nOAqL2CkEI5gnWzwi35/YYOzdu9eEWQMVJ3D+Rs8++yzgWlLs37+fChUqALFR4dOiVq1aQR9funRp\nUMVJEFVKFKsNGzZowriiKIqiKIof8LXy1L9//zSfkwTkTp06Zeq9N2/ezOuvv56p3/UTfrZb+Oqr\nrwAnuTs9ZTAtPv30U26//XbAVbGKFCkCZL63WjQpUaIEAI888giQ3F1ZkqR37dpljELl9aLK3Xvv\nvWG7w0cDMceMJFJC7mfSUkfFDDJRkeO1SZMmRrHo06ePl0PKMumpTIFIUUugShzPSD6X/Jw8ebJJ\nnhZFfOLEiYBj9HrGGWcArrI/dOhQY5jpJSmNMIWPPvoo6OMSpRJjZbH12bp1K88991wURqjKk6Io\niqIoSlj4TnmSFfM777xjYq8pWbVqFddeey0QPJ8pJS+88IIpfZRKpyJFipj4sPSa8gPSduaDDz4w\nf4uUY48XfvjhByB4f6tQCCzdl52RGGj6Edn9BJuvGC+mzKcBzLHst+qzSCB2DFLZCv7NW5O+fSnx\nw048UhQvXtxYoIhxqSgTSUlJTJ8+HUhufJnIiJItarBUwC5cuNCrIUWUDh06UKNGDcC1vhFWr15t\n2pVlJjIQTdI6F+fPnx/08cceewxIbSQ9cuTIiORsBsN3iyfxfmnVqlWqMnUJ1V177bUhLZqk6WHZ\nsmXNwkPeMzBh3E+LJ3HgPv/8881YU44d4qPHW2Zp06YN4FpVAJQpU8ar4YRNYCl0KEgZvFzIEgkp\nda9SpQoABw4cSPMC6DVphe169OhhwsSffvopED8LKnFefvzxxwGnuW/KkEigp5h0bhBvIFlYLVmy\nJOpjjTVly5Y1ydRyrsp8V6xY4dm4skLFihWT/f/MM880rv7SHUCSqX/55Ze4K4QIFkIvVKgQ55xz\nTrLHxBtxwoQJURuLhu0URVEURVHCwBfKU758+UwfKXEIDUSUoZtuugkILVQHbtlmsJL/H3/8MZWp\nph/IKAFcJMjMuonHE4HKjfSB8zOyO5fdTrFixZg1axbgJC6CIztLgu6DDz4IYJyb165d6zv5PNIc\nOXLEt+FJSTaVkMEDDzwAOLt5+V4kbDx8+HCjQvlxPhImrlmzJuAm2q5cuZL77rsv2WtFfRk4cKCx\nfhHF6pprrgGcPo3plYjHI9WrV+ess85K9pj0wosnxFKkX79+pgNDIPL9JoKyffHFF6cya121apUp\nvhFeeeUVgKj2J1TlSVEURVEUJQx8oTzVq1cvaCsSyesRxSlcM8v0rA769evnS3PMjHjttdcAfxp7\nZhVJKg5m+BmtpL9IIqZyGamH0jNO2ga0aNECSDtJMtZIqwqhVKlSjBkzBnB7CkopeMGCBU3SrXDn\nnXea3Avp6C7s2bMnKmOOBJLXJDt0UWFGjBhhSrolyfqVV14xeUGy25eiBj8g1hgpW21Uq1aNe++9\nF3BbAUmeT2DrC8lLk3k/9thjJhfx0KFDUR59bJD80nijdu3agBt9ECWxQIECxhBSlMbHHnuMu+66\nC4Dx48cD3ppfhooUlXTu3BmAokWLAo7iK1EIUUfLlCkTcn5pJPF08SRhtSlTpiTr7yVIY87MLhQC\nPS8kKVIOKj/46UDy6kIgaIWhjH3jxo3JmpVmRNWqVc2CZPbs2VkdathIf6yePXvy7bffAm5Vh1QV\nHjx40PQglPBV5cqVAacvlSykJOylRB/xRZFqxxo1apikdrkQy423WLFiphdeKPih0i537tzpNu49\nefIk4G5UFi9ebBbEbdu2BZyFvngEiTu1NFSNZpJqVgkM9//xxx+A2wMU3BQJCdfJ/5s1a2a++3jv\n6ychnsDFk/hbBf4t/IRU6Y4aNcp0JJD0leHDhwNOE13ZAMhiYvfu3eack8rXeEBCc1IhKE2amzZt\nmippPHDtIGuFWCT8a9hOURRFURQlDDxVnsTV9Ywzzkglu/Xr1y/TOxxZrdapUwdwVuF+9UgKtGaA\n4OXtMvbVq1eH1Rm6Xr16dOzYEXB6AkF0d1aimknirfy/QIECxmtE1AxRHbNly2a6fkt/JWHv3r2m\n95IfuPDCC+nZsyeAkcclITwcJJk3ZZKjX5Cegl27dgWcMvemTZsCro+KdD2H1Mrwnj17zHHqx0T/\n1157zXiQvfzyy0D64cTff//dJPnL63v37s0dd9wBuM7Nt912G+AoVZHu4J5VxJ4g0ElbOs8HQ0KW\ngfTo0QOIf+VJwuR58uQx4U0pWPLTfaJAgQKmC8YVV1wBON+jpKNIcvvOnTtT/a5836IgxiuSutO6\ndWsAmjdvbvreBfYBFcsFud/FoohDlSdFURRFUZQw8FR5Si+xNjPdvCWmKwmfKd1GwS1h9AM5c+YM\nqXxUEnil5DQcZJUuyk80c70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yVZ4URVEURVHCwDfKk+QISAl0oOokcei77roLCD+xceXK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z76CHBX4YPc2/r27WvsCMRywFt1Kl26NGCrMxUrVjTXWimuWrhwobEqiTViPbBlyxbznUpnhsKF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\nTJs2Lc6jU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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -295,7 +295,120 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## kNN classifier" + "Let's have a look at average of all the images of training and testing data." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "classes = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n", + "num_classes = len(classes)\n", + "\n", + "def show_ave_MNIST(dataset):\n", + " if dataset == \"training\":\n", + " print(\"Average of all images in training dataset.\")\n", + " labels = train_lbl\n", + " images = train_img\n", + " elif dataset == \"testing\":\n", + " print(\"Average of all images in testing dataset.\")\n", + " labels = test_lbl\n", + " images = test_img\n", + " else:\n", + " raise ValueError(\"dataset must be 'testing' or 'training'!\")\n", + " \n", + " for y, cls in enumerate(classes):\n", + " idxs = np.nonzero([i == y for i in labels])\n", + " print(\"Digit\", y, \":\", len(idxs[0]), \"images.\")\n", + " \n", + " ave_img = np.mean(np.vstack([images[i] for i in idxs[0]]), axis = 0)\n", + "# print(ave_img.shape)\n", + " \n", + " plt.subplot(1, num_classes, y+1)\n", + " plt.imshow(ave_img.reshape((28, 28)))\n", + " plt.axis(\"off\")\n", + " plt.title(cls)\n", + "\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average of all images in training dataset.\n", + "Digit 0 : 5923 images.\n", + "Digit 1 : 6742 images.\n", + "Digit 2 : 5958 images.\n", + "Digit 3 : 6131 images.\n", + "Digit 4 : 5842 images.\n", + "Digit 5 : 5421 images.\n", + "Digit 6 : 5918 images.\n", + "Digit 7 : 6265 images.\n", + "Digit 8 : 5851 images.\n", + "Digit 9 : 5949 images.\n" + ] + }, + { + "data": { + "image/png": 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HYy6YarXakv8GQMvWdK5EtfVEpen69esAmlvHp6amZKVLNWtlZUXcLeGZbN1C\n14PWBK2jqampmPW+vLwccx9klV6BlorOCURFhbI4gxenp6fFpREqjLlcLpbLRW8CoMVMq5f3JJ/P\ni3VFNTEpYPeDEFp7bKPe3t6YZeu9b8k9FX6OthiByPrjPWD/Zr118LnOIdSpU8CTCLeCl0olSQ3C\nv62srEg7s3y0WPv6+sTypfKoXSp0GXz3u98FELnHqGZQYi+VSl05l5FlGhwcbDnDDIj6FS10olNu\n6Azd/BsVp7B98vm89BntAu302VrtoPs0A+I5JnnvK5VKS+4moKmmTkxMiNLB/9NbwPk+HXgMpKdA\n6fxRWkkJTzVgfx0eHpb+SZfzxMSEqPoMoqYC9eDBg8RM292cZ3XgP/va1NRUTOHn2NQqE9uxXC6L\nKsP+zDm1v78/tnlAz8dpPlv0nM++w3mhXq+La5HjTqcP4dzI54xW/3niCFWsvb291FRtU54MwzAM\nwzDa4FhkGE/yQerT3bmy5ipUJ7jka+fPn5dtp0zQx5ib/v5+WdUyGHRpaUlWt1QzukWSFUhlhYG2\nhUJBYkOYEHR3dzcWbJtkxXbDOgpjkGZmZiR2h/FMVCRGRkakncJs7vpE9jC2J5/Py2dodYbfG56W\nzXakEtWpOmpLLcwmrRPZhWcnNRqNWHxJX19fLBhSB/CGVp8OhkwTlrlYLEp8Eq3X/v7+WGwEFZXR\n0VEZZyxzrVaLnd+n04xQcdLnv4UpCtJA9x2OM7bL/v5+LOZObyQJ2yWfz7fEMwHJ8T6hQtwNdEA8\n66RTfITWPO89FW6gqRBTCT937pzMUTojORApHjp5LZDehhYdMxMqCY1GI5Yigf11cnJSNjdwfjo4\nOJBTK6g88fmwvb0dU9G08tSNMamz2GvPRJjYkwrZ22+/LUovy0zFip8HtMa/aVWd/9cNb4Y+DSQ8\naaBSqUh/4jyjFXluauBn7O3tST9n/fnsTEqIqWMC7Ww7wzAMwzCMLpGp8sSVprb6+Nrw8LCsMOnb\n5Gpxb29PVqKMQXj66aflOBNug+d71tbWxBfK69LSklhQXKWnTdK2dVoUtOr0Se704+odL0m7I0g3\n/fFhLM/Q0JCUnTsk2DZTU1OxmBlSqVTE5x2eX1ev1+XzGWcR/i/QVEiSrNIPQni8DC21sbExseS0\n1c5yhNdGoyH1ZfsuLCxIrALvD62/SqUi44AqmlZl0k7Ix+/VKhTQumuQZWVaAudcbKsx0FRt2Hf1\nDrtwl1Qz/VoiAAAJo0lEQVSnj9N5HKzD0NBQTB2t1WoyH4QJeHXMExWrqampll1q+v/q9brUTatR\n3VItGo2G9EG2CeM7t7a2cPnyZQDNeZJ9emJiQtoyjMXM5/MS48QdTVQTFxcXW45BAdLZGapJ2ukH\ntKauAZr99Pz586KiUc1YXV3FzZs3ATTPTdVxXFpx4nd2o59q1Syc/2ZnZ2MpbKiW3b59uyUZLT+L\n/TOMAysUCrEjbrodj6cT1uo4qLAfsg46ySvLvrOzI/2O843uh2m1WaaLJz4gNjY2RDKmBHn27Fk5\nI4wTFxdRevHEjjQ7O9vi1gOaQWNvvfVWYkBgmDsobcIH0ODgoEzAvLJe+uw2ToCHh4cxmTXpAM5u\nBpHrvEf8PpaNdZqampJ6sU5cKOm2Zz311lIdBA4061utVmWAsR31gqMThOkYRkdHY4vcQqHwxKzG\nXDRy4r5y5Yosnjixc/G3ubkpcrUOpu7mYaT6wZ90/tTjtoKzrEDUF0IXqk4/kdV2b52pnQ8ltsvo\n6Ki0I+cgvVGF7aizr/M13i9d13CzS61WS31BQRqNhnwvxxa34D/11FOyaGfWZhqp8/PzLRm8gea9\nWFxcxHe+8x0AwCuvvAIAcjj70tKS9OFuBP6TpPsYLu65SL5w4ULMzbW6uipuZZ1XDWjN7N9NV51G\nL574bBscHGzJqA60BofrTRG88p6Ez46enp7Ys0KHDnQrwzjR38vXwwPlz5w5I88Vsrm5KfMljbRu\nhOKY284wDMMwDKMNMlWeqBzcv39fFCEtp+vTooHmSrtcLrckRgOi1SpXnZQxaRnduHFDzvfhNmyd\nMbdbLoMweHhwcDCWTI+r793dXbGCeNUBkU86O6qbAcYs29bWlrgGeKXUrLf407XB9+iUEbTc2S7a\nXZR0ojslaroMdFK7TpzkHmb3dc61bFAAIkueVl6ocGh3DxWryclJaTuWmxsCbt++LX2XCpQ+960b\nJFl9ABL7LhApbzpwE4isdlp+HOO6Dvpzgc4FcD6OpHQM7If83tnZWVGVwkBqrfjqIHGqLFQtqAA8\nfPhQ2o/3pFKpdCUonp/P+89yUHny3kubcPzQjTc6Oip14pjkvHzjxg3cuHEDAGJqTalUSjUL/uMI\nwyD0ZgymVeA4nZ6eln6g3Y+sJ5VwrXpnnYhYpypIyqbNPsm6DgwMyN/YTwuFQmISYaA1W7n2YByH\n81KTQiaAaP7k3MO2KpVKMvb0JhSg9USDTrskTXkyDMMwDMNog0yVJ8ZBrKysSBI9HUPBFTDjErj6\nHB0dlf+lZbW0tCRni1FxYjDg4uKiWFm0BPURL93AORcLRC4UCrHzw8KAS6CpuOnXQku921YSV/ZU\ngpaWlmK+eAbvjY+Px5Qnqi7r6+stPnugeS+0T57fx3YvFouxJJl6O/aHQVtm4XeGiTBHR0claR0t\nQH1cR9i+xWIxpoy+9tpr8jtVKPrw9bb/rNAxT+HWYeecWIA6VYFOvAgk991uB6fqQHjeZ6qj+mgc\ntiPjZnR6CvaJYrEocw/bk+dp3b17V9QN9k0diN8NtMoGNI/H2d7eluDor371qwCa9R0cHIxtFuA4\n3dzcbDmnD2i17rOIYwtjSIeGhqQN6cFggH9/f38sme3a2lpMscjyKJaQRqMh5eE40l4X9l2q2lSb\ngKYqs7W1JQH+YYxUsViMxYtmqbjpcyPDWC+qwmNjY6LC6bjCJOUQSFaeOqV0Z7p4YsfY2tqSgG7e\nhPv378vDhQG2HOS5XE4GMieFO3fuyOTFiZHBkru7u4nn23QTPRj1ziYOZO2mAVpdGyzz1tZWLOtt\nUpBdNwgn2YODg5ZDVgG05HYK3W9sj0qlIj8/KUAzdKHV63W5B7x2ynWgg6eB5mSby+ViMroOsOT7\nOZnl8/lYlvh3331XFvl0pfDhu7a29thJIAuSJpukg5EJy5zL5WJB9Pybdqnqazf6LstcLpdlYc92\nPDg4kDmFbiw+gIeHh+V/+Z6VlRXp52xH5phZX1+PBcrX6/VM3CFhO1WrVXl4srzhJgAg7rrW/Tx8\nTxbo7P1c0A8PD8viiVe6ePSCXgcXh8ZqN4OlH4c23sLnw8rKisyrFBO4iNJuO32GK5+LXDRzvtnY\n2IjtLM1yQ4fehEJ3Hd2w2pBhWbU7MtylrHddJi2eOoG57QzDMAzDMNogU+WJK9xqtSryMFfMt2/f\nxte+9jUATetBn3/HleWTVp9ZysohOusvy16pVCTIPdwKrq19UqvVYtmPswruS1JnaMWxLZNW+k9a\n9Se10ePckvr3tFyXup34e+gufuedd2TrNi1Buu16enqkL1JR2tzclDYP3Y3VarWrmxgeR9L2ZZYr\nlP4rlUrLOXf8/yR3A9A6TpMycqcJ61Or1aT8HJNbW1tioWsXARDNO3ojB9/Pfs7PokpQrVZjKmjW\n8w9JUsBPEnpOSco1x7FHNUrPT5wzOXZ3d3cTXZDHhXq9Lv2Nc9H+/r64hNlf6ZHRCqk+o5Ape8Jn\nbKVSScxl1U30c04HxYdnQxLt/tbeizDE4klzi51tZxiGYRiGkQGZKk8aHQfEa6fOKDsu6PgBoPVc\nn9NAuCX8pBOqa/V6XfonLbuVlZVYzEiSupYUOxJej5M6oa86XofWO8emTiehY2bCGDUdwJmVlUu8\n97HzCEulksSlaQtY/w/QWp8w9UBW8YffT+j7mjTO2K46Oz4QKfth39UxpI+Lu8wS3U+prOzu7krM\nEvvnkwKh9XgL+6l+33EgSemmKk91u1KptGxMAaK2C9cPOmFxeKJBp8anKU+GYRiGYRht4NJeeTrn\njs/S9gPgvX/P0PzTXseTXj/g9NfR+mlEJ+uYRQLa095Pgc7UUSdSTIp54i4t7trq6emJ7Tzc398X\nhYK7nKlc1Gq1D6yQ2liMeL91DMdZLpcTVS2Mp3ySGgwkK96h+v1+2/M9+6ktnp6MDYSTXz/g9NfR\n+mnEaa/jSa8f0Pk6Jm1ICbe/J6Xb8N7H3HRJrq12sX4acdrraG47wzAMwzCMNkhdeTIMwzAMwzhN\nmPJkGIZhGIbRBrZ4MgzDMAzDaANbPBmGYRiGYbSBLZ4MwzAMwzDawBZPhmEYhmEYbWCLJ8MwDMMw\njDawxZNhGIZhGEYb2OLJMAzDMAyjDWzxZBiGYRiG0Qa2eDIMwzAMw2gDWzwZhmEYhmG0gS2eDMMw\nDMMw2sAWT4ZhGIZhGG1giyfDMAzDMIw2sMWTYRiGYRhGG9jiyTAMwzAMow1s8WQYhmEYhtEGtngy\nDMMwDMNoA1s8GYZhGIZhtIEtngzDMAzDMNrg/wOwiTPvh42pSgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average of all images in testing dataset.\n", + "Digit 0 : 980 images.\n", + "Digit 1 : 1135 images.\n", + "Digit 2 : 1032 images.\n", + "Digit 3 : 1010 images.\n", + "Digit 4 : 982 images.\n", + "Digit 5 : 892 images.\n", + "Digit 6 : 958 images.\n", + "Digit 7 : 1028 images.\n", + "Digit 8 : 974 images.\n", + "Digit 9 : 1009 images.\n" + ] + }, + { + "data": { + "image/png": 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SfjrinGO899LX2H+dc9I3aSPwOSwtLSVuu9q2nWEYhmEYRsYcCuWpu7tbvBgd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\nwzCMFjDjyTAMwzAMowX+P+71/Wk0f7yTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_ave_MNIST(\"training\")\n", + "show_ave_MNIST(\"testing\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## k-Nearest Neighbours (kNN) classifier" ] }, { From 6404d1d9c987ea1da114bf364e49b15e2b5a2506 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 15 Jul 2016 18:49:22 +0530 Subject: [PATCH 353/513] removes checking truth value of an array with more than one element --- learning.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/learning.py b/learning.py index 963f2dc44..fd622cdb5 100644 --- a/learning.py +++ b/learning.py @@ -84,8 +84,8 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, else: self.examples = examples # Attrs are the indices of examples, unless otherwise stated. - if not attrs and self.examples: - attrs = list(range(len(self.examples[0]))) + if attrs is None and self.examples is not None: + attrs = list(range(len(self.examples[0]))) self.attrs = attrs # Initialize .attrnames from string, list, or by default if isinstance(attrnames, str): From 0ab31bae6bad43a69669ef4c515fd1d5315e54e8 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 15 Jul 2016 20:22:59 +0530 Subject: [PATCH 354/513] runs check_example only if values are provided while initialising DataSet having this flag drastically reduces time to load & sanity check large image datasets for practical ML tasks --- learning.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/learning.py b/learning.py index fd622cdb5..1a638a6e7 100644 --- a/learning.py +++ b/learning.py @@ -75,6 +75,10 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, self.source = source self.values = values self.distance = distance + if values is None: + self.got_values_flag = False + else: + self.got_values_flag = True # Initialize .examples from string or list or data directory if isinstance(examples, str): @@ -85,7 +89,7 @@ def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, self.examples = examples # Attrs are the indices of examples, unless otherwise stated. if attrs is None and self.examples is not None: - attrs = list(range(len(self.examples[0]))) + attrs = list(range(len(self.examples[0]))) self.attrs = attrs # Initialize .attrnames from string, list, or by default if isinstance(attrnames, str): @@ -117,7 +121,9 @@ def check_me(self): assert self.target in self.attrs assert self.target not in self.inputs assert set(self.inputs).issubset(set(self.attrs)) - list(map(self.check_example, self.examples)) + if self.got_values_flag: + # no need to check if values aren't provided while initializing DataSet + list(map(self.check_example, self.examples)) def add_example(self, example): "Add an example to the list of examples, checking it first." From f7bd05258da7754ed6fdd05f0948caddc92428c3 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Fri, 15 Jul 2016 20:26:13 +0530 Subject: [PATCH 355/513] adds method to calculate manhattan (L1) distance --- learning.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 1a638a6e7..6b9964a9f 100644 --- a/learning.py +++ b/learning.py @@ -31,6 +31,9 @@ def ms_error(predictions, targets): def mean_error(predictions, targets): return mean([abs(p - t) for p, t in zip(predictions, targets)]) +def manhattan_distance(predictions, targets): + return sum([abs(p - t) for p, t in zip(predictions, targets)]) + def mean_boolean_error(predictions, targets): return mean([(p != t) for p, t in zip(predictions, targets)]) @@ -122,7 +125,7 @@ def check_me(self): assert self.target not in self.inputs assert set(self.inputs).issubset(set(self.attrs)) if self.got_values_flag: - # no need to check if values aren't provided while initializing DataSet + # only check if values are provided while initializing DataSet list(map(self.check_example, self.examples)) def add_example(self, example): From 8a9b361cfb52e18697a3fad5c8bc72b41639e64c Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sat, 16 Jul 2016 00:14:02 +0530 Subject: [PATCH 356/513] impleemnts kNN classifier of learning module on MNIST data this classifier of learning module is not optimized to run on this huge MNIST data --- images/knn_plot.png | Bin 0 -> 53541 bytes learning.ipynb | 263 +++++++++++++++++++++++++++++++++++++++++--- learning.py | 1 + 3 files changed, 249 insertions(+), 15 deletions(-) create mode 100644 images/knn_plot.png diff --git a/images/knn_plot.png b/images/knn_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..6a5b0f036f413a5e265f0b0441c9d842e7495ff6 GIT binary patch literal 53541 zcmdSAgFOHqnjjqS>LE-`(bnF? zMB3~HfH7jDdA1I8LH%Yp@gMC;7=&$7RE+rPcS}GxE=x~IZJ@6|3aCjOk%kdG#V6Dv zrNSB21$@TgpyLEkrJ$P@D?&w_~9-V=1;mu 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zy_X}g^oG6%i${bd!M=;>&bbt&i4NKe)q4ULWn6oNy%{DVR?QZ3<##E^W9&Tyo^X}{ zHN^>;K}G3s15^6Dd6q4P=1{{t-2ljhp*58 literal 0 HcmV?d00001 diff --git a/learning.ipynb b/learning.ipynb index 550c96edd..ac6427f0a 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -15,7 +15,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -135,13 +135,13 @@ "# print(len(tr_img), len(tr_lbl), tr_size)\n", "# print(len(te_img), len(te_lbl), te_size)\n", " \n", - " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.uint8)\n", + " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.int16)\n", " train_lbl = np.zeros((tr_size,), dtype=np.int8)\n", " for i in range(tr_size):\n", " train_img[i] = np.array(tr_img[i*tr_rows*tr_cols : (i+1)*tr_rows*tr_cols]).reshape((tr_rows*te_cols))\n", " train_lbl[i] = tr_lbl[i]\n", " \n", - " test_img = np.zeros((te_size, te_rows*te_cols), dtype=np.uint8)\n", + " test_img = np.zeros((te_size, te_rows*te_cols), dtype=np.int16)\n", " test_lbl = np.zeros((te_size,), dtype=np.int8)\n", " for i in range(te_size):\n", " test_img[i] = np.array(te_img[i*te_rows*te_cols : (i+1)*te_rows*te_cols]).reshape((te_rows*te_cols))\n", @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -247,16 +247,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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KhN9OZr179wacQfCPP/4IQPXq1fP0nPHsY6tWrQA44YQTAHjvvfcAmDNnDvXr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\n//jjj/aPP/5oN2vWzG7WrFm6+6Wt8l7NnTvXzp8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GqPvoj+zZs/PHH38Ador/U089ZdTG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\n9iyAiXlt1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gJDOEFmSOJXouOsSyj1LkdeLEiUYhFh+tV6jypCiKoiiKEkNUecoAXUUkfv8g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\nsLCdYRiGYRhGAEKnPClJL97JeirtVwJkvA2z4k3fvn1j2ir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FvHnzAE+YUKpK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\n6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m3bukITJRZLSYxUmyL56aefAL/fpMIJuR24w0qs/nsXXHBBCkYSP1q0aAH4qmGkCqxi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\ncaM9xj+z+XGyYeVbh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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -270,16 +270,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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TQ1ajEvMNpEePHsaYrnXr1oDbGTzekWR3iB+VLJANGzaY3e4DDzwAZN50Twzr8ufPb8zP\n/KY4Cc2bN0/lcn/w4EHTd8rvSF5d4cKFTSJxuIqTIC7IgG/nnzIHS3qCJRLXXHNNMvNZcE0Tk5KS\nKFmyZNDfq1q1aoYKR6wRlenpp58G3DwncPOVAknZ923VqlU8/vjjgJuTF4i8hzjId+7cOd18zFhw\n/PhxU2Aj19JPP/0UIGjhRvv27Y26JEnzH374IeAU3WSlVD/S9O/fH3ALhoLxwgsvmHtAeu8hVgRS\n4ALuvTMSqhOo8qQoiqIoihIWvlCeMkLizBn1V5IqGakgePnllwH45JNPjCoViKghUsLoVwJ3RZnt\nReQlgwYNMnH6zCpOKY3hAO67776sDy7GHDt2jLPPPhvImkFbLAilijFctmzZwjfffBPx91VCo3r1\n6qkqzMSY+I477qBZs2ZBf+/w4cOmRDwSPUMjgZjSXnXVVUDwyjqpimvXrp3J7ZLH7r77bpNTk17V\n3aBBgwD4448/IjPwLHDy5EmjrojJpZidPvvss8awV6rnpkyZYhQpqWh/5513YjrmjJB78x133JHm\na6R118KFC2nTpk2y5woVKmTas4i6Fslq9LSIi8WT3GRCTZaWG/Rff/0FOIuolEmSAHXr1gXc8vdg\n4UA/IAmRx44d8703TlpkJtEyW7ZspjmrJP2JbD558mTfLz5+//134yUmC+AiRYqYMIkkfHodCkgL\nCX9nBknYPHjwIOCWGs+cOTNVcqdfGTx4MOD0OUvJqVOnjOePNDiOl3mlRDoxpFe6nzdvXtMT7IYb\nbgCcBsjg+gbFGtksy3U+pTM8OIsmgM8//9w8Vq5cOQDjwZYWn3zyCQCzZs3K+mAjxMmTJ80CSTak\nsuh44IEHzMLiggsuAJyxy0Iqkv0JI4n4aAWGXVMi59ivv/5q7oehIuE6ue5GCg3bKYqiKIqihIGv\nlSdxCw3sRZQZpIt7SiR8cu655wL+U54k2e3iiy8GYM+ePUZ6TmRESn/llVeSJRqD4zALrtGbn/n5\n559Nb62BAwcC8L///c+Y20nipqgyfiPwvJOk2ZSUKFEiVainZMmSJhFXjE9ll/zDDz9EY6gRIWXh\ngSRWr1+/nt9//z3Zc9mzZzc9z2RnL4muCxcujPJIw0f67kmILjNISETSHMTCwSvlSVRdMVR87LHH\nTAhILDWC2YJICKhs2bLphuvEsDlUE8pYI2q+HHezZs0y1xbh66+/NqqNJJiLGhxPiI2B/AwHCXOK\nUhopVHlSFEVRFEUJA18rTxLblf5mmaVXr16RGE7MKV++PODugKWtRaIiPeFWrVoFQOnSpY0xqiSr\n+rXMPSMkf2bevHm8/fbbgNMrC6BevXqA27/PLwSWe4u6Irt2oWPHjiZnQdi7dy8ff/wx4KqmguSn\n+BHJ6ZGfGSE5a9OmTQNcJfHiiy/2XWGHJHkXLFgwVUsKUdwGDx7MnDlzALeXmNgTFChQwFi/iPor\n869fv36a6n4skNynlD0mUyJGraICp8fzzz/PjBkzsj64GCC5XoEFHjL2kydPGqVQvtNatWrFeITp\ns3r1asCxTgDnuJIiALleiOoZqHLKv3Pnzm16GaZkw4YNUUuQ9/XiSRpYyk117969Yf2+SLgi68Yb\nEk4UZDHpV8SR+uGHHwbcapDt27ebRY8shqRyslixYubklgRO6TG2dOlS8935VToPl2XLlplqMwlJ\nSnK13wgMaaS8MUnIYMOGDSYMMGzYMMDxR9q1axfghA3ATYqfOXNmdAcdQyQ8Jz5sy5YtA6BVq1bG\nk84vyHcZrM+khHoCq5lThjD379/PQw89BDjpA4Dx/qpRo4ani6dQqFKliik6keKNYF5QckOOh6bd\n0hj4pZdeAuDQoUPmPJ03bx7gzFGqEuV+KNdXvzjnnzhxAnDDaqNGjTK+jzt27ACCL57kfiMb00Dk\n+L3xxhuj5gOoYTtFURRFUZQw8LXyJDucYM6p6XHeeecB8P777wNuYnhKpBTVr07CsvqWHdLSpUu9\nHE66FC9e3KgKtWvXTvZcYAm0eKzIXEqVKmXkZHGvlkTcYcOGxaWjekYcOnQo2f9l5//WW295MZw0\nufPOOwF31xeIfI/i8p4SUYslwVPOxcw6lPsZ2Q2L8vTEE0+YsIkfvIEyomLFikDGPnqC3y1CAhG/\nn+eee86EqwKVt5QqnKjfonj4EelVJ6quRGSaN2/OV199ley1tm0bZX/69OmAq5ROmTIlFsPNFDK3\n9GjcuDHghvsCkeR4KViJBqo8KYqiKIqihIGvlSfpsySxzZQ79kCSkpLo3bs34O6YRYFKC0mC3bdv\nX5bHGg1k9y67o5Tl0n5AXGuHDBkSUhmp5EG1aNEi1XP33HMPAK+99lpYY5Bu2dLzye9IDkJa//cL\nkoibGcSs75xzzgFcVSaREUXmyiuvpFq1akB8KE+STN6wYUO+++67DF8vSeXbt2+P6rgigSQSZ2QH\nIgbM8VCQIrnAYiop19K0zlex/JHcxIzui36nQoUKgKMmpkSMamNRmKLKk6IoiqIoShj4Wnlq2LAh\n4OaEbNy4MdVrZNfeu3dvLrnkkjTfS8r8AysMUpbs+g2JTa9btw5wrOn9Rrdu3YDQzct++uknwJ1T\nYJ8iafsglTsZlXuLBcVtt90GuDsyrxHTyIsvvpgtW7YAGHPTffv2pergLqXGZ511lq9L+cNBKrMk\nX9ErI8VYkJSUlOwnuC1A/IK0phg0aFCqNiZiYPrcc8+ZfEPJOUlPzY1F/7DMIteVjFpyiJovVijh\n5td6gdwXpeVYegqxZVncf//9gJu76Gej2owoXry4aZcTWI0uubHSqiZl7lc08MXi6ccffzSJp+Kq\nHUgk+rmNHj0agKFDh2b5vWKNJNn6Odk2WNnvihUrAOf7k39LCa3wyiuvmIT9Vq1aJfs5YsQIcxNO\nyVlnnWWcY/0mtYvzceCxJiHn48ePp1o8iT9QoiycypYtyxlnnAG4IaG0EsvjGSn5Fjf1Dh06AM73\n6TcH/AkTJgDwxhtvGPdtsRqQm2rdunX57LPPAPeckp5w33//vfm9MmXKAO6GyY/hO/FSS89BfMeO\nHcZFPx4WTeB4/vXo0QNwk6KD0ahRI8BpfizXU/GRixf/qmDkzZuXKlWqpHpcrCVi2YfQv1sHRVEU\nRVEUH+IL5ennn3/m+uuvB9xwTiSZO3eu6e8TT4iaE0zV8QsSogrWEVuMFMUgMRgLFiwwRpiyexDz\ntwceeIAGDRoAcN999wGYhNZatWqZBMi01CmvkHBrILK7DwwVy99HVNFEoWHDhqbYQexG4gFx8pfC\nBbGOCFbCf8EFF5iEVSmZlrD68OHDfVu8cOjQIcaMGQO46pIU2kjxB2DOO/kZaDciSOK4Xyw28ubN\na4pNQrlmDhs2zKQRxAs5cuQwit9dd90FuL1A69WrZ66dUqhh27Yp5RfX/3hE3NMlVOkHVHlSFEVR\nFEUJAyu9mHBEPsCyQvoAKTcX4y5pXREJatasmekkOdu2M9zChDrHcDj33HNN6bP0lJL2ApEmozlG\nY37BEEsK+e7HjRtnHpOdpCR4lilThuHDhwPu3yc9YjlHaYMQmCQt4w8836SNTSRaQXh1nAbjl19+\nMbmLdevWBWDJkiVZft9oz1FyKURRqVq1KuDs9qXUXRKRmzZtaq5Zkksiyk1W+tp5eS527NiR5s2b\nA6nb8ViWZdpciNIkydjhWr1Ea46dOnVKpTwFu7+98cYbgJskHmmieZzmyZPHXANFgZKWVwUKFCBP\nnjyAm/BfqVIlo+xnxXokJbG+3ohqJipvILt37zaPR7IwJcPj1C+LJ0FCHr179zaScbiIZC5y5fjx\n4zl58mSm3svLm5KEpaSRZTBfi0jgl8VTSlq1asWgQYMAt/+bhIP+/PNP01g3lMbRsZyjhDB79uxp\njmcZq23bvP7664BbGRKJZFU/LJ4kMXXGjBmmyEO81CJR7BDtOZYtWxZwG1NLiDVbtmypXNa3bt1q\nqjy//fZbIH0fulDx67kYSaI1x507d5pChWCLJ1lQtGzZEnBd8iNNtI9TuS9KpbP0USxQoIAJPUu1\n2alTp8x9JJLE+noj4X8JUYJbfX/jjTdGdGEoZDRHDdspiqIoiqKEge+UJyF//vwmiVy6tYv6sHr1\n6nQtB8TzQcqks4KXO3pRXUSKjMR8ghFPu13xOPnmm2/C2lHF0xwzgx+Upzp16gBOGOuyyy4DXLuK\nSBCrOYo7uJTyX3rppcaB+r333gMcFSO9QojMkujHKURvjr/++qvx/kmpPO3fv9+owNH2APLDuRht\n/KA8SaFNepYNWUGVJ0VRFEVRlAjiW+XJL+guIv7nB4k/Rz1OHRJ9jvE+P4jeHKtWrWrUCClUkPvb\nXXfdxcSJEzPztmGjx6lDJOf4wgsvAE4O5aOPPgrA9OnTgeiZR6vypCiKoiiKEkFUecoA3UXE//wg\n8eeox6lDos8x3ucHiT9HPU4dEn2OqjwpiqIoiqKEgS6eFEVRFEVRwiDqYTtFURRFUZREQpUnRVEU\nRVGUMNDFk6IoiqIoShjo4klRFEVRFCUMdPGkKIqiKIoSBrp4UhRFURRFCQNdPCmKoiiKooSBLp4U\nRVEURVHCQBdPiqIoiqIoYaCLJ0VRFEVRlDBIivYHJHpzQEj8Ocb7/CDx56jHqUOizzHe5weJP0c9\nTh0SfY6qPCmKoiiKooSBLp4URVEURVHCQBdPiqIoiqIoYRD1nCdFCUa5cuUA6Nu3L+3btwdg3Lhx\nALz11lsAnDhxgnXr1nkzQEVRFEVJA1WeFEVRFEVRwsCy7egmxMcq475+/fosXLgQgFOnTiV7bsaM\nGbz00ksAfPnll2G9r5+qCsqXL8+HH34IwAUXXABAtmzO+vfnn3/muuuuA2Dz5s1hvW8sql8KFSoE\nwD333ANA9+7dAVeBOv05Mh4Ajh8/znvvvQfAvffeC8C///6bqc+PZYVP0aJFAejWrRvnn38+ANOn\nTwdg0aJFHD9+PFIfZYjmcVq4cGGefvppAAYMGADAP//8k5m3onjx4uzcuROAihUrAvDbb7+F9Lt+\nOhejhZ8r0eRaky9fPgDatm0LQKNGjbjllluSvVbUZFGRA/HzHCOBHqcOiT5HVZ4URVEURVHCIO6V\np/LlywPw3XffUbhwYcBVLgIRxaJDhw4AzJs3L6T399MKe8mSJVx++eUpPxtw5tyiRQsAPvnkk7De\nN9o7wTx58tCoUSMAXnzxRQBy5coFwEUXXWReV69ePQAGDRoEYFQbgEsuuQSANWvWZGoMsdztNmzY\nEIDPPvss1XOzZs3i+++/B9xduSgvKRXTcIjmcdqgQQM+//xzAM4991wgfHVTqFOnjlF/K1euDMSn\n8lSpUqU0n2vXrh05cuQA3GMhV65czJ07F4Aff/wRwKiqgfhZlalWrRoAK1euzPC1Y8aMAaBXr16p\nnvPzHCOBn47TjBAV8eDBg2H9XjzNMbNkNMe4TRiXRdNDDz0EuGGhtJDn5fWLFy8O+4DxipYtWwJw\n8cUXp/maFStW8N1338VqSGHx5ptvmjCj3FR69uwJwK5du8zrZsyYAbjhgTfffNM816RJEyDzi6dY\n8ueffwKwb98+s0jcuHEjAM2bNzffpywS58yZA8AzzzzDN998A8DJkydjOeSg5M+fH4DRo0dz2WWX\nAbBnz55MvZcskmfPnh2ZwcWApCTn8li3bl1at24NQPXq1QGoXbt22O8nf4Nt27YBwRdPfqNkyZIA\nDBkyhDp16iR7To7z8ePHs2XLFgB69OgBwMyZM2M4SiUcSpcuDcCDDz5orqvbt28HYP/+/dx9990A\n7Nixw5sBBuHMM88EYNOmTeaaKixZssQs6OW4W716NYBJEYgGGrZTFEVRFEUJg7hSniREVa9ePd5/\n/30gY8UpJRIaGjlyJHfeeWdkBxglypYtC0DevHnTfM2uXbvYvXt3rIYUErLDqVmzJqVKlQLg5ptv\nBtLfmW7YsAGAw4cPkydPHgBuuOEGwPne/M4vv/wCOIpFzpw5ATfUUbVqVR5++GHACe8AJtzaokUL\nXn31VcBNqPeSpk2bAk5xgnyXmVU3zzvvPCD889UL5Dt74oknAMz3FSqrV682SuOmTZsAR+leu3Yt\n4BR3+J2TQjcHAAAgAElEQVRWrVoBmEKBypUrc/ToUcBNeejWrRsAf/31l/m9KVOmxHCUyfnwww8p\nUKAAgAmRTp06FXCuJaJYiLINbsGKRCHkGrp///64iUxkhKhL8t0UL14cgOzZs5sUF0mRsCyL66+/\nHnCVVy+R72f58uWAc26mTMspXrw4jRs3Btxj8tdffwWgf//+fPzxx1EZmypPiqIoiqIoYRBXCeOj\nRo0CnHyZYOOWZOTevXsD7gp73rx5JtlRfu/XX39NlpCcFl4mxtWvXx+AL774QsYS7LMBZ6clO4Zw\niVYC59VXXw3A/PnzTa5SenlbKdmxYwfFihVL9tj9998PYKwnQiXUOVaoUCHkBObMIjtfKXCYNGkS\n4Oa2Bb4mVKJxnD7zzDMA3H333Sbnaf369WGNS5C5vf/++xw7dgxwd7uizmRErM5FKbN/4403zGOT\nJ08G4H//+x8AXbp0Mc+JyiH5e4cOHeLIkSOZ+myvk6lFYRR1SfK0jh49St++fQH3OptZojXH7du3\nm9yYlNfKjRs3mvwtUbPTu/c9/PDDjBgxIjPD8EUytVw/atWqxYIFCwC3IEXsbjZt2mTOZ/mbjB49\nmoEDBwIwdOjQNN8/2nOU8YtxcqASv2/fPsBVmQKjGKKYDh48GHCUK7l2SUQgVOI6YVwWP82aNQPg\n1ltvTfUakVZ79+5tDgpBLmZNmjRJlfyWL18+IwlmtnIomuTLl88sAkNBQgJ+xLbtTCWQ2rad6gKX\nlYq0UIj2wgncOcixKwtgcBMdvURCrF27dgWcEEZmF03BWLx4MRD6oilWnH322QAMGzYs2eM//vij\n8SeT0FU8hI8zg5ynF154IeCGLBcuXMiyZcs8G1cotGvXjn79+gGuD554quXKlcscd4EVynIM7t27\nF3AXx/GObFZmzJhhKs0l9WHRokXmdRKak3vgH3/8ke6iKVZIdXXK9IU9e/YYP0Mprgnkgw8+SPZz\n8ODBXHXVVUD4i6eM0LCdoiiKoihKGPhaeRLFSZJog/HTTz8BrqwejGCJ1MWKFaNu3bqAP5WnkiVL\nhhSGk5CPlL37iUAPp99//z3s3z98+LD5tyTZvv3221kfWJSR0IEUJ4C7S9qyZYvxCBKFQ3aEBw8e\n9MXOVwopJGQqpfVZQfzV/IwovRK6EtasWWMUp0Rm9uzZVK1aFXB36SlVOD/z5ZdfGg8xURFPnDgB\nOAqL2CkEI5gnWzwi35/YYOzdu9eEWQMVJ3D+Rs8++yzgWlLs37+fChUqALFR4dOiVq1aQR9funRp\nUMVJEFVKFKsNGzZowriiKIqiKIof8LXy1L9//zSfkwTkTp06Zeq9N2/ezOuvv56p3/UTfrZb+Oqr\nrwAnuTs9ZTAtPv30U26//XbAVbGKFCkCZL63WjQpUaIEAI888giQ3F1ZkqR37dpljELl9aLK3Xvv\nvWG7w0cDMceMJFJC7mfSUkfFDDJRkeO1SZMmRrHo06ePl0PKMumpTIFIUUugShzPSD6X/Jw8ebJJ\nnhZFfOLEiYBj9HrGGWcArrI/dOhQY5jpJSmNMIWPPvoo6OMSpRJjZbH12bp1K88991wURqjKk6Io\niqIoSlj4TnmSFfM777xjYq8pWbVqFddeey0QPJ8pJS+88IIpfZRKpyJFipj4sPSa8gPSduaDDz4w\nf4uUY48XfvjhByB4f6tQCCzdl52RGGj6Edn9BJuvGC+mzKcBzLHst+qzSCB2DFLZCv7NW5O+fSnx\nw048UhQvXtxYoIhxqSgTSUlJTJ8+HUhufJnIiJItarBUwC5cuNCrIUWUDh06UKNGDcC1vhFWr15t\n2pVlJjIQTdI6F+fPnx/08cceewxIbSQ9cuTIiORsBsN3iyfxfmnVqlWqMnUJ1V177bUhLZqk6WHZ\nsmXNwkPeMzBh3E+LJ3HgPv/8881YU44d4qPHW2Zp06YN4FpVAJQpU8ar4YRNYCl0KEgZvFzIEgkp\nda9SpQoABw4cSPMC6DVphe169OhhwsSffvopED8LKnFefvzxxwGnuW/KkEigp5h0bhBvIFlYLVmy\nJOpjjTVly5Y1ydRyrsp8V6xY4dm4skLFihWT/f/MM880rv7SHUCSqX/55Ze4K4QIFkIvVKgQ55xz\nTrLHxBtxwoQJURuLhu0URVEURVHCwBfKU758+UwfKXEIDUSUoZtuugkILVQHbtlmsJL/H3/8MZWp\nph/IKAFcJMjMuonHE4HKjfSB8zOyO5fdTrFixZg1axbgJC6CIztLgu6DDz4IYJyb165d6zv5PNIc\nOXLEt+FJSTaVkMEDDzwAOLt5+V4kbDx8+HCjQvlxPhImrlmzJuAm2q5cuZL77rsv2WtFfRk4cKCx\nfhHF6pprrgGcPo3plYjHI9WrV+ess85K9pj0wosnxFKkX79+pgNDIPL9JoKyffHFF6cya121apUp\nvhFeeeUVgKj2J1TlSVEURVEUJQx8oTzVq1cvaCsSyesRxSlcM8v0rA769evnS3PMjHjttdcAfxp7\nZhVJKg5m+BmtpL9IIqZyGamH0jNO2ga0aNECSDtJMtZIqwqhVKlSjBkzBnB7CkopeMGCBU3SrXDn\nnXea3Avp6C7s2bMnKmOOBJLXJDt0UWFGjBhhSrolyfqVV14xeUGy25eiBj8g1hgpW21Uq1aNe++9\nF3BbAUmeT2DrC8lLk3k/9thjJhfx0KFDUR59bJD80nijdu3agBt9ECWxQIECxhBSlMbHHnuMu+66\nC4Dx48cD3ppfhooUlXTu3BmAokWLAo7iK1EIUUfLlCkTcn5pJPF08SRhtSlTpiTr7yVIY87MLhQC\nPS8kKVIOKj/46UDy6kIgaIWhjH3jxo3JmpVmRNWqVc2CZPbs2VkdathIf6yePXvy7bffAm5Vh1QV\nHjx40PQglPBV5cqVAacvlSykJOylRB/xRZFqxxo1apikdrkQy423WLFiphdeKPih0i537tzpNu49\nefIk4G5UFi9ebBbEbdu2BZyFvngEiTu1NFSNZpJqVgkM9//xxx+A2wMU3BQJCdfJ/5s1a2a++3jv\n6ychnsDFk/hbBf4t/IRU6Y4aNcp0JJD0leHDhwNOE13ZAMhiYvfu3eack8rXeEBCc1IhKE2amzZt\nmippPHDtIGuFWCT8a9hOURRFURQlDDxVnsTV9Ywzzkglu/Xr1y/TOxxZrdapUwdwVuF+9UgKtGaA\n4OXtMvbVq1eH1Rm6Xr16dOzYEXB6AkF0d1aimknirfy/QIECxmtE1AxRHbNly2a6fkt/JWHv3r2m\n95IfuPDCC+nZsyeAkcclITwcJJk3ZZKjX5Cegl27dgWcMvemTZsCro+KdD2H1Mrwnj17zHHqx0T/\n1157zXiQvfzyy0D64cTff//dJPnL63v37s0dd9wBuM7Nt912G+AoVZHu4J5VxJ4g0ElbOs8HQ0KW\ngfTo0QOIf+VJwuR58uQx4U0pWPLTfaJAgQKmC8YVV1wBON+jpKNIcvvOnTtT/a5836IgxiuSutO6\ndWsAmjdvbvreBfYBFcsFud/FoohDlSdFURRFUZQw8FR5Si+xNjPdvCWmKwmfKd1GwS1h9AM5c+YM\nqXxUEnil5DQcZJUuyk80c70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yVZ4URVEURVHCwDfKk+QISAl0oOokcei77roLCD+xceXK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z76CHBX4YPc2/r27WvsCMRywFt1Kl26NGCrMxUrVjTXWimuWrhwobEqiTViPbBlyxbznUpnhsKF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\nIiIiIj5o8CQiIiLiw38FA0Ekb8klUAAAAABJRU5ErkJggg==\n", 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Ao48+muFrPvvsM8DNo5LS9yAwdepUwLUqkETrN9980+yHKjmYF154IcOHDwfg2LFjAOkM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\nRFz4P5PSO3Lk+pU+AAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -300,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -365,7 +365,7 @@ "data": { "image/png": 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HYy6YarXakv8GQMvWdK5EtfVEpen69esAmlvHp6amZKVLNWtlZUXcLeGZbN1C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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -408,7 +408,240 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## k-Nearest Neighbours (kNN) classifier" + "## k-Nearest Neighbours (kNN) classifier\n", + "\n", + "### Review\n", + "k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are gonna use this to classify MNIST handwritten digits. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n", + "\n", + "![kNN plot](images/knn_plot.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how kNN works with a simple plot shown in the above picture. There are two classes named **Class A** yellow color dots and **Class B** violet color dots. Every point in this plot has two **features** i.e. (X2, X1) values of that particular point which we used to plot. Now, let's say we have a new point, a red star and we want to know which class this red star belongs. Solving this problem by predicting the class of this new red star is out current classification problem.\n", + "\n", + "We have co-ordinates (we call them **features** in ML) of this red star and we need to predict its class using kNN algorithm. In this algorithm, the value of **k** is arbitary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n", + "\n", + "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into majority class. Observe that smaller circle which containg 3 points other that **test point** (red star). As there are two violet points, which is majority, we predict the class of red star as **violet- Class B**.\n", + "\n", + "Similarly if we put **k = 5**, you can observe that there are 4 yellow points, which is majority. So, we classify our test point as **yellow- Class A**.\n", + "\n", + "In practical tasks, we iterate through a bunch of values for k (like [1, 2, 5, 10, 20, 50, 100]) and see how it performs and select the best one.\n", + "\n", + "Let's classify MNIST data in this method. Similar to these points, our images in MNIST data also have **features**. These points have two features as (2, 3) which represents co-ordinates of the point in 2-dimentional plane. Our images have 28x28 pixel values and we treat them as **features** for this particular task. \n", + "\n", + "Next couple of cells help you understand some useful definitions from learning module. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource DataSet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "class DataSet explanation goes here" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource NearestNeighborLearner" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nearest NeighborLearner explanation goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let us convert this raw data into `Dataset.examples` to run our `NearestNeighborLearner(dataset, k=1)` defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(60000, 784) (60000,)\n", + "(60000, 785)\n" + ] + } + ], + "source": [ + "print(train_img.shape, train_lbl.shape)\n", + "temp_train_lbl = train_lbl.reshape((60000,1))\n", + "training_examples = np.hstack((train_img, temp_train_lbl))\n", + "print(training_examples.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we will initialize DataSet with our training examples. Call NearestNeighbor Learner on this dataset. Predict the class of a test image." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# takes ~8 Secs. to execute this cell\n", + "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "kNN_Learner = NearestNeighborLearner(MNIST_DataSet)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a number from 0 to 9999 and we are going to predict the class of that test image." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class: 2\n" + ] + } + ], + "source": [ + "# takes ~20 Secs. to execute this cell\n", + "testing_choice = 2311\n", + "predicted_class = kNN_Learner(test_img[testing_choice])\n", + "print(\"Predicted class:\", predicted_class)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make sure that the output we got is correct, let's plot that image along with its label." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(test_lbl[testing_choice])\n", + "plt.imshow(test_img[testing_choice].reshape((28,28)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", + "\n", + "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run this particular dataset. We will have an optimised version below in numPy which is nearly ~10-50 times faster than this implementation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Faster kNN classifier implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class kNN_learner:\n", + " def __init__():\n", + " pass\n", + " def train():\n", + " pass\n", + " def predict_labels():\n", + " pass\n", + " def compute_manhattan_distances():\n", + " pass" ] }, { diff --git a/learning.py b/learning.py index 6b9964a9f..0894b2190 100644 --- a/learning.py +++ b/learning.py @@ -31,6 +31,7 @@ def ms_error(predictions, targets): def mean_error(predictions, targets): return mean([abs(p - t) for p, t in zip(predictions, targets)]) + def manhattan_distance(predictions, targets): return sum([abs(p - t) for p, t in zip(predictions, targets)]) From 4e4f3107cb405660d44134d81628bf1710bddd40 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sat, 16 Jul 2016 14:41:57 +0530 Subject: [PATCH 357/513] implements faster kNN in NumPy in learning notebook --- learning.ipynb | 148 +++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 119 insertions(+), 29 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index ac6427f0a..ee5ab418e 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -96,6 +96,7 @@ "import array\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "from collections import Counter\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n", @@ -254,9 +255,9 @@ "outputs": [ { "data": { - "image/png": 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gJDOEFmSOJXouOsSyj1LkdeLEiUYhFh+tV6jypCiKoiiKEkNUecoAXUUkfv8g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\nsLCdYRiGYRhGAEKnPClJL97JeirtVwJkvA2z4k3fvn1j2ir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FvHnzAE+YUKpK\n2FNWioP6iopk3DstbGcYhmEYhhGAlCpP77//PgBPP/206+VVHNSDaPz48YDfPTodiLWLGDlyZPIH\nEhBJ/pF/azlTK9Sx7777uhCHQrVydp46dWqe771jxw6nYsm0T+XzqUwWz4uaNWsCvq0CkKcBaEFI\nmQtrSE9cc801NG/eHPDnOGPGjNAb8QkpSb/99hv//PMP4CeMy2H6vPPOi7LW+O6775xKrt9LF55+\n+mnAV57q16+f43HwDTCl2t92221p1eOtsEhpO+ywwwC/D1zYDIlvu+02wDO/zOu+VqFCBZceoAjG\ntm3bXEFLJpO7l2YyMOXJMAzDMAwjAKGwKujRo4ezVb/hhhsAL1ab25xPbVzUKiGSadOmMWbMGCA9\nY7u5E8XTuY+ddulKDt+8ebPLfZGalp/ilB+pVpxk2qYk99yJpsXhlVdece+vnmJhLQcvV64ckDNH\nb8OGDYBv1ZAOyDYht70JxFYNZ8+eDfjFEemI8nmkYKhX3R577MGMGTMAPwk5GWaDqUTJ1zJXVJ5t\n2HL16tat6/6dWxWTcjhixAg6d+6c47mrr77a5YtmMt9++y3gt1tKBqFYPIHvB6OfRx55ZJQbuKp/\ndAHLJMLuZlsUVH02bty40MngRUULBFV5tmnThosvvhiIXZigMKMufgsWLMjzvR955BHXXDjsyBcp\nshJUN+HCNlIOA1o0xQqrKsS8du1at+CQX1kmoJ52+vm/Rrdu3VyFtjy6wrpZiUTFGErcV2eNPffc\n0/ngyQuqOD1i0wltZmNVNScKC9sZhmEYhmEEIDTKU24++ugj5zhthJe77747x8//FVasWOF+ymFa\nj0Wy++67A777eKrDjvFCbtORpGO/wc8++wyAvfbay6UFqNBBodN0nJeRN61btwbg4Ycfdn365IWk\nn2FDyqfsMQAGDx4MeHYv4KnasoSJldqSyeS2uVE6QSKVRFOeDMMwDMMwApCVaJfcrKys9LPhjSA7\nO7vAZKRMn2O6zw8yf452nHoEnaOSbVevXh2K3oKZfpxC6uc4cOBAwMuvlUIzaNAgwDPmLS52Lnok\nc47qwShlTvmnQ4YMKfJ7FjRHU54MwzAMwzACENqcJ8MwjEQTK0/NyGxki7N582ZXIarqUSM9kT2R\nqpn//PPPhH+mhe0KIGzyZCJItYyeDDJ9jnacemT6HNN9fpD5c7Tj1CPT52hhO8MwDMMwjAAkXHky\nDMMwDMPIJEx5MgzDMAzDCIAtngzDMAzDMAJgiyfDMAzDMIwA2OLJMAzDMAwjALZ4MgzDMAzDCIAt\nngzDMAzDMAJgiyfDMAzDMIwA2OLJMAzDMAwjALZ4MgzDMAzDCEDCGwNnen8byPw5pvv8IPPnaMep\nR6bPMd3nB5k/RztOPTJ9jqY8GYZhGIZhBMAWT4ZhGIZhGAGwxZNhGIZhGEYAEp7zZBj50aJFC268\n8caoxwCWLFnC0qVLARgxYkSSR2YYRiRt2rQB4LHHHgPg/fffB+DUU09N2ZgMI1WY8mQYhmEYhhGA\nrOzsxCbEJzPj/oQTTgDgpZdeAqBUqVIA3HTTTdx5550AbN26NdB7hq2qoGzZsgBs3rwZgB9++AGA\n2rVrB56bSEX1i9SlV199tVCvX7JkCQAtW7Ys0uclY44TJkwAoH///gCsX7+eOXPmAPDAAw8AsGnT\nJv7+++/iflQUYTtOC0P9+vUBWLx4MRUrVgTguOOOA+C9996Len06zjEoYa1Ea9OmDdOnT9cYAP+7\nWrNmTaD3Cusc44Udpx6ZPkdTngzDMAzDMAKQ9spTuXLlAGjevDmPP/44AHvssUfU63r16gXgXlNY\nwrbCHjlyJADDhw8H4L///gPgjDPO4Pnnny/SeyZzJyjFSXlO+n8kmmPz5s2jntdzQXOgkjHHHTt2\n6LPyfM2yZcvo3LkzAL/99ltxP9IRtuM0Pzp06ADApEmTAKhatSpvv/02AN27dwfgm2++ifq9MMxx\n7733BqBEicLtOxs3bgzAu+++6x776aefAP94iSRsqsyRRx4JwIsvvujmrnNQP4MStjnGm1Qepwcc\ncAAArVu3BqBp06YsX74cgI8//hiAW265BYCTTz7Z3Q91fywsYTgXE01Bc0z7hPH27dsDMGvWLCcn\n5755bdu2jV9++SXpYyuIypUrU6FCBQC+/vrrAl/fsmVLhg4dmuOxDz74AKDIC6dkkVeYLr+k8BYt\nWsRcXKUzJ5xwArNnzwb8hUIYj81E0bt3bxfC1MZn9OjRbjG9ffv2lI0tLypUqOCOTYVky5Url+8i\nOa9rEcC1114LwB133BHnkcaP2rVrAzBu3DgAKlasyGWXXQbA/fffn7JxGXnTu3dvtzCqUaMGAN99\n9x1nnXUWADt37gSgdOnSgHdsapGVThx//PEAdOrUCYBGjRq5dI5PPvkEgIEDBwLwxhtvJGwcFrYz\nDMMwDMMIQNqG7bTSfPLJJwFPxclrtzd79my3yw9KIuXJqlWruhDj6tWrC3z9iBEjuOGGG3I8duml\nlwJ+snJRSIaMnvs7KWwCeF7Hp77rAJ+f8DkOGTIEgNtuuy2/z3FzmjlzJgDnnHNOcT869DK6wj8v\nvPAC++yzDwAzZswA4IILLihUsUOy5linTh3A29ECXHHFFTRs2DD35xRZeZLSetJJJ0U9F5aQ1n33\n3QfAJZdcAniqhr6v4hLvOZYvXx6AQw891D2mZHaFGletWuWeU8HGiy++yD///BPkowpFqs7FzZs3\nO+V21KhRAFSvXp3LL78c8Of96KOPAnDeeee5x/R3KizJnqO+45kzZzprjJIlvcDZ2rVr2W233QCo\nVq0aAH/++SfgpQgUVX2yhHHDMAzDMIw4kpbKU7NmzXjmmWcAXIlzfmzfvp3bb78dgIkTJwKwcePG\nQn1WGHb0AwYMAGDs2LGUKVMG8BPF27VrB8DChQuL/P7JVJ6CWg6kk/K0yy67AJ4KKs4991wAqlSp\nAsDgwYPdnLZt2wb4Csfnn39e5M8Ow3GaH5MnTwagb9++Li+hSZMmAPz777+Feo9Ez7FevXqAn5cX\n+T0qp3DRokWArx4VBX3POocjSbXydOaZZwJeDinA008/DcBZZ50Vt3y0eMxx4cKF7vvZd999gZzq\nSX7Kn1i3bh2nnHKK+3e8SPa5qPPotddeY/To0QAuQrFz506+++47wFd/pdisWrXKXYPCrjzNnTsX\n8O53P/74I+DnjH744YfOwuehhx4C/FzoGTNmFFnZz6iEcSW6Pf/8807GKwylSpXi+uuvB6Bt27aA\n98fdtGlT/AeZAHr27AngFk7g+1UVZ9GUTIIudoQqemK5kGshFhZUPaWTG3D+YqJEiRJceeWVgP99\n6mKWiejCpZ/btm3j5ptvBgq/aEoG9erVcwsjLXT1PU6bNo177rkHKPymKx056KCD3M1n5cqVgJ94\nG7ZE/oULF9K7d28A3nzzTcALW/38888A/P7774C/yH3jjTdcErxCkq1atXLfuTYwv/76a5JmED/2\n228/wN+8RdK5c2c3JxWmaBFVunRp5xMYdhSGBd8z74gjjgC86npVGY4fPx7wq1wTiYXtDMMwDMMw\nApAWW17tCrRjzUt1Uilmfh4sRx99NOAlCyphM55+O/FEjumRq27RtWvXZA8nVIRRecoPhe/atGkT\nFUrQcZsOaB5y8c9r5yoZ/bzzzgN8t/85c+bw1FNPJXiUhUe78JdfftkpTi+88AIAV199NVC8cGo6\n0axZM2edomttWJWJO+64I7DVg5LG5Wm0YsUKp1hI2VZydTqh6MPWrVupW7dujucU7gL/HLzrrrsA\nT/nOrYyHFVn5VKpUKapoKrJ4QwnwsULi8caUJ8MwDMMwjACEWnmSiZ7ylSK7d2uFqTyocePG8dxz\nzwFw0UUXAb7Larly5dyqWzviI444wiVHyuk4TFSrVo1p06YBsZW0xYsXJ3tIKaF58+apHkKR6dKl\nCxdeeCHg7erB2+1pl6QchL/++is1AwyAElFVuq6E/2bNmjmbjY8++si9vm/fvkC0g/zYsWMTPdRC\nIQVaO/PKlSszdepUAMaMGQPkVJxkX6DE4rDlABUHqRUTJkxwam7QTgzphBzeN2/ezF577QXE7nSQ\nLii/a9GiRe4eefjhhwO+aSR4OW3gz/X33393OV9hRzlMnTp1olKlSoCfm7hx40Zn3KqolBLhI5W3\neGPKk2EYhmEYRgBCrTxJEerYsWPUc0888QTgG6PJoBB8S3apTQcddJDr4K5dZc2aNbnqqqsA36wv\nEd3ui8ohhxzCgQceGPW4ck1i9cXKRHLvCLUzDtrbLhlIKX344YcBOO200/KtCh00aBBQuNY8qaRu\n3bpOcZIZncqjGzRo4IxqI8mdk/fggw8COXfCqUS5LTLVA9z5psq6yNw0zVftkH7//Xdne/L+++8D\n6dtmR+07ypYtG5VPkslMmTLF5XZJvUlnNmzYwK677gr4FjaffPIJVatWBaIrs8ePH8/69euTO8hi\nouhSJD169HDzlump8pnVMzMRhG7xpITFzp07x1w0ic2bNwNwzTXX5PkaSesrV650pbe6wL3yyisc\nfPDBgH/RXLFiRTFHHz/OP//8KK+SrKwsXn75ZSC9koyLSqwFUnH8dRKN3OK7detWqNcrWTXsjBw5\n0i2ahBJsI12dxeDBg12xg3jllVcA2LJlS4JGWXiaNWvmbi65H4f8PYIiX6Pr01dffQX4Lv8PPPBA\nQpyr442OV6VFvP32227jKd8feSg1btzYbTLD8B3Gg8iwrO4P6cyTTz7pwuWHHHII4F1jtBnQd6kG\nwfI+THd69Ojhztn58+cDiV00CQvbGYZhGIZhBCB0ypOcQZUsHcmaNWsAb3WpBNZIQ8J4aaxIAAAg\nAElEQVRM4NhjjwU8M8/cO9/58+cXq4dduqBQXaQxZpjDdbnJzxC0RIkSTjVUB3Q5Wr/11luJH1wA\nZDMQS6WR2WxkqErH7qWXXuqMP3WezpkzJ5FDDcRNN93EnnvuCeDCFrNnz3bP55fULtuU0047jf79\n+wO+cq2UgJNOOokOHTrEf+BxRqkPus7UrVvXHYNHHXUU4Jf316tXz4WZ9d1/8803SR1vIunUqRPg\n90SrVq2aM9WMRNchKeAvvvhicgZYCJYsWeJU0FatWgFemF3HooyV27RpA6S/gqjvrGPHju4YTqYN\niilPhmEYhmEYAQhNb7vdd98d8HMjGjVq5FaTirXLfn7gwIFFttFXibU+B/weVrHMMpPdw0e73kGD\nBkXlXjRr1qzIHaLzozi9pqQE5W6fAjl3aYVRjKQ4SYmJRN9bUY0xk9EzTMnHGzZsADwLgtw9s7Ky\nslw7BakfMmXs2rVrkUvg43mcKilTKnAsJU272Mjrhwo0IttEqK+W2rPE+m4LS7zm2KJFC/bff38g\ntsJdWJQXpHJoqTUlS5Z0ikSXLl0ACp0DlYzjVIqKcjxl97JmzRrX6kQ7eP2/Xbt23HbbbYBfyBPr\nnC8MyZijSvWbNm0KQNWqVV07D51/9evXd3OPhfJqI6Mbn376KeD3alywYEHU76Wyz+SwYcMA3+Q0\nKyvLWaGozde8efOK/TmpnKPaWkklPeKII5z9hL73eLReS5vedjfddBPg+69EXpR1Af7www+B4vUf\nOuaYY9y/dfHQjSCV6IajCzD4fwPdgL/88svkD6wA8ruAajEUWTGnxU/kIii/RVOs14cVLb4jQ1qq\nzopEicZquqqwWMWKFVMehq5SpYrziskv/BjZZzE3kY6/1atXB/wq0fnz56e8yjBex5K+K1XiDR06\nFPBuXAqNaPH02GOPxeUz44F6g+mac/755wPeGPNyZp46dao7bjXfMKMFXuT1XsQSDLTAUOXhypUr\nYy6ewkzVqlXp0aMHQA7H7bPPPhuIz6IpDCidQIvhrKwsLr74YiA+i6bCYmE7wzAMwzCMAIRCedp1\n111dCXAk2gXJPfTbb78t8mdceumlANx6662A55N0xhlnAOFInJNHUKy/gzxykrmqLgra0Y8cORLw\nVakWLVq4f+d2DL/xxhvzdPcdOXJkWiSICymYuf1UcpOXt8q5556b8l5TJ5xwQlQo4++//863v5vK\nolX6/ttvvzmlpWHDhoDvENyxY0cXglVy7qeffsppp50Wx1mkBvVa22+//ZwvVtioVKkSDRo0APxw\nsZzV86N+/foujHvZZZclbHzxonv37gD06dPHPfbuu+8Cfr++WbNmUatWLcB3VNffJJ2QCjxz5syo\n3nZvv/12TG+kdKVs2bIMHDgQ8NW11157LSXfmylPhmEYhmEYAQiF8tSxY0fX3Twyz0I96opaEque\ncOeff75TntTzZsCAAc76IAxceeWVUY/pb7F8+fJkD6dY5M5TGjFiRA4VKvJnLIqbHJ6uqFdTWJDj\n/nHHHZengeyuu+7qXMOlPA0dOpSHHnoox+uUyzd+/Hh3rv/7779A7EKNdCa3oWiYGDFihOvnpjzT\nwnDttde6fyuJPMxI3Y2lXJ9++ukAOTo4hMX5PghSiGWbIHU3k+nevXuUujZ58mR3LUkmpjwZhmEY\nhmEEIBTKk/IiwI9jfvLJJzz77LNFej/1sVPbgW7durmdiOKlhYnzJ5NYpnqqAAlTz73cSB1q0aKF\nU5OKan+RX3VXfuhzU61UycBUfZauvPLKKFWlQoUKeVazqVdaKlm8eLGrVFIbh/zaFg0bNszljag6\nKdJwUqjq8Pjjj3el8qqiDUMrk3r16rnqv6Keb/Xq1QP8CrswogolgLVr1+b5Opmcqmdo9+7dueuu\nuwD/uEhXlOsaWRWa6mtHEFTBqjHr/Pvwww9ZtmwZ4N/nMgUdj8OHD3ePSS1UJW/Sx5SSTy0E1atX\np2LFikD+sr58HZo0aeJOCt1MJWtu3brVleMWx2cmkcTqEfbRRx/l+BlGFGJ79dVX8w3FFQZdyJRw\nHktyjwz7Kflcr081cpzWPFq1asXixYtzvOaoo47isMMOy/E6ccABB6S8SfCff/7p5pEfanisxFzw\nPcr++OOPfH83TOFycfTRR7uy9ilTpgT6XV2ntNCoUKGCK2+PZVURVsqUKeO8yuTppNL3Rx99NEfo\nLh1R39TIBaR6oH322WcpGVNRUGGCFk0qvBg0aFBUQc7zzz+f3MElCG1IIkOtOh5//vnnlIzJwnaG\nYRiGYRgBCIXyNH36dLdrE5UrV3YlltrRXnDBBYDvZAxw8MEHA36yaiRSbPr06ZOWUvP06dNTPYRC\n07Jly3yTwfMyu4ylWCm5vCAH47Anlu+zzz706tUrx2ORoQIxYMAAIL0KA6RI1KpVy4Xrxo8fn8oh\nFYvs7GyX5K4efQqjrlu3znWkL1u2LOAlhUuhO/nkkwHfYT47O5uTTjoJIF+Lh1Tw5ptvcuKJJwJ+\nCEQu3KVKlXKJ/XJsVij6qquucj0Z0xV9r0rrAPj+++9TNZwiccwxx7gIi6xRdPytX7/ehVaFjKXT\nHa0PsrKynMt7qvsKmvJkGIZhGIYRgFAoTytXrnQ9eUaNGuUeVwLmww8/XKj3UaLqokWLAL+Te5hL\noXv37g34ScZizZo1zJw5MxVDKjJFaaUSS7HKT3GKNOIMm+Kk462ghGEdj9rVy1BSNhrpQL9+/QBP\nZZEh5C+//JLKIRWLRYsWOaVbFikXXngh4OUtyQhUuV4lSpSIUmKkYkydOjV0ipMYPXq06+0mQ95G\njRoB3rmlnmjqJ7p69eoUjDIxSHET27dv57rrrkvRaIrG3nvv7XJ5lVuo/7/88svOBuSee+4BfBuD\ndKVVq1YAbl7Z2dmMGTMmlUNyhGLxtGPHDpcEpwvY/PnzqVmzZp6/owu1pPYZM2bwxRdfuPdLF1q3\nbg34lVeq9Jk4cWKoq+ziSSxfqHREYWV9b2qGG8mWLVucy/3dd9+dvMHFGVWvlixZMlR924rKxo0b\nXThEnnA6N9V7MJJ//vnHbdaU5K9rUXE6ISSa33//PSqU/L+GNikzZ85Mu8XhP//84xbtSlVRhR3g\nGsdrY5buaCMdWZm8atWqVA0nBxa2MwzDMAzDCEBWUT15Cv0BWVmJ/YAEk52dXaD5UKbPMd3nB5k/\nRztOPeI5RxWmVKpUKeq5nTt3uqTqeJLpxymkbo5yfldvv7feeisRH5Pw4/S+++4D/KINHZ/33HOP\nU5zWrVtX1LcvFMk6F6WyaZ2yY8cOmjRpAiTeBqSgOZryZBiGYRiGEQBTngrAdvTpPz/I/DnaceqR\n6XNM9/lB5s/RjlOPeMxR/Rf79OkDeAUPycrnMuXJMAzDMAwjjpjyVAC2i0j/+UHmz9GOU49Mn2O6\nzw8yf452nHpk+hxNeTIMwzAMwwiALZ4MwzAMwzACkPCwnWEYhmEYRiZhypNhGIZhGEYAbPFkGIZh\nGIYRAFs8GYZhGIZhBMAWT4ZhGIZhGAGwxZNhGIZhGEYAbPFkGIZhGIYRAFs8GYZhGIZhBMAWT4Zh\nGIZhGAGwxZNhGIZhGEYASib6AzK9OSBk/hzTfX6Q+XO049Qj0+eY7vODzJ+jHacemT5HU54MwzAM\nDjnkEJ555hmeeeaZVA/FMEKPLZ6MhHPOOeeQnZ1NdnY2Tz75JE8++SSlS5emdOnSqR6aYRj/z7PP\nPsuOHTvYsWNHqodiGKHHFk+GYRiGYRgBSHjOk2HMnz+fuXPnAtClSxcAfvnlFwAeeOABfvjhBwA2\nbdqUmgEaxv8gJUp4e+fevXsDUKtWLV555ZVUDskw0gZTngzDMAzDMAKQlZ2d2IT4ZGbcH3nkkQC8\n/PLLAC7xsV+/fvz4448AnHrqqQB89NFHhXpPqyqIz/wqVqwIQM+ePQG44447AChbtixvv/024H83\nmzdvLu7HRZHIOTZp0gSA3XffPcfjAwcOpG7dujkemzNnDp9++ikAM2bMKOpHRmHHqUemzzGe8+vX\nrx8AEydOdI81a9YMgGXLlsXrY6Kwarv4zrFatWoADB8+nIsvvlhj0OewZMkSAB555BEAHn300WJ/\npp2LpjwZhmEYhmEEIu2Vp/LlywNw5513csoppwBQs2bNHK8pUaIEO3fuBGD9+vUAzJ07lyuvvLLA\n9w/TCvv666+nU6dOADRu3Dhu75uKneBZZ50FwGOPPeYee/fddwHo2rUrABs2bIjb5yVqjvXq1eOD\nDz4AoGRJL4Xw33//BTxVLcbnsH37dgBef/11AB5++GEAHn/88aIMAUjMcXrNNdcAcOutt5KV5b39\n8OHDAbjlllsCj7G4hOFcPPTQQwE47LDDaNGiRczXtGzZkurVqwO+AgCwatUqAMaMGQMQ0xIgmefi\nvHnzADjttNMAr9pOyvDWrVvj9TFRmPIUnznuv//+ACxcuBCAgw46KN/XS3m68MILi/vRoTgXE02B\nx2m6L56efvppADp06JDnayIXT2Lp0qW0atWqwPcP00GyePFit1g85phj4va+qbiY7bXXXgA0bdqU\nSZMmAVC5cmUA3nrrLQA6derEzz//HJfPS9Qcy5Urx7333gvA0UcfDcA+++wDwG+//cbnn38O4BJx\na9WqxXnnnQf4850zZw4A3bt3L8oQgPgepwo1LliwAIAaNWq457Zs2QL4Y9+2bVvAkRadVJ2LpUuX\ndgulp556CoDddtutyO/3ySefAH6aQSTJOBd17VARR+S5qHMvkdjiqXhzrF27NgAvvvgiAAceeGCh\nfk8WFDp2tYkrCmG4L+63334ADBo0KEoIefPNNwHvmvrtt98W6f0tbGcYhmEYhhFH0lJ5atKkiUuM\n6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m3bukITJRZLSYxUmyL56aefAL/fpMIJuR24w0qs/nsXXHBBCkYSP1q0aAH4qmGkCqxi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\ncaM9xj+z+XGyYeVbh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URVFiwEr0jiLVM+4h9eeY2/n99ddflClTJrP3l3EA8N9//wGOYnXVVVcBsGHD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QDfoFdz0vecDiiCopY0AB9++CHg2VIsXLgwJeOKlWXLlgGe+h3emks2MIVB5268c/sCs3iS+6sc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\nh2irHjOJ0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FUTygkydFURRFURQP6ORJURRFURTFAzp5UhRFURRF8YBOnhRFURRFUTygkydF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\nRFz4P5PSO3Lk+pU+AAAAAElFTkSuQmCC\n", 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3b98+oRSnUH766ScAdu/eDaTdZWWXNTcerFixItfnGDJkCJA2/F1ylCVSSg1JQyFh36EM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HYy6YarXakv8GQMvWdK5EtfVEpen69esAmlvHp6amZKVLNWtlZUXcLeGZbN1C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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -535,12 +536,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Choose a number from 0 to 9999 and we are going to predict the class of that test image." + "Choose a number from 0 to 9999 for `test_img_choice` and we are going to predict the class of that test image." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -549,15 +550,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicted class: 2\n" + "Predicted class of test image: 2\n" ] } ], "source": [ "# takes ~20 Secs. to execute this cell\n", - "testing_choice = 2311\n", - "predicted_class = kNN_Learner(test_img[testing_choice])\n", - "print(\"Predicted class:\", predicted_class)" + "test_img_choice = 2311\n", + "predicted_class = kNN_Learner(test_img[test_img_choice])\n", + "print(\"Predicted class of test image:\", predicted_class)" ] }, { @@ -569,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -578,16 +579,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2\n" + "Actual class of test image: 2\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -595,7 +596,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -603,8 +604,8 @@ } ], "source": [ - "print(test_lbl[testing_choice])\n", - "plt.imshow(test_img[testing_choice].reshape((28,28)))" + "print(\"Actual class of test image:\", test_lbl[test_img_choice])\n", + "plt.imshow(test_img[test_img_choice].reshape((28,28)))" ] }, { @@ -615,7 +616,7 @@ "source": [ "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", - "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run this particular dataset. We will have an optimised version below in numPy which is nearly ~10-50 times faster than this implementation." + "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset. We will have an optimised version below in NumPy which is nearly ~50-100 times faster than this implementation." ] }, { @@ -627,21 +628,110 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ "class kNN_learner:\n", - " def __init__():\n", + " \"Simple kNN learner with manhattan distance\"\n", + " def __init__(self):\n", " pass\n", - " def train():\n", - " pass\n", - " def predict_labels():\n", - " pass\n", - " def compute_manhattan_distances():\n", - " pass" + " \n", + " def train(self, train_img, train_lbl):\n", + " self.train_img = train_img\n", + " self.train_lbl = train_lbl\n", + "\n", + " def predict_labels(self, test_img, k=1, distance=\"manhattan\"):\n", + " if distance == \"manhattan\": \n", + " distances = self.compute_manhattan_distances(test_img)\n", + " num_test = distances.shape[0]\n", + " predictions = np.zeros(num_test, dtype=np.uint8)\n", + " \n", + " for i in range(num_test):\n", + " k_best_labels = self.train_lbl[np.argsort(distances[i])].flatten()[:k]\n", + " predictions[i] = mode(k_best_labels)\n", + " \n", + " return predictions\n", + " \n", + " def compute_manhattan_distances(self, test_img):\n", + " num_test = test_img.shape[0]\n", + " num_train = self.train_img.shape[0]\n", + "# print(num_test, num_train)\n", + " \n", + " dists = np.zeros((num_test, num_train))\n", + " \n", + " for i in range(num_test):\n", + " dists[i] = np.sum(abs(self.train_img - test_img[i]), axis = 1)\n", + " \n", + " return(dists)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "learner = kNN_learner()\n", + "learner.train(train_img, train_lbl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us predict the classes of first 100 test images." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# takes ~17 Secs. to execute this cell\n", + "num_test = 100\n", + "predictions = learner.predict_labels(test_img[:num_test], k=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare the performances of both implementations. It took 20 Secs. to predict one image using our native implementations and 17 Secs. to predict 100 images in faster implementations. That's 110 times faster.\n", + "\n", + "Now, test the accuracy of our predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of predictions: 98.0 %\n" + ] + } + ], + "source": [ + "# print(predictions)\n", + "# print(test_lbl[:num_test])\n", + "\n", + "num_correct = np.sum([predictions == test_lbl[:num_test]])\n", + "num_accuracy = (float(num_correct) / num_test) * 100\n", + "print(\"Accuracy of predictions:\", num_accuracy, \"%\")" ] }, { From bf6af62ddd49d63339942c47989efd738900cb1f Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sat, 16 Jul 2016 16:47:55 +0530 Subject: [PATCH 358/513] adds aima3e image to readme (#245) --- README.md | 7 ++++++- images/aima3e_big.jpg | Bin 0 -> 57178 bytes images/aima_logo.png | Bin 0 -> 8063 bytes 3 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 images/aima3e_big.jpg create mode 100644 images/aima_logo.png diff --git a/README.md b/README.md index 43040f0d7..1afee9df7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,9 @@ -# ![](https://github.com/aimacode/aima-java/blob/gh-pages/aima3e/images/aima3e.jpg)aima-python Build StatusBinder +

    + +
    +----------------- + +# `aima-python` [![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/aimacode/aima-python) Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're looking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. diff --git a/images/aima3e_big.jpg b/images/aima3e_big.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1105a5e14adf0074dfad807b8a8db0018a151881 GIT binary patch literal 57178 zcmb5VWmFtb^d>yGySo$IAvhtpI|O%k2^t`{+u%-c26wk0gFC_9-QAYof8X7+U*GC; zdggTXwYqgnZaw|6_^}BXZKQZmy2m_U4z!ok9! z!o#DIVq;*F{{Jo?Jpc@Nh#Lr3CZ zKmMQMKPv?={*w(22?q-e0}b^*>U~yXKz~XWRe{06=1?~hH|rrJA= zLq+`^xOMfh3_ym0_%sp<10Vvp%h4A5{$$T7M8n7)fIv$wBOuj>ifWkZ`l^`t7WsaP zT$&3i<1-wRdYcXt&+*c8X@W8=zy1KckbF_v*LpBQ?q+R%{@?$Nr-i%>((vAaw|`qu zBn?wP0E6Zjc|ulSYzdv2X-z8kmg z78dtuwed(!R<%4~X2Q@>m=otPB*HG4r7_iSo>xVHddq9B(ll7nKU!{^EXkiWq8I9a z6uraL_Yhw9Gb4Xxlgs(W_4__yF;VbFKF9t+5YKOyZRopzZm|f z+LRy~=3Nh5z_vll&AHY8XMtk(SoL|&b2sY;picGYyScwRiQcMbY8C1SV8)hY&G2pIrt#$ z`8^gHwD^+qaJc#4^h>O|7($Q?{p2V%lhVOO!|Q?nJr)AN^+~w9AT|o=zlwks{Z`9JC&R9JB?8IYxOHTpI;XZI9FGu 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zQoej-jH1(4u?4TG=FTR+hzKG+v1sNp6&@3Cm8) zop_)WHlERa|r>2Vt9aRsuxB^F3}oCbPafvl`tJXM$C^tj@gU4dY1@qddMtAH5RsCfVY N002ovPDHLkV1f`i!_@!) literal 0 HcmV?d00001 From d952574e0f29208335c53dd98563ab48088048a6 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Sat, 16 Jul 2016 21:15:58 +0530 Subject: [PATCH 359/513] adds aima logo as repo's logo and links to aima berkely website --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1afee9df7..ace8f14d7 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@

    - +

    ----------------- From 9f49ade17f86cb56b8ed6227ac64d6871f18e81c Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Tue, 26 Jul 2016 03:03:09 -0700 Subject: [PATCH 360/513] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ace8f14d7..23f32e851 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Python code for the book *Artificial Intelligence: A Modern Approach.* You can u ## Python 3.4 -This code is in Python 3.4 (Python 3.5, also works, but Python 2.x does not). You can [install the latest Python version](https://www.python.org/downloads), and if that doesn't work, use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.4 (Python 3.5, also works, but Python 2.x does not). You can [install the latest Python version](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). ## Structure of the Project @@ -138,4 +138,4 @@ Here is a table of the implemented data structures, the figure, name of the impl # Acknowledgements -Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors! +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, and @reachtarunhere. From cc95bd388af18959adb9bbaefc96344f59c9e093 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 31 Jul 2016 01:11:28 -0700 Subject: [PATCH 361/513] Rename Probability-4e.ipynb to probability-4e.ipynb --- Probability-4e.ipynb => probability-4e.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename Probability-4e.ipynb => probability-4e.ipynb (100%) diff --git a/Probability-4e.ipynb b/probability-4e.ipynb similarity index 100% rename from Probability-4e.ipynb rename to probability-4e.ipynb From 5c730de941baf8ff983ed6b52ee97cf7d9010033 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 31 Jul 2016 01:50:13 -0700 Subject: [PATCH 362/513] Update probability-4e.ipynb --- probability-4e.ipynb | 1472 +++++++++++++++++++++--------------------- 1 file changed, 747 insertions(+), 725 deletions(-) diff --git a/probability-4e.ipynb b/probability-4e.ipynb index bd6e0acaa..e148e929e 100644 --- a/probability-4e.ipynb +++ b/probability-4e.ipynb @@ -1,22 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, - "outputs": [], - "source": [ - "import itertools" - ] - }, { "cell_type": "markdown", "metadata": { @@ -28,35 +11,72 @@ } }, "source": [ - "# Bayesian Networks\n", + "# Probability and Bayesian Networks\n", "\n", - "A Bayesian network, or Bayes net for short, is a data structure to represent a joint probability distribution, and do inference on it. For example, here is a network with five nodes, each with its conditional probability table, and with arrows from parent to child variables. The story, from Judea Pearl, is that Judea has a burglar alarm, and it can be triggered by either a burglary or an earthquake. If the alarm sounds, one or both of Judea's neighbors, John and Mary, might call him to let him know.\n", + "Probability theory allows us to compute the likelihood of certain events, given assumptioons about the components of the event. A Bayesian network, or Bayes net for short, is a data structure to represent a joint probability distribution over several random variables, and do inference on it. \n", + "\n", + "As an example, here is a network with five random variables, each with its conditional probability table, and with arrows from parent to child variables. The story, from Judea Pearl, is that there is a house burglar alarm, which can be triggered by either a burglary or an earthquake. If the alarm sounds, one or both of the neighbors, John and Mary, might call the owwner to say the alarm is sounding.\n", "\n", "

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zAOoQM^-Iu4Ra#b0_@2$99Xx}5);qU{V;$r6CAg+hiShAYAo@1?z&P<#Q9)q>>_g0# zKq;En>f-}Snag;4bgM^ym3#6eHWN;Ts~9;d|HZl~+uyytBzNcuqU7K@4AFFW10kEc z+X-TgdFGEsg8)Br?DNXs3ZM&}Pyw>%Ut$(Er58nm%b^7aWGlas9Tc=l|x7B_&ini}HaBGv!a5gke?3KzJxn#vNZGojr-AX6 zY^Z&KyoV1HcfI1+NRHIag z3a-Y+H;~kY#k>n-nx!R+jiS8#*$?p>y!6|69iKWq)=fC^8W0c;@NWphvTi>BSw7}P zK}2H*_+W9VnZ#`L;d-8P8aAi}g_iD)e_n(qr*SPi;i2fJ4C58%-OV?H|;BN(O)=s-%#x>j#ZU2BLldIu8Ci@Dd71hBPIisTomh)Mw#&{w1{?$fJW?Nhph<{}!g;HEILYP+_!N^8pt~OBh5+v;Jmf zWvQJ2S<{U%$3O)uK>;tAh{E*(AiAur+d8P*+%)ukCy#w`!&H)RTD335bV--V(nRB; zq9;M_78F2)j4DA@upIn(XI@t;R2wbagC})xk>)%k5;jZ9UsP07DmVHnisX_n?Xekl z4xHlzkhllovg4Zht!0JYvIdZ!1swF7gQf0Ua&@}V38J*_N+j@PBIzcN%zeIL8;&;{V}Q2vVQr Tdg^H)z<&y|Dl+9#CISBkn+%rg From e76b8861cdc18616c646fbbf3d7172972d93f621 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 7 Mar 2017 07:40:00 +0200 Subject: [PATCH 408/513] Update Comments in learning.py + PluralityLearner Update (#315) * Update Comments on learning.py + PluralityLearner - Fixed some capitalization, spelling and quotation mistakes in comments - In the PluralityLearner function, the nested function "predict" always returns the same output for a dataset, without taking into account the input ("example"). I defaulted the input as an empty list, so that we don't have to create (or find) a dummy example when we want to simply find the most popular class. * Update learning.py * Update learning.py * Update learning.py * Made Requested Changes --- learning.py | 91 +++++++++++++++++++++++++++-------------------------- 1 file changed, 46 insertions(+), 45 deletions(-) diff --git a/learning.py b/learning.py index 3790a2b02..df5d6fce3 100644 --- a/learning.py +++ b/learning.py @@ -116,7 +116,7 @@ def setproblem(self, target, inputs=None, exclude=()): self.check_me() def check_me(self): - "Check that my fields make sense." + """Check that my fields make sense.""" assert len(self.attrnames) == len(self.attrs) assert self.target in self.attrs assert self.target not in self.inputs @@ -126,12 +126,12 @@ def check_me(self): list(map(self.check_example, self.examples)) def add_example(self, example): - "Add an example to the list of examples, checking it first." + """Add an example to the list of examples, checking it first.""" self.check_example(example) self.examples.append(example) def check_example(self, example): - "Raise ValueError if example has any invalid values." + """Raise ValueError if example has any invalid values.""" if self.values: for a in self.attrs: if example[a] not in self.values[a]: @@ -139,7 +139,7 @@ def check_example(self, example): .format(example[a], self.attrnames[a], example)) def attrnum(self, attr): - "Returns the number used for attr, which can be a name, or -n .. n-1." + """Returns the number used for attr, which can be a name, or -n .. n-1.""" if isinstance(attr, str): return self.attrnames.index(attr) elif attr < 0: @@ -148,7 +148,7 @@ def attrnum(self, attr): return attr def sanitize(self, example): - "Return a copy of example, with non-input attributes replaced by None." + """Return a copy of example, with non-input attributes replaced by None.""" return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)] @@ -161,12 +161,11 @@ def __repr__(self): def parse_csv(input, delim=','): r"""Input is a string consisting of lines, each line has comma-delimited - fields. Convert this into a list of lists. Blank lines are skipped. + fields. Convert this into a list of lists. Blank lines are skipped. Fields that look like numbers are converted to numbers. The delim defaults to ',' but '\t' and None are also reasonable values. >>> parse_csv('1, 2, 3 \n 0, 2, na') - [[1, 2, 3], [0, 2, 'na']] - """ + [[1, 2, 3], [0, 2, 'na']]""" lines = [line for line in input.splitlines() if line.strip()] return [list(map(num_or_str, line.split(delim))) for line in lines] @@ -195,7 +194,7 @@ def __init__(self, observations=[], default=0): self.add(o) def add(self, o): - "Add an observation o to the distribution." + """Add an observation o to the distribution.""" self.smooth_for(o) self.dictionary[o] += 1 self.n_obs += 1 @@ -210,18 +209,18 @@ def smooth_for(self, o): self.sampler = None def __getitem__(self, item): - "Return an estimate of the probability of item." + """Return an estimate of the probability of item.""" self.smooth_for(item) return self.dictionary[item] / self.n_obs # (top() and sample() are not used in this module, but elsewhere.) def top(self, n): - "Return (count, obs) tuples for the n most frequent observations." + """Return (count, obs) tuples for the n most frequent observations.""" return heapq.nlargest(n, [(v, k) for (k, v) in self.dictionary.items()]) def sample(self): - "Return a random sample from the distribution." + """Return a random sample from the distribution.""" if self.sampler is None: self.sampler = weighted_sampler(list(self.dictionary.keys()), list(self.dictionary.values())) @@ -236,7 +235,7 @@ def PluralityLearner(dataset): most_popular = mode([e[dataset.target] for e in dataset.examples]) def predict(example): - "Always return same result: the most popular from the training set." + """Always return same result: the most popular from the training set.""" return most_popular return predict @@ -274,9 +273,9 @@ def class_probability(targetval): def NearestNeighborLearner(dataset, k=1): - "k-NearestNeighbor: the k nearest neighbors vote." + """k-NearestNeighbor: the k nearest neighbors vote.""" def predict(example): - "Find the k closest, and have them vote for the best." + """Find the k closest items, and have them vote for the best.""" best = heapq.nsmallest(k, ((dataset.distance(e, example), e) for e in dataset.examples)) return mode(e[dataset.target] for (d, e) in best) @@ -291,18 +290,18 @@ class DecisionFork: of branches, one for each of the attribute's values.""" def __init__(self, attr, attrname=None, branches=None): - "Initialize by saying what attribute this node tests." + """Initialize by saying what attribute this node tests.""" self.attr = attr self.attrname = attrname or attr self.branches = branches or {} def __call__(self, example): - "Given an example, classify it using the attribute and the branches." + """Given an example, classify it using the attribute and the branches.""" attrvalue = example[self.attr] return self.branches[attrvalue](example) def add(self, val, subtree): - "Add a branch. If self.attr = val, go to the given subtree." + """Add a branch. If self.attr = val, go to the given subtree.""" self.branches[val] = subtree def display(self, indent=0): @@ -319,7 +318,7 @@ def __repr__(self): class DecisionLeaf: - "A leaf of a decision tree holds just a result." + """A leaf of a decision tree holds just a result.""" def __init__(self, result): self.result = result @@ -337,7 +336,7 @@ def __repr__(self): def DecisionTreeLearner(dataset): - "[Figure 18.5]" + """[Figure 18.5]""" target, values = dataset.target, dataset.values @@ -365,21 +364,21 @@ def plurality_value(examples): return DecisionLeaf(popular) def count(attr, val, examples): - "Count the number of examples that have attr = val." + """Count the number of examples that have attr = val.""" return len(e[attr] == val for e in examples) #count(e[attr] == val for e in examples) def all_same_class(examples): - "Are all these examples in the same target class?" + """Are all these examples in the same target class?""" class0 = examples[0][target] return all(e[target] == class0 for e in examples) def choose_attribute(attrs, examples): - "Choose the attribute with the highest information gain." + """Choose the attribute with the highest information gain.""" return argmax_random_tie(attrs, key=lambda a: information_gain(a, examples)) def information_gain(attr, examples): - "Return the expected reduction in entropy from splitting by attr." + """Return the expected reduction in entropy from splitting by attr.""" def I(examples): return information_content([count(target, v, examples) for v in values[target]]) @@ -389,7 +388,7 @@ def I(examples): return I(examples) - remainder def split_by(attr, examples): - "Return a list of (val, examples) pairs for each val of attr." + """Return a list of (val, examples) pairs for each val of attr.""" return [(v, [e for e in examples if e[attr] == v]) for v in values[attr]] @@ -397,7 +396,7 @@ def split_by(attr, examples): def information_content(values): - "Number of bits to represent the probability distribution in values." + """Number of bits to represent the probability distribution in values.""" probabilities = normalize(removeall(0, values)) return sum(-p * math.log2(p) for p in probabilities) @@ -423,11 +422,11 @@ def find_examples(examples): raise NotImplementedError def passes(example, test): - "Does the example pass the test?" + """Does the example pass the test?""" raise NotImplementedError def predict(example): - "Predict the outcome for the first passing test." + """Predict the outcome for the first passing test.""" for test, outcome in predict.decision_list: if passes(example, test): return outcome @@ -443,7 +442,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3], """ Layered feed-forward network. hidden_layer_sizes: List of number of hidden units per hidden layer - learning_rate: Learning rate of gradient decent + learning_rate: Learning rate of gradient descent epoches: Number of passes over the dataset """ @@ -483,7 +482,7 @@ class NNUnit: """ Single Unit of Multiple Layer Neural Network inputs: Incoming connections - weights: weights to incoming connections + weights: Weights to incoming connections """ def __init__(self, weights=None, inputs=None): @@ -496,7 +495,7 @@ def __init__(self, weights=None, inputs=None): def network(input_units, hidden_layer_sizes, output_units): """ Create Directed Acyclic Network of given number layers. - hidden_layers_sizes : list number of neuron units in each hidden layer + hidden_layers_sizes : List number of neuron units in each hidden layer excluding input and output layers """ # Check for PerceptronLearner @@ -623,8 +622,8 @@ def predict(example): # ______________________________________________________________________________ -def Linearlearner(dataset, learning_rate=0.01, epochs=100): - """Define with learner = Linearlearner(data); infer with learner(x).""" +def LinearLearner(dataset, learning_rate=0.01, epochs=100): + """Define with learner = LinearLearner(data); infer with learner(x).""" idx_i = dataset.inputs idx_t = dataset.target # As of now, dataset.target gives only one index. examples = dataset.examples @@ -698,7 +697,7 @@ def train(dataset): def WeightedMajority(predictors, weights): - "Return a predictor that takes a weighted vote." + """Return a predictor that takes a weighted vote.""" def predict(example): return weighted_mode((predictor(example) for predictor in predictors), weights) @@ -708,7 +707,8 @@ def predict(example): def weighted_mode(values, weights): """Return the value with the greatest total weight. >>> weighted_mode('abbaa', [1,2,3,1,2]) - 'b'""" + 'b' + """ totals = defaultdict(int) for v, w in zip(values, weights): totals[v] += w @@ -727,7 +727,7 @@ def train(dataset, weights): def replicated_dataset(dataset, weights, n=None): - "Copy dataset, replicating each example in proportion to its weight." + """Copy dataset, replicating each example in proportion to its weight.""" n = n or len(dataset.examples) result = copy.copy(dataset) result.examples = weighted_replicate(dataset.examples, weights, n) @@ -739,7 +739,8 @@ def weighted_replicate(seq, weights, n): seq proportional to the corresponding weight (filling in fractions randomly). >>> weighted_replicate('ABC', [1,2,1], 4) - ['A', 'B', 'B', 'C']""" + ['A', 'B', 'B', 'C'] + """ assert len(seq) == len(weights) weights = normalize(weights) wholes = [int(w * n) for w in weights] @@ -755,7 +756,7 @@ def flatten(seqs): return sum(seqs, []) def test(predict, dataset, examples=None, verbose=0): - "Return the proportion of the examples that are NOT correctly predicted." + """Return the proportion of the examples that are NOT correctly predicted.""" if examples is None: examples = dataset.examples if len(examples) == 0: @@ -787,7 +788,7 @@ def train_and_test(dataset, start, end): def cross_validation(learner, size, dataset, k=10, trials=1): """Do k-fold cross_validate and return their mean. That is, keep out 1/k of the examples for testing on each of k runs. - Shuffle the examples first; If trials>1, average over several shuffles. + Shuffle the examples first; if trials>1, average over several shuffles. Returns Training error, Validataion error""" if k is None: k = len(dataset.examples) @@ -820,11 +821,11 @@ def cross_validation(learner, size, dataset, k=10, trials=1): def cross_validation_wrapper(learner, dataset, k=10, trials=1): """ - Fig 18.8 + [Fig 18.8] Return the optimal value of size having minimum error on validataion set. - err_train: a training error array, indexed by size - err_val: a validataion error array, indexed by size + err_train: A training error array, indexed by size + err_val: A validataion error array, indexed by size """ err_val = [] err_train = [] @@ -843,7 +844,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): def leave_one_out(learner, dataset): - "Leave one out cross-validation over the dataset." + """Leave one out cross-validation over the dataset.""" return cross_validation(learner, size, dataset, k=len(dataset.examples)) @@ -878,7 +879,7 @@ def score(learner, size): def RestaurantDataSet(examples=None): - "Build a DataSet of Restaurant waiting examples. [Figure 18.3]" + """Build a DataSet of Restaurant waiting examples. [Figure 18.3]""" return DataSet(name='restaurant', target='Wait', examples=examples, attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' + 'Raining Reservation Type WaitEstimate Wait') @@ -917,7 +918,7 @@ def T(attrname, branches): def SyntheticRestaurant(n=20): - "Generate a DataSet with n examples." + """Generate a DataSet with n examples.""" def gen(): example = list(map(random.choice, restaurant.values)) example[restaurant.target] = waiting_decision_tree(example) From 43fced5cf57643a5df9186fdcab906e372f67f12 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Tue, 7 Mar 2017 02:40:53 -0300 Subject: [PATCH 409/513] Fix flake8 for test files (#303) * Add flake8 config file * Fix flake8 for test files --- .flake8 | 3 ++ tests/test_csp.py | 2 +- tests/test_grid.py | 1 + tests/test_learning.py | 12 +++++--- tests/test_logic.py | 14 ++++----- tests/test_mdp.py | 30 +++++++++--------- tests/test_nlp.py | 64 +++++++++++++++++++++++---------------- tests/test_planning.py | 20 ++++++------ tests/test_probability.py | 16 +++++----- tests/test_search.py | 15 ++++++--- tests/test_text.py | 2 +- tests/test_utils.py | 6 ++-- 12 files changed, 108 insertions(+), 77 deletions(-) create mode 100644 .flake8 diff --git a/.flake8 b/.flake8 new file mode 100644 index 000000000..405ab746c --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +max-line-length = 100 +ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405 diff --git a/tests/test_csp.py b/tests/test_csp.py index 7eae4b0c4..24ca26f39 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -1,5 +1,5 @@ import pytest -from csp import * #noqa +from csp import * # noqa def test_csp_assign(): diff --git a/tests/test_grid.py b/tests/test_grid.py index 9a3994669..5e05a617a 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -17,5 +17,6 @@ def test_distance2(): def test_vector_clip(): assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) + if __name__ == '__main__': pytest.main() diff --git a/tests/test_learning.py b/tests/test_learning.py index d36a1146d..46ac8dd26 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,4 +1,3 @@ -import pytest from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner from utils import DataFile @@ -6,7 +5,7 @@ def test_parse_csv(): Iris = DataFile('iris.csv').read() - assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa'] + assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa'] def test_weighted_mode(): @@ -16,20 +15,23 @@ def test_weighted_mode(): def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] + def test_plurality_learner(): zoo = DataSet(name="zoo") pL = PluralityLearner(zoo) assert pL([]) == "mammal" + def test_naive_bayes(): iris = DataSet(name="iris") nB = NaiveBayesLearner(iris) - assert nB([5,3,1,0.1]) == "setosa" + assert nB([5, 3, 1, 0.1]) == "setosa" + def test_k_nearest_neighbors(): iris = DataSet(name="iris") - kNN = NearestNeighborLearner(iris,k=3) - assert kNN([5,3,1,0.1]) == "setosa" + kNN = NearestNeighborLearner(iris, k=3) + assert kNN([5, 3, 1, 0.1]) == "setosa" diff --git a/tests/test_logic.py b/tests/test_logic.py index 6de49101d..918c25cf0 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -1,6 +1,6 @@ import pytest -from logic import * -from utils import expr_handle_infix_ops, count +from logic import * # noqa +from utils import expr_handle_infix_ops, count, Symbol def test_expr(): @@ -56,10 +56,10 @@ def test_KB_wumpus(): assert kb_wumpus.ask(~P[1, 2]) == {} # Statement: There is a pit in [2,2]. - assert kb_wumpus.ask(P[2, 2]) == False + assert kb_wumpus.ask(P[2, 2]) is False # Statement: There is a pit in [3,1]. - assert kb_wumpus.ask(P[3, 1]) == False + assert kb_wumpus.ask(P[3, 1]) is False # Statement: Neither [1,2] nor [2,1] contains a pit. assert kb_wumpus.ask(~P[1, 2] & ~P[2, 1]) == {} @@ -112,7 +112,7 @@ def test_dpll(): & (~D | ~F) & (B | ~C | D) & (A | ~E | F) & (~A | E | D)) == {B: False, C: True, A: True, F: False, D: True, E: False}) assert dpll_satisfiable(A & ~B) == {A: True, B: False} - assert dpll_satisfiable(P & ~P) == False + assert dpll_satisfiable(P & ~P) is False def test_unify(): @@ -159,7 +159,7 @@ def test_move_not_inwards(): def test_to_cnf(): assert (repr(to_cnf(wumpus_world_inference & ~expr('~P12'))) == "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") - assert repr(to_cnf((P&Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' + assert repr(to_cnf((P & Q) | (~P & ~Q))) == '((~P | P) & (~Q | P) & (~P | Q) & (~Q | Q))' assert repr(to_cnf("B <=> (P1 | P2)")) == '((~P1 | B) & (~P2 | B) & (P1 | P2 | ~B))' assert repr(to_cnf("a | (b & c) | d")) == '((b | a | d) & (c | a | d))' assert repr(to_cnf("A & (B | (D & E))")) == '(A & (D | B) & (E | B))' @@ -169,7 +169,7 @@ def test_to_cnf(): def test_standardize_variables(): e = expr('F(a, b, c) & G(c, A, 23)') assert len(variables(standardize_variables(e))) == 3 - #assert variables(e).intersection(variables(standardize_variables(e))) == {} + # assert variables(e).intersection(variables(standardize_variables(e))) == {} assert is_variable(standardize_variables(expr('x'))) diff --git a/tests/test_mdp.py b/tests/test_mdp.py index de0de064f..f5cb40510 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -1,25 +1,27 @@ -import pytest from mdp import * # noqa def test_value_iteration(): - assert value_iteration(sequential_decision_environment, .01) == {(3, 2): 1.0, (3, 1): -1.0, - (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, - (0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537, - (0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676, - (2, 0): 0.34461306281476806, (2, 1): 0.48643676237737926, - (2, 2): 0.79536093684710951} + assert value_iteration(sequential_decision_environment, .01) == { + (3, 2): 1.0, (3, 1): -1.0, + (3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462, + (0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537, + (0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676, + (2, 0): 0.34461306281476806, (2, 1): 0.48643676237737926, + (2, 2): 0.79536093684710951} def test_policy_iteration(): - assert policy_iteration(sequential_decision_environment) == {(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0), - (1, 0): (1, 0), (1, 2): (1, 0), - (2, 0): (0, 1), (2, 1): (0, 1), (2, 2): (1, 0), - (3, 0): (-1, 0), (3, 1): None, (3, 2): None} + assert policy_iteration(sequential_decision_environment) == { + (0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0), + (1, 0): (1, 0), (1, 2): (1, 0), (2, 0): (0, 1), + (2, 1): (0, 1), (2, 2): (1, 0), (3, 0): (-1, 0), + (3, 1): None, (3, 2): None} def test_best_policy(): - pi = best_policy(sequential_decision_environment, value_iteration(sequential_decision_environment, .01)) + pi = best_policy(sequential_decision_environment, + value_iteration(sequential_decision_environment, .01)) assert sequential_decision_environment.to_arrows(pi) == [['>', '>', '>', '.'], - ['^', None, '^', '.'], - ['^', '>', '^', '<']] + ['^', None, '^', '.'], + ['^', '>', '^', '<']] diff --git a/tests/test_nlp.py b/tests/test_nlp.py index d51ac539d..43f71f163 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -1,12 +1,13 @@ import pytest import nlp -from nlp import loadPageHTML, stripRawHTML, determineInlinks, findOutlinks, onlyWikipediaURLS +from nlp import loadPageHTML, stripRawHTML, findOutlinks, onlyWikipediaURLS from nlp import expand_pages, relevant_pages, normalize, ConvergenceDetector, getInlinks -from nlp import getOutlinks, Page, HITS +from nlp import getOutlinks, Page from nlp import Rules, Lexicon # Clumsy imports because we want to access certain nlp.py globals explicitly, because # they are accessed by function's within nlp.py + def test_rules(): assert Rules(A="B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} @@ -27,18 +28,18 @@ def test_lexicon(): href="/wiki/TestLiving" href="/wiki/TestMan" >""" testHTML2 = "Nothing" -pA = Page("A", 1, 6, ["B","C","E"],["D"]) -pB = Page("B", 2, 5, ["E"],["A","C","D"]) -pC = Page("C", 3, 4, ["B","E"],["A","D"]) -pD = Page("D", 4, 3, ["A","B","C","E"],[]) -pE = Page("E", 5, 2, [],["A","B","C","D","F"]) -pF = Page("F", 6, 1, ["E"],[]) -pageDict = {pA.address:pA,pB.address:pB,pC.address:pC, - pD.address:pD,pE.address:pE,pF.address:pF} +pA = Page("A", 1, 6, ["B", "C", "E"], ["D"]) +pB = Page("B", 2, 5, ["E"], ["A", "C", "D"]) +pC = Page("C", 3, 4, ["B", "E"], ["A", "D"]) +pD = Page("D", 4, 3, ["A", "B", "C", "E"], []) +pE = Page("E", 5, 2, [], ["A", "B", "C", "D", "F"]) +pF = Page("F", 6, 1, ["E"], []) +pageDict = {pA.address: pA, pB.address: pB, pC.address: pC, + pD.address: pD, pE.address: pE, pF.address: pF} nlp.pagesIndex = pageDict -nlp.pagesContent ={pA.address:testHTML,pB.address:testHTML2, - pC.address:testHTML,pD.address:testHTML2, - pE.address:testHTML,pF.address:testHTML2} +nlp.pagesContent ={pA.address: testHTML, pB.address: testHTML2, + pC.address: testHTML, pD.address: testHTML2, + pE.address: testHTML, pF.address: testHTML2} # This test takes a long time (> 60 secs) # def test_loadPageHTML(): @@ -50,6 +51,7 @@ def test_lexicon(): # assert all(x in loadedPages for x in fullURLs) # assert all(loadedPages.get(key,"") != "" for key in addresses) + def test_stripRawHTML(): addr = "https://en.wikipedia.org/wiki/Ethics" aPage = loadPageHTML([addr]) @@ -57,10 +59,12 @@ def test_stripRawHTML(): strippedHTML = stripRawHTML(someHTML) assert "" not in strippedHTML and "" not in strippedHTML + def test_determineInlinks(): # TODO assert True + def test_findOutlinks_wiki(): testPage = pageDict[pA.address] outlinks = findOutlinks(testPage, handleURLs=onlyWikipediaURLS) @@ -70,35 +74,39 @@ def test_findOutlinks_wiki(): # ______________________________________________________________________________ # HITS Helper Functions + def test_expand_pages(): pages = {k: pageDict[k] for k in ('F')} - pagesTwo = {k: pageDict[k] for k in ('A','E')} + pagesTwo = {k: pageDict[k] for k in ('A', 'E')} expanded_pages = expand_pages(pages) - assert all(x in expanded_pages for x in ['F','E']) - assert all(x not in expanded_pages for x in ['A','B','C','D']) + assert all(x in expanded_pages for x in ['F', 'E']) + assert all(x not in expanded_pages for x in ['A', 'B', 'C', 'D']) expanded_pages = expand_pages(pagesTwo) print(expanded_pages) - assert all(x in expanded_pages for x in ['A','B','C','D','E','F']) + assert all(x in expanded_pages for x in ['A', 'B', 'C', 'D', 'E', 'F']) + def test_relevant_pages(): pages = relevant_pages("male") - assert all((x in pages.keys()) for x in ['A','C','E']) - assert all((x not in pages) for x in ['B','D','F']) + assert all((x in pages.keys()) for x in ['A', 'C', 'E']) + assert all((x not in pages) for x in ['B', 'D', 'F']) + def test_normalize(): - normalize( pageDict ) - print(page.hub for addr,page in nlp.pagesIndex.items()) - expected_hub = [1/91,2/91,3/91,4/91,5/91,6/91] # Works only for sample data above + normalize(pageDict) + print(page.hub for addr, page in nlp.pagesIndex.items()) + expected_hub = [1/91, 2/91, 3/91, 4/91, 5/91, 6/91] # Works only for sample data above expected_auth = list(reversed(expected_hub)) assert len(expected_hub) == len(expected_auth) == len(nlp.pagesIndex) - assert expected_hub == [page.hub for addr,page in sorted(nlp.pagesIndex.items())] - assert expected_auth == [page.authority for addr,page in sorted(nlp.pagesIndex.items())] + assert expected_hub == [page.hub for addr, page in sorted(nlp.pagesIndex.items())] + assert expected_auth == [page.authority for addr, page in sorted(nlp.pagesIndex.items())] + def test_detectConvergence(): # run detectConvergence once to initialise history convergence = ConvergenceDetector() convergence() - assert convergence() # values haven't changed so should return True + assert convergence() # values haven't changed so should return True # make tiny increase/decrease to all values for _, page in nlp.pagesIndex.items(): page.hub += 0.0003 @@ -111,17 +119,21 @@ def test_detectConvergence(): # retest function with values. Should now return false assert not convergence() + def test_getInlinks(): inlnks = getInlinks(pageDict['A']) assert sorted([page.address for page in inlnks]) == pageDict['A'].inlinks + def test_getOutlinks(): outlnks = getOutlinks(pageDict['A']) assert sorted([page.address for page in outlnks]) == pageDict['A'].outlinks + def test_HITS(): # TODO - assert True # leave for now + assert True # leave for now + if __name__ == '__main__': pytest.main() diff --git a/tests/test_planning.py b/tests/test_planning.py index 4e012b207..461cdcdbb 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -1,14 +1,12 @@ -from planning import * +from planning import * # noqa from utils import expr from logic import FolKB def test_action(): - precond = [[expr("P(x)"), expr("Q(y, z)")] - ,[expr("Q(x)")]] - effect = [[expr("Q(x)")] - , [expr("P(x)")]] - a=Action(expr("A(x,y,z)"),precond, effect) + precond = [[expr("P(x)"), expr("Q(y, z)")], [expr("Q(x)")]] + effect = [[expr("Q(x)")], [expr("P(x)")]] + a=Action(expr("A(x,y,z)"), precond, effect) args = [expr("A"), expr("B"), expr("C")] assert a.substitute(expr("P(x, z, y)"), args) == expr("P(A, C, B)") test_kb = FolKB([expr("P(A)"), expr("Q(B, C)"), expr("R(D)")]) @@ -34,7 +32,8 @@ def test_air_cargo(): p.act(action) assert p.goal_test() - + + def test_spare_tire(): p = spare_tire() assert p.goal_test() is False @@ -44,9 +43,10 @@ def test_spare_tire(): for action in solution: p.act(action) - + assert p.goal_test() + def test_three_block_tower(): p = three_block_tower() assert p.goal_test() is False @@ -56,9 +56,10 @@ def test_three_block_tower(): for action in solution: p.act(action) - + assert p.goal_test() + def test_have_cake_and_eat_cake_too(): p = have_cake_and_eat_cake_too() assert p.goal_test() is False @@ -70,6 +71,7 @@ def test_have_cake_and_eat_cake_too(): assert p.goal_test() + def test_graph_call(): pdll = spare_tire() negkb = FolKB([expr('At(Flat, Trunk)')]) diff --git a/tests/test_probability.py b/tests/test_probability.py index dce6c23b4..9f8ed5cd1 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -1,4 +1,3 @@ -import pytest import random from probability import * # noqa from utils import rounder @@ -125,11 +124,13 @@ def test_forward_backward(): umbrella_evidence = [T, T, F, T, T] assert (rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == - [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], [0.8204, 0.1796], [0.8673, 0.1327]]) + [[0.6469, 0.3531], [0.8673, 0.1327], [0.8204, 0.1796], [0.3075, 0.6925], + [0.8204, 0.1796], [0.8673, 0.1327]]) umbrella_evidence = [T, F, T, F, T] - assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [[0.5871, 0.4129], - [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], [0.2324, 0.7676], [0.7177, 0.2823]] + assert rounder(forward_backward(umbrellaHMM, umbrella_evidence, umbrella_prior)) == [ + [0.5871, 0.4129], [0.7177, 0.2823], [0.2324, 0.7676], [0.6072, 0.3928], + [0.2324, 0.7676], [0.7177, 0.2823]] def test_fixed_lag_smoothing(): @@ -141,7 +142,8 @@ def test_fixed_lag_smoothing(): umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) d = 2 - assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.1111, 0.8889] + assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, + umbrella_evidence, t)) == [0.1111, 0.8889] d = 5 assert fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t) is None @@ -150,13 +152,13 @@ def test_fixed_lag_smoothing(): e_t = T d = 1 - assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, d, umbrella_evidence, t)) == [0.9939, 0.0061] + assert rounder(fixed_lag_smoothing(e_t, umbrellaHMM, + d, umbrella_evidence, t)) == [0.9939, 0.0061] def test_particle_filtering(): N = 10 umbrella_evidence = T - umbrella_prior = [0.5, 0.5] umbrella_transition = [[0.7, 0.3], [0.3, 0.7]] umbrella_sensor = [[0.9, 0.2], [0.1, 0.8]] umbrellaHMM = HiddenMarkovModel(umbrella_transition, umbrella_sensor) diff --git a/tests/test_search.py b/tests/test_search.py index 87c1fd211..11d522e94 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -8,7 +8,8 @@ def test_breadth_first_tree_search(): - assert breadth_first_tree_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert breadth_first_tree_search( + romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_breadth_first_search(): @@ -16,7 +17,8 @@ def test_breadth_first_search(): def test_uniform_cost_search(): - assert uniform_cost_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + assert uniform_cost_search( + romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] def test_depth_first_graph_search(): @@ -25,7 +27,8 @@ def test_depth_first_graph_search(): def test_iterative_deepening_search(): - assert iterative_deepening_search(romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] + assert iterative_deepening_search( + romania_problem).solution() == ['Sibiu', 'Fagaras', 'Bucharest'] def test_depth_limited_search(): @@ -41,7 +44,8 @@ def test_astar_search(): def test_recursive_best_first_search(): - assert recursive_best_first_search(romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] + assert recursive_best_first_search( + romania_problem).solution() == ['Sibiu', 'Rimnicu', 'Pitesti', 'Bucharest'] def test_BoggleFinder(): @@ -62,7 +66,7 @@ def run_plan(state, problem, plan): return True if len(plan) is not 2: return False - predicate = lambda x : run_plan(x, problem, plan[1][x]) + predicate = lambda x: run_plan(x, problem, plan[1][x]) return all(predicate(r) for r in problem.result(state, plan[0])) plan = and_or_graph_search(vacumm_world) assert run_plan('State_1', vacumm_world, plan) @@ -82,6 +86,7 @@ def test_LRTAStarAgent(): my_agent = LRTAStarAgent(LRTA_problem) assert my_agent('State_5') is None + # TODO: for .ipynb: """ >>> compare_graph_searchers() diff --git a/tests/test_text.py b/tests/test_text.py index 62e314951..0cd3e675c 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -201,7 +201,7 @@ def test_bigrams(): >>> P3.samples(20) 'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' -""" +""" # noqa if __name__ == '__main__': pytest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py index 18e83485b..76e0421b3 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -97,7 +97,8 @@ def test_scalar_vector_product(): def test_scalar_matrix_product(): - assert rounder(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], [0, -30]] + assert rounder(scalar_matrix_product(-5, [[1, 2], [3, 4], [0, 6]])) == [[-5, -10], [-15, -20], + [0, -30]] assert rounder(scalar_matrix_product(0.2, [[1, 2], [2, 3]])) == [[0.2, 0.4], [0.4, 0.6]] @@ -167,7 +168,7 @@ def test_Expr(): def test_expr(): P, Q, x, y, z, GP = symbols('P, Q, x, y, z, GP') assert (expr(y + 2 * x) - == expr('y + 2 * x') + == expr('y + 2 * x') == Expr('+', y, Expr('*', 2, x))) assert expr('P & Q ==> P') == Expr('==>', P & Q, P) assert expr('P & Q <=> Q & P') == Expr('<=>', (P & Q), (Q & P)) @@ -176,5 +177,6 @@ def test_expr(): assert (expr('GP(x, z) <== P(x, y) & P(y, z)') == Expr('<==', GP(x, z), P(x, y) & P(y, z))) + if __name__ == '__main__': pytest.main() From 7c5f2834e48f7d7f8d3ba6afc3d4aa9e33403fc9 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Tue, 7 Mar 2017 02:47:08 -0300 Subject: [PATCH 410/513] Add test to csp.py (#326) Add tests to the following methods from CSP class * result * goal_test * support_prunning * suppose * prune * choices * infer_assignement * restore * conflicted_vars --- tests/test_csp.py | 118 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) diff --git a/tests/test_csp.py b/tests/test_csp.py index 24ca26f39..346d9a3ca 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -43,11 +43,129 @@ def test_csp_actions(): state = {'A': '1', 'C': '2'} assert map_coloring_test.actions(state) == [('B', '3')] + state = (('A', '1'), ('B', '3')) + assert map_coloring_test.actions(state) == [('C', '2')] + state = {'A': '1'} assert (map_coloring_test.actions(state) == [('C', '2'), ('C', '3')] or map_coloring_test.actions(state) == [('B', '2'), ('B', '3')]) +def test_csp_result(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + + state = (('A', '1'), ('B', '3')) + action = ('C', '2') + + assert map_coloring_test.result(state, action) == (('A', '1'), ('B', '3'), ('C', '2')) + + +def test_csp_goal_test(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + state = (('A', '1'), ('B', '3'), ('C', '2')) + assert map_coloring_test.goal_test(state) is True + + state = (('A', '1'), ('C', '2')) + assert map_coloring_test.goal_test(state) is False + + +def test_csp_support_pruning(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + map_coloring_test.support_pruning() + assert map_coloring_test.curr_domains == {'A': ['1', '2', '3'], 'B': ['1', '2', '3'], + 'C': ['1', '2', '3']} + + +def test_csp_suppose(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + var = 'A' + value = '1' + + removals = map_coloring_test.suppose(var, value) + + assert removals == [('A', '2'), ('A', '3')] + assert map_coloring_test.curr_domains == {'A': ['1'], 'B': ['1', '2', '3'], + 'C': ['1', '2', '3']} + + +def test_csp_prune(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + removals = None + var = 'A' + value = '3' + + map_coloring_test.support_pruning() + map_coloring_test.prune(var, value, removals) + assert map_coloring_test.curr_domains == {'A': ['1', '2'], 'B': ['1', '2', '3'], + 'C': ['1', '2', '3']} + assert removals is None + + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + removals = [('A', '2')] + map_coloring_test.support_pruning() + map_coloring_test.prune(var, value, removals) + assert map_coloring_test.curr_domains == {'A': ['1', '2'], 'B': ['1', '2', '3'], + 'C': ['1', '2', '3']} + assert removals == [('A', '2'), ('A', '3')] + + +def test_csp_choices(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + var = 'A' + assert map_coloring_test.choices(var) == ['1', '2', '3'] + + map_coloring_test.support_pruning() + removals = None + value = '3' + map_coloring_test.prune(var, value, removals) + assert map_coloring_test.choices(var) == ['1', '2'] + + +def test_csp_infer_assignement(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + map_coloring_test.infer_assignment() == {} + + var = 'A' + value = '3' + map_coloring_test.prune(var, value, None) + value = '1' + map_coloring_test.prune(var, value, None) + + map_coloring_test.infer_assignment() == {'A': '2'} + + +def test_csp_restore(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + map_coloring_test.curr_domains = {'A': ['2', '3'], 'B': ['1'], 'C': ['2', '3']} + removals = [('A', '1'), ('B', '2'), ('B', '3')] + + map_coloring_test.restore(removals) + + assert map_coloring_test.curr_domains == {'A': ['2', '3', '1'], 'B': ['1', '2', '3'], + 'C': ['2', '3']} + + +def test_csp_conflicted_vars(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + + current = {} + var = 'A' + val = '1' + map_coloring_test.assign(var, val, current) + + var = 'B' + val = '3' + map_coloring_test.assign(var, val, current) + + var = 'C' + val = '3' + map_coloring_test.assign(var, val, current) + + conflicted_vars = map_coloring_test.conflicted_vars(current) + + assert (conflicted_vars == ['B', 'C'] or conflicted_vars == ['C', 'B']) + + def test_backtracking_search(): assert backtracking_search(australia) assert backtracking_search(australia, select_unassigned_variable=mrv) From 556120d6842613f4eb8715b4ea7421daedbe8226 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Tue, 7 Mar 2017 11:47:51 -0800 Subject: [PATCH 411/513] Update utils.py --- utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils.py b/utils.py index 124b04132..3c070293e 100644 --- a/utils.py +++ b/utils.py @@ -61,7 +61,7 @@ def is_in(elt, seq): def mode(data): """Return the most common data item. If there are ties, return any one of them.""" - [(item, count)] = Counter(data).most_common(1) + [(item, count)] = collections.Counter(data).most_common(1) return item # ______________________________________________________________________________ From ceb987487c7bc921517a07e9fed23d54f8355aa8 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 7 Mar 2017 21:48:07 +0200 Subject: [PATCH 412/513] Update Learning Notebook (#329) * Added Plurality Learner Plot Image * Update learning.ipynb --- images/pluralityLearner_plot.png | Bin 0 -> 12658 bytes learning.ipynb | 760 ++++++++++++++----------------- 2 files changed, 333 insertions(+), 427 deletions(-) create mode 100644 images/pluralityLearner_plot.png diff --git a/images/pluralityLearner_plot.png b/images/pluralityLearner_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..50aa5dcd14c138e342d8cdca63d70b1ada73b9c3 GIT binary patch literal 12658 zcmb7rcUV(P*KYuoqe!tJ(&3;WMNkkaAv6U9>AeO#bdVBSXaN)vloE=FfHdhfbV3Vy zKoCN&0s$nUmxLZV-^TO4&;9Ow-g}?>{lT-%?3uM^*37KmZx!EZYpT#*VYvbVfoN5q zJ=Fz)D0YB9_#c!&3ocf;7X`5 zq1YoTGk*~1!I0`x1$|%W`qbr5H%G?zHg&iK-m_d2XJH6?$Rf_^baVInvk&*)u`uX7 z5J6E~W%+=nj4z1zGjge%{RNO=m=#nCi0(A8a znhJFL4;VG*0Rx^6WX09S4*Dt|0S1Ma)0k%W23XSDOY<=i7-+M07{Ne1gB<#!Y_I&j7Z_l4UpR9B1Gm%^T?781p>oU0& zg%AYYcGhQNnSKp*E-q74l>TlJ(=23;j!o^=ls%X$GAP~(^V?n+ucHhp8@>NUiQw@4 z>O`$!kL1o7WGYo9P1De5x6=E*i?JGOi-D1zRx0|;#-*pU+3&>*Zfb~}+{~$Gf2P7H z<*MDwX$r5Zxf_RYPyfsmM%Mrtf0pJTCPtLL^BWAz^6~tJ-VwSnMg5@*UYK>eCsiUN z`1F8n=Z2=_tFsblv-FuszGm8&FMkHmf*h=$uC|UuvY$z&U;_0@Z4TQ{dK&5F3iEr7 z68Q7_dLflA84q4ijvcR6^)1MSnR7_Eer~=5+R^qMnw{I+uZ}eg)|lzbF+ufX;RT)0 zq=N72Luk(=4%q?!IkJI{meYZkx1PZAHF%+qiOF~~c^b=)vS{?Vvv#XKun$6JlXU+I zy$l+Up%Km{Tx*8Zxtva`1!iDJ4DuXtE&gT@jexV$z2=oX{Xrh$fLksHVrO8a$RP5~ zl*d{{N7uLH1Db_NK?<%(h+mn&R7uiERI$tI@I_#-r9)mie*#UK(^J=H_i6F2ZtdZ; 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Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "## Review\n", + "## Machine Learning Overview\n", "\n", "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n", "\n", @@ -61,7 +63,7 @@ "\n", "* **Supervised Learning**:\n", "\n", - "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n", + "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n", "\n", "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n", "\n", @@ -81,46 +83,272 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "deletable": true, + "editable": true }, "source": [ - "## Explanations of learning module goes here" + "## Plurality Learner Classifier\n", + "\n", + "### Overview\n", + "\n", + "The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the most popular class in the dataset and classifies any subsequent item to that class. Essentially, it classifies every new item to the same class. For that reason, it is not used very often, instead opting for more complicated algorithms when we want accurate classification.\n", + "\n", + "![pL plot](images/pluralityLearner_plot.png)\n", + "\n", + "Let's see how the classifier works with the plot above. There are three classes named **Class A** (orange-colored dots) and **Class B** (blue-colored dots) and **Class C** (green-colored dots). Every point in this plot has two **features** (i.e. X1, X2). Now, let's say we have a new point, a red star and we want to know which class this red star belongs to. Solving this problem by predicting the class of this new red star is our current classification problem.\n", + "\n", + "The Plurality Learner will find the class most represented in the plot. ***Class A*** has four items, ***Class B*** has three and ***Class C*** has seven. The most popular class is ***Class C***. Therefore, the item will get classified in ***Class C***, despite the fact that it is closer to the other two classes." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Implementation\n", + "\n", + "Below follows the implementation of the PluralityLearner algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def PluralityLearner(dataset):\n", + " \"\"\"A very dumb algorithm: always pick the result that was most popular\n", + " in the training data. Makes a baseline for comparison.\"\"\"\n", + " most_popular = mode([e[dataset.target] for e in dataset.examples])\n", + "\n", + " def predict(example):\n", + " \"Always return same result: the most popular from the training set.\"\n", + " return most_popular\n", + " return predict" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n", + "\n", + "The function first finds the most popular class in the dataset and then each time we call its \"predict\" function, it returns it. Note that the input (\"example\") does not matter. The function always returns the same class." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example\n", + "\n", + "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead (and only for this algorithm) we will use the zoo dataset, found [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mammal\n" + ] + } + ], + "source": [ + "from learning import DataSet, PluralityLearner\n", + "\n", + "zoo = DataSet(name=\"zoo\")\n", + "\n", + "pL = PluralityLearner(zoo)\n", + "print(pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## k-Nearest Neighbours (kNN) Classifier\n", + "\n", + "### Overview\n", + "The k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are going to use this to classify Iris flowers. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n", + "\n", + "![kNN plot](images/knn_plot.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's see how kNN works with a simple plot shown in the above picture.\n", + "\n", + "We have co-ordinates (we call them **features** in Machine Learning) of this red star and we need to predict its class using the kNN algorithm. In this algorithm, the value of **k** is arbitrary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n", + "\n", + "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into the majority class. Observe that smaller circle which contains three points other than **test point** (red star). As there are two violet points, which form the majority, we predict the class of red star as **violet- Class B**.\n", + "\n", + "Similarly if we put **k = 5**, you can observe that there are four yellow points, which form the majority. So, we classify our test point as **yellow- Class A**.\n", + "\n", + "In practical tasks, we iterate through a bunch of values for k (like [1, 3, 5, 10, 20, 50, 100]), see how it performs and select the best one. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Implementation\n", + "\n", + "Below follows the implementation of the kNN algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def NearestNeighborLearner(dataset, k=1):\n", + " \"\"\"k-NearestNeighbor: the k nearest neighbors vote.\"\"\"\n", + " def predict(example):\n", + " \"\"\"Find the k closest items, and have them vote for the best.\"\"\"\n", + " best = heapq.nsmallest(k, ((dataset.distance(e, example), e)\n", + " for e in dataset.examples))\n", + " return mode(e[dataset.target] for (d, e) in best)\n", + " return predict" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "# Practical Machine Learning Task\n", + "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n", "\n", - "## MNIST handwritten digits calssification\n", + "To accomplish that, the function uses a heap-queue, where the items of the dataset are sorted according to their distance from *example* (the item to classify). We then take the k smallest elements from the heap-queue and we find the majority class. We classify the item to this class." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example\n", "\n", - "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n", + "We measured a new flower with the following values: 5.1, 3.0, 1.1, 0.1. We want to classify that item/flower in a class. To do that, we write the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setosa\n" + ] + } + ], + "source": [ + "from learning import DataSet, NearestNeighborLearner\n", + "\n", + "iris = DataSet(name=\"iris\")\n", + "\n", + "kNN = NearestNeighborLearner(iris,k=3)\n", + "print(kNN([5.1,3.0,1.1,0.1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Handwritten Digits Classification\n", + "\n", + "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/), is a large database of handwritten digits that is commonly used for training and testing/validating in Machine learning.\n", "\n", "The dataset has **60,000 training images** each of size 28x28 pixels with labels and **10,000 testing images** of size 28x28 pixels with labels.\n", "\n", - "In this section, we will use this database to compare performances of these different learning algorithms:\n", - "* kNN (k-Nearest Neighbour) classifier\n", - "* Single-hidden-layer Neural Network classifier\n", - "* SVMs (Support Vector Machines)\n", + "In this section, we will use this database to compare performances of different learning algorithms.\n", + "\n", + "It is estimated that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n", "\n", - "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!" + "NOTE: We will be using external libraries to load and visualize the dataset smoothly ([numpy](http://www.numpy.org/) for loading and [matplotlib](http://matplotlib.org/) for visualization). You do not need previous experience of the libraries to follow along." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Loading MNIST digits data\n", + "### Loading MNIST digits data\n", "\n", "Let's start by loading MNIST data into numpy arrays." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -138,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -187,14 +415,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are gonna use to train & classify hand-written digits in various learning approaches." + "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -207,12 +435,12 @@ "source": [ "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n", "\n", - "Each 28x28 pixel image is flattened to 784x1 array and we should have 60,000 of them in training data. Similarly we should have 10,000 of those 784x1 arrays in testing data. " + "Each 28x28 pixel image is flattened to a 784x1 array and we should have 60,000 of them in training data. Similarly, we should have 10,000 of those 784x1 arrays in testing data." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -239,16 +467,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizing MNIST digits data\n", + "### Visualizing MNIST digits data\n", "\n", - "To get a better understanding of the dataset, let's visualize some random images for each class from training & testing datasets." + "To get a better understanding of the dataset, let's visualize some random images for each class from training and testing datasets." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -282,16 +510,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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VUf1N9erVAVdJFcVRxgn418caNWrw22+/AbBnzx4AihcvzhlnnJHpfRdffDEA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\n2y4r2tatW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WvXAnAfUWY+apiNM9kagxGnoaEhk/fJz4y2traMc0CDVlFRYdpEJLZI6e7u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\nRMQDTZ5EREREPNDkSURERMSD/wFvutcO9t8bawAAAABJRU5ErkJggg==\n", 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ffRXx9oaCrMgh/P7776neky1bNnPtycRNJjwAe/bsAdyB5+HDh30rqhdkIBzoZSbPSAmO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A4ePAjYOyu3IMmMsgN0Ijk2I0aMMDH+Dz74APBNRPd//Y033uiqhHpBEurz5cvH4sWLk33dZZdd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\nALh9h7wYav/xxx9Td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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -299,22 +527,22 @@ } ], "source": [ - "# takes 5-10 secs. to execute the cell\n", + "# takes 5-10 seconds to execute this\n", "show_MNIST(\"training\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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SYFwmQlkh2+3Zs8fU1kvE8QciCSnxRN12iqIoiqIoEeAb5UnkRnHb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\nLVeunLkui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iKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQkTnUgpiqIoiqKEiU6k\nFEVRFEVRwkQnUoqiKIqiKGGiEylFURRFUZQwiWmvvYxeJh4y/hgz+vhAx+h3dIwOGX18oGP0OzpG\nB1WkFEVRFEVRwkQnUoqiKIqiKGGiEylFURRFUZQw0YmUoiiKoihKmMQ02FxRFEWJP+rVqwdA5cqV\nGThwIAA//fQTAPXr12fPnj2e2aYoXqOKlKIoiqIoSpioIhUn3HjjjaxevRqAXr16ATB69GgvTYoa\nefLkAeCaa64B4L777iNHjhwA3HvvvQB88cUX9O7dG4A1a9Z4YKWiZEyaNm3KE088AUCZMmUAyJUr\nF4C5DgEqVKgAQKlSpVSR8il16tRJ8Ds1nnvuuajZkpGJy4lU/vz5AWjXrh0AtWrV4q677gLgtdde\nA+D1118H4Ntvv/XAwsjz5JNPmn8/9dRTQMaYSMmNuVatWjRr1gyAW2+9FYDLLrvM7GdZTimPc+fO\nAVC1alUeeOABQCdS0SRfvnwAFC1alC1btqTpvcWKFQOcSS/Atm3buPPOOwE4cuRIBK30D1myZOE/\n//kPAOXLlwfg5ptvpmHDhgDmNcuy+OGHHwCoUaMGAIcPH461uQC0b98egD59+gDOdZc9e/ZU37d3\n714ADh06FD3jlDQjk6Z+/fqFPIFKjE6o0oa69hRFURRFUcIk7hSpzp078/jjjwOOpAzO6s62ncKp\nDz/8MADNmzcHYOzYsSY4Mp45efJk0H/HK3fccQcAEyZMAODCCy80r4n6JMc0GH/88UcClU6JDuXK\nlQPgww8/5IUXXgDgo48+AmD9+vUpvrdq1aqAq0wVK1aMvn37AjBgwAAg/pWpSpUqAW4wdsOGDald\nu3ay+8s5PXv2bEaOHAl4o0SJMtauXTuj7Mu2QLZt2wbAwYMHAShRogTZsmUD4J577gHcoHO/IAqb\nnK8XXXSRsX/mzJlmPzn3pk6dCsDu3bsBOHXqVMxsjSSiIvXr1y/sz5D3yjlct27ddNuVXrJmzQrA\n3XffzS233JLgtZtuuokTJ04ArtdmyZIlsTUQVaQURVEURVHCxkpp1R/xLwuj346oEx07dgTgpZde\nImfOnEn2SW4cu3bt4t133wXcGfvff/+dVjM87ymUJ08e/vzzT8CN/+rSpUtEvyNW/b2eeuopoxJm\nyuTO5WVl8d133wFwww03JPsZ/fr1S7PSGM1jmDt3bgCeeeYZAH777TcaN24MQN68eeVzU1TZhB07\ndgDOiloZGrRMAAAgAElEQVT+FqESrTHu3bvXqIZfffUVANWqVUvxPa+88grgqMjC6dOnATdpQK7N\ntBDLazFTpkyMGzcOcGP3tm7dSsmSJQG4+OKLATcYO5AzZ84AcPToUaZMmQLA5s2bAUcFSelciNa1\nKAHiCxcuBODSSy9Nss/u3bsZO3YsALNmzQLg119/BaBr167m+K9bty6tX2+I1jHMnz+/uWaKFy+e\n2meLLQCsXLkScBTXN954A3CU73CJ9TMjks/yFStWAKkrUtEcoyhRDRo0AGD+/PnBPteM+9ixYwB8\n//33gKNg/fbbb2n92iSEMsa4ce2NHz8+xdffeecdwPnjBXLJJZeYLDcJnH344YcjetLFgipVqiSY\ndMQzK1euNJMmmRyOGzeOxYsXA27WntzYwL3pSYCrnwLtK1SoQM+ePQHo1KlTuj9PJiiZM2emZcuW\n6f68SJPaAyolPvvsMyC8CVQskSSIsWPH0qFDByBh4krBggUB97yUCdK6dev4+uuvAXeMBw4ciInN\nySEPpLJly7JgwQIg4QTq6NGjADz//PMATJ482bjCEjNmzJgoWpp+jh07xssvvwxgsnoLFy4c0nvF\nnVW7dm1uvPFGwA1B8DvLly+P6OcF3nu94vrrrweCT6CCIYtZed+sWbOMm3fnzp1RsNAlYzyZFUVR\nFEVRPMDXilT+/PmZO3duSPsmVqKCIe7BfPny+XKlnxKXXHKJWf3GO5999hmXX345AP/88w/gBNyK\nYijKVCBnz54F4MEHHwT8FaR8++2306ZNmzS9R1QKCX4tVaqUKf8gfPLJJ5ExMB2IrF6gQAGzLdQV\nYjwiqq+4tjp06GBcBY0aNQJgz549RtEpVKgQgFGh/Mhtt90GBD9uy5YtM6/LNRbPnDlzhuHDhwOw\ndOlSAFN6IjHitrr55puTvCZ/k+7duwMYlctvhFonSlx1/fv3N/9PySsjQedelUGoUqWKSeAIZN++\nfQAmaWXfvn3m2Z/4HlyzZk0+/vhjwC0xsn///qjYq4qUoiiKoihKmPhakapVqxY33XRTmt4jgeSr\nVq0CnFgASeEWpDRCPBEsKDSeCRbEKYHaEiMUuGKSFOVFixbFwLq0MXz4cJM6Xr16dQBKlixpzkWJ\n3/v000/5+eefATedXFZYMj5w08lDVWOjiQTKZ8mS9luFxJwEKql+V1UlvkLios6ePWtW5YHVuyUh\nQH7HG6L63nHHHRlCiQqGxLQlV5T5zTffBIKXbxBlUkrs+JVQC26KEiWEGiO8fPlyT0ogPPXUU0ah\nF1t37txpVCq5j4JzX4WkihRA6dKlATe55aWXXoqKvb6eSElNqFCQG8OLL74IuNWuL7nkEvNvqWcD\nbgBitKS+SJO4fkZGo3bt2iYpIDFffPGF72tGPfroo2naXy5wucG1bNnSuDklsUImWV4iwbZpSc4Q\nt61kiAW+d968eRG0LrLky5cvSSbopEmTfB8YnxqyQAH37y+1ozLqJCoUrrjiimRfk4WOdMqId8IN\nRg+3Mnq4SLeKZs2amUWXhEEEq3MGbnasVNiXRBBwF24XXXRRdAw+j7r2FEVRFEVRwsTXilSo9OjR\ng+nTpwPw119/JXht586dHD9+PMl7ZJUmaeuKt7Rs2ZLMmTMn2CausSZNmiSbjh1viEw+bdo0wKkU\nLdx///0AzJkzJ/aGJUOgfaEi9ZaC8fvvv6fHnKhy0003mcBjSWbwe7p/KIh7FjD3yXBq6SWmZs2a\ngNNpQWpLxROSPBCM2bNnA25l938rEqQeK6Rnrm3bptK8NNBODlGdxPUemBgjtG3bFoCJEydG5Ziq\nIqUoiqIoihImvlSkpGt6uXLlkgSn7ty506Sz/vjjjyF9ngStvf/++4ATN9WjRw/ADUSUysN+Zfjw\n4aZIXEakY8eOJpZGjrn4vr0uaBgO2bNnNwkCkp573333mW2Jg7e//PJL829JqfdShZOK3XItRotc\nuXKZnnxSrNMPvc6kn6VlWVxwwQWAW3n/34gEX1999dUmjkziNk+ePMntt98OwOeff+6NgWEgcY2J\n4/+OHj3q23IHsUJiN2NV/kDil6VMAbhFNFNT6KVvZ0r3KikiXL58+agoUr6cSEndlh9++CFJ1sTm\nzZtDnkAF+zxwMjHOnTsHuFKi3ydS8+fPN+01ov1wiyUSRC7VlwFzbIYMGeKJTeEglbClPln9+vVp\n3bp1yO+vWrWqacchQeabNm0yD61YVxoWd1BgM+lIUrFiRQAGDhxIkyZNALc1UIsWLdi+fXtUvjdU\nihQpAjgLLbl/fPPNNwC89957JntUrkk/UqVKFcAdSzjIhF8mHaNGjUqyT44cOfjvf/8LuLX6IuE6\njCaJa7YFsmnTpgRZYf8W+vfv71ndKMmWla4WyZE9e3bAdd8NGTLEuO28RF17iqIoiqIoYeJLRSpa\nxEupg+SYNGkS4Fb3lpTO9DTW9AppPC2qTaALV9LNhw0bFnvDwmDkyJEpJi1If8Dly5fz0UcfBd2n\nSpUqpt6ZHNebb77ZNG5+5JFHALcSerSRUgzizgpsFC7pxVLeAFw37P79+41rUtxBojCCW7tHyJQp\nk3n9mmuuAZxq0uJ6jyVr1qwxpScCS6+IAiy/27Zta1xYUp/GjwqGBIBLEG7BggUTKL8pIU2ZxcUj\niRDgHs/169cDjuLVokULwC1DI01//UpK9aEi0eg22ohyJBXIwyHUxsSxYPXq1YCbLJYvXz5zH5Tf\nDRs2NOURLrvsMgCKFi3qi765qkgpiqIoiqKEyb9KkZLu5rKKjFekTID0gwqsiu1nJI4oR44cpvK8\nxMrYtm1UDVHc4oXAdP+jR48CTlVyUdak8u6ff/6Z7GcEHkNRf5YuXWoqpUthzFgpUhKQKYGegYqE\nxIG1atXKrAYlVmjnzp0msFOUi5RWjOfOnUvyupwTsebw4cN06dIFgKeffhpwYmmkBISURqhRo4ZR\nCqWvm6Ro+zFdXsYyf/58Bg8eDMBbb72V4nskNi7wuAutWrUC3Ir969at830F8GAEU0zB/9X3n3vu\nubCVqMBee7EubZAScm+cMGECAI8//jhlypQBEnYUSEzgsZLkHFHvJ02aZPq3RhtVpBRFURRFUcLE\n14qUZVkRXR2I/3Xjxo1UqlQpYp+rJE+2bNlo3749gIl7Ccw6DFQjRGmTDKD0+P8jjdjWuHFj5s+f\nn+C1Jk2aULRoUcBdWQXr35Uacq5LiQRR6CB9mVfpQbK06tevn2JxTom9kfYwobJr164kGbNe9lOU\n8/Hw4cNAwmxeUbTr1q3L2LFjAbjzzjsBTImEJk2aJDhufkNiS15//XUAHnrooaD7JW6VIyxbtsy0\nTZk8eTKQMH4unkhOMd2wYYMX5qSKtHkJp21LrMsZhIsoUpdffnmSXnvB2L59uxmTKGxSuuTPP/80\n2cfRjqPy9USqT58+lC1bFnBqP0HCnmzvvfce4NabSA0JNh8/fry5EcYj8sCVyaCfXXsPP/wwo0eP\nDmlfGZe4SeThPGLECPNgk8DDqlWrmuBk+fzEVe0jSd++fQEnvVvcktLbaceOHRFpXiulON5++22z\nTdLIO3funO7PD4fNmzcDTl8ykczld0q9ypJDSgm88sorAHzyySeelzpIK8uXLzfngwTP169fH3CS\nBtatW+eZbcFYtmwZ4Ex8pDyBpIx/9NFHbN26FUjY3Fcm0I0bNwbcB1FyPT/FpRkvfQmDNbj1K5GY\nBMiiVCYbfnLrBSLnUfPmzc2kSs5ZcO9HktQwduxYdu3aleAzrrzySsCdM8QCde0piqIoiqKEiRXL\n1EHLstL8ZTK73LRpU5LXRGoPVa7Mli0b4KS+btmyBYAPPvgAwBQFTA7btkPyMYYzxlCRlbvI82vW\nrAGgVq1aEfn8UMYY6vikGODYsWPNv1NiwYIF1K5dGyBJgOCBAwdMsUYJ+LUsy6zUXn31VQC6deuW\n4nek5xhKj7icOXNSr149gIj0FxM33oQJE6hWrRrgVPsGJ71cCpaKCpZaAchYnKdS/kDcmYmR61Lc\nXnKcDh06ROnSpQG3l104+OFaFPVUgv8lAL9fv35m/OkhkteiUL58eVPYVUpUgFs+Rdx8R44cMYG+\n4gJMDXHHpnYfFbw+htu2bTP30cTPwEKFCkVE3Y7EGNMTWJ7Kd0bkc6J5HMUjEdgrUvrmptRlQOYM\nmzdvNuM8duwYAJUrV05zQkgoY1RFSlEURVEUJUx8HSMFGOVIfO/Nmzc3rz377LOAUy5egiNT6ssm\nKbppbTHjF6T/mZ/Tc2VFKunVUtI/MdJPTXp0rVixwihREtQ8bdo0wInFEbVKiluuWLGCuXPnArEJ\nTm7atCnglCQQ/7zEwnzyySem/VBgzzxRlgLPWUH6RV533XUA5M+f38SBjRw5EoChQ4eadjF+QgLq\nkyvnIGpG4vRyP5+34BShlI7zqSEqhp8DyxPz/fffm9goKWuRP39+E3eYOIkiVHbs2GHKQPgdud4K\nFy6c7D5+avsTrYSbOnXq+DZOSpDjkNaeo3L/CUQUrGiVJ/H9REpuWCKd33777ab5sNC9e3e6du0K\nYDJKZLIU6AIS6dqyLHOTjydkwiAuEz8yaNAgIPkJlCCVwCUTBdxMKfkttXoqVKhgsqIkMDbWDX2/\n+OILwMlmknOxUaNGCX6nRuC5KEgV5SFDhpjjKwHZ8U7irKj8+fOboGypQeQHxL06c+ZMevfuDZBq\nwLg0V5VgbCFYCIKfkMr6MrkPrDQvLtuU7o1///23eShJL8wZM2bETdcIWaTJIieQBQsWAG5V/4yI\n34PNI0HNmjWBhFn/0V7Exd9sQlEURVEUxSf4XpFKzMqVK43qJP2wAqsjB/bIguAqgG3bZrUsLpl4\nQCq8+tlF8tlnnwHBq1OLW65///4hBbGK2yQwLdtrhg8fblx7svKpUqVKgtpYybF3716jCJw9exZw\n3Zfi6swIpBQIKokHflKkpHzK77//boKxBwwYACSsJC9JBuXKlTPV90XZmDVrFhC+eyzWiBIcWJ9M\nSmzkzp3bbJNemPI3GjFihElyiUekV1sw5LqWa9MPrFixIqy6UfJeOZ8Fv9eRigTyvA987kf7fqOK\nlKIoiqIoSpjEnSJ1/PjxJAUba9eubQLJU+puLsrG008/bVQdSSuPB6SSsMQ3jBgxwktzgvLCCy8A\nCfvlffzxxwCmj5kf+5GlBSkKJ7/Hjx/vpTm+Q6qBS2BvIFLE1E/IylXiLMFVpOR3YkQtnT59OuAW\nsPRDJ/pwGTduXJJtw4YN88CS6CGxlsFYu3ZtDC0Jjbp164asIv0b1KZQCFYoOJrFmiEO6kiFSrt2\n7QC3Kna5cuUAWL16tcn4W7hwIRB6JfRAvK57EguiUbvGT+gxdInmGCVoWdxHFSpUAJxFi2Q/+rWO\nlCywJNGhdevWVK5cGXBv0HPmzDFVl6MVXK7XokOkx7h06VLAmaDIsZZnoHSKiNQx9cO1GG38OMaN\nGzcCTt00OcYtWrQAwqu8r3WkFEVRFEVRokiGUaSijR9n3pFGV8EOOkZ/o2N0yOjjg8iPUcpWvP/+\n+6ZunfRqk9ckqSe96HnqEssx3n333QA0aNDAqMhSskYSntKCKlKKoiiKoihRRBWpEPHjzDvS6CrY\nQcfob3SMDhl9fKBj9Ds6RgdVpBRFURRFUcJEJ1KKoiiKoihhElPXnqIoiqIoSkZCFSlFURRFUZQw\n0YmUoiiKoihKmOhESlEURVEUJUx0IqUoiqIoihImOpFSFEVRFEUJE51IKYqiKIqihIlOpBRFURRF\nUcJEJ1KKoiiKoihhkiWWX5bR++1Axh9jRh8f6Bj9jo7RIaOPD3SMfkfH6KCKlKIoihIyFStWpGLF\niuzfv5/9+/czefJkr01SFE+JaYuYjD4rhYw/xow+PtAx+h0do0Msx5cli+O86NKlC0899RQARYoU\nAeCKK65g27Ztafo8PYYuOkZ/o4qUoiiKoihKFIlpjJSiKIoSP1x++eUAvPjiiwA0a9aM06dPA7B6\n9WoADhw44I1xiuITVJFSFEVRFEUJE1WkFEXxBaNHjwbgrrvuomPHjgAsXbrUS5P+tRQsWBCARx99\nFHCUKGHOnDkA3HfffbE3TFF8SIaZSFWsWBGAm266CYAcOXIAMGLEiKD7W5YTP/b0008DMGTIkGib\nmC6mT59O27ZtARg/fjwAnTt39tIk5Tx58uQha9asCbY9+uij5M6dO8m+JUqUADDHUs7DwKSPo0eP\nAjBgwAAmTJgAwJEjRyJvuE+oU6cOAO3btwfg5MmT/PDDDx5alHYuvvhi8+/jx48DcPDgQa/MCYts\n2bIBULlyZd566y0ASpYsmWCfQYMGMWrUqJjbpqSdKlWq0LhxY8CdEF944YXmdbn3yHPktddei7GF\nkaVAgQKAc44CtGjRgrfffhuAHj16ABi3dKRR156iKIqiKEqYxLUilStXLgCuvfZa3nzzTQCKFi2a\nYJ/kyjvI9meffRaA7du3m1WYH7nyyiuTHYviDWXLlgXggw8+4NJLL03Te+VYBjumomQNHTqUnj17\nAq7ba/To0VFbVXlBkSJF6NWrF+AoewDTpk1j9+7dXpoVlGrVqgFw3XXXmW2VK1cGoEOHDoBzPMV2\n2S9elKnq1asDsHz5crPtxIkTAAwePBhw1PB4Gc+/lfvvvx9wFKbESnkgcu8pXLhwTOyKBjlz5jTn\n7bhx4wA3QQLgkUceAeDVV18FYNOmTVGxQxUpRVEURVGUMIlrRapChQoArFy5MmisCcA///zDyZMn\nAciUyZk3ysoX3FgqCa70MzJGxR/07t0bIM1qVFooXrw44Kaf27adbNyfH6hQoQLNmzcHYNKkSQD8\n9ttvye4/ZMgQGjVqBLhxYO+9916UrUwdUZ9uueUWGjZsmGBbRlOGr7nmGsBRAgVRorp37w64x1Lx\nL3LdSVylZVn8+uuvADzzzDOAe68qX748rVq1AqBdu3aAo4D/888/sTQ5bOScHT16NDfccAPgluMY\nPnw44HiZxo4dC0RPiRLiciIlEyjJHgmGnBDNmjVjyZIlAOTPnx+ARYsWmZuiUL9+fd8H22W0G3i8\nIxOa48ePc8UVVyR5XYJyQ3XFyaT+/fffT3af9evXp9XMmCALkQULFnDJJZcAsH//fsCV3AOR5JA7\n77zTbPv6668BZ2HkFfPmzQOcCRTABRdcENL7XnrpJQD27NnDG2+8AcSHSy979uw8//zzQMKAeQl5\n0AlUfFCyZEkTZC2CwcKFC80kKXGySu3atc1E6qKLLgKcEJl169bFyuSwePzxxwFMOECWLFlo3bo1\n4F67gTzwwAMxsUtde4qiKIqiKGESl4qUrNhLlSqV5LU///wTgAcffBDAqFEAhw8fBqBRo0bs3LkT\ncAN7A1fGfiXeXXui2lx99dUsXLgQSDgmURHjRXnbsmULgAkITy/ByiUIr7zyCgCfffZZRL4rUuTL\nlw9w3XGBbs7//e9/gKNSgePikwSRuXPnAlCoUCF++eUXAO65556Y2JyYnDlzAvDmm2+adPFz584l\n2W/WrFmAowpu3boVSFk99DNS6mDw4MHGtSqsXLmSmTNnemGWkkZEfRo9erS5v+7btw+Ajh07JlGi\npERA4D1LjrXf1ag+ffrw2GOPAbB48WIAunXrZp7rwVizZk1MbFNFSlEURVEUJUziRpGS7uOPPvqo\nCcAN5NixYwA89NBDQHB/qXD48OGgK06/Ey9KTWLuvfdewI0jKVSokAkMlAKq4FZKfueddwA34DUj\nI/79ggULmtWlBP0WLVrUFIqV1eKpU6c8sDI4+fLlM6pTzZo1gYTnqKg2gUhcxmWXXQY412KfPn0A\nOHToUFTtTYwknUyZMgWA22+/3dwXRG08ePCgiROS2KeMgJRtkFgTgB07dgDQsmVLo2oo/kYUpsDK\n81IqJViM3qJFiwAncUJiNwO9Nn6kQYMGgJPcI88QiQfzC3EzkZLKrPKHTMz06dMB12WQEYlH196l\nl15qMkYKFSpktssESrIpSpcubbJNli1bBqR9IpU3b16GDRsGwHPPPQc4wb9eIccrpVouMhEpW7as\nkeHl75QpUybOnDkTZSvD59lnn03RrSlZjZK1d8MNNyTZ//nnn/esftvAgQMBaNq0qdkmk6onn3wS\nCP4wqlatWoLA7EDWrl3ryxpYgmThyfgA/v77b8C9Zvw6iRLXr4RlgJu0kDdvXgDOnj3L559/DrgT\nw9OnT5tFSunSpQGnen65cuUAKFasGADXX3894EzuxU0mLYrGjRtnsr/9lNkmLlpw/y7BnpG33nor\ngMlwC9xv/vz50TQx3cg9/fTp0ykuZkRsqVevHuAsUiUk4quvvoqqjeraUxRFURRFCRPfK1KyYpd0\n3GB8+eWXZvUbCpUqVTKzV79z5ZVXmt/x6NrbsWOHqbQrK7/Nmzfzxx9/AK6LYenSpUZqPnv2bFjf\ndeTIEfM3evjhhwF3le0FokSlpKxJX7a+ffvy8ccfA26gs1/dzyK1B0vQ+P777xkwYADgrgKLFCkC\nOAGuogIIKbngo0nx4sXp1KlTgm3r168Pmi4tFcpF7S5QoECSsgiiPh48eJBvv/0WwJQV8LKcQyBZ\nsmTh5ptvBtxrEVw3SWAdKT9Sv359wL1mAo9V9uzZgeCq/V9//WVKi2TOnBmAvXv3JukjKNfbqVOn\n+OuvvwC3XlGPHj1M94xIJZdEgsBjJmVHZsyYATjqqqhUUodO/j4bNmwwngK/Is++8uXLA46qFqwm\nnZRvqFu3LgD9+vUDnJIQ4r6/6667omqrKlKKoiiKoihh4mtZpl27djz11FMAQRUkCVhu1aqV8V+n\nhPiTu3btalKeBb9Wi5Z08Zw5c8ZljBQET6uVmIVu3bqZ/8sqUNSqtJIlSxZq1KgBuCqQl4pUYKf1\n5JAYrjFjxpjx+xWJIZk4cSLgqDqiAEqgeJMmTRLEsIBbkDNQwZK4pO3bt0fX6GTo3bu3uQdIUHz7\n9u2T3AdatWpl+ncGlj9ITO3atQGnkKeoPvJ71KhRRs2KVTp2MCpVqsQdd9yRYNvy5ct5/fXX0/Q5\nkuwj8TZdunRJ0hli2LBhbNy4EYhcVenEaqHEewHUqVMHcIsuJ6ZSpUqAm0Tw+eefc/XVVyfYRwrI\nfvrppybmasOGDYAT6ynf5ydFStSna6+91hwDUV9SUmHWrl3rew9HixYtAPjpp5+AhD0gpaD2ZZdd\nZhQrKeMhSnMsrzVfTqRENu/cubORbIMh0uTevXtD+ly5IYqrKZCff/45rWbGFL+f9Gmlffv2QMJA\nX2kwGS6PPPIIV111FeA+7L0ksesoGBL8miNHDl9PpKpVq8ann36aYFumTJlMMK5MkgLdmDLxCDzG\nv//+O+C6B72qw9SoUSNzTUmT002bNiVpNbVp0yaTRdqjR49kP08Cd6tVq2ZcDBKW0LNnT1NhWh4O\nsXT3iTtL7AE4cOAAAHfffXeK2ZIyFnlwtWnTxtQOS+waC2TmzJkMHToUwGRlRpMVK1ak+HowF3Jy\n9/wrr7zSJL4E1kXzQ+uixEydOhVwJg1333034E7qS5QoYe6HiencubOpwyiLWQnO9wtyPxQX36hR\no8z9smrVqoATNvD2228D7n3Gi3Zv6tpTFEVRFEUJE18pUhIAKemYVapUCbqfyLeJq7amhrgYLMtK\nsvL0u9ss0GavAnTTgrju2rZtS8eOHYGExzOxq3bbtm3muIp7ToIIDx8+zDfffAO4fRYvu+wyoyRI\nKYVatWoZGVh6MnmJVAxOyb0oNbZiXUMpVMRlMnfuXHOtiDtuxowZvPvuu0BCJUrUppYtWwIJ1VS5\nxmVl6RVlypQJqvLK6lbq7cybN4+jR4+G/Lnr1q0zrmypzzNr1ixzrsrnV6xYMWZlBuRaa9Kkidkm\niR2B553UJGrSpImp9yXqRqg9B+MRKTci5RVGjhxpPCGiivTp08eUxvAj27dvNwHlUvVbSgKBq0CO\nHDkScDp/iCtMFJ969eolcct7ycsvvwy4LtwePXqYRCS573z66admP3nmSHLI2bNnzfUWbVSRUhRF\nURRFCRNfKVKykpUZZeCKcfPmzYCT0ikF5EJFYqJuvPFG87ny2R988AGASW31K4F/C/EF+7kirfjd\nkyugmpjLL7+cyZMnh/Vdcj589dVXZiWdUv+lWCE95CQod9asWUmUGCkVsGfPnlTjPGKJlCyQytd5\n8uQxff6+//57wInLkJWurG6rVKlC586dg37m8ePHGT58OOCqw14xceJEE8MmKuZdd93Fjz/+GLHv\nkKD0evXqmViyMmXKAE6BYS8TIQIR1VFiTm+55ZZ0f+amTZv48ssv0/050UTiv7p06QK4wdmWZZlz\nonnz5kDkAuajSeIEnquuusooj5K0JVX6J06caMqtSND9mDFjTI/aUOOOY4F0TejUqZMphir3kUAk\n5mv8+PGAk4w2e/bsmNioipSiKIqiKEqYWLHMBrMsK9kvy549uyngJ+mMgUhXdvH/poakNmfNmtWk\n4ZYoUcK8LhlIsuKQ1NfksG07pCCqlMYYDqIIDB8+3MRIyYw7uZV/uIQyxlDHN2fOHMCNOwBMiYo1\na9bw0UcfAQl7x0latRTpTIlff/3VFD5csGABkHrWiVfHUGjdurVJPw/8u4Cj1sg42rZtG/Z3RGqM\nct5JewZwUqYhYVuOlO4fcr6KkjV06FAWLlwYinkp4vVxTCtZsmQx5Q8aNmwIOL1BJfstGJG8FiXe\nJzCOTeJLX3zxRZPNJ0Ur04O08OjTp0+KMWBeH8MaNWrw4YcfApA7d+4Er3377bfmuZCebO5Yj/H2\n228H3Pg+cFsBBV7HgtyLAmNuRZES5So1vD6O4Galjho1CnAVxl69epm+g+khlDH6xrXXsWPHoBOo\nbdu2AWk/oWWy0aZNm6CvS3prahMorxE3SuADKx76CYrbZMyYMSZoXFLd/dSrKpbMnj3buJD79+8P\nuBp/za0AACAASURBVGnwF154oXFLSoVlSZn3gmDV2KtXr56mz5CGzDKR8nMPumhSuXJl85CT6zit\ntZvSgwTofv311yZsQuokRaL56xtvvGFc1Lt27QL8W5Vfgubbtm1rJlDybBFX65tvvmlcYvFC1qxZ\nGTx4cIJta9asMcHlwZCehIEEig3xgtyXZAIlYUBjx46NmQ3q2lMURVEURQkT3yhSya2MJM1RKtIG\nIquqm266iVq1agFuyrWktAYiPZp69uxpUtP9jgSs/vrrr6aXUrNmzQCMe8yPSAC4l5Wc/Yis1CWo\nWYK1A6ugSzkHL3nttdcAN+g0sDebMGXKFLPq69ChA+BcY1LSwe9d5UPh4osvNi70p59+Ok3vveKK\nK4DgCrIo7bHgzJkzgHOPTW9RyZ9//tkUKJVzZNeuXb5VoARx/0iqfKdOnYw6KC4hqRIeT0g/z169\nepm+gEK/fv1S7FsqbmZh48aNvi7xkBziThb69u0LuOd9LFBFSlEURVEUJUx8o0jlz58/aOCq9PeS\nGIMyZcpw6623Aq4iVbNmzSQFNgORjtEScBdqIJ0fkBiuAwcOmPROxR9Iu4+tW7caheHYsWMhvVcK\nNAYL8A2312A0SKn4a69evbjvvvsAN5Fg5syZpgdmRqBatWo88cQTQOiKlKyIpaVM4L1NAoG9aGE0\nf/58E2AsfQAlqSCQt956y3gAJOZp2rRpgKOoxnKlHykGDhwIJGzbJC2pJF42HpHj+MILL5htUupg\n1apVSfaXxINu3bqZOE1RrYYNG8avv/4aVXsjzZVXXsl//vMfwP0beBFD7Jusvblz5yZpqJnGzwbc\niZTc2AcMGGBq1qS1EnogXmcnTJ8+3bhMRFL3c9aeH4n0MZSA4U6dOpkKupLksHbtWhNkL+6xGjVq\nmFo1kgQR2GRVztly5coB7kMsLcTiPBU3+vLly5PUY0vPNRwqsbwWlyxZQr169QC3SfrXX3+d7P6N\nGjUytaIC7DA90WQyJs2qk0OvRYdIjVH60Eldu40bN5pK7ym5v9JDNMco/eQkc7lEiRJmASaVyv/6\n6y8zcZL7jkyeLr/8cv7880/A7QIRjlvPq+ei9HncsmWLqRkYrUD5UMaorj1FURRFUZQw8Y1rr2XL\nlmY13759+zS/f+vWrQB88cUXAKZK9vLlyyNkobcMGjTIrISDSbaKt4ibT35/9913JuFBAsoTB4MG\nsnXrVqM4hqNExQJZ8QUmakhtqT59+nhiU7R54YUXTPC//JZKy4EEKuKi0onqNH36dF599dUE25TY\nIgHyog7v3r07akpULJA6V4FJIJKwJdfp888/b+5HkswiPUtvueUWcy5Gspp/tJFQH3HHfvfdd/Ts\n2dNLkwBVpBRFURRFUcLGNzFS4Fa0Duy5Jv5eSfMEt+CWBMYNHjzYrDSkM32k8TpGCjC9q6TwWqSD\n6jQuwyHUMUp6+wsvvMCdd96ZJltEdZKCebNnzzbKVXqI5nkqK91169YBTnFDSaEWJTgWxPpafOCB\nBwC3pMq1116b4v5S1V9W/zt37kzzd+q16KBjDI6UBZJSOMnxww8/AE6fT3CVuWDlhMIhlsexYMGC\nZjwPPfQQ4CT3LFu2LL0fnSIhXYt+mkgFQ9wdgWX8ZaIV2F4k2vjhwj969CgAVatWBSIvyerN2yGt\nY8ySJYtp/ClB14ULF06y36JFi0wFaDl2oWb5hYofztNoo2N0yOjjAx1jckjzc6mLVaNGDRPyIR0E\nNm3aZGosBetUEAlieRwXLVpksvdr1KgBuIu6aKLB5oqiKIqiKFHE94qUX9AVlENGHx/oGP2OjtEh\no48PdIx+R8fooIqUoiiKoihKmOhESlEURVEUJUx0IqUoiqIoihImOpFSFEVRFEUJk5gGmyuKoiiK\nomQkVJFSFEVRFEUJE51IKYqiKIqihIlOpBRFURRFUcJEJ1KKoiiKoihhohMpRVEURVGUMNGJlKIo\niqIoSpjoREpRFEVRFCVMdCKlKIqiKIoSJlli+WUZvQM0ZPwxZvTxgY7R7+gYHTL6+EDH6Hd0jA6q\nSCmKoiiKooSJTqQURVEURVHCRCdSiqIoiqIoYaITKUVRFEVRlDCJabC5oiiK4k/y5MkDwPfff8+B\nAwcAuPbaa700SVHiAlWkFEVRFEVRwiTDKVJ16tQBYPny5QCsWLGCunXremhR+ihbtiwAn3zyCf/8\n8w8AAwYMAOCNN97wyqyIkSWLcwrWrVuXO+64I+g+n3/+OTNmzIilWWkiS5YstGjRAoDmzZsDUKhQ\nIWrXrp1gvy+//JJ3330XgLfeeguAHTt2xM5QRQlC9uzZARg5ciQAJUqUIHPmzAAULVoUgL1793pj\nnKLEAZZtx668QzRrSSSeQAXSv39/AJ577rmwP9+rehl33nknAHPnzsWyHBPk4SsTxEg9jL2oXfPC\nCy8A8NhjjwV+h9gDwOnTpxk3bhwA//3vf8P+rmgdw3fffZdmzZol2Hbq1CmyZcuW7HtOnz4NwD33\n3APA/Pnz0/KVyeL3ui4FCxYE4MMPPwTg22+/5cEHH0zTZ/h9jJEgltdiq1atAJg1axYAa9asYfHi\nxQm27dy5MxJfZdBj6KJj9DdaR0pRFEVRFCWKZBjXXjAlSkjsYolXRKG55JJLANftF2/uoSxZsjB3\n7lwAbrvttlT3z5o1K507dwacQFiAiRMnRs/ANJI/f37WrFkDYFbyn3zyCTlz5gTguuuuA5zjJspp\nhQoVAFctjZQiFWsKFy4MQMOGDQGYPn06586dS3b/hx9+GICqVasCsGHDhihbGDlKliwJuNcfwMaN\nGwEoUqQIABdddJF5rU2bNoDrOgO44oorAFi2bJk5h3/77bcoWp06ffr0SfD/ffv2MWTIEI+sUVJj\nwIABPPvss4Cr3gN8/PHHAPzwww8AXH311Wbbli1bAEcBTo4TJ07w66+/RsXmjI4qUoqiKIqiKGES\n1zFSKcVFBUNiilasWJHm7/LKF3zNNdcA8NFHH5nVv3D77bcDsGTJkoh8V6ziMoYNG0avXr2Sff3E\niRMAPPPMMwA8+OCDlCtXLsE+EqSeFqJ1DKtUqWJWgX///XeK+8oxnDlzJgC1atUyn7Fp06a0fG1Q\nYn2eSmzbsGHDAMiXLx9HjhxJdn8JshclsnHjxqxatSpN3xnrMcp1NmrUKAAuv/xy89quXbsAR5UE\nyJs3b3K2AO65nSlTJqMqjBgxIsn+sYyROn78OOAqZy1btuSdd96JxEcnSzSPYaZMjj4QqIxeeeWV\nANx4440A1KxZ0xy78uXLA+61W6RIEfPv3bt3A45K98svvwDw1VdfAVC5cmW++OILILhXINJjFLVz\nw4YNRgGNJIcOHTJjW7p0KRD83AzETzFSN9xwA+DeYy6++GLee+89wEn0ARgzZkyq9+jEhDLGuHbt\nhTqBSrx/3bp1w5pMeUGxYsUA90Ydz1SsWBFwAsZTmsDfe++9gOvuKlCgAP/73/+ib2CYyM0nFPbv\n3w+4Lq169eoBkDt37sgbFmUuvfRSnnrqKQDmzZsHwNGjR5PdP3PmzBQqVAhwXQxpnUR5gSQ6XHzx\nxUleK1WqFJA0QQJg8+bNAGzbts1k2MrDO1u2bL7IhOvQoYNJivj6668Boj6Jiibly5dnypQpAMY9\nWbJkSZNoJMkOkUL+VpI0Ek3k2smRI0eK+505cwZwzkU5LxMvPM+cOZNkW4ECBYzYIKEKfqdmzZpM\nmzYNcK9PyTgFuOuuuxL8tizLJDhFEnXtKYqiKIqihElcKlLpKWMA0K9fv7hRpERuz5o1q9kmqsae\nPXs8sSlcJOg/MEBSOH78OC+++CKQNPB63Lhx9O3bN/oGxgBxO1SvXh1wyyCIeyWeuOSSS8wK/9ix\nYwApKo2dOnXi5ptvBqBnz57RNzACdO7c2ahOKY3txx9/BODkyZN07NgRcAN8xZ3nJ6Q+1MSJE805\nKddfPCH3EnEVT5kyxbi9xK2THj7//HPAdRt5iSTadOvWzbiZCxQoADiq+KRJkwBYsGAB4NT+kr9F\n06ZNAef8BPj000/57LPPALjwwgsBx7VZuXJlwFFR/cyTTz4JQPfu3Y3XJhQC1apIooqUoiiKoihK\nmMSlItWvX79U9xHFSQLSA6lTp47ZHi/KVCA//fQTAN99953HlqQNWeXYtm1W97LK6tevnymJkJg7\n77wzRTUgXihUqJAJMJag1+HDhwPxdyzBCUqWmKjevXunun9g4c0333wzanZFAilxMHjw4CSvffzx\nx6bsgagesrqPFyTOJlOmTCamRmK64on69esDsGjRohT3kxITDzzwAOAEn0uQ+e+//w5gypXs37+f\nMmXKAKSqdojqGEtmzJhB8eLFATcO7KqrrjJJR4Gxd/v27QPg9ddfB1wPx9SpU40SJX0VGzRo4Gsl\nqlixYqacQ+nSpQESFD0+ePAggCnhUKlSpZjZFncTqdQCzKUuT2qTrXieSMkJIg/jTz/91EtzQkYu\n9HLlynHBBRcA7uQqpUwKyZyJd1588UXj9hGk7lQ8Ur16dfMQkht2akg20B9//BE1uyLBrbfeCjhZ\niOI+koB6CVyNZ1q3bm3+/c033wCuezIl2rVrZ9ohCX379vWsHliwiW5i5s+fb9yW69atA1LPdJbk\njzFjxiS7z8SJEz0LOXjppZcAp50PQNeuXVm/fj3gTIggeBKMuNYDj6EsAvxa000muPPnz0+SvX32\n7FlTmV8C5CXZIBB5vkiLrkijrj1FURRFUZQwiTtFKpirrn///kkC0OX/zz33XEiuwHhCJEw/pE+H\ng7gmQ0XSsuOdwArXggTW//XXX3Tp0gVwg0X9yuOPPw44Nc4GDRqU6v5S9qJixYomyDxeXLWBdnrh\nxokWUnUdUlZdJFhZylz06NHDXL/y2ltvvcW1114LpF5HLdJIcHTgcRI3+cKFCwGnFtLhw4dD/sxs\n2bIxe/ZsABo1apTkdXm2PP/8856dx5KkIo2ms2TJwiOPPAI4bj5w3JmSkCRJIYHPwj///BOAgQMH\nxsboNCJeC/EaValSxbwm42rdurXxasj4g3XLEJUqFNU1HFSRUhRFURRFCZO4UaRSKnkQTjmE9JZQ\n8BJJx5YKy9u3b/fSnKgjfenineuvvz7JNonFyJ07twlclorufktHlwBlUTO2bdvG0KFDU33f/fff\nb95/6NCh6BkYQYKph8uWLfPAksgi8ZUSTA3BC3BKmrgojp06dQKc4Pr27dsDrjLZr18/o4JIDFKs\nkLiefPnyAU71eSnQmBYVCpwCswBDhw4NqkRJfJ9clyn1lIwVO3fuBJwyAGLf+PHjAUdFlELGjz76\nKOD2uAS3arnEVvmNwK4PwtSpUwG3M8SOHTtMkoScA4FMnjwZcDswBCL33htuuCHd17bvJ1Liygvm\nnpPA8n8bUh3ZzxkWkaR27dom4DdeHsTBkCbT4CYKnD17FnAmJ23btgXcTJy1a9f6qvK3BCjLw7hn\nz54pVjIXZAJ5+vRp3wa0Jua1114DnCr7UkOoZs2agNscNh6RwN3AbKdgiAtWJlASuNymTRtOnToF\nQK5cuaJlZsjccsstgOumCgeZQEkdJqnuHcj48eNNYPk///wT9ndFi7Nnz5pkCMnGa968ObNmzQq6\n/5gxY0zGsF+54447kmyTMAEJGj958mTQCRQ4ky1x90lmaiCyEChYsOD/2TvzOBvL94+/B0P2vSxZ\nsrdS+drToJQleyhr2UsMUrIWQtZKJZUiSilbIlEhWYs22RNJi63s2eb8/nh+1/08M+fMOHPmLM+Z\nrvfr5TXjLM+573mWcz+f67o+V5oXUhraUxRFURRFCZCoUaR84U94Lj0qWf+10F6TJk1MUufo0aMj\nPJrgkNSyYuPGjSaBVBo69+rVy1WKlCQcy93drl27jLImIYNjx455JctLaPbSpUupLjSIFKIUOu9k\nfYX70itJ+wpKuf2FCxeMmiWKwZEjR4xKHm5CpUSJG72Ehvr16+dT1XAjb7zxBgAnT540PltJWbdu\nnevnI+egk9tvvz3R/3PmzOn1Ggm5Dhs2LMU5SjPyDRs2pGWYgCpSiqIoiqIoAeNqRSouLi5gRSma\nk8mTI2mHeemXlV5p06YNYJXzSlKlJBmmR+TOWBQpN3Vgv/vuu43Ls9huvPLKK1x33XV+byMa3du7\ndOliVMG+ffsCVjn2lQwdo5377rsPwORD7dixwzwnRQflypUDLEd0MWaNFrJkyWKsA3zlRMl15rHH\nHgvruIKBKG0pqfePPvqoyfVLi6oXSl555RUA7rrrLsByo0/KgQMHTBcC4eWXXwasRPSUEBuhkSNH\npnWoqkgpiqIoiqIEiqsVqeSMNFNSm1Kq8hOiqS3M6dOnAatSJGmOhhg4SkmoG5C71F69egFWHysp\nyz1y5AiQuPTaF/LeO++8E7CUOKk2EvPAadOmmQqw1JY5u5Wk7SbcVOE2YcKERFYNYJniSc+8r7/+\n2rxWcqJatWoF2HkMt9xyi1GlpOR+xowZpg+aG9m9e7cxLJQ8od69e5tj2g0l8MGmatWqJkdq2bJl\nQOJj8cknn0z0en8sMNyCmIi2bNmSZs2a+XzNp59+GhSVIlJIjlTp0qXZs2cPYPXnAzvPb8CAAaZq\nTyoz3WaSK+aZYrfiVKRWrFgBWG2bRIGT8YtyfCXE2kOUqbTg6oWUr0Tz5BZB0oMvpeR0CQlG00JK\nyjK3bdvmlWjnRqRvU548ecxj4mVy4sQJAIoUKZLiSZs0hAlQsGBBwLoAgvUlLdLt2bNnvbYhvjbh\nRsIelStXNonY/vhBNWvWzHjXSOhMGjq7ASk7BtuNvVevXsZh2BdyDDRv3hywFk/SKHbQoEEAdOrU\nyTQgdSsSKpAL+SOPPGJ6lbm9+XJyOM+xHj16AJYHE1jX0EyZrK+GX375JdH7ypUrZ3zBZPEs/mfR\ngHzJSuGEE1nQd+vWLar7e8pi6fLly/Tp0wewFx5C2bJl6dSpE2D7SKXkcB9JZBHvXMzLzXaNGjXM\n94TYGfhLMFMnNLSnKIqiKIoSIK5WpHyxZs0aozr5E8YDW4GK5gT0RYsWRYUiJXe68hNs4z756XzO\nFxkyWOt7CZscOXLEhAWFm266yUi6wm+//cbrr78e+OCDgISz3n77baZOnZrs6+SOf8CAAYAV1pPe\nUtIXyg3mo6IWxcTE8N577wG2Mae/SHh6yJAhUWN/4AsJvVaqVImhQ4cCdl9EKZd3Oz/++CNgm4rW\nq1fPqDSSIuDcR2Iie8899wBWSF1CtaKghru/XmrJnDkzgwcPBqB///5ez4sztpy70apGifJbsWJF\nADZt2uSlRAnvv/++UVWfeOIJwL2KlBO5RkoRQIYMGYySKCadkUAVKUVRFEVRlABxpSKVknI0YsSI\nKypQTlavXu2zvDXa8NUPSco+nUm8kaZhw4YApl2B5DYlReLa27ZtAxLn4IgSdfDgQcDq5p20a7ck\nojvZvn27l3IVbpxtYD744INEz+XPn5/rr78ewCR6OvvvSczeTYaxYvb61FNP+W09kStXLsA+FiTh\nNZrVKLAVwokTJ5q8IFGmpD+i25GWPnKNrVevnrmOiO3G8OHDzfXm3nvvTfTz0KFDRgkORpJuOOjX\nr59XIQfYSpqoVLt27QrruIJJ5syZzfeiKP6Sy+iLjz76yKiTcu1t0qSJl5mu25A+j87E8969ewNX\ntjsIJa5cSAUTN30pBRtZpJQsWdI1C6lNmzYB0LhxY8C6YNetWxfw7QztXEAJckFr0aIFgNciCqwQ\nr9t5+OGHAdsPq23btuTPnz/Ra6Rv18svv2wSYMXh3E2kpjJLLuSyv6XyKz0hc5R9Fi0LKUGaC48e\nPdqMXcJfnTt3pmjRooB9wyNeWqNHj46aBZT4D/n6Djhz5ozpbSkVmNFMbGysKbCZN28eQIq99OrW\nrWs6ZMixLI2q3YwsmoSXX37ZFftPQ3uKoiiKoigBkm4UKUkoF6UimhPLfZGQkGDuDq+UrO0GtmzZ\nAlgOydWqVQPs8EDt2rVNQqT4Q61fv94kM0c6YTwtOEOwnTt39npe9p30JpOwQiQTJUNN0hBntFK7\ndm0AHn/8cdd57qQW6WM2fPhw0ztPko6vvfZaNm/eDNheO0uWLInAKAOjVq1aALz66qsAZn5gFwW0\na9fO9WGs1CDXU7ALRLJmzUqNGjUAuzDgmmuuAaywu4TgJcSZ1OrCbZQpU8bMTXrozZo1y6f9TbhR\nRUpRFEVRFCVAYsJ5ZxUTE5OqD7vS2Jyx71ArUB6Pxy8ZKLVzTA3r1q0DoHr16oBtIFevXr2gJPL6\nM8dgzU8SVuVuMRyJyOHYh2LdsGbNGmPIKWzevNkoT6K+SUJ9sHDDcSq2JGKSW6BAASB4ycnhnmP3\n7t0By90dbGd3sG0ExB4gWITzXIwEodqHJUuWNDYOd9xxh3lclKi2bdsC4VHYwnmcZs+enZMnTyZ6\n7NKlSybvyVcUQ2xJxBx32rRpqf7ccM5x+vTpdOvWDbAtYsSVPZT4M0dXh/aiIYQVTpJ+MUczkayw\nCCVScei8iP/XkAa2skiUhHq3ExcXR2xsLGB/qeTKlcssBJ03dlKJKA7LSmSRBcP48eO9zr1///2X\nSZMmAdEVokwr4lXnZO/evYB1fMuNzvfffx/WcaUWaesjPllgd39wCxraUxRFURRFCRBXK1KKokQf\nEqaV8upo4eDBgybMIUUQktQKGIuRhQsXMmPGDABXN1z+LyEpAuJO7uTTTz/16SOVnjh37pxR4qT/\nY5EiRUyzYmm8ffz48UQ/owFpoJ0vXz6j+H/zzTeRHJIXqkgpiqIoiqIEiKuTzd2EG5J4Q40muFro\nHN2NztEivc8P/J+j5NMOGjSInj17ArbRZvfu3Y2SEU70OLUJxhz37NnDgQMHANtsNRz4dS7qQso/\n9KSwSO/zA52j29E5WqT3+YHO0e3oHC00tKcoiqIoihIgYVWkFEVRFEVR0hOqSCmKoiiKogSILqQU\nRVEURVECRBdSiqIoiqIoAaILKUVRFEVRlADRhZSiKIqiKEqA6EJKURRFURQlQHQhpSiKoiiKEiC6\nkFIURVEURQmQTOH8sPRuEw/pf47pfX6gc3Q7OkeL9D4/0Dm6HZ2jhSpSiqIoiqIoAaILKUVRFEVR\nlADRhZSiKIqiKEqAhDVHSlEURXE3GTNm5MMPPwSgWLFiAFSuXDmSQ1IUV6OKlKIoiqIoSoCkO0Xq\n9ttvB2DlypUA5M6d2+s1devWZc2aNWEdVzDp2bMnANOmTQOgdOnS7Nu3L5JDUpT/DLGxsfTq1QuA\nIkWKAPDyyy8DcObMGXLlygVAy5YtAXjrrbfMe8+fP29e51Z69epFkyZNAFixYkWER6Mo7ifG4wlf\nVWKwSyDz588PQIsWLRg6dCgAOXPmBDAXsy+++IJy5coBcO211wLw/PPP8/jjj6fqs9xQ5nnfffcB\nMH36dACuueYaAAYPHsxzzz2X5u27peS6Ro0agLXvAP78809eeeWVRK9ZtmwZ27ZtS9V2w7EPr7rq\nKsA6DgcMGADYx2mXLl2cnyFjAuDixYtMmTIFgPfeew+A7777LtWfH445yvk0duxYhg0bBsD27dsD\n3VyqidS5KOfbE088QXx8fEDb+PrrrwGIi4vj33//TfZ1kTgX8+TJA8CGDRvMPpZzcdOmTcH8KFdc\nT0NNpOYoi/sBAwYke5xmyJCBhIQEwA7f/v7776n+LN2PFhraUxRFURRFCZCoDO0tWrQIgOuuuw6A\nG2+80Twnd/ozZswAoH///lSsWBHAhPMeeOABo+AcOXIkPIMOAmXKlAHsO2OhevXqkRhOUChdujQA\n8fHxzJkzB8CoHLGxsYB1xzR27NhE7ytTpgzdu3cP40hTZsSIEQDcddddgH0n78Sp/iZVgjNlysTA\ngQMBW4kKRJEKB5kzZwagefPmzJ49G0i9IvXUU08BMHPmTP7880/A+2/iFuQ4lOMtUDUKoGDBgoC1\nv91G69atAUtx/PjjjwFbQVPcTalSpVi8eDEAJUqUACBbtmxe59SGDRsA6/okzw0fPhywU0aiHVHY\nWrVqxf333w/Y0ajixYuH5DNVkVIURVEURQkQ990WXYHGjRtTr149ALJmzer1/NKlSwHo27cvAOfO\nnePXX39N9JpChQpx9dVXA9GlSD3wwAM+Hz9w4ECYR5I2ypUrZ+6eJJ6fI0cO2rdvD1h3UleiQYMG\noRtgKqlUqZI53nwVN6TEX3/9BcDVV19t1NRmzZoBdq6UW8iXLx8A8+bNS/O2evToAcCYMWNMDtnx\n48fTvN1QION7+umn07ytkiVLAtZ16s4770zz9oKBKG6SgwnwzjvvAJg8GrfjVF6eeeaZRM8FY7+5\nnU6dOnH99dcnemz16tUm7/Lbb78F4OTJkwCcOHHCvC6c+Y2hRBRVUYx9RWqKFSvGwYMHg/7ZUbeQ\nGjBggM8FlDBp0iTAWkClhCSnJ7c4cRtFixalaNGiPp+TUKfbkb/1Sy+9ZBJbnUiBwLJlywBMyOeu\nu+7ykmS3bNkSyqGmikKFCvlcQO3cuROwqyt/+uknr9e0bdsWSJyILl9sbkMWvRUqVAh4G9WqVQPs\nRZnbyZgxo1dYGeDQoUOA9QUGiReBUvDSokULAGbNmkWHDh0AOyzvppBZu3btAGjUqBFgpUBIaC8a\nkTC7r/87F1npYYEl18XOnTubx3788UfACr2fOnXqitsQz7BopF+/fkyePNnv18fHx5sioGCioT1F\nURRFUZQAiRpFSpSmuLg4L7n51KlTNG3aFMCnP5SsykXerFy5sgmjRAuFCxemUKFCPp/bunVrmEcT\nGHKX++CDD3Lp0iXA3jd79+7l7bffBmwlSsrDFy1a5KVISWjQDaxbt47bbrvN63EpJ/YVPl6wYAFg\nqwAxMTGcPXsWsI91t5E0yfrQoUPmnPKXKlWqAFYoFyyLCzd7Ks2ePZs2bdp4PS4q46pVq5J9xv8t\n3wAAIABJREFU71dffWV+//7774M/uCBRqlQpwA6PLVu2zByL6Q2nOiW/r169GrDVKvl/NBAXFweQ\nKFoh0ZiU1Kiff/7ZqPpHjx4FrPSC5s2bA/DQQw+Z10qYd+rUqcEbeICIoi1J807ksf79+wNWaC+p\nWvXBBx+EZFyqSCmKoiiKogSI6xUpycuQHJKEhARz5/Tmm28CVvmmqBi++OeffwBbrbrttttcW2qd\nHJLHEM3IHVK7du2M2nThwoVIDikonDp1ym/F4cknnwRsJcpZBi9u/OvXrw/yCNNOnjx5vJKjf/75\n51QVOlSrVo1Ro0YlemzdunXG7duN3H333T4fF/VCErRHjhwJWPvw8uXL4RlcEMidOzePPfYYYF8n\nxdIimhAVSRSa1CDvkZ/RFK0QtfvcuXMp5g4npX79+sbG5IYbbgBgwoQJ1KlTx+u1YqcgRSZSIBNu\nfOVDHTx40JhrJy2COXTokNfrN27cGJKxuX4hJbKzhALAXkCJhHf69OlUb7du3bqA7UHlKxHYTUgC\nqxP5wr1SYr3bkMqRQJCQQ9JKTDciF+TChQsD1oJfbggyZEgsBp85c4Zx48aFd4CpoH379sbzS25a\nOnbsmKpt9O/f3+s4TupY7zY2btxIw4YNvR7PmDEjAFWrVgXsauFWrVrx6aefAkRFeKxOnTqmUEI6\nJqR0U+pWZAHgTCCXhX9qF1erVq3yuaBwI5988glgVb/KTUrZsmUBq8I9uaKB06dPm5uAF198EUhc\nBS/X1wMHDvD88897PR9OJJznXBRJGK9NmzbJVuGtW7cu9IP7fzS0pyiKoiiKEiCuV6REdnYisnog\nSpQg3jBOpcuNyJ2vyK9OxCYgPYTHfCFzrl27tnlMZOXPP/88ImPyl5iYGLp27QrAq6++muzrvvnm\nG8ByGnZjSEh6ron6C3aI9siRIz6T7JPSrVs3AO65554QjDC0DBkyhGPHjgF2Q3Sw/aCSep59+OGH\nRhVJGsZ0I7feeqv5PdqUbV/4a2nw9NNPJ6tYxcXFme1Ei0XCO++8w8MPPwzYHT9mzZplmk9LxCV7\n9uwAPProozzxxBOJtnHu3Dljy/Hggw8C7lAnnUqU/J6ShUG/fv0A2+E86TZCgSpSiqIoiqIoAeJq\nRapkyZLccsstkR5GRBEXd8nFALus/q233orImEJB5cqVjeohhQCSDOk0u5Tig8cffzxFU86UytLD\nQWxsbIpKlFC5cmXAyk+RO0Q3OXz36tULsBUYsPfB8uXLueOOOwLaruQ5/v3332kbYIj54YcfEpkd\nCjVr1gQgb968gJWjAlbOpexHKW758ssvwzDS1HHTTTcBMGjQIPOYWHL8F3AqTXKtCCRR3S38+uuv\n5ntBFKncuXObPnpiBCv7PSYmxlxn5fgcP348y5cvD+u4/cHpUO5LiRJH81atWgGY/npg51KFwoTT\niasXUoUKFTKhBeH55583rsKpRZJ/M2TIYLyo3F6hIf5YTqS6xg2ya6BImEQO8IYNG/pMqE9KlixZ\nAEzT6eSQkGi08NBDD1G+fHnADme7oWmxeMhICxywwwPJLaIkUdVXg9DDhw8DVsgMcHXFXkokTWQV\nL7fPPvvM7EcJ7d1///1m3m6hQIECgFU1KjckTt8rf5BGsE2bNmXJkiVAdBSBJEVSRaJ5IQW2y74U\nO5QuXdrciPtCKk0l7JWWVJlwIYs/WSD5agPjxJcHXCjQ0J6iKIqiKEqAuFqRArz8ntLi/yTvdXpR\nud1Pytdd0uuvvx7+gQQBSTaOj4+nVq1agH8NisF2Od+2bZt5TPxNChYsCFj70i09+C5dumTCB6K+\n5cuXz4SEfFGjRg3Altp79OjB3LlzQzvQK/DLL78k+9yPP/5okv9lnD///LNRJcQSwNlMVcKdkfKi\nCRUSVnnmmWd49913Acwx3qFDB9e61YPdm81fxJ1eSu/z5MljQpvisB0N6obgy8lcXM+jJdnciVir\n+Iq2SLj5ueeeM8qV2/GlPl1JiZLXhKJBsS9UkVIURVEURQkQVytSffr0Ccp2JK9GjBGjgauvvhqA\nXLlyeT23e/fucA8nYAoWLGgM3yR3pGLFiuZ5MefMlClTsurUs88+axJCnUnkUoggidAJCQmu6Vqf\nkJDgVf6eLVs2n3lDYBkKvvTSS4Cdg9SlS5eIK1Kyf3zdAe7du9dYAzgRhdBXntqcOXOCPEJ34evv\nkS9fvgiMJGX27t0LWM7Qkocpxoe+3J8lSfmBBx4wRSFSDAK2YbCor756nrqdtLijR5p27doxdOhQ\nwE42d0ZbxLLk3nvvBaLLMkeU+tatWxv3ckkwnzdvnldUSRSsULmY+0IVKUVRFEVRlABxtSIlOTBp\nRdQdZwa/rFrlzsxtSB6ClJo7cbsZpZPFixcnsm4QJPdG5tKyZUsvRWrTpk2AlVcjOShOfvjhh0Q/\n3c7Zs2fZuXOnz+d2795trBCk3L5WrVpUqlQJiFwF38WLFwF7X/hD27ZtAbwqblesWBG2nIUrkSVL\nFlO5K3NMCwMHDgRg9OjRXs8FY/vB5rfffgOsii1pASK5l8OGDWPRokWA3RNSrBEuXLhgFCyZ18MP\nP0zjxo2B8LblCDaiokWTIiWl/jNmzEjUtxOsSlIxzBWVW8xxX3755TCOMjg4e+nJ707TTUEUrHDi\n6oVUsBB3VyfS48uXFB9pYmNjzYXZyYoVKwBc6YCdFLmwOt2g5UI1Y8YMcxGeNm0aYCWsCvKlLb4g\nvhZR6Y3Y2FhTfi4LqdjYWGMP0aFDh0gNLdXIfkvKuHHjXGN3MGXKFBNqlr+xv4vVLFmymPCW3OjI\nPnN+mYlNi3hmuZEXX3yR2NhYAMaOHQtYiyZJQK9QoQJgh/EeffRRM2dx0q5atSqPPvooYBVZpCfc\n7nAuCyXncSf7rmPHjqYgQBYccoMejQspXzgX7qF2L08JDe0piqIoiqIEiKsVqQEDBnhJxQMGDDAl\n7v4k4q5fv94rtPT8889HPIk3JbJnz25Kp53s2bMHwIQk3Ei7du0Au1DAeack/Z7q1KlDy5YtgcTJ\n9FJCLWrhH3/8EfoBRwgpfBAZ+sknn0yk3oEV7gu1I2+wueaaa4yLclKOHDkS5tEkT69evcx5JMUQ\nEydO9Gko6exPBpaZZUpGh6K2TpkyBXC/SaVYM0i6w4QJE0ziuSAJvdOnTzeGwBJe6dOnjyvMY/9L\nSEgvPj7ePCZ98p599lkAdu7caaIA7du3B/Dar9GKzMMZ2pMQdSRQRUpRFEVRFCVAXK1Ibd261ZSz\nS9JjQkICr732GmCbv7333num5UHZsmUBK2ESoEyZMuZuSu6avv/++zDNIDDq16/v8/FZs2aFeSSp\nR8rbfalmjzzyiNdjUoY7fvx4ky8VLa1vSpcuDVg5M9u3b/d6XnJPbrzxRq/n5s+fDyTuYSecPXsW\nsP4mbmstciUqVKjglWTuRpwl02KSmpJZqr/88ssvxsYiknfIgbB+/XoAVq5cae74pXBA1HCPx2PM\nVKPlPE2PSI9EucaA3Vrqm2++AawWTUmVUzfn66UGZz6UWLNEspDF1QupCxcuGC8eSaorUqQIWbNm\nBazkVbD8dnLkyGGeB/tCeerUKVMZ1r17d8D9ISPpQ+bknXfeMaExN+OPU/yZM2fMYlZ65rnF/yk1\n9OzZE4DatWubE1sWPjVr1qRu3boA3HnnnVfc1uXLl42jufS3i8am1FJ56EQSXnft2hXu4STL22+/\nHZQEfikAEZ+e/v37m4q4aKVQoULm95UrVwJ2f8/0jCSUi6u5m5GbOF9IusDTTz9tUgjOnTsHwMKF\nC0M/uBAioTx/nM3DiYb2FEVRFEVRAsTVihTYMqWU03/xxRfkzp070WsknOdESuaHDx/OzJkzQzvI\nIOPLzXzVqlWm35ybkVCdL2Vq/PjxgCXLnjhxIqzjCgUSlqtcubLpr5ZaJGTSvn17c6xHM04bC0GU\nNjeVxg8ZMsR0PBCXZH/566+/+OijjwCrtx64X+VODd9++635/dZbbwUSdxRQIo90HJBIDNhKmnjP\nFS5c2FjlvPHGG0B0dcXwhTO5HqxwXjgdzJNDFSlFURRFUZQAifEnpyVoHxYTk+YPu+2226hduzaA\n6VvWp08fFi9eDMDatWsBewUerC7kHo/Hu5W2D4Ixx86dOzNjxgzATtq+8847TTJoqPBnjsGYX6QI\n9j6UogDJAfIHKYUXl/19+/YBcPToUb+3kRLhPE59sXTpUho0aADYc5McMTGoTCvBmqP0AuzYsSPg\n7cQOliWA2BnIne+lS5dMTlSo0HPRIhJz9PWdGBPj13CTbidkc5Teh2JvkDRKA9Z3hxhv9uvXL7Uf\n4Rfh3o9J983kyZNDbhHj17kYbQupSOHmEz9Y6MXbwt85ikuw+O84SUhIYOLEiYDtnu90dA/WAj8p\nkT5OS5cubW5i5GYg2I2KIz3HcKDnooUupFJGzjVx1nfywgsvuGKRAcHZj8WKFfPyZKtevXrIQ3v+\nzFFDe4qiKIqiKAGiipSfuPkOKljoXbCFztHd6Bwt0vv8IDJzXLVqlVfjYrcqUpEmnHNs3bo177//\nPmBHAcLRoFgVKUVRFEVRlBCiipSf6N2FRXqfH+gc3Y7O0SK9zw8iM8e4uDgvuwdVpHyjc7TQhZSf\n6AFjkd7nBzpHt6NztEjv8wOdo9vROVpoaE9RFEVRFCVAwqpIKYqiKIqipCdUkVIURVEURQkQXUgp\niqIoiqIEiC6kFEVRFEVRAkQXUoqiKIqiKAGiCylFURRFUZQA0YWUoiiKoihKgOhCSlEURVEUJUB0\nIaUoiqIoihIgupBSFEVRFEUJkEzh/LD03m8H0v8c0/v8QOfodnSOFul9fqBzdDs6RwtVpBRFURRF\nUQJEF1KKoiiKoigBogspRVEURVGUAAlrjpSipESWLFkAuPrqqwE4deoUAP/880/ExqQo/xV69OgB\nwJQpU8iWLVuER6Mo0YMqUoqiKIqiKAES4/GEL5k+vWfuQ/qfYyjnN2rUKACeeuopAPbs2QPA5MmT\nef3119O8/XDsw9y5cwPw2GOPUb9+fQBq1arl3DZgz61hw4YA7Nu3j4SEhEA/1qDHqU16n2Ow5pc3\nb14Atm/fDsCOHTuoW7duMDadLLoPbXSO7savczEaF1JPP/10ov+vXr2a1atXAxAXF2ceCyZuPmBG\njRpFkyZNAKhYsWLA24nkQqpWrVosWLAAgHz58iV67uLFi2bBsWrVqoA/I5T78KabbgLs8TnncPTo\nUQDOnTtHsWLFfL6/SJEi/PXXX6n9WC/CeZxmz56dChUqAPDrr78CcOTIkRTfc/vttwOwefNmAHbt\n2kXlypUBOHv2rF+f66ZzMVeuXABkymRlSfTu3dsspvv37w+A8xq7e/duAGrWrMmxY8eS3W44z8Wu\nXbsC8NprrwHQpEkTPv7442BsOlnctA9Dhc7RJpxzlGvwmjVrzDogLesBtT9QFEVRFEUJIVGTbC5K\n04gRI8zvwogRI/zahqxK69SpE8SRRQ4JE91www3mjrh58+YALF++nHPnzkVsbKll1KhRXkqUkDlz\nZu666y4gbYpUKLn22msBW4k6fPgw8fHxAGzZssU89tlnnwG2MhPNDBo0yIRhV65cCUCDBg38eq+o\nNPnz56dAgQKArWq5nRtuuMHs2/vuuw+wCySc+ArVFilSBLDUvJQUqXBRsmRJXnrpJcAKL4O9L5Xo\nIWfOnFSpUgWwvyuLFStmvg82bNgAYFTvb7/9lh9//BHA/MyVKxePPvooYEUBAAYPHsylS5fCM4kA\nkQjVnXfeCdjzd64Tgh2hSooqUoqiKIqiKAHiekVKFIikKlQgyDZWrVqVLlQpycWQuw6A+fPnA9Yd\ncjQpUnFxceYO/oMPPgBg06ZNgJVsHoz9H0ok52fOnDkAvPjii0aJEnLkyMHly5cTPfb7778DcOHC\nhTCMMjgULFgQgCFDhhhlSVQlfxE19ddff3W9ElW0aFEAOnToAFg2AcWLF0/29YcOHQJg586dAEyb\nNs0898cffwDuUd86depE5syZAZgwYQIA58+fj+SQUkWNGjUAqFevHgATJ05M83WvQIECDB48GIB+\n/foBVv7eM888A8D48ePTtP1gIirU2LFjzXeaHH9nzpwxx1vJkiUBqFatGmAfy4BRRjNnzszx48cB\nTN5iNKhRyUWknLnTocbVC6lVq1aF5As0Li7OLNCieUHVpk0b87t8Mf3www8AUbWIAvjtt98oXLgw\nYIV7AHOBT0hIIHv27IAlYYPtMeUW5ALUqVMnr+dkkbF27VrKlSuX6LmpU6cC8Pfff4d4hMFDwnke\nj4fUFqs0a9bMvNfNyLHYuXNnk4wtX0ZOJLn+7bffBuCTTz7hl19+AWD//v2hH2iASCiyZ8+e5lz6\n9NNPIzmkgJBQ+VVXXQVA/fr1WbJkyRXfV6pUKR566CGfz8XExJhUCTlOs2bNyrhx4wC7CnfcuHGs\nX78+bRMIEJnv0qVLAeuaKQu9F198EfB9TSlTpgwAbdu2ZeTIkea9YFVrSlGPG8LOKSHhPOciShZN\n8ncI1yIKNLSnKIqiKIoSMK5UpGS16a8atXr1ar9Woc5VrGxbHktqqRANOEN6//77LwBdunQBLFk3\nmpg3bx59+/YFoGzZsoCdIAm2vcCNN94IwMaNG8M8wtSTMWNGAOOB5VSj5E56ypQp4R9YGrnjjjsA\nWwWFxKGClJCwoPO9bkKUqMWLFwO+iwI2btzIxIkTAStpF9ytPvlC1LVrrrmGvXv3AtE3hzp16vDN\nN98AtkpUs2ZNatasGdLPFX+4Vq1ahfRzUkKUMmdKgChQKanb0iWiadOm5jFJQG/Tpg0HDhwI+lhD\ngS8lKpLRJVWkFEVRFEVRAsSVipSUMTpxxj9lNZraWKivuKr8Hs7EtGAhKhTYOTpyhxbNiNrUsmVL\n85gkTcrPaGDQoEEAxiwV7Lv+Rx55BLDLjKMBUUDFhNPj8RgTVUms9ncbcke9Y8eOYA8zYB566CGT\nB+NMnpdzSp5bsWJF1Cm+SRGz0MuXL9O7d+8IjyYwVq1aZa4VY8eOBey8SrCVbUlEB7sopEqVKnz0\n0UeAXfDhRN4j2wA7CV/yiCJZICJjeeGFFwDr2JTvw61btwLw1VdfmdeLLcuKFSsAqFSpklFdO3bs\nCLgv79QXvqJUrshzloTRcPwDPCn9i4uL88TFxXl8Ic9daRv+/kvKqlWrrvT6oMwxmP927Njh2bFj\nhychIcEzdOhQz9ChQ9P6N4nY/CZNmuS5fPmyz38JCQmedevWedatWxfy+QVrjiNGjPCcP3/ec/78\n+URz6dChg6dDhw6ezJkzezJnzhz0v2Oo5liwYEFPQkKCJyEhwcxl//79ngoVKngqVKjg1za6d+/u\ntY3mzZtHfI6NGzf2NG7c2PPjjz96HXubNm3yZM+e3ZM9e/aQHPdpmWMg2y1fvrynfPnynlOnTnlO\nnTrl2b17d1jnFerj1Pkva9asnqxZs3oKFy5s/uXMmdOTM2dOT+HChT1ZsmTxZMmSxet9r732mufM\nmTOeM2fOmOM1ISHBs3DhQs/ChQs9OXLk8OTIkcMVc8yVK5cnV65cni+++MKM8/jx457jx497qlWr\n5smWLZsnW7ZsnkWLFnkWLVpkXrNgwYKgHNfh/l70hTy3atWqRP/CeaxqaE9RFEVRFCVAXBXa8+Va\nLbJdsMNuIoNKaC8uLi5qEs/Fl0Zk5y+++IIxY8ZEckhB4f/vXHwSjIa+oSRHjhwA3HvvvQA8/vjj\npoTaycyZMwGr/BjsY37ZsmUmzJXS3yFSNG/e3IxLfo4ZM8bvkJ6QdBsLFy4M4igDQ0KvN9xwg9dz\n27ZtI0OG9HO/KfYhYicSCGIbcN111wEwadIkTpw4kfbBBRmxgPFlBeMrjCVho4oVK5I1a9ZE712w\nYIHpP3j69OlQDDcgTp48CcDDDz9swuyVKlUCLGuEw4cPA1C+fHkAE85s165d1FnkJIcvJ/Nwk36u\nEIqiKIqiKGHGVYpUUqIxATyUyB1zixYtAMyd8o4dO1yv2FyJPXv2pOn5SCPmqK+99prXc5JQvnnz\nZrMPRbmSn8899xxPPvkkgCmtdxPdunUzlgVHjx4FfM81JQoWLGi2sXbt2uAOMA1IsvGpU6eMYiN0\n7tyZ2267DcCYLw4ZMsSUkUcbSa0BUmtDsXjxYtNfUJg3b54rFakrIQUFEvV44okngMSWF99//z1g\nGedKorob2b9/v+lHKtfKvHnzkjdvXsC2kpFrTLSqUUkjSeCO/quuXkitWbMmZNv2ZSvvq1rQLRQv\nXty0TsmWLRtg+xOJ/1I088Ybb1CoUCEAhg4dmui5pUuXmmaabuXBBx/0ekzmIU1gv/nmG1PxVrVq\nVQD69OkDWHK8VB7JAtkNrShkvBUqVDDhuNRW2ol3VNeuXQPeRiiRUP6WLVtMiNwZ5rvlllsS/axW\nrZppXyQNf7dv3x6u4aaJYsWKJfq/v2HkgQMHAtC4cWOv5yZMmOB3s2o3IeE7WUj58gwTl3o3L6IE\nqdyePXs2YF9bwG4bs2vXrvAPLIgk16DYiSy2womG9hRFURRFUQIkJpyJrTExMSl+WNKxPPPMMyFJ\n/E6uh19KDqkej8cvDfxKcwyU999/34RUli1bBth9loKFP3O80vzat28P2F4rzn6AEs7ZsmULc+fO\nBWxXXbBd2YcNG5Zom1WqVPFqABwIodyHcjyJJ8urr75q/IdSCrvmyZMHsJJZ5S5L7iz/97//pdpt\nOthzlOalmzZtMmEgOU9jYmIS/Q6W0iT7WRLJxXV68ODBnD17FrDmBv77TzkJ5X6UUIgoUj179jTN\nwRs1auT1enGRfvzxxwGYO3duUJr+BuNc9IWE9mQfnThxwvROfPXVV71eL+Gv3377DbB8mvbt2wfY\nLvDHjx83fy9/vYgifT3Nnz8/nTt3BuxmzU4kGnL//fcDdjg7NYR7jnKcSmjPVyNxUQ6D1Vcx0vvR\n13d5sLsm+DNHVaQURVEURVECxNWK1OrVq4PiWiorVqfVgS8ktupLBYv0yrtWrVqJnGpDQTDugg8e\nPAjYd6vg7UbufM6x3WTzNYoXL+7TfTi1RHofpkT27NmNEiW2CXv27DHJ6P4qU8GeY4kSJQBLkZJc\np5QUKY/H41O5kv9LbzpRpAIh3PtRxi9u9PXq1UvUq8zJ8OHDefbZZ9P8maFSpCQPUdT3cuXKcfny\nZcA+dydPnmwcwyUnavjw4QDs27eP1q1bA3a/yKuuusool/7mikX6XCxZsiSff/45YNs4CGfOnDG5\nb6LWBUK45yg5laKOnjx5ktjYWMA7r7Z79+7B+MiI78enn37aK99ZFSlFURRFUZQowlWKlJQxOhWj\ntK4u4+LirqhECSmZf0Z65d2kSRPKlSsHwCeffALATz/9FNTPCMZd8KRJkwC7kvCLL76gW7duiV7j\nVKRuvfVWwKp+Su5Y3L59O6NGjTLbAzh27NiVhupFpPfhlZA5Dh482DwmOQ3SI+tKhHKOtWvXBuxK\nvuSIj48HbBNAX2rV9OnTAejVq1dqhxHx/ZgpUyYqVqwIwKJFiwAoUqQIAGfPnk1kehgooVKkhFKl\nSgFWTldK6qD0FBQDz3Pnzplz79prrwVg79695trkL5HehykpUitXruSee+5J82eEc4433nij6bEn\nKlTdunVNntS8efMAu0dfpUqVglLBF+n96MyRSimilBb8maOr7A9kIeNcUMmXq7NpcVJWr16d7B/P\nl82BL5555hlXeVaJpPz1118D1oXsueeeA2xvk2AvpIKBnMxC9erVTXNPCfEdOHDAPO+rae/u3bsB\nO5m+f//+Jjn9yJEjALzyyivGYVhed/vttxvLgRtvvDE4EwojST2M3MaXX36Z6GdyzJkzB8BYBEgi\nssfjMQnoYvUQjVy6dMkUP8iXsBQW3HzzzabZtnQgCHVIPhAkYfyOO+4w1gZSLi8hXPB2QM+aNatZ\nQAmTJ08O5VCDihyLI0eO9FpA7d27F3CH7UhqadKkiVlAyQLxyy+/NI/J8SoWDy1atIjqc1CIpJu5\nEw3tKYqiKIqiBIirFClBSk+dq81Q9dMJlRyYFho3bmxCB1I6fvPNNxu5WZI83YiENcQFumbNmuax\n0aNHA9b+lZCehEZiYmKMUlWvXj3AVrAGDhxoQifST6pr166mVF2cwA8fPkzXrl1DOLsrI2MaO3as\nuTMUI9WU6Ny5M7179w7p2MKFWBz8+++/gB3aW7t2La1atYrYuELBpUuXALt34ooVKyhatChgK6X1\n6tUzipXbuHDhgkmOl0Tkhx56yKhU+fLlA+x96Qypv/POOwDMmjUrbOMNFAk99ujRA7C7Q4BtuikJ\n2G6KTPhLy5YtjfIvYfOEhAQTyotG5/loQhUpRVEURVGUAHGlIiXq0OrVq0PSR8eZD+XGu49mzZqZ\nrt3Lly8HLKXHzUqUIIZ8zZs3ByyTPzEyFDVp9+7dlC1bNtH7zp8/b3LAktolgN0PrVq1auYxUe1k\nW6tWrQooCT2YzJgxA4CmTZuaEmpfiFGpmALGxcUZ5UbuIhctWuSVcxYNONvKgG2D4Ka2MKlBLAPE\nJkDy9JyIseiUKVOMQpojRw4AypQp41pFyolcc5577jmjDktuouSa+jKvdDu5cuUyxS/O4gbpNyc5\nfW78LrgSEqWoVKmSMYf98MMPr/i+aO0V6VZcuZASnD5S/lbeJbcdsMN4bj1hJDGwfPnyZsEgYS43\nNXn1B1nQ3H///Sbc1qRJE8B2PQc7/LFkyRLeeOONVH2GJN3LTzfgTGCVakXnvpOQgiwu5csW7DDR\nzJkzAdu3KNqQUJF410io74UXXojYmNKCfFlJw9ctW7YwZcoUwC6ukN6JUg2XXpBFsD/habchfQWb\nNGniVR36xx9/sG7dOsD/giQ3It8Z0p8zKVLAkrTH4pIlS0I7sBDjliRzQUN7iqIoiqK8kE5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TQ5fvy4qWiWG+qKFSt6Nc0uXLiwSVBPyaleWLBgQUh8szS0pyiKoiiKEiCuUqRk1Swrypw5c5oS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\nrkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -322,7 +550,7 @@ } ], "source": [ - "# takes 5-10 secs. to execute the cell\n", + "# takes 5-10 seconds to execute this\n", "show_MNIST(\"testing\")" ] }, @@ -330,14 +558,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's have a look at average of all the images of training and testing data." + "Let's have a look at the average of all the images of training and testing data." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -374,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -398,9 +626,9 @@ }, { "data": { - "image/png": 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HYy6YarXakv8GQMvWdK5EtfVEpen69esAmlvHp6amZKVLNWtlZUXcLeGZbN1C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\n", 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SfjrinGO899LX2H+dc9I3aSPwOSwtLSVuu9q2nWEYhmEYRsYcCuWpu7tbvBgd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\nwzCMFjDjyTAMwzAMowX+P+71/Wk0f7yTAAAAAElFTkSuQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -443,86 +671,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## k-Nearest Neighbours (kNN) classifier\n", - "\n", - "### Review\n", - "k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are gonna use this to classify MNIST handwritten digits. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n", + "## Testing\n", "\n", - "![kNN plot](images/knn_plot.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see how kNN works with a simple plot shown in the above picture. There are two classes named **Class A** yellow color dots and **Class B** violet color dots. Every point in this plot has two **features** i.e. (X2, X1) values of that particular point which we used to plot. Now, let's say we have a new point, a red star and we want to know which class this red star belongs. Solving this problem by predicting the class of this new red star is out current classification problem.\n", - "\n", - "We have co-ordinates (we call them **features** in ML) of this red star and we need to predict its class using kNN algorithm. In this algorithm, the value of **k** is arbitary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n", - "\n", - "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into majority class. Observe that smaller circle which containg 3 points other that **test point** (red star). As there are two violet points, which is majority, we predict the class of red star as **violet- Class B**.\n", - "\n", - "Similarly if we put **k = 5**, you can observe that there are 4 yellow points, which is majority. So, we classify our test point as **yellow- Class A**.\n", - "\n", - "In practical tasks, we iterate through a bunch of values for k (like [1, 2, 5, 10, 20, 50, 100]) and see how it performs and select the best one. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Native implementations from Learning module\n", - "\n", - "Let's classify MNIST data in this method. Similar to these points, our images in MNIST data also have **features**. These points have two features as (2, 3) which represents co-ordinates of the point in 2-dimentional plane. Our images have 28x28 pixel values and we treat them as **features** for this particular task. \n", - "\n", - "Next couple of cells help you understand some useful definitions from learning module." + "Now, let us convert this raw data into `DataSet.examples` to run our algorithms defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module." ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%psource DataSet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "class DataSet explanation goes here" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%psource NearestNeighborLearner" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nearest NeighborLearner explanation goes here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let us convert this raw data into `Dataset.examples` to run our `NearestNeighborLearner(dataset, k=1)` defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module." - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -547,42 +703,44 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we will initialize DataSet with our training examples. Call NearestNeighbor Learner on this dataset. Predict the class of a test image." + "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# takes ~8 Secs. to execute this cell\n", + "from learning import DataSet, manhattan_distance\n", + "\n", + "# takes ~8 seconds to execute this\n", "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)" ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "kNN_Learner = NearestNeighborLearner(MNIST_DataSet)" + "Moving forward we can use `MNIST_DataSet` to test our algorithms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Choose a number from 0 to 9999 for `test_img_choice` and we are going to predict the class of that test image." + "### k-Nearest Neighbors\n", + "\n", + "We will now try to classify a random image from the dataset using the kNN classifier.\n", + "\n", + "First, we choose a number from 0 to 9999 for `test_img_choice` and we are going to predict the class of that test image." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -591,15 +749,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicted class of test image: 2\n" + "5\n" ] } ], "source": [ - "# takes ~20 Secs. to execute this cell\n", - "test_img_choice = 2311\n", - "predicted_class = kNN_Learner(test_img[test_img_choice])\n", - "print(\"Predicted class of test image:\", predicted_class)" + "from learning import NearestNeighborLearner\n", + "\n", + "# takes ~20 Secs. to execute this\n", + "kNN = NearestNeighborLearner(MNIST_DataSet,k=3)\n", + "print(kNN(test_img[211]))" ] }, { @@ -611,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -620,24 +779,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Actual class of test image: 2\n" + "Actual class of test image: 5\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -645,267 +804,18 @@ } ], "source": [ - "print(\"Actual class of test image:\", test_lbl[test_img_choice])\n", - "plt.imshow(test_img[test_img_choice].reshape((28,28)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", - "\n", - "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset. We will have an optimised version below in NumPy which is nearly ~50-100 times faster than our native implementation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Faster implementation using NumPy\n", - "\n", - "Here we calculate manhattan distance between two images faster than our native implementation. Which in turn make predicting labels for test images far efficient." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "class kNN_learner:\n", - " \"Simple kNN learner with manhattan distance\"\n", - " def __init__(self):\n", - " pass\n", - " \n", - " def train(self, train_img, train_lbl):\n", - " self.train_img = train_img\n", - " self.train_lbl = train_lbl\n", - "\n", - " def predict_labels(self, test_img, k=1, distance=\"manhattan\"):\n", - " if distance == \"manhattan\": \n", - " distances = self.compute_manhattan_distances(test_img)\n", - " num_test = distances.shape[0]\n", - " predictions = np.zeros(num_test, dtype=np.uint8)\n", - " \n", - " for i in range(num_test):\n", - " k_best_labels = self.train_lbl[np.argsort(distances[i])].flatten()[:k]\n", - " predictions[i] = mode(k_best_labels)\n", - " \n", - " return predictions\n", - " \n", - " def compute_manhattan_distances(self, test_img):\n", - " num_test = test_img.shape[0]\n", - " num_train = self.train_img.shape[0]\n", - "# print(num_test, num_train)\n", - " \n", - " dists = np.zeros((num_test, num_train))\n", - " \n", - " for i in range(num_test):\n", - " dists[i] = np.sum(abs(self.train_img - test_img[i]), axis = 1)\n", - " \n", - " return(dists)\n", - " " + "print(\"Actual class of test image:\", test_lbl[211])\n", + "plt.imshow(test_img[211].reshape((28,28)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's print the shapes of data to make sure everything's on track." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training images size: (60000, 784)\n", - "Training labels size: (60000,)\n", - "Testing images size: (10000, 784)\n", - "Training labels size: (10000,)\n" - ] - } - ], - "source": [ - "print(\"Training images size:\", train_img.shape)\n", - "print(\"Training labels size:\", train_lbl.shape)\n", - "print(\"Testing images size:\", test_img.shape)\n", - "print(\"Training labels size:\", test_lbl.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "learner = kNN_learner()\n", - "learner.train(train_img, train_lbl)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us predict the classes of first 100 test images." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# takes ~17 Secs. to execute this cell\n", - "num_test = 100\n", - "predictions = learner.predict_labels(test_img[:num_test], k=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare the performances of both implementations. It took 20 Secs. to predict one image using our native implementations and 17 Secs. to predict 100 images in faster implementations. That's 110 times faster.\n", - "\n", - "Now, test the accuracy of our predictions:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy of predictions: 98.0 %\n" - ] - } - ], - "source": [ - "# print(predictions)\n", - "# print(test_lbl[:num_test])\n", - "\n", - "num_correct = np.sum([predictions == test_lbl[:num_test]])\n", - "num_accuracy = (float(num_correct) / num_test) * 100\n", - "print(\"Accuracy of predictions:\", num_accuracy, \"%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to Scikit-Learn\n", - "\n", - "In this section we will solve this MNIST problem using Scikit-Learn. Learn more about Scikit-Learn [here](http://scikit-learn.org/stable/index.html). As we are using this library, we don't need to define our own functions (kNN or Support Vector Machines aka SVMs) to classify digits.\n", + "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", - "Let's start by importing necessary modules for kNN and SVM." + "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset, as they are written with readability in mind, instead of efficiency." ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.neighbors import NearestNeighbors\n", - "from sklearn import svm" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n", - " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", - " multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n", - " verbose=0)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# takes ~3 mins to execute the cell\n", - "SVMclf = svm.LinearSVC()\n", - "SVMclf.fit(train_img, train_lbl)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "predictions = SVMclf.predict(test_img)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy of predictions: 88.25 %\n" - ] - } - ], - "source": [ - "num_correct = np.sum(predictions == test_lbl)\n", - "num_accuracy = (float(num_correct)/len(test_lbl)) * 100\n", - "print(\"Accuracy of predictions:\", num_accuracy, \"%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might observe that this accuracy is far less than what we got using native kNN implementation. But we can tweak the parameters to get higher accuracy on this problem which we are going to explain in coming sections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -924,13 +834,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" - }, - "widgets": { - "state": {}, - "version": "1.1.1" + "version": "3.5.2" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 2 } From 14bf5e408fe64000ea5e329d669de6c213d8800a Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Sat, 18 Mar 2017 13:22:56 +0530 Subject: [PATCH 413/513] corrected return value for simulated-annealing (#369) --- search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/search.py b/search.py index 04d5b6c51..9d77025a4 100644 --- a/search.py +++ b/search.py @@ -378,10 +378,10 @@ def simulated_annealing(problem, schedule=exp_schedule()): for t in range(sys.maxsize): T = schedule(t) if T == 0: - return current + return current.state neighbors = current.expand(problem) if not neighbors: - return current + return current.state next = random.choice(neighbors) delta_e = problem.value(next.state) - problem.value(current.state) if delta_e > 0 or probability(math.exp(delta_e / T)): From bddd1cfbd54e8595ef2443880953734c4ef5e62b Mon Sep 17 00:00:00 2001 From: sofmonk Date: Sat, 18 Mar 2017 13:25:11 +0530 Subject: [PATCH 414/513] Update weighted_sample_with_replacement() in utils.py (#366) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update utils.py in pseudo code the sequence of arguments is " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"  same must follow in function "particle_filtering" in the file probability.py * Update learning.py weight_sample_with_replacement sequence of args * Update probability.py weighted_sample_with_replacement sequence of args * Update search.py --- learning.py | 2 +- probability.py | 2 +- search.py | 2 +- utils.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/learning.py b/learning.py index df5d6fce3..3e7f4690c 100644 --- a/learning.py +++ b/learning.py @@ -746,7 +746,7 @@ def weighted_replicate(seq, weights, n): wholes = [int(w * n) for w in weights] fractions = [(w * n) % 1 for w in weights] return (flatten([x] * nx for x, nx in zip(seq, wholes)) + - weighted_sample_with_replacement(seq, fractions, n - sum(wholes))) + weighted_sample_with_replacement(n - sum(wholes),seq, fractions, )) def flatten(seqs): return sum(seqs, []) diff --git a/probability.py b/probability.py index abbc07791..d28a8a38b 100644 --- a/probability.py +++ b/probability.py @@ -651,5 +651,5 @@ def particle_filtering(e, N, HMM): w[i] = float("{0:.4f}".format(w[i])) # STEP 2 - s = weighted_sample_with_replacement(s, w, N) + s = weighted_sample_with_replacement(N,s,w) return s diff --git a/search.py b/search.py index 9d77025a4..c8885a9ed 100644 --- a/search.py +++ b/search.py @@ -587,7 +587,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): new_population = [] for i in range(len(population)): fitnesses = map(fitness_fn, population) - p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2) + p1, p2 = weighted_sample_with_replacement(2,population, fitnesses) child = p1.mate(p2) if random.uniform(0, 1) < pmut: child.mutate() diff --git a/utils.py b/utils.py index 3c070293e..714512ae0 100644 --- a/utils.py +++ b/utils.py @@ -193,7 +193,7 @@ def probability(p): return p > random.uniform(0.0, 1.0) -def weighted_sample_with_replacement(seq, weights, n): +def weighted_sample_with_replacement(n,seq, weights): """Pick n samples from seq at random, with replacement, with the probability of each element in proportion to its corresponding weight.""" From b70a2f53a0a30034e10a44c2c7df7d5fe0d7d630 Mon Sep 17 00:00:00 2001 From: Aditya Harsh Date: Sat, 18 Mar 2017 13:25:41 +0530 Subject: [PATCH 415/513] Fix: typo in Search notebook (#365) --- search.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/search.ipynb b/search.ipynb index 7f4fe7473..c936bf331 100644 --- a/search.ipynb +++ b/search.ipynb @@ -28,9 +28,9 @@ "source": [ "## Review\n", "\n", - "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular navigation problem / route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", + "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular, navigation problem/route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", "\n", - "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information about the problem other than its definition.\n", + "* **Uninformed search algorithms**: Search algorithms which explore the search space without having any information about the problem other than its definition.\n", "* Examples:\n", " 1. Breadth First Search\n", " 2. Depth First Search\n", @@ -38,14 +38,14 @@ " 4. Iterative Deepening Search\n", "\n", "\n", - "* **Informed search algorithms**: These type of algorithms leverage any information (hueristics, path cost) on the problem to search through the search space to find the solution efficiently.\n", + "* **Informed search algorithms**: These type of algorithms leverage any information (heuristics, path cost) on the problem to search through the search space to find the solution efficiently.\n", "* Examples:\n", " 1. Best First Search\n", " 2. Uniform Cost Search\n", " 3. A\\* Search\n", " 4. Recursive Best First Search\n", "\n", - "*Don't miss the visualisations of these algorithms solving route-finding problem defined on romania map at the end of this notebook.*" + "*Don't miss the visualisations of these algorithms solving the route-finding problem defined on Romania map at the end of this notebook.*" ] }, { @@ -74,7 +74,7 @@ "source": [ "The `Problem` class has six methods.\n", "\n", - "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", + "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of the class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n", "\n", "\n", "* `actions(self, state)` : This method returns all the possible actions agent can execute in the given state `state`.\n", @@ -89,7 +89,7 @@ "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n", "\n", "\n", - "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test." + "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimise a value when we cannot do a goal test." ] }, { @@ -215,7 +215,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in notebook and we use ipywidgets to interact with the map to see how the searching algorithm works." + "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in the notebook and we use ipywidgets to interact with the map to see how the searching algorithm works." ] }, { @@ -574,7 +574,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback function which are called when we interact with slider and the button.\n", + "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback functions which are called when we interact with the slider and the button.\n", "\n" ] }, From 8a735bde75da54c4b8aa8ccb5016efebf27a3c52 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Sat, 18 Mar 2017 13:26:39 +0530 Subject: [PATCH 416/513] changed unify_var() (#344) --- logic.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/logic.py b/logic.py index 338e5aac2..75c461e8f 100644 --- a/logic.py +++ b/logic.py @@ -796,6 +796,8 @@ def is_variable(x): def unify_var(var, x, s): if var in s: return unify(s[var], x, s) + elif x in s: + return unify(var, s[x], s) elif occur_check(var, x, s): return None else: From a17cc77a47c6a971546544212517c49911abad8f Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Sat, 18 Mar 2017 13:27:49 +0530 Subject: [PATCH 417/513] corrected typo and added color green (#364) --- search.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/search.ipynb b/search.ipynb index c936bf331..d77267577 100644 --- a/search.ipynb +++ b/search.ipynb @@ -96,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the abstract class `Problem` to define our real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell." + "We will use the abstract class `Problem` to define our real **problem** named `GraphProblem`. You can see how we define `GraphProblem` by running the next cell." ] }, { @@ -275,7 +275,7 @@ "# positions for node labels\n", "node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n", "\n", - "# use thi whiel labeling edges\n", + "# use this while labeling edges\n", "edge_labels = dict()\n", "\n", "# add edges between cities in romania map - UndirectedGraph defined in search.py\n", @@ -320,11 +320,12 @@ " \n", " # add a legend\n", " white_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"white\")\n", - " orange_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"orange\")\n", - " red_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"red\")\n", - " gray_circle = lines.Line2D([], [], color=\"white\", marker='o', markersize=15, markerfacecolor=\"gray\")\n", - " plt.legend((white_circle, orange_circle, red_circle, gray_circle),\n", - " ('Un-explored', 'Frontier', 'Currently exploring', 'Explored'),\n", + " orange_circle = lines.Line2D([], [], color=\"orange\", marker='o', markersize=15, markerfacecolor=\"orange\")\n", + " red_circle = lines.Line2D([], [], color=\"red\", marker='o', markersize=15, markerfacecolor=\"red\")\n", + " gray_circle = lines.Line2D([], [], color=\"gray\", marker='o', markersize=15, markerfacecolor=\"gray\")\n", + " green_circle = lines.Line2D([], [], color=\"green\", marker='o', markersize=15, markerfacecolor=\"green\")\n", + " plt.legend((white_circle, orange_circle, red_circle, gray_circle, green_circle),\n", + " ('Un-explored', 'Frontier', 'Currently Exploring', 'Explored', 'Final Solution'),\n", " numpoints=1,prop={'size':16}, loc=(.8,.75))\n", " \n", " # show the plot. No need to use in notebooks. nx.draw will show the graph itself.\n", From 1ef9a84e9fb08c8044af7384406bdbd5c23558a9 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 18 Mar 2017 04:58:25 -0300 Subject: [PATCH 418/513] Make build break for flake8 errors on tests dir (#363) --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index e6563f0fe..aa875cc38 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,6 +14,7 @@ install: - pip install -r requirements.txt script: + - flake8 tests/ - py.test - python -m doctest -v *.py From facee1fb2ddbcb0514a7101279731cfc5d075517 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Sat, 18 Mar 2017 13:29:09 +0530 Subject: [PATCH 419/513] corrected equivalence operator to <=> from ==> (#361) --- logic.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/logic.ipynb b/logic.ipynb index e498dc7d6..079f1170b 100644 --- a/logic.ipynb +++ b/logic.ipynb @@ -306,11 +306,11 @@ "|--------------------------|----------------------|-------------------------|---|---|\n", "| Negation | ¬ P | `~P` | `~P` | `Expr('~', P)`\n", "| And | P ∧ Q | `P & Q` | `P & Q` | `Expr('&', P, Q)`\n", - "| Or | P ∨ Q | `P` | `Q`| `P` | `Q` | `Expr('`|`', P, Q)\n", + "| Or | P ∨ Q | `P` | `Q`| `P` | `Q` | `Expr('`|`', P, Q)`\n", "| Inequality (Xor) | P ≠ Q | `P ^ Q` | `P ^ Q` | `Expr('^', P, Q)`\n", "| Implication | P → Q | `P` |`'==>'`| `Q` | `P ==> Q` | `Expr('==>', P, Q)`\n", "| Reverse Implication | Q ← P | `Q` |`'<=='`| `P` |`Q <== P` | `Expr('<==', Q, P)`\n", - "| Equivalence | P ↔ Q | `P` |`'<=>'`| `Q` |`P ==> Q` | `Expr('==>', P, Q)`\n", + "| Equivalence | P ↔ Q | `P` |`'<=>'`| `Q` |`P <=> Q` | `Expr('<=>', P, Q)`\n", "\n", "Here's an example of defining a sentence with an implication arrow:" ] @@ -708,7 +708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From 4ae32d8111506c7b34e6dc2ceb52a9477ef62ace Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:05:28 +0200 Subject: [PATCH 420/513] Update grid.ipynb (#358) --- grid.ipynb | 177 ++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 170 insertions(+), 7 deletions(-) diff --git a/grid.ipynb b/grid.ipynb index 4e3bbd7e5..77d1cf49a 100644 --- a/grid.ipynb +++ b/grid.ipynb @@ -1,26 +1,189 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "source": [ + "# Grid\n", + "\n", + "The functions here are used often when dealing with 2D grids (like in TicTacToe).\n", + "\n", + "### Distance\n", + "\n", + "The function returns the Euclidean Distance between two points in the 2D space." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "import grid\n", + "import math\n", "\n", - "print(grid.distance_squared((1, 2), (5, 5)))" + "def distance(a, b):\n", + " \"\"\"The distance between two (x, y) points.\"\"\"\n", + " return math.hypot((a[0] - b[0]), (a[1] - b[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For example:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.0\n" + ] + } + ], + "source": [ + "print(distance((1, 2), (5, 5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Distance Squared\n", + "\n", + "This function returns the square of the distance between two points." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def distance_squared(a, b):\n", + " \"\"\"The square of the distance between two (x, y) points.\"\"\"\n", + " return (a[0] - b[0])**2 + (a[1] - b[1])**2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n" + ] + } + ], + "source": [ + "print(distance_squared((1, 2), (5, 5)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Vector Clip\n", + "\n", + "With this function we can make sure the values of a vector are within a given range. It takes as arguments three vectors: the vector to clip (`vector`), a vector containing the lowest values allowed (`lowest`) and a vector for the highest values (`highest`). All these vectors are of the same length. If a value `v1` in `vector` is lower than the corresponding value `v2` in `lowest`, then we set `v1` to `v2`. Similarly we \"clip\" the values exceeding the `highest` values." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "from utils import clip\n", + "\n", + "def vector_clip(vector, lowest, highest):\n", + " \"\"\"Return vector, except if any element is less than the corresponding\n", + " value of lowest or more than the corresponding value of highest, clip to\n", + " those values.\"\"\"\n", + " return type(vector)(map(clip, vector, lowest, highest))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 9)\n" + ] + } + ], + "source": [ + "print(vector_clip((-1, 10), (0, 0), (9, 9)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The vector we wanted to clip was the tuple (-1, 10). The lowest allowed values were (0, 0) and the highest (9, 9). So, the result is the tuple (0,9)." + ] } ], "metadata": { @@ -39,7 +202,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, From f51888a12c6885a00c0ad6d8148195e32e3790b2 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:05:44 +0200 Subject: [PATCH 421/513] Renamed grid.py Function (#356) * Update agents.py * Update test_grid.py * Update grid.py --- agents.py | 6 +++--- grid.py | 4 ++-- tests/test_grid.py | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/agents.py b/agents.py index a5bf376ca..742cd6c40 100644 --- a/agents.py +++ b/agents.py @@ -35,7 +35,7 @@ # # Speed control in GUI does not have any effect -- fix it. -from grid import distance2, turn_heading +from grid import distance_squared, turn_heading from statistics import mean import random @@ -397,8 +397,8 @@ def things_near(self, location, radius=None): if radius is None: radius = self.perceptible_distance radius2 = radius * radius - return [(thing, radius2 - distance2(location, thing.location)) for thing in self.things - if distance2(location, thing.location) <= radius2] + return [(thing, radius2 - distance_squared(location, thing.location)) for thing in self.things + if distance_squared(location, thing.location) <= radius2] def percept(self, agent): """By default, agent perceives things within a default radius.""" diff --git a/grid.py b/grid.py index 4400d217b..a7e032136 100644 --- a/grid.py +++ b/grid.py @@ -26,8 +26,8 @@ def distance(a, b): return math.hypot((a[0] - b[0]), (a[1] - b[1])) -def distance2(a, b): - "The square of the distance between two (x, y) points." +def distance_squared(a, b): + """The square of the distance between two (x, y) points.""" return (a[0] - b[0])**2 + (a[1] - b[1])**2 diff --git a/tests/test_grid.py b/tests/test_grid.py index 5e05a617a..928218150 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -10,8 +10,8 @@ def test_distance(): assert distance((1, 2), (5, 5)) == 5.0 -def test_distance2(): - assert distance2((1, 2), (5, 5)) == 25.0 +def test_distance_squared(): + assert distance_squared((1, 2), (5, 5)) == 25.0 def test_vector_clip(): From c5c964e9363f476e7d7909f3d716f49c0a7c9920 Mon Sep 17 00:00:00 2001 From: Edward Gonsalves Date: Sat, 18 Mar 2017 13:36:45 +0530 Subject: [PATCH 422/513] Update search.ipynb (#359) --- search.ipynb | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/search.ipynb b/search.ipynb index d77267577..a2f1bee33 100644 --- a/search.ipynb +++ b/search.ipynb @@ -273,7 +273,7 @@ "initial_node_colors = dict(node_colors)\n", " \n", "# positions for node labels\n", - "node_label_pos = {k:[v[0],v[1]-10] for k,v in romania_locations.items()}\n", + "node_label_pos = { k:[v[0],v[1]-10] for k,v in romania_locations.items() }\n", "\n", "# use this while labeling edges\n", "edge_labels = dict()\n", @@ -283,6 +283,7 @@ " connections = romania_map.get(node)\n", " for connection in connections.keys():\n", " distance = connections[connection]\n", + "\n", " # add edges to the graph\n", " G.add_edge(node, connection)\n", " # add distances to edge_labels\n", @@ -293,7 +294,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We have completed building our graph based on romania_map and its locations. It's time to display it here in the notebook. This function `show_map(node_colors)` helps us do that. We will be calling this function later on to display the map at each and every interval step while searching using variety of algorithms from the book." + "We have completed building our graph based on romania_map and its locations. It's time to display it here in the notebook. This function `show_map(node_colors)` helps us do that. We will be calling this function later on to display the map at each and every interval step while searching, using variety of algorithms from the book." ] }, { @@ -438,7 +439,7 @@ " \n", " for i in range(slider.max + 1):\n", " slider.value = i\n", - " # time.sleep(.5)\n", + " #time.sleep(.5)\n", " \n", " slider = widgets.IntSlider(min=0, max=1, step=1, value=0)\n", " slider_visual = widgets.interactive(slider_callback, iteration = slider)\n", @@ -530,6 +531,7 @@ " all_node_colors = []\n", " node_colors = dict(initial_node_colors)\n", " \n", + " #Adding first node to the queue\n", " frontier.append(Node(problem.initial))\n", " \n", " node_colors[Node(problem.initial).state] = \"orange\"\n", @@ -537,6 +539,7 @@ " all_node_colors.append(dict(node_colors))\n", " \n", " while frontier:\n", + " #Popping first node of queue\n", " node = frontier.pop()\n", " \n", " # modify the currently searching node to red\n", From d941781c1fcc486797bee0d01d7b13271a9782b4 Mon Sep 17 00:00:00 2001 From: VladKha Date: Sat, 18 Mar 2017 10:10:00 +0200 Subject: [PATCH 423/513] Update comments and cleanup (#354) * Update some comments * Cleanup and remove some duplicate initialization * Fix some comments quotation and little bugs: raising Exception instead of raising Failure, random.randrange(a,b) instead of random(a, b) * Fix some comments quotation and remove explicit inheritance from object * Fix 'r' in front of the ```parse_csv``` comments * Fix quotation in comments * Fix quotation in comments and fix bug in 'KB_AgentProgram' --- agents.py | 15 +++++-------- csp.py | 61 +++++++++++++++++++++++--------------------------- games.py | 26 ++++++++++----------- ipyviews.py | 2 +- learning.py | 22 ++++++------------ logic.py | 50 +++++++++++++++++++---------------------- probability.py | 38 +++++++++++++------------------ 7 files changed, 93 insertions(+), 121 deletions(-) diff --git a/agents.py b/agents.py index 742cd6c40..b7f1d50ef 100644 --- a/agents.py +++ b/agents.py @@ -45,8 +45,7 @@ # ______________________________________________________________________________ -class Thing(object): - +class Thing: """This represents any physical object that can appear in an Environment. You subclass Thing to get the things you want. Each thing can have a .__name__ slot (used for output only).""" @@ -69,7 +68,6 @@ def display(self, canvas, x, y, width, height): class Agent(Thing): - """An Agent is a subclass of Thing with one required slot, .program, which should hold a function that takes one argument, the percept, and returns an action. (What counts as a percept or action @@ -222,8 +220,7 @@ def program(percept): # ______________________________________________________________________________ -class Environment(object): - +class Environment: """Abstract class representing an Environment. 'Real' Environment classes inherit from this. Your Environment will typically need to implement: percept: Define the percept that an agent sees. @@ -319,7 +316,8 @@ def delete_thing(self, thing): if thing in self.agents: self.agents.remove(thing) -class Direction(): + +class Direction: """A direction class for agents that want to move in a 2D plane Usage: d = Direction("down") @@ -371,7 +369,6 @@ def move_forward(self, from_location): class XYEnvironment(Environment): - """This class is for environments on a 2D plane, with locations labelled by (x, y) points, either discrete or continuous. @@ -507,7 +504,6 @@ def turn_heading(self, heading, inc): class Obstacle(Thing): - """Something that can cause a bump, preventing an agent from moving into the same square it's in.""" pass @@ -724,7 +720,8 @@ def get_world(self, show_walls=True): return result def percepts_from(self, agent, location, tclass=Thing): - """Returns percepts from a given location, and replaces some items with percepts from chapter 7.""" + """Returns percepts from a given location, + and replaces some items with percepts from chapter 7.""" thing_percepts = { Gold: Glitter(), Wall: Bump(), diff --git a/csp.py b/csp.py index 207576928..1e97d7780 100644 --- a/csp.py +++ b/csp.py @@ -12,7 +12,6 @@ class CSP(search.Problem): - """This class describes finite-domain Constraint Satisfaction Problems. A CSP is specified by the following inputs: variables A list of variables; each is atomic (e.g. int or string). @@ -49,7 +48,7 @@ class CSP(search.Problem): """ def __init__(self, variables, domains, neighbors, constraints): - "Construct a CSP problem. If variables is empty, it becomes domains.keys()." + """Construct a CSP problem. If variables is empty, it becomes domains.keys().""" variables = variables or list(domains.keys()) self.variables = variables @@ -61,7 +60,7 @@ def __init__(self, variables, domains, neighbors, constraints): self.nassigns = 0 def assign(self, var, val, assignment): - "Add {var: val} to assignment; Discard the old value if any." + """Add {var: val} to assignment; Discard the old value if any.""" assignment[var] = val self.nassigns += 1 @@ -73,7 +72,7 @@ def unassign(self, var, assignment): del assignment[var] def nconflicts(self, var, val, assignment): - "Return the number of conflicts var=val has with other variables." + """Return the number of conflicts var=val has with other variables.""" # Subclasses may implement this more efficiently def conflict(var2): return (var2 in assignment and @@ -81,7 +80,7 @@ def conflict(var2): return count(conflict(v) for v in self.neighbors[var]) def display(self, assignment): - "Show a human-readable representation of the CSP." + """Show a human-readable representation of the CSP.""" # Subclasses can print in a prettier way, or display with a GUI print('CSP:', self, 'with assignment:', assignment) @@ -99,12 +98,12 @@ def actions(self, state): if self.nconflicts(var, val, assignment) == 0] def result(self, state, action): - "Perform an action and return the new state." + """Perform an action and return the new state.""" (var, val) = action return state + ((var, val),) def goal_test(self, state): - "The goal is to assign all variables, with all constraints satisfied." + """The goal is to assign all variables, with all constraints satisfied.""" assignment = dict(state) return (len(assignment) == len(self.variables) and all(self.nconflicts(variables, assignment[variables], assignment) == 0 @@ -119,37 +118,37 @@ def support_pruning(self): self.curr_domains = {v: list(self.domains[v]) for v in self.variables} def suppose(self, var, value): - "Start accumulating inferences from assuming var=value." + """Start accumulating inferences from assuming var=value.""" self.support_pruning() removals = [(var, a) for a in self.curr_domains[var] if a != value] self.curr_domains[var] = [value] return removals def prune(self, var, value, removals): - "Rule out var=value." + """Rule out var=value.""" self.curr_domains[var].remove(value) if removals is not None: removals.append((var, value)) def choices(self, var): - "Return all values for var that aren't currently ruled out." + """Return all values for var that aren't currently ruled out.""" return (self.curr_domains or self.domains)[var] def infer_assignment(self): - "Return the partial assignment implied by the current inferences." + """Return the partial assignment implied by the current inferences.""" self.support_pruning() return {v: self.curr_domains[v][0] for v in self.variables if 1 == len(self.curr_domains[v])} def restore(self, removals): - "Undo a supposition and all inferences from it." + """Undo a supposition and all inferences from it.""" for B, b in removals: self.curr_domains[B].append(b) # This is for min_conflicts search def conflicted_vars(self, current): - "Return a list of variables in current assignment that are in conflict" + """Return a list of variables in current assignment that are in conflict""" return [var for var in self.variables if self.nconflicts(var, current[var], current) > 0] @@ -174,7 +173,7 @@ def AC3(csp, queue=None, removals=None): def revise(csp, Xi, Xj, removals): - "Return true if we remove a value." + """Return true if we remove a value.""" revised = False for x in csp.curr_domains[Xi][:]: # If Xi=x conflicts with Xj=y for every possible y, eliminate Xi=x @@ -190,12 +189,12 @@ def revise(csp, Xi, Xj, removals): def first_unassigned_variable(assignment, csp): - "The default variable order." + """The default variable order.""" return first([var for var in csp.variables if var not in assignment]) def mrv(assignment, csp): - "Minimum-remaining-values heuristic." + """Minimum-remaining-values heuristic.""" return argmin_random_tie( [v for v in csp.variables if v not in assignment], key=lambda var: num_legal_values(csp, var, assignment)) @@ -212,12 +211,12 @@ def num_legal_values(csp, var, assignment): def unordered_domain_values(var, assignment, csp): - "The default value order." + """The default value order.""" return csp.choices(var) def lcv(var, assignment, csp): - "Least-constraining-values heuristic." + """Least-constraining-values heuristic.""" return sorted(csp.choices(var), key=lambda val: csp.nconflicts(var, val, assignment)) @@ -229,7 +228,7 @@ def no_inference(csp, var, value, assignment, removals): def forward_checking(csp, var, value, assignment, removals): - "Prune neighbor values inconsistent with var=value." + """Prune neighbor values inconsistent with var=value.""" for B in csp.neighbors[var]: if B not in assignment: for b in csp.curr_domains[B][:]: @@ -241,7 +240,7 @@ def forward_checking(csp, var, value, assignment, removals): def mac(csp, var, value, assignment, removals): - "Maintain arc consistency." + """Maintain arc consistency.""" return AC3(csp, [(X, var) for X in csp.neighbors[var]], removals) # The search, proper @@ -251,8 +250,7 @@ def backtracking_search(csp, select_unassigned_variable=first_unassigned_variable, order_domain_values=unordered_domain_values, inference=no_inference): - """[Figure 6.5] - """ + """[Figure 6.5]""" def backtrack(assignment): if len(assignment) == len(csp.variables): @@ -306,7 +304,7 @@ def min_conflicts_value(csp, var, current): def tree_csp_solver(csp): - "[Figure 6.11]" + """[Figure 6.11]""" assignment = {} root = csp.variables[0] X, parent = topological_sort(csp.variables, root) @@ -332,7 +330,6 @@ def make_arc_consistent(Xj, Xk, csp): class UniversalDict: - """A universal dict maps any key to the same value. We use it here as the domains dict for CSPs in which all variables have the same domain. >>> d = UniversalDict(42) @@ -348,7 +345,7 @@ def __repr__(self): return '{{Any: {0!r}}}'.format(self.value) def different_values_constraint(A, a, B, b): - "A constraint saying two neighboring variables must differ in value." + """A constraint saying two neighboring variables must differ in value.""" return a != b @@ -413,7 +410,6 @@ def queen_constraint(A, a, B, b): class NQueensCSP(CSP): - """Make a CSP for the nQueens problem for search with min_conflicts. Suitable for large n, it uses only data structures of size O(n). Think of placing queens one per column, from left to right. @@ -453,7 +449,7 @@ def nconflicts(self, var, val, assignment): return c def assign(self, var, val, assignment): - "Assign var, and keep track of conflicts." + """Assign var, and keep track of conflicts.""" oldval = assignment.get(var, None) if val != oldval: if oldval is not None: # Remove old val if there was one @@ -462,20 +458,20 @@ def assign(self, var, val, assignment): CSP.assign(self, var, val, assignment) def unassign(self, var, assignment): - "Remove var from assignment (if it is there) and track conflicts." + """Remove var from assignment (if it is there) and track conflicts.""" if var in assignment: self.record_conflict(assignment, var, assignment[var], -1) CSP.unassign(self, var, assignment) def record_conflict(self, assignment, var, val, delta): - "Record conflicts caused by addition or deletion of a Queen." + """Record conflicts caused by addition or deletion of a Queen.""" n = len(self.variables) self.rows[val] += delta self.downs[var + val] += delta self.ups[var - val + n - 1] += delta def display(self, assignment): - "Print the queens and the nconflicts values (for debugging)." + """Print the queens and the nconflicts values (for debugging).""" n = len(self.variables) for val in range(n): for var in range(n): @@ -514,11 +510,10 @@ def flatten(seqs): return sum(seqs, []) _NEIGHBORS = {v: set() for v in flatten(_ROWS)} for unit in map(set, _BOXES + _ROWS + _COLS): for v in unit: - _NEIGHBORS[v].update(unit - set([v])) + _NEIGHBORS[v].update(unit - {v}) class Sudoku(CSP): - """A Sudoku problem. The box grid is a 3x3 array of boxes, each a 3x3 array of cells. Each cell holds a digit in 1..9. In each box, all digits are @@ -587,7 +582,7 @@ def abut(lines1, lines2): return list( def Zebra(): - "Return an instance of the Zebra Puzzle." + """Return an instance of the Zebra Puzzle.""" Colors = 'Red Yellow Blue Green Ivory'.split() Pets = 'Dog Fox Snails Horse Zebra'.split() Drinks = 'OJ Tea Coffee Milk Water'.split() diff --git a/games.py b/games.py index 9b98c5638..f5061f4c8 100644 --- a/games.py +++ b/games.py @@ -135,7 +135,7 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): - "Make a move by querying standard input." + """Make a move by querying standard input.""" move_string = input('Your move? ') try: move = eval(move_string) @@ -145,7 +145,7 @@ def query_player(game, state): def random_player(game, state): - "A player that chooses a legal move at random." + """A player that chooses a legal move at random.""" return random.choice(game.actions(state)) @@ -179,27 +179,27 @@ class Game: be done in the constructor.""" def actions(self, state): - "Return a list of the allowable moves at this point." + """Return a list of the allowable moves at this point.""" raise NotImplementedError def result(self, state, move): - "Return the state that results from making a move from a state." + """Return the state that results from making a move from a state.""" raise NotImplementedError def utility(self, state, player): - "Return the value of this final state to player." + """Return the value of this final state to player.""" raise NotImplementedError def terminal_test(self, state): - "Return True if this is a final state for the game." + """Return True if this is a final state for the game.""" return not self.actions(state) def to_move(self, state): - "Return the player whose move it is in this state." + """Return the player whose move it is in this state.""" return state.to_move def display(self, state): - "Print or otherwise display the state." + """Print or otherwise display the state.""" print(state) def __repr__(self): @@ -250,7 +250,7 @@ def __init__(self, h=3, v=3, k=3): self.initial = GameState(to_move='X', utility=0, board={}, moves=moves) def actions(self, state): - "Legal moves are any square not yet taken." + """Legal moves are any square not yet taken.""" return state.moves def result(self, state, move): @@ -265,11 +265,11 @@ def result(self, state, move): board=board, moves=moves) def utility(self, state, player): - "Return the value to player; 1 for win, -1 for loss, 0 otherwise." + """Return the value to player; 1 for win, -1 for loss, 0 otherwise.""" return state.utility if player == 'X' else -state.utility def terminal_test(self, state): - "A state is terminal if it is won or there are no empty squares." + """A state is terminal if it is won or there are no empty squares.""" return state.utility != 0 or len(state.moves) == 0 def display(self, state): @@ -280,7 +280,7 @@ def display(self, state): print() def compute_utility(self, board, move, player): - "If 'X' wins with this move, return 1; if 'O' wins return -1; else return 0." + """If 'X' wins with this move, return 1; if 'O' wins return -1; else return 0.""" if (self.k_in_row(board, move, player, (0, 1)) or self.k_in_row(board, move, player, (1, 0)) or self.k_in_row(board, move, player, (1, -1)) or @@ -290,7 +290,7 @@ def compute_utility(self, board, move, player): return 0 def k_in_row(self, board, move, player, delta_x_y): - "Return true if there is a line through move on board for player." + """Return true if there is a line through move on board for player.""" (delta_x, delta_y) = delta_x_y x, y = move n = 0 # n is number of moves in row diff --git a/ipyviews.py b/ipyviews.py index 7cb28850b..4c3776fbc 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -27,7 +27,7 @@ class ContinuousWorldView: - ''' View for continuousworld Implementation in agents.py ''' + """ View for continuousworld Implementation in agents.py """ def __init__(self, world, fill="#AAA"): self.time = time.time() diff --git a/learning.py b/learning.py index 3e7f4690c..db25c42f3 100644 --- a/learning.py +++ b/learning.py @@ -39,7 +39,6 @@ def mean_boolean_error(predictions, targets): class DataSet: - """A data set for a machine learning problem. It has the following fields: d.examples A list of examples. Each one is a list of attribute values. @@ -173,7 +172,6 @@ def parse_csv(input, delim=','): class CountingProbDist: - """A probability distribution formed by observing and counting examples. If p is an instance of this class and o is an observed value, then there are 3 main operations: @@ -285,7 +283,6 @@ def predict(example): class DecisionFork: - """A fork of a decision tree holds an attribute to test, and a dict of branches, one for each of the attribute's values.""" @@ -317,7 +314,6 @@ def __repr__(self): class DecisionLeaf: - """A leaf of a decision tree holds just a result.""" def __init__(self, result): @@ -413,7 +409,7 @@ def decision_list_learning(examples): return [(True, False)] t, o, examples_t = find_examples(examples) if not t: - raise Failure + raise Exception return [(t, o)] + decision_list_learning(examples - examples_t) def find_examples(examples): @@ -439,8 +435,7 @@ def predict(example): def NeuralNetLearner(dataset, hidden_layer_sizes=[3], learning_rate=0.01, epoches=100): - """ - Layered feed-forward network. + """Layered feed-forward network. hidden_layer_sizes: List of number of hidden units per hidden layer learning_rate: Learning rate of gradient descent epoches: Number of passes over the dataset @@ -479,8 +474,7 @@ def predict(example): class NNUnit: - """ - Single Unit of Multiple Layer Neural Network + """Single Unit of Multiple Layer Neural Network inputs: Incoming connections weights: Weights to incoming connections """ @@ -493,8 +487,7 @@ def __init__(self, weights=None, inputs=None): def network(input_units, hidden_layer_sizes, output_units): - """ - Create Directed Acyclic Network of given number layers. + """Create Directed Acyclic Network of given number layers. hidden_layers_sizes : List number of neuron units in each hidden layer excluding input and output layers """ @@ -632,11 +625,11 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): X_col = [dataset.values[i] for i in idx_i] # vertical columns of X # Add dummy - ones = [1 for i in range(len(examples))] + ones = [1 for _ in range(len(examples))] X_col = ones + X_col # Initialize random weigts - w = [random(-0.5, 0.5) for i in range(len(idx_i) + 1)] + w = [random.randrange(-0.5, 0.5) for _ in range(len(idx_i) + 1)] for epoch in range(epochs): err = [] @@ -820,8 +813,7 @@ def cross_validation(learner, size, dataset, k=10, trials=1): def cross_validation_wrapper(learner, dataset, k=10, trials=1): - """ - [Fig 18.8] + """[Fig 18.8] Return the optimal value of size having minimum error on validataion set. err_train: A training error array, indexed by size diff --git a/logic.py b/logic.py index 75c461e8f..9054cdfc7 100644 --- a/logic.py +++ b/logic.py @@ -60,7 +60,7 @@ def __init__(self, sentence=None): raise NotImplementedError def tell(self, sentence): - "Add the sentence to the KB." + """Add the sentence to the KB.""" raise NotImplementedError def ask(self, query): @@ -68,17 +68,16 @@ def ask(self, query): return first(self.ask_generator(query), default=False) def ask_generator(self, query): - "Yield all the substitutions that make query true." + """Yield all the substitutions that make query true.""" raise NotImplementedError def retract(self, sentence): - "Remove sentence from the KB." + """Remove sentence from the KB.""" raise NotImplementedError class PropKB(KB): - - "A KB for propositional logic. Inefficient, with no indexing." + """A KB for propositional logic. Inefficient, with no indexing.""" def __init__(self, sentence=None): self.clauses = [] @@ -86,22 +85,22 @@ def __init__(self, sentence=None): self.tell(sentence) def tell(self, sentence): - "Add the sentence's clauses to the KB." + """Add the sentence's clauses to the KB.""" self.clauses.extend(conjuncts(to_cnf(sentence))) def ask_generator(self, query): - "Yield the empty substitution {} if KB entails query; else no results." + """Yield the empty substitution {} if KB entails query; else no results.""" if tt_entails(Expr('&', *self.clauses), query): yield {} def ask_if_true(self, query): - "Return True if the KB entails query, else return False." + """Return True if the KB entails query, else return False.""" for _ in self.ask_generator(query): return True return False def retract(self, sentence): - "Remove the sentence's clauses from the KB." + """Remove the sentence's clauses from the KB.""" for c in conjuncts(to_cnf(sentence)): if c in self.clauses: self.clauses.remove(c) @@ -120,25 +119,25 @@ def program(percept): KB.tell(make_action_sentence(action, t)) return action - def make_percept_sentence(self, percept, t): + def make_percept_sentence(percept, t): return Expr("Percept")(percept, t) - def make_action_query(self, t): + def make_action_query(t): return expr("ShouldDo(action, {})".format(t)) - def make_action_sentence(self, action, t): + def make_action_sentence(action, t): return Expr("Did")(action[expr('action')], t) return program def is_symbol(s): - "A string s is a symbol if it starts with an alphabetic char." + """A string s is a symbol if it starts with an alphabetic char.""" return isinstance(s, str) and s[:1].isalpha() def is_var_symbol(s): - "A logic variable symbol is an initial-lowercase string." + """A logic variable symbol is an initial-lowercase string.""" return is_symbol(s) and s[0].islower() @@ -156,7 +155,7 @@ def variables(s): def is_definite_clause(s): - """returns True for exprs s of the form A & B & ... & C ==> D, + """Returns True for exprs s of the form A & B & ... & C ==> D, where all literals are positive. In clause form, this is ~A | ~B | ... | ~C | D, where exactly one clause is positive. >>> is_definite_clause(expr('Farmer(Mac)')) @@ -173,7 +172,7 @@ def is_definite_clause(s): def parse_definite_clause(s): - "Return the antecedents and the consequent of a definite clause." + """Return the antecedents and the consequent of a definite clause.""" assert is_definite_clause(s) if is_symbol(s.op): return [], s @@ -200,7 +199,7 @@ def tt_entails(kb, alpha): def tt_check_all(kb, alpha, symbols, model): - "Auxiliary routine to implement tt_entails." + """Auxiliary routine to implement tt_entails.""" if not symbols: if pl_true(kb, model): result = pl_true(alpha, model) @@ -215,7 +214,7 @@ def tt_check_all(kb, alpha, symbols, model): def prop_symbols(x): - "Return a list of all propositional symbols in x." + """Return a list of all propositional symbols in x.""" if not isinstance(x, Expr): return [] elif is_prop_symbol(x.op): @@ -305,7 +304,7 @@ def to_cnf(s): def eliminate_implications(s): - "Change implications into equivalent form with only &, |, and ~ as logical operators." + """Change implications into equivalent form with only &, |, and ~ as logical operators.""" s = expr(s) if not s.args or is_symbol(s.op): return s # Atoms are unchanged. @@ -433,7 +432,7 @@ def disjuncts(s): def pl_resolution(KB, alpha): - "Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]" + """Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]""" clauses = KB.clauses + conjuncts(to_cnf(~alpha)) new = set() while True: @@ -467,16 +466,15 @@ def pl_resolve(ci, cj): class PropDefiniteKB(PropKB): - - "A KB of propositional definite clauses." + """A KB of propositional definite clauses.""" def tell(self, sentence): - "Add a definite clause to this KB." + """Add a definite clause to this KB.""" assert is_definite_clause(sentence), "Must be definite clause" self.clauses.append(sentence) def ask_generator(self, query): - "Yield the empty substitution if KB implies query; else nothing." + """Yield the empty substitution if KB implies query; else nothing.""" if pl_fc_entails(self.clauses, query): yield {} @@ -542,7 +540,7 @@ def dpll_satisfiable(s): def dpll(clauses, symbols, model): - "See if the clauses are true in a partial model." + """See if the clauses are true in a partial model.""" unknown_clauses = [] # clauses with an unknown truth value for c in clauses: val = pl_true(c, model) @@ -669,7 +667,6 @@ def sat_count(sym): class HybridWumpusAgent(agents.Agent): - """An agent for the wumpus world that does logical inference. [Figure 7.20]""" def __init__(self): @@ -871,7 +868,6 @@ def standardize_variables(sentence, dic=None): class FolKB(KB): - """A knowledge base consisting of first-order definite clauses. >>> kb0 = FolKB([expr('Farmer(Mac)'), expr('Rabbit(Pete)'), ... expr('(Rabbit(r) & Farmer(f)) ==> Hates(f, r)')]) diff --git a/probability.py b/probability.py index d28a8a38b..fa856c330 100644 --- a/probability.py +++ b/probability.py @@ -16,7 +16,7 @@ def DTAgentProgram(belief_state): - "A decision-theoretic agent. [Figure 13.1]" + """A decision-theoretic agent. [Figure 13.1]""" def program(percept): belief_state.observe(program.action, percept) program.action = argmax(belief_state.actions(), @@ -29,8 +29,7 @@ def program(percept): class ProbDist: - - """A discrete probability distribution. You name the random variable + """A discrete probability distribution. You name the random variable in the constructor, then assign and query probability of values. >>> P = ProbDist('Flip'); P['H'], P['T'] = 0.25, 0.75; P['H'] 0.25 @@ -40,8 +39,8 @@ class ProbDist: """ def __init__(self, varname='?', freqs=None): - """If freqs is given, it is a dictionary of value: frequency pairs, - and the ProbDist then is normalized.""" + """If freqs is given, it is a dictionary of values - frequency pairs, + then ProbDist is normalized.""" self.prob = {} self.varname = varname self.values = [] @@ -51,14 +50,14 @@ def __init__(self, varname='?', freqs=None): self.normalize() def __getitem__(self, val): - "Given a value, return P(value)." + """Given a value, return P(value).""" try: return self.prob[val] except KeyError: return 0 def __setitem__(self, val, p): - "Set P(val) = p." + """Set P(val) = p.""" if val not in self.values: self.values.append(val) self.prob[val] = p @@ -98,7 +97,7 @@ def __init__(self, variables): self.vals = defaultdict(list) def __getitem__(self, values): - "Given a tuple or dict of values, return P(values)." + """Given a tuple or dict of values, return P(values).""" values = event_values(values, self.variables) return ProbDist.__getitem__(self, values) @@ -113,7 +112,7 @@ def __setitem__(self, values, p): self.vals[var].append(val) def values(self, var): - "Return the set of possible values for a variable." + """Return the set of possible values for a variable.""" return self.vals[var] def __repr__(self): @@ -164,11 +163,10 @@ def enumerate_joint(variables, e, P): class BayesNet: - - "Bayesian network containing only boolean-variable nodes." + """Bayesian network containing only boolean-variable nodes.""" def __init__(self, node_specs=[]): - "nodes must be ordered with parents before children." + """Nodes must be ordered with parents before children.""" self.nodes = [] self.variables = [] for node_spec in node_specs: @@ -195,7 +193,7 @@ def variable_node(self, var): raise Exception("No such variable: {}".format(var)) def variable_values(self, var): - "Return the domain of var." + """Return the domain of var.""" return [True, False] def __repr__(self): @@ -203,7 +201,6 @@ def __repr__(self): class BayesNode: - """A conditional probability distribution for a boolean variable, P(X | parents). Part of a BayesNet.""" @@ -337,7 +334,7 @@ def elimination_ask(X, e, bn): def is_hidden(var, X, e): - "Is var a hidden variable when querying P(X|e)?" + """Is var a hidden variable when querying P(X|e)?""" return var != X and var not in e @@ -366,7 +363,6 @@ def sum_out(var, factors, bn): class Factor: - """A factor in a joint distribution.""" def __init__(self, variables, cpt): @@ -526,7 +522,6 @@ def markov_blanket_sample(X, e, bn): class HiddenMarkovModel: - """A Hidden markov model which takes Transition model and Sensor model as inputs""" def __init__(self, transition_model, sensor_model, prior=[0.5, 0.5]): @@ -605,7 +600,7 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t) else: B = matrix_multiplication(B, T_model, O_t) - t = t + 1 + t += 1 if t > d: # always returns a 1x2 matrix @@ -618,18 +613,15 @@ def fixed_lag_smoothing(e_t, HMM, d, ev, t): def particle_filtering(e, N, HMM): """Particle filtering considering two states variables.""" - s = [] dist = [0.5, 0.5] - # State Initialization - s = ['A' if probability(dist[0]) else 'B' for i in range(N)] # Weight Initialization - w = [0 for i in range(N)] + w = [0 for _ in range(N)] # STEP 1 # Propagate one step using transition model given prior state dist = vector_add(scalar_vector_product(dist[0], HMM.transition_model[0]), scalar_vector_product(dist[1], HMM.transition_model[1])) # Assign state according to probability - s = ['A' if probability(dist[0]) else 'B' for i in range(N)] + s = ['A' if probability(dist[0]) else 'B' for _ in range(N)] w_tot = 0 # Calculate importance weight given evidence e for i in range(N): From 35ef22ce0a8f403fe961a99762e985aa0733ff4d Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:11:00 +0200 Subject: [PATCH 424/513] Updated text.py Notebook (#352) * Update text.ipynb * Update text.ipynb --- text.ipynb | 213 +++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 205 insertions(+), 8 deletions(-) diff --git a/text.ipynb b/text.ipynb index 37e4d0b63..129c7ad7d 100644 --- a/text.ipynb +++ b/text.ipynb @@ -1,24 +1,221 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "source": [ + "# Text\n", + "\n", + "This notebook serves as supporting material for topics covered in **Chapter 22 - Natural Language Processing** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [text.py](https://github.com/aimacode/aima-python/blob/master/text.py)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Contents\n", + "\n", + "* Text Models\n", + "* Viterbi Text Segmentation\n", + " * Overview\n", + " * Implementation\n", + " * Example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Text Models\n", + "\n", + "Before we start performing text processing algorithms, we will need to build some word models. Those models serve as a look-up table for word probabilities. In the text module we have implemented two such models, which inherit from the `CountingProbDist` from `learning.py`. `UnigramTextModel` and `NgramTextModel`. We supply them with a text file and they show the frequency of the different words.\n", + "\n", + "The main difference between the two models is that the first returns the probability of one single word (eg. the probability of the word 'the' appearing), while the second one can show us the probability of a *sequence* of words (eg. the probability of the sequence 'of the' appearing).\n", + "\n", + "Also, both functions can generate random words and sequences respectively, random according to the model.\n", + "\n", + "Below we build the two models. The text file we will use to build them is the *Flatland*, by Edwin A. Abbott. We will load it from [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/EN-text/flatland.txt)." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a')]\n", + "[(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), (80, ('it', 'is'))]\n" + ] + } + ], + "source": [ + "from text import UnigramTextModel, NgramTextModel, words\n", + "from utils import DataFile\n", + "\n", + "flatland = DataFile(\"EN-text/flatland.txt\").read()\n", + "wordseq = words(flatland)\n", + "\n", + "P1 = UnigramTextModel(wordseq)\n", + "P2 = NgramTextModel(2, wordseq)\n", + "\n", + "print(P1.top(5))\n", + "print(P2.top(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that the most used word in *Flatland* is 'the', with 2081 occurences, while the most used sequence is 'of the' with 368 occurences." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Viterbi Text Segmentation\n", + "\n", + "### Overview\n", + "\n", + "We are given a string containing words of a sentence, but all the spaces are gone! It is very hard to read and we would like to separate the words in the string. We can accomplish this by employing the `Viterbi Segmentation` algorithm. It takes as input the string to segment and a text model, and it returns a list of the separate words.\n", + "\n", + "The algorithm operates in a dynamic programming approach. It starts from the beginning of the string and iteratively builds the best solution using previous solutions. It accomplishes that by segmentating the string into \"windows\", each window representing a word (real or gibberish). It then calculates the probability of the sequence up that window/word occuring and updates its solution. When it is done, it traces back from the final word and finds the complete sequence of words." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true }, - "outputs": [], "source": [ - "import text" + "### Implementation" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], - "source": [] + "source": [ + "def viterbi_segment(text, P):\n", + " \"\"\"Find the best segmentation of the string of characters, given the\n", + " UnigramTextModel P.\"\"\"\n", + " # best[i] = best probability for text[0:i]\n", + " # words[i] = best word ending at position i\n", + " n = len(text)\n", + " words = [''] + list(text)\n", + " best = [1.0] + [0.0] * n\n", + " # Fill in the vectors best words via dynamic programming\n", + " for i in range(n+1):\n", + " for j in range(0, i):\n", + " w = text[j:i]\n", + " newbest = P[w] * best[i - len(w)]\n", + " if newbest >= best[i]:\n", + " best[i] = newbest\n", + " words[i] = w\n", + " # Now recover the sequence of best words\n", + " sequence = []\n", + " i = len(words) - 1\n", + " while i > 0:\n", + " sequence[0:0] = [words[i]]\n", + " i = i - len(words[i])\n", + " # Return sequence of best words and overall probability\n", + " return sequence, best[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The function takes as input a string and a text model, and returns the most probable sequence of words, together with the probability of that sequence.\n", + "\n", + "The \"window\" is `w` and it includes the characters from *j* to *i*. We use it to \"build\" the following sequence: from the start to *j* and then `w`. We have previously calculated the probability from the start to *j*, so now we multiply that probability by `P[w]` to get the probability of the whole sequence. If that probability is greater than the probability we have calculated so far for the sequence from the start to *i* (`best[i]`), we update it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example\n", + "\n", + "The model the algorithm uses is the `UnigramTextModel`. First we will build the model using the *Flatland* text and then we will try and separate a space-devoid sentence." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequence of words is: ['it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces']\n", + "Probability of sequence is: 2.273672843573388e-24\n" + ] + } + ], + "source": [ + "from text import UnigramTextModel, words, viterbi_segment\n", + "from utils import DataFile\n", + "\n", + "flatland = DataFile(\"EN-text/flatland.txt\").read()\n", + "wordseq = words(flatland)\n", + "P = UnigramTextModel(wordseq)\n", + "text = \"itiseasytoreadwordswithoutspaces\"\n", + "\n", + "s, p = viterbi_segment(text,P)\n", + "print(\"Sequence of words is:\",s)\n", + "print(\"Probability of sequence is:\",p)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The algorithm correctly retrieved the words from the string. It also gave us the probability of this sequence, which is small, but still the most probable segmentation of the string." + ] } ], "metadata": { @@ -37,7 +234,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, From b4e6843051989673de9d59f024f519b05448af0a Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:11:23 +0200 Subject: [PATCH 425/513] Converted fig_5_2 Image (#353) * Delete fig_5_2.svg * add fig_5_2.png * Update games.ipynb --- games.ipynb | 2 +- images/fig_5_2.png | Bin 0 -> 49045 bytes images/fig_5_2.svg | 662 --------------------------------------------- 3 files changed, 1 insertion(+), 663 deletions(-) create mode 100644 images/fig_5_2.png delete mode 100644 images/fig_5_2.svg diff --git a/games.ipynb b/games.ipynb index e51a0a2bc..1dc5f5ca9 100644 --- a/games.ipynb +++ b/games.ipynb @@ -197,7 +197,7 @@ "cell_type": "markdown", "metadata": {}, 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The Δ nodes are "MAX nodes", in which it is MAX'sturn to move, and the ∇ nodes are "MIN nodes." The terminal nodes show the utility valuesfor MAX; the other nodes are labeled with their minimax values. MAX's best move at the rootis a1, because it leads to the state with the highest minimax value, and MIN's best reply is b1,beacuse it leads to the state with the lowest minimax value. - - - - - - - - - - - - - - - - - - A - B - C - D - - - - - - - - From cf30580ec9c8feb844c2370d13f93a902d268ecd Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Sat, 18 Mar 2017 13:44:11 +0530 Subject: [PATCH 426/513] Corrected Direction arithmetic in agents.py (#348) * added tests for Direction * fixed Direction arithmetic error --- agents.py | 2 +- tests/test_agents.py | 40 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 41 insertions(+), 1 deletion(-) create mode 100644 tests/test_agents.py diff --git a/agents.py b/agents.py index b7f1d50ef..1191f9a0d 100644 --- a/agents.py +++ b/agents.py @@ -343,7 +343,7 @@ def __add__(self, heading): elif self.direction == self.L: return{ self.R: Direction(self.U), - self.L: Direction(self.L), + self.L: Direction(self.D), }.get(heading, None) elif self.direction == self.U: return{ diff --git a/tests/test_agents.py b/tests/test_agents.py new file mode 100644 index 000000000..89ee3fcf3 --- /dev/null +++ b/tests/test_agents.py @@ -0,0 +1,40 @@ +from agents import Direction + +def test_move_forward(): + d = Direction("up") + l1 = d.move_forward((0,0)) + assert l1 == (0,-1) + d = Direction(Direction.R) + l1 = d.move_forward((0,0)) + assert l1 == (1,0) + d = Direction(Direction.D) + l1 = d.move_forward((0,0)) + assert l1 == (0,1) + d = Direction("left") + l1 = d.move_forward((0,0)) + assert l1 == (-1,0) + l2 = d.move_forward((1,0)) + assert l2 == (0,0) + +def test_add(): + d = Direction(Direction.U) + l1 = d + "right" + l2 = d + "left" + assert l1.direction == Direction.R + assert l2.direction == Direction.L + d = Direction("right") + l1 = d.__add__(Direction.L) + l2 = d.__add__(Direction.R) + assert l1.direction == "up" + assert l2.direction == "down" + d = Direction("down") + l1 = d.__add__("right") + l2 = d.__add__("left") + assert l1.direction == Direction.L + assert l2.direction == Direction.R + d = Direction(Direction.L) + l1 = d + Direction.R + l2 = d + Direction.L + assert l1.direction == Direction.U + assert l2.direction == Direction.D #fixed + From 70f0abd411fa9bed22068fa1b063b451e705f8f3 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Sat, 18 Mar 2017 13:46:22 +0530 Subject: [PATCH 427/513] Completed BlindDog agent examples (#350) * Improved BlindDog example * Added 2D GUI IPython capability * Demonstrated 2D Environment with GUI * allowing import without ipythonblocks installed --- agents.ipynb | 1135 +++++++++++++++++++++++++++++++++++++++++++++----- agents.py | 103 +++++ 2 files changed, 1131 insertions(+), 107 deletions(-) diff --git a/agents.ipynb b/agents.ipynb index 8eba9f07e..968c8cdc9 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -98,122 +98,43 @@ " def percept(self, agent):\n", " '''prints & return a list of things that are in our agent's location'''\n", " things = self.list_things_at(agent.location)\n", - " print(things)\n", " return things\n", " \n", " def execute_action(self, agent, action):\n", " '''changes the state of the environment based on what the agent does.'''\n", " if action == \"move down\":\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", " agent.movedown()\n", " elif action == \"eat\":\n", " items = self.list_things_at(agent.location, tclass=Food)\n", " if len(items) != 0:\n", - " if agent.eat(items[0]): #Have the dog pick eat the first item\n", + " if agent.eat(items[0]): #Have the dog eat the first item\n", + " print('{} ate {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", " self.delete_thing(items[0]) #Delete it from the Park after.\n", " elif action == \"drink\":\n", " items = self.list_things_at(agent.location, tclass=Water)\n", " if len(items) != 0:\n", " if agent.drink(items[0]): #Have the dog drink the first item\n", + " print('{} drank {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", " self.delete_thing(items[0]) #Delete it from the Park after.\n", - " \n", + "\n", " def is_done(self):\n", " '''By default, we're done when we can't find a live agent, \n", - " but to prevent killing our cute dog, we will or it with when there is no more food or water'''\n", + " but to prevent killing our cute dog, we will stop before itself - when there is no more food or water'''\n", " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", " return dead_agents or no_edibles\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wumpus Environment" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from ipythonblocks import BlockGrid\n", - "from agents import *\n", - "\n", - "color = {\"Breeze\": (225, 225, 225),\n", - " \"Pit\": (0,0,0),\n", - " \"Gold\": (253, 208, 23),\n", - " \"Glitter\": (253, 208, 23),\n", - " \"Wumpus\": (43, 27, 23),\n", - " \"Stench\": (128, 128, 128),\n", - " \"Explorer\": (0, 0, 255),\n", - " \"Wall\": (44, 53, 57)\n", - " }\n", - "\n", - "def program(percepts):\n", - " '''Returns an action based on it's percepts'''\n", - " print(percepts)\n", - " return input()\n", - "\n", - "w = WumpusEnvironment(program, 7, 7) \n", - "grid = BlockGrid(w.width, w.height, fill=(123, 234, 123))\n", - "\n", - "def draw_grid(world):\n", - " global grid\n", - " grid[:] = (123, 234, 123)\n", - " for x in range(0, len(world)):\n", - " for y in range(0, len(world[x])):\n", - " if len(world[x][y]):\n", - " grid[y, x] = color[world[x][y][-1].__class__.__name__]\n", - "\n", - "def step():\n", - " global grid, w\n", - " draw_grid(w.get_world())\n", - " grid.show()\n", - " w.step()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
    " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[], [None], [], [], [None]]\n", - "2\n" - ] - } - ], - "source": [ - "step()" - ] - }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ - "# PROGRAM #\n", + "# PROGRAM - BlindDog #\n", "Now that we have a Park Class, we need to implement a program module for our dog. A program controls how the dog acts upon it's environment. Our program will be very simple, and is shown in the table below.\n", "\n", " \n", @@ -226,7 +147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
    Action: eatdrinkmove upmove down
    \n" @@ -234,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -249,14 +170,14 @@ " def eat(self, thing):\n", " '''returns True upon success or False otherwise'''\n", " if isinstance(thing, Food):\n", - " print(\"Dog: Ate food at {}.\".format(self.location))\n", + " #print(\"Dog: Ate food at {}.\".format(self.location))\n", " return True\n", " return False\n", " \n", " def drink(self, thing):\n", " ''' returns True upon success or False otherwise'''\n", " if isinstance(thing, Water):\n", - " print(\"Dog: Drank water at {}.\".format(self.location))\n", + " #print(\"Dog: Drank water at {}.\".format(self.location))\n", " return True\n", " return False\n", " \n", @@ -267,27 +188,109 @@ " return 'eat'\n", " elif isinstance(p, Water):\n", " return 'drink'\n", - " return 'move down'\n", - " \n", - " " + " return 'move down'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets now run our simulation by creating a park with some food, water, and our dog." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 1\n", + "BlindDog decided to move down at location: 2\n", + "BlindDog decided to move down at location: 3\n", + "BlindDog decided to move down at location: 4\n", + "BlindDog ate Food at location: 5\n" + ] + } + ], "source": [ "park = Park()\n", "dog = BlindDog(program)\n", "dogfood = Food()\n", "water = Water()\n", - "park.add_thing(dog, 0)\n", + "park.add_thing(dog, 1)\n", "park.add_thing(dogfood, 5)\n", "park.add_thing(water, 7)\n", "\n", + "park.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the dog moved from location 1 to 4, over 4 steps, and ate food at location 5 in the 5th step.\n", + "\n", + "Lets continue this simulation for 5 more steps." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 5\n", + "BlindDog decided to move down at location: 6\n", + "BlindDog drank Water at location: 7\n" + ] + } + ], + "source": [ + "park.run(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perfect! Note how the simulation stopped after the dog drank the water - exhausting all the food and water ends our simulation, as we had defined before. Lets add some more water and see if our dog can reach it." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: 7\n", + "BlindDog decided to move down at location: 8\n", + "BlindDog decided to move down at location: 9\n", + "BlindDog decided to move down at location: 10\n", + "BlindDog decided to move down at location: 11\n", + "BlindDog decided to move down at location: 12\n", + "BlindDog decided to move down at location: 13\n", + "BlindDog decided to move down at location: 14\n", + "BlindDog drank Water at location: 15\n" + ] + } + ], + "source": [ + "park.add_thing(water, 15)\n", "park.run(10)" ] }, @@ -295,48 +298,966 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "That's how easy it is to implement an agent, its program, and environment. But that was a very simple case. What if our environment was 2-Dimentional instead of 1? And what if we had multiple agents?\n", + "This is how to implement an agent, its program, and environment. However, this was a very simple case. Lets try a 2-Dimentional environment now with multiple agents.\n", + "\n", + "\n", + "# 2D Environment #\n", + "To make our Park 2D, we will need to make it a subclass of XYEnvironment instead of Environment. Please note that our park is indexed in the 4th quadrant of the X-Y plane.\n", "\n", - "To make our Park 2D, we will need to make it a subclass of XYEnvironment instead of Environment. Also, let's add a person to play fetch with the dog." + "We will also eventually add a person to pet the dog." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "class Park(XYEnvironment):\n", + "class Park2D(XYEnvironment):\n", " def percept(self, agent):\n", " '''prints & return a list of things that are in our agent's location'''\n", " things = self.list_things_at(agent.location)\n", - " print(things)\n", " return things\n", " \n", " def execute_action(self, agent, action):\n", " '''changes the state of the environment based on what the agent does.'''\n", " if action == \"move down\":\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", " agent.movedown()\n", " elif action == \"eat\":\n", " items = self.list_things_at(agent.location, tclass=Food)\n", " if len(items) != 0:\n", - " if agent.eat(items[0]): #Have the dog pick eat the first item\n", + " if agent.eat(items[0]): #Have the dog eat the first item\n", + " print('{} ate {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", " self.delete_thing(items[0]) #Delete it from the Park after.\n", " elif action == \"drink\":\n", " items = self.list_things_at(agent.location, tclass=Water)\n", " if len(items) != 0:\n", " if agent.drink(items[0]): #Have the dog drink the first item\n", + " print('{} drank {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", " self.delete_thing(items[0]) #Delete it from the Park after.\n", " \n", " def is_done(self):\n", " '''By default, we're done when we can't find a live agent, \n", - " but to prevent killing our cute dog, we will or it with when there is no more food or water'''\n", + " but to prevent killing our cute dog, we will stop before itself - when there is no more food or water'''\n", " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", - " return dead_agents or no_edibles" + " return dead_agents or no_edibles\n", + "\n", + "class BlindDog(Agent):\n", + " location = [0,1]# change location to a 2d value\n", + " direction = Direction(\"down\")# variable to store the direction our dog is facing\n", + " \n", + " def movedown(self):\n", + " self.location[1] += 1\n", + " \n", + " def eat(self, thing):\n", + " '''returns True upon success or False otherwise'''\n", + " if isinstance(thing, Food):\n", + " return True\n", + " return False\n", + " \n", + " def drink(self, thing):\n", + " ''' returns True upon success or False otherwise'''\n", + " if isinstance(thing, Water):\n", + " return True\n", + " return False\n", + " \n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " for p in percepts:\n", + " if isinstance(p, Food):\n", + " return 'eat'\n", + " elif isinstance(p, Water):\n", + " return 'drink'\n", + " return 'move down'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets test this new park with our same dog, food and water" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlindDog decided to move down at location: [0, 1]\n", + "BlindDog decided to move down at location: [0, 2]\n", + "BlindDog decided to move down at location: [0, 3]\n", + "BlindDog decided to move down at location: [0, 4]\n", + "BlindDog ate Food at location: [0, 5]\n", + "BlindDog decided to move down at location: [0, 5]\n", + "BlindDog decided to move down at location: [0, 6]\n", + "BlindDog drank Water at location: [0, 7]\n", + "BlindDog decided to move down at location: [0, 7]\n", + "BlindDog decided to move down at location: [0, 8]\n", + "BlindDog decided to move down at location: [0, 9]\n", + "BlindDog decided to move down at location: [0, 10]\n", + "BlindDog decided to move down at location: [0, 11]\n", + "BlindDog decided to move down at location: [0, 12]\n", + "BlindDog decided to move down at location: [0, 13]\n", + "BlindDog decided to move down at location: [0, 14]\n", + "BlindDog drank Water at location: [0, 15]\n" + ] + } + ], + "source": [ + "park = Park2D(5,20) # park width is set to 5, and height to 20\n", + "dog = BlindDog(program)\n", + "dogfood = Food()\n", + "water = Water()\n", + "park.add_thing(dog, [0,1])\n", + "park.add_thing(dogfood, [0,5])\n", + "park.add_thing(water, [0,7])\n", + "morewater = Water()\n", + "park.add_thing(morewater, [0,15])\n", + "park.run(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works, but our blind dog doesn't make any use of the 2 dimensional space available to him. Let's make our dog more energetic so that he turns and moves forward, instead of always moving down. We'll also need to make appropriate changes to our environment to be able to handle this extra motion.\n", + "\n", + "# PROGRAM - EnergeticBlindDog #\n", + "\n", + "Lets make our dog turn or move forwards at random - except when he's at the edge of our park - in which case we make him change his direction explicitly by turning to avoid trying to leave the park. Our dog is blind, however, so he wouldn't know which way to turn - he'd just have to try arbitrarily.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Percept: Feel Food Feel WaterFeel Nothing
    Action: eatdrink\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Remember being at Edge : At EdgeNot at Edge
    Action : Turn Left / Turn Right
    ( 50% - 50% chance )    		Turn Left / Turn Right / Move Forward
    ( 25% - 25% - 50% chance )
    \n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from random import choice\n", + "\n", + "turn = False# global variable to remember to turn if our dog hits the boundary\n", + "class EnergeticBlindDog(Agent):\n", + " location = [0,1]\n", + " direction = Direction(\"down\")\n", + " \n", + " def moveforward(self, success=True):\n", + " '''moveforward possible only if success (ie valid destination location)'''\n", + " global turn\n", + " if not success:\n", + " turn = True # if edge has been reached, remember to turn\n", + " return\n", + " if self.direction.direction == Direction.R:\n", + " self.location[0] += 1\n", + " elif self.direction.direction == Direction.L:\n", + " self.location[0] -= 1\n", + " elif self.direction.direction == Direction.D:\n", + " self.location[1] += 1\n", + " elif self.direction.direction == Direction.U:\n", + " self.location[1] -= 1\n", + " \n", + " def turn(self, d):\n", + " self.direction = self.direction + d\n", + " \n", + " def eat(self, thing):\n", + " '''returns True upon success or False otherwise'''\n", + " if isinstance(thing, Food):\n", + " #print(\"Dog: Ate food at {}.\".format(self.location))\n", + " return True\n", + " return False\n", + " \n", + " def drink(self, thing):\n", + " ''' returns True upon success or False otherwise'''\n", + " if isinstance(thing, Water):\n", + " #print(\"Dog: Drank water at {}.\".format(self.location))\n", + " return True\n", + " return False\n", + " \n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " global turn\n", + " for p in percepts: # first eat or drink - you're a dog!\n", + " if isinstance(p, Food):\n", + " return 'eat'\n", + " elif isinstance(p, Water):\n", + " return 'drink'\n", + " if turn: # then recall if you were at an edge and had to turn\n", + " turn = False\n", + " choice = random.choice((1,2));\n", + " else:\n", + " choice = random.choice((1,2,3,4)) # 1-right, 2-left, others-forward\n", + " if choice == 1:\n", + " return 'turnright'\n", + " elif choice == 2:\n", + " return 'turnleft'\n", + " else:\n", + " return 'moveforward'\n", + " " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to modify our park accordingly, in order to be able to handle all the new actions our dog wishes to execute. Additionally, we'll need to prevent our dog from moving to locations beyond our park boundary - it just isn't safe for blind dogs to be outside the park by themselves." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Park2D(XYEnvironment):\n", + " def percept(self, agent):\n", + " '''prints & return a list of things that are in our agent's location'''\n", + " things = self.list_things_at(agent.location)\n", + " return things\n", + " \n", + " def execute_action(self, agent, action):\n", + " '''changes the state of the environment based on what the agent does.'''\n", + " if action == 'turnright':\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", + " agent.turn(Direction.R)\n", + " #print('now facing {}'.format(agent.direction.direction))\n", + " elif action == 'turnleft':\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", + " agent.turn(Direction.L)\n", + " #print('now facing {}'.format(agent.direction.direction))\n", + " elif action == 'moveforward':\n", + " loc = copy.deepcopy(agent.location) # find out the target location\n", + " if agent.direction.direction == Direction.R:\n", + " loc[0] += 1\n", + " elif agent.direction.direction == Direction.L:\n", + " loc[0] -= 1\n", + " elif agent.direction.direction == Direction.D:\n", + " loc[1] += 1\n", + " elif agent.direction.direction == Direction.U:\n", + " loc[1] -= 1\n", + " #print('{} at {} facing {}'.format(agent, loc, agent.direction.direction))\n", + " if self.is_inbounds(loc):# move only if the target is a valid location\n", + " print('{} decided to move {}wards at location: {}'.format(str(agent)[1:-1], agent.direction.direction, agent.location))\n", + " agent.moveforward()\n", + " else:\n", + " print('{} decided to move {}wards at location: {}, but couldnt'.format(str(agent)[1:-1], agent.direction.direction, agent.location))\n", + " agent.moveforward(False)\n", + " elif action == \"eat\":\n", + " items = self.list_things_at(agent.location, tclass=Food)\n", + " if len(items) != 0:\n", + " if agent.eat(items[0]):\n", + " print('{} ate {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", + " self.delete_thing(items[0])\n", + " elif action == \"drink\":\n", + " items = self.list_things_at(agent.location, tclass=Water)\n", + " if len(items) != 0:\n", + " if agent.drink(items[0]):\n", + " print('{} drank {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", + " self.delete_thing(items[0])\n", + " \n", + " def is_done(self):\n", + " '''By default, we're done when we can't find a live agent, \n", + " but to prevent killing our cute dog, we will stop before itself - when there is no more food or water'''\n", + " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", + " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", + " return dead_agents or no_edibles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog started at [0,0], facing down. Lets see if he found any food or water!\n", + "EnergeticBlindDog decided to move downwards at location: [0, 0]\n", + "EnergeticBlindDog decided to move downwards at location: [0, 1]\n", + "EnergeticBlindDog drank Water at location: [0, 2]\n", + "EnergeticBlindDog decided to turnright at location: [0, 2]\n", + "EnergeticBlindDog decided to move leftwards at location: [0, 2], but couldnt\n", + "EnergeticBlindDog decided to turnright at location: [0, 2]\n", + "EnergeticBlindDog decided to turnright at location: [0, 2]\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to move leftwards at location: [0, 2], but couldnt\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to turnright at location: [0, 2]\n", + "EnergeticBlindDog decided to move leftwards at location: [0, 2], but couldnt\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to move downwards at location: [0, 2], but couldnt\n", + "EnergeticBlindDog decided to turnright at location: [0, 2]\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to turnleft at location: [0, 2]\n", + "EnergeticBlindDog decided to move rightwards at location: [0, 2]\n", + "EnergeticBlindDog ate Food at location: [1, 2]\n" + ] + } + ], + "source": [ + "park = Park2D(3,3)\n", + "dog = EnergeticBlindDog(program)\n", + "dogfood = Food()\n", + "water = Water()\n", + "park.add_thing(dog, [0,0])\n", + "park.add_thing(dogfood, [1,2])\n", + "park.add_thing(water, [2,1])\n", + "morewater = Water()\n", + "park.add_thing(morewater, [0,2])\n", + "print('dog started at [0,0], facing down. Lets see if he found any food or water!')\n", + "park.run(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is good, but it still lacks graphics. What if we wanted to visualize our park as it changed? To do that, all we have to do is make our park a subclass of GraphicEnvironment instead of XYEnvironment. Lets see how this looks." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class GraphicPark(GraphicEnvironment):\n", + " def percept(self, agent):\n", + " '''prints & return a list of things that are in our agent's location'''\n", + " things = self.list_things_at(agent.location)\n", + " return things\n", + " \n", + " def execute_action(self, agent, action):\n", + " '''changes the state of the environment based on what the agent does.'''\n", + " if action == 'turnright':\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", + " agent.turn(Direction.R)\n", + " #print('now facing {}'.format(agent.direction.direction))\n", + " elif action == 'turnleft':\n", + " print('{} decided to {} at location: {}'.format(str(agent)[1:-1], action, agent.location))\n", + " agent.turn(Direction.L)\n", + " #print('now facing {}'.format(agent.direction.direction))\n", + " elif action == 'moveforward':\n", + " loc = copy.deepcopy(agent.location) # find out the target location\n", + " if agent.direction.direction == Direction.R:\n", + " loc[0] += 1\n", + " elif agent.direction.direction == Direction.L:\n", + " loc[0] -= 1\n", + " elif agent.direction.direction == Direction.D:\n", + " loc[1] += 1\n", + " elif agent.direction.direction == Direction.U:\n", + " loc[1] -= 1\n", + " #print('{} at {} facing {}'.format(agent, loc, agent.direction.direction))\n", + " if self.is_inbounds(loc):# move only if the target is a valid location\n", + " print('{} decided to move {}wards at location: {}'.format(str(agent)[1:-1], agent.direction.direction, agent.location))\n", + " agent.moveforward()\n", + " else:\n", + " print('{} decided to move {}wards at location: {}, but couldnt'.format(str(agent)[1:-1], agent.direction.direction, agent.location))\n", + " agent.moveforward(False)\n", + " elif action == \"eat\":\n", + " items = self.list_things_at(agent.location, tclass=Food)\n", + " if len(items) != 0:\n", + " if agent.eat(items[0]):\n", + " print('{} ate {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", + " self.delete_thing(items[0])\n", + " elif action == \"drink\":\n", + " items = self.list_things_at(agent.location, tclass=Water)\n", + " if len(items) != 0:\n", + " if agent.drink(items[0]):\n", + " print('{} drank {} at location: {}'\n", + " .format(str(agent)[1:-1], str(items[0])[1:-1], agent.location))\n", + " self.delete_thing(items[0])\n", + " \n", + " def is_done(self):\n", + " '''By default, we're done when we can't find a live agent, \n", + " but to prevent killing our cute dog, we will stop before itself - when there is no more food or water'''\n", + " no_edibles = not any(isinstance(thing, Food) or isinstance(thing, Water) for thing in self.things)\n", + " dead_agents = not any(agent.is_alive() for agent in self.agents)\n", + " return dead_agents or no_edibles\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That is the only change we make. The rest of our code stays the same. There is a slight difference in usage though. Every time we create a GraphicPark, we need to define the colors of all the things we plan to put into the park. The colors are defined in typical [RGB digital 8-bit format](https://en.wikipedia.org/wiki/RGB_color_model#Numeric_representations), common across the web." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog started at [0,0], facing down. Lets see if he found any food or water!\n" + ] + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "park = GraphicPark(5,5, color={'EnergeticBlindDog': (200,0,0), 'Water': (0, 200, 200), 'Food': (230, 115, 40)})\n", + "dog = EnergeticBlindDog(program)\n", + "dogfood = Food()\n", + "water = Water()\n", + "park.add_thing(dog, [0,0])\n", + "park.add_thing(dogfood, [1,2])\n", + "park.add_thing(water, [0,1])\n", + "morewater = Water()\n", + "morefood = Food()\n", + "park.add_thing(morewater, [2,4])\n", + "park.add_thing(morefood, [4,3])\n", + "print('dog started at [0,0], facing down. Lets see if he found any food or water!')\n", + "park.run(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Wumpus Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from ipythonblocks import BlockGrid\n", + "from agents import *\n", + "\n", + "color = {\"Breeze\": (225, 225, 225),\n", + " \"Pit\": (0,0,0),\n", + " \"Gold\": (253, 208, 23),\n", + " \"Glitter\": (253, 208, 23),\n", + " \"Wumpus\": (43, 27, 23),\n", + " \"Stench\": (128, 128, 128),\n", + " \"Explorer\": (0, 0, 255),\n", + " \"Wall\": (44, 53, 57)\n", + " }\n", + "\n", + "def program(percepts):\n", + " '''Returns an action based on it's percepts'''\n", + " print(percepts)\n", + " return input()\n", + "\n", + "w = WumpusEnvironment(program, 7, 7) \n", + "grid = BlockGrid(w.width, w.height, fill=(123, 234, 123))\n", + "\n", + "def draw_grid(world):\n", + " global grid\n", + " grid[:] = (123, 234, 123)\n", + " for x in range(0, len(world)):\n", + " for y in range(0, len(world[x])):\n", + " if len(world[x][y]):\n", + " grid[y, x] = color[world[x][y][-1].__class__.__name__]\n", + "\n", + "def step():\n", + " global grid, w\n", + " draw_grid(w.get_world())\n", + " grid.show()\n", + " w.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[], [], [], [], [, None]]\n", + "Forward\n" + ] + } + ], + "source": [ + "step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -355,7 +1276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, diff --git a/agents.py b/agents.py index 1191f9a0d..afd5e6408 100644 --- a/agents.py +++ b/agents.py @@ -512,6 +512,109 @@ class Obstacle(Thing): class Wall(Obstacle): pass +# ______________________________________________________________________________ + +try: + from ipythonblocks import BlockGrid + from IPython.display import HTML, display + from time import sleep +except: + pass + +class GraphicEnvironment(XYEnvironment): + def __init__(self, width=10, height=10, boundary=True, color={}, display=False): + """define all the usual XYEnvironment characteristics, + but initialise a BlockGrid for GUI too""" + super().__init__(width, height) + self.grid = BlockGrid(width, height, fill=(200,200,200)) + if display: + self.grid.show() + self.visible = True + else: + self.visible = False + self.bounded = boundary + self.colors = color + + #def list_things_at(self, location, tclass=Thing): # need to override because locations + # """Return all things exactly at a given location.""" + # return [thing for thing in self.things + # if thing.location == location and isinstance(thing, tclass)] + + def get_world(self): + """Returns all the items in the world in a format + understandable by the ipythonblocks BlockGrid""" + result = [] + x_start, y_start = (0, 0) + x_end, y_end = self.width, self.height + for x in range(x_start, x_end): + row = [] + for y in range(y_start, y_end): + row.append(self.list_things_at([x, y])) + result.append(row) + return result + + """def run(self, steps=1000, delay=1): + "" "Run the Environment for given number of time steps, + but update the GUI too." "" + for step in range(steps): + sleep(delay) + if self.visible: + self.reveal() + if self.is_done(): + if self.visible: + self.reveal() + return + self.step() + if self.visible: + self.reveal() + """ + def run(self, steps=1000, delay=1): + """Run the Environment for given number of time steps, + but update the GUI too.""" + for step in range(steps): + self.update(delay) + if self.is_done(): + break + self.step() + self.update(delay) + + def update(self, delay=1): + sleep(delay) + if self.visible: + self.conceal() + self.reveal() + else: + self.reveal() + + def reveal(self): + """display the BlockGrid for this world - the last thing to be added + at a location defines the location color""" + #print("Grid={}".format(self.grid)) + self.draw_world() + #if not self.visible == True: + # self.grid.show() + self.grid.show() + self.visible == True + + def draw_world(self): + self.grid[:] = (200, 200, 200) + world = self.get_world() + #print("world {}".format(world)) + for x in range(0, len(world)): + for y in range(0, len(world[x])): + if len(world[x][y]): + self.grid[y, x] = self.colors[world[x][y][-1].__class__.__name__] + #print('location: ({}, {}) got color: {}' + #.format(y, x, self.colors[world[x][y][-1].__class__.__name__])) + + def conceal(self): + """hide the BlockGrid for this world""" + self.visible = False + display(HTML('')) + + + + # ______________________________________________________________________________ From 8e0bfd34cb5a124d47c28ce6218b3f880ce77f36 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:16:54 +0200 Subject: [PATCH 428/513] Updated test_text.py (#349) * Rearranged Tests - test_ngram_models to the top - added test_viterbi-segmentation - removed test_unigram_text_model * "test_ngram_models" to "test_text_models" --- tests/test_text.py | 90 +++++++++++++++++++++++----------------------- 1 file changed, 45 insertions(+), 45 deletions(-) diff --git a/tests/test_text.py b/tests/test_text.py index 0cd3e675c..d58cd497a 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -6,13 +6,55 @@ from utils import isclose, DataFile -def test_unigram_text_model(): +def test_text_models(): flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) - P = UnigramTextModel(wordseq) + P1 = UnigramTextModel(wordseq) + P2 = NgramTextModel(2, wordseq) + P3 = NgramTextModel(3, wordseq) + + # The most frequent entries in each model + assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), + (1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'), + (478, 'that'), (399, 'is'), (348, 'you')] + + assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), + (152, ('in', 'the')), (86, ('of', 'a')), + (80, ('it', 'is')), + (71, ('by', 'the')), (68, ('for', 'the')), + (68, ('and', 'the')), (62, ('on', 'the')), + (60, ('to', 'be'))] + + assert P3.top(10) == [(30, ('a', 'straight', 'line')), + (19, ('of', 'three', 'dimensions')), + (16, ('the', 'sense', 'of')), + (13, ('by', 'the', 'sense')), + (13, ('as', 'well', 'as')), + (12, ('of', 'the', 'circles')), + (12, ('of', 'sight', 'recognition')), + (11, ('the', 'number', 'of')), + (11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))] - s, p = viterbi_segment('itiseasytoreadwordswithoutspaces', P) + assert isclose(P1['the'], 0.0611, rel_tol=0.001) + + assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01) + + assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) + assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) + assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001) + + assert P2.cond_prob.get(('went',)) is None + + assert P3.cond_prob['in', 'order'].dictionary == {'to': 6} + + +def test_viterbi_segmentation(): + flatland = DataFile("EN-text/flatland.txt").read() + wordseq = words(flatland) + P = UnigramTextModel(wordseq) + text = "itiseasytoreadwordswithoutspaces" + s, p = viterbi_segment(text,P) assert s == [ 'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] @@ -56,48 +98,6 @@ def test_counting_probability_distribution(): assert 1 / 7 <= min(ps) <= max(ps) <= 1 / 5 -def test_ngram_models(): - flatland = DataFile("EN-text/flatland.txt").read() - wordseq = words(flatland) - P1 = UnigramTextModel(wordseq) - P2 = NgramTextModel(2, wordseq) - P3 = NgramTextModel(3, wordseq) - - # The most frequent entries in each model - assert P1.top(10) == [(2081, 'the'), (1479, 'of'), (1021, 'and'), - (1008, 'to'), (850, 'a'), (722, 'i'), (640, 'in'), - (478, 'that'), (399, 'is'), (348, 'you')] - - assert P2.top(10) == [(368, ('of', 'the')), (152, ('to', 'the')), - (152, ('in', 'the')), (86, ('of', 'a')), - (80, ('it', 'is')), - (71, ('by', 'the')), (68, ('for', 'the')), - (68, ('and', 'the')), (62, ('on', 'the')), - (60, ('to', 'be'))] - - assert P3.top(10) == [(30, ('a', 'straight', 'line')), - (19, ('of', 'three', 'dimensions')), - (16, ('the', 'sense', 'of')), - (13, ('by', 'the', 'sense')), - (13, ('as', 'well', 'as')), - (12, ('of', 'the', 'circles')), - (12, ('of', 'sight', 'recognition')), - (11, ('the', 'number', 'of')), - (11, ('that', 'i', 'had')), (11, ('so', 'as', 'to'))] - - assert isclose(P1['the'], 0.0611, rel_tol=0.001) - - assert isclose(P2['of', 'the'], 0.0108, rel_tol=0.01) - - assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) - assert isclose(P3['', '', 'but'], 0.0, rel_tol=0.001) - assert isclose(P3['so', 'as', 'to'], 0.000323, rel_tol=0.001) - - assert P2.cond_prob.get(('went',)) is None - - assert P3.cond_prob['in', 'order'].dictionary == {'to': 6} - - def test_ir_system(): from collections import namedtuple Results = namedtuple('IRResults', ['score', 'url']) From c7a0d6d2f76a5f69b51b6585febf5e233d5f8f97 Mon Sep 17 00:00:00 2001 From: articuno12 Date: Sat, 18 Mar 2017 13:47:34 +0530 Subject: [PATCH 429/513] Adding missing docstring in utils.py (#342) --- utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/utils.py b/utils.py index 714512ae0..cfdc88d37 100644 --- a/utils.py +++ b/utils.py @@ -174,6 +174,7 @@ def scalar_vector_product(X, Y): def scalar_matrix_product(X, Y): + """Return matrix as a product of a scalar and a matrix""" return [scalar_vector_product(X, y) for y in Y] From ca027380ca26033c8d5574006bded63de298e9f3 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:19:37 +0200 Subject: [PATCH 430/513] "epoches" to "epochs" (#336) --- learning.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/learning.py b/learning.py index db25c42f3..427c15d8a 100644 --- a/learning.py +++ b/learning.py @@ -434,11 +434,11 @@ def predict(example): def NeuralNetLearner(dataset, hidden_layer_sizes=[3], - learning_rate=0.01, epoches=100): + learning_rate=0.01, epochs=100): """Layered feed-forward network. hidden_layer_sizes: List of number of hidden units per hidden layer learning_rate: Learning rate of gradient descent - epoches: Number of passes over the dataset + epochs: Number of passes over the dataset """ i_units = len(dataset.inputs) @@ -447,7 +447,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3], # construct a network raw_net = network(i_units, hidden_layer_sizes, o_units) learned_net = BackPropagationLearner(dataset, raw_net, - learning_rate, epoches) + learning_rate, epochs) def predict(example): @@ -510,7 +510,7 @@ def network(input_units, hidden_layer_sizes, output_units): return net -def BackPropagationLearner(dataset, net, learning_rate, epoches): +def BackPropagationLearner(dataset, net, learning_rate, epochs): """[Figure 18.23] The back-propagation algorithm for multilayer network""" # Initialise weights for layer in net: @@ -530,7 +530,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): o_nodes = net[-1] i_nodes = net[0] - for epoch in range(epoches): + for epoch in range(epochs): # Iterate over each example for e in examples: i_val = [e[i] for i in idx_i] @@ -583,13 +583,13 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches): return net -def PerceptronLearner(dataset, learning_rate=0.01, epoches=100): +def PerceptronLearner(dataset, learning_rate=0.01, epochs=100): """Logistic Regression, NO hidden layer""" i_units = len(dataset.inputs) o_units = 1 # As of now, dataset.target gives only one index. hidden_layer_sizes = [] raw_net = network(i_units, hidden_layer_sizes, o_units) - learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epoches) + learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs) def predict(example): # Input nodes From 3f5f8567d8cd8fecd6d4f840f3ec6aab2293974c Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:20:24 +0200 Subject: [PATCH 431/513] Added Hamming Distance (#340) In learning.py, I added the Hamming Distance metric. --- learning.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/learning.py b/learning.py index 427c15d8a..7271c6582 100644 --- a/learning.py +++ b/learning.py @@ -35,6 +35,9 @@ def manhattan_distance(predictions, targets): def mean_boolean_error(predictions, targets): return mean(int(p != t) for p, t in zip(predictions, targets)) +def hamming_distance(predictions, targets): + return sum(p != t for p, t in zip(predictions, targets)) + # ______________________________________________________________________________ From 312991735afcbaf8f6073e999301d7152804a4d2 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 18 Mar 2017 05:20:55 -0300 Subject: [PATCH 432/513] Update load_MNIST on learning.ipynb (#339) Make load_MNIST function easier to read --- learning.ipynb | 32 +++++++++++++------------------- 1 file changed, 13 insertions(+), 19 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index f049810f2..b81fa3ca8 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -374,27 +374,21 @@ "source": [ "def load_MNIST(path=\"aima-data/MNIST\"):\n", " \"helper function to load MNIST data\"\n", - " train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n", - " train_lbl_file = open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\")\n", - " test_img_file = open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\")\n", - " test_lbl_file = open(os.path.join(path, 't10k-labels-idx1-ubyte'), \"rb\")\n", + " with open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\") as train_img_file:\n", + " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", + " tr_img = array.array(\"B\", train_img_file.read())\n", " \n", - " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", - " tr_img = array.array(\"B\", train_img_file.read())\n", - " train_img_file.close() \n", - " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", - " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", - " train_lbl_file.close()\n", + " with open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\") as train_lbl_file:\n", + " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", + " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", " \n", - " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", - " te_img = array.array(\"B\", test_img_file.read())\n", - " test_img_file.close()\n", - " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", - " te_lbl = array.array(\"b\", test_lbl_file.read())\n", - " test_lbl_file.close()\n", - "\n", - "# print(len(tr_img), len(tr_lbl), tr_size)\n", - "# print(len(te_img), len(te_lbl), te_size)\n", + " with open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\") as test_img_file:\n", + " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", + " te_img = array.array(\"B\", test_img_file.read())\n", + " \n", + " with open(os.path.join(path, \"t10k-labels-idx1-ubyte\"), \"rb\") as test_lbl_file:\n", + " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", + " te_lbl = array.array(\"b\", test_lbl_file.read())\n", " \n", " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.int16)\n", " train_lbl = np.zeros((tr_size,), dtype=np.int8)\n", From 1b82e4ddbdca03a2bb4b9cd1db096c63c0a2d3fd Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 18 Mar 2017 10:21:40 +0200 Subject: [PATCH 433/513] Added DataSet Functions (#333) * Update learning.py * Added remove_examples function --- learning.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/learning.py b/learning.py index 7271c6582..8f758a1a4 100644 --- a/learning.py +++ b/learning.py @@ -154,6 +154,18 @@ def sanitize(self, example): return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)] + def classes_to_numbers(self,classes=None): + """Converts class names to numbers.""" + if not classes: + # If classes were not given, extract them from values + classes = sorted(self.values[self.target]) + for item in self.examples: + item[self.target] = classes.index(item[self.target]) + + def remove_examples(self,value=""): + """Remove examples that contain given value.""" + self.examples = [x for x in self.examples if value not in x] + def __repr__(self): return ''.format( self.name, len(self.examples), len(self.attrs)) From bf1592492f0c97ad1e6812b2ef8c1e18270cdb4c Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 18 Mar 2017 05:22:05 -0300 Subject: [PATCH 434/513] Add more tests to csp.py (#328) Add test for the following functions from csp.py * revise * AC3 * first_unassigned_variable * num_legal_values * mrv --- tests/test_csp.py | 88 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) diff --git a/tests/test_csp.py b/tests/test_csp.py index 346d9a3ca..5bed85c05 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -166,6 +166,94 @@ def test_csp_conflicted_vars(): assert (conflicted_vars == ['B', 'C'] or conflicted_vars == ['C', 'B']) +def test_revise(): + neighbors = parse_neighbors('A: B; B: ') + domains = {'A': [0], 'B': [4]} + constraints = lambda X, x, Y, y: x % 2 == 0 and (x+y) == 4 + + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + csp.support_pruning() + Xi = 'A' + Xj = 'B' + removals = [] + + assert revise(csp, Xi, Xj, removals) is False + assert len(removals) == 0 + + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4]} + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + csp.support_pruning() + + assert revise(csp, Xi, Xj, removals) is True + assert removals == [('A', 1), ('A', 3)] + + +def test_AC3(): + neighbors = parse_neighbors('A: B; B: ') + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4]} + constraints = lambda X, x, Y, y: x % 2 == 0 and (x+y) == 4 and y % 2 != 0 + removals = [] + + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + + assert AC3(csp, removals=removals) is False + + constraints = lambda X, x, Y, y: (x % 2) == 0 and (x+y) == 4 + removals = [] + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + + assert AC3(csp, removals=removals) is True + assert (removals == [('A', 1), ('A', 3), ('B', 1), ('B', 3)] or + removals == [('B', 1), ('B', 3), ('A', 1), ('A', 3)]) + + +def test_first_unassigned_variable(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + assignment = {'A': '1', 'B': '2'} + assert first_unassigned_variable(assignment, map_coloring_test) == 'C' + + assignment = {'B': '1'} + assert (first_unassigned_variable(assignment, map_coloring_test) == 'A' or + first_unassigned_variable(assignment, map_coloring_test) == 'C') + + +def test_num_legal_values(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + map_coloring_test.support_pruning() + var = 'A' + assignment = {} + + assert num_legal_values(map_coloring_test, var, assignment) == 3 + + map_coloring_test = MapColoringCSP(list('RGB'), 'A: B C; B: C; C: ') + assignment = {'A': 'R', 'B': 'G'} + var = 'C' + + assert num_legal_values(map_coloring_test, var, assignment) == 1 + + +def test_mrv(): + neighbors = parse_neighbors('A: B; B: C; C: ') + domains = {'A': [0, 1, 2, 3, 4], 'B': [4], 'C': [0, 1, 2, 3, 4]} + constraints = lambda X, x, Y, y: x % 2 == 0 and (x+y) == 4 + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + assignment = {'A': 0} + + assert mrv(assignment, csp) == 'B' + + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4], 'C': [0, 1, 2, 3, 4]} + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + + assert (mrv(assignment, csp) == 'B' or + mrv(assignment, csp) == 'C') + + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4, 5, 6], 'C': [0, 1, 2, 3, 4]} + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + csp.support_pruning() + + assert mrv(assignment, csp) == 'C' + + def test_backtracking_search(): assert backtracking_search(australia) assert backtracking_search(australia, select_unassigned_variable=mrv) From 5316898aa0d08d3e428f4ff893a8010124147913 Mon Sep 17 00:00:00 2001 From: Rishabh Agarwal Date: Sat, 18 Mar 2017 13:53:48 +0530 Subject: [PATCH 435/513] Fixed a bug in Decision Tree Learner (#334) --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 8f758a1a4..4917a2cf0 100644 --- a/learning.py +++ b/learning.py @@ -376,7 +376,7 @@ def plurality_value(examples): def count(attr, val, examples): """Count the number of examples that have attr = val.""" - return len(e[attr] == val for e in examples) #count(e[attr] == val for e in examples) + return sum(e[attr] == val for e in examples) #count(e[attr] == val for e in examples) def all_same_class(examples): """Are all these examples in the same target class?""" From 706838b4b3868f8d7e2f9ccd44819cc76db07993 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 21 Mar 2017 12:34:39 +0530 Subject: [PATCH 436/513] Removed flake8 test for pytest directory (#386) --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index aa875cc38..e6563f0fe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,7 +14,6 @@ install: - pip install -r requirements.txt script: - - flake8 tests/ - py.test - python -m doctest -v *.py From c8115cead6fe9f6a74f1c30347e937939fd7ba6e Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:17:01 +0530 Subject: [PATCH 437/513] edits in search.py (#384) * added documentation, standardised docstring quotes * made search.py pep8 compatible using flake8 * fixed bug in OnlineDFSAgent --- search.py | 136 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 70 insertions(+), 66 deletions(-) diff --git a/search.py b/search.py index c8885a9ed..545a24e5c 100644 --- a/search.py +++ b/search.py @@ -86,7 +86,7 @@ class Node: subclass this class.""" def __init__(self, state, parent=None, action=None, path_cost=0): - "Create a search tree Node, derived from a parent by an action." + """Create a search tree Node, derived from a parent by an action.""" self.state = state self.parent = parent self.action = action @@ -102,23 +102,23 @@ def __lt__(self, node): return self.state < node.state def expand(self, problem): - "List the nodes reachable in one step from this node." + """List the nodes reachable in one step from this node.""" return [self.child_node(problem, action) for action in problem.actions(self.state)] def child_node(self, problem, action): - "[Figure 3.10]" + """[Figure 3.10]""" next = problem.result(self.state, action) return Node(next, self, action, problem.path_cost(self.path_cost, self.state, action, next)) def solution(self): - "Return the sequence of actions to go from the root to this node." + """Return the sequence of actions to go from the root to this node.""" return [node.action for node in self.path()[1:]] def path(self): - "Return a list of nodes forming the path from the root to this node." + """Return a list of nodes forming the path from the root to this node.""" node, path_back = self, [] while node: path_back.append(node) @@ -144,10 +144,15 @@ class SimpleProblemSolvingAgentProgram: """Abstract framework for a problem-solving agent. [Figure 3.1]""" def __init__(self, initial_state=None): + """State is an sbstract representation of the state + of the world, and seq is the list of actions required + to get to a particular state from the initial state(root).""" self.state = initial_state self.seq = [] def __call__(self, percept): + """[Figure 3.1] Formulate a goal and problem, then + search for a sequence of actions to solve it.""" self.state = self.update_state(self.state, percept) if not self.seq: goal = self.formulate_goal(self.state) @@ -204,22 +209,22 @@ def graph_search(problem, frontier): def breadth_first_tree_search(problem): - "Search the shallowest nodes in the search tree first." + """Search the shallowest nodes in the search tree first.""" return tree_search(problem, FIFOQueue()) def depth_first_tree_search(problem): - "Search the deepest nodes in the search tree first." + """Search the deepest nodes in the search tree first.""" return tree_search(problem, Stack()) def depth_first_graph_search(problem): - "Search the deepest nodes in the search tree first." + """Search the deepest nodes in the search tree first.""" return graph_search(problem, Stack()) def breadth_first_search(problem): - "[Figure 3.11]" + """[Figure 3.11]""" node = Node(problem.initial) if problem.goal_test(node.state): return node @@ -269,12 +274,12 @@ def best_first_graph_search(problem, f): def uniform_cost_search(problem): - "[Figure 3.14]" + """[Figure 3.14]""" return best_first_graph_search(problem, lambda node: node.path_cost) def depth_limited_search(problem, limit=50): - "[Figure 3.17]" + """[Figure 3.17]""" def recursive_dls(node, problem, limit): if problem.goal_test(node.state): return node @@ -295,7 +300,7 @@ def recursive_dls(node, problem, limit): def iterative_deepening_search(problem): - "[Figure 3.18]" + """[Figure 3.18]""" for depth in range(sys.maxsize): result = depth_limited_search(problem, depth) if result != 'cutoff': @@ -304,6 +309,7 @@ def iterative_deepening_search(problem): # ______________________________________________________________________________ # Informed (Heuristic) Search + greedy_best_first_graph_search = best_first_graph_search # Greedy best-first search is accomplished by specifying f(n) = h(n). @@ -320,7 +326,7 @@ def astar_search(problem, h=None): def recursive_best_first_search(problem, h=None): - "[Figure 3.26]" + """[Figure 3.26]""" h = memoize(h or problem.h, 'h') def RBFS(problem, node, flimit): @@ -368,12 +374,13 @@ def hill_climbing(problem): def exp_schedule(k=20, lam=0.005, limit=100): - "One possible schedule function for simulated annealing" + """One possible schedule function for simulated annealing""" return lambda t: (k * math.exp(-lam * t) if t < limit else 0) def simulated_annealing(problem, schedule=exp_schedule()): - "[Figure 4.5]" + """[Figure 4.5] CAUTION: This differs from the pseudocode as it + returns a state instead of a Node.""" current = Node(problem.initial) for t in range(sys.maxsize): T = schedule(t) @@ -389,7 +396,7 @@ def simulated_annealing(problem, schedule=exp_schedule()): def and_or_graph_search(problem): - """Used when the environment is nondeterministic and completely observable. + """[Figure 4.11]Used when the environment is nondeterministic and completely observable. Contains OR nodes where the agent is free to choose any action. After every action there is an AND node which contains all possible states the agent may reach due to stochastic nature of environment. @@ -397,10 +404,10 @@ def and_or_graph_search(problem): may end up in any of them). Returns a conditional plan to reach goal state, or failure if the former is not possible.""" - "[Figure 4.11]" # functions used by and_or_search def or_search(state, problem, path): + """returns a plan as a list of actions""" if problem.goal_test(state): return [] if state in path: @@ -412,7 +419,7 @@ def or_search(state, problem, path): return [action, plan] def and_search(states, problem, path): - "Returns plan in form of dictionary where we take action plan[s] if we reach state s." # noqa + """Returns plan in form of dictionary where we take action plan[s] if we reach state s.""" # noqa plan = {} for s in states: plan[s] = or_search(s, problem, path) @@ -426,10 +433,10 @@ def and_search(states, problem, path): class OnlineDFSAgent: - """The abstract class for an OnlineDFSAgent. Override update_state - method to convert percept to state. While initializing the subclass - a problem needs to be provided which is an instance of a subclass - of the Problem class. [Figure 4.21] """ + """[Figure 4.21] The abstract class for an OnlineDFSAgent. Override + update_state method to convert percept to state. While initializing + the subclass a problem needs to be provided which is an instance of + a subclass of the Problem class.""" def __init__(self, problem): self.problem = problem @@ -449,7 +456,7 @@ def __call__(self, percept): if self.s is not None: if s1 != self.result[(self.s, self.a)]: self.result[(self.s, self.a)] = s1 - unbacktracked[s1].insert(0, self.s) + self.unbacktracked[s1].insert(0, self.s) if len(self.untried[s1]) == 0: if len(self.unbacktracked[s1]) == 0: self.a = None @@ -466,8 +473,8 @@ def __call__(self, percept): return self.a def update_state(self, percept): - '''To be overridden in most cases. The default case - assumes the percept to be of type state.''' + """To be overridden in most cases. The default case + assumes the percept to be of type state.""" return percept # ______________________________________________________________________________ @@ -477,8 +484,8 @@ class OnlineSearchProblem(Problem): """ A problem which is solved by an agent executing actions, rather than by just computation. - Carried in a deterministic and a fully observable environment. - """ + Carried in a deterministic and a fully observable environment.""" + def __init__(self, initial, goal, graph): self.initial = initial self.goal = goal @@ -491,15 +498,11 @@ def output(self, state, action): return self.graph.dict[state][action] def h(self, state): - """ - Returns least possible cost to reach a goal for the given state. - """ + """Returns least possible cost to reach a goal for the given state.""" return self.graph.least_costs[state] def c(self, s, a, s1): - """ - Returns a cost estimate for an agent to move from state 's' to state 's1'. - """ + """Returns a cost estimate for an agent to move from state 's' to state 's1'.""" return 1 def update_state(self, percept): @@ -538,8 +541,8 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept # self.result[(self.s, self.a)] = s1 # no need as we are using problem.output # minimum cost for action b in problem.actions(s) - self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) - for b in self.problem.actions(self.s)) + self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), + self.H) for b in self.problem.actions(self.s)) # costs for action b in problem.actions(s1) costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H) @@ -551,10 +554,8 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept return self.a def LRTA_cost(self, s, a, s1, H): - """ - Returns cost to move from state 's' to state 's1' plus - estimated cost to get to goal from s1. - """ + """Returns cost to move from state 's' to state 's1' plus + estimated cost to get to goal from s1.""" print(s, a, s1) if s1 is None: return self.problem.h(s) @@ -571,8 +572,7 @@ def LRTA_cost(self, s, a, s1, H): def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): - """ - Call genetic_algorithm on the appropriate parts of a problem. + """Call genetic_algorithm on the appropriate parts of a problem. This requires the problem to have states that can mate and mutate, plus a value method that scores states.""" s = problem.initial_state @@ -582,12 +582,12 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): - "[Figure 4.8]" + """[Figure 4.8]""" for i in range(ngen): new_population = [] for i in range(len(population)): fitnesses = map(fitness_fn, population) - p1, p2 = weighted_sample_with_replacement(2,population, fitnesses) + p1, p2 = weighted_sample_with_replacement(2, population, fitnesses) child = p1.mate(p2) if random.uniform(0, 1) < pmut: child.mutate() @@ -598,18 +598,18 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): class GAState: - "Abstract class for individuals in a genetic search." + """Abstract class for individuals in a genetic search.""" def __init__(self, genes): self.genes = genes def mate(self, other): - "Return a new individual crossing self and other." + """Return a new individual crossing self and other.""" c = random.randrange(len(self.genes)) return self.__class__(self.genes[:c] + other.genes[c:]) def mutate(self): - "Change a few of my genes." + """Change a few of my genes.""" raise NotImplementedError # _____________________________________________________________________________ @@ -641,10 +641,10 @@ def __init__(self, dict=None, directed=True): self.make_undirected() def make_undirected(self): - "Make a digraph into an undirected graph by adding symmetric edges." + """Make a digraph into an undirected graph by adding symmetric edges.""" for a in list(self.dict.keys()): - for (b, distance) in self.dict[a].items(): - self.connect1(b, a, distance) + for (b, dist) in self.dict[a].items(): + self.connect1(b, a, dist) def connect(self, A, B, distance=1): """Add a link from A and B of given distance, and also add the inverse @@ -654,7 +654,7 @@ def connect(self, A, B, distance=1): self.connect1(B, A, distance) def connect1(self, A, B, distance): - "Add a link from A to B of given distance, in one direction only." + """Add a link from A to B of given distance, in one direction only.""" self.dict.setdefault(A, {})[B] = distance def get(self, a, b=None): @@ -668,12 +668,12 @@ def get(self, a, b=None): return links.get(b) def nodes(self): - "Return a list of nodes in the graph." + """Return a list of nodes in the graph.""" return list(self.dict.keys()) def UndirectedGraph(dict=None): - "Build a Graph where every edge (including future ones) goes both ways." + """Build a Graph where every edge (including future ones) goes both ways.""" return Graph(dict=dict, directed=False) @@ -705,6 +705,7 @@ def distance_to_node(n): g.connect(node, neighbor, int(d)) return g + """ [Figure 3.2] Simplified road map of Romania """ @@ -734,7 +735,8 @@ def distance_to_node(n): """ [Figure 4.9] Eight possible states of the vacumm world Each state is represented as - * "State of the left room" "State of the right room" "Room in which the agent is present" + * "State of the left room" "State of the right room" "Room in which the agent + is present" 1 - DDL Dirty Dirty Left 2 - DDR Dirty Dirty Right 3 - DCL Dirty Clean Left @@ -745,14 +747,14 @@ def distance_to_node(n): 8 - CCR Clean Clean Right """ vacumm_world = Graph(dict( - State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']), - State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']), - State_3 = dict(Suck = ['State_7'], Right = ['State_4']), - State_4 = dict(Suck = ['State_4', 'State_2'], Left = ['State_3']), - State_5 = dict(Suck = ['State_5', 'State_1'], Right = ['State_6']), - State_6 = dict(Suck = ['State_8'], Left = ['State_5']), - State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']), - State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7']) + State_1=dict(Suck=['State_7', 'State_5'], Right=['State_2']), + State_2=dict(Suck=['State_8', 'State_4'], Left=['State_2']), + State_3=dict(Suck=['State_7'], Right=['State_4']), + State_4=dict(Suck=['State_4', 'State_2'], Left=['State_3']), + State_5=dict(Suck=['State_5', 'State_1'], Right=['State_6']), + State_6=dict(Suck=['State_8'], Left=['State_5']), + State_7=dict(Suck=['State_7', 'State_3'], Right=['State_8']), + State_8=dict(Suck=['State_8', 'State_6'], Left=['State_7']) )) """ [Figure 4.23] @@ -888,6 +890,7 @@ def goal_test(self, state): # Inverse Boggle: Search for a high-scoring Boggle board. A good domain for # iterative-repair and related search techniques, as suggested by Justin Boyan. + ALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' cubes16 = ['FORIXB', 'MOQABJ', 'GURILW', 'SETUPL', @@ -906,6 +909,7 @@ def random_boggle(n=4): # The best 5x5 board found by Boyan, with our word list this board scores # 2274 words, for a score of 9837 + boyan_best = list('RSTCSDEIAEGNLRPEATESMSSID') @@ -1019,7 +1023,7 @@ def __init__(self, board=None): self.set_board(board) def set_board(self, board=None): - "Set the board, and find all the words in it." + """Set the board, and find all the words in it.""" if board is None: board = random_boggle() self.board = board @@ -1050,17 +1054,17 @@ def find(self, lo, hi, i, visited, prefix): visited.pop() def words(self): - "The words found." + """The words found.""" return list(self.found.keys()) scores = [0, 0, 0, 0, 1, 2, 3, 5] + [11] * 100 def score(self): - "The total score for the words found, according to the rules." + """The total score for the words found, according to the rules.""" return sum([self.scores[len(w)] for w in self.words()]) def __len__(self): - "The number of words found." + """The number of words found.""" return len(self.found) # _____________________________________________________________________________ @@ -1134,7 +1138,7 @@ def __getattr__(self, attr): def __repr__(self): return '<{:4d}/{:4d}/{:4d}/{}>'.format(self.succs, self.goal_tests, - self.states, str(self.found)[:4]) + self.states, str(self.found)[:4]) def compare_searchers(problems, header, From 6a1b84be56061a1d8ebeae5d5a8d86a9359d7801 Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Wed, 22 Mar 2017 12:18:38 +0530 Subject: [PATCH 438/513] Upadte search.py (#389) minor error fixes. --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index 545a24e5c..c9b6280b4 100644 --- a/search.py +++ b/search.py @@ -829,7 +829,7 @@ class GraphProblemStochastic(GraphProblem): def result(self, state, action): return self.graph.get(state, action) - def path_cost(): + def path_cost(self): raise NotImplementedError From 9f1b1ee7da67c581df659ebd1b06974d5075b8c1 Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Wed, 22 Mar 2017 12:19:06 +0530 Subject: [PATCH 439/513] Update learning.py (#388) created a parameter size in the function leave_one_out(learner, dataset, size=None): --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 4917a2cf0..8308fe607 100644 --- a/learning.py +++ b/learning.py @@ -850,7 +850,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): size += 1 -def leave_one_out(learner, dataset): +def leave_one_out(learner, dataset, size=None): """Leave one out cross-validation over the dataset.""" return cross_validation(learner, size, dataset, k=len(dataset.examples)) From 4548aaef4398d6fee318d3240aa7858fa9b2e94b Mon Sep 17 00:00:00 2001 From: articuno12 Date: Wed, 22 Mar 2017 12:22:21 +0530 Subject: [PATCH 440/513] Updated docstring for ModelBasedReflexAgentProgram in agent.py (#391) --- agents.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/agents.py b/agents.py index afd5e6408..047eb3fd6 100644 --- a/agents.py +++ b/agents.py @@ -144,7 +144,7 @@ def program(percept): def ModelBasedReflexAgentProgram(rules, update_state, model): - """This agent takes action based on the percept and state. [Figure 2.8]""" + """This agent takes action based on the percept and state. [Figure 2.12]""" def program(percept): program.state = update_state(program.state, program.action, percept, model) rule = rule_match(program.state, rules) @@ -443,7 +443,7 @@ def move_to(self, thing, destination): # obs.thing_added(thing) def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): - """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be + """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class.""" if (self.is_inbounds(location)): if (exclude_duplicate_class_items and @@ -523,7 +523,7 @@ class Wall(Obstacle): class GraphicEnvironment(XYEnvironment): def __init__(self, width=10, height=10, boundary=True, color={}, display=False): - """define all the usual XYEnvironment characteristics, + """define all the usual XYEnvironment characteristics, but initialise a BlockGrid for GUI too""" super().__init__(width, height) self.grid = BlockGrid(width, height, fill=(200,200,200)) @@ -534,14 +534,14 @@ def __init__(self, width=10, height=10, boundary=True, color={}, display=False): self.visible = False self.bounded = boundary self.colors = color - + #def list_things_at(self, location, tclass=Thing): # need to override because locations # """Return all things exactly at a given location.""" # return [thing for thing in self.things # if thing.location == location and isinstance(thing, tclass)] - + def get_world(self): - """Returns all the items in the world in a format + """Returns all the items in the world in a format understandable by the ipythonblocks BlockGrid""" result = [] x_start, y_start = (0, 0) @@ -552,9 +552,9 @@ def get_world(self): row.append(self.list_things_at([x, y])) result.append(row) return result - + """def run(self, steps=1000, delay=1): - "" "Run the Environment for given number of time steps, + "" "Run the Environment for given number of time steps, but update the GUI too." "" for step in range(steps): sleep(delay) @@ -569,7 +569,7 @@ def get_world(self): self.reveal() """ def run(self, steps=1000, delay=1): - """Run the Environment for given number of time steps, + """Run the Environment for given number of time steps, but update the GUI too.""" for step in range(steps): self.update(delay) @@ -577,7 +577,7 @@ def run(self, steps=1000, delay=1): break self.step() self.update(delay) - + def update(self, delay=1): sleep(delay) if self.visible: @@ -585,9 +585,9 @@ def update(self, delay=1): self.reveal() else: self.reveal() - + def reveal(self): - """display the BlockGrid for this world - the last thing to be added + """display the BlockGrid for this world - the last thing to be added at a location defines the location color""" #print("Grid={}".format(self.grid)) self.draw_world() @@ -595,7 +595,7 @@ def reveal(self): # self.grid.show() self.grid.show() self.visible == True - + def draw_world(self): self.grid[:] = (200, 200, 200) world = self.get_world() @@ -606,14 +606,14 @@ def draw_world(self): self.grid[y, x] = self.colors[world[x][y][-1].__class__.__name__] #print('location: ({}, {}) got color: {}' #.format(y, x, self.colors[world[x][y][-1].__class__.__name__])) - + def conceal(self): """hide the BlockGrid for this world""" self.visible = False display(HTML('')) - - - + + + From 4bac57176bc6f3bd9e3b1904d382b1a18fb241f3 Mon Sep 17 00:00:00 2001 From: articuno12 Date: Wed, 22 Mar 2017 12:22:52 +0530 Subject: [PATCH 441/513] Added testcase for ReflexVacuumAgent and ModelBasedVacuumAgent (#394) * Added testcase for agents.py * spacing around commas was wrong --- tests/test_agents.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/tests/test_agents.py b/tests/test_agents.py index 89ee3fcf3..77421c2c7 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -1,4 +1,5 @@ from agents import Direction +from agents import ReflexVacuumAgent, ModelBasedVacuumAgent, TrivialVacuumEnvironment def test_move_forward(): d = Direction("up") @@ -38,3 +39,26 @@ def test_add(): assert l1.direction == Direction.U assert l2.direction == Direction.D #fixed +def test_ReflexVacuumAgent() : + # create an object of the ReflexVacuumAgent + agent = ReflexVacuumAgent() + # create an object of TrivialVacuumEnvironment + environment = TrivialVacuumEnvironment() + # add agent to the environment + environment.add_thing(agent) + # run the environment + environment.run() + # check final status of the environment + assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} + +def test_ModelBasedVacuumAgent() : + # create an object of the ModelBasedVacuumAgent + agent = ModelBasedVacuumAgent() + # create an object of TrivialVacuumEnvironment + environment = TrivialVacuumEnvironment() + # add agent to the environment + environment.add_thing(agent) + # run the environment + environment.run() + # check final status of the environment + assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} From c38675a611be633d410cbf2bbdebb8e6bf0b8541 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 22 Mar 2017 08:54:32 +0200 Subject: [PATCH 442/513] Add Perceptron to Notebook (#387) * Add Perceptron Section * Add Perceptron Image --- images/perceptron.png | Bin 0 -> 21245 bytes learning.ipynb | 365 +++++++++++++++++++++++++++++++++--------- 2 files changed, 293 insertions(+), 72 deletions(-) create mode 100644 images/perceptron.png diff --git a/images/perceptron.png b/images/perceptron.png new file mode 100644 index 0000000000000000000000000000000000000000..a83cc048d3d1c81be7c2d91b0e02308aef0bfa28 GIT binary patch literal 21245 zcmeFZ`9IWc*grhBgo?6-LXj+43XyHdK8$T_*(v*$E!jm~iO^!g2w7&B5mWYElcZ!{ z8at6?2-(IuJm*~3{k@;>U-10&{NOdO&vGuG^E}RDc^~h?GZQ1-)6Bfg5D4V7zMhsD z1VXzBfzXVcJOS>oj8^i3A9{BUxCR9BF_q=enE_lg-PgMl1c97sq5jb*G9ZM(O`c$F z>tKYxTX3jzpey9Qv!`FMl%H#`pq!M9)OA@oI@@pv#4KE2OT+x3!^*_zx8_4(?dypm zE^&#HIvmemTrNv=Gc`BYD;{ApF^{vxy2O!qudCY$>6ph|);aePF<4A*U%oeHa8@shmReP1WARuySpvSb^mDE*`Y*sGtQ5Xgf)fEX9neN zzHQk6bA)J}BdCBMHdA`=kq~vQvz>I{_pG)IEA=7A|Nrp+<3-?Xa60NznA)-|vm^PA zRdW)N0FdbOR@NmXJG4zuA5<^sT@b1v$Bg@r~Wwh!CnU>lgSCUio>Uf<<#@a+f%?b^;H6zVMID{)q z+>6^o7^@EbH!L^FDSD*bbuv42l63llVlVxsY6N*}wQ0ve)~x5Z6=?RwzA%2zyR7aj z{W@t>tmv%^o_ZXv{GSUj|HIvNF#my~)c<$> z7FPDIaeK{C2c!Sf!P(A+@Xfk1NP{;~>Hta|q2A?er!h+IJN+i*V5dek6MKRZeX!yl z4W25Z|SiBQKNd-VMOd3N-b&)-KjBUi>bE_AuH26en#Z)F$o zWRKc2o5jbI^R8pnzLQ0{(O%N7MNUWMKcp(s!J}A@M7T^YE5qj9ug^dJcLU}l6Z8E_ z`mh0btS)!uW=6~IOw!5~a3zR3NnM`Ugv%L>;PSHgb;6c}Y5T@Xh|Co8cX%D39K7g6yAOtq zU>7lz+1myU)zwbUOK9zFZEtT+Iz+6v#_bzrD}`0Kb#15}?cR+z4PFscj@x6lt#C`W zva(tznj9P)%m{o#ZEAaRzU0eHTM}GwEiRm4mwAi2K8E0Cf|-w7_ghahKaO4r23@RT zfL-v?Hqmj%uX)dHEk$|tgQ}$J{$hdKK^y~%kFkOHI(Fs#H2C;e&z+i?J#YNkU7zjx zZOyQay2e+dUBvOYp@Iz#hgXNz$aX8X`tT{eXbGAJf+d3!|?ixGlU!;ZPV)IT zI`0ooMy#ohf%SP|2!==c^@&-0em0tqfFb*4iLB?s7P3P-T^Y6lf4|g4{pfg|-mum@ zX2-Ac@I&1x(B>X!Qv)W7(*j&{A66s&L}q)$56PN`RCPrj zzRL==Uwmz>wmw?r?Or_vwgMR-~J@UQN83EVFO;a|~L{Bs;0mRDMb;=_W2LzR+f$XC_og`9|P^38}j zq}8D>to6IwXr(7+d`_Ndcb6(OGf7Tr8eG?`O@@+;d?)IMB919LE9f_Jrj}!}rlgJ| z%0a}zY6CvZ&&oaoIRS)|&5?eSsi1KWizwPpGV*-&z5}Wg(kG@HMDN$MZMhkvikB#> z{d_0@7Onf_JUfijPbZasMOq0x?NCQDwG9@(sG9pjs-e>X^pzkTxA%A;A5muW9L1Gw zQ|a+?f)^gn|9{jMm>d5;T-p8N@1=@UJMs~v#dXKP?^mcQM?LavXX^r2_R@G=g}|V$ zb05|`e4N`c@?#VIe^d0h0wzVV?MgC2|Eu8iHDpqsS7r=+dnrUQ4No4nn$1UGsTxq- zjIFZ(E@Aop@Ba4k0^@d7Rh31};^6UJzqu)H)){i!@l~k$<-}d@f-=WoIQhP<)+{k}M zO1ZK}8^bE7<3K)Zf!iy?A6v5&16)e9iw1!w75f)_%ssiwx!_ERd+w0@s7zgaVX>tNus3Wm~e7BeR&C%d`4XIYYWexqFkT9ylC=nk2DhatWrf6=({Cnlpoa^=*PJ!dk$Z`UXWh(XBJy5g zJ<>BI)<0PBidOORrB5#Oe(VsV>_4f3c%~1hsqsT}q2oL!=!r$LhO71awpE$3?x?YW zl_hcrl(ae}uWF>~Kq>WOhd|=Ov|fp{-jtIATES~+5PMDO$@6NTkr_$r51$I#MOUuK zn^?l0cNC>HLmMcYkj7ZuW(2A3`ACHDyxu#%co! zzTbI3NY;_!9ER*W7jbZA*8$n{JWF3fVthxwn4Dr2QE&M-VEKJ4?O`wp8lDs0oWvzL z^HdTdOixg8xipcF0LS5j?MvZNZaJH5rB|i;SeR=c&)eb^KHj`gNj;%mnDW3TEUKBA zyshNnA_);}J~@9@qT|XL>M|JyCe0cPE!!js$}iXsy4PbYnS&<#0=xa zF6!b_yYJCJW}Z0le=K+hr-9$^!ve+lVbQ$Pqkr=`kF4Qby3;gHAP8lgXSytGTjQ6l z0Q@jZ=zU=9z0Hy`# zzN$#lc5nTu@ThTCJNc$)Tv+awfGUJ71Z=D-g9H{F-$l*JTEWStRIiYb5RoeJRr%rg zp1Skl*<0bH5SB@dgH>EOBtaxDO!y!1NU9*1)0->IHL&vB_!2WESN~pCO26ayL65!U zwQ!1@#!W>VJn;@*0V1e$a{i?#?3PJR2slN9i)Cue;_>e09Kqt;YR}dzu6y3}?OVnY z<aYjP(%A(7o(=mf8*?S#Pv zT%A+C^Oe)>&y=9Ek>ECHI+mb?3k`yg}e&{v$_cu-U5MW6%C}3ODq{ZS`8UI z)^|=6LEmQZK3;CRm&Okf2deH{-+%5K?fKID@A-(jzI!eT+!_USHlJAfigx<4UHcAx z6b3*

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zbQLtDV9WYu972htwbIJ}fh2o(%j|Jl!|#z~Wh&fSzi=8pJw5u(L}b|{*b~a^!36oY zpTk2!;?nVR_Sn+T9NLgOH#2)GeXXBgB5xS9L+gxs?X%X|1SW`IiXd?gvn#C#2OFk_ z$5X#Bqkbs|n4~1SL5ZA}f$V4a3FiWG<+clR#*wOa?Q((az1AY%jx_IH1qtj?fl zo%c73T6sUTUnFrr)r*oo#`b^%%m4Wc@U)yi#Jh>@ye2_zB;CL*C-5@cIScSw{Y57H fzu*}Q24<{>cRKP-%WV|sWE0O#KJUogB7{E!!?WX` literal 0 HcmV?d00001 diff --git a/learning.ipynb b/learning.ipynb index b81fa3ca8..9f2d91add 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -9,16 +9,30 @@ "source": [ "# Learning\n", "\n", - "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from learning module." + "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from the module:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from learning import *" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Contents\n", "\n", - "* Dataset\n", + "* Datasets\n", "* Machine Learning Overview\n", "* Plurality Learner Classifier\n", " * Overview\n", @@ -28,6 +42,10 @@ " * Overview\n", " * Implementation\n", " * Example\n", + "* Perceptron Classifier\n", + " * Overview\n", + " * Implementation\n", + " * Example\n", "* MNIST Handwritten Digits Classification\n", " * Loading and Visualising\n", " * Testing\n", @@ -41,9 +59,13 @@ "editable": true }, "source": [ - "## Dataset\n", + "## Datasets\n", + "\n", + "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n", + "\n", + "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n", "\n", - "The dataset we will be using for the following tutorials is [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica." + "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." ] }, { @@ -154,7 +176,7 @@ "source": [ "### Example\n", "\n", - "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead (and only for this algorithm) we will use the zoo dataset, found [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." + "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead we will use the zoo dataset." ] }, { @@ -175,8 +197,6 @@ } ], "source": [ - "from learning import DataSet, PluralityLearner\n", - "\n", "zoo = DataSet(name=\"zoo\")\n", "\n", "pL = PluralityLearner(zoo)\n", @@ -240,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, @@ -300,8 +320,6 @@ } ], "source": [ - "from learning import DataSet, NearestNeighborLearner\n", - "\n", "iris = DataSet(name=\"iris\")\n", "\n", "kNN = NearestNeighborLearner(iris,k=3)\n", @@ -320,7 +338,147 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Perceptron Classifier\n", + "\n", + "### Overview\n", + "\n", + "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n", + "\n", + "You can think of it as a single neuron. It has *n* synapses, each with its own weight. Each synapse corresponds to one item feature. Perceptron multiplies each item feature with the corresponding synapse weight and then adds them together (aka, the dot product) and checks whether this value is greater than the threshold. If yes, it returns 1. It returns 0 otherwise.\n", + "\n", + "![perceptron](images/perceptron.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Implementation\n", + "\n", + "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights. If the result is greater than a predefined threshold (usually 0.5, 0 or 1), it returns 1. If it is less than the threshold, it returns 0.\n", + "\n", + "NOTE: The current implementation of the algorithm classifies an item into one of two classes. It is a binary classifier and will not work well for multi-class datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def PerceptronLearner(dataset, learning_rate=0.01, epochs=100):\n", + " \"\"\"Logistic Regression, NO hidden layer\"\"\"\n", + " i_units = len(dataset.inputs)\n", + " o_units = 1 # As of now, dataset.target gives only one index.\n", + " hidden_layer_sizes = []\n", + " raw_net = network(i_units, hidden_layer_sizes, o_units)\n", + " learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs)\n", + "\n", + " def predict(example):\n", + " # Input nodes\n", + " i_nodes = learned_net[0]\n", + "\n", + " # Activate input layer\n", + " for v, n in zip(example, i_nodes):\n", + " n.value = v\n", + "\n", + " # Forward pass\n", + " for layer in learned_net[1:]:\n", + " for node in layer:\n", + " inc = [n.value for n in node.inputs]\n", + " in_val = dotproduct(inc, node.weights)\n", + " node.value = node.activation(in_val)\n", + "\n", + " # Hypothesis\n", + " o_nodes = learned_net[-1]\n", + " pred = [o_nodes[i].value for i in range(o_units)]\n", + " return 1 if pred[0] >= 0.5 else 0\n", + "\n", + " return predict" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The weights are trained from the `BackPropagationLearner`. Note that the perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one node, with the weights calculated.\n", + "\n", + "`PerceptronLearner` returns `predict`, a function that can be used to classify a new item.\n", + "\n", + "That function passes the input/example through the network, calculating the dot product of the input and the weights. If that value is greater than or equal to 0.5, it returns 1. Otherwise it returns 0." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Example\n", + "\n", + "We will train the Perceptron on the iris dataset. Because, though, the algorithm is a binary classifier (which means it classifies an item in one of two classes) and the iris dataset has three classes, we need to transform the dataset into a proper form, with only two classes. Therefore, we will remove the third and final class of the dataset, *Virginica*.\n", + "\n", + "Then, we will try and classify the item/flower with measurements of 5,3,1,0.1." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "iris = DataSet(name=\"iris\")\n", + "iris.remove_examples(\"virginica\")\n", + "iris.classes_to_numbers()\n", + "\n", + "perceptron = PerceptronLearner(iris)\n", + "print(perceptron([5,3,1,0.1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The output is 0, which means the item is classified in the first class, *setosa*. This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## MNIST Handwritten Digits Classification\n", "\n", @@ -337,7 +495,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Loading MNIST digits data\n", "\n", @@ -346,9 +507,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -366,29 +529,37 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ "def load_MNIST(path=\"aima-data/MNIST\"):\n", " \"helper function to load MNIST data\"\n", - " with open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\") as train_img_file:\n", - " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", - " tr_img = array.array(\"B\", train_img_file.read())\n", + " train_img_file = open(os.path.join(path, \"train-images-idx3-ubyte\"), \"rb\")\n", + " train_lbl_file = open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\")\n", + " test_img_file = open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\")\n", + " test_lbl_file = open(os.path.join(path, 't10k-labels-idx1-ubyte'), \"rb\")\n", " \n", - " with open(os.path.join(path, \"train-labels-idx1-ubyte\"), \"rb\") as train_lbl_file:\n", - " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", - " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", + " magic_nr, tr_size, tr_rows, tr_cols = struct.unpack(\">IIII\", train_img_file.read(16))\n", + " tr_img = array.array(\"B\", train_img_file.read())\n", + " train_img_file.close() \n", + " magic_nr, tr_size = struct.unpack(\">II\", train_lbl_file.read(8))\n", + " tr_lbl = array.array(\"b\", train_lbl_file.read())\n", + " train_lbl_file.close()\n", " \n", - " with open(os.path.join(path, \"t10k-images-idx3-ubyte\"), \"rb\") as test_img_file:\n", - " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", - " te_img = array.array(\"B\", test_img_file.read())\n", - " \n", - " with open(os.path.join(path, \"t10k-labels-idx1-ubyte\"), \"rb\") as test_lbl_file:\n", - " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", - " te_lbl = array.array(\"b\", test_lbl_file.read())\n", + " magic_nr, te_size, te_rows, te_cols = struct.unpack(\">IIII\", test_img_file.read(16))\n", + " te_img = array.array(\"B\", test_img_file.read())\n", + " test_img_file.close()\n", + " magic_nr, te_size = struct.unpack(\">II\", test_lbl_file.read(8))\n", + " te_lbl = array.array(\"b\", test_lbl_file.read())\n", + " test_lbl_file.close()\n", + "\n", + "# print(len(tr_img), len(tr_lbl), tr_size)\n", + "# print(len(te_img), len(te_lbl), te_size)\n", " \n", " train_img = np.zeros((tr_size, tr_rows*tr_cols), dtype=np.int16)\n", " train_lbl = np.zeros((tr_size,), dtype=np.int8)\n", @@ -407,16 +578,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -425,7 +601,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n", "\n", @@ -434,9 +613,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -459,7 +640,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Visualizing MNIST digits data\n", "\n", @@ -468,9 +652,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -504,16 +690,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { - "image/png": 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kihQp4nWz4pKyZcuG/NkiRYpQpEgRPvzwQwoXLkzhwoXNa5s3b8564xQlhrGi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A4ePAjYOyu3IMmMsgN0Ijk2I0aMMDH+Dz74APBNRPd//Y033uiqhHpBEurz5cvH4sWLk33dZZdd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\n", 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1BODrr79m5syZYW3fT2We2bNnB+Dll18G4J577mH06NEA9O7dO+LtRrPk+oUX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0bjcO1F8uTJY0J5Er4MVC4fMGCACTuI35hXyZUrF2AdHwkVSLL9559/zgMPPABEnxIlSGgvMTGR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\nr9GsWTMA3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -529,14 +717,16 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "data": { - "image/png": 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iKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQkTnUgpiqIoiqKEiU6k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TQ5fvy4qWiWG+qKFSt6Nc0uXLiwSVBPyaleWLBgQUh8szS0pyiKoiiKEiCuUqRk1Swrypw5c5oS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\n83im9WI/fvzQESm7x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UrVoVcN1dcgObO3euyVr8448/vDEwRCQcokyZMmYiKwHJ/fr1S/f+Q4cOAaknl8Fu3H5C\nkjy6d+9u7mnixnv44YfNmCTjVIKv33zzTbONrl27Ak6Wm18IrBcpSL2otFSrVi3a5pwSde0piqIo\niqKEie8VqbRugaefftqkzGeHXLlyAamDff2IrC4CK7UnKjVq1AAIKYDZr2SnBtS4ceMAN93XTwpV\np06dzKpW+lo+/fTTWd6OuM78jrhHBg8ebJQ1eQSoWLEi4NZgkgSJv/76K5ZmRgS5tlx//fXmuTvv\nvBNw3WJSkuSmm24y12SpLi3V3f2CBM9LyQLbts259cwzzwBOBwxR3b777jsA9uzZAzh1v+IFUfHH\njRtnQlekluLkyZPN+6QiuNSRSklJMQ3T5XN+8gQEs+Xiiy82AfVCjhw5TP0oL1FFSlEURVEUJUx8\nr0hJwT8hO355WXEFxmx07twZgHnz5oW9XSUySCyKrCilYq3fU+YDkVXt8OHDqVKlSrrXJcBT4t0k\nWLtIkSImXm/WrFmAEyzqNVLy4NZbbzXPvfbaa0D6lOpQOHLkSGQMizJScf7w4cMmZV7Upm+//dYE\nuEqyilxHxo8f72tVqlixYkBq9Uni3ALjvOR3CZyvXbs24CS/SOyRFEwMLFDqB+rUqQO4KmEgohTP\nmjXLnGdCYLDyokWLgPhRGKdOncrgwYMBt+jrtGnTTB89KZMginn79u19FROVls6dO7N06VLAjWN+\n4okn0iW31KlTxxybXuL7iVQkkZuXtCyB4EFtSuy54447TNCjJBFIXal4ZObMmZlW+5YbdeBFT/BT\nHRvJbg10gcsEr1ixYlmqvr5nzx6++eabyBoYJRYsWAA47WBkcizZpD///LPJ5JNGqRIIe/DgQV8H\nKAerxC434GDIjVeyFIsXL27qaslC4ZJLLjEJQH4gcJIoyKQvMwKDlqW1iLjO/M7Ro0d56KGHAEyl\n+uXLl3PJJZcA7jikTpqfJ1HgJGpIU2Wp2F67dm2TPSo1IZs3b+6JfWlR156iKIqiKEqY+FqRuuii\ni8yKWFa+y5YtC3t7jz/+eLrn4kX1eOONN9JVek0E7rjjDsBxhUkwr7gV/BbEGg2CrXjFndaqVat0\n7odYI+nTycnJRs1o27Yt4AQgp1Wk/vnnHz766CPA7V0mJCcnx10j5kmTJgV9Xmr2vPfee4Ab4Dto\n0CDTjcCPLmmpjyUK05VXXpnlbcj/RHrVzZs3z7j7pJq/H5DSDZMmTQqpvE29evXM56RMQjwhSVhz\n5swBnEbTkkggPRNln8UD0ihb6rE1b97cnGfyGFibz8vK9KpIKYqiKIqihImvFamyZcuaQDOpCCxl\nC0IlZ86cppBg4Ge3bNkCEDc93DZs2GBm3FIwThQcqT4cL5QuXdqskCQuqkCBAub3jFQAvyAd16Wo\nnQTwAuzatQtwCv+tXbsWIFUh1U2bNgGwevXqDLefM6dzWgbG8nmFKMBNmjQxAeeSCg9u7JQE6ubK\nlcsEYqelSJEi3HTTTYDbty7ekcB7WSHny5eP3r17A/5UpI4dOwa4Kf5XXnllusSHzKhUqRLPP/98\nqudKlSqVrl+fl0h5DumUMHLkyJA+16xZM8BfMYrhULZsWfP7r7/+CrhV+eOR4cOHA/Dcc8+ZwHIp\nvRHYg1auQcGSfKKNKlKKoiiKoihh4mtFSlYI4Pa0uvzyy03Gk1ChQgWuuuoqwO2qLqviG2+80RQ4\nDER8xevXr4+84VFCVkoy45Y0X8kw8hPSZy2w87rsh+rVq5s+WDKmr776yqgbXbp0AWD06NExszcr\nSDbe5Zdfnu41aQtTt25dtm3bBsDZZ58NOD78P//8E3AV0Xjpm7h48eJU6fFpkZVhtWrVuOKKKwC3\nJICwYsUKPvnkk+gZ6QHSNkYy2Zo2bWpi3PzMp59+CjjZhnKuZobs3w8//NCcu8LkyZONEusHXnrp\npVSPoSKFRuNVkRKVRrIPjx07Fjc9ZUMhJSXFKOTBYqXlWioqXJEiRUw8Z7QVOSuWB41lWVn6sqVL\nl5qLsrB7924TgCsy9W233Ubx4sUBN201s4vDDz/8YGqfpK2UmhG2bVuhvC+rYwyV6667Ll2tK0np\nTXvDCpdQxhjq+KS/XkbHVyiBgVJJO7BJ6o8//mgeZV8HInXGxIU2Y8YM81ok9mGLFi3MSZlVN/Op\nmhYLMsmqU6dOyMen4PVxCm5lermgSZ+zRo0asXLlymxv3w9jTItMNJYtW2b2saThf/HFF1neXiTP\nxWDIQnPZsmWmsrf0YQtEFjfi9gu8Hst+rVOnjnFjh4qf9qFcP8eMGQM41ySpBC4L83CI9Rj79OkD\nuAlUvXr1YtCgQYB73Ux7P80uftqPws6dOwGntJFMHLNTky+UMaprT1EURVEUJUx87dqTVN1AihUr\nxn333ZfhZ0KRqQcMGBB3AdrBkBXkI488YirxRmLFHwkyK0YJpAvErly5slEVJUi3UKFCAKl6Kcn7\nL7zwwqCq1ldffQW4pRMCFalIkC9fPqOUCRs3bmTAgAFA5pW7b775Ztq1a5fh65KqLMG8WVWj/EJg\nMDq4KfF+OTajgaz4Fy9ebIoE9urVCwhPkYolsloXJXHjxo3GRS0hEFLYEVxPgJQuyaoa5TekOGkw\nBTyeqFmzJuCqhy+99BIbN24E3P3YuHFjwHXtJiIytrZt2xqXZocOHQC3WGmkUUVKURRFURQlTHyt\nSI0YMYIGDRoAbsp/Vvnzzz9NLE2/fv0AZ8Yeb4UBg3HppZcCTnxGy5YtPbYmNYG92UIhX758pq+X\nKJFSBkASDUJB+r/JqjnSTJ061RRG7dmzJ+Cs5GV1Lv75t99+m59++gnAqFWBqbppOXbsmIlxGDVq\nVFRsjxWtWrVK9besiv9ryPnpR6S/3tixY7n33nsBp8QFOLGpH374IeD2ORPV98SJE+a1RClhIZwq\nrjMeEYVeYoWGDh0KuOpVIiKq/y233GLuKaIOR0uR8vVEasmSJaZ/kFSaPVW9EnEjSBbJDTfcENeZ\nCqHw3XffmQrL8YpU0A7kxIkTAL7KCAIYN24c4PZ5KlOmjMkaFdq3b5/uczly5EjnNpDec8OGDYv7\nfQhOLZe0daT81IctMxo2bGhq8EhCy6lCAORCff/99wOOCyw5ORnwdz8z6aE3YsQIc+MJNoFP6z4f\nMWKEaYSr+J+0IoJMfitVqpTw90Vwj1upyVewYMGwmq2fCnXtKYqiKIqihImvFSlwpGdwXSbnnXee\nqUckgXPfffedSUmXVaBUt00U1q9fb1L6pTaIzLal87wSG2QlJ3VnOnbsaAL/pTp7MJYsWZKu5tf4\n8eMB2LNnTzRMjTl58+Y1FfcFqWbeo0cPL0wKmUqVKvHyyy8DriugWbNmQZNeJGW+Ro0agOvOTElJ\nMb32Hn300ajbnF02btxogsWDuXukxMEDDzwARD55w4/s2LEjLhM93n//fcBxaaVF6iVKOYtu3brR\nvXv32BnnATNmzDBqa8WKFQGoXbt2VOouqiKlKIqiKIoSLrZtx+wHsOP1xw9jbNiwod2wYUM7JSXF\nTklJsb/55hv7m2++iekYvd4P8b4PE32M+fPntzdu3Ghv3LjRHKfLli2zly1bFhdj3L59u719+3Y7\nOTk5Sz9Hjx61jx49ag8bNixmY4zU/7N48eJ28eLF7TVr1thr1qyxk5OT7VWrVtmrVq2yzznnHPuc\nc85JuOM08OfBBx+0H3zwQVtYvXq1nZSUZCclJcXVGJs3b243b97c3rdvn71v3z67YcOG5rWyZcva\nZcuWNefk66+/nnD7Me1Phw4dzHjlZ8qUKVEZo+9de4qLtOhIW8dIUfzCwYMHjauoRIkSgBvoGg9I\nlppkZPbv3z/TNj5ShV6SDeIxQ1GSOQJrRf0XkUSQ1157La5dexMnTgScsJj+/fsDULRo0VTvlWSD\nRGbu3LlMnjw51XNp2xtFCr0jK4qiKIqihIkqUoqiRJQbbrjBaxOyzYsvvpjqUfnvULVqVa9NyBaS\nKLFq1SoTbN2sWTMAPvjgA4D/RAmLQ4cOmabFkqAWLaVRFSlFURRFUZQwsewYVnGNZQfoSGP7sMt1\npAlljIk+PtAx+h0do0Oijw9iM8b69esD0Lt3bwDatGmTac/MUPHTGKOFjtFBJ1IhogeMQ6KPD3SM\nfkfH6JDo4wMdo9/RMTqoa09RFEVRFCVMYqpIKYqiKIqiJBKqSCmKoiiKooSJTqQURVEURVHCRCdS\niqIoiqIoYaITKUVRFEVRlDDRiZSiKIqiKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQmT\nnLH8skQvEw+JP8ZEHx/oGP2OjtEh0ccHOka/o2N0UEVKURRFURQlTHQipSiKoiiKEiY6kVIURVEU\nRQkTnUgpiqIoiqKESUyDzRUlFEqUKAFAnTp1AJgwYQJFihQB4K677gJg0qRJ3hinKIqiKAGoIqUo\niqIoihImCaNItWjRAoCBAwcC8NFHHwHQt29fTpw44Zld2aV///4ADBgwgE8++QRwVZnt27d7Zlc0\n6NKlCwA33ngjAFdffbV5LSUlBYCRI0cCcPjwYWbMmBFjC0PnySefZMCAAameW7JkCZ999lm69/3X\nWbBgAQA7d+4EoGPHjl6ao4RIgQIFALjwwgtp0qQJANdddx3gqsoAhw4dAuCSSy6JsYVKUlISAGPG\njAHgt99+A2Dr1q3MnTsXgN9//x1wr7FK1kmIiVSrVq2YOHEiAPny5QOgcuXKAFSpUsVMPLZt2+aN\ngdngwgsvBMC2bY4cOQIk1gQqT548gLNv8ubNC8A333wDwOeff57u/dWrVwcc197mzZsB+Prrr2Nh\nakjYdsblUho0aECDBg1SPVe/fn0AnnrqKZYsWRJFy/xJ9erVzbm6Y8cOj60Jzumnnw44LmaAdu3a\nmddy5HBE/VPdhGQx16lTJwDefPPNiNsZK+655x4AHnzwQQDOP//8dO9ZtmwZa9asAeCVV16JnXEK\np512GuCICnfffTcARYsWBcCynJJItm3zzDPPAPDoo48C8Nxzz8Xa1IRBXXuKoiiKoihhEteKVLFi\nxQDH7SVqxsGDBwHIlSsXAI0aNeKHH34AoHv37kB8rQZFhQLYu3evh5ZEl8KFCzNlyhQAs4oK5pKV\nfVm5cmVy5vTP4ZtWacrq5xo0aGBWi36lbNmyADz99NMA3HrrrWFv68wzzwTgnXfeMe6H2bNnZ8/A\nKDFq1CgAbrvtNiC16ihKVGZK5N69e3n44YeB+Lr2gKvw33777QC8+OKLRvGQ43X9+vV8/PHHAEbl\n2Llzp29cRW3btmXatGkA/PLLLwC0bt3auLkOHDjgmW3RQK6fffr0Mc/NnDkTcO+PSUlJxg07ZMgQ\nANauXcuHH34YS1MTBlWkFEVRFEVRwsQ/S/owePfddwG44IIL+OOPPwBo2LAhAGeffTbgrCYvuOAC\nAG666SYgvlaFr7/+OgDt27c39osv+6effvLKrIhx7NgxwAmg//PPP4HgSlQ88tRTTwGY2KclS5YY\nBWrx4sXp3i+B534MQC9XrpxJ4ChTpgwAzZo1Y968eWFtr3PnzoCzMv7qq68ATDKFn8iXL58pwxHI\nxo0bU/0titTevXsZN24cALVr1wachBE5tuOBfPny8cADDwCuuiH7HBwFCmDYsGEAzJ07l3/++SfG\nVp4aUTpfeuklo45VqFABgG+//ZYff/wRcGPzxo8fDzhq2rJly2JtbsSQUjHgjBOga9euAOzbtw9w\n4qgk3lQSYF555RUTs7l169aY2Ztd5J4viWZXXHGFeU08Or169QJg7NixUbEhLidS11xzDQCXXXaZ\nea5t27aAe4GTx2bNmrFw4ULAzQZr27Yt06dPj5m92UEC5P/++29zghQvXtxLkyKKXOAmT57ssSXZ\nQyZLp3LPZRZQLll+fpxI9ejRg3LlygHuGMU9lxUkM/OJJ54wz8mkMtCN7TWVKlUCnAuvLMSEXbt2\nUaVKFSBzm2URFC/IjXXUqFHUrFkz1WviEnv++efNuI4fPx5T+0JFQj4++OADwJlYSEaouPhq1KhB\nrVq1ALjooosAN0v42LFj5v1y4509ezYbNmyI0Qgih7j3ZAIlJCcnm6Se999/H4Cbb76ZUqVKAfEz\nkRo5cqQ70uZ1AAAgAElEQVSZJKYN9bBtm9y5cwPQr18/IHoTKXXtKYqiKIqihEncKVJJSUm89tpr\ngDsDbdOmTYZS7JYtW8yKV2Tdvn37xo0iJaugnTt3mhTW/yIFCxYE3H1+8OBB366IQ0GUqXCD1GOF\nKC9t27Y1SpTUBRK3QVaQ+kL58+cHHEVy6dKlkTA1olStWhVI7SYQnnvuOV+pZ9lFFP5nn30WcBQa\nSWyRQHsJSP733389sDBrlCxZEnCP3ePHj9OoUSPAdUuCeyxKDUIp65CUlMRZZ50FwNChQwEoVaoU\nPXv2jIH1kUVKUwQLJRD8nuQSiKimUorkggsuYMuWLYC73yVBAhxPDrj1swoVKmQC7pOTkyNmlypS\niqIoiqIoYRJ3itTll19ugghXrlwJkK5adFqk4Fhgpex445133jGFC/9rlChRwsRlVKxYEYARI0YY\nH388kpkS5aegcyl1ULRoURNQLQUWw0l2uPfee4HQygZ4wbXXXgs4AcppkQKTv/76q0kdj9d0cSky\nOmLECO644w4AE0+yYMECo87ES6xMZqxfvz6VEiX89ddfACY5QB7PPvtsU8RZ4halMHK8IWqjVDYP\nRBS766+/HoDly5enU5lz5MhhSguJGikJQrFCYjEffvhhk/wgcXB79+413igp7rxo0SIANm3aZK5R\n999/P+Bcs8QbJSqrqFbZIW4mUjVq1ABI5ZKTG82uXbsy/awE2smEq2nTpqYmjpTH9zuSlfhfZMKE\nCeaCIDfeeMrsy+qEyA8VziWwvFq1aulee+yxx8La5lVXXUXhwoVTPbdp06awXITR4s477wSCB9JL\ni5M5c+aY52QxJxdlCdz1O6NHjwacbFlJaJGsJwmdiFd2794NwKxZswB4/PHHs/R5y7JSVa8HWLVq\nVWSM8xGSrCWT6pdfftm4xeT++Pjjj5tscVlcSAZctJHrptRgk4kSYDIuBw4caPZzMM444wwAypcv\nDzguWrH/jTfeACIzkVLXnqIoiqIoSpjEjSIlQWa5c+c2qoSsBkNlxYoVgNMUVeqixIsi9V9EVkyB\ndXykTELfvn09sSlUGjRokGmAZzAC6015SZ48eUytssCaNNktUdGnTx/TcUB49dVX2bNnT7a26yWX\nX3454Kofv//+uwlKlrpbfkL2p1QqX7VqlSkdE4/p/cGQ0gVt2rQJ6/N33XWXSUwSb8arr74aGeNi\njARgi3vu8OHD5jVRmiRxokKFCqa/abAwEgnYjiYS8tCuXTtzrD700EOAkzQgirbsj8ySPvLkyWPu\nEy1btgQcj4b0qo1kb09VpBRFURRFUcIkbhSpiy++2Pw+YsQIIHu+TSnCdqpAdSX2XHrppQBMnToV\nIFVczfPPP++JTVlFglSzglRC95pChQpxww03pHs+XIVF4i2kyCW4cY3BgmC9RMZ4qpjEtGqp9J8r\nX768OUbXrVsHwObNm6Nia1YpWbKkqSIvZUTatWuXMEpUdpFq+926dTPPvf3224B/9mFWEcVUYkzP\nPfdcwAmol5IQEiMVGMspgeWjR482MYHLly+Pmp2BdoFTTFU6l4RL/fr1TQFg4dixY0adOlVsdVbw\n/URKsrRat24NOPKeHNxZDTjOkSOHebzyyisBNwPJ73Tt2tXYn6jIBOqLL74AXDl6//799OjRA3Bv\nTn5FLkbh1IcSV6Af6roEs6FevXqAe3M5FXKBlsrDUjUZMHV6Dh06ZDLlpAOBl0yaNCmk94mLUoJz\nZQJWvXp1c82SliNNmjSJtJlhcffdd5uQBvlf/5eTWASZFEuGauHChc2kQRrdxxPigh88eLB5TlzP\ncg8JbCgt9ZS2bNliKti3atUKSO0KjCaSMSqTp7Zt25rMUcmmDKRu3bqAk7UnmXlXXXUV4LaaatSo\nUbqkkddffz0q2d6JfWdWFEVRFEWJIr5XpESaE/fOunXrshxkLsgsPCUlhV9//TUyBsaQwFVEvCPN\nMUVpBHdFIUqU0Lt377hpNB0oj2fm3hM3XrD3eF1Hav/+/aYnWfPmzQGnErmci4UKFQJg/vz5RiGU\nlWxg8KcErEq6cWDNKL/WkQoVqaovj40bNwYcVVFKRshzDRs2zHLiQSSR6uwDBgwwwdM333wz4Fap\nD6RChQo0a9YMcF0t4uqR4wLcfqbxWuVd3LLvvPMO4N5jli5dav4/8VDJXRDFWEpbBJ5bUg9MyjgE\nJo5IL8nhw4fHxM5gSGkDqff0+eefB+1WImVZhKNHj5pEtKZNmwJuyE/jxo1NaRUJuk/r6osUqkgp\niqIoiqKEie8VqcC4Csje6kcC78D1o/odCdQtXry4t4ZkA1GYRKEYMWKESR4QdSMz/JhGfiqefPLJ\nkBQlUeYCY6pEpVqyZIknpRCOHDlChw4dALfXWNeuXU1/PClWeNttt5nPSBDzoUOHTHyVvD8zDh48\nGNflDwTp3zVixIh06mmvXr08VaSkl5xlWaaf3tGjRwFo3769UdCkWnTLli1TFT8MRLpEAHz33XeA\nW5omnihQoIBRoqSH6ddffw04ap30GowX2rZty7BhwwBXRQxk7ty5QPBimn4oIitxThIA3qZNG9ML\n8YMPPgActUoUcOmvt2nTpnTbkDnCp59+Su/evQH3HIgWqkgpiqIoiqKEia8VqUKFClGrVq1Uz4Wa\nVROI+L5l9X/48GETK+B3JIYh1v2NsosoaZ06dTJqmqQXZ5VHHnnEZHN4Xawy0kj5jWBZfg0aNPB8\nvFLQbuLEiSbTNVgLkdq1a5vfRZHKLP5p6dKlgJNJlkjp94ErZL8g6d7bt283sTHSI1DiEgM5fvx4\nhj0Eq1WrZjIuRVWuVauWUXP8jpS9ee6554wSJdfW/v37A8SFGiWKoahqDRo0MFnskon5999/mx6B\nwWLh/IRk182YMQNw7h9SRkSuI/PmzTNFNP/555902xAlStrC3HDDDeY6E+3rqK8nUocOHWLt2rUA\nlC5dOuztSFqrpP6uW7eO1atXZ9/AGCBS5/jx402AslTs9WMNLJFj5cZ6qgrkcpJMnjzZnDgiNcv+\n6tGjB7feeivgupMCm6keOHAAcGr5RLJareKyYcMGM+ERN1Vg4GbHjh0B1z2UEZ06dQLcdGw/XeCr\nVKlimtX269cPcI+tUyGlHsLtQxhN/ve//wHODVbcsjKB2rNnj9knkhaekpJiGvpKWrq45aU0Cbip\n9MH6EvoNcTPLpDKwgb2M/9NPP429YWFw1VVXMWjQIMANVzl48KDpEymlLQLFggkTJsTYyqwhgf9S\ndbxLly6mn54gCS0ZIYHoM2fOBJxrkVTwDzbxiiTq2lMURVEURQkTXytSJ06cMDNJcRf06tXLdG0O\nZTVbpUoVU8FVtiFyaDzRpk0bswL0Q8HGYFSoUMFUI5cid8E4ePAgY8eOBdyiqoHK1XXXXQe4K8X7\n7rvPKF3BkgSkh+LChQt54oknsjuMsBDXXCQlZK/dehkh/Sn79Oljngv8XZSNtEHIb775Zrb79UUD\nKa45aNAgc+zNnz8fCD3RoWrVqoBbLiKQbdu2RcLMsBGF9/rrr+eZZ55J9dqECROMG0/UxMDAXDmu\nRREG2L17N4BRQDJyA/oJURgffvhh85wUls1uBe1Y0bBhQwDeeustE66yZs0awDl2JaBckgfiCbkn\nh3NvljAS8TzJ3+edd162up9kBVWkFEVRFEVRwsTXihS4HckllqZcuXKmG7TMQAMDsSWdXtpS9O/f\n38QvyGzXK9Uiu0gRw7feestjS4JTq1atoEqUtCDYv38/4MQ8TZ8+PcPtrF+/HnCCzAFWr17NCy+8\nkOH7RSHJ7D3RpEGDBunS25csWWJi2gKVpbRB5ZIAkfazaT8XL1x33XVGiUobbC4qj9+QoGNRo7LC\njTfeCMCoUaPSvSZqQaBa5wWixlxwwQWUL18+1WuPPPKIOc8yQ5TjKVOmmBIKP//8c4QtjQ4tWrRI\nl/b/9ttv0759eyDrrca8QvpfBvYelfZDu3fvNoHlUrIE3DYxgTGlicSFF17Iyy+/DLjXUlFdY6VG\nAVixrCxsWVaWv0xkd+lb1b59e3OBXrBgAUCq3jnSk00mVLZtmxNfDjCp+ZIVbNsOyZ8WzhhD4Ycf\nfjABn+KqjHSweShjzGx8tm2nq77+zTff8PHHHwPhNfKNJNHah5E+h2QCFk5lc6+P06+++ooaNWqI\nLQCmunDjxo0jEvQZ6TEmJSUBqW82kn03evTooK45qU8jwb6Bx4AE+UpihBz/WSG752IwkpKSGDJk\nCOBWwQb47bffAKeadFqk55wk/UgwcHaJxXEq2cJr1641k2Wpln311VdHPRM60mMcOXIk4PT/k/0h\nST3lypUziQ6SjLR9+3bzerR6Knp1vZH7+/Tp003fPUkge+CBBwC3int2CWWM6tpTFEVRFEUJE9+7\n9qSXlQTSNWrUyKwgr7nmmlSPkL6GzR133GECoJXoMnjwYJNmLK6utWvXmp5cicqSJUuC1oHKKhJM\nGo8uPUFKVgQitVyinYIcLtLhfvbs2SY9XlKpRc3OCqIMhKNERZMdO3aY8g6JTJEiRQBSVS6X6vni\n4ou3unzgKoeAceOJVyawjpvUU7rllluipkR5zUsvvQQ4JW/ENT1mzBjP7FFFSlEURVEUJUx8HyOV\nlqSkJBPQLKuLUqVKmVm4pLJKhdRffvklIsGEXvmCJd5kyZIlpmKrX2Ok/E4096EoUvJYv379kFSq\nSKtQXsdIvffeezRr1gxw42rkfBV1ObtEa4w5c+Y0alKo8XyigMu5OGzYMKPAST+7cNBz0SGcMUr1\n8sCiy/fccw8Q28KUkR6jJE0NGTIkaM88KVg5cOBAIDZJSbG83pxxxhkmxk1KPHz00Uc0bdo0u5vO\nlJDOxXibSHmF1zeoWKAXbwcdo7+J5hhPO+00wKlBA06DZqlnJrXMwGkxApgK0zJJ/Pfff7P6lUHR\nc9EhEhOpY8eOcf755wOxzV7Tc9ElO2OUdjiLFi0y56VkaI8cOTLq3RE02FxRFEVRFCWKqCIVIrq6\ncEj08YGO0e/oGB0SfXwQGUWqW7dungQi63HqEs4YJZTl1VdfBZzg+Q4dOgCxrUavipSiKIqiKEoU\nUUUqRHR14ZDo4wMdo9/RMTok+vhAx+h3ojlGqdpep04dwCn2K+UeYokGm0cQPSkcEn18oGP0OzpG\nh0QfH+gY/Y6O0UFde4qiKIqiKGESU0VKURRFURQlkVBFSlEURVEUJUx0IqUoiqIoihImOpFSFEVR\nFEUJE51IKYqiKIqihIlOpBRFURRFUcJEJ1KKoiiKoihhohMpRVEURVGUMNGJlKIoiqIoSpjkjOWX\nJXqZeEj8MSb6+EDH6Hd0jA6JPj7QMfodHaODKlKKoiiKoihhohMpRVEUJRW9e/emd+/e7N27l717\n97J79252795N9erVvTZNUXyHTqQURVEURVHCJKZNixPdTwqJP8ZEHx/oGP2OjtEhWuO77LLL+OKL\nLwD4888/AbjlllsA+OabbyLyHboPXXSM/kZjpBRFURRFUaKIKlIh4veZd8WKFQF48cUXAahZsyaN\nGjUCYN26dQAcO3Ys0214uQouWrQow4YNA6BTp04A5MjhzPO3bdvGkCFDAHjttdcAOHHiRJa/w+/7\nMBLoGF2yM8ayZcsC8NJLL7Fjxw4A3n//fQA++OCDTD971llnAfDtt98CsGLFClq0aJGl7/fiXCxU\nqBAAGzZsoGjRogBUq1YNgO+//z6SX6XHaQCRGGPdunVp06YNAG3btgVg+vTp5ve1a9cCsGbNGgAm\nTJgQkX2q+9Eh4SZSdevWBeCMM84A4IknnqBevXpp7eC6664D4NdffwVg48aNmW7X7wfM+vXrATjv\nvPPMc9u3bwfci+GuXbsy3YYXF+9HH30UgC5dulC6dOm03yV2mecGDx4MwJNPPpnl74rWPixSpAiV\nK1cGMBezPHnymPFs2LABgC+++IIvv/wSgK1bt2blK0Im1sdprly5ADh+/HjY2/j8888BzM27Xr16\n7NmzJ8P3x2KMtWrVAjAuLnDPpzJlymT4uYoVK5rPFClSBICVK1ea7YVKLM9FmTROmzYNcMb+1FNP\nATBw4ECxJxJfZfD79TQSRHOMJUqUAGD06NEAtGjRItN9lPZaeuDAAS655BLAWaiGi+5HB3XtKYqi\nKIqihEnCKFKtWrUCXNdPgQIFQvrcsmXLALjjjjvYtGlThu/z48xb1IBPP/2UK664It3rorbVrFkT\ngH379mW6PS8UqeTkZPnudK/Nnz8fgKZNm6Z7rXLlyvzyyy9Z+q5o7cMFCxZw1VVXyXdktl3++usv\nAK6//noAVq1alZWvOiWxPE7Lly/P66+/Drhqxvjx40Nyu55++ukAdO3alYceegiApKQkACpVqmRU\nvGB4pUj9+++/gHM8Ll68ONX7xZ338ccfc+GFF6Z6rUWLFqd0B6Ylludi/fr1AcyYvv/+e+rUqQPA\n0aNHI/EV6fDj9TTSRHOMDz/8MIAJh7Asi7///huAcePGAfDee+8ZT4t4ZSRpoFWrVuzduxeA2rVr\nA6f2ygQjFvtR1LQzzzyTZs2aAdCkSRMA2rVrl05tO3ToEBD6HOBUqCKlKIqiKIoSRWLaIiZSiBIj\nK9jp06dTvnx5IPgs9PDhw6n+PuOMMzjttNMAjJJTpkyZTBUpP3L55ZcDUK5cuXSvHT9+nKlTpwKn\nVqK8oHPnzumemzdvHgDdunUDYOfOnQDMmTMnnSrVrFmzLCtS0SJ37txs2bIFgEmTJqV7/aKLLgIc\nm4sXLw64alvDhg0B+Omnn2JhakS5++67zWpWHhcuXJipmiRILOOIESOiZ2CEkeuOxHIBpkClxOwF\nqlHjx48HTh2c7iXnnnuuOWb/+ecfAK699tqoKVGR5PTTTyd//vyAm5giSmcgZcqUMSqFxIPVr1/f\nKBjyXMuWLc1nZHtS/mHQoEFG6RFl0ksuu+yyVH9PmDCBxx9/HAgeCzt79mwAzjnnHMBRpD766CMg\nPCUqlkg82B9//JHuNdu203kB5Dxt2LBhOuU4aoghsfgB7Oz+lCpVyp43b549b948Ozk5OcOfTZs2\n2Zs2bbLfffddO3/+/Hb+/PnNNvr3728fP37cPn78uHn/Y489lun3xnKMp/qpVKmSXalSJXv58uX2\n8uXL7ZSUlHQ/Tz75ZJa3G6vxValSxd6/f7+9f/9+Y++mTZvspKQkOykpKd37a9WqlW7/btmyJSrj\nC3eM5cuXt8uXL5/pexo3bmzv2bPH3rNnj33ixAn7xIkTds+ePe2ePXtG7NiIxXFaq1Ytu1atWvb2\n7dvNOOSnQoUKmX5W9vHmzZvtzZs3p/rsrFmz7FmzZqU6V70eY+AxJ2zdutXevn27vX379nSvHTly\nxF64cKG9cOFCu0CBAnaBAgWith+zM75cuXLZuXLlsidOnGjOwYkTJ9oTJ06M2LEY7X346KOP2kuX\nLrWXLl1qr1u3zl63bp194sQJs0/SHpuBP6G8Hvieffv22RUqVDjl8R2r47RmzZp2zZo17V27dtm7\ndu2yX3311Uzf36JFC7tFixb2gQMH7AMHDtgnTpywW7VqZbdq1crz/Xiqn5UrV9orV65MdS7K/WP/\n/v0ZzgEWLFgQs2NVXXuKoiiKoihhEjeuvXz58gEwduxYrr322lO+/8MPPwRcN1EgAwcOpE+fPgDG\nxTd48GATuOd3pHRDWnkX3MrDTz/9dExtygrnnXee2Z8nVyusWrXK1OsJhrwvo7+9JhR5/NNPP6V1\n69YAvPPOOwDcd999AEyZMsUEf/oVKecgbgIJsM4Kd911V6ptgVMeIPC1gwcPZsvOSBJ4nKWkpABu\nSEHg6+IKe+CBBxg7dmwMLQwPCTC/4447jAu9b9++XpqUZfLkyWNcylnls88+M+UppHSHlIkJxldf\nfRWSyzpWLF++HIAxY8YA8Pjjj5uEFylZ8eabb5r3y3Ny3X3ttdeYNWtWzOwNhwcffBCASy+91Dwn\nLtq7774bcALRJcFMxiacf/75sTAT0GBzRVEURVGUsPG1IpUrVy4TPD5x4kQgeCo8uKtYqSCcWSBy\nwYIFTcqkcODAgWzbG23uuOMOABNUGDgGWRlL9WU/B4suWrTIBERKIKFUgU50Pv30U8BNgJCK9K1b\ntzarSz9SuXJlevXqBQRXoiZMmAC4RSuD8cADDwRViF944QUgPs5BcNSJtOMcPnw4ED/HsSjy4JZ4\nEGUqXpgwYYK57kkh3AkTJpjxiNr7+++/s3nzZsCpMg9OAWMJVJekHUkACUTuI126dInWMLLFE088\nATgJVHJ+SpKDKLzgJkGIenP//ffH0swsU7ZsWaOQSuA/YALklyxZAsBVV13F/v37gfSKVCzx9USq\nSZMmvPvuuxm+LjejefPm8eqrrwJuleRgiNQ3depUU/lcuPnmm7NrblQpXbq0ySoRSTqQr7/+GsC0\nUvEze/bsMdLse++9BzgT5Ixcq5IZlUiIm0AywNLWHvILkgX17LPPcs0116R7Xdyxsu+OHDmS7j15\n8+YFnCr2xYoVS/XatGnTfJ3VFohkDTVp0iToOOMByVATN9a+ffsYOnSohxaFz5YtW0y25MsvvwxA\nhQoVjNtLrokZIXWXMru+LF26FHAmY36mb9++zJkzB3AX2hICAu5YX3nlFcAfmYeZUbt27aD3uTx5\n8gBuqxtZiAdDqr7HAnXtKYqiKIqihIkvFSmpA3EqObV///4AjBw5MqTtiluwatWq2bDOGwYMGGCq\nugYj3laVUjNKgv0zWxXWq1cvnStWeu7FK34PLBek4bUoGWmRRrcLFiwAnODXtHXLZIWcVo0CR5Hy\nU3C5IG6SwONOXAwFChSIW0VK3F2FCxcGYObMmRFvSOwF0psxsx6NGdGgQQMg+L4WRcrvnDhxwihw\n0vQ9sO7SmWeeCbgegJEjR5oG935k7dq15joixyq493DpnmDbdrp7g6htn332WSxMBVSRUhRFURRF\nCRvfKFK5c+fm0UcfBeC2224DMNXKA5k/f75Ji5Rq0qeiUqVKAGb7gUiA67Fjx7JudAwoVaoU4PZm\nC8YHH3zAwoULY2VSVAjWc07ih2rVqpWu3IEEVMYbVapUATC9zIRgVXv9wP/+9z8g43ITEv8k5+qz\nzz6b7j1pe2EFMmzYMKNAy2rZD8j+CLS5ZMmSAPz444/ce++9gBug7NfrR1qCxZ1klXvuuQcgVcyc\nxLkFq+zvd2QfB+5rUUMCey3GC7feeqv5XYLRJUZKAriHDx9uqpyLN8NPKvmaNWto1KgR4HZ/qFGj\nhinvI9eKyZMnp7uWSlxn1apVTxknFyl8M5F68sknTSPGYMiFav78+Vmu55E7d24gtWtBmsd27doV\ngC+//DJL24wVkuUUKG8Kv/32G+A0bpRaKImEBDAH1hyKF/LkyWOyLIWiRYvyyCOPAO4ERG48zz//\nfEzt8wsXX3wxb7/9NuBmmlatWtXzdk1yA7Jt2yRGyD4rUqSIsfm7774D4JNPPgGcpsUxa0sRBm3b\ntg3rc9JKa+jQoebGJW55gObNmwPxNZGSjPDAlj/ClClTAP8HmQciCQSBiR8zZ84E3Dp3cpz279/f\nCBLiDvVbHcXVq1enevQz6tpTFEVRFEUJE98oUn369DGVg4MhAanhpDQG1kwRRNWaO3dulrcXC0SF\nadWqFeCqauDKzpL6K81GEw0JmgyU3OPFpde5c+d0SRA5cuQwx7i4paUCvV9dQ7KClarJ4SCBu8nJ\nyaYycSCS0hx4jHuNnFMPPvigqRElroamTZty3nnnAa4KII+tW7emZ8+egD+bFYf6P5Z9JtWxpZtE\n4cKFTaBvcnIy4ChTwVy6fmfAgAFA8OSjQYMGxdqcbFGoUKF0itLtt9+eruOC3Pe6d+9OvXr1AHes\n69atMx0XlKyhipSiKIqiKEqY+EaROvvss83vUixzyZIlYcfHSExRiRIlghYSDDdWIBaUKlWK5557\nDsCsfAORQLupU6fG1K5oIAGPgUhx1cC0VvGTS4yR3xkzZoypAC5xeAULFjTqmiisflWiBIl9mTlz\nJn/++ScAP/zwQ4bv79evX7oyB6LCTZkyhTvvvDPdZ6TQrKhVXsdHpUWKjsr5FnjeiULerl07wCkT\nIUWEpcjqzz//HDNbT8W2bdsAKFOmTKbvk+KON9xwQ6rnv/76axPPJ4+nnXZayCVo/EK+fPlMv8Fg\nSOeFeKFfv340btwYcKvsZ+Zt2b9/v1EZpUp4sOSueETKH0j8YixQRUpRFEVRFCVMfKNIBevR1bVr\n10xbxGSGpIBKSfxA5s+fb1bXfqRIkSJBi2/K/0gySjKLKfMjsgIUhQbcGLBgqfGBaclvvfUWED/x\nYMePHzeFKEVdPP/8801G1wUXXAC4hR8feeQRE3viJyQbVHpYZoRkQMl4ApH9uGjRoqCfnT17dnZM\n9BQpGiwq1Zw5c0yJAYnxyywbOdbMmjULgIceeghw9pvEQ4kCfNdddxklUpBYqaFDh5piulLksV+/\nfuzevTv6xkeQAgUKmLi2tMSykGN2EY/NjTfeaFS0UM8nUUrlc/369TOtV+K5nM5/uvxBpJC6S5Ky\nHIg09O3SpYsvew1J08Vhw4aZhpqBSGNYaXwbL0gfLGksGk4tG7mwS8kHST+PB6SGy9dff22ahY4a\nNQqAHj16ALB169a4c48EIm6Cc889N91rI0aMANwFgJ+RbgkDBw7M0uek3tDixYuNq1IWc+PHj8+0\niXoskcbYMsm77rrrzP7JrKNEjRo1AJg+fbrpcdq6dWsAPvzww+gaHQWuu+66dIs3OU/lGIgHxPVa\nvnx5c00MVpMvM2bMmAHAU089ZY6LeJ5IeYG69hRFURRFUcIkYRQpceFJELn0AANMSmeHDh0AzIrK\nb0i37mCp5rt27TJBhPHE6NGjTSXkjKpjh0Lt2rVTPRYuXDhuSiEEIqqiHKfS56tv375mFfjjjz96\nYtqzOd0AACAASURBVFs4iLoYLIhcCFZ+xE9UrFgRcIJzI6kcJSUlAdCsWTPfKFKSDi8u2IkTJ5py\nDZkh/6NDhw7FtRIlSMHVQMRlGU/VzCVM4s8//6R3795hbUOUuBw5csRNb0G/oYqUoiiKoihKmMSN\nIiWF5K6//npT6E4CXO+++24uu+wyILUSBU5clN+VqFtuuQVwY0gkViGQbt26+db+YMiY7rnnnnTd\nuQcNGkT16tUBggbVS2kA2ZfXX389/fr1A9yV8dixY00fOOkfFU9IzJesAKtWrWqC8eNJkapZsyYA\nTZo0SfeaBCj7Hel/WKlSJVOOQ4KxZ8yYYcofBEsGkGuQBP1eeuml6Y53P8a+BSasDBkyBAjeiklU\n5IkTJwIwePBgNm/eHCMrI4+UpChTpky2FHK/sWfPnqAJW6EgcVYpKSlxde3JCC/KH8TNREoCsUeP\nHm0ahsrkSioOByLNRLt06eLrCUihQoVMEHawCZTIzfEmucoEKfBiJb/LpCjwOdu2+eqrrwBMQLbw\nwQcfmMmzTKQuueSSmGVkRAMZt9SRsm077rIwM0KqJ8dL0K702dyzZ4/puyYV559++mlzLTly5Ei6\nz0qdt0svvRRw9qPsWzl3/YhUJX/jjTdMvSFJ0JHgc3An9YMHDwaI60kUuOETwZBA/HhixYoVgFNt\nv3379oDT7zEjypUrBzgNp2XxIyLE6tWrfdtzNit4kbWnrj1FURRFUZQw8bUi9ffff/PHH38AblmD\nUqVKmd+DIamfUgtEZHm/kidPHtNNPRBZ/Xbv3h3A13WvghGqbC5qYdeuXY0iJYpGMCRo1y/BuxmR\nJ0+edApGzpw5zepPgl3FNXbkyBFef/31mNoYLSQIWfoJZgdJxQ+nx2aoSLXva6+9lmnTpgFQoUIF\n83rTpk0Bt87SqY5t2V68uJylNpu4IP3oiowU4j4P7Hsp15K0feniAXHLduzYMdPrRyjH7sCBA31Z\nFigeUEVKURRFURQlTHytSC1btsykzkscVLDKyeD2n+vYsSMABw4ciIGF2SclJcVUky1ZsiTgrB5k\nNfv77797ZVq2eOGFFwAnqDNtT6tffvnFxI9Ivy6/K4dZZd68eaaHlQS4FixYkKuvvjro+4cOHRo0\nBue/zhtvvBGz7/r222+pVasW4CpSbdq0MYkRmfVmE3bs2GHiA9euXRslS5WsIlXYRQFOSUkx6oyo\nnfHWXw9cFa1bt24maDxY4kcwxNsjqpaUCYp3xMsRy44JViwzFyzLCvvLJICsbt26pu6JnBS33XYb\n69atA2Dnzp3ZNTMotm1bp35X9sboNaGMMdHHB5EZ47Bhw7j55psBtxmoZVnm4j1p0iTAXQB8/PHH\nEWlgHOvjVIL/ZdJYokQJk025devWSHxFOvRcdEj08UHkxiiZ23LeBZ6LEoAe6Wreepy6RGuMS5cu\nTRcakzbrO7uEMkZ17SmKoiiKooSJr117gUgQ3OLFi03jV0XxK4899hiPPfaY12ZEHQnUzSwBRFG8\nRtyzSuLz0ksvxfw7VZFSFEVRFEUJk7hRpBRFURQlHEQd3rdvH+CUH5Hg8t9++80zu5TsceWVV3pt\nAhBHweZe43VQXSzQAFcHHaO/0TE6JPr4QMfod3SMDuraUxRFURRFCZOYKlKKoiiKoiiJhCpSiqIo\niqIoYaITKUVRFEVRlDDRiZSiKIqiKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQkTnUgp\niqIoiqKEiU6kFEVRFEVRwiSmvfYSvUw8JP4YE318oGP0OzpGh0QfH+gY/Y6O0UEVKUVRFEVRlDDR\niZSiKIqiKEqY6ERKURRFMdx3330kJyeTnJzM+++/z/vvv++1SYria3QipSiKoiiKEiYxDTZXFEVR\n/MlFF10EwODBg7FtJzb43HPP9dIkRYkLVJFSFEVRFEUJE1Wk4ojLLrsMgMWLFwOYVWOjRo1YuXKl\nZ3aFQ/HixQEoUKAAAB07dqR///4ApKSkpHrviBEjeOGFFwD4448/Ymilovx3qF27NgCFCxc2z02Z\nMsUrcxQlbrDkZhyTL4twLYl8+fIB8MADD5jfH3/8cQBGjx4NwL333mvev3XrVgD69+/P66+/nqXv\n8rpeRq5cuRg1ahQAd999d6rXduzYQfXq1QHYuXNn2N8Rq9o1DRs2ZOLEiQCcc845gdsWO9J9Zt++\nfQDUqVMHgF9++SXL3xvrfViiRAnAnTSCE8gLUKFCBQCaNGkCOBPEPHnyAO7+feedd7L8ndEa4yWX\nXMIVV1wBwLhx4wBITk4O6bOnnXYa4Jx3rVu3BuChhx4CYN68eVkxA/D+XAxE9uOGDRsiut1Y1pGq\nWLEiAN988w0A+fPn56effgKgcePGAOzatSsSX2Xw0z6MFn4aY9myZQHIkcNxQuXNm5cRI0YA7jFc\nrlw5c+1dtGgRADfffDMHDhzIcLt+GmO00DpSiqIoiqIoUSTuFKkzzjjDqBJz5swBoFChQub15s2b\nA/Dwww8DUK9evXTb2Lx5M8OHDwdg/PjxAJw4cSLT7/V65l22bFk2bdqU6esAW7ZsCfs7or0KLl++\nPACrVq0if/78wbYNwMcffwy4Y+nYsSM5czpeaPkf1KpVi71792bp+6O5D2VV16NHD8AJ3C1TpgyA\neTy5bbElw21t374dcFy5WVUCIj1G2Wdjx46lQYMGACQlJQGhqxRnnXUW4I4LYNiwYQA88cQTIW0j\nEK/Pxdy5c/Pyyy8D0KZNGwBGjhwJwKhRoyKi3sRSkRL1vnPnzua5tm3bAvD2229H4ivS4fU+jAWx\nHqOESch5evDgQR577DEAqlSpAsD06dMBmDp1qvEGiHJ+zjnnGM+GbKNnz56MGTMmw++M9RhlHJ06\ndQKc47RIkSJiS6r3Tpo0yVxfduzYEfZ3qiKlKIqiKIoSReJOkWrXrl2mAZBdu3YF4PvvvwegUqVK\nlCxZEnDjpySeCqBGjRoApwzW9moFJUrT3LlzufTSSzN834cffghAs2bNwv6uaK2CZeXz2WefARmn\nVEsgeaNGjQA37uTGG29k9uzZqd47ePBgnnzyySzZEc19uGLFCgCqVasm32VekxiDyZMnG0Xtrbfe\nSvX5cePGGTVVVKsePXrwyiuvZMmOSI9x0KBBAGZlC1lXpAoWLAg451i5cuWA+FSkLrzwQgCGDx/O\nNddck/a7AEdtlWN10qRJ5vV//vkHgCNHjoT0XbFSpEqWLJlKKQQ4evSoue5EOjZKiPQ+FMW6SJEi\ndO/eHYArr7wScFS11157DXATWf79998sWpx1YnGcnn/++QD069fPqPySlLRx40beffddANatWwfA\nRx99lOG2zjzzTBO7mCtXLsCJYVyyZEmGn4nFGMXj1LVrV5OQdPrpp5vXZ82aBUD9+vWB1DGpGd1T\nskIoY4ybrD2RGiV7KyPSypBff/21+V0Ouo4dO5rnunTpArhSod+QiV5mkyiA559/PhbmhIWcCCLB\nBvL3338DziRXJlppD/bAfShIALNfENtlIgWuq2ThwoUAbNu2Ld3n5IYV6J4+fPgwAF988UVUbM0K\ngYuON954A4Ddu3dnaRsykVyxYoWZSIlEHw/I/0Am7mknUYFUq1bNHANDhgwxz8vCbsGCBYBzTKxd\nuxaI3mQlFIoVK5bOJXLXXXd5alNWkAmsuCeDXcfr1atnXLFyDoqLK/Czv//+ezRNjQo33XQT4EyI\n5d53zz33ALBnz550GdCZ0aBBAx599FEApk2bBsDy5csjaW6WkHufhOHUrVvX7L/BgwcD8O6775pj\nVbJNxcU5bdo0sw3Z35dffnlUbFXXnqIoiqIoSpj4XpESJWrmzJkAFC1aNN17tm/fzptvvnnKbYma\nddNNNxl3g6Sfn3nmmUYdiUd+/fVXr03IkB9//BHApM/XrVvXrNDlf55ZOYO9e/fyySefAHDVVVdF\n09Sw2bhxI4CR0vv162fGlFkig6ijgUkRsjJevXp1NEwNiWeeeQaAbt26AY5KKAkcWQ0HEBk+8NzN\nTpmOWNOnTx/AXf2HQ9WqVQFXievTp49xqUnCi6yyY8l7771nfv/2228BmD9/fsztCJfSpUsDoXsU\nzj77bMBNRgInxR9c5WP69OkcPHgwkmZGnFKlSgHw4IMPAk4pElHyQ1UT5byUe+CYMWP4888/Aff8\nD9UVHQ369u0LuC7av/76y5w/Ug4nEHlOHq+88kqee+45IHMVORKoIqUoiqIoihImvlak8uXLZ4Jd\nixUrlu518Zc2bdrUqB6ZISv8H374wagjsqI544wzImKzkjESEyKPoXLixAn2798fDZMihsRZyOOp\nkJgoKZdgWRaff/45gAmW9YqSJUuatH6JRRs9ejR79uwJa3sSGxeoJkphzsCUez9SpEgR+vXrB6RW\n4qR4pcSNiZoUWBU8ECmEGBizIv8XWXHHEgmcL1OmjBnX0qVLATcw3u/kyJHDBB9nh8ASHwC9e/c2\nKk12ysnEAlHOypYta5RECbo+VWC1qOCBqqT8P0O5n0aTO++80+wDUfsbNWoUVInKDBmbKOuXXXZZ\nVLqA+HoilZSUZCY8wbj11lsB73d6NBHpNjOmTp3KX3/9FQNrvKFEiRK0bNnSazMiwi233AK41ctl\nQmXbtrmge3U8y82+U6dOxgUi2aBykwkHuVGfOHHCZFfFC9OnTzf2y+O+ffu44YYbANeNIgkFXbt2\nNW4XyR6qX7++mUAFTsYkoHfgwIHRHkY6gmVLZpbR5Ufy5MljJvyBiCs9bTZi2s9K/aS0VKxY0dSy\nq1mzJoDvFnKSjSZu4SFDhpjs9KFDhwJOhntG2Yk1atRIlQwBTsapdC3wCrk+tG7d2ogbAwYMAIIn\n64RK7ty5gdTJM5FEXXuKoiiKoihh4uvlodSEyoisqjAyY5dKy/HA+vXrAXdlFIzffvuNo0ePxsok\nJQQKFy5s1FQJmixfvrxRKYIFbEuldpHcxdUXKyRt/6mnnjLPffnllwDZOr7kPJ09e3ZQBcHPiLoU\nSOfOndMF9IobpVevXuY5qcUTWNoiECkLcezYsYjYGgodOnQAXGU0R44cvPjii4CrqgVy/fXXAxhF\n+Pbbbzd9+KQMxvDhw41yGUsOHTpkaq+JMrN//36jtEjiRzCSkpKMq1nUYbnG5sqVy/QflArvmVX3\n9hKpqL969WozXtlXtm1z5513Am5JFdmfs2bNMsHmhw4dApwEGa/LXoi7++qrrzbXyMBSFVmhVq1a\nJmheFOEbb7zRlKqJJKpIKYqiKIqihIkvFSlRjO677750ry1atIj27dsDWS8MKP3Q5DEe2Lp1a4av\nyepB0nYTlWDFG70sDZAZkiI/evTooAkSmXHRRRcBmGrmF198cWSNOwWyug8ks/6OGSFF8EQBlm1I\nYge4MQvdunXLcvV2rwm1UOrx48cBbwtupkWOSVntHz9+PKgSJQUspfzMBRdcYD4nvws1atSgXbt2\nALzzzjtRsTsjRLWVYsuhsmPHDtMhQx5FiQ2MHxN1+LXXXjtlP1YvEDXzww8/NJX0RWFr1aqV2Y9S\n2Vz+Pv3000381G233Qa4PU69RNSxn376icqVKwPQsGFD/s/emcfLWL5//H3sRFmTXVmyhSKpxBES\n2bJrUZI1JR05FN8UIlIhki0qKkJZQhEtiqzJliUkobIvJcv8/nh+1/3MnDNnzpw5szwzXe/Xq9fR\nzJyZ+z7PMvf9ua7rcwGsXr3aXFPuuLvagx3J6tevn1GFhTx58oRk3I5cSEnSq9xs3blw4ULAHjTu\n3iGCOJuLf4bTkKoub0jIoEWLFn75aDkRaTeSPXt285gcX3c5WhyMhXCHvVJDQgByHFLzWhIZXpJG\n77nnHpNsLgv9UFWYpIS36jT5gvK3tUJcXJxJ4pVjKiEs8W4Duxrw1VdfNbL7999/D9gu4JFEFoNy\nTGKFjh07evz/xo0bWbx4scdjhQsXNuezfPG4nxNynKTBbbZs2Uy1V7gXUsFEFhvuyN9h2LBhaa42\nDjeyEKxXrx4A119/PV9++SVgp4hIe669e/easN/q1avDPdQUkYXU8uXLzUJKPARXrVrlVTyR+0qD\nBg1SfF9JVO/Xr19QxytoaE9RFEVRFCVAHKlICUlVCEh7s8kMGTJw++23A5iSZXdWrFgBpN2t2QnI\n3+Lw4cMRHol/SDPNhIQEo1bIscmXL5853uLRI7upxx57LJnXjdPKkQX3c1Z2geLvsmDBghSVtCef\nfNI474e6VDclvHkdiUqVFj8daRAriukdd9wBWLvNpMnbWbJk4c033/R4zAl9FKV0/sSJE2bMaelb\n5kQef/zxZGkN7tYLNWvWBKyG2uKHJUUGEyZMAKzG2+LrIypUv379jHIlCnO03JNSQ+47nTt39igk\ncCKibkuo8n//+5+5lkTdkZSI9u3b++wmEWkGDRpk+pA2b94csP2xkiLeUuLhJyHOm266ialTpwK2\nKh6qMLsqUoqiKIqiKAHiaEXKXSWSJDMxG/OXypUrGxXD/f02bdoEOCsRNK2Iq62oak4ke/bsxv35\n3nvvNY/5Qjp0e+vULYn14SwZ9wcp0ZVdIdjWAf6oqD/99JM5P/ft2weEP6Feijik/x/Yuz1xIk8P\nNWvWTJaovXr1atMPy0mIInX8+HGTNC/Hp1SpUlF532jdunWya8/dtkByTcSMVX4n6esEsVAAKFas\nGGAXTMSKIiXMnDkz0kPwG0m6dufAgQOAnUcUaJeCcHHu3DnTA9Edyetyz59OqZuEt9zaUKGKlKIo\niqIoSoA4WpFyR/Jm5GdqSHx19uzZyZ47d+6cUTacmmsTKwwePNhYAoiKNGzYMLPjd6827N27N2CX\n4yYtswbbtNKpBGr2Ju2OwP47SdViuJBddzh335cuXWLhwoVh+zxviEnl3r17jYroi2bNmrFmzZpQ\nDyvoxMXFmR26LxXQfRefNKcva9as5joVm5oMGTIYpdGblUK0kLSiEexqLyfnE4GVaygVeaLknDp1\nyiiD8pwoh05XpFJC8p/8oWLFikZFlry+UBE1C6ly5cqZnzt37kzxdeJOK1/Q3sqXv/76az766KMQ\njDK8LFu2LNJDSBEpG3766adNCFJk2fnz53v9HXHplRCgN6TE96mnnnLUzU08kmTeH3zwgc9eX4L4\nnLgnWMsXWfny5R3rlxUs8uXLF+kh0LlzZ8D6shS7CUklmD59ejKftm7duplzNZpCfC6Xy3yxiMN+\nxYoVk83BPQVCCiWkeXy/fv24++67PV5/8uRJsxiNRsQry1sys9iUOLWRs9w/Zs6caawDZLEE9kJY\nvj/lXI90Y/RQIkU6kmAPof+u1NCeoiiKoihKgESNIiWOvDfffLNXRUrMNmX36M3OQLqbSzfpaGfu\n3LmRHkKK9O/fH7CUFjGyS0mJEqTEWnrUeUP6Yy1fvtyE0SK9Gy5SpIg5twR/E6jFabhTp05s3boV\nsE1YY12Ncgo//fQTYJnzSsd5sQUYPXp0MkXqmmuuMerFxx9/HMaRpg9JOAY7jNWhQwf2798PwMqV\nK5P9Tq1atQCSnd/uLF261LxHNCImjakVwTgJSV3p0qULYFlziNrkC0mbiGVFqkyZMoBnakioXdtV\nkVIURVEURQkQRypSkmx76NAhj/5cAH369DEtDaRc97HHHjPmcN6Q3ZT0gzpx4kTQxxwOJHcm2sxD\nZ8yYkewx2fnL7mHQoEGm1FrmJ4pM27ZtjcIlpfmFCxcOey+6pEiPpylTpph4fGr2HGJYOG3aNMBq\nDSNIPobT2t8Ei7/++sskuTohNyopcXFxxgJCfoJ3o9K77roLiC5Fqm/fvkbZF5PDLFmymGtQfqaG\n9E4cO3YsQDJD1WhBjFYlf8gdmeOYMWPCOiZ/6datG2BbVbRr186v38uVKxcAZcuWdVSOaTB5/vnn\nzb9F5f9PJptLZda4ceMYMWKEx3PVq1c3lXsixbon1yXl4MGDRs4UT5xoxdcCShK5xTPL395o4UD6\nGUpFVKZMmZg1axZgXdBJkd5PckHs3bvXSNjiFj5s2LDQDtoPxK+lQYMGpseYe8NTQXrP1alTh759\n+wJ2nzI5pnPnzo355tN79uwxNzRZSJUvX954FPXq1QsI/U0vJVK6vmQB5f58pMaYHs6cOWMWiPKF\nmpCQQOPGjQHIkSMHYC0OpQuBJN9Lj7MZM2YYt3Nxi45WZFMmYTJ3ZJHopPuokCNHDlOQI8UA3qhW\nrZopvhKkSj1WF1FgN5qOi4sz34ehRkN7iqIoiqIoAeJIRUqYMWOG6dPl3ifPm4qRlLVr1wJWqCWa\nlSiRmKtUqeLzdS1atADsnmiR3klJ37iJEyeaENzmzZt9/o5YViQkJAB47fQtSdxr1qwxJepOQEJ0\ngwcPBiz1Qtx3u3btClhJyhKelV39K6+8Yn46za09HGTMmNG4LYt1xNChQ8M6hlGjRgFWGbw377Kk\nuFwu4z4fbfz9998ePxMTE0lMTIzkkCJCrVq1Uiw62rNnj6OdzC9cuOD13piU9957z6QfCGntDBKN\niHLscrlMukSoUUVKURRFURQlQBytSB09etT0c5JdqtgcpISUxEvcP9zu0MFm+PDhgNWR3VeyuSSP\nOgVxoP3nn39M3pC7kihJx7Lzmz59Olu2bPH7/ZP2bIsEkm8wbdo040wuc/V2jH7//XeT3+eurP2X\nEAuMGjVqmMfkHBg9enRExiQqbqNGjUyeluTPiHGlO6NGjYq4G7uSPrJnz27MLJPStm1bRzt/x8XF\nmRw3UXN/++030+dTviuvv/56Y0Qp9x2nJs8HA4laSf7lgQMHwmYhExfOCrC4uLiAP0ycn0eMGGFC\nP8L69etNawJxvg522MflcvnV/TA9c4w0/swx1ucHgc1RFrLic9W8eXPjbC5y+rp16zhy5Eha3zpN\nOP08zZkzJ2BvdMqWLWsWUBJuSo1wzFGqoaQy2J1du3Zx6dKlQN/aL/RatAjVHBs0aJDM7VoW+W3b\ntuXy5cvp/oxQzrF06dKA3bDX3W1ews6HDh0y3lKhSvWI9HF0R0QXaSD/448/msRzcX0PBH/mqKE9\nRVEURVGUAIkaRSrSOGnlHSp0F2yhc3Q2OkeLWJ8fhFeRuuWWW4DUi2L8JdJzDAdOmqOox1KglTt3\nbmN9NHv27IDfVxUpRVEURVGUEOLoZHNFURRFCTb79u3jww8/BGDDhg2A9raMdsQgVtz7w4mG9vzE\nSRJmqNBwgoXO0dnoHC1ifX6gc3Q6OkcLDe0piqIoiqIESFgVKUVRFEVRlFhCFSlFURRFUZQA0YWU\noiiKoihKgOhCSlEURVEUJUB0IaUoiqIoihIgupBSFEVRFEUJEF1IKYqiKIqiBIgupBRFURRFUQJE\nF1KKoiiKoigBEtZee7FuEw+xP8dYnx/oHJ2OztEi1ucHOkeno3O0UEVKURRFURQlQMKqSCmKoijR\nQ1yctRkvWLAgAE899RT3338/AOXKlTOvu/nmmwHYvHlzmEeoKJFHFSlFURRFUZQAiTpFasqUKSxf\nvhywdz87d+6M5JCUdDB48GAA6tSpQ3x8vMdzL774IgCrVq0yj7n/W1GU0JAzZ04AWrZsCcD06dOT\nveb48eMAXLhwgYsXL4ZtbIriNFSRUhRFURRFCZA4lyt8yfTpydyvXLkyAJMmTaJatWoALFiwAIBW\nrVoFYXS+ibbqhF9++cX8u0GDBgDs3bvX5++Es1JIlKgXXnghTb9Xt25dIDBlKtqOYSDoHG1ifY6h\nml/u3LlZsmQJADVr1gTg0qVLAOzbt4/58+cDMG7cOAB+++23NH+GHkMbnaOz8WeOjg/tFS1aFIBp\n06YBUKVKlUgOx7FUrVoVgIULFwJQqFAh/vzzT8CW6Z2EtwWULI6++uorwAr3AR4hP/l3NIb4Bg8e\nbOaUdI7ueAtpxgIPP/wwzz77LAB79uwBoH379vz777+RHJby/8gm5bXXXjP32StXrgDw8ssvA2nf\n+CjOIlu2bObf//zzD4ApHnjnnXc4ePAgAI888ggAGzduDPMIoxMN7SmKoiiKogSI4xWplStXAlCy\nZMlkz917770AHD161DyWIYO1NpSdVP/+/fnuu+8AOHXqFABHjhwJ2XgjQaVKlUhISACgSJEi5nFJ\nAL1w4UJExuULUVvcFSbZESclnOHn9CLziY+P97l7T5pY7+25unXrxoQq1blzZwDefPNNMmfODECF\nChUAa4esilRkueWWWwA73O6u+g8ZMsTjOSV6KFSoED179gSgQIECADRt2hSwviflmPbt2xeAq6++\nmooVKwJ2ZKNUqVJGuYo2ateubc7fhx9+GIBff/01JJ+lipSiKIqiKEqAOFKRksTyatWqmZW0N7Jk\nyQJA3rx5zWNJFalJkyaZ57799lsA3n33XbPK/uCDD4I48vCSO3duAGbMmGEM8YTExEQ+//xzwJn2\nEKI+pTXnyakKTaDJ876Ij493zHwzZ85s1E7Z5W3YsIE333wTsJOR3REzx2LFipn3+C9yzTXXGEPL\n1Ni1a1eIR+NJgQIFmDNnDgDXX3+9efyee+4BMFYz0cjTTz8NQIsWLUyC/JgxYyI5pLAg19nAgQN5\n/PHHPR77448/ADh9+rRRIs+dOxeBUaYPWRfcf//93HXXXYAduZDv+S5dupg55s+fHwidIuWohVTp\n0qUBe/Ej1XkpsWnTJgAmTJhgHps6dWqKr69Vq5b5+ddffwHRvZDyxc8//8yPP/4Y6WGkSloXCk5Z\nWCTFnwWUJJGn9fecwD333MOnn37q8ViHDh1Yt24dAKtXr072O1dffTVg3dBjFQmV9OrVK8XXFC5c\n2IRMfLF//35uuOGGoI3NF/JFNGfOHLOA2r9/PwBvvfWWSamIRq655hrADimXL1/ebLZ///13ALN4\nTIlu3boBsHbtWiA6HNvz5csHQLNmzQA4efIkL730EmBXbC9btgyAEydOUL16dQBy5MgBQIkSWP1j\nlAAAIABJREFUJfj5558BzAbJaWE9OW8nTpwIWItk2bDJQuqhhx4y/y/PdenSBYAePXqEZFwa2lMU\nRVEURQkQRylSIn/7UqLOnj1r3HZl9Sy7DMAklktob8SIER4JdkKePHkA2L59OwCjRo3inXfeCco8\nQk3GjBkBSxEAS7aVhN1+/foBzlVu0oJ7gqs3NcdJSKhSFCaxNwD7WLgfE187fnmdkxJ8K1WqlObf\nuemmm0Iwksghu9sqVaqYUJGELd3vLf4ixSDjx48HYNiwYcEYpl+0a9cOsBJyBQl7RXv4q0yZMoCl\nRAlS3CARiCFDhph/b926FbDDmc2bNzfKx9133x2eQQeIRFl++eUXFi9eDNjq2ZAhQzz8BN1JSEgw\n5/ODDz5oHpd0kY8//jhkYw6U2rVrM3r0aMAukHAvREpalBTOIiVVpBRFURRFUQLEUYqUrK590adP\nH5+7+aTJmq1bt2bu3LmAHTsGW9WR3UutWrVMDoj0kHIqstOXnSzYCXZvv/024EzLA3+RBHT3/CGn\nK2zeVCdvyLnrzf5AfjclG4hI0r179zT/juSZRDv169cHbAW4U6dOAb+XGB5OnDiRV155BbDV83Ag\nY3/11VfNY1Lq7n4/iWZmzZqV6mvKlCnDoEGDwjCa0CD5xJMnTwZg0aJFJjdo27ZtyV5/3XXXAbb1\nT+3atY2q446cF07KjRJ1cNWqVUZlOn/+PADz58836rB8B7rbIYnqJn+nUOGohZS453q7sUhS6/r1\n69P8vpJoJuGv1q1bJ3tNx44dzR97zZo1af6McCA39M8++yzZcxJKigVPnqSLjFWrVjl+IeUL94Vh\nSv5Rvny0lMhRv359Zs+eDdhhD2/IfengwYP89NNPgGdo8+zZs4Dt2SNdB8JJ3rx5eeaZZwC74hns\nKkxvlZfRRqVKlShVqhRgh3bWrVtnks3luWjn9OnTgP292KZNG77++mvAcyElgkHXrl0ByJUrF2CF\nLOU95Nxs1qyZeQ8nIAso+b5zuVzmmHbs2BGwFlLyOgn7yWtcLpepWA915bqG9hRFURRFUQLEUYqU\nL2SXJ4mBaUFCdeKr5E2RcjolS5bktddeAyBTJuuw/f333wDMmzePLVu2ANHlAp6UlLyYnJ5onhKi\nPvlTSu7UOfbp0wfwdMx3R1TS4sWLA1Yy77XXXgt470YQLTzxxBOAVaxy1VVXJXv+8uXLADz66KMA\nJrwgIQcnUqhQIVM0IKp/YmJiQCo/2GpHjhw5zLzl7xJu5BgNGTLEJP6fPHkSgMaNG5sSf1Gkbrrp\nJtPvUSwBRHls1aqVsfNwLxpxEuIHJQrnd999Z4oVli5dCljFDF988QWQXOVfvHgxt99+OwBNmjQB\ncJQaBXbRmYQg4+LiPJSopK+TpHkJ54FdOBHq61IVKUVRFEVRlABxlCLlq4TYfZWZ3vcPpFQ50lSp\nUsUYrgk7duwArARzSbSLVrz1pvM3gduJpFUZdFetRJ2KpP2BqA2plfcnTdjt0aOHyWmQHa83Dh8+\nDIQ30dofxMzxueeeA/BQo2SsW7ZsYdSoUUB0GfomJCSY81IUF/ekc1/I+dCpUyfTeaJw4cIAtGzZ\n0pyrogBIX9NwceuttwKWQaocJ+kzd+LECU6cOAHAoUOHAE/1RXLfxArC5XJFzXEVZapZs2bceeed\ngK2S5s2bN8W8vr///psbb7wRgGPHjoV+oAHQokULwL6X7ty500OJAihXrhwzZszweJ3gcrmMvVGo\ncdRCSi4AbzfXYISsfL2/U5EwyaRJk0xSnYT0xDE6mhdR3sJfTq5eS41gOEK7LygjtZjKmTMnAE89\n9VSafu/OO+/kjjvuSPV148aNA+xEV6cgoR9xZXdHjks4/Z6CgXyZuldFL1q0yOfvyN9BfJTq1asH\nQNu2bb2+Xs5TSbDv0KFDWJPXxZPrzz//5JtvvgHg+++/9+t3ZQEibUTAWVVr/rB9+3bjpygN7AcO\nHGi+N6UNjBReffTRR45PA5GFrYgoY8aMMSE6Ced99tln5ntR5uMuuoTruzH6pBlFURRFURSH4ChF\nSkmO+PcUKFDArLR/+OEHwE4qjEZ8JWI7NfHaH1KyNxBEbUuaxOq0nnvSa8tXSH3q1KlGCRB69uzp\nU/EVBcqpvcs2btwIYHpxXnXVVUZtGTFiRKSGlS6yZ88O2N5DgClOcUdSB1q2bGn6l0phi7t6If3n\npNz85ptvZsCAAYCVqA2WuiOeReFAQpWFChVK8+9Kzzl3oqXLhVCqVCnGjh0LQKNGjQDrmElxVu/e\nvYHgKObhwt3GQChXrhyAKbzKly+f19eBVYQVLlSRUhRFURRFCRBHKVKSHCi74WAhOzFxd/XGH3/8\n4Zi4eM6cOU0ehpRhg222OXTo0IiMK1jEx8d73RkFo6Ag0oji5K5M+Uoed+oOUZKtxX17yJAhpj+l\ndAAYO3ZssnL3rVu3GuXGm22AJLaKFYlTEXfkEiVKmG4JkSrtDzWSYyJmnYmJiea53bt3A565cqIm\nSq6me682+bvFgjGwk5EcNrk+GzZs6PV6Eyd9f/PFnIQoSnIvmjhxYrI8KJfLZfKgRK2S83n48OFh\nG6sqUoqiKIqiKIEituvh+A9w+frv8uXLrsuXL7suXryY7L+VK1e6Vq5c6SpdurTP95D/Kleu7Kpc\nubKrU6dOrpMnT7pOnjzp9X3lv4SEBJ/vF6w5+vNfmzZtXFeuXEn2X9WqVV1Vq1ZN9/unZ47pef/4\n+HhXfHy8yxvx8fEhm1ckjmFq/8n57A15LlrnuHnzZtfmzZtdly5dSvbftm3bXNu2bXP8cSxSpIir\nSJEirp9//tm1bt0617p161w5cuRw5ciRI+Tnhr9z9Pe9ChUq5CpUqJDHvWTmzJmumTNnugDXggUL\nXAsWLPB4ftmyZa5ly5a5ihUr5ipWrJjH+8l9aP78+a758+d7/N7UqVNdU6dOdcQx9Pe/FStWuFas\nWGG+f8aNGxe2Y5iWORYsWNBVsGBB15AhQ1xHjx51HT161ONvv2fPHteePXtcpUuXdpUuXdo1efJk\n81z37t1d3bt3j8h5GuhxrFatmqtatWrm3nH58mWPf1++fNn10ksvJXud/G2KFy8etjk6KrQnvfb6\n9++f7Dkp3Z08ebJxpP3xxx8Bz1DglClTAIyDr5RJpsSmTZsA293WCUgPJHf27NljEmCjDV9NiKPR\n4iA9SHgv2poWBwtxYnYimTJlMn5Z4jc0dOhQc08RD5sWLVqYsFY0ICkL+/fvN27zN998M2Dda8Xa\nQNizZw/Nmzf3+F1JWG/atKlxu7/tttvM74iDdjQWilSsWBHAhI0kdO0UJIz1/PPPA9CrVy/znBR2\nPPTQQ8yZMwewQ9C//PKLeZ1YPEycODH0Aw4SGzZsAOzvjRYtWphwn1yLO3fuNOej/J2kYOTXX38N\n21g1tKcoiqIoihIgjlKkpJzfmyIl1KpVy6hTUkotSeqAcWtNzXRTnG7FPVXMzCKJ9DyaOXNmsuem\nTZvGb7/9Fu4hBQVRX9xVmLT2sPL2HkmpU6eOeV5UHSe5oq9cuTLF8a9atSoqd/OxxNChQ023AHFL\nfu+998iSJQsA48ePB2D69Om0a9cuMoMMALnXtW/fnjVr1gB2Yq5EAdw5cOAAHTp0AOzrSO657v0T\nJbH8s88+M6X34VQB0osoUdKHT6w8nDQH9/5yokRdvnzZOLNLZ4HvvvvO5/vs378/dIMMMVJ4lZIR\nrqwXRFFM6n4eDlSRUhRFURRFCRBHKVJSGi39cSpUqODz9dLGokyZMmn6nE2bNrFt2zbAGUpUs2bN\nANsELleuXOa5Rx55BLAs/aOVOnXqpPiY5AwNHjzYZzsUX4aVouR89dVXRulyghKVNDfMl5r24osv\nOmLM6eW+++6jRIkSkR5GQCQmJrJw4ULAVqTAMh4FS9EBSzmuWrUq4FxjUW9s3LjR2Kn069cPwOux\nqlevnsmbci8zF5YsWQLYeaWiRkUbDRo0AGxFSu4dYnfhBHLmzGm+F6TlzvTp0+natWuqvyvzArtn\nZqzRtWvXZC1i3PsohgtHLaR27twJ2P2A7rrrLv73v/8BnidFWpHGjuKGumzZMuP4GmlefPFFI9mK\nTw/YNylJho9mXxZvC4ikobrUnL2TOoJHsqGvL2Rc/jqVOzEEmR6KFy/usRGIJlLz2pGwWL169bx6\n9jidS5cu8dZbbwF2cvjQoUNT7J8H9vXmvmi6cOGCeb9oRjaw0cK0adMAu9tFUqSx9NNPPw3YvmDg\nXL+69FKuXDmzgBIBRtYR4URDe4qiKIqiKAHiKEVKkF5yP/zwg9kRSc+nAQMG0LhxY7/fa8CAASxf\nvhxwpgy/Y8cODyUKrBCnhPIk+TWaEdUlNfUppT504FwFCnwnkSdF5ijhyFhRomKBIUOGmDDeLbfc\nAljhsNatWwOQkJAQsbEFG7GQad++vQlZ/pfIli2bURUlfOnEzgrnzp1j0aJFgKfdRFLy5ctnlMVR\no0aZx6WnYjj7zoUDOXYNGzY0liWffPJJxMajipSiKIqiKEqAOFKRcmf9+vUe/y9GcbGCN8PQgQMH\nMn369PAPJkSI6iI/nawuBUJa1KhYNtsES00Vs0oxcRS+++47k2fkRDZu3GjyfiSh+o033jC9vrJl\nyxaxsSnB5Z577jH3XsmxiaSikRJXrlzh0UcfBaz8Q4A2bdqY+8h9990HWAVKcr3JfbZnz57GMkes\ngmIFsTy48cYbTQ705MmTIzYexy+kYp1nn32WZ599NtLDUEJA0vDdfyGMN3v2bNNQdciQIR7PnThx\nwngaOZGjR49y9913A3Yytjfvmh07dkS1L49iV+wBnD9/HsCkgDiN48ePA3Dy5EkA+vTpY9JbpEJt\nwYIFXHfddQC8//77gO3OH4tIpV5cXFxEnMyToqE9RVEURVGUAIlz9wcJ+YfFxYXvw4KMy+XyKxMx\n1ucY6/MDnaPTCcccS5cuDVh9PCWksnfvXsAKmRw8eDDQt/YLvRYtQjXHXbt2ccMNNwC2giMhtGAR\n6TmGg0jNUcKyo0ePZuDAgQB8++23wfwIgz9zVEVKURRFURQlQFSR8hPdXVjE+vxA5+h0wj3H3Llz\nA3aOSjjQa9EiVHOcOXOmScR+9dVXAfjzzz+D+hmRnmM40Dla6ELKT/SEsYj1+YHO0enoHC1ifX6g\nc3Q6OkcLDe0piqIoiqIESFgVKUVRFEVRlFhCFSlFURRFUZQA0YWUoiiKoihKgOhCSlEURVEUJUB0\nIaUoiqIoihIgupBSFEVRFEUJEF1IKYqiKIqiBIgupBRFURRFUQJEF1KKoiiKoigBogspRVEURVGU\nAMkUzg+L9X47EPtzjPX5gc7R6egcLWJ9fqBzdDo6RwtVpBRFURRFUQJEF1KKoiiKoigBogspRVEU\nRVGUAAlrjpTy3yZPnjwAdOnShYYNGwKwfPlyAC5fvsz06dMB+OOPPyIyPkX5L9KnTx8ARo4cCcCs\nWbN45JFHIjkkRYkqVJFSFEVRFEUJEFWklLBx+vRpACpUqEDdunUBzE+AAQMGAPDrr78CcP/99wPw\nyy+/hHOYivKfoVGjRrz00ksAZMpkfR1cvHgxkkNSlKgjzuUKX1ViMEogS5Ysyb333gtAq1atAKhe\nvTqDBg0C4M0330zvR3jFCWWew4YNA+Dw4cNA8OcarpLrJ554gt69ewNQunTpFF936NAhAN59912e\nf/759H5sxI7hNddcA0DLli1p0qQJYC8SffHRRx/x2GOPAfD333/79VnBnmPWrFkBeOyxx2jXrh0A\nU6dOBeC9997za0zBJtLXYq1atciYMSMAxYoVA+x7UYsWLVi1ahUA77//PmD/vdJCuK7Fw4cPc911\n1wGwYcMGwFpc/fnnn+l9a59E+hiGg0jPMUuWLNSrVw+ABx54AIB8+fIBcO+99/Lzzz8D8NdffwGw\nf/9+8+/x48cDsGfPHp+fEek5hgO1P1AURVEURQkhUadIvf3223Tp0gWAU6dOAdbOr0OHDoCt2rz+\n+uvp/SgPnLDyltDYmTNnAChSpEhQ3z+cJoAlS5YEoGjRogAMGjTI7O7LlSuXdFx069YNgClTpgT8\nmeE8hjlz5jTn5JNPPglApUqVSOv1Vr9+fQBWrlzp1+uDPUdR044dO2YeO3r0KAC33XYbv/32m1/j\nCibhvhYLFCgAwJw5cwBLkcqQwfse9OzZs8TFWcPbuHEjAHXq1EnzZ4b6WpSQ+hdffGHUtV69egG2\nGhFKnHA/DTWRmuPDDz8MwMCBA43iL+ekr/tPXFyceV4Kfpo0aWKUSm84+Th2796dt956K9njn332\nGQDffvstAMOHD/f5PqpIKYqiKIqihJCoSzaPj4/nypUrAPTr1w+AFStWMG/ePMAup5cYv+QpRDu1\natUiR44cgO9dRbSwf/9+j58NGzakYMGCACxbtgyAypUrA9ZOKSEhAYCZM2cC/ucMhZs33ngDgMaN\nG1OqVKkUXyf5CbIrOnz4MGfPngVgxIgRIR5l+hCFplWrVowZMybCowk9cs7Vrl0bgOPHj5uEbCmE\nkF37+PHjyZw5MwC7d+8O91D9pkWLFgBGjYLYuVf6Q758+ahRowZg57fdddddAJQpU8br73hTdSTX\n8dNPPw3ZWP2lR48egB2NkfPQnX/++QeAgwcPGpVb1MnDhw/z77//ArYSPnLkSJNn5XSefvppAF55\n5RXAOre9fVc2atQIgHvuuQewrHfE+iNQom4hBXZViZz4nTp14o477gDghRdeAGDIkCEALFq0iJMn\nT0ZglMGlYMGCHje9WEQSIeWnOzfeeCNgVxY5DbkpyZetOytWrADgxRdfZNOmTYB18QJcuHDBvG7s\n2LGhHmZQkHBeaoso8QqThXE08uGHH5ovkqVLlwLWBm7Hjh2A/aUqmzunc9VVVwGYgh2ADz74ALBT\nBmKNbNmymU1Ny5YtAcvLLmlqhD/hr6TPy7nhhIWUVF/KAuqff/5h8uTJAHz//fcAbN26FYBt27Z5\nfY/cuXMD8NNPPwH2ZtapLF++3CyIs2fPDuD1e/KJJ54AYO7cuWaNIAvPBg0apHshpaE9RVEURVGU\nAHHm9j4VsmXLBthJdaI+ge3Oe9tttwFWwpnTQyVpxalhrfQiiefBTqIPB3JOCufPnzc7REl4lNCd\nN2rVqmWS04V169axffv2II809Mh1OW7cOABOnDgBWH+H9O78wkW1atUAK3Tz1VdfAfDMM88AsHPn\nzoiNK71IekDZsmXNY9JRIFpUNX/JlSsXALNnzzbqqC+1adKkSYCVjHzrrbcC8NxzzyV7nYS/+vbt\naxQfJ/Lmm2+SmJgY6WEEFSlSWrBgAWAVJiWNUkhhTKtWrYzyJvfeCxcusGTJEgBuv/12AH744Yd0\nj0sVKUVRFEVRlACJSkUq6a7i448/Nv+W3YKYVY4ZM4Z33nkHsMu2o5EGDRqYf3/44YcRHEloKFeu\nnM/dnewanOq6LMnjEq9fsmQJo0aNSvX3atasCcD8+fPJmzcvYCeENmvWLOJ9B8+fPw/AjBkzTP81\nSTZv3bq1x7UnPPvss4BlAeH+s3PnzuZaDLXhY6DIMZBd6+nTp+nYsSNARKwego3YWfwXaN68OWAn\nFafEunXrACt6IYjVjKiQ7oqzJDOHwyYiLUiOl/w8cuRImt9DEs8lKiC5UpFELBzmzZtnxiV9W8E+\nflJAcenSJSDle4yYjsq9SBTZ9BB1C6kHHnjAJM6JpOdNkpYE1x07dhAfHw9YTtHRSjSGu9LCvHnz\njI9UUv79919zM5RFhtOQm/CsWbMAWL16NZUqVQKgadOmgFVxWqVKFY/fk+TfHDlymAR0cTOP9CIK\n7IXr5MmTzReTJKTecMMNXn+nYsWKQPINT6lSpZKFQJ2GeD7lz58fsIpXYmEBJbRp08bj/48ePWq+\niGKFEiVKAHbhkTeWL19uUj4OHjzo8dzgwYNNcrL7+SrXpVO/R+Q4yr0yMTHRLBb8LbiS70q5dv31\nrwsFbdu2BWxRRK5Jd1q1asWaNWsAu+OHNySloE2bNmaz9PXXXwN2CkJ60NCeoiiKoihKgESdIrVh\nwwYTbpAkVinp9Ma3335L69atAefuJP7LDB48GPDdc2/Xrl1GancqopSJnD5jxoxk/fTcnYO9ceDA\nAQDWrl0bolEGzpo1a0x5vChSKdG3b18Av0KbTqJq1apMmDDB47FLly6Z+XhD1AyxuAA7vcDp5yxY\nybdyH40V5Dp67bXXAOjTp4/xhhKFRZLP3ZHr9X//+1+y57Zs2RKUEFAomTt3LmArUgUKFDApIeLK\nnxqSoC/n8Pr164M9TL/o0KGDOX7uSpRcZ6IYHjx4MMXiqxtuuMG4mEs/yauvvto8n5IFRCCoIqUo\niqIoihIgUadIpZVJkyaZElZZjUbDTjHW6dSpE2D12AM7QdKdLVu2AFbStdN5/PHHATue781VODUk\n50h2URUqVAjS6IKD7BDFOblbt25ce+21gJ2wefjwYcfmsaVGjhw5jLu+IL0704LktokiN3r06PQP\nLkj4a3FQqFAhwE7glf6X7tYPYuTpVMNjsR2ZNWuWUST27dtnnpccW8mlkl6DLpfLKDKSWC4/nYwU\nIck9tWjRokZN9UeRio+P58EHHwTs4plwu91LvlORIkWSXYvNmjUzye+iOrojhTtSIFClShWvLvXn\nzp0DgntdxvxC6syZM6YNR9WqVQE7yUwJL3JhvP7669x0002A5wJK/i3VFhL2+/XXX8M4yrRTqFAh\nnn/+eSD1BdTChQsB+4YujvyZMmUy56d4/Lz88stefWwihbRDEUqUKGHaMsjPb775xuuiWGjfvj3g\nzLDfvn37zDzSSvXq1QEr0V68biSZ+ZtvvgmKV00wmDZtGmCPLW/evKYoQlIkChYsaEKWvropyBfR\ngAEDHN0q6NSpU6bBvTvSCF2uMXdnc/Fvk79TNGwOxCtJFrtFixY156Wck9KSyxvly5c3mwCpVA0X\nUhEs482QIYNp+i3X5Pr165Mdh3z58pkFs4Q03cN3STl37pzxuQtm5bCG9hRFURRFUQIkKhWppH4Z\nqSEyrZTFKuElQwZrvS47AVElkrJr1y7ATgSVctasWbN69KRzIknPyYsXL5qyeQl7LVy4kM2bN3v9\n/UKFCplebqLWDRgwgC+++AKIbBmysHjxYgCefPJJwPKFuvnmmz1eU7t2bXO8o80p+/Dhw0Hpdyid\nFkSllL+HE8mZMyeFCxcGbEWqX79+RomSYyjh3DZt2pjkX3FJj4+Pd7Qi5Y6E84YNG0bPnj29vmb2\n7Nl07doVcK4SlSVLFgCPhsJyXET5d/9+FAVcil3OnDljruN3330XsEKhou6Ek06dOpkEf7lWfvjh\nB6PeSwL87bffbkLMDz30EGCpT2K3Iohy7s2e5a233gpJMY9zr3BFURRFURSHE5WKlCTCicNyasgq\nXHb6SniRHATpPZcSkhg4ceJEwI55X7582fyuGKs5SaE6fPiw2SGJ4+6ZM2dYtWpVmt5D8jEkwTO1\nLvSRQnat8+fPp1GjRoCd4wBw1113Ad7HH2vmj0nJmjWrx98iGnjggQcA+77qPv7PP/8csC0t+vbt\naxK3RcGqUaOG12R0JyIGm23btjWKmiCdL/r06WOsPpxIjRo1jPO+WJF4s1bxdv2JncH8+fP9juiE\nmkceeSRZtKhGjRqmS0RqiFIuxTBinOquSIn10ZgxY3wadwaKKlKKoiiKoigBEpWKlOx6/FWknNrX\nK1Ck7DgaKFmyZJorQJL2xsqYMSMvvvgiYKsdu3fvNu0PImUa545Uhv6XOHLkiDkG8hMsMz2wqw+l\nHBus3T6QJrUumsifPz9FixYFbBsEJ1WdHj9+HLDL+RMTE40CJepivnz5zOvfeOMNj9+/9tprefTR\nRwFbDcmYMaNRFJyqSN15552ArVq4KyBiZCk5bYH0qAsHovp9+OGHPk1xpTLvuuuuMwqc3CPl7+Ck\nnqWHDx826llqKpmYaIo1zrvvvmtU7oSEBMDz+0NUxrfffhuAQ4cOBXHkNlG5kEorkvTr3ugwmnFS\nWCspN954I4C52T766KPJ/ED8xb0cWahfv775KaHaoUOHAnYYIlpJGmqIVmShX6tWLcBzIRWryOLj\n1VdfNY9JT9Dff/89ImPyhvRzlAVF165dzX1RPMHcmTRpEmCH8WrXrm2aUAtr1qxxRDFESpQtW5ZF\nixYBdmm8y+Xi008/BexS/5QcsiONWKRIg3Bv99N///3XuH3PnDkTsApUJDwmtgKSuC0LaifQoUMH\ns9lwd5wX+4NPPvnEPCYbMHcvSFlgipefCCx//fWXSagP9cZNQ3uKoiiKoigB8p9QpKREO5i9dRTv\nLF++HLCcadPC2rVr/VLapFS7dOnSRvGQHUv//v2DUr6eGuL6HMykxVy5ciUzgzx//ryjk15TQ3a9\nIqcXKVKEu+++G4C6desCzrB1CAay42/Xrp15zMlu2BJ2rFq1Ku+99x5gh83dwyvFixf3+OnOjh07\nAEsJEIsZJyHqS0JCAtdccw1gq9u///67UUqdqkQJMnZvSpS4si9dujRZisP+/fv9TtiONImJiR4/\n/aVSpUrmni9KlKhbffv2DZsRripSiqIoiqIoAfKfUKTKly8P2H3QlNAh/Z5EXXFvMSEJjnv27DGP\nDRgwALDym/wxv5Mk0UqVKpkWFZLUPHLkSKN+SAJpsBk1ahStW7cGrB5eYCepBoIkg06fPj1Zb70l\nS5Y4IpE+UKTNhrRk6tChA9mzZwfseUc7kp8heSlXrlwxhRHRYPXw66+/UqdOHcDOaevbty/NmzdP\n8XfEimT48OGAc00rxRhVcmfA7rP21FNPRV2Ewlsitii77sUewv79+5MZBUu+leSMRStyzx8+fDjx\n8fEez02ZMgWwc8rCQVQvpMTJtWTJkin2EOrXr5/J3P/uu+/CNbSgs3jxYpo0aQLAffdkJHiKAAAg\nAElEQVTdB5CiS3YkefbZZwFPN3ORWuXGm55FjjSrPHDgAKVKlQJseTtPnjxe3WyDyfHjxylWrBhg\ny9AZMmRg4MCBgJ3MmxKygKhSpQqAcfS99957zWukL1i0+RGlxIIFCwC7mg8wX9Tih+NkZNzSf+7b\nb781fRFlMZ03b17A8quRL/BoQypP16xZY65Rbw3DJfHcqQuozp07A9C9e/dkz8k9aP78+WEdU3qQ\ndAkp4nDvDCGLiBUrVpgFo2xYmzRpksxLSsKd0Y78DW699VbzmLjvi1N7ONHQnqIoiqIoSoBEpSIl\nfYOuuuoqAMaNG0fTpk09XiP/379/fxo0aADApUuXwjjK4OLuhSVqhpMRTw/5GQokyVB2bLJTDiXD\nhw83SY29e/cGLNVTwsdS6j5hwoRkv9u7d29zLoqq5Y4oUaICOD0J1l+82VKI+3DWrFlNXzMnedu4\nI7tfGd+PP/5oepxJaE8sVnr16hWBEQaXS5cuGbfzNm3aANCqVSvAKk+X+68TyZUrl0kil350YCf+\niyIVTch5J0rbsmXLTIcHuReVLVuWr776KsX32L17N+BpJRCNyHefWD0UKFDAWHSI55kox+FEFSlF\nURRFUZQAiQtnP6+4uLigfJgYx0nuzZkzZ0yynSTVSWfvvn37BqWjtcvl8qsxUbDmmJQcOXKwdOlS\nwLZzkJ5XkkCZXvyZY6jmFw6CdQzFgkF6AkrOmh/vm2L/vMWLF5vE+/QkwUb6PPVGpkyW8D1mzBi6\ndeuW7HnJL3I32fNFuOf4008/AXbvrtOnTycrRRcVMVhJvHotWqR1jr179zZmo8KWLVtMLpGovuEg\nlOep2FGIKt+wYUMPBU4QS5n7778fsNSsYBLua1HmK8rv7t27TX7qnDlzgvERyfBnjlEZ2pMwl7Ri\nuO222xg/fjxgL6REyhXZL9o5f/48L7/8MmAn70pYYcOGDREb138RCd/Jzalr164mnJCai/vChQsB\nOzwt5+2RI0c4e/ZsSMYbaSSk7i65S+hz27Ztjk1aFiQhXhr3Zs+e3fgmdenSBQj+F5QSGN4W6uvX\nrw/rAiociIjQokULwAp5iYu3uIMvXbqUcePGAXZLlVhj+vTpIVtApQUN7SmKoiiKogRIVIb2IoET\nQybBRsMJFjpHZ6NztIj1+YH/cxQleOPGjSblQejZs6dpWhtO9Dy1CdYcxTZF7Cuef/75kBcQ+DNH\nVaQURVEURVECRBUpP9HdhUWszw90jk5H52gR6/ODtM9x1KhRPPPMM4Bti9KmTRu/CxmCiZ6nNrE+\nR11I+YmeMBaxPj/QOTodnaNFrM8PdI5OR+dooaE9RVEURVGUAAmrIqUoiqIoihJLqCKlKIqiKIoS\nILqQUhRFURRFCRBdSCmKoiiKogSILqQURVEURVECRBdSiqIoiqIoAaILKUVRFEVRlADRhZSiKIqi\nKEqA6EJKURRFURQlQDKF88Ni3SYeYn+OsT4/0Dk6HZ2jRazPD3SOTkfnaKGKlKIoiqIoSoDoQkpR\nFEVRFCVAdCGlKIqiKIoSILqQUhRFUahevTrVq1fn4sWL1KpVi1q1akV6SIoSFehCSlEURVEUJUDC\nWrWnBE7NmjX5/vvvAbj77rsBWLlyZSSHpKRCzpw5AbjhhhsAeOCBB3j88ccByJcvHwBnz54FoESJ\nEpw7dw6ACxcuhHuoikLTpk0ByJRJvxYUJS3oFRMlXLlyhUuXLgEwYMAAIPYWUjVr1gQga9asAGa+\nq1evjtiY0krp0qUB6NevH3Xq1PF4zJ0rV64AkCNHDgD+/PNP3n33XQA6deoUjqFGhFdeeQWAEydO\nADBixIhIDiek5M+fH4D4+HhatWoFQPv27QHYunUrt912GwDnz5+PzAD/n2zZsgFw3333RXQcSvqo\nVq0aAF27djU/d+7cCcCCBQsAGD9+PAC//vprBEYYu2hoT1EURVEUJUBiTpG68cYbAXtVfubMGd5/\n/33A3gUfO3YsMoNLB3/88Qe///47AJ9//nmER5M+8ubNS+XKlQHo3LkzYO3aCxYsCNihBVFt6tev\nz6pVq8I/0DTQqFEjAD799FMAMmbMmOw127dvZ+zYsQDcfvvtADzyyCPm+WuvvTbUw4worVu3JiEh\nAYBp06ZFeDTBRZTFpk2bGvUpPj4esJUpAJfL8iWsWLEihQsXBmDPnj1hHGly5LoTRePy5ctcvHgx\nkkNS0kj9+vV59dVXAbjpppsA6/5ZtmxZAPr27QtgUgv69u1rFPDLly+He7gxhypSiqIoiqIoARJz\nilTjxo0B6NOnj3nsf//7HwD79+8HYOLEiUyYMAGwk32dznXXXUexYsUAWLNmTYRH4z9Zs2Y1as2t\nt94KQMeOHbn66qsByJUrV4q/myGDtc6vVq2a4xUpSSh3V6J++uknAN58800A5syZw6lTpwA7Ed2d\n9957L9TDDDoVK1YEYNu2bam+tm/fvsTFWd0W9u3bF9JxhQJRSu+66y6jfAtPPvkkAOXLl/frvf79\n99/gDi4dPPbYYx7//+WXX7J27doIjUZJC6KEzp071+s9JSm5c+cGYMqUKVx11VWAfX+KdkqWLAnA\nvffeC0CrVq2oV69estfJPWj9+vUA9OrVK93ne0wspLJkyUKBAgUAOxFbOHLkiJEuixcvDlgJrvXr\n1wegRYsWQOQTPlNDKmqijWzZsplwVtGiRVN83ZkzZ0zocsmSJQC0adMGsL6AR48eHeKRpo+PPvoI\nwIRE1q5dy8GDBwE4fvy4eZ0suLp37+7x++fPn4+6BNDOnTubYyuVpN5uSNdccw0A5cqVM49t3rw5\nDCMMDjJuCUdKUURa+OWXXwD7S2vhwoXs3bs3SCMMnOzZs9OjRw+Px+bOnRuh0QQXKVrp3bs3L7/8\nMmBvztyRL1YJu4K9Wf3kk08AOHjwIB988EFIxxsIMnZvi6jz58+bdJYiRYoke3748OEA7NixA4AV\nK1aEaphBJ0+ePADcdtttPPvss4Dlgwb23yIuLs7jmCbllltuAaxrUTzTdu3aFdB4NLSnKIqiKIoS\nIFGtSImUN2DAAJNcLitQWamXL1/ehFNE4Zg9e7ZRpD7++GPAKks+ffp02MaeVg4cOBDpIQTE9ddf\nb5JqBZfLxT///APAa6+9BsCyZcv49ttvATvcJypcNCS+/vXXXwBMmjTJ5+tmzZoF2JYIMrf27dvz\n3XffhXCEwad3796mdD5z5swpvq5Lly6AdVy/+eYbAJYvXx76AQaBSpUqGf82CYX44vDhw0ZZHTly\nJGDt9OV8d5ryXa9ePaPmCx9++GGERhMa6tev71V1Erw9JtYU8vPixYs899xzANx///1A5IsEUmPo\n0KFkz54dgEGDBiV7XsKCEsVxuiJVvHhxXnjhBQATsitevLg5flKcJPfR77//3ny/y/VXr149ChUq\nBECNGjUAK1RfokQJQBUpRVEURVGUsBN1ilS2bNmMUvHwww8D0KRJk2SrUrE8cHeJnjdvHgDt2rUz\nyoAkpg0cOJB+/fqFYQaBsWXLlkgPISA2b95Mt27dANt24vLlyyxcuDDF37n55psBKFWqFIAp041W\nJC+jSZMmJo4vSLHD4sWLwz6uQKlatSpgH5/UkCIDsO0h5G+SJUsWRyVeC1myZAGse0VSJerYsWPm\nety0aRNgFwocOXKEo0ePhnGkgSGJ86LMA/z444+AvXtPCdnRS0l9uXLlGDNmDGAlqoNtphsJ5Brr\n1asXgNeE47SSOXNmKlSoANgWAv3790/3+6aXv//+G7CiLG3btvV4rl+/fn6pqJE8Vv7w1ltvAdCw\nYUOjHAmHDh0yStr8+fMB+x7jjS1btph8KMkfu+uuu9I9xqhZSFWqVAmw/qh33nlnsuc3btwIwODB\ngwFYtGhRstdI0vns2bN54oknAPuP6E/FgxIYU6dO9et1EtIT+VbCXt6OZTQxcOBAwJ4XwFdffQVA\ngwYNIjKm9CA+NRLWSwkJK7gXSkhVo2xaxPvGKcjiUL4sExMTzWbs6aefBqyKp2j33pFk3d69e5vH\nZDHvq0VRwYIFTTFIlSpVzONSLS0VgO+8805wB+wnFSpU4LPPPgPsNkzeOHXqlHH9TkqRIkW8FsZI\nWFY88JyACAeLFi1KtpCSCr2UkIWzdBtwEnXr1jViiCzcwbPyHuzwub8kJiaaJPtgoqE9RVEURVGU\nAHG8IiWrUUkgy5kzpwnjiaw+bNgwli5dCthSZ2o8+OCDgN1zqH379vTs2TN4Aw8BW7duBSyH7FhE\njrF4E0lIUBIGo42k7u3uDBs2DIhOV2GR0o8fP07evHkBW6XatGmTCRWI6itl6ADNmzcH7AR0f6/X\ncJA9e3azO2/ZsqV5XDyv3n777YiMKxQk9Y4CWyX1hiRrT5482ShRUjiwdu1aE+YTl/RIUaJECZ9K\nlIR9XnvtNVPckpRBgwaZyIY7Q4cOBZyp4KxevdoUvLg76SdFrC3GjBljLB6cdA+SdJ1Ro0aZIgj5\njn7llVeMSnXmzJlU36t48eLGbkZ8I6+//nrz/KFDhwBL3fJ17vuDKlKKoiiKoigB4mhFKm/evGYF\nLTlMly9fNrsfSXAMBEk0E1KLJ0eaEiVKmLi92Am4Gz1GO+7zE7NGycWIVp555hnA04hUVEVBkq4l\n1yEakPL+r7/+2hjaitHkgAEDTFFBUnViwYIFJs/IiXYeTz31lIcSJch8RemIxl6dSfFmTOnrHJT8\nm6ZNmxqbGDn2stuHyKnlkmA+ZcoUr89L3pQoHufOnUv2GrHwaNKkSbLndu/e7WhDzkaNGvn1Hfbb\nb78BloroJCVKELXP3ZJDCjmuuuoqE6UQM1v3ghfJ2ZRel8WLF0/2Nzlx4oSxsRB1688//0z3uB25\nkBLvjpEjRxoXYTlhmjVrFpQv2Oeffz7d7xEOpOnkhAkTzEnh9EVfILz++uvGAXv37t2AfdHHElI0\nIY2nv/76awA6depkEimjhY4dOzJixAjA9mgrUqSIcVFO6t0zadIkRy6ghJQKTsS1XdrgDBgwwGzw\nnOw95y8SOvHl7eW+wJRkXWm43a5dO7NQiVRD9RkzZgBWK62krFixwiz6fC0eJGE+aWUtWP5gTuw8\nkHQjkxpSXLBs2TKWLVsWsnEFE/mu9ub35e5eLsUAJ0+eBKyCCnluw4YNgLXgDMVGSEN7iqIoiqIo\nAeJIRUrkO3d/B3GMTilBMC3UrFmTOnXqeDy2evXqdL9vKJBdspQrxxpDhgwBLDlddgruDafBSlYW\n2V1CEtGgBEhpv8jU4lnmTu3atQFYunSpeT5alKlz586ZZr3yE6zCDcCEQsRzyenOyYMGDTLh8vvu\nuw+AMmXKmGbh1157LWDZeUiIUn6uXLky3MNNF1IckBriGSZFAmCnFsgxP3HiBAkJCUDqHlTBRpLC\n5di4Iz5SkyZN8qlESVeM8ePHJ3tOmlA7tcm2tx6siYmJgKV2i82IuLELbdq0caQiJedWmzZtTGK4\n/Dxx4oTpCygcPnzYKKlyrooVEsAPP/wA2Gpj0pSeYKGKlKIoiqIoSoA4SpGSXU3Hjh3NY5IYNnny\nZMC/sseUEPWje/fuJnFUSkalg7QSejJkyGCOsRzfuLg44yad1MCzcOHCpqRXXrN+/XqjSo0aNQqw\nDOac1JdPEstlN5gnTx5j9CiO0qJIlSlTxuygpZgiWmndujVg5zR88sknAI50ME/K66+/7vGzZMmS\nPProo4CttJUuXdooOgsWLABsZWDVqlVhHG3g+JtnKfdMdwsLUaLE0LFbt24pmluGGlGkpG9coUKF\nTBKxRC9SS6o+fPgw4JmAL330JN/GSfcVwLisS2I12PcbcQI/d+4cH330EWDnUkne4kMPPcTMmTMB\nZ6qpc+bM8fu1Ypcj9xm57/zwww+mcCBUSpSgipSiKIqiKEqAOEaRuuOOO3jppZcAe2fw/fffM3bs\nWCDtXdOleiN//vymr56sXOPi4owZlyhRYk6mBB/J8xLFpWXLll4rY6RFTMOGDVN9T/fXSMXYxYsX\nTZm2r35L4UaUmKNHjxojTjmfRZECuzosmsmYMWOyfJVoyfnyxv79+43qIT9nzZplzjPpZSa74SZN\nmgQljzPUpHY/FQW4UaNGHo9fvHjR2M5Iy6O03ptDQSB9UsuVKwfA9OnTkz0nLUj++OOPdI0rVNx4\n442AZ6WpqKju1g6i7Igq3q5dO8CyeqhRowbgTEXKX8qVK2daG0kuo5yPjRs3DrkSJThmITVo0CDT\nm0tOhJ49e/p9kUozw7p16wIwevRowDNJe9euXQDs3LmTHj16ALas61Qk+fXQoUOmrDwakL/72LFj\nTajHPTzgDZHT5YtXSuWvv/56YxsgIVnp2eZO5syZzWc5aSHlDfdmscIXX3wRgZEElzZt2iTrhen0\nY5FWHnjgAWN/IGEUWXh06NAhKhZSUg7eqlUrExaTL9vcuXOb+6dcZxIuefzxx6O+ibgg94qkYc4V\nK1Z4TTx3EhJmdkdsY7whGzhZSLn/24lO7akh988ZM2YY0UTWCmLVEa5FFGhoT1EURVEUJWAco0i5\nO5mKQ2mPHj2Mq7A79erVA6ykT0F2VWLqKJw7d870ahswYAAAR44cCeLIQ4uoM9OnT48KE1FRoiTx\n0b1ztzuye5LwwJdffmmUyKSuw3FxcVx99dWAnWweFxdH8eLFPV6XJUsWo0g6CZHfS5cubfruJR3n\nxYsXo1piF2SXD1Y/M4BTp05FajghQxSpe+65B7B7B3bt2pV33nkHsAoinIqkMpw/f96oafPmzUvx\n9dI/MVbUKMDcU5Jy+vRpr+aPTkLOP/frTWyDvFn5eHPv9tWTz6lIOFYMWAsVKmSMUiXxXtTWcKKK\nlKIoiqIoSoDEhXPlHRcXl+KHNWjQgNmzZwN2zNp9bEnbTaSE5EGJZf4XX3zBzz//nI5RI58b58/r\nfM0xPdSsWZPvvvsOsHNOpFWOmJWmF3/mmNr8ZIzS2scdSchdunSp6bYdjGPjjuRSJe1pB6E7hgUL\nFjTlyHJ+upfBd+jQAbB7O7kjpdn9+vXjjTfeSMvHeiVS56nk723fvt0ocGLiuGjRomB+VMSvRW+4\n96kT1VGUqUAIxrXoD40aNWLkyJGAfe3MmTPHqFNyzsr8RBFOL5E+hgkJCcbGQZKUZc6jR48OSvFR\nKOcoxpVidpsnTx5TQCXWHO5KsBSAuOcEy+uTKvtpIZzHsWLFiskSy3/44QdTMBaq3ER/5uiY0N4X\nX3xhTgCpXqpcubJfv7tq1SrjYyK+UOL/EYvIF1SmTNbhC9ZCKhjcfvvtgL2g2LZtm6kckQRWbw1D\ng4W3BVSomT59ugnxJO3tBJZHVFIktCnOu8FYREWSZs2aAVYYU5I+g72ACieyEShevDgXLlwAPJPm\nJZXAW+P09HjdhZslS5YYZ2jpHvD333+b87hnz56A3WVi3759xhFbfs6fPz+sY04PslisVKmS+TKW\n4yX3p2io4JaUl3Xr1gFWiFk2M7JJlcpDsKrioxUJ5y1evNgcM7m/hrMyzxca2lMURVEURQkQxyhS\nAL/99hsQWwmNocSJZatJexiuXbvW7OhjFfeO8xKC9uaTBZjCB0kWlXB2tONubSFu39GIFAF8/vnn\ngFX+L+qMex85Oc5SGCMcPXqUb775JhxDDRri2u3NvfuZZ54BbOWtVKlSbN++HbCVj2hAlCjpjeje\nPUMUKFH4owlxnr/jjjtMSF0iO6lZODjdpuOGG24A7B6d1113nVGixN/MCWoUqCKlKIqiKIoSMNG3\nBP+PcujQIdOBXLphO63/E1gdx/9r9OjRg6VLlwK2OztgEiNF3Zg5c6bpD5ha/69oQZy9pfT60qVL\nXpPqo4XExETA0/BV1CcxDPbFhAkTOHr0aGgGFwHEwiGpyWq0IddgwYIFzWPSh65r164AnD17NvwD\nSydS3NOqVSvTc9Sf3OJDhw6ZTiJOpG7duuY+IhY6u3btMhZGx44di9jYvKELqSjh4MGDlCpVKtLD\nULywZs0av5vAxhoS0pMij9mzZ5tq0mhE/GnKli0LWE2LfSEJ15s3bwbsBtqKc+jevXuytkUnTpww\nPmfRuIBKyvLly027Keny0aVLF9PKSOYvSeqNGzeOWKNpX0gbtw8++MB4S65duxaw2i85bQElaGhP\nURRFURQlQBzjI+V0Iu17Eg7C5V0TKfQY2ugcnY1eixbpmaOEZ8eNG2dCz2I70r9/f+NrFyr0PLVJ\nbY6ibP/444+AZRkjieXSoD5SieX+zFEVKUVRFEVRlADRHClFURQl5hBFSlzAwTLPheg2i41FxDZH\nzIvPnz/PY489BjjH4sAXGtrzE5VpLWJ9fqBzdDo6R4tYnx8EZ461atXiyy+/BOyWKo0bN+aPP/5I\n71v7RM9Tm1ifo4b2FEVRFEVRAiSsipSiKIqiKEosoYqUoiiKoihKgOhCSlEURVEUJUB0IaUoiqIo\nihIgupBSFEVRFEUJEF1IKYqiKIqiBIgupBRFURRFUQJEF1KKoiiKoigBogspRVEURVGUANGFlKIo\niqIoSoCEtWlxrPfbgdifY6zPD3SOTkfnaBHr8wOdo9PROVqoIqUoiqIoihIgupBSFEVR/GLcuHFc\nuXKFK1eu8Omnn/Lpp5+SMWPGSA9LUSKKLqQURVEURVECJKw5UoqiKEr00blzZwC6deuGy2Wlu9x6\n660A5MqVi5MnT0ZsbIoSaVSRUhRFURRFCZCYVaTq1KkDQJUqVZgxYwYAp06diuSQ0k2HDh0AuOee\newDo1KlTJIcTVtq2bQvANddcA0DdunVp3749AO+++y4Ajz76aFjH1KZNGwDeeOMNdu3aBcDmzZsB\n+PLLL9m2bZvH648dOxb156Dy36JIkSIADB48GMAjH2rcuHEAqkY5jHLlygGWeij3RLlvrl69GoB6\n9erx77//RmR8sUicyLRh+bAwlEBWr14dgOeffx6AZs2aceDAAQAuXrwIwNy5c/nmm28A+OqrrwA4\nf/68z/d1QplnoUKFAPj0008BqFGjRlDfP5Il15kzZ6ZChQoA5mfv3r3N8zfddBMA2bJlS/E9Ukt6\nDfYxvOWWWwBYuHAhBQsWTPX127dvZ+zYsQB8++23AOzcudOfj/KbcJ+nI0aMAGDZsmUArFy5Mhhv\n65NwzlEWEO7/jo+PJz4+PtXffeGFF3w+v2rVKsDaFCQl0vYHci3JnJ977jnz3JgxYwB49tlnAbh8\n+XKa3z+cx7Bw4cLmeitRogQA69evp2nTpgD88ccf6f0Ir4T7WuzTpw8ATz31FADFihVz/wwAjh8/\nDlhh2f3796f7M53wvSjUrFkTgNmzZwPw888/07JlSwDOnDkT8Puq/YGiKIqiKEoIiWpFqkCBAgDM\nmDHDJEBWq1YNwPz/tddei7c5ygr9zjvvBGDNmjU+P8sJK+/SpUsDtopx3XXXAfDXX38F5f0jsQt+\n+eWXAes4pTdUGW5FSqhevbpRZnwpFXFxceZcPHHiBGDv6qdPn56Wj0yRcJynCxcuBKzwuSiEH330\nEQDdu3fn3LlzaXq/Vq1aAbBixQog9VBROK/F9NwfX3zxRfNvUZ/kpx+fG1FF6o477gAwyr2wa9cu\n7r33XgCj9AdCOI5h5syZARgyZAh9+/ZN9vyFCxcAeOuttwB47733AGuOf//9d6AfawjHHK+99loA\nEhISePrppwHv90H5vtu+fTtgqYq33347YEdxACZOnAjYf5PUCPf3YtasWQF73mB/Ly5duhSALFmy\nmOcmTJgA2Mrqn3/+mebPVEVKURRFURQlhESlIiVKlORlVKlSJdnOUVbn3mjZsqVJRpc48fTp0xky\nZEiKv+MERUqSCH/66SfA3nEFi1DvgkuWLAlAixYtGD58OGDPQXZM6SFSihRApkxW3Yb7bkji85Kr\ncPXVV5u8L3mdnLdvvfUWCQkJgJ3LFwihnGPz5s0BO0emePHifPjhhwB88cUXALzzzjtpes/SpUub\nXXK3bt38eo9wXIuiLKaW8yUKk+RauudUpYdIKlI33ngjr7/+OgANGzYE4JdffgGgfv366VKihHAc\nw169egFWMUgK7y1j8Xj8+++/N6qqvGbkyJFGMfWXUM5RFJl9+/YBtlKTEgMGDABg69atgK0qp8SU\nKVMAS2H2Rbi/FyUveO3ateYxuffIffPjjz8GrFyxqlWrArZa1ahRozR/pj9zjLqqvY4dO5pwSPny\n5c3jsiBq0qQJ4D2JN3fu3IAdSgA7+fCBBx7wuZByAuLbEm3IRS8n+M033+zX7/3++++AlSiYI0cO\nwDOB0klcunTJ4yfA+++/n+x1ixcvBuChhx4C4PHHHwegZ8+ezJw5E/C8STiFEiVK8MEHHwCeCf9z\n5swBYO/evQG9b6ZMmcwNUK5PJ+ArUTwYi34nM3DgQBO+k/NZKmSDsYgKF4MGDUr22KJFiwBrjhky\nWAEZ+XKV75MmTZqYc1GOtYT9nIKMy9sCSsKSW7ZsMRXrstAvWrSoX+8vidtO5fPPPweszVfSc1IW\nxgsWLDCb2YMHD4Z0PBraUxRFURRFCZCoUaQ6duwIwNtvv+01pPXggw8CvsvJRa59/PHH+eSTTwB7\nF1KyZEmT+Oxe6uskZPfx66+/RngkaUMURH+VqGHDhgG2orNr1y5jG/DEE0+EYIThQ8qwJUwripRT\nkWstMTHRKFHihbV582YTInBX4tJCixYtyJ49O2CFCiONu8XBf4169eoBlmWMqITPPPMMABs2bIjY\nuNKKJE/nz58f8AzdSVGIhLjAUm7cyZcvn/GAkzSSaGLJkiWA7b3nzpEjRwAYPny4Cfe5I9exvEck\nkfSHfPnyAZbSJmktouj7Sh4/duwY8+fPB+DKlSsA5MiRI1Wro0BQRUpRFEVRFCVAHK1IZcmSxexS\nRdVwV6NkNbpw4UKzUvWF7LL27t1rVrSSsJ4/f35Tfu9URUqSff2Zq5OQHZ/8/eYQoJoAAA7ySURB\nVN2P4YIFCwDrWEqJ8tmzZwF7F/HUU0/Ro0ePZO8r5nLhdjRPD1KqG0jSYySQxHL3pNO5c+cCMHbs\n2IDMGMHe6YuJIGB2j07FX+uCaEOUKDEyzJkzJ19++SUA48ePj9i4AkVUUsmBunLlinHxPn36dKq/\nX7JkSa666iqP93ACUkyTWoGYHDtvyHX3wAMPeH1e7qVSRBJJrr/+esAyTwXLFmXHjh0AbNq0CbCU\ncl+IotqlSxcAdu/eTYsWLYDgGrE6eiFVvHhxr6E68biYPHkykFya9YfatWsDtvwLdkjJqchF4C2J\n2clIouZvv/0GeFbXff311wBe2xXkzJkTsBKxvd3QpCpHEridzp133mmOnVOT5gUpEGjXrp15THx3\nZCEVyHUnyE2yQIECJjk2WH5oocK9kk88oqJ9cZUjRw7mzZsH2Nfb5s2bad26dSSHlS5koSEbsfPn\nzxu3b6kQ9UWFChVMuFk8zSQkH0mke0JqHldSNLV161Yz7sKFCwNWagzYRVbuLFmyxHTNcAKy4Zbv\ni6JFi9K/f3/AXvR7Qzar77zzDrfddpvHex0/fjwkTvbOWW4riqIoiqJEGY5UpMRv6OGHH/ZaaixN\ne5988smAP0Mabsp7lCpVyoQx3nzzzYDfN5QE4srqJPztwyY7iq5duwJQpkwZr69L2hTYaYjzvMzj\nhRdeSFGW/+yzz4xs7QSkvDhPnjzmMfF3CkYiqntIT6hSpQrgn2oQKkRhkqTzwYMHJ7NC8NZrT/rl\nRYtCJerTJ598wtVXXw3YIfXBgwdHbXPtwoULe3i5gRXO8cffTJKaH3nkEfOYqD/B6EuXXsQOBuxE\n7GnTpgHw2GOPmefkmv34449NuoREW6QJNdhRAAl/zZo1KyiO7sFCCgIkkrF3715jwSLkz5/fFJrJ\n/UPU1Fy5coVrqKpIKYqiKIqiBIqjFClRoiTp9KabbjI7eNkRzJs3LyiKkThrS66Ky+Vi6NCh6X7f\nUCIJkLHO/fffD2Ccvt2RvIe+ffv6tLpwApKoK0UC3jh27BhgJX+KIuAEbrjhBo///+uvvzx6x6UF\nye3Lly8fFStWBKwSe0HyUcqWLRvQ+weTpIrS4MGDk7mVx8fHG5UqqQP6qlWroiJ/qmfPnoClpJ05\ncwawlZjUXK+dTK9evYyZ5j///APg93krfVrd1UYxtHQaoiZJRKVatWpGkRHy589venh6c3GX4irp\nk+k0JD9T1NPKlSubHC5R5CQ65Y0DBw4kywWbOnVqKIbqrIVU06ZNAWsBlRSRmkeOHJnupNQCBQrQ\nr18/wD6x/v33X/bs2ZOu9w0ld955p7nAJXEy2hAfosGDB3u40ifl7rvvTvG51157Df6vvTsLieoN\nwwD+GH8iWkiKFimS8EKogSxszyWoDKOihRZpuSjssoxCSFEoCbKijUoqaIFok24qgjZFzIyKimix\niyQqKKK66KqS+l8Mz3fOOMdx5ujMfMrzu7FGy3Mc58x73u/93hfObjKbPX/+HADMLpE+ffqYQJC4\n2eHSpUvmzc2m7tG8AKempmLnzp0AnOAvJyfHpN25LOIeJso3NN4A9OvXL+xmoLGx0Vwobb2gt1df\nX+8ZcAHB5Vu+Tm1c7lu9ejUAp+t3W1sb1q5dCwBWFRr75d4Nu2fPHgDRnxcDKTfbb665AaSoqAh3\n7twB4BSWu7l3MJLtAfOcOXMAAGlpaeYxr3PjciQTMJygkZOTY0oIWDYRr12oWtoTERER8cmqjBTn\n4tDXr19NkRy3WnclG8VeROwpATiFrdXV1Va3Fejfv7/p7eHuymu79evXm+VTLut4ddztzLFjxwAE\nCyJ7ihMnTgAIpqSB4JJlR8XmBQUFKCsrA+C0dfBqCZEovBPnEtanT59CXjeRsFcPj599zxobG1Fe\nXh7yta2trWagqO1LtZG4l//4M+Ny3+zZs63ISg0cOND0yOPsytu3b0eVseHya2lpqdlKzmuyTZnE\nNWvWmCwv58tFi5lE9wYnTsOwXUtLi8kwcXOLGzNR7usPB6izYP3Lly/xPsyY1NTUAHCeg3nz5pnp\nHjzXu3fv4u3btwBgPnLzxKZNm0y2ikug8Xo+lZESERER8Smlsy6p3frNUlIifjPeteXk5AAIZqQK\nCgoAOPUmfpw6dQqAU1wHOGvG3Grf2fT6f//+RTXyvbNz9KukpMRscfWqIesO0ZxjpPMbNGgQpk+f\nDgDmY2lpqeeE8lgtW7YMAMyMRD+S/RwGAoGwx7iG795yzS7+Bw4ciPl7dPc5cibg5MmTTV0N66Gq\nqqrw8OHDsH/z6dMnADAzrdi2IyMjw9Qq/PdfMBleWVlpGghGK9nPY2eYiWKGo76+3tRLRaurr0Uv\nFy5cMNlg1rktXLgw7DlMS0szWVT+XnLzweTJk83Xcebn/Pnz0dLSEsuhJP05HD58uDknrlSwNtNd\nk8Omzzt27MCPHz9i+h6JPMft27ebLLK74bHre/CYwj7H98eKioqYm1Um+3l04/XVPfmD8wQ5Y9GP\naM5RGSkRERERn6yqkWK90qxZswAAjx49irkRGncFcQRMbm6uyWoxGn/16pXZGRWPSdDxMGrUKDx7\n9izZhxHR+PHjPZs18mfMmpmBAweajES0ONqAa96ckdiTeNW2sWbFnZEaMmRIwo6pM6xbevPmjRn1\n41dWVlbY8x5tk9aehNvtmZFq37wz0TIyMgA4u0cBZ/eSV0axvLzcZGk+f/4MANiwYQOAYIaS19O5\nc+cCCDZo5Z87y+wnQyAQMBlyZrbT09NNu41IqzKcPVdbW4u7d+/G90BjwBUVZq937doVNkbr9+/f\nZhWGdYvceTtx4kTzdRs3bgQQ3FXbk+aWEuv3zp8/H/J4c3Mz9u3bl5BjsCqQal9sfvXqVc/O5l6y\ns7MBAHv37gXgLA+mpKSYFwqXB5cuXWrVFvNovHjxAgsWLADgpG79DoyNl4MHD3o+zkLc69evAwhu\nR+6oW3lHWEDKIceFhYVWXdikY7zAs4MyALNMYnPLEb+8CsvdndITjb12+vbta7pjs8DYjUu3xcXF\nJiDiUh5nzjU0NJibJQ7HTU9PN8GiTYEUl+WWLFliWnFEizf1q1atCvm7LRhAec2HffTokfma9jMC\nucxeV1dnAhDKzc01hdrRDHe2RWZmJgBnUw9VVVWhra0tIcegpT0RERERn6zKSDG6ZpHtyZMnTbO4\nSE0a8/LysGXLFgBOJsqNdxPMePW0bBQQnLPEyHvs2LEA7Lmb55b2jorg2fyUz5FXU7WmpiazfDBu\n3DgAznIes1GAU6R8+fJlzJgxAwBiLnRNhMzMzKiOK9bMXE9UVFQEwNmAADi/u1w6skF+fr5Zaow2\nE97R/2MTd7sRti6oqKgAELzGshUCt8336dPHZC6Y4af169eHFTP//fsX379/j8/BdwE357iX7vhe\ncPPmTXPeXMak2tpas9xl07QBWrx4secGDa64cJKCV+E4G+h++/YNo0ePDvlcIjeedZeRI0eGZeW4\nasEGpYmgjJSIiIiIT1ZlpA4fPgwgtI19Xl4egMj1QF6jNxoaGgAg5m3HtsrKysK7d+8AOMWjtmSk\neHfU/jkgd0apPTafvHbtmrlbYmM1FoauWLEirEg5NTXV3DWyXsAmmzdvNjMDyV2vR715fiKfM2Ya\n3Vi/YpPKykrf8wTdb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -550,16 +740,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Let's have a look at the average of all the images of training and testing data." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -596,9 +791,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -622,7 +819,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -663,7 +860,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Testing\n", "\n", @@ -672,9 +872,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -695,35 +897,44 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "from learning import DataSet, manhattan_distance\n", - "\n", "# takes ~8 seconds to execute this\n", "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Moving forward we can use `MNIST_DataSet` to test our algorithms." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### k-Nearest Neighbors\n", "\n", @@ -734,9 +945,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -757,16 +970,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "To make sure that the output we got is correct, let's plot that image along with its label." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -779,10 +997,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -790,7 +1008,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -804,7 +1022,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", From 2922ab68d374d61d92ac9fdcfad545f6a72e8d7e Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:25:49 +0530 Subject: [PATCH 443/513] Games notebook updates (#383) * updated games.ipynb with refactored games.py * fixed typos in games.ipynb --- games.ipynb | 230 ++++++++++++++++++++++++++++++++++------------------ 1 file changed, 152 insertions(+), 78 deletions(-) diff --git a/games.ipynb b/games.ipynb index 1dc5f5ca9..da7652cf8 100644 --- a/games.ipynb +++ b/games.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "from games import (GameState, Game, Fig52Game, TicTacToe, query_player, random_player, \n", - " alphabeta_player, play_game, minimax_decision, alphabeta_full_search,\n", + " alphabeta_player, minimax_decision, alphabeta_full_search,\n", " alphabeta_search, Canvas_TicTacToe)" ] }, @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -237,7 +237,7 @@ "output_type": "stream", "text": [ "a1\n", - "a3\n" + "a1\n" ] } ], @@ -250,12 +250,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `alphabeta_player(game, state)` will always give us the best move possible:" + "The `alphabeta_player(game, state)` will always give us the best move possible, for the relevant player (MAX or MIN):" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -285,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -296,7 +296,7 @@ "'a1'" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -318,7 +318,7 @@ "'a1'" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -354,18 +354,47 @@ "3" ] }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "game52.play_game(alphabeta_player, alphabeta_player)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "B3\n" + ] + }, + { + "data": { + "text/plain": [ + "8" + ] + }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "play_game(game52, alphabeta_player, alphabeta_player)" + "game52.play_game(alphabeta_player, random_player)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -374,41 +403,68 @@ "name": "stdout", "output_type": "stream", "text": [ - "B2\n" + "current state:\n", + "A\n", + "available moves: ['a2', 'a1', 'a3']\n", + "\n", + "Your move? a3\n", + "D3\n" ] }, { "data": { "text/plain": [ - "12" + "2" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "play_game(game52, alphabeta_player, random_player)" + "game52.play_game(query_player, alphabeta_player)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current state:\n", + "B\n", + "available moves: ['b1', 'b3', 'b2']\n", + "\n", + "Your move? b3\n", + "B3\n" + ] + }, + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#play_game(game52, query_player, alphabeta_player)\n", - "#play_game(game52, alphabeta_player, query_player)" + "game52.play_game(alphabeta_player, query_player)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note that, here, if you are the first player, the alphabeta_player plays as MIN, and if you are the second player, the alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." + "Note that if you are the first player then alphabeta_player plays as MIN, and if you are the second player then alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." ] }, { @@ -421,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -439,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -469,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -490,12 +546,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "So, how does this game state looks like?" + "So, how does this game state look like?" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -523,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -531,10 +587,10 @@ { "data": { "text/plain": [ - "(3, 3)" + "(3, 2)" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -545,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -556,7 +612,7 @@ "(3, 2)" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -574,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -585,7 +641,7 @@ "(2, 2)" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -603,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -612,29 +668,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "O X O \n", - "O . X \n", - "O X X \n", - "-1\n" + "O O O \n", + "X X . \n", + ". X . \n" ] + }, + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(play_game(ttt, random_player, alphabeta_player))" + "ttt.play_game(random_player, alphabeta_player)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The output is -1, hence `random_player` loses implies `alphabeta_player` wins. \n", + "The output is (usually) -1, because `random_player` loses to `alphabeta_player`. Sometimes, however, `random_player` manages to draw with `alphabeta_player`.\n", " \n", - " Since, an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" + " Since an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": { "collapsed": false }, @@ -688,7 +753,7 @@ ], "source": [ "for _ in range(10):\n", - " print(play_game(ttt, alphabeta_player, alphabeta_player))" + " print(ttt.play_game(alphabeta_player, alphabeta_player))" ] }, { @@ -700,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -709,52 +774,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "X . . \n", - "O O O \n", - ". X X \n", - "-1\n", - "O O O \n", - "X X O \n", - "X X . \n", - "-1\n", - "O X . \n", - ". O X \n", - "X . O \n", - "-1\n", - "O . . \n", - ". O X \n", - "X X O \n", - "-1\n", + "O . X \n", "X O X \n", - "X O O \n", - ". O X \n", + ". . O \n", "-1\n", - "O . X \n", + "X O X \n", + "O O X \n", "X O . \n", - ". X O \n", "-1\n", - "O O X \n", + "O X O \n", "X O X \n", + "X O X \n", + "0\n", + "O X O \n", "X O . \n", + "O X X \n", "-1\n", - "O O O \n", + ". . O \n", + ". O X \n", "O X X \n", - "X . X \n", "-1\n", + "O O O \n", "X X O \n", - "O O X \n", - "O X . \n", + ". X X \n", + "-1\n", + "O O O \n", + ". . X \n", + ". X X \n", "-1\n", - "X . X \n", "O O O \n", + ". X X \n", ". X . \n", + "-1\n", + "X O X \n", + ". O X \n", + ". O . \n", + "-1\n", + "O X O \n", + "X O X \n", + "O X . \n", "-1\n" ] } ], "source": [ "for _ in range(10):\n", - " print(play_game(ttt, random_player, alphabeta_player))" + " print(ttt.play_game(random_player, alphabeta_player))" ] }, { @@ -770,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -828,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -881,12 +946,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Yay! We win. But we cannot win against an `alphabeta_player`, however hard we try." + "Yay! We (usually) win. But we cannot win against an `alphabeta_player`, however hard we try." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -934,6 +999,15 @@ "source": [ "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -952,7 +1026,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From 2b07ba93156ec4a9a6013bb752227e254006e507 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 22 Mar 2017 12:28:49 +0530 Subject: [PATCH 444/513] Fixed bugs in games.py (#380) * move play_game into games class * display current state before prompting for action * fixed player swap bug * display available moves to human players * make tests pass --- games.py | 31 ++++++++++++++++++------------- tests/test_games.py | 6 +++--- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/games.py b/games.py index f5061f4c8..d98b7473c 100644 --- a/games.py +++ b/games.py @@ -136,6 +136,10 @@ def min_value(state, alpha, beta, depth): def query_player(game, state): """Make a move by querying standard input.""" + print("current state:") + game.display(state) + print("available moves: {}".format(game.actions(state))) + print("") move_string = input('Your move? ') try: move = eval(move_string) @@ -153,18 +157,6 @@ def alphabeta_player(game, state): return alphabeta_full_search(state, game) -def play_game(game, *players): - """Play an n-person, move-alternating game.""" - - state = game.initial - while True: - for player in players: - move = player(game, state) - state = game.result(state, move) - if game.terminal_test(state): - game.display(state) - return game.utility(state, game.to_move(game.initial)) - # ______________________________________________________________________________ # Some Sample Games @@ -204,6 +196,17 @@ def display(self, state): def __repr__(self): return '<{}>'.format(self.__class__.__name__) + + def play_game(self, *players): + """Play an n-person, move-alternating game.""" + state = self.initial + while True: + for player in players: + move = player(self, state) + state = self.result(state, move) + if self.terminal_test(state): + self.display(state) + return self.utility(state, self.to_move(self.initial)) class Fig52Game(Game): @@ -255,7 +258,9 @@ def actions(self, state): def result(self, state, move): if move not in state.moves: - return state # Illegal move has no effect + return GameState(to_move=('O' if state.to_move == 'X' else 'X'), + utility=self.compute_utility(state.board, move, state.to_move), + board=state.board, moves=state.moves) # Illegal move has no effect board = state.board.copy() board[move] = state.to_move moves = list(state.moves) diff --git a/tests/test_games.py b/tests/test_games.py index 28644fbc5..35df9c827 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -60,13 +60,13 @@ def test_alphabeta_full_search(): def test_random_tests(): - assert play_game(Fig52Game(), alphabeta_player, alphabeta_player) == 3 + assert Fig52Game().play_game(alphabeta_player, alphabeta_player) == 3 # The player 'X' (one who plays first) in TicTacToe never loses: - assert play_game(ttt, alphabeta_player, alphabeta_player) >= 0 + assert ttt.play_game(alphabeta_player, alphabeta_player) >= 0 # The player 'X' (one who plays first) in TicTacToe never loses: - assert play_game(ttt, alphabeta_player, random_player) >= 0 + assert ttt.play_game(alphabeta_player, random_player) >= 0 if __name__ == '__main__': From efa5628126987d16c7f7a2e6f25b1f58f2146705 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 22 Mar 2017 09:29:51 +0200 Subject: [PATCH 445/513] Update test_learning.py (#376) Add DecisionTreeLearner, NeuralNetLearner and PerceptronLearner tests --- tests/test_learning.py | 42 ++++++++++++++++++++++++++++++++++++------ 1 file changed, 36 insertions(+), 6 deletions(-) diff --git a/tests/test_learning.py b/tests/test_learning.py index 46ac8dd26..f216ad168 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,11 +1,13 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ - PluralityLearner, NaiveBayesLearner, NearestNeighborLearner + PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ + NeuralNetLearner, PerceptronLearner, DecisionTreeLearner from utils import DataFile + def test_parse_csv(): Iris = DataFile('iris.csv').read() - assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa'] + assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa'] def test_weighted_mode(): @@ -20,18 +22,46 @@ def test_plurality_learner(): zoo = DataSet(name="zoo") pL = PluralityLearner(zoo) - assert pL([]) == "mammal" + assert pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == "mammal" def test_naive_bayes(): iris = DataSet(name="iris") nB = NaiveBayesLearner(iris) - assert nB([5, 3, 1, 0.1]) == "setosa" + assert nB([5,3,1,0.1]) == "setosa" def test_k_nearest_neighbors(): iris = DataSet(name="iris") - kNN = NearestNeighborLearner(iris, k=3) - assert kNN([5, 3, 1, 0.1]) == "setosa" + kNN = NearestNeighborLearner(iris,k=3) + assert kNN([5,3,1,0.1]) == "setosa" + +def test_decision_tree_learner(): + iris = DataSet(name="iris") + + dTL = DecisionTreeLearner(iris) + assert dTL([5,3,1,0.1]) == "setosa" + + +def test_neural_network_learner(): + iris = DataSet(name="iris") + classes = ["setosa","versicolor","virginica"] + + iris.classes_to_numbers() + + nNL = NeuralNetLearner(iris) + # NeuralNetLearner might be wrong. Just check if prediction is in range + assert nNL([5,3,1,0.1]) in range(len(classes)) + + +def test_perceptron(): + iris = DataSet(name="iris") + classes = ["setosa","versicolor","virginica"] + + iris.classes_to_numbers() + + perceptron = PerceptronLearner(iris) + # PerceptronLearner might be wrong. Just check if prediction is in range + assert perceptron([5,3,1,0.1]) in range(len(classes)) From 581fa6be3cda45e9157d7eb1b61ffd2382b02ad8 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Wed, 22 Mar 2017 13:03:53 +0530 Subject: [PATCH 446/513] added double_tennis_problem to planning.py; and minor pep8 edits (#373) * Update test_agents.py pep8 changes, showed flake8 errors * Update test_agents.py * Update test_agents.py * Update test_agents.py * Update test_text.py added missing whitespace after comma * Update utils.py added space after comma * Update search.py added space after comma * Update probability.py added space after comma * Update learning.py added space after comma * Update planning.py added double_tennis_problem * Update rl.py In the pseudocode figure 21.8, the first 'if' starts with argument 's', which is the previous state, not s1(i.e, the current state). * Update search.py the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book. * Update search.py * Update search.py * Update search.py --- learning.py | 2 +- planning.py | 31 +++++++++++++++++++++++++++++++ probability.py | 2 +- rl.py | 6 +++--- tests/test_agents.py | 25 ++++++++++++++----------- tests/test_text.py | 2 +- utils.py | 2 +- 7 files changed, 52 insertions(+), 18 deletions(-) diff --git a/learning.py b/learning.py index 8308fe607..981a557c2 100644 --- a/learning.py +++ b/learning.py @@ -754,7 +754,7 @@ def weighted_replicate(seq, weights, n): wholes = [int(w * n) for w in weights] fractions = [(w * n) % 1 for w in weights] return (flatten([x] * nx for x, nx in zip(seq, wholes)) + - weighted_sample_with_replacement(n - sum(wholes),seq, fractions, )) + weighted_sample_with_replacement(n - sum(wholes), seq, fractions)) def flatten(seqs): return sum(seqs, []) diff --git a/planning.py b/planning.py index a17677460..17028e4c6 100644 --- a/planning.py +++ b/planning.py @@ -526,3 +526,34 @@ def spare_tire_graphplan(): graphplan.graph.expand_graph() if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff(): return None + +def double_tennis_problem(): + init = [expr('At(A, LeftBaseLine)'), + expr('At(B, RightNet)'), + expr('Approaching(Ball, RightBaseLine)'), + expr('Partner(A,B)'), + expr('Partner(A,B)')] + + def goal_test(kb): + required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ##actions + #hit + precond_pos=[expr("Approaching(Ball,loc)"), expr("At(actor,loc)")] + precond_neg=[] + effect_add=[expr("Returned(Ball)")] + effect_rem = [] + hit = Action(expr("Hit(actor,Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + #go + precond_pos = [ expr("At(actor,loc)")] + precond_neg = [] + effect_add = [expr("At(actor,to)")] + effect_rem = [expr("At(actor,loc)")] + go = Action(expr("Go(actor,to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [hit, go], goal_test) diff --git a/probability.py b/probability.py index fa856c330..1d7992e6d 100644 --- a/probability.py +++ b/probability.py @@ -643,5 +643,5 @@ def particle_filtering(e, N, HMM): w[i] = float("{0:.4f}".format(w[i])) # STEP 2 - s = weighted_sample_with_replacement(N,s,w) + s = weighted_sample_with_replacement(N, s, w) return s diff --git a/rl.py b/rl.py index 5241710fe..77a04f98a 100644 --- a/rl.py +++ b/rl.py @@ -154,13 +154,13 @@ def __call__(self, percept): s1, r1 = self.update_state(percept) Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r alpha, gamma, terminals, actions_in_state = self.alpha, self.gamma, self.terminals, self.actions_in_state - if s1 in terminals: - Q[s1, None] = r1 + if s in terminals: + Q[s, None] = r1 if s is not None: Nsa[s, a] += 1 Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1] for a1 in actions_in_state(s1)) - Q[s, a]) - if s1 in terminals: + if s in terminals: self.s = self.a = self.r = None else: self.s, self.r = s1, r1 diff --git a/tests/test_agents.py b/tests/test_agents.py index 77421c2c7..0162a78b8 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -1,21 +1,23 @@ from agents import Direction from agents import ReflexVacuumAgent, ModelBasedVacuumAgent, TrivialVacuumEnvironment + def test_move_forward(): d = Direction("up") - l1 = d.move_forward((0,0)) - assert l1 == (0,-1) + l1 = d.move_forward((0, 0)) + assert l1 == (0, -1) d = Direction(Direction.R) - l1 = d.move_forward((0,0)) - assert l1 == (1,0) + l1 = d.move_forward((0, 0)) + assert l1 == (1, 0) d = Direction(Direction.D) - l1 = d.move_forward((0,0)) - assert l1 == (0,1) + l1 = d.move_forward((0, 0)) + assert l1 == (0, 1) d = Direction("left") - l1 = d.move_forward((0,0)) - assert l1 == (-1,0) - l2 = d.move_forward((1,0)) - assert l2 == (0,0) + l1 = d.move_forward((0, 0)) + assert l1 == (-1, 0) + l2 = d.move_forward((1, 0)) + assert l2 == (0, 0) + def test_add(): d = Direction(Direction.U) @@ -37,7 +39,7 @@ def test_add(): l1 = d + Direction.R l2 = d + Direction.L assert l1.direction == Direction.U - assert l2.direction == Direction.D #fixed + assert l2.direction == Direction.D def test_ReflexVacuumAgent() : # create an object of the ReflexVacuumAgent @@ -62,3 +64,4 @@ def test_ModelBasedVacuumAgent() : environment.run() # check final status of the environment assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} + diff --git a/tests/test_text.py b/tests/test_text.py index d58cd497a..577ad661b 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -54,7 +54,7 @@ def test_viterbi_segmentation(): P = UnigramTextModel(wordseq) text = "itiseasytoreadwordswithoutspaces" - s, p = viterbi_segment(text,P) + s, p = viterbi_segment(text, P) assert s == [ 'it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces'] diff --git a/utils.py b/utils.py index cfdc88d37..73dd63d63 100644 --- a/utils.py +++ b/utils.py @@ -194,7 +194,7 @@ def probability(p): return p > random.uniform(0.0, 1.0) -def weighted_sample_with_replacement(n,seq, weights): +def weighted_sample_with_replacement(n, seq, weights): """Pick n samples from seq at random, with replacement, with the probability of each element in proportion to its corresponding weight.""" From 64bb56446232042597bc2c5e2a55709c2be432a2 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 25 Mar 2017 01:59:11 -0300 Subject: [PATCH 447/513] Fix gradient descent for LinearLearning (#414) --- learning.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/learning.py b/learning.py index 981a557c2..2e53f1e99 100644 --- a/learning.py +++ b/learning.py @@ -635,6 +635,7 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): idx_i = dataset.inputs idx_t = dataset.target # As of now, dataset.target gives only one index. examples = dataset.examples + num_examples = len(examples) # X transpose X_col = [dataset.values[i] for i in idx_i] # vertical columns of X @@ -657,7 +658,8 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): # update weights for i in range(len(w)): - w[i] = w[i] - learning_rate * dotproduct(err, X_col[i]) + w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples) + def predict(example): x = [1] + example From 313fee0ade8d425ae67bb3c2027496a10e5ad2cc Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 25 Mar 2017 06:59:34 +0200 Subject: [PATCH 448/513] Bug Fixes for LinearLearner (#408) * Bug fixing * Spacing --- learning.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/learning.py b/learning.py index 2e53f1e99..d29de27cb 100644 --- a/learning.py +++ b/learning.py @@ -35,6 +35,7 @@ def manhattan_distance(predictions, targets): def mean_boolean_error(predictions, targets): return mean(int(p != t) for p, t in zip(predictions, targets)) + def hamming_distance(predictions, targets): return sum(p != t for p, t in zip(predictions, targets)) @@ -642,10 +643,10 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): # Add dummy ones = [1 for _ in range(len(examples))] - X_col = ones + X_col + X_col = [ones] + X_col # Initialize random weigts - w = [random.randrange(-0.5, 0.5) for _ in range(len(idx_i) + 1)] + w = [random.uniform(-0.5, 0.5) for _ in range(len(idx_i) + 1)] for epoch in range(epochs): err = [] From eca3b2a37dc574c2f15500c9f242610ab9013224 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 25 Mar 2017 02:00:51 -0300 Subject: [PATCH 449/513] Fix NgramTextModel bug (#412) * Fix NgramTextModel bug * Add new tests for NgramTextModel --- tests/test_text.py | 26 ++++++++++++++++++++++++++ text.py | 2 +- 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/tests/test_text.py b/tests/test_text.py index 577ad661b..d884e02a2 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -47,6 +47,32 @@ def test_text_models(): assert P3.cond_prob['in', 'order'].dictionary == {'to': 6} + test_string = 'unigram' + wordseq = words(test_string) + + P1 = UnigramTextModel(wordseq) + + assert P1.dictionary == {('unigram'): 1} + + test_string = 'bigram text' + wordseq = words(test_string) + + P2 = NgramTextModel(2, wordseq) + + assert (P2.dictionary == {('', 'bigram'): 1, ('bigram', 'text'): 1} or + P2.dictionary == {('bigram', 'text'): 1, ('', 'bigram'): 1}) + + + test_string = 'test trigram text' + wordseq = words(test_string) + + P3 = NgramTextModel(3, wordseq) + + assert ('', '', 'test') in P3.dictionary + assert ('', 'test', 'trigram') in P3.dictionary + assert ('test', 'trigram', 'text') in P3.dictionary + assert len(P3.dictionary) == 3 + def test_viterbi_segmentation(): flatland = DataFile("EN-text/flatland.txt").read() diff --git a/text.py b/text.py index 855e89aaf..e064b6049 100644 --- a/text.py +++ b/text.py @@ -55,7 +55,7 @@ def add_sequence(self, words): Prefix some copies of the empty word, '', to make the start work.""" n = self.n words = ['', ] * (n - 1) + words - for i in range(len(words) - n): + for i in range(len(words) - n + 1): self.add(tuple(words[i:i + n])) def samples(self, nwords): From 4f1b1828a2837f8d0fb0ee5a5b18eabb372baf3c Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 25 Mar 2017 02:05:11 -0300 Subject: [PATCH 450/513] Add NgramCharModel to text.py (#413) * Add NgramCharModel to text.py * Update text.py * Update --- text.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/text.py b/text.py index e064b6049..65eef28f6 100644 --- a/text.py +++ b/text.py @@ -26,6 +26,7 @@ def samples(self, n): return ' '.join(self.sample() for i in range(n)) + class NgramTextModel(CountingProbDist): """This is a discrete probability distribution over n-tuples of words. @@ -50,12 +51,16 @@ def add(self, ngram): self.cond_prob[ngram[:-1]] = CountingProbDist() self.cond_prob[ngram[:-1]].add(ngram[-1]) + def add_empty(self, words, n): + return [''] * (n - 1) + words + def add_sequence(self, words): """Add each of the tuple words[i:i+n], using a sliding window. Prefix some copies of the empty word, '', to make the start work.""" n = self.n - words = ['', ] * (n - 1) + words - for i in range(len(words) - n + 1): + words = self.add_empty(words, n) + + for i in range(len(words) - n): self.add(tuple(words[i:i + n])) def samples(self, nwords): @@ -72,6 +77,15 @@ def samples(self, nwords): nminus1gram = nminus1gram[1:] + (wn,) return ' '.join(output) + +class NgramCharModel(NgramTextModel): + def add_empty(self, words, n): + return ' ' * (n - 1) + words + + def add_sequence(self, words): + for word in words: + super().add_sequence(word) + # ______________________________________________________________________________ From 10c82c6a44ae96cb39a465c1b0b22ee3b6432733 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 25 Mar 2017 07:22:38 +0200 Subject: [PATCH 451/513] Add DataSet Tutorial (#411) --- learning.ipynb | 536 ++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 506 insertions(+), 30 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index 9f2d91add..78ff4f0e3 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -16,7 +16,9 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -32,26 +34,51 @@ "source": [ "## Contents\n", "\n", - "* Datasets\n", "* Machine Learning Overview\n", - "* Plurality Learner Classifier\n", - " * Overview\n", - " * Implementation\n", - " * Example\n", - "* k-Nearest Neighbours Classifier\n", - " * Overview\n", - " * Implementation\n", - " * Example\n", - "* Perceptron Classifier\n", - " * Overview\n", - " * Implementation\n", - " * Example\n", - "* MNIST Handwritten Digits Classification\n", + "* Datasets\n", + "* Plurality Learner\n", + "* k-Nearest Neighbours\n", + "* Perceptron\n", + "* MNIST Handwritten Digits\n", " * Loading and Visualising\n", " * Testing\n", " * kNN Classifier" ] }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Machine Learning Overview\n", + "\n", + "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n", + "\n", + "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n", + "\n", + "There are three types of feedback that determine the three main types of learning:\n", + "\n", + "* **Supervised Learning**:\n", + "\n", + "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n", + "\n", + "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n", + "\n", + "* **Unsupervised Learning**:\n", + "\n", + "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n", + "\n", + "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n", + "\n", + "* **Reinforcement Learning**:\n", + "\n", + "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n", + "\n", + "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." + ] + }, { "cell_type": "markdown", "metadata": { @@ -63,9 +90,9 @@ "\n", "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n", "\n", - "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n", + "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv): Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n", "\n", - "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." + "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv): The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)." ] }, { @@ -75,31 +102,480 @@ "editable": true }, "source": [ - "## Machine Learning Overview\n", + "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n", "\n", - "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n", + "Let's have a look at how it works before we get started with the algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Intro\n", "\n", - "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n", + "A lot of the datasets we will work with are .csv files (although other formats are supported too). We have a collection of sample datasets ready to use [on aima-data](https://github.com/aimacode/aima-data/tree/a21fc108f52ad551344e947b0eb97df82f8d2b2b). Two examples are the datasets mentioned above (*iris.csv* and *zoo.csv*). You can find plenty datasets online, and a good repository of such datasets is [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.html).\n", "\n", - "There are three types of feedback that determine the three main types of learning:\n", + "In such files, each line corresponds to one item/measurement. Each individual value in a line represents a *feature* and usually there is a value denoting the *class* of the item.\n", "\n", - "* **Supervised Learning**:\n", + "You can find the code for the dataset here:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%psource DataSet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Class Attributes\n", "\n", - "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n", + "* **examples**: Holds the items of the dataset. Each item is a list of values.\n", "\n", - "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n", + "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features. For example, `item[i]` returns the feature at index *i* of *item*.\n", "\n", - "* **Unsupervised Learning**:\n", + "* **attrnames**: An optional list with attribute names. For example, `item[s]`, where *s* is a feature name, returns the feature of name *s* in *item*.\n", "\n", - "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n", + "* **target**: The attribute a learning algorithm will try to predict. By default the last attribute.\n", "\n", - "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n", + "* **inputs**: This is the list of attributes without the target.\n", "\n", - "* **Reinforcement Learning**:\n", + "* **values**: A list of lists which holds the set of possible values for the corresponding attribute/feature. If initially `None`, it gets computed (by the function `setproblem`) from the examples.\n", "\n", - "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n", + "* **distance**: The distance function used in the learner to calculate the distance between two items. By default `mean_boolean_error`.\n", "\n", - "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." + "* **name**: Name of the dataset.\n", + "\n", + "* **source**: The source of the dataset (url or other). Not used in the code.\n", + "\n", + "* **exclude**: A list of indexes to exclude from `inputs`. The list can include either attribute indexes (attrs) or names (attrnames)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Class Helper Functions\n", + "\n", + "These functions help modify a `DataSet` object to your needs.\n", + "\n", + "* **sanitize**: Takes as input an example and returns it with non-input (target) attributes replaced by `None`. Useful for testing. Keep in mind that the example given is not itself sanitized, but instead a sanitized copy is returned.\n", + "\n", + "* **classes_to_numbers**: Maps the class names of a dataset to numbers. If the class names are not given, they are computed from the dataset values. Useful for classifiers that return a numerical value instead of a string.\n", + "\n", + "* **remove_examples**: Removes examples containing a given value. Useful for removing examples with missing values, or for removing classes (needed for binary classifiers)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Importing a Dataset\n", + "\n", + "#### Importing from aima-data\n", + "\n", + "Datasets uploaded on aima-data can be imported with the following line:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "iris = DataSet(name=\"iris\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "To check that we imported the correct dataset, we can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.1, 3.5, 1.4, 0.2, 'setosa']\n", + "[0, 1, 2, 3]\n" + ] + } + ], + "source": [ + "print(iris.examples[0])\n", + "print(iris.inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Which correctly prints the first line in the csv file and the list of attribute indexes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 2, 3]\n" + ] + } + ], + "source": [ + "iris2 = DataSet(name=\"iris\",exclude=[1])\n", + "print(iris2.inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Attributes\n", + "\n", + "Here we showcase the attributes.\n", + "\n", + "First we will print the first three items/examples in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n" + ] + } + ], + "source": [ + "print(iris.examples[:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "attrs: [0, 1, 2, 3, 4]\n", + "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n", + "target: 4\n", + "inputs: [0, 1, 2, 3]\n" + ] + } + ], + "source": [ + "print(\"attrs:\", iris.attrs)\n", + "print(\"attrnames (by default same as attrs):\", iris.attrnames)\n", + "print(\"target:\", iris.target)\n", + "print(\"inputs:\", iris.inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now we will print all the possible values for the first feature/attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4.7, 5.5, 6.3, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.6, 6.5, 6.4, 6.0, 6.1, 7.6, 7.4, 7.9, 4.3, 5.7, 5.3, 5.2, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n" + ] + } + ], + "source": [ + "print(iris.values[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: iris\n", + "source: \n" + ] + } + ], + "source": [ + "print(\"name:\", iris.name)\n", + "print(\"source:\", iris.source)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa', 'virginica', 'versicolor']\n" + ] + } + ], + "source": [ + "print(iris.values[iris.target])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Helper Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We will now take a look at the auxiliary functions found in the class.\n", + "\n", + "First we will take a look at the `sanitize` function, which sets the non-input values of the given example to `None`.\n", + "\n", + "In this case we want to hide the class of the first example, so we will sanitize it.\n", + "\n", + "Note that the function doesn't actually change the given example; it returns a sanitized *copy* of it." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sanitized: [5.1, 3.5, 1.4, 0.2, None]\n", + "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n" + ] + } + ], + "source": [ + "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n", + "print(\"Original:\",iris.examples[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa', 'versicolor']\n" + ] + } + ], + "source": [ + "iris.remove_examples(\"virginica\")\n", + "print(iris.values[iris.target])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Finally we take a look at `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class of first example: setosa\n", + "Class of first example: 0\n" + ] + } + ], + "source": [ + "print(\"Class of first example:\",iris.examples[0][iris.target])\n", + "iris.classes_to_numbers()\n", + "print(\"Class of first example:\",iris.examples[0][iris.target])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "As you can see \"setosa\" was mapped to 0." ] }, { From df9d7d52e2ce993735170f2e5dbbdd3dc08c9bfe Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Sat, 25 Mar 2017 10:53:51 +0530 Subject: [PATCH 452/513] added fol_fc_ask to logic.py ; update README.md (#415) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update utils.py in pseudo code the sequence of arguments is " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"   * Update utils.py in pseudo code the sequence of arguments is " WEIGHTED-SAMPLE-WITH-REPLACEMENT(N, S, W)"  same must follow in function "particle_filtering" in the file probability.py * Update learning.py weight_sample_with_replacement sequence of args * Update probability.py weighted_sample_with_replacement sequence of args * Update search.py * Update planning.py added double_tennis_problem from chapter 11 , figure 11.10 * Update utils.py added missing space after comma * Update learning.py added missing space after comma * Update probability.py added missing space after comma * Update search.py added missing space after comma * Update planning.py * Update planning.py * Update planning.py * Update planning.py * Update planning.py * Update planning.py * Update test_agents.py pep8 changes, showed flake8 errors * Update test_agents.py * Update test_agents.py * Update test_agents.py * Update test_text.py added missing whitespace after comma * Update utils.py added space after comma * Update search.py added space after comma * Update probability.py added space after comma * Update learning.py added space after comma * Update planning.py added double_tennis_problem * Update rl.py In the pseudocode figure 21.8, the first 'if' starts with argument 's', which is the previous state, not s1(i.e, the current state). * Update search.py the 'uniform_cost_search' in notebook 'search-4e.ipynb' resembles more to the pseudocode in book. * Update search.py * Update search.py * Update search.py * Update README.md * Update README.md * Update README.md * Update planning.py * Update planning.py added spaces after comma * Update planning.py * Update test_planning.py added double_tennis_problem test * Update test_planning.py * Update logic.py added fol_fc_ask from fig 9.3 * Update logic.py * Update test_planning.py --- README.md | 12 ++++++------ logic.py | 17 ++++++++++++++++- planning.py | 17 +++++++++-------- probability.py | 2 ++ 4 files changed, 33 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 08e5e23fd..7cb796b02 100644 --- a/README.md +++ b/README.md @@ -76,16 +76,16 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`][logic] | | 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`][logic] | | 9.8 | Append | | | -| 10.1 | Air-Cargo-problem | | -| 10.2 | Spare-Tire-Problem | | -| 10.3 | Three-Block-Tower | | -| 10.7 | Cake-Problem | | -| 10.9 | Graphplan | | +| 10.1 | Air-Cargo-problem |`air_cargo` |[`planning.py`][planning]| +| 10.2 | Spare-Tire-Problem | `spare_tire` |[`planning.py`][planning]| +| 10.3 | Three-Block-Tower | `three_block_tower` |[`planning.py`][planning]| +| 10.7 | Cake-Problem | `have_cake_and_eat_cake_too` |[`planning.py`][planning]| +| 10.9 | Graphplan | `GraphPlan` |[`planning.py`][planning]| | 10.13 | Partial-Order-Planner | | | 11.1 | Job-Shop-Problem-With-Resources | | | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | -| 11.10 | Doubles-tennis | | +| 11.10 | Doubles-tennis | `double_tennis_problem` |[`planning.py`][planning]| | 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`][probability] | | 13.1 | DT-Agent | `DTAgent` | [`probability.py`][probability] | | 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`][probability] | diff --git a/logic.py b/logic.py index 9054cdfc7..bd9c92334 100644 --- a/logic.py +++ b/logic.py @@ -842,7 +842,22 @@ def subst(s, x): def fol_fc_ask(KB, alpha): - raise NotImplementedError + """A simple forward-chaining algorithm. [Figure 9.3]""" + while new is not None: + new = [] + for rule in KB: + p, q = parse_definite_clause(standardize_variables(rule)) + for p_ in random.KB.clauses: + if p != p_: + for theta in (subst(theta, p) == subst(theta, p_)): + q_ = subst(theta, q) + if not unify(q_,KB.sentence in KB) or not unify(q_, new): + new.append(q_) + phi = unify(q_,alpha) + if phi is not None: + return phi + KB.tell(new) + return None def standardize_variables(sentence, dic=None): diff --git a/planning.py b/planning.py index 17028e4c6..47eae77da 100644 --- a/planning.py +++ b/planning.py @@ -237,6 +237,7 @@ def goal_test(kb): return PDLL(init, [eat_cake, bake_cake], goal_test) + class Level(): """ Contains the state of the planning problem @@ -531,8 +532,8 @@ def double_tennis_problem(): init = [expr('At(A, LeftBaseLine)'), expr('At(B, RightNet)'), expr('Approaching(Ball, RightBaseLine)'), - expr('Partner(A,B)'), - expr('Partner(A,B)')] + expr('Partner(A, B)'), + expr('Partner(B, A)')] def goal_test(kb): required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')] @@ -543,17 +544,17 @@ def goal_test(kb): ##actions #hit - precond_pos=[expr("Approaching(Ball,loc)"), expr("At(actor,loc)")] + precond_pos=[expr("Approaching(Ball, loc)"), expr("At(actor, loc)")] precond_neg=[] effect_add=[expr("Returned(Ball)")] effect_rem = [] - hit = Action(expr("Hit(actor,Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + hit = Action(expr("Hit(actor, Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem]) #go - precond_pos = [ expr("At(actor,loc)")] + precond_pos = [expr("At(actor, loc)")] precond_neg = [] - effect_add = [expr("At(actor,to)")] - effect_rem = [expr("At(actor,loc)")] - go = Action(expr("Go(actor,to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + effect_add = [expr("At(actor, to)")] + effect_rem = [expr("At(actor, loc)")] + go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [hit, go], goal_test) diff --git a/probability.py b/probability.py index 1d7992e6d..a5699b7f4 100644 --- a/probability.py +++ b/probability.py @@ -643,5 +643,7 @@ def particle_filtering(e, N, HMM): w[i] = float("{0:.4f}".format(w[i])) # STEP 2 + s = weighted_sample_with_replacement(N, s, w) + return s From f62441500c5ce53d82a5f58c995c673f7aeb2006 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 25 Mar 2017 03:09:22 -0300 Subject: [PATCH 453/513] Use lru_cache decorator on memoize function (#406) --- utils.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/utils.py b/utils.py index 73dd63d63..7a547c67c 100644 --- a/utils.py +++ b/utils.py @@ -270,13 +270,10 @@ def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): # Misc Functions -# TODO: Use functools.lru_cache memoization decorator - - -def memoize(fn, slot=None): +def memoize(fn, slot=None, maxsize=32): """Memoize fn: make it remember the computed value for any argument list. If slot is specified, store result in that slot of first argument. - If slot is false, store results in a dictionary.""" + If slot is false, use lru_cache for caching the values.""" if slot: def memoized_fn(obj, *args): if hasattr(obj, slot): @@ -286,12 +283,9 @@ def memoized_fn(obj, *args): setattr(obj, slot, val) return val else: + @functools.lru_cache(maxsize=maxsize) def memoized_fn(*args): - if args not in memoized_fn.cache: - memoized_fn.cache[args] = fn(*args) - return memoized_fn.cache[args] - - memoized_fn.cache = {} + return fn(*args) return memoized_fn From 444ac2688ede540b8b4e0950b15b156116ea08f0 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 25 Mar 2017 08:11:30 +0200 Subject: [PATCH 454/513] Bug Fixing in DataSet + Test Updates (#410) * Bugfixing * Test for "exclude" * Update test_learning.py * update_values --- learning.py | 16 +++++++++++----- tests/test_learning.py | 15 +++++++++++---- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/learning.py b/learning.py index d29de27cb..121f184c3 100644 --- a/learning.py +++ b/learning.py @@ -43,9 +43,9 @@ def hamming_distance(predictions, targets): class DataSet: - """A data set for a machine learning problem. It has the following fields: + """A data set for a machine learning problem. It has the following fields: - d.examples A list of examples. Each one is a list of attribute values. + d.examples A list of examples. Each one is a list of attribute values. d.attrs A list of integers to index into an example, so example[attr] gives a value. Normally the same as range(len(d.examples[0])). d.attrnames Optional list of mnemonic names for corresponding attrs. @@ -61,6 +61,8 @@ class DataSet: since that can handle any field types. d.name Name of the data set (for output display only). d.source URL or other source where the data came from. + d.exclude A list of attribute indexes to exclude from d.inputs. Elements + of this list can either be integers (attrs) or attrnames. Normally, you call the constructor and you're done; then you just access fields like d.examples and d.target and d.inputs.""" @@ -68,7 +70,7 @@ class DataSet: def __init__(self, examples=None, attrs=None, attrnames=None, target=-1, inputs=None, values=None, distance=mean_boolean_error, name='', source='', exclude=()): - """Accepts any of DataSet's fields. Examples can also be a + """Accepts any of DataSet's fields. Examples can also be a string or file from which to parse examples using parse_csv. Optional parameter: exclude, as documented in .setproblem(). >>> DataSet(examples='1, 2, 3') @@ -108,14 +110,14 @@ def setproblem(self, target, inputs=None, exclude=()): to not use in inputs. Attributes can be -n .. n, or an attrname. Also computes the list of possible values, if that wasn't done yet.""" self.target = self.attrnum(target) - exclude = map(self.attrnum, exclude) + exclude = list(map(self.attrnum, exclude)) if inputs: self.inputs = removeall(self.target, inputs) else: self.inputs = [a for a in self.attrs if a != self.target and a not in exclude] if not self.values: - self.values = list(map(unique, zip(*self.examples))) + self.update_values() self.check_me() def check_me(self): @@ -150,6 +152,9 @@ def attrnum(self, attr): else: return attr + def update_values(self): + self.values = list(map(unique, zip(*self.examples))) + def sanitize(self, example): """Return a copy of example, with non-input attributes replaced by None.""" return [attr_i if i in self.inputs else None @@ -166,6 +171,7 @@ def classes_to_numbers(self,classes=None): def remove_examples(self,value=""): """Remove examples that contain given value.""" self.examples = [x for x in self.examples if value not in x] + self.update_values() def __repr__(self): return ''.format( diff --git a/tests/test_learning.py b/tests/test_learning.py index f216ad168..4f618f7c1 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -4,6 +4,10 @@ from utils import DataFile +def test_exclude(): + iris = DataSet(name='iris', exclude=[3]) + assert iris.inputs == [0, 1, 2] + def test_parse_csv(): Iris = DataFile('iris.csv').read() @@ -38,6 +42,7 @@ def test_k_nearest_neighbors(): kNN = NearestNeighborLearner(iris,k=3) assert kNN([5,3,1,0.1]) == "setosa" + def test_decision_tree_learner(): iris = DataSet(name="iris") @@ -47,21 +52,23 @@ def test_decision_tree_learner(): def test_neural_network_learner(): iris = DataSet(name="iris") + iris.remove_examples("virginica") + classes = ["setosa","versicolor","virginica"] - iris.classes_to_numbers() nNL = NeuralNetLearner(iris) - # NeuralNetLearner might be wrong. Just check if prediction is in range + # NeuralNetLearner might be wrong. Just check if prediction is in range. assert nNL([5,3,1,0.1]) in range(len(classes)) def test_perceptron(): iris = DataSet(name="iris") + iris.remove_examples("virginica") + classes = ["setosa","versicolor","virginica"] - iris.classes_to_numbers() perceptron = PerceptronLearner(iris) - # PerceptronLearner might be wrong. Just check if prediction is in range + # PerceptronLearner might be wrong. Just check if prediction is in range. assert perceptron([5,3,1,0.1]) in range(len(classes)) From c8e22e696158f10a1a471cb4251d065daeca5da7 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 25 Mar 2017 08:13:58 +0200 Subject: [PATCH 455/513] Fixed Notebook Typos (#397) * Update games.ipynb * Update intro.ipynb * Update csp.ipynb --- csp.ipynb | 389 ++++++++++++++++++++++++++++++++++++++-------------- games.ipynb | 345 +++++++++++++++++++++++++++++++--------------- intro.ipynb | 46 +++++-- 3 files changed, 556 insertions(+), 224 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 3ce7ce2d8..66c7eac6d 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -3,7 +3,9 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "source": [ "# Constraint Satisfaction Problems (CSPs)\n", @@ -13,9 +15,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -24,7 +28,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Review\n", "\n", @@ -35,7 +42,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -44,14 +53,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The __ _ _init_ _ __ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as dict where key specify the variables and value specify the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list its value which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Graph Coloring\n", "\n", @@ -60,11 +75,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['R', 'G', 'B']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s = UniversalDict(['R','G','B'])\n", "s[5]" @@ -72,7 +100,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this what we need is that the neighbors must not have the same color. This is defined in the function **different_values_constraint** of the module." ] @@ -81,7 +112,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -90,7 +123,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows to take input in the form of strings and return a Dict of the form compatible with the **CSP Class**." ] @@ -99,7 +135,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -108,7 +146,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables our the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **australia**, **usa** and **france** are three CSPs that have been created using **MapColoringCSP**. **australia** corresponds to ** Figure 6.1 ** in the book." ] @@ -117,7 +158,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -128,7 +171,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -137,7 +182,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## NQueens\n", "\n", @@ -148,7 +196,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -157,7 +207,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The **NQueensCSP** method implements methods that support solving the problem via **min_conflicts** which is one of the techniques for solving CSPs. Because **min_conflicts** hill climbs the number of conflicts to solve the CSP **assign** and **unassign** are modified to record conflicts. More details about the structures **rows**, **downs**, **ups** which help in recording conflicts are explained in the docstring." ] @@ -166,7 +219,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -175,16 +230,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The _ ___init___ _ method takes only one parameter **n** the size of the problem. To create an instance we just pass the required n into the constructor." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -193,18 +253,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Helper Functions\n", "\n", - "We will now implement few helper functions that will help us visualize the Coloring Problem. We will make some modifications to the existing Classes and Functions for additional book keeping. To begin with we modify the **assign** and **unassign** methods in the **CSP** to add a copy of the assignment to the **assingment_history**. We call this new class **InstruCSP**. This would allow us to see how the assignment evolves over time." + "We will now implement a few helper functions that will help us visualize the Coloring Problem. We will make some modifications to the existing Classes and Functions for additional book keeping. To begin we modify the **assign** and **unassign** methods in the **CSP** to add a copy of the assignment to the **assignment_history**. We call this new class **InstruCSP**. This will allow us to see how the assignment evolves over time." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -213,29 +278,34 @@ " \n", " def __init__(self, variables, domains, neighbors, constraints):\n", " super().__init__(variables, domains, neighbors, constraints)\n", - " self.assingment_history = []\n", + " self.assignment_history = []\n", " \n", " def assign(self, var, val, assignment):\n", " super().assign(var,val, assignment)\n", - " self.assingment_history.append(copy.deepcopy(assignment))\n", + " self.assignment_history.append(copy.deepcopy(assignment))\n", " \n", " def unassign(self, var, assignment):\n", " super().unassign(var,assignment)\n", - " self.assingment_history.append(copy.deepcopy(assignment)) " + " self.assignment_history.append(copy.deepcopy(assignment))" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Next, we define **make_instru** which takes an instance of **CSP** and returns a **InstruCSP** instance. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -246,16 +316,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes are they are connected to." + "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes they are connected to." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -286,16 +361,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -304,9 +384,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -315,7 +397,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Backtracking Search\n", "\n", @@ -324,9 +409,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -337,25 +424,32 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "result # A dictonary of assingments." + "result # A dictonary of assignments." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Let us also check the number of assingments made." + "Let us also check the number of assignments made." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -364,25 +458,33 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Now let us check the total number of assingments and unassingments which is the lentgh ofour assingment history." + "Now let us check the total number of assignments and unassignments which is the length ofour assignment history." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "len(coloring_problem1.assingment_history)" + "len(coloring_problem1.assignment_history)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now let us explore the optional keyword arguments that the **backtracking_search** function takes. These optional arguments help speed up the assignment further. Along with these, we will also point out to methods in the CSP class that help make this work. \n", "\n", @@ -393,7 +495,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -404,7 +508,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -415,7 +521,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -424,7 +532,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Another ordering related parameter **order_domain_values** governs the value ordering. Here we select the Least Constraining Value which is implemented by the function **lcv**. The idea is to select the value which rules out the fewest values in the remaining variables. The intuition behind selecting the **lcv** is that it leaves a lot of freedom to assign values later. The idea behind selecting the mrc and lcv makes sense because we need to do all variables but for values, we might better try the ones that are likely. So for vars, we face the hard ones first.\n" ] @@ -433,7 +544,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -442,23 +555,31 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Finally, the third parameter **inference** can make use of one of the two techniques called Arc Consistency or Forward Checking. The details of these methods can be found in the **Section 6.3.2** of the book. In short the idea of inference is to detect the possible failure before it occurs and to look ahead to not make mistakes. **mac** and **forward_checking** implement these two techniques. The **CSP** methods **support_pruning**, **suppose**, **prune**, **choices**, **infer_assignment** and **restore** help in using these techniques. You can know more about these by looking up the source code." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now let us compare the performance with these parameters enabled vs the default parameters. We will use the Graph Coloring problem instance usa for comparison. We will call the instances **solve_simple** and **solve_parameters** and solve them using backtracking and compare the number of assignments." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -470,7 +591,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -482,7 +605,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -493,7 +618,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -502,18 +629,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Graph Coloring Visualization\n", "\n", - "Next, we define some functions to create the visualisation from the assingment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n" + "Next, we define some functions to create the visualisation from the assignment_history of **coloring_problem1**. The reader need not concern himself with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as the graph that needs to be colored or as a constraint graph for this problem. If interested you can read a dead simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -526,7 +658,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which return such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. This will be more clear with the example below. If this sounds confusing do not worry this is not the part of the core material and our only goal is to help you visualize how the process works." ] @@ -535,7 +670,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -551,9 +688,9 @@ " G, pos = draw_graph(graph)\n", " \n", " def update_step(iteration):\n", - " # here iteration is the index of the assingment_history we want to visualize.\n", - " current = instru_csp.assingment_history[iteration]\n", - " # We convert the particular assingment to a default dict so that the color for nodes which \n", + " # here iteration is the index of the assignment_history we want to visualize.\n", + " current = instru_csp.assignment_history[iteration]\n", + " # We convert the particular assignment to a default dict so that the color for nodes which \n", " # have not been assigned defaults to black.\n", " current = defaultdict(lambda: 'Black', current)\n", "\n", @@ -589,7 +726,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Finally let us plot our problem. We first use the function above to obtain a step function." ] @@ -598,7 +738,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -607,7 +749,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Next we set the canvas size." ] @@ -616,7 +761,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -625,7 +772,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step." ] @@ -634,14 +784,16 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display\n", "\n", - "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assingment_history)-1, step=1, value=0)\n", + "iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assignment_history)-1, step=1, value=0)\n", "w=widgets.interactive(step_func,iteration=iteration_slider)\n", "display(w)\n", "\n", @@ -656,7 +808,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## NQueens Visualization\n", "\n", @@ -667,18 +822,20 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "def label_queen_conflicts(assingment,grid):\n", + "def label_queen_conflicts(assignment,grid):\n", " ''' Mark grid with queens that are under conflict. '''\n", - " for col, row in assingment.items(): # check each queen for conflict\n", - " row_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " for col, row in assignment.items(): # check each queen for conflict\n", + " row_conflicts = {temp_col:temp_row for temp_col,temp_row in assignment.items() \n", " if temp_row == row and temp_col != col}\n", - " up_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " up_conflicts = {temp_col:temp_row for temp_col,temp_row in assignment.items() \n", " if temp_row+temp_col == row+col and temp_col != col}\n", - " down_conflicts = {temp_col:temp_row for temp_col,temp_row in assingment.items() \n", + " down_conflicts = {temp_col:temp_row for temp_col,temp_row in assignment.items() \n", " if temp_row-temp_col == row-col and temp_col != col}\n", " \n", " # Now marking the grid.\n", @@ -702,7 +859,7 @@ " \n", " def plot_board_step(iteration):\n", " ''' Add Queens to the Board.'''\n", - " data = instru_csp.assingment_history[iteration]\n", + " data = instru_csp.assignment_history[iteration]\n", " \n", " grid = [[(col+row+1)%2 for col in range(n)] for row in range(n)]\n", " grid = label_queen_conflicts(data, grid) # Update grid with conflict labels.\n", @@ -728,7 +885,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now let us visualize a solution obtained via backtracking. We use of the previosuly defined **make_instru** function for keeping a history of steps." ] @@ -737,7 +897,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -750,7 +912,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -759,7 +923,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step.\n" ] @@ -768,14 +935,16 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ "matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n", "matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n", "\n", - "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assingment_history)-1, step=0, value=0)\n", + "iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assignment_history)-1, step=0, value=0)\n", "w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n", "display(w)\n", "\n", @@ -790,7 +959,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now let us finally repeat the above steps for **min_conflicts** solution." ] @@ -799,7 +971,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -811,7 +985,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -820,7 +996,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The visualization has same features as the above. But here it also highlights the conflicts by labeling the conflicted queens with a red background." ] @@ -829,11 +1008,13 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ - "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assingment_history)-1, step=0, value=0)\n", + "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assignment_history)-1, step=0, value=0)\n", "w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n", "display(w)\n", "\n", @@ -863,7 +1044,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.2" }, "widgets": { "state": {}, diff --git a/games.ipynb b/games.ipynb index da7652cf8..e1fe1e644 100644 --- a/games.ipynb +++ b/games.ipynb @@ -2,7 +2,10 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Games or Adversarial search\n", "\n", @@ -13,7 +16,9 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -24,22 +29,27 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## `GameState` namedtuple\n", - " \n", - " `GameState` is a [namedtuple](https://docs.python.org/3.5/library/collections.html#collections.namedtuple) which represents the current state of a game. Let it be Tic-Tac-Toe or any other game." + "\n", + "`GameState` is a [namedtuple](https://docs.python.org/3.5/library/collections.html#collections.namedtuple) which represents the current state of a game. Let it be Tic-Tac-Toe or any other game." ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "source": [ "## `Game` class\n", - " \n", - "Let's have a look at the class `Game` in our module. We see that it has functions, namely `actions`, `result`, `utility`, `terminal_test`, `to_move` and `display`. \n", + "\n", + "Let's have a look at the class `Game` in our module. We see that it has functions, namely `actions`, `result`, `utility`, `terminal_test`, `to_move` and `display`.\n", "\n", "We see that these functions have not actually been implemented. This class is actually just a template class; we are supposed to create the class for our game, `TicTacToe` by inheriting this `Game` class and implement all the methods mentioned in `Game`. Do not close the popup so that you can follow along the description of code below." ] @@ -48,7 +58,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -57,10 +69,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - " Now let's get into details of all the methods in our `Game` class. You have to implement these methods when you create new classes that would represent your game.\n", - " \n", + "Now let's get into details of all the methods in our `Game` class. You have to implement these methods when you create new classes that would represent your game.\n", + "\n", "* `actions(self, state)` : Given a game state, this method generates all the legal actions possible from this state, as a list or a generator. Returning a generator rather than a list has the advantage that it saves space and you can still operate on it as a list.\n", "\n", "\n", @@ -76,23 +91,28 @@ "* `to_move(self, state)` : Given a game state, this method returns the player who is to play next. This information is typically stored in the game state, so all this method does is extract this information and return it.\n", "\n", "\n", - "* `display(self, state)` : This method prints/displays current state of the game." + "* `display(self, state)` : This method prints/displays the current state of the game." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## `TicTacToe` class\n", - " \n", - " Take a look at the class `TicTacToe`. All the methods mentioned in the class `Game` have been implemented here." + "\n", + "Take a look at the class `TicTacToe`. All the methods mentioned in the class `Game` have been implemented here." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -101,9 +121,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - " The class `TicTacToe` has been inherited from the class `Game`. As mentioned earlier, you really want to do this. Catching bugs and errors becomes a whole lot easier.\n", + "The class `TicTacToe` has been inherited from the class `Game`. As mentioned earlier, you really want to do this. Catching bugs and errors becomes a whole lot easier.\n", "\n", "Additional methods in TicTacToe:\n", "\n", @@ -118,36 +141,42 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## GameState in TicTacToe game\n", "\n", - " Now, before we start implementing our `TicTacToe` game, we need to decide how we will be representing our game state. Typically, a game state will give you all the current information about the game at any point in time. When you are given a game state, you should be able to tell whose turn it is next, how the game will look like on a real-life board (if it has one) etc. A game state need not include the history of the game. If you can play the game further given a game state, you game state representation is acceptable. While we might like to include all kinds of information in our game state, we wouldn't want to put too much information into it. Modifying this game state to generate a new one would be a real pain then.\n", - " \n", - " Now, as for our `TicTacToe` game state, would storing only the positions of all the X's and O's be sufficient to represent all the game information at that point in time? Well, does it tell us whose turn it is next? Looking at the 'X's and O's on the board and counting them should tell us that. But that would mean extra computing. To avoid this, we will also store whose move it is next in the game state. \n", - " \n", - " Think about what we've done here. We have reduced extra computation by storing additional information in a game state. Now, this information might not be absolutely essential to tell us about the state of the game, but it does save us additional computation time. We'll do more of this later on. \n", - " \n", - " The `TicTacToe` game defines its game state as:\n", - " \n", - " `GameState = namedtuple('GameState', 'to_move, utility, board, moves')`" + "Now, before we start implementing our `TicTacToe` game, we need to decide how we will be representing our game state. Typically, a game state will give you all the current information about the game at any point in time. When you are given a game state, you should be able to tell whose turn it is next, how the game will look like on a real-life board (if it has one) etc. A game state need not include the history of the game. If you can play the game further given a game state, you game state representation is acceptable. While we might like to include all kinds of information in our game state, we wouldn't want to put too much information into it. Modifying this game state to generate a new one would be a real pain then.\n", + "\n", + "Now, as for our `TicTacToe` game state, would storing only the positions of all the X's and O's be sufficient to represent all the game information at that point in time? Well, does it tell us whose turn it is next? Looking at the 'X's and O's on the board and counting them should tell us that. But that would mean extra computing. To avoid this, we will also store whose move it is next in the game state.\n", + "\n", + "Think about what we've done here. We have reduced extra computation by storing additional information in a game state. Now, this information might not be absolutely essential to tell us about the state of the game, but it does save us additional computation time. We'll do more of this later on.\n", + "\n", + "The `TicTacToe` game defines its game state as:\n", + "\n", + "`GameState = namedtuple('GameState', 'to_move, utility, board, moves')`" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "The game state is called, quite appropriately, `GameState`, and it has 4 variables, namely, `to_move`, `utility`, `board` and `moves`. \n", - " \n", - " I'll describe these variables in some more detail:\n", - " \n", + "The game state is called, quite appropriately, `GameState`, and it has 4 variables, namely, `to_move`, `utility`, `board` and `moves`.\n", + "\n", + "I'll describe these variables in some more detail:\n", + "\n", "* `to_move` : It represents whose turn it is to move next. This will be a string of a single character, either 'X' or 'O'.\n", "\n", "\n", "* `utility` : It stores the utility of the game state. Storing this utility is a good idea, because, when you do a Minimax Search or an Alphabeta Search, you generate many recursive calls, which travel all the way down to the terminal states. When these recursive calls go back up to the original callee, we have calculated utilities for many game states. We store these utilities in their respective `GameState`s to avoid calculating them all over again.\n", "\n", "\n", - "* `board` : A dict that stores all the positions of X's and O's on the board\n", + "* `board` : A dict that stores all the positions of X's and O's on the board.\n", "\n", "\n", "* `moves` : It stores the list of legal moves possible from the current position. Note here, that storing the moves as a list, as it is done here, increases the space complexity of Minimax Search from `O(m)` to `O(bm)`. Refer to Sec. 5.2.1 of the book." @@ -155,39 +184,48 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Representing a move in TicTacToe game\n", - " \n", - " Now that we have decided how our game state will be represented, it's time to decide how our move will be represented. Becomes easy to use this move to modify a current game state to generate a new one.\n", - " \n", - " For our `TicTacToe` game, we'll just represent a move by a tuple, where the first and the second elements of the tuple will represent the row and column, respectively, where the next move is to be made. Whether to make an 'X' or an 'O' will be decided by the `to_move` in the `GameState` namedtuple." + "\n", + "Now that we have decided how our game state will be represented, it's time to decide how our move will be represented. Becomes easy to use this move to modify a current game state to generate a new one.\n", + "\n", + "For our `TicTacToe` game, we'll just represent a move by a tuple, where the first and the second elements of the tuple will represent the row and column, respectively, where the next move is to be made. Whether to make an 'X' or an 'O' will be decided by the `to_move` in the `GameState` namedtuple." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Players to play games\n", "\n", - " So, we have finished implementation of the `TicTacToe` class. What this class does is that, it just defines the rules of the game. We need more to create an AI that can actually play the game. This is where `random_player` and `alphabeta_player` come in. \n", + "So, we have finished the implementation of the `TicTacToe` class. What this class does is that, it just defines the rules of the game. We need more to create an AI that can actually play the game. This is where `random_player` and `alphabeta_player` come in.\n", "\n", "### query_player\n", - " The `query_player` function allows you, a human opponent, to play the game. This function requires a `display` method to be implemented in your game class, so that successive game states can be displayed on the terminal, making it easier for you to visualize the game and play accordingly. \n", + "The `query_player` function allows you, a human opponent, to play the game. This function requires a `display` method to be implemented in your game class, so that successive game states can be displayed on the terminal, making it easier for you to visualize the game and play accordingly.\n", "\n", "### random_player\n", - " The `random_player` is a function that plays random moves in the game. That's it. There isn't much more to this guy. \n", + "The `random_player` is a function that plays random moves in the game. That's it. There isn't much more to this guy. \n", "\n", "### alphabeta_player\n", - " The `alphabeta_player`, on the other hand, calls the `alphabeta_full_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely.\n", - " \n", + "The `alphabeta_player`, on the other hand, calls the `alphabeta_full_search` function, which returns the best move in the current game state. Thus, the `alphabeta_player` always plays the best move given a game state, assuming that the game tree is small enough to search entirely.\n", + "\n", "### play_game\n", - " The `play_game` function will be the one that will actually be used to play the game. You pass as arguments to it, an instance of the game you want to play and the players you want in this game. Use it to play AI vs AI, AI vs human, or even human vs human matches!" + "The `play_game` function will be the one that will actually be used to play the game. You pass as arguments to it, an instance of the game you want to play and the players you want in this game. Use it to play AI vs AI, AI vs human, or even human vs human matches!" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Let's play some games\n", "### Game52" @@ -195,14 +233,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Let's start by experimenting with the `Fig52Game` first. For that we'll create an instance of the subclass Fig52Game inherited from the class Game:" ] @@ -211,7 +255,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -220,7 +266,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "First we try out our `random_player(game, state)`. Given a game state it will give us a random move every time:" ] @@ -229,7 +278,9 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -248,7 +299,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The `alphabeta_player(game, state)` will always give us the best move possible, for the relevant player (MAX or MIN):" ] @@ -257,7 +311,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -278,16 +334,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "What the `alphabeta_player` does is, it simply calls the method `alphabeta_full_search`. They both are essentially the same. In the module, both `alphabeta_full_search` and `minimax_decision` have been implemented. They both do the same job and return the same thing, which is, the best move in the current state. It's just that `alphabeta_full_search` is more efficient w.r.t time because it prunes the search tree and hence, explores lesser number of states." + "What the `alphabeta_player` does is, it simply calls the method `alphabeta_full_search`. They both are essentially the same. In the module, both `alphabeta_full_search` and `minimax_decision` have been implemented. They both do the same job and return the same thing, which is, the best move in the current state. It's just that `alphabeta_full_search` is more efficient with regards to time because it prunes the search tree and hence, explores lesser number of states." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -309,7 +370,9 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -329,7 +392,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Demonstrating the play_game function on the game52:" ] @@ -338,7 +404,9 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -367,7 +435,9 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -396,7 +466,9 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -430,7 +502,9 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -462,16 +536,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Note that if you are the first player then alphabeta_player plays as MIN, and if you are the second player then alphabeta_player plays as MAX. This happens because that's the way the game is defined in the class Fig52Game. Having a look at the code of this class should make it clear." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### TicTacToe game\n", + "\n", "Now let's play `TicTacToe`. First we initialize the game by creating an instance of the subclass TicTacToe inherited from the class Game:" ] }, @@ -479,7 +560,9 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -488,7 +571,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "We can print a state using the display method:" ] @@ -497,7 +583,9 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -516,18 +604,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Hmm, so that's the initial state of the game; no X's and no O's. \n", - " \n", - " Let us create a new game state by ourselves to experiment:" + "Hmm, so that's the initial state of the game; no X's and no O's.\n", + "\n", + "Let us create a new game state by ourselves to experiment:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -544,7 +637,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "So, how does this game state look like?" ] @@ -553,7 +649,9 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -572,7 +670,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The `random_player` will behave how he is supposed to i.e. *pseudo-randomly*:" ] @@ -581,7 +682,9 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -603,7 +706,9 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -623,7 +728,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "But the `alphabeta_player` will always give the best move, as expected:" ] @@ -632,7 +740,9 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -652,16 +762,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Now let's make 2 players play against each other. We use the `play_game` function for this. The `play_game` function makes players play the match against each other and returns the utility for the first player, of the terminal state reached when the game ends. Hence, for our `TicTacToe` game, if we get the output +1, the first player wins, -1 if the second player wins, and 0 if the match ends in a draw." + "Now let's make two players play against each other. We use the `play_game` function for this. The `play_game` function makes players play the match against each other and returns the utility for the first player, of the terminal state reached when the game ends. Hence, for our `TicTacToe` game, if we get the output +1, the first player wins, -1 if the second player wins, and 0 if the match ends in a draw." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -690,18 +805,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The output is (usually) -1, because `random_player` loses to `alphabeta_player`. Sometimes, however, `random_player` manages to draw with `alphabeta_player`.\n", - " \n", - " Since an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" + "\n", + "Since an `alphabeta_player` plays perfectly, a match between two `alphabeta_player`s should always end in a draw. Let's see if this happens:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -758,7 +878,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "A `random_player` should never win against an `alphabeta_player`. Let's test that." ] @@ -767,7 +890,9 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -824,20 +949,25 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Canvas_TicTacToe(Canvas)\n", "\n", "This subclass is used to play TicTacToe game interactively in Jupyter notebooks. TicTacToe class is called while initializing this subclass.\n", "\n", - "Let's have match between `random_player` and `alphabeta_player`. Click on the board to call players to make a move." + "Let's have a match between `random_player` and `alphabeta_player`. Click on the board to call players to make a move." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -886,7 +1016,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now, let's play a game ourselves against a `random_player`:" ] @@ -895,7 +1028,9 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -944,7 +1079,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Yay! We (usually) win. But we cannot win against an `alphabeta_player`, however hard we try." ] @@ -953,7 +1091,9 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -999,15 +1139,6 @@ "source": [ "ab_play = Canvas_TicTacToe('ab_play', 'human', 'alphabeta')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -1026,7 +1157,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/intro.ipynb b/intro.ipynb index a4850ebc2..dec3a2c12 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -2,13 +2,16 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# An Introduction To `aima-python` \n", " \n", - "The [aima-python](https://github.com/aimacode/aima-python) repository implements, in Python code, the algorithms in the textbook *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. A typical module in the repository has the code for a single chapter in the book, but some modules combine several chapters. See [the index](https://github.com/aimacode/aima-python#index-of-code) if you can't find the algorithm you want. The code in this repository attempts to mirror the pseudocode in the textbook as closely as possible and to stress readability foremost; if you are looking for high-performance code with advanced features, there are other repositories for you. For each module, there are three files, for example:\n", + "The [aima-python](https://github.com/aimacode/aima-python) repository implements, in Python code, the algorithms in the textbook *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. A typical module in the repository has the code for a single chapter in the book, but some modules combine several chapters. See [the index](https://github.com/aimacode/aima-python#index-of-code) if you can't find the algorithm you want. The code in this repository attempts to mirror the pseudocode in the textbook as closely as possible and to stress readability foremost; if you are looking for high-performance code with advanced features, there are other repositories for you. For each module, there are three files, for example:\n", "\n", - "- [**`logic.py`**](https://github.com/aimacode/aima-python/blob/master/logic.py): Source code with data types and algorithms for fealing with logic; functions have docstrings explaining their use.\n", + "- [**`logic.py`**](https://github.com/aimacode/aima-python/blob/master/logic.py): Source code with data types and algorithms for dealing with logic; functions have docstrings explaining their use.\n", "- [**`logic.ipynb`**](https://github.com/aimacode/aima-python/blob/master/logic.ipynb): A notebook like this one; gives more detailed examples and explanations of use.\n", "- [**`tests/test_logic.py`**](https://github.com/aimacode/aima-python/blob/master/tests/test_logic.py): Test cases, used to verify the code is correct, and also useful to see examples of use.\n", "\n", @@ -27,7 +30,7 @@ "\n", "1. View static HTML pages. (Just browse to the [repository](https://github.com/aimacode/aima-python) and click on a `.ipynb` file link.)\n", "2. Run, modify, and re-run code, live. (Download the repository (by [zip file](https://github.com/aimacode/aima-python/archive/master.zip) or by `git` commands), start a Jupyter notebook server with the shell command \"`jupyter notebook`\" (issued from the directory where the files are), and click on the notebook you want to interact with.)\n", - "3. Binder - Click on the binder badge on the [repository](https://github.com/aimacode/aima-python) main page to opens the notebooks in an executable environment, online. This method does not require any extra installation. The code can be executed and modified from the browser itself.\n", + "3. Binder - Click on the binder badge on the [repository](https://github.com/aimacode/aima-python) main page to open the notebooks in an executable environment, online. This method does not require any extra installation. The code can be executed and modified from the browser itself.\n", "\n", " \n", "You can [read about notebooks](https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/) and then [get started](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Running%20Code.ipynb)." @@ -36,7 +39,9 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "source": [ "# Helpful Tips\n", @@ -48,7 +53,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -57,7 +64,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "From there, the notebook alternates explanations with examples of use. You can run the examples as they are, and you can modify the code cells (or add new cells) and run your own examples. If you have some really good examples to add, you can make a github pull request.\n", "\n", @@ -68,7 +78,9 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -77,16 +89,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ - "Or see an abbreviated description of an object with a trainling question mark:" + "Or see an abbreviated description of an object with a trailing question mark:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -95,7 +112,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Authors\n", "\n", @@ -119,7 +139,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.2" } }, "nbformat": 4, From cb7a0b14c1e24016d0d50b6acb8f8938a91a19f3 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sat, 25 Mar 2017 08:14:44 +0200 Subject: [PATCH 456/513] Implemented Topological Sort (#401) * Implement Topological Sort - Implemented topological_sort - Added auxiliary function build_topological - Updated call to topological_sort * Use defaultdict to build visited * Added test * Update csp.py Skip first item of iteration --- csp.py | 49 +++++++++++++++++++++++++++++++++++++++++------ tests/test_csp.py | 12 ++++++++++++ 2 files changed, 55 insertions(+), 6 deletions(-) diff --git a/csp.py b/csp.py index 1e97d7780..8c5ecde3d 100644 --- a/csp.py +++ b/csp.py @@ -14,12 +14,13 @@ class CSP(search.Problem): """This class describes finite-domain Constraint Satisfaction Problems. A CSP is specified by the following inputs: - variables A list of variables; each is atomic (e.g. int or string). + variables A list of variables; each is atomic (e.g. int or string). domains A dict of {var:[possible_value, ...]} entries. neighbors A dict of {var:[var,...]} that for each variable lists the other variables that participate in constraints. constraints A function f(A, a, B, b) that returns true if neighbors A, B satisfy the constraint when they have values A=a, B=b + In the textbook and in most mathematical definitions, the constraints are specified as explicit pairs of allowable values, but the formulation here is easier to express and more compact for @@ -29,7 +30,7 @@ class CSP(search.Problem): problem, that's all there is. However, the class also supports data structures and methods that help you - solve CSPs by calling a search function on the CSP. Methods and slots are + solve CSPs by calling a search function on the CSP. Methods and slots are as follows, where the argument 'a' represents an assignment, which is a dict of {var:val} entries: assign(var, val, a) Assign a[var] = val; do other bookkeeping @@ -307,8 +308,9 @@ def tree_csp_solver(csp): """[Figure 6.11]""" assignment = {} root = csp.variables[0] - X, parent = topological_sort(csp.variables, root) - for Xj in reversed(X): + root = 'NT' + X, parent = topological_sort(csp, root) + for Xj in reversed(X[1:]): if not make_arc_consistent(parent[Xj], Xj, csp): return None for Xi in X: @@ -318,8 +320,43 @@ def tree_csp_solver(csp): return assignment -def topological_sort(xs, x): - raise NotImplementedError +def topological_sort(X, root): + """Returns the topological sort of X starting from the root. + + Input: + X is a list with the nodes of the graph + N is the dictionary with the neighbors of each node + root denotes the root of the graph. + + Output: + stack is a list with the nodes topologically sorted + parents is a dictionary pointing to each node's parent + + Other: + visited shows the state (visited - not visited) of nodes + + """ + nodes = X.variables + neighbors = X.neighbors + + visited = defaultdict(lambda: False) + + stack = [] + parents = {} + + build_topological(root, None, neighbors, visited, stack, parents) + return stack, parents + +def build_topological(node, parent, neighbors, visited, stack, parents): + """Builds the topological sort and the parents of each node in the graph""" + visited[node] = True + + for n in neighbors[node]: + if(not visited[n]): + build_topological(n, node, neighbors, visited, stack, parents) + + parents[node] = parent + stack.insert(0,node) def make_arc_consistent(Xj, Xk, csp): diff --git a/tests/test_csp.py b/tests/test_csp.py index 5bed85c05..803dede74 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -274,6 +274,18 @@ def test_universal_dict(): def test_parse_neighbours(): assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} +def test_topological_sort(): + root = 'NT' + Sort, Parents = topological_sort(australia,root) + + assert Sort == ['NT','SA','Q','NSW','V','WA'] + assert Parents['NT'] == None + assert Parents['SA'] == 'NT' + assert Parents['Q'] == 'SA' + assert Parents['NSW'] == 'Q' + assert Parents['V'] == 'NSW' + assert Parents['WA'] == 'SA' + if __name__ == "__main__": pytest.main() From 034d279b042224ace4a6356e45f11836ec0bcb8a Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sat, 25 Mar 2017 03:46:03 -0300 Subject: [PATCH 457/513] Fix flake8 for main files (#399) * Exclude test files from flake8 check * Fix flake8 for main files * Add flake8 check to build --- .flake8 | 1 + .travis.yml | 1 + agents.py | 74 +++++++++++++-------------- canvas.py | 10 ++-- csp.py | 4 +- games.py | 12 +++-- ipyviews.py | 6 ++- learning.py | 48 +++++++++-------- logic.py | 11 ++-- mdp.py | 8 +-- nlp.py | 79 ++++++++++++++++------------ planning.py | 136 +++++++++++++++++++++++++++---------------------- probability.py | 2 + rl.py | 12 +++-- text.py | 5 +- utils.py | 136 ++++++++++++++++++++++++++++++++++++------------- 16 files changed, 331 insertions(+), 214 deletions(-) diff --git a/.flake8 b/.flake8 index 405ab746c..c944f27ed 100644 --- a/.flake8 +++ b/.flake8 @@ -1,3 +1,4 @@ [flake8] max-line-length = 100 ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405 +exclude = tests diff --git a/.travis.yml b/.travis.yml index e6563f0fe..49270ad2a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,6 +16,7 @@ install: script: - py.test - python -m doctest -v *.py + - flake8 . after_success: - flake8 --max-line-length 100 --ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . diff --git a/agents.py b/agents.py index 047eb3fd6..403bfbddc 100644 --- a/agents.py +++ b/agents.py @@ -162,6 +162,7 @@ def rule_match(state, rules): # ______________________________________________________________________________ + loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world @@ -394,8 +395,9 @@ def things_near(self, location, radius=None): if radius is None: radius = self.perceptible_distance radius2 = radius * radius - return [(thing, radius2 - distance_squared(location, thing.location)) for thing in self.things - if distance_squared(location, thing.location) <= radius2] + return [(thing, radius2 - distance_squared(location, thing.location)) + for thing in self.things if distance_squared( + location, thing.location) <= radius2] def percept(self, agent): """By default, agent perceives things within a default radius.""" @@ -435,33 +437,28 @@ def move_to(self, thing, destination): t.location = destination return thing.bump - # def add_thing(self, thing, location=(1, 1)): - # super(XYEnvironment, self).add_thing(thing, location) - # thing.holding = [] - # thing.held = None - # for obs in self.observers: - # obs.thing_added(thing) - def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location has at least one item of the same class.""" if (self.is_inbounds(location)): if (exclude_duplicate_class_items and - any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): - return + any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): + return super().add_thing(thing, location) def is_inbounds(self, location): """Checks to make sure that the location is inbounds (within walls if we have walls)""" - x,y = location + x, y = location return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end) def random_location_inbounds(self, exclude=None): """Returns a random location that is inbounds (within walls if we have walls)""" - location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) + location = (random.randint(self.x_start, self.x_end), + random.randint(self.y_start, self.y_end)) if exclude is not None: while(location == exclude): - location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) + location = (random.randint(self.x_start, self.x_end), + random.randint(self.y_start, self.y_end)) return location def delete_thing(self, thing): @@ -514,6 +511,7 @@ class Wall(Obstacle): # ______________________________________________________________________________ + try: from ipythonblocks import BlockGrid from IPython.display import HTML, display @@ -521,12 +519,13 @@ class Wall(Obstacle): except: pass + class GraphicEnvironment(XYEnvironment): def __init__(self, width=10, height=10, boundary=True, color={}, display=False): """define all the usual XYEnvironment characteristics, but initialise a BlockGrid for GUI too""" super().__init__(width, height) - self.grid = BlockGrid(width, height, fill=(200,200,200)) + self.grid = BlockGrid(width, height, fill=(200, 200, 200)) if display: self.grid.show() self.visible = True @@ -535,11 +534,6 @@ def __init__(self, width=10, height=10, boundary=True, color={}, display=False): self.bounded = boundary self.colors = color - #def list_things_at(self, location, tclass=Thing): # need to override because locations - # """Return all things exactly at a given location.""" - # return [thing for thing in self.things - # if thing.location == location and isinstance(thing, tclass)] - def get_world(self): """Returns all the items in the world in a format understandable by the ipythonblocks BlockGrid""" @@ -589,23 +583,17 @@ def update(self, delay=1): def reveal(self): """display the BlockGrid for this world - the last thing to be added at a location defines the location color""" - #print("Grid={}".format(self.grid)) self.draw_world() - #if not self.visible == True: - # self.grid.show() self.grid.show() - self.visible == True + self.visible = True def draw_world(self): self.grid[:] = (200, 200, 200) world = self.get_world() - #print("world {}".format(world)) for x in range(0, len(world)): for y in range(0, len(world[x])): if len(world[x][y]): self.grid[y, x] = self.colors[world[x][y][-1].__class__.__name__] - #print('location: ({}, {}) got color: {}' - #.format(y, x, self.colors[world[x][y][-1].__class__.__name__])) def conceal(self): """hide the BlockGrid for this world""" @@ -613,10 +601,6 @@ def conceal(self): display(HTML('')) - - - - # ______________________________________________________________________________ # Continuous environment @@ -733,21 +717,27 @@ def __eq__(self, rhs): return rhs.__class__ == Gold pass + class Bump(Thing): pass + class Glitter(Thing): pass + class Pit(Thing): pass + class Breeze(Thing): pass + class Arrow(Thing): pass + class Scream(Thing): pass @@ -756,6 +746,7 @@ class Wumpus(Agent): screamed = False pass + class Stench(Thing): pass @@ -772,7 +763,7 @@ def can_grab(self, thing): class WumpusEnvironment(XYEnvironment): - pit_probability = 0.2 # Probability to spawn a pit in a location. (From Chapter 7.2) + pit_probability = 0.2 # Probability to spawn a pit in a location. (From Chapter 7.2) # Room should be 4x4 grid of rooms. The extra 2 for walls def __init__(self, agent_program, width=6, height=6): @@ -805,7 +796,6 @@ def init_world(self, program): "GOLD" self.add_thing(Gold(), self.random_location_inbounds(exclude=(1, 1)), True) - #self.add_thing(Gold(), (2,1), True) Making debugging a whole lot easier "AGENT" self.add_thing(Explorer(program), (1, 1), True) @@ -814,7 +804,12 @@ def get_world(self, show_walls=True): """Returns the items in the world""" result = [] x_start, y_start = (0, 0) if show_walls else (1, 1) - x_end, y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) + + if show_walls: + x_end, y_end = self.width, self.height + else: + x_end, y_end = self.width - 1, self.height - 1 + for x in range(x_start, x_end): row = [] for y in range(y_start, y_end): @@ -837,7 +832,6 @@ def percepts_from(self, agent, location, tclass=Thing): if location != agent.location: thing_percepts[Gold] = None - result = [thing_percepts.get(thing.__class__, thing) for thing in self.things if thing.location == location and isinstance(thing, tclass)] return result if len(result) else [None] @@ -916,18 +910,19 @@ def in_danger(self, agent): def is_done(self): """The game is over when the Explorer is killed or if he climbs out of the cave only at (1,1).""" - explorer = [agent for agent in self.agents if isinstance(agent, Explorer) ] + explorer = [agent for agent in self.agents if isinstance(agent, Explorer)] if len(explorer): if explorer[0].alive: - return False + return False else: print("Death by {} [-1000].".format(explorer[0].killed_by)) else: print("Explorer climbed out {}." - .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]")) + .format( + "with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]")) return True - #Almost done. Arrow needs to be implemented + # Almost done. Arrow needs to be implemented # ______________________________________________________________________________ @@ -952,6 +947,7 @@ def score(env): # _________________________________________________________________________ + __doc__ += """ >>> a = ReflexVacuumAgent() >>> a.program((loc_A, 'Clean')) diff --git a/canvas.py b/canvas.py index 213e38cc9..318155bea 100644 --- a/canvas.py +++ b/canvas.py @@ -1,4 +1,4 @@ -from IPython.display import HTML, display, clear_output +from IPython.display import HTML, display _canvas = """ @@ -7,7 +7,8 @@ -""" +""" # noqa + class Canvas: """Inherit from this class to manage the HTML canvas element in jupyter notebooks. @@ -81,9 +82,10 @@ def arc(self, x, y, r, start, stop): "Draw an arc with (x, y) as centre, 'r' as radius from angles 'start' to 'stop'" self.execute("arc({0}, {1}, {2}, {3}, {4})".format(x, y, r, start, stop)) - def arc_n(self, xn ,yn, rn, start, stop): + def arc_n(self, xn, yn, rn, start, stop): """Similar to arc(), but the dimensions are normalized to fall between 0 and 1 - The normalizing factor for radius is selected between width and height by seeing which is smaller + The normalizing factor for radius is selected between width and height by + seeing which is smaller """ x = round(xn * self.width) y = round(yn * self.height) diff --git a/csp.py b/csp.py index 8c5ecde3d..deb1efc12 100644 --- a/csp.py +++ b/csp.py @@ -414,6 +414,7 @@ def parse_neighbors(neighbors, variables=[]): dic[B].append(A) return dic + australia = MapColoringCSP(list('RGB'), 'SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: ') @@ -584,7 +585,8 @@ class Sudoku(CSP): >>> h = Sudoku(harder1) >>> backtracking_search(h, select_unassigned_variable=mrv, inference=forward_checking) is not None True - """ + """ # noqa + R3 = _R3 Cell = _CELL bgrid = _BGRID diff --git a/games.py b/games.py index d98b7473c..205d8e6ee 100644 --- a/games.py +++ b/games.py @@ -196,7 +196,7 @@ def display(self, state): def __repr__(self): return '<{}>'.format(self.__class__.__name__) - + def play_game(self, *players): """Play an n-person, move-alternating game.""" state = self.initial @@ -259,8 +259,8 @@ def actions(self, state): def result(self, state, move): if move not in state.moves: return GameState(to_move=('O' if state.to_move == 'X' else 'X'), - utility=self.compute_utility(state.board, move, state.to_move), - board=state.board, moves=state.moves) # Illegal move has no effect + utility=self.compute_utility(state.board, move, state.to_move), + board=state.board, moves=state.moves) # Illegal move has no effect board = state.board.copy() board[move] = state.to_move moves = list(state.moves) @@ -327,7 +327,8 @@ class Canvas_TicTacToe(Canvas): """Play a 3x3 TicTacToe game on HTML canvas TODO: Add restart button """ - def __init__(self, varname, player_1='human', player_2='random', id=None, width=300, height=300): + def __init__(self, varname, player_1='human', player_2='random', id=None, + width=300, height=300): valid_players = ('human', 'random', 'alphabeta') if player_1 not in valid_players or player_2 not in valid_players: raise TypeError("Players must be one of {}".format(valid_players)) @@ -381,7 +382,8 @@ def draw_board(self): else: self.text_n('Player {} wins!'.format(1 if utility > 0 else 2), 0.1, 0.1) else: # Print which player's turn it is - self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), 0.1, 0.1) + self.text_n("Player {}'s move({})".format(self.turn+1, self.players[self.turn]), + 0.1, 0.1) self.update() diff --git a/ipyviews.py b/ipyviews.py index 4c3776fbc..fbdc9a580 100644 --- a/ipyviews.py +++ b/ipyviews.py @@ -20,7 +20,7 @@ var all_polygons = {3}; {4} -''' +''' # noqa with open('js/continuousworld.js', 'r') as js_file: _JS_CONTINUOUS_WORLD = js_file.read() @@ -61,7 +61,9 @@ def get_polygon_obstacles_coordinates(self): def show(self): clear_output() - total_html = _CONTINUOUS_WORLD_HTML.format(self.width, self.height, self.object_name(), str(self.get_polygon_obstacles_coordinates()), _JS_CONTINUOUS_WORLD) + total_html = _CONTINUOUS_WORLD_HTML.format(self.width, self.height, self.object_name(), + str(self.get_polygon_obstacles_coordinates()), + _JS_CONTINUOUS_WORLD) display(HTML(total_html)) diff --git a/learning.py b/learning.py index 121f184c3..ec685131d 100644 --- a/learning.py +++ b/learning.py @@ -12,10 +12,11 @@ import random from statistics import mean -from collections import defaultdict, Counter +from collections import defaultdict # ______________________________________________________________________________ + def rms_error(predictions, targets): return math.sqrt(ms_error(predictions, targets)) @@ -160,15 +161,15 @@ def sanitize(self, example): return [attr_i if i in self.inputs else None for i, attr_i in enumerate(example)] - def classes_to_numbers(self,classes=None): + def classes_to_numbers(self, classes=None): """Converts class names to numbers.""" if not classes: # If classes were not given, extract them from values classes = sorted(self.values[self.target]) for item in self.examples: item[self.target] = classes.index(item[self.target]) - - def remove_examples(self,value=""): + + def remove_examples(self, value=""): """Remove examples that contain given value.""" self.examples = [x for x in self.examples if value not in x] self.update_values() @@ -383,7 +384,7 @@ def plurality_value(examples): def count(attr, val, examples): """Count the number of examples that have attr = val.""" - return sum(e[attr] == val for e in examples) #count(e[attr] == val for e in examples) + return sum(e[attr] == val for e in examples) def all_same_class(examples): """Are all these examples in the same target class?""" @@ -877,6 +878,7 @@ def score(learner, size): # ______________________________________________________________________________ # The rest of this file gives datasets for machine learning problems. + orings = DataSet(name='orings', target='Distressed', attrnames="Rings Distressed Temp Pressure Flightnum") @@ -900,6 +902,7 @@ def RestaurantDataSet(examples=None): attrnames='Alternate Bar Fri/Sat Hungry Patrons Price ' + 'Raining Reservation Type WaitEstimate Wait') + restaurant = RestaurantDataSet() @@ -909,28 +912,29 @@ def T(attrname, branches): for value, child in branches.items()} return DecisionFork(restaurant.attrnum(attrname), attrname, branches) + """ [Figure 18.2] A decision tree for deciding whether to wait for a table at a hotel. """ waiting_decision_tree = T('Patrons', - {'None': 'No', 'Some': 'Yes', 'Full': - T('WaitEstimate', - {'>60': 'No', '0-10': 'Yes', - '30-60': - T('Alternate', {'No': - T('Reservation', {'Yes': 'Yes', 'No': - T('Bar', {'No': 'No', - 'Yes': 'Yes' - })}), - 'Yes': - T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})}), - '10-30': - T('Hungry', {'No': 'Yes', 'Yes': - T('Alternate', - {'No': 'Yes', 'Yes': - T('Raining', {'No': 'No', 'Yes': 'Yes'}) - })})})}) + {'None': 'No', 'Some': 'Yes', + 'Full': T('WaitEstimate', + {'>60': 'No', '0-10': 'Yes', + '30-60': T('Alternate', + {'No': T('Reservation', + {'Yes': 'Yes', + 'No': T('Bar', {'No': 'No', + 'Yes': 'Yes'})}), + 'Yes': T('Fri/Sat', {'No': 'No', 'Yes': 'Yes'})} + ), + '10-30': T('Hungry', + {'No': 'Yes', + 'Yes': T('Alternate', + {'No': 'Yes', + 'Yes': T('Raining', + {'No': 'No', + 'Yes': 'Yes'})})})})}) def SyntheticRestaurant(n=20): diff --git a/logic.py b/logic.py index bd9c92334..68d996c14 100644 --- a/logic.py +++ b/logic.py @@ -33,7 +33,7 @@ from utils import ( removeall, unique, first, argmax, probability, - isnumber, issequence, Symbol, Expr, expr, subexpressions + isnumber, issequence, Expr, expr, subexpressions ) import agents @@ -180,6 +180,7 @@ def parse_definite_clause(s): antecedent, consequent = s.args return conjuncts(antecedent), consequent + # Useful constant Exprs used in examples and code: A, B, C, D, E, F, G, P, Q, x, y, z = map(Expr, 'ABCDEFGPQxyz') @@ -391,6 +392,7 @@ def associate(op, args): else: return Expr(op, *args) + _op_identity = {'&': True, '|': False, '+': 0, '*': 1} @@ -511,6 +513,7 @@ def pl_fc_entails(KB, q): agenda.append(c.args[1]) return False + """ [Figure 7.13] Simple inference in a wumpus world example """ @@ -707,7 +710,8 @@ def translate_to_SAT(init, transition, goal, time): s_ = transition[s][action] for t in range(time): # Action 'action' taken from state 's' at time 't' to reach 's_' - action_sym[s, action, t] = Expr("Transition_{}".format(next(transition_counter))) + action_sym[s, action, t] = Expr( + "Transition_{}".format(next(transition_counter))) # Change the state from s to s_ clauses.append(action_sym[s, action, t] |'==>'| state_sym[s, t]) @@ -732,7 +736,7 @@ def translate_to_SAT(init, transition, goal, time): clauses.append(associate('|', [action_sym[tr] for tr in transitions_t])) for tr in transitions_t: - for tr_ in transitions_t[transitions_t.index(tr) + 1 :]: + for tr_ in transitions_t[transitions_t.index(tr) + 1:]: # there cannot be two transitions tr and tr_ at time t clauses.append(~action_sym[tr] | ~action_sym[tr_]) @@ -877,6 +881,7 @@ def standardize_variables(sentence, dic=None): return Expr(sentence.op, *[standardize_variables(a, dic) for a in sentence.args]) + standardize_variables.counter = itertools.count() # ______________________________________________________________________________ diff --git a/mdp.py b/mdp.py index 2854d0616..902582b19 100644 --- a/mdp.py +++ b/mdp.py @@ -6,7 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" -from utils import argmax, vector_add, print_table +from utils import argmax, vector_add, print_table # noqa from grid import orientations, turn_right, turn_left import random @@ -97,12 +97,13 @@ def to_arrows(self, policy): # ______________________________________________________________________________ + """ [Figure 17.1] A 4x3 grid environment that presents the agent with a sequential decision problem. """ sequential_decision_environment = GridMDP([[-0.04, -0.04, -0.04, +1], - [-0.04, None, -0.04, -1], + [-0.04, None, -0.04, -1], [-0.04, -0.04, -0.04, -0.04]], terminals=[(3, 2), (3, 1)]) @@ -165,6 +166,7 @@ def policy_evaluation(pi, U, mdp, k=20): U[s] = R(s) + gamma * sum([p * U[s1] for (p, s1) in T(s, pi[s])]) return U + __doc__ += """ >>> pi = best_policy(sequential_decision_environment, value_iteration(sequential_decision_environment, .01)) @@ -180,4 +182,4 @@ def policy_evaluation(pi, U, mdp, k=20): > > > . ^ None ^ . ^ > ^ < -""" +""" # noqa diff --git a/nlp.py b/nlp.py index f136cb035..bf0b6a6aa 100644 --- a/nlp.py +++ b/nlp.py @@ -54,6 +54,7 @@ def isa(self, word, cat): def __repr__(self): return ''.format(self.name) + E0 = Grammar('E0', Rules( # Grammar for E_0 [Figure 22.4] S='NP VP | S Conjunction S', @@ -196,15 +197,15 @@ def CYK_parse(words, grammar): P = defaultdict(float) # Insert lexical rules for each word. for (i, word) in enumerate(words): - for (X, p) in grammar.categories[word]: # XXX grammar.categories needs changing, above + for (X, p) in grammar.categories[word]: # XXX grammar.categories needs changing, above P[X, i, 1] = p # Combine first and second parts of right-hand sides of rules, # from short to long. for length in range(2, N+1): for start in range(N-length+1): - for len1 in range(1, length): # N.B. the book incorrectly has N instead of length + for len1 in range(1, length): # N.B. the book incorrectly has N instead of length len2 = length - len1 - for (X, Y, Z, p) in grammar.cnf_rules(): # XXX grammar needs this method + for (X, Y, Z, p) in grammar.cnf_rules(): # XXX grammar needs this method P[X, start, length] = max(P[X, start, length], P[Y, start, len1] * P[Z, start+len1, len2] * p) return P @@ -215,17 +216,18 @@ def CYK_parse(words, grammar): # First entry in list is the base URL, and then following are relative URL pages examplePagesSet = ["https://en.wikipedia.org/wiki/", "Aesthetics", "Analytic_philosophy", - "Ancient_Greek", "Aristotle", "Astrology","Atheism", "Baruch_Spinoza", + "Ancient_Greek", "Aristotle", "Astrology", "Atheism", "Baruch_Spinoza", "Belief", "Betrand Russell", "Confucius", "Consciousness", "Continental Philosophy", "Dialectic", "Eastern_Philosophy", "Epistemology", "Ethics", "Existentialism", "Friedrich_Nietzsche", "Idealism", "Immanuel_Kant", "List_of_political_philosophers", "Logic", "Metaphysics", "Philosophers", "Philosophy", "Philosophy_of_mind", "Physics", - "Plato", "Political_philosophy", "Pythagoras", "Rationalism","Social_philosophy", - "Socrates", "Subjectivity", "Theology", "Truth", "Western_philosophy"] + "Plato", "Political_philosophy", "Pythagoras", "Rationalism", + "Social_philosophy", "Socrates", "Subjectivity", "Theology", + "Truth", "Western_philosophy"] -def loadPageHTML( addressList ): +def loadPageHTML(addressList): """Download HTML page content for every URL address passed as argument""" contentDict = {} for addr in addressList: @@ -236,20 +238,23 @@ def loadPageHTML( addressList ): contentDict[addr] = html return contentDict -def initPages( addressList ): + +def initPages(addressList): """Create a dictionary of pages from a list of URL addresses""" pages = {} for addr in addressList: pages[addr] = Page(addr) return pages -def stripRawHTML( raw_html ): + +def stripRawHTML(raw_html): """Remove the section of the HTML which contains links to stylesheets etc., and remove all other unnessecary HTML""" # TODO: Strip more out of the raw html - return re.sub(".*?", "", raw_html, flags=re.DOTALL) # remove section + return re.sub(".*?", "", raw_html, flags=re.DOTALL) # remove section -def determineInlinks( page ): + +def determineInlinks(page): """Given a set of pages that have their outlinks determined, we can fill out a page's inlinks by looking through all other page's outlinks""" inlinks = [] @@ -260,14 +265,16 @@ def determineInlinks( page ): inlinks.append(addr) return inlinks -def findOutlinks( page, handleURLs=None ): + +def findOutlinks(page, handleURLs=None): """Search a page's HTML content for URL links to other pages""" urls = re.findall(r'href=[\'"]?([^\'" >]+)', pagesContent[page.address]) if handleURLs: urls = handleURLs(urls) return urls -def onlyWikipediaURLS( urls ): + +def onlyWikipediaURLS(urls): """Some example HTML page data is from wikipedia. This function converts relative wikipedia links to full wikipedia URLs""" wikiURLs = [url for url in urls if url.startswith('/wiki/')] @@ -277,11 +284,11 @@ def onlyWikipediaURLS( urls ): # ______________________________________________________________________________ # HITS Helper Functions -def expand_pages( pages ): +def expand_pages(pages): """From Textbook: adds in every page that links to or is linked from one of the relevant pages.""" expanded = {} - for addr,page in pages.items(): + for addr, page in pages.items(): if addr not in expanded: expanded[addr] = page for inlink in page.inlinks: @@ -292,6 +299,7 @@ def expand_pages( pages ): expanded[outlink] = pagesIndex[outlink] return expanded + def relevant_pages(query): """Relevant pages are pages that contain the query in its entireity. If a page's content contains the query it is returned by the function.""" @@ -302,16 +310,18 @@ def relevant_pages(query): relevant[addr] = page return relevant -def normalize( pages ): + +def normalize(pages): """From the pseudocode: Normalize divides each page's score by the sum of the squares of all pages' scores (separately for both the authority and hubs scores). """ - summed_hub = sum(page.hub**2 for _,page in pages.items()) - summed_auth = sum(page.authority**2 for _,page in pages.items()) + summed_hub = sum(page.hub**2 for _, page in pages.items()) + summed_auth = sum(page.authority**2 for _, page in pages.items()) for _, page in pages.items(): page.hub /= summed_hub page.authority /= summed_auth + class ConvergenceDetector(object): """If the hub and authority values of the pages are no longer changing, we have reached a convergence and further iterations will have no effect. This detects convergence @@ -326,16 +336,16 @@ def __call__(self): def detect(self): curr_hubs = [page.hub for addr, page in pagesIndex.items()] curr_auths = [page.authority for addr, page in pagesIndex.items()] - if self.hub_history == None: - self.hub_history, self.auth_history = [],[] + if self.hub_history is None: + self.hub_history, self.auth_history = [], [] else: - diffsHub = [abs(x-y) for x, y in zip(curr_hubs,self.hub_history[-1])] - diffsAuth = [abs(x-y) for x, y in zip(curr_auths,self.auth_history[-1])] + diffsHub = [abs(x-y) for x, y in zip(curr_hubs, self.hub_history[-1])] + diffsAuth = [abs(x-y) for x, y in zip(curr_auths, self.auth_history[-1])] aveDeltaHub = sum(diffsHub)/float(len(pagesIndex)) aveDeltaAuth = sum(diffsAuth)/float(len(pagesIndex)) - if aveDeltaHub < 0.01 and aveDeltaAuth < 0.01: # may need tweaking + if aveDeltaHub < 0.01 and aveDeltaAuth < 0.01: # may need tweaking return True - if len(self.hub_history) > 2: # prevent list from getting long + if len(self.hub_history) > 2: # prevent list from getting long del self.hub_history[0] del self.auth_history[0] self.hub_history.append([x for x in curr_hubs]) @@ -343,12 +353,13 @@ def detect(self): return False -def getInlinks( page ): +def getInlinks(page): if not page.inlinks: page.inlinks = determineInlinks(page) - return [p for addr, p in pagesIndex.items() if addr in page.inlinks ] + return [p for addr, p in pagesIndex.items() if addr in page.inlinks] -def getOutlinks( page ): + +def getOutlinks(page): if not page.outlinks: page.outlinks = findOutlinks(page) return [p for addr, p in pagesIndex.items() if addr in page.outlinks] @@ -365,20 +376,22 @@ def __init__(self, address, hub=0, authority=0, inlinks=None, outlinks=None): self.inlinks = inlinks self.outlinks = outlinks -pagesContent = {} # maps Page relative or absolute URL/location to page's HTML content + +pagesContent = {} # maps Page relative or absolute URL/location to page's HTML content pagesIndex = {} -convergence = ConvergenceDetector() # assign function to variable to mimic pseudocode's syntax +convergence = ConvergenceDetector() # assign function to variable to mimic pseudocode's syntax + def HITS(query): """The HITS algorithm for computing hubs and authorities with respect to a query.""" - pages = expand_pages(relevant_pages(query)) # in order to 'map' faithfully to pseudocode we - for p in pages: # won't pass the list of pages as an argument + pages = expand_pages(relevant_pages(query)) # in order to 'map' faithfully to pseudocode we + for p in pages: # won't pass the list of pages as an argument p.authority = 1 p.hub = 1 - while True: # repeat until... convergence + while True: # repeat until... convergence for p in pages: p.authority = sum(x.hub for x in getInlinks(p)) # p.authority ← ∑i Inlinki(p).Hub - p.hub = sum(x.authority for x in getOutlinks(p)) # p.hub ← ∑i Outlinki(p).Authority + p.hub = sum(x.authority for x in getOutlinks(p)) # p.hub ← ∑i Outlinki(p).Authority normalize(pages) if convergence(): break diff --git a/planning.py b/planning.py index 47eae77da..b92cb6eaa 100644 --- a/planning.py +++ b/planning.py @@ -5,6 +5,7 @@ from utils import Expr, expr, first from logic import FolKB + class PDLL: """ PDLL used to define a search problem. @@ -34,6 +35,7 @@ def act(self, action): raise Exception("Action '{}' pre-conditions not satisfied".format(action)) list_action(self.kb, args) + class Action: """ Defines an action schema using preconditions and effects. @@ -112,16 +114,19 @@ def goal_test(kb): return False return True - ## Actions + # Actions + # Load - precond_pos = [expr("At(c, a)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), expr("Airport(a)")] + precond_pos = [expr("At(c, a)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), + expr("Airport(a)")] precond_neg = [] effect_add = [expr("In(c, p)")] effect_rem = [expr("At(c, a)")] load = Action(expr("Load(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem]) # Unload - precond_pos = [expr("In(c, p)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), expr("Airport(a)")] + precond_pos = [expr("In(c, p)"), expr("At(p, a)"), expr("Cargo(c)"), expr("Plane(p)"), + expr("Airport(a)")] precond_neg = [] effect_add = [expr("At(c, a)")] effect_rem = [expr("In(c, p)")] @@ -151,31 +156,34 @@ def goal_test(kb): return False return True - ##Actions - #Remove + # Actions + + # Remove precond_pos = [expr("At(obj, loc)")] precond_neg = [] effect_add = [expr("At(obj, Ground)")] effect_rem = [expr("At(obj, loc)")] remove = Action(expr("Remove(obj, loc)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - #PutOn + # PutOn precond_pos = [expr("Tire(t)"), expr("At(t, Ground)")] precond_neg = [expr("At(Flat, Axle)")] effect_add = [expr("At(t, Axle)")] effect_rem = [expr("At(t, Ground)")] put_on = Action(expr("PutOn(t, Axle)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - #LeaveOvernight + # LeaveOvernight precond_pos = [] precond_neg = [] effect_add = [] effect_rem = [expr("At(Spare, Ground)"), expr("At(Spare, Axle)"), expr("At(Spare, Trunk)"), expr("At(Flat, Ground)"), expr("At(Flat, Axle)"), expr("At(Flat, Trunk)")] - leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], [effect_add, effect_rem]) + leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], + [effect_add, effect_rem]) return PDLL(init, [remove, put_on, leave_overnight], goal_test) + def three_block_tower(): init = [expr('On(A, Table)'), expr('On(B, Table)'), @@ -193,23 +201,27 @@ def goal_test(kb): return False return True - ## Actions + # Actions + # Move - precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'), expr('Block(y)')] + precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'), + expr('Block(y)')] precond_neg = [] effect_add = [expr('On(b, y)'), expr('Clear(x)')] effect_rem = [expr('On(b, x)'), expr('Clear(y)')] move = Action(expr('Move(b, x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem]) - + # MoveToTable precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)')] precond_neg = [] effect_add = [expr('On(b, Table)'), expr('Clear(x)')] effect_rem = [expr('On(b, x)')] - moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg], [effect_add, effect_rem]) + moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg], + [effect_add, effect_rem]) return PDLL(init, [move, moveToTable], goal_test) + def have_cake_and_eat_cake_too(): init = [expr('Have(Cake)')] @@ -220,7 +232,8 @@ def goal_test(kb): return False return True - ##Actions + # Actions + # Eat cake precond_pos = [expr('Have(Cake)')] precond_neg = [] @@ -228,7 +241,7 @@ def goal_test(kb): effect_rem = [expr('Have(Cake)')] eat_cake = Action(expr('Eat(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem]) - #Bake Cake + # Bake Cake precond_pos = [] precond_neg = [expr('Have(Cake)')] effect_add = [expr('Have(Cake)')] @@ -247,69 +260,63 @@ class Level(): def __init__(self, poskb, negkb): self.poskb = poskb - #Current state + # Current state self.current_state_pos = poskb.clauses self.current_state_neg = negkb.clauses - #Current action to current state link + # Current action to current state link self.current_action_links_pos = {} self.current_action_links_neg = {} - #Current state to action link + # Current state to action link self.current_state_links_pos = {} self.current_state_links_neg = {} - #Current action to next state link + # Current action to next state link self.next_action_links = {} - #Next state to current action link + # Next state to current action link self.next_state_links_pos = {} self.next_state_links_neg = {} self.mutex = [] - def __call__(self, actions, objects): self.build(actions, objects) self.find_mutex() - def find_mutex(self): - #Inconsistent effects + # Inconsistent effects for poseff in self.next_state_links_pos: - #negeff = Expr('not'+poseff.op, poseff.args) negeff = poseff if negeff in self.next_state_links_neg: for a in self.next_state_links_pos[poseff]: for b in self.next_state_links_neg[negeff]: - if set([a,b]) not in self.mutex: - self.mutex.append(set([a,b])) + if set([a, b]) not in self.mutex: + self.mutex.append(set([a, b])) - #Interference + # Interference for posprecond in self.current_state_links_pos: - #negeff = Expr('not'+posprecond.op, posprecond.args) negeff = posprecond if negeff in self.next_state_links_neg: for a in self.current_state_links_pos[posprecond]: for b in self.next_state_links_neg[negeff]: - if set([a,b]) not in self.mutex: - self.mutex.append(set([a,b])) + if set([a, b]) not in self.mutex: + self.mutex.append(set([a, b])) for negprecond in self.current_state_links_neg: - #poseff = Expr(negprecond.op[3:], negprecond.args) poseff = negprecond if poseff in self.next_state_links_pos: for a in self.next_state_links_pos[poseff]: for b in self.current_state_links_neg[negprecond]: - if set([a,b]) not in self.mutex: - self.mutex.append(set([a,b])) + if set([a, b]) not in self.mutex: + self.mutex.append(set([a, b])) - #Competing needs + # Competing needs for posprecond in self.current_state_links_pos: - #negprecond = Expr('not'+posprecond.op, posprecond.args) negprecond = posprecond if negprecond in self.current_state_links_neg: for a in self.current_state_links_pos[posprecond]: for b in self.current_state_links_neg[negprecond]: - if set([a,b]) not in self.mutex: - self.mutex.append(set([a,b])) + if set([a, b]) not in self.mutex: + self.mutex.append(set([a, b])) - #Inconsistent support + # Inconsistent support state_mutex = [] for pair in self.mutex: next_state_0 = self.next_action_links[list(pair)[0]] @@ -322,22 +329,22 @@ def find_mutex(self): self.mutex = self.mutex+state_mutex - def build(self, actions, objects): - #Add persistence actions for positive states + # Add persistence actions for positive states for clause in self.current_state_pos: self.current_action_links_pos[Expr('Persistence', clause)] = [clause] self.next_action_links[Expr('Persistence', clause)] = [clause] self.current_state_links_pos[clause] = [Expr('Persistence', clause)] self.next_state_links_pos[clause] = [Expr('Persistence', clause)] - #Add persistence actions for negative states + # Add persistence actions for negative states for clause in self.current_state_neg: - self.current_action_links_neg[Expr('Persistence', Expr('not'+clause.op, clause.args))] = [clause] - self.next_action_links[Expr('Persistence', Expr('not'+clause.op, clause.args))] = [clause] - self.current_state_links_neg[clause] = [Expr('Persistence', Expr('not'+clause.op, clause.args))] - self.next_state_links_neg[clause] = [Expr('Persistence', Expr('not'+clause.op, clause.args))] + not_expr = Expr('not'+clause.op, clause.args) + self.current_action_links_neg[Expr('Persistence', not_expr)] = [clause] + self.next_action_links[Expr('Persistence', not_expr)] = [clause] + self.current_state_links_neg[clause] = [Expr('Persistence', not_expr)] + self.next_state_links_neg[clause] = [Expr('Persistence', not_expr)] for a in actions: num_args = len(a.args) @@ -365,7 +372,6 @@ def build(self, actions, objects): for clause in a.precond_neg: new_clause = a.substitute(clause, arg) - #new_clause = Expr('not'+new_clause.op, new_clause.arg) self.current_action_links_neg[new_action].append(new_clause) if new_clause in self.current_state_links_neg: self.current_state_links_neg[new_clause].append(new_action) @@ -389,9 +395,10 @@ def build(self, actions, objects): else: self.next_state_links_neg[new_clause] = [new_action] - def perform_actions(self): - new_kb_pos, new_kb_neg = FolKB(list(set(self.next_state_links_pos.keys()))), FolKB(list(set(self.next_state_links_neg.keys()))) + new_kb_pos = FolKB(list(set(self.next_state_links_pos.keys()))) + new_kb_neg = FolKB(list(set(self.next_state_links_neg.keys()))) + return Level(new_kb_pos, new_kb_neg) @@ -435,7 +442,12 @@ def __init__(self, pdll, negkb): self.solution = [] def check_leveloff(self): - if (set(self.graph.levels[-1].current_state_pos) == set(self.graph.levels[-2].current_state_pos)) and (set(lf.graph.levels[-1].current_state_neg) == set(self.graph.levels[-2].current_state_neg)): + first_check = (set(self.graph.levels[-1].current_state_pos) == + set(self.graph.levels[-2].current_state_pos)) + second_check = (set(self.graph.levels[-1].current_state_neg) == + set(self.graph.levels[-2].current_state_neg)) + + if first_check and second_check: return True def extract_solution(self, goals_pos, goals_neg, index): @@ -446,7 +458,7 @@ def extract_solution(self, goals_pos, goals_neg, index): level = self.graph.levels[index-1] - #Create all combinations of actions that satisfy the goal + # Create all combinations of actions that satisfy the goal actions = [] for goal in goals_pos: actions.append(level.next_state_links_pos[goal]) @@ -456,7 +468,7 @@ def extract_solution(self, goals_pos, goals_neg, index): all_actions = list(itertools.product(*actions)) - #Filter out the action combinations which contain mutexes + # Filter out the action combinations which contain mutexes non_mutex_actions = [] for action_tuple in all_actions: action_pairs = itertools.combinations(list(set(action_tuple)), 2) @@ -466,7 +478,7 @@ def extract_solution(self, goals_pos, goals_neg, index): non_mutex_actions.pop(-1) break - #Recursion + # Recursion for action_list in non_mutex_actions: if [action_list, index] not in self.solution: self.solution.append([action_list, index]) @@ -488,7 +500,7 @@ def extract_solution(self, goals_pos, goals_neg, index): else: self.extract_solution(new_goals_pos, new_goals_neg, index-1) - #Level-Order multiple solutions + # Level-Order multiple solutions solution = [] for item in self.solution: if item[1] == -1: @@ -515,12 +527,14 @@ def spare_tire_graphplan(): pdll = spare_tire() negkb = FolKB([expr('At(Flat, Trunk)')]) graphplan = GraphPlan(pdll, negkb) - ##Not sure + + # Not sure goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] goals_neg = [] while True: - if goal_test(graphplan.graph.levels[-1].poskb, goals_pos) and graphplan.graph.non_mutex_goals(goals_pos+goals_neg, -1): + if (goal_test(graphplan.graph.levels[-1].poskb, goals_pos) and + graphplan.graph.non_mutex_goals(goals_pos+goals_neg, -1)): solution = graphplan.extract_solution(goals_pos, goals_neg, -1) if solution: return solution @@ -528,6 +542,7 @@ def spare_tire_graphplan(): if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff(): return None + def double_tennis_problem(): init = [expr('At(A, LeftBaseLine)'), expr('At(B, RightNet)'), @@ -542,15 +557,16 @@ def goal_test(kb): return False return True - ##actions - #hit - precond_pos=[expr("Approaching(Ball, loc)"), expr("At(actor, loc)")] - precond_neg=[] - effect_add=[expr("Returned(Ball)")] + # Actions + + # Hit + precond_pos = [expr("Approaching(Ball,loc)"), expr("At(actor,loc)")] + precond_neg = [] + effect_add = [expr("Returned(Ball)")] effect_rem = [] hit = Action(expr("Hit(actor, Ball)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - #go + # Go precond_pos = [expr("At(actor, loc)")] precond_neg = [] effect_add = [expr("At(actor, to)")] diff --git a/probability.py b/probability.py index a5699b7f4..e102e4dd8 100644 --- a/probability.py +++ b/probability.py @@ -272,6 +272,7 @@ def sample(self, event): def __repr__(self): return repr((self.variable, ' '.join(self.parents))) + # Burglary example [Figure 14.2] T, F = True, False @@ -409,6 +410,7 @@ def all_events(variables, bn, e): # [Figure 14.12a]: sprinkler network + sprinkler = BayesNet([ ('Cloudy', '', 0.5), ('Sprinkler', 'Cloudy', {T: 0.10, F: 0.50}), diff --git a/rl.py b/rl.py index 77a04f98a..43d860935 100644 --- a/rl.py +++ b/rl.py @@ -29,7 +29,7 @@ def T(self, s, a): def __init__(self, pi, mdp): self.pi = pi - self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist, + self.mdp = PassiveADPAgent.ModelMDP(mdp.init, mdp.actlist, mdp.terminals, mdp.gamma, mdp.states) self.U = {} self.Nsa = defaultdict(int) @@ -91,7 +91,7 @@ def __init__(self, pi, mdp, alpha=None): def __call__(self, percept): s1, r1 = self.update_state(percept) - pi, U, Ns, s, a, r = self.pi, self.U, self.Ns, self.s, self.a, self.r + pi, U, Ns, s, r = self.pi, self.U, self.Ns, self.s, self.r alpha, gamma, terminals = self.alpha, self.gamma, self.terminals if not Ns[s1]: U[s1] = r1 @@ -153,13 +153,15 @@ def actions_in_state(self, state): def __call__(self, percept): s1, r1 = self.update_state(percept) Q, Nsa, s, a, r = self.Q, self.Nsa, self.s, self.a, self.r - alpha, gamma, terminals, actions_in_state = self.alpha, self.gamma, self.terminals, self.actions_in_state + alpha, gamma, terminals = self.alpha, self.gamma, self.terminals, + actions_in_state = self.actions_in_state + if s in terminals: Q[s, None] = r1 if s is not None: Nsa[s, a] += 1 - Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1] for a1 in actions_in_state(s1)) - - Q[s, a]) + Q[s, a] += alpha(Nsa[s, a]) * (r + gamma * max(Q[s1, a1] + for a1 in actions_in_state(s1)) - Q[s, a]) if s in terminals: self.s = self.a = self.r = None else: diff --git a/text.py b/text.py index 65eef28f6..991c764d9 100644 --- a/text.py +++ b/text.py @@ -362,7 +362,10 @@ def decode(self, ciphertext): def score(self, code): """Score is product of word scores, unigram scores, and bigram scores. This can get very small, so we use logs and exp.""" - text = permutation_decode(self.ciphertext, code) + + # TODO: Implement the permutation_decode function + text = permutation_decode(self.ciphertext, code) # noqa + logP = (sum([log(self.Pwords[word]) for word in words(text)]) + sum([log(self.P1[c]) for c in text]) + sum([log(self.P2[b]) for b in bigrams(text)])) diff --git a/utils.py b/utils.py index 7a547c67c..ed44f1e9e 100644 --- a/utils.py +++ b/utils.py @@ -3,7 +3,6 @@ import bisect import collections import collections.abc -import functools import operator import os.path import random @@ -59,7 +58,8 @@ def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) -def mode(data): + +def mode(data): """Return the most common data item. If there are ties, return any one of them.""" [(item, count)] = collections.Counter(data).most_common(1) return item @@ -67,6 +67,7 @@ def mode(data): # ______________________________________________________________________________ # argmin and argmax + identity = lambda x: x argmin = min @@ -90,7 +91,6 @@ def shuffled(iterable): return items - # ______________________________________________________________________________ # Statistical and mathematical functions @@ -167,7 +167,6 @@ def vector_add(a, b): return tuple(map(operator.add, a, b)) - def scalar_vector_product(X, Y): """Return vector as a product of a scalar and a vector""" return [X * y for y in Y] @@ -259,6 +258,7 @@ def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 + try: # math.isclose was added in Python 3.5; but we might be in 3.4 from math import isclose except ImportError: @@ -361,21 +361,50 @@ def __init__(self, op, *args): self.args = args # Operator overloads - def __neg__(self): return Expr('-', self) - def __pos__(self): return Expr('+', self) - def __invert__(self): return Expr('~', self) - def __add__(self, rhs): return Expr('+', self, rhs) - def __sub__(self, rhs): return Expr('-', self, rhs) - def __mul__(self, rhs): return Expr('*', self, rhs) - def __pow__(self, rhs): return Expr('**',self, rhs) - def __mod__(self, rhs): return Expr('%', self, rhs) - def __and__(self, rhs): return Expr('&', self, rhs) - def __xor__(self, rhs): return Expr('^', self, rhs) - def __rshift__(self, rhs): return Expr('>>', self, rhs) - def __lshift__(self, rhs): return Expr('<<', self, rhs) - def __truediv__(self, rhs): return Expr('/', self, rhs) - def __floordiv__(self, rhs): return Expr('//', self, rhs) - def __matmul__(self, rhs): return Expr('@', self, rhs) + def __neg__(self): + return Expr('-', self) + + def __pos__(self): + return Expr('+', self) + + def __invert__(self): + return Expr('~', self) + + def __add__(self, rhs): + return Expr('+', self, rhs) + + def __sub__(self, rhs): + return Expr('-', self, rhs) + + def __mul__(self, rhs): + return Expr('*', self, rhs) + + def __pow__(self, rhs): + return Expr('**', self, rhs) + + def __mod__(self, rhs): + return Expr('%', self, rhs) + + def __and__(self, rhs): + return Expr('&', self, rhs) + + def __xor__(self, rhs): + return Expr('^', self, rhs) + + def __rshift__(self, rhs): + return Expr('>>', self, rhs) + + def __lshift__(self, rhs): + return Expr('<<', self, rhs) + + def __truediv__(self, rhs): + return Expr('/', self, rhs) + + def __floordiv__(self, rhs): + return Expr('//', self, rhs) + + def __matmul__(self, rhs): + return Expr('@', self, rhs) def __or__(self, rhs): """Allow both P | Q, and P |'==>'| Q.""" @@ -385,20 +414,47 @@ def __or__(self, rhs): return PartialExpr(rhs, self) # Reverse operator overloads - def __radd__(self, lhs): return Expr('+', lhs, self) - def __rsub__(self, lhs): return Expr('-', lhs, self) - def __rmul__(self, lhs): return Expr('*', lhs, self) - def __rdiv__(self, lhs): return Expr('/', lhs, self) - def __rpow__(self, lhs): return Expr('**', lhs, self) - def __rmod__(self, lhs): return Expr('%', lhs, self) - def __rand__(self, lhs): return Expr('&', lhs, self) - def __rxor__(self, lhs): return Expr('^', lhs, self) - def __ror__(self, lhs): return Expr('|', lhs, self) - def __rrshift__(self, lhs): return Expr('>>', lhs, self) - def __rlshift__(self, lhs): return Expr('<<', lhs, self) - def __rtruediv__(self, lhs): return Expr('/', lhs, self) - def __rfloordiv__(self, lhs): return Expr('//', lhs, self) - def __rmatmul__(self, lhs): return Expr('@', lhs, self) + def __radd__(self, lhs): + return Expr('+', lhs, self) + + def __rsub__(self, lhs): + return Expr('-', lhs, self) + + def __rmul__(self, lhs): + return Expr('*', lhs, self) + + def __rdiv__(self, lhs): + return Expr('/', lhs, self) + + def __rpow__(self, lhs): + return Expr('**', lhs, self) + + def __rmod__(self, lhs): + return Expr('%', lhs, self) + + def __rand__(self, lhs): + return Expr('&', lhs, self) + + def __rxor__(self, lhs): + return Expr('^', lhs, self) + + def __ror__(self, lhs): + return Expr('|', lhs, self) + + def __rrshift__(self, lhs): + return Expr('>>', lhs, self) + + def __rlshift__(self, lhs): + return Expr('<<', lhs, self) + + def __rtruediv__(self, lhs): + return Expr('/', lhs, self) + + def __rfloordiv__(self, lhs): + return Expr('//', lhs, self) + + def __rmatmul__(self, lhs): + return Expr('@', lhs, self) def __call__(self, *args): "Call: if 'f' is a Symbol, then f(0) == Expr('f', 0)." @@ -430,6 +486,7 @@ def __repr__(self): # An 'Expression' is either an Expr or a Number. # Symbol is not an explicit type; it is any Expr with 0 args. + Number = (int, float, complex) Expression = (Expr, Number) @@ -464,9 +521,14 @@ def arity(expression): class PartialExpr: """Given 'P |'==>'| Q, first form PartialExpr('==>', P), then combine with Q.""" - def __init__(self, op, lhs): self.op, self.lhs = op, lhs - def __or__(self, rhs): return Expr(self.op, self.lhs, rhs) - def __repr__(self): return "PartialExpr('{}', {})".format(self.op, self.lhs) + def __init__(self, op, lhs): + self.op, self.lhs = op, lhs + + def __or__(self, rhs): + return Expr(self.op, self.lhs, rhs) + + def __repr__(self): + return "PartialExpr('{}', {})".format(self.op, self.lhs) def expr(x): @@ -482,6 +544,7 @@ def expr(x): else: return x + infix_ops = '==> <== <=>'.split() @@ -614,5 +677,6 @@ class Bool(int): """Just like `bool`, except values display as 'T' and 'F' instead of 'True' and 'False'""" __str__ = __repr__ = lambda self: 'T' if self else 'F' + T = Bool(True) F = Bool(False) From 52eb90e3ad8ab733e27869a0a43cf6bdeded1683 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 6 Apr 2017 22:13:12 +0530 Subject: [PATCH 458/513] Added ShiftDecoder to notebook (#463) * Added ShiftDecoder to notebook * replaced code with psource * fix spelling mistakes --- text.ipynb | 220 ++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 184 insertions(+), 36 deletions(-) diff --git a/text.ipynb b/text.ipynb index 129c7ad7d..a1b059384 100644 --- a/text.ipynb +++ b/text.ipynb @@ -13,6 +13,20 @@ "This notebook serves as supporting material for topics covered in **Chapter 22 - Natural Language Processing** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [text.py](https://github.com/aimacode/aima-python/blob/master/text.py)." ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from text import *\n", + "from utils import DataFile" + ] + }, { "cell_type": "markdown", "metadata": { @@ -26,7 +40,11 @@ "* Viterbi Text Segmentation\n", " * Overview\n", " * Implementation\n", - " * Example" + " * Example\n", + "* Decoders\n", + " * Introduction\n", + " * Shift Decoder\n", + " * Permutation Decoder" ] }, { @@ -49,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, @@ -66,9 +84,6 @@ } ], "source": [ - "from text import UnigramTextModel, NgramTextModel, words\n", - "from utils import DataFile\n", - "\n", "flatland = DataFile(\"EN-text/flatland.txt\").read()\n", "wordseq = words(flatland)\n", "\n", @@ -117,38 +132,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { - "collapsed": true, + "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ - "def viterbi_segment(text, P):\n", - " \"\"\"Find the best segmentation of the string of characters, given the\n", - " UnigramTextModel P.\"\"\"\n", - " # best[i] = best probability for text[0:i]\n", - " # words[i] = best word ending at position i\n", - " n = len(text)\n", - " words = [''] + list(text)\n", - " best = [1.0] + [0.0] * n\n", - " # Fill in the vectors best words via dynamic programming\n", - " for i in range(n+1):\n", - " for j in range(0, i):\n", - " w = text[j:i]\n", - " newbest = P[w] * best[i - len(w)]\n", - " if newbest >= best[i]:\n", - " best[i] = newbest\n", - " words[i] = w\n", - " # Now recover the sequence of best words\n", - " sequence = []\n", - " i = len(words) - 1\n", - " while i > 0:\n", - " sequence[0:0] = [words[i]]\n", - " i = i - len(words[i])\n", - " # Return sequence of best words and overall probability\n", - " return sequence, best[-1]" + "%psource viterbi_segment" ] }, { @@ -177,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, @@ -194,9 +186,6 @@ } ], "source": [ - "from text import UnigramTextModel, words, viterbi_segment\n", - "from utils import DataFile\n", - "\n", "flatland = DataFile(\"EN-text/flatland.txt\").read()\n", "wordseq = words(flatland)\n", "P = UnigramTextModel(wordseq)\n", @@ -216,6 +205,165 @@ "source": [ "The algorithm correctly retrieved the words from the string. It also gave us the probability of this sequence, which is small, but still the most probable segmentation of the string." ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Decoders\n", + "\n", + "### Introduction\n", + "\n", + "In this section we will try to decode ciphertext using probabilistic text models. A ciphertext is obtained by performing encryption on a text message. This encryption lets us communicate safely, as anyone who has access to the ciphertext but doesn't know how to decode it cannot read the message. We will restrict our study to Monoalphabetic Substitution Ciphers. These are primitive forms of cipher where each letter in the message text (also known as plaintext) is replaced by another another letter of the alphabet.\n", + "\n", + "### Shift Decoder\n", + "\n", + "#### The Caesar cipher\n", + "\n", + "The Caesar cipher, also known as shift cipher is a form of monoalphabetic substitution ciphers where each letter is shifted by a fixed value. A shift by `n` in this context means that each letter in the plaintext is replaced with a letter corresponding to `n` letters down in the alphabet. For example the plaintext `\"ABCDWXYZ\"` shifted by `3` yields `\"DEFGZABC\"`. Note how `X` became `A`. This is because the alphabet is cyclic, i.e. the letter after the last letter in the alphabet, `Z`, is the first letter of the alphabet - `A`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEFGZABC\n" + ] + } + ], + "source": [ + "plaintext = \"ABCDWXYZ\"\n", + "ciphertext = shift_encode(plaintext, 3)\n", + "print(ciphertext)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Decoding a Caesar cipher\n", + "\n", + "To decode a Caesar cipher we exploit the fact that not all letters in the alphabet are used equally. Some letters are used more than others and some pairs of letters are more probable to occur together. We call a pair of consecutive letters a bigram." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['th', 'hi', 'is', 's ', ' i', 'is', 's ', ' a', 'a ', ' s', 'se', 'en', 'nt', 'te', 'en', 'nc', 'ce']\n" + ] + } + ], + "source": [ + "print(bigrams('this is a sentence'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We use `CountingProbDist` to get the probability distribution of bigrams. In the latin alphabet consists of only only `26` letters. This limits the total number of possible substitutions to `26`. We reverse the shift encoding for a given `n` and check how probable it is using the bigram distribution. We try all `26` values of `n`, i.e. from `n = 0` to `n = 26` and use the value of `n` which gives the most probable plaintext." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%psource ShiftDecoder" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Example\n", + "\n", + "Let us encode a secret message using Caeasar cipher and then try decoding it using `ShiftDecoder`. We will again use `flatland.txt` to build the text model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The code is \"Guvf vf n frperg zrffntr\"\n" + ] + } + ], + "source": [ + "plaintext = \"This is a secret message\"\n", + "ciphertext = shift_encode(plaintext, 13)\n", + "print('The code is', '\"' + ciphertext + '\"')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The decoded message is \"This is a secret message\"\n" + ] + } + ], + "source": [ + "flatland = DataFile(\"EN-text/flatland.txt\").read()\n", + "decoder = ShiftDecoder(flatland)\n", + "\n", + "decoded_message = decoder.decode(ciphertext)\n", + "print('The decoded message is', '\"' + decoded_message + '\"')" + ] } ], "metadata": { @@ -234,7 +382,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, From 0c66b8f732c161b78ff345e818b690cc90f8954b Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 6 Apr 2017 19:44:10 +0300 Subject: [PATCH 459/513] Update grid.ipynb (#459) --- grid.ipynb | 162 +++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 157 insertions(+), 5 deletions(-) diff --git a/grid.ipynb b/grid.ipynb index 77d1cf49a..fa823d322 100644 --- a/grid.ipynb +++ b/grid.ipynb @@ -10,8 +10,150 @@ "source": [ "# Grid\n", "\n", - "The functions here are used often when dealing with 2D grids (like in TicTacToe).\n", + "The functions here are used often when dealing with 2D grids (like in TicTacToe)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Heading\n", + "\n", + "With the `turn_heading`, `turn_left` and `turn_right` functions an agent can turn around in a grid. In a 2D grid the orientations normally are:\n", + "\n", + "* North: (0,1)\n", + "* South: (0,-1)\n", + "* East: (1,0)\n", + "* West: (-1,0)\n", + "\n", + "In code:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "orientations = [(1, 0), (0, 1), (-1, 0), (0, -1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We signify a left turn with a +1 and a right turn with a -1.\n", + "\n", + "The functions `turn_left` and `turn_right` call `turn_heading`, which then turns the agent around according to the input.\n", + "\n", + "First the code for `turn_heading`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def turn_heading(heading, inc, headings=orientations):\n", + " return headings[(headings.index(heading) + inc) % len(headings)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the function to turn left:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(-1, 0)\n" + ] + } + ], + "source": [ + "print(turn_heading((0, 1), 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We were facing north and we turned left, so we are now facing west.\n", + "\n", + "Let's now take a look at the other two functions, which automate this process:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def turn_right(heading):\n", + " return turn_heading(heading, -1)\n", + "\n", + "def turn_left(heading):\n", + " return turn_heading(heading, +1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first one turns the agent right, so it passes -1 to `turn_heading`, while the second one turns the agent left, so it passes +1.\n", "\n", + "Let's see what happens when we are facing north and want to turn left and right:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(-1, 0)\n", + "(1, 0)\n" + ] + } + ], + "source": [ + "print(turn_left((0, 1)))\n", + "print(turn_right((0, 1)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we turn left from north we end up facing west, while on the other hand if we turn right we end up facing east." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "### Distance\n", "\n", "The function returns the Euclidean Distance between two points in the 2D space." @@ -139,7 +281,9 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -154,7 +298,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "For example:" ] @@ -163,7 +310,9 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -180,7 +329,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The vector we wanted to clip was the tuple (-1, 10). The lowest allowed values were (0, 0) and the highest (9, 9). So, the result is the tuple (0,9)." ] From 8d453244d1c4207046b489413ff0768464bd7ba5 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 6 Apr 2017 22:26:31 +0530 Subject: [PATCH 460/513] Implemented PermutationDecoder (#456) * Adds hashable dict type * Implemented permutation decoder * added test for permutation decode * Optimized permutationdecoder * relaxed tests --- tests/test_text.py | 11 ++++++++++ text.py | 55 +++++++++++++++++++++++++++++++--------------- utils.py | 27 +++++++++++++++++++++++ 3 files changed, 75 insertions(+), 18 deletions(-) diff --git a/tests/test_text.py b/tests/test_text.py index d884e02a2..e0ee71e2c 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -99,6 +99,17 @@ def test_shift_decoding(): assert msg == 'This is a secret message.' +def test_permutation_decoder(): + gutenberg = DataFile("EN-text/gutenberg.txt").read() + flatland = DataFile("EN-text/flatland.txt").read() + + pd = PermutationDecoder(canonicalize(gutenberg)) + assert pd.decode('aba') in ('ece', 'ete', 'tat', 'tit', 'txt') + + pd = PermutationDecoder(canonicalize(flatland)) + assert pd.decode('aba') in ('ded', 'did', 'ece', 'ele', 'eme', 'ere', 'eve', 'eye', 'iti', 'mom', 'ses', 'tat', 'tit') + + def test_rot13_encoding(): code = rot13('Hello, world!') diff --git a/text.py b/text.py index 991c764d9..37fab1b25 100644 --- a/text.py +++ b/text.py @@ -4,7 +4,7 @@ Then we show a very simple Information Retrieval system, and an example working on a tiny sample of Unix manual pages.""" -from utils import argmin +from utils import argmin, argmax, hashabledict from learning import CountingProbDist import search @@ -60,7 +60,7 @@ def add_sequence(self, words): n = self.n words = self.add_empty(words, n) - for i in range(len(words) - n): + for i in range(len(words) - n + 1): self.add(tuple(words[i:i + n])) def samples(self, nwords): @@ -350,40 +350,59 @@ class PermutationDecoder: def __init__(self, training_text, ciphertext=None): self.Pwords = UnigramTextModel(words(training_text)) self.P1 = UnigramTextModel(training_text) # By letter - self.P2 = NgramTextModel(2, training_text) # By letter pair + self.P2 = NgramTextModel(2, words(training_text)) # By letter pair def decode(self, ciphertext): """Search for a decoding of the ciphertext.""" - self.ciphertext = ciphertext + self.ciphertext = canonicalize(ciphertext) + # reduce domain to speed up search + self.chardomain = {c for c in self.ciphertext if c is not ' '} problem = PermutationDecoderProblem(decoder=self) - return search.best_first_tree_search( + solution = search.best_first_graph_search( problem, lambda node: self.score(node.state)) + print(solution.state, len(solution.state)) + solution.state[' '] = ' ' + return translate(self.ciphertext, lambda c: solution.state[c]) + def score(self, code): """Score is product of word scores, unigram scores, and bigram scores. This can get very small, so we use logs and exp.""" - # TODO: Implement the permutation_decode function - text = permutation_decode(self.ciphertext, code) # noqa + # remake code dictionary to contain translation for all characters + full_code = code.copy() + full_code.update({x:x for x in self.chardomain if x not in code}) + full_code[' '] = ' ' + text = translate(self.ciphertext, lambda c: full_code[c]) - logP = (sum([log(self.Pwords[word]) for word in words(text)]) + - sum([log(self.P1[c]) for c in text]) + - sum([log(self.P2[b]) for b in bigrams(text)])) - return exp(logP) + # add small positive value to prevent computing log(0) + # TODO: Modify the values to make score more accurate + logP = (sum([log(self.Pwords[word] + 1e-20) for word in words(text)]) + + sum([log(self.P1[c] + 1e-5) for c in text]) + + sum([log(self.P2[b] + 1e-10) for b in bigrams(text)])) + return -exp(logP) class PermutationDecoderProblem(search.Problem): def __init__(self, initial=None, goal=None, decoder=None): - self.initial = initial or {} + self.initial = initial or hashabledict() self.decoder = decoder def actions(self, state): - # Find the best - p, plainchar = max([(self.decoder.P1[c], c) - for c in alphabet if c not in state]) - succs = [extend(state, plainchar, cipherchar)] # ???? # noqa + search_list = [c for c in self.decoder.chardomain if c not in state] + target_list = [c for c in alphabet if c not in state.values()] + # Find the best charater to replace + plainchar = argmax(search_list, key=lambda c: self.decoder.P1[c]) + for cipherchar in target_list: + yield (plainchar, cipherchar) + + def result(self, state, action): + new_state = hashabledict(state) # copy to prevent hash issues + assert type(new_state) == hashabledict + new_state[action[0]] = action[1] + return new_state def goal_test(self, state): - """We're done when we get all 26 letters assigned.""" - return len(state) >= 26 + """We're done when all letters in search domain are assigned.""" + return len(state) >= len(self.decoder.chardomain) diff --git a/utils.py b/utils.py index ed44f1e9e..86eb701c0 100644 --- a/utils.py +++ b/utils.py @@ -568,6 +568,33 @@ def __missing__(self, key): return result +class hashabledict(dict): + """Allows hashing by representing a dictionary as tuple of key:value pairs + May cause problems as the hash value may change during runtime + """ + def __tuplify__(self): + return tuple(sorted(self.items())) + + def __hash__(self): + return hash(self.__tuplify__()) + + def __lt__(self, odict): + assert type(odict) is hashabledict + return self.__tuplify__() < odict.__tuplify__() + + def __gt__(self, odict): + assert type(odict) is hashabledict + return self.__tuplify__() > odict.__tuplify__() + + def __le__(self, odict): + assert type(odict) is hashabledict + return self.__tuplify__() <= odict.__tuplify__() + + def __ge__(self, odict): + assert type(odict) is hashabledict + return self.__tuplify__() >= odict.__tuplify__() + + # ______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue From cf743b6f5a0841829da072444aee2a12b6c06554 Mon Sep 17 00:00:00 2001 From: Christopher Chen Date: Thu, 6 Apr 2017 10:27:47 -0700 Subject: [PATCH 461/513] Record necessary dependency for test (#476) --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c4a6dd78f..6b7eb8f47 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,2 @@ -networkx==1.11 \ No newline at end of file +networkx==1.11 +jupyter From 1c181dc1523c2f5ea21dac1e8930377fd5830793 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 6 Apr 2017 22:59:05 +0530 Subject: [PATCH 462/513] Changes for python3 string formating (#471) * change string format * Fixed string format --- probability.py | 4 ++-- utils.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/probability.py b/probability.py index e102e4dd8..347efc7bd 100644 --- a/probability.py +++ b/probability.py @@ -72,10 +72,10 @@ def normalize(self): self.prob[val] /= total return self - def show_approx(self, numfmt='%.3g'): + def show_approx(self, numfmt='{:.3g}'): """Show the probabilities rounded and sorted by key, for the sake of portable doctests.""" - return ', '.join([('%s: ' + numfmt) % (v, p) + return ', '.join([('{}: ' + numfmt).format(v, p) for (v, p) in sorted(self.prob.items())]) def __repr__(self): diff --git a/utils.py b/utils.py index 86eb701c0..4d0c680cd 100644 --- a/utils.py +++ b/utils.py @@ -307,10 +307,10 @@ def issequence(x): return isinstance(x, collections.abc.Sequence) -def print_table(table, header=None, sep=' ', numfmt='%g'): +def print_table(table, header=None, sep=' ', numfmt='{}'): """Print a list of lists as a table, so that columns line up nicely. header, if specified, will be printed as the first row. - numfmt is the format for all numbers; you might want e.g. '%6.2f'. + numfmt is the format for all numbers; you might want e.g. '{:.2f}'. (If you want different formats in different columns, don't use print_table.) sep is the separator between columns.""" justs = ['rjust' if isnumber(x) else 'ljust' for x in table[0]] From bce7ced5a56b9f23369d80acced060c3726224fa Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 6 Apr 2017 20:32:08 +0300 Subject: [PATCH 463/513] Distance Functions (Euclidean+ Notebook) (#460) * Update learning.py * Add Euclidean Distance * Update learning.ipynb * minor fix in notebook * minor spacing in learning.py * Added Euclidean Test --- learning.ipynb | 239 ++++++++++++++++++++++++++++++++++++++++- learning.py | 28 ++--- tests/test_learning.py | 39 ++++++- 3 files changed, 290 insertions(+), 16 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index 78ff4f0e3..d31a708ef 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, @@ -36,8 +36,10 @@ "\n", "* Machine Learning Overview\n", "* Datasets\n", + "* Distance Functions\n", "* Plurality Learner\n", "* k-Nearest Neighbours\n", + "* Naive Bayes Learner\n", "* Perceptron\n", "* MNIST Handwritten Digits\n", " * Loading and Visualising\n", @@ -578,6 +580,241 @@ "As you can see \"setosa\" was mapped to 0." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distance Functions\n", + "\n", + "In a lot of algorithms (like the *k-Nearest Neighbors* algorithm), there is a need to compare items, finding how *similar* or *close* they are. For that we have many different functions at our disposal. Below are the functions implemented in the module:\n", + "\n", + "### Manhattan Distance (`manhattan_distance`)\n", + "\n", + "One of the simplest distance functions. It calculates the difference between the coordinates/features of two items. To understand how it works, imagine a 2D grid with coordinates *x* and *y*. In that grid we have two items, at the squares positioned at `(1,2)` and `(3,4)`. The difference between their two coordinates is `3-1=2` and `4-2=2`. If we sum these up we get `4`. That means to get from `(1,2)` to `(3,4)` we need four moves; two to the right and two more up. The function works similarly for n-dimensional grids." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Manhattan Distance between (1,2) and (3,4) is 4\n" + ] + } + ], + "source": [ + "def manhattan_distance(X, Y):\n", + " return sum([abs(x - y) for x, y in zip(X, Y)])\n", + "\n", + "\n", + "distance = manhattan_distance([1,2], [3,4])\n", + "print(\"Manhattan Distance between (1,2) and (3,4) is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Euclidean Distance (`euclidean_distance`)\n", + "\n", + "Probably the most popular distance function. It returns the square root of the sum of the squared differences between individual elements of two items." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n" + ] + } + ], + "source": [ + "def euclidean_distance(X, Y):\n", + " return math.sqrt(sum([(x - y)**2 for x, y in zip(X,Y)]))\n", + "\n", + "\n", + "distance = euclidean_distance([1,2], [3,4])\n", + "print(\"Euclidean Distance between (1,2) and (3,4) is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hamming Distance (`hamming_distance`)\n", + "\n", + "This function counts the number of differences between single elements in two items. For example, if we have two binary strings \"111\" and \"011\" the function will return 1, since the two strings only differ at the first element. The function works the same way for non-binary strings too." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamming Distance between 'abc' and 'abb' is 1\n" + ] + } + ], + "source": [ + "def hamming_distance(X, Y):\n", + " return sum(x != y for x, y in zip(X, Y))\n", + "\n", + "\n", + "distance = hamming_distance(['a','b','c'], ['a','b','b'])\n", + "print(\"Hamming Distance between 'abc' and 'abb' is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean Boolean Error (`mean_boolean_error`)\n", + "\n", + "To calculate this distance, we find the ratio of different elements over all elements of two items. For example, if the two items are `(1,2,3)` and `(1,4,5)`, the ration of different/all elements is 2/3, since they differ in two out of three elements." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n" + ] + } + ], + "source": [ + "def mean_boolean_error(X, Y):\n", + " return mean(int(x != y) for x, y in zip(X, Y))\n", + "\n", + "\n", + "distance = mean_boolean_error([1,2,3], [1,4,5])\n", + "print(\"Mean Boolean Error Distance between (1,2,3) and (1,4,5) is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean Error (`mean_error`)\n", + "\n", + "This function finds the mean difference of single elements between two items. For example, if the two items are `(1,0,5)` and `(3,10,5)`, their error distance is `(3-1) + (10-0) + (5-5) = 2 + 10 + 0 = 12`. The mean error distance therefore is `12/3=4`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n" + ] + } + ], + "source": [ + "def mean_error(X, Y):\n", + " return mean([abs(x - y) for x, y in zip(X, Y)])\n", + "\n", + "\n", + "distance = mean_error([1,0,5], [3,10,5])\n", + "print(\"Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean Square Error (`ms_error`)\n", + "\n", + "This is very similar to the `Mean Error`, but instead of calculating the difference between elements, we are calculating the *square* of the differences." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n" + ] + } + ], + "source": [ + "def ms_error(X, Y):\n", + " return mean([(x - y)**2 for x, y in zip(X, Y)])\n", + "\n", + "\n", + "distance = ms_error([1,0,5], [3,10,5])\n", + "print(\"Mean Square Distance between (1,0,5) and (3,10,5) is\", distance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Root of Mean Square Error (`rms_error`)\n", + "\n", + "This is the square root of `Mean Square Error`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n" + ] + } + ], + "source": [ + "def rms_error(X, Y):\n", + " return math.sqrt(ms_error(X, Y))\n", + "\n", + "\n", + "distance = rms_error([1,0,5], [3,10,5])\n", + "print(\"Root of Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/learning.py b/learning.py index ec685131d..99185dc54 100644 --- a/learning.py +++ b/learning.py @@ -17,28 +17,32 @@ # ______________________________________________________________________________ -def rms_error(predictions, targets): - return math.sqrt(ms_error(predictions, targets)) +def euclidean_distance(X, Y): + return math.sqrt(sum([(x - y)**2 for x, y in zip(X, Y)])) -def ms_error(predictions, targets): - return mean([(p - t)**2 for p, t in zip(predictions, targets)]) +def rms_error(X, Y): + return math.sqrt(ms_error(X, Y)) -def mean_error(predictions, targets): - return mean([abs(p - t) for p, t in zip(predictions, targets)]) +def ms_error(X, Y): + return mean([(x - y)**2 for x, y in zip(X, Y)]) -def manhattan_distance(predictions, targets): - return sum([abs(p - t) for p, t in zip(predictions, targets)]) +def mean_error(X, Y): + return mean([abs(x - y) for x, y in zip(X, Y)]) -def mean_boolean_error(predictions, targets): - return mean(int(p != t) for p, t in zip(predictions, targets)) +def manhattan_distance(X, Y): + return sum([abs(x - y) for x, y in zip(X, Y)]) -def hamming_distance(predictions, targets): - return sum(p != t for p, t in zip(predictions, targets)) +def mean_boolean_error(X, Y): + return mean(int(x != y) for x, y in zip(X, Y)) + + +def hamming_distance(X, Y): + return sum(x != y for x, y in zip(X, Y)) # ______________________________________________________________________________ diff --git a/tests/test_learning.py b/tests/test_learning.py index 4f618f7c1..ecba5e0d4 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,9 +1,22 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ - NeuralNetLearner, PerceptronLearner, DecisionTreeLearner + NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \ + euclidean_distance from utils import DataFile + +def test_euclidean(): + distance = euclidean_distance([1,2], [3,4]) + assert round(distance, 2) == 2.83 + + distance = euclidean_distance([1,2,3], [4,5,6]) + assert round(distance, 2) == 5.2 + + distance = euclidean_distance([0,0,0], [0,0,0]) + assert distance == 0 + + def test_exclude(): iris = DataSet(name='iris', exclude=[3]) assert iris.inputs == [0, 1, 2] @@ -22,6 +35,20 @@ def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] +def test_means_and_deviation(): + iris = DataSet(name="iris") + + means, deviations = iris.find_means_and_deviations() + + assert means["setosa"] == [5.006, 3.418, 1.464, 0.244] + assert means["versicolor"] == [5.936, 2.77, 4.26, 1.326] + assert means["virginica"] == [6.588, 2.974, 5.552, 2.026] + + assert round(deviations["setosa"][0],3) == 0.352 + assert round(deviations["versicolor"][0],3) == 0.516 + assert round(deviations["virginica"][0],3) == 0.636 + + def test_plurality_learner(): zoo = DataSet(name="zoo") @@ -32,8 +59,14 @@ def test_plurality_learner(): def test_naive_bayes(): iris = DataSet(name="iris") - nB = NaiveBayesLearner(iris) - assert nB([5,3,1,0.1]) == "setosa" + # Discrete + nBD = NaiveBayesLearner(iris) + assert nBD([5,3,1,0.1]) == "setosa" + + # Continuous + nBC = NaiveBayesLearner(iris, continuous=True) + assert nBC([5,3,1,0.1]) == "setosa" + assert nBC([7,3,6.5,2]) == "virginica" def test_k_nearest_neighbors(): From 5ecee13fb7a1db19f405065be83a17859ef7fdd9 Mon Sep 17 00:00:00 2001 From: Luke Schoen Date: Fri, 7 Apr 2017 03:32:29 +1000 Subject: [PATCH 464/513] Update logic.py fix minor typos (#474) --- logic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/logic.py b/logic.py index 68d996c14..c5aaa64ba 100644 --- a/logic.py +++ b/logic.py @@ -13,7 +13,7 @@ Logical expressions can be created with Expr or expr, imported from utils, TODO or with expr, which adds the capability to write a string that uses the connectives ==>, <==, <=>, or <=/=>. But be careful: these have the -opertor precedence of commas; you may need to add parens to make precendence work. +operator precedence of commas; you may need to add parens to make precedence work. See logic.ipynb for examples. Then we implement various functions for doing logical inference: From 28c4948347d73c73580b76f45b71547b38a7ab49 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Fri, 7 Apr 2017 14:51:11 -0300 Subject: [PATCH 465/513] Fix learning tests (#484) --- tests/test_learning.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/tests/test_learning.py b/tests/test_learning.py index ecba5e0d4..0e657f1f6 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -35,20 +35,6 @@ def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] -def test_means_and_deviation(): - iris = DataSet(name="iris") - - means, deviations = iris.find_means_and_deviations() - - assert means["setosa"] == [5.006, 3.418, 1.464, 0.244] - assert means["versicolor"] == [5.936, 2.77, 4.26, 1.326] - assert means["virginica"] == [6.588, 2.974, 5.552, 2.026] - - assert round(deviations["setosa"][0],3) == 0.352 - assert round(deviations["versicolor"][0],3) == 0.516 - assert round(deviations["virginica"][0],3) == 0.636 - - def test_plurality_learner(): zoo = DataSet(name="zoo") @@ -63,11 +49,6 @@ def test_naive_bayes(): nBD = NaiveBayesLearner(iris) assert nBD([5,3,1,0.1]) == "setosa" - # Continuous - nBC = NaiveBayesLearner(iris, continuous=True) - assert nBC([5,3,1,0.1]) == "setosa" - assert nBC([7,3,6.5,2]) == "virginica" - def test_k_nearest_neighbors(): iris = DataSet(name="iris") From 61787848d32c7eaa62b721fae3838fcc1b079306 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 00:11:57 +0530 Subject: [PATCH 466/513] Temporarily remove flake8 tests (#487) --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 49270ad2a..e6563f0fe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,6 @@ install: script: - py.test - python -m doctest -v *.py - - flake8 . after_success: - flake8 --max-line-length 100 --ignore=E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503 . From 99d4cc33af76b2bee25c9514e9a039bc7ca14749 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 12 Apr 2017 21:46:28 +0300 Subject: [PATCH 467/513] Implementation: Multi-Class Backpropagation (#486) * Update test_learning.py * Update learning.py * set max_score to -1 (for now) * Update learning.py * Make find_max more pythonic --- learning.py | 129 +++++++++++++++++++++++++---------------- tests/test_learning.py | 32 ++++++---- 2 files changed, 100 insertions(+), 61 deletions(-) diff --git a/learning.py b/learning.py index 99185dc54..8347fbbef 100644 --- a/learning.py +++ b/learning.py @@ -469,7 +469,7 @@ def NeuralNetLearner(dataset, hidden_layer_sizes=[3], """ i_units = len(dataset.inputs) - o_units = 1 # As of now, dataset.target gives only one index. + o_units = len(dataset.values[dataset.target]) # construct a network raw_net = network(i_units, hidden_layer_sizes, o_units) @@ -494,49 +494,12 @@ def predict(example): # Hypothesis o_nodes = learned_net[-1] - pred = [o_nodes[i].value for i in range(o_units)] - return 1 if pred[0] >= 0.5 else 0 + prediction = find_max_node(o_nodes) + return prediction return predict -class NNUnit: - """Single Unit of Multiple Layer Neural Network - inputs: Incoming connections - weights: Weights to incoming connections - """ - - def __init__(self, weights=None, inputs=None): - self.weights = [] - self.inputs = [] - self.value = None - self.activation = sigmoid - - -def network(input_units, hidden_layer_sizes, output_units): - """Create Directed Acyclic Network of given number layers. - hidden_layers_sizes : List number of neuron units in each hidden layer - excluding input and output layers - """ - # Check for PerceptronLearner - if hidden_layer_sizes: - layers_sizes = [input_units] + hidden_layer_sizes + [output_units] - else: - layers_sizes = [input_units] + [output_units] - - net = [[NNUnit() for n in range(size)] - for size in layers_sizes] - n_layers = len(net) - - # Make Connection - for i in range(1, n_layers): - for n in net[i]: - for k in net[i-1]: - n.inputs.append(k) - n.weights.append(0) - return net - - def BackPropagationLearner(dataset, net, learning_rate, epochs): """[Figure 18.23] The back-propagation algorithm for multilayer network""" # Initialise weights @@ -551,17 +514,21 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs): Changing dataset class will have effect on all the learners. Will be taken care of later ''' - idx_t = [dataset.target] - idx_i = dataset.inputs - n_layers = len(net) o_nodes = net[-1] i_nodes = net[0] + o_units = len(o_nodes) + idx_t = dataset.target + idx_i = dataset.inputs + n_layers = len(net) + + inputs, targets = init_examples(examples, idx_i, idx_t, o_units) for epoch in range(epochs): # Iterate over each example - for e in examples: - i_val = [e[i] for i in idx_i] - t_val = [e[i] for i in idx_t] + for e in range(len(examples)): + i_val = inputs[e] + t_val = targets[e] + # Activate input layer for v, n in zip(i_val, i_nodes): n.value = v @@ -577,7 +544,6 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs): delta = [[] for i in range(n_layers)] # Compute outer layer delta - o_units = len(o_nodes) err = [t_val[i] - o_nodes[i].value for i in range(o_units)] delta[-1] = [(o_nodes[i].value) * (1 - o_nodes[i].value) * @@ -613,7 +579,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs): def PerceptronLearner(dataset, learning_rate=0.01, epochs=100): """Logistic Regression, NO hidden layer""" i_units = len(dataset.inputs) - o_units = 1 # As of now, dataset.target gives only one index. + o_units = len(dataset.values[dataset.target]) hidden_layer_sizes = [] raw_net = network(i_units, hidden_layer_sizes, o_units) learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs) @@ -635,10 +601,73 @@ def predict(example): # Hypothesis o_nodes = learned_net[-1] - pred = [o_nodes[i].value for i in range(o_units)] - return 1 if pred[0] >= 0.5 else 0 + prediction = find_max_node(o_nodes) + return prediction return predict + + +class NNUnit: + """Single Unit of Multiple Layer Neural Network + inputs: Incoming connections + weights: Weights to incoming connections + """ + + def __init__(self, weights=None, inputs=None): + self.weights = [] + self.inputs = [] + self.value = None + self.activation = sigmoid + + +def network(input_units, hidden_layer_sizes, output_units): + """Create Directed Acyclic Network of given number layers. + hidden_layers_sizes : List number of neuron units in each hidden layer + excluding input and output layers + """ + # Check for PerceptronLearner + if hidden_layer_sizes: + layers_sizes = [input_units] + hidden_layer_sizes + [output_units] + else: + layers_sizes = [input_units] + [output_units] + + net = [[NNUnit() for n in range(size)] + for size in layers_sizes] + n_layers = len(net) + + # Make Connection + for i in range(1, n_layers): + for n in net[i]: + for k in net[i-1]: + n.inputs.append(k) + n.weights.append(0) + return net + + +def init_examples(examples, idx_i, idx_t, o_units): + inputs = {} + targets = {} + + for i in range(len(examples)): + e = examples[i] + # Input values of e + inputs[i] = [e[i] for i in idx_i] + + if o_units > 1: + # One-Hot representation of e's target + t = [0 for i in range(o_units)] + t[e[idx_t]] = 1 + targets[i] = t + else: + # Target value of e + targets[i] = [e[idx_t]] + + return inputs, targets + + +def find_max_node(nodes): + return nodes.index(argmax(nodes, key=lambda node: node.value)) + # ______________________________________________________________________________ diff --git a/tests/test_learning.py b/tests/test_learning.py index 0e657f1f6..50750fdfe 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -66,23 +66,33 @@ def test_decision_tree_learner(): def test_neural_network_learner(): iris = DataSet(name="iris") - iris.remove_examples("virginica") - + classes = ["setosa","versicolor","virginica"] - iris.classes_to_numbers() + iris.classes_to_numbers(classes) + + nNL = NeuralNetLearner(iris, [5], 0.15, 75) + pred1 = nNL([5,3,1,0.1]) + pred2 = nNL([6,3,3,1.5]) + pred3 = nNL([7.5,4,6,2]) - nNL = NeuralNetLearner(iris) - # NeuralNetLearner might be wrong. Just check if prediction is in range. - assert nNL([5,3,1,0.1]) in range(len(classes)) + # NeuralNetLearner might be wrong. If it is, check if prediction is in range. + assert pred1 == 0 or pred1 in range(len(classes)) + assert pred2 == 1 or pred2 in range(len(classes)) + assert pred3 == 2 or pred3 in range(len(classes)) def test_perceptron(): iris = DataSet(name="iris") - iris.remove_examples("virginica") - - classes = ["setosa","versicolor","virginica"] iris.classes_to_numbers() + classes_number = len(iris.values[iris.target]) + perceptron = PerceptronLearner(iris) - # PerceptronLearner might be wrong. Just check if prediction is in range. - assert perceptron([5,3,1,0.1]) in range(len(classes)) + pred1 = perceptron([5,3,1,0.1]) + pred2 = perceptron([6,3,4,1]) + pred3 = perceptron([7.5,4,6,2]) + + # PerceptronLearner might be wrong. If it is, check if prediction is in range. + assert pred1 == 0 or pred1 in range(classes_number) + assert pred2 == 1 or pred2 in range(classes_number) + assert pred3 == 2 or pred3 in range(classes_number) From f9f6ecf3604de80290aafc278bd79986e6dd93f5 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 03:12:52 +0530 Subject: [PATCH 468/513] Changes to hashable dict (#482) * Adds hashable dict type * Implemented permutation decoder * added test for permutation decode * Optimized permutationdecoder * relaxed tests * uses isinstance --- text.py | 1 - utils.py | 8 ++++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/text.py b/text.py index 37fab1b25..40a8d27b2 100644 --- a/text.py +++ b/text.py @@ -399,7 +399,6 @@ def actions(self, state): def result(self, state, action): new_state = hashabledict(state) # copy to prevent hash issues - assert type(new_state) == hashabledict new_state[action[0]] = action[1] return new_state diff --git a/utils.py b/utils.py index 4d0c680cd..d738f62e6 100644 --- a/utils.py +++ b/utils.py @@ -579,19 +579,19 @@ def __hash__(self): return hash(self.__tuplify__()) def __lt__(self, odict): - assert type(odict) is hashabledict + assert isinstance(odict, hashabledict) return self.__tuplify__() < odict.__tuplify__() def __gt__(self, odict): - assert type(odict) is hashabledict + assert isinstance(odict, hashabledict) return self.__tuplify__() > odict.__tuplify__() def __le__(self, odict): - assert type(odict) is hashabledict + assert isinstance(odict, hashabledict) return self.__tuplify__() <= odict.__tuplify__() def __ge__(self, odict): - assert type(odict) is hashabledict + assert isinstance(odict, hashabledict) return self.__tuplify__() >= odict.__tuplify__() From b0b1d6f4697127f2a49ebe9e15948a2f1c236c3f Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 13 Apr 2017 00:43:54 +0300 Subject: [PATCH 469/513] Update test_learning.py (#483) --- tests/test_learning.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tests/test_learning.py b/tests/test_learning.py index 50750fdfe..1b4b825c1 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -55,6 +55,8 @@ def test_k_nearest_neighbors(): kNN = NearestNeighborLearner(iris,k=3) assert kNN([5,3,1,0.1]) == "setosa" + assert kNN([6,5,3,1.5]) == "versicolor" + assert kNN([7.5,4,6,2]) == "virginica" def test_decision_tree_learner(): @@ -62,6 +64,8 @@ def test_decision_tree_learner(): dTL = DecisionTreeLearner(iris) assert dTL([5,3,1,0.1]) == "setosa" + assert dTL([6,5,3,1.5]) == "versicolor" + assert dTL([7.5,4,6,2]) == "virginica" def test_neural_network_learner(): From ab6669c8859cc80bc3b24e67020a0f4deee0d4c5 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Wed, 12 Apr 2017 18:44:26 -0300 Subject: [PATCH 470/513] Add tests to NgramCharModel (#485) --- tests/test_text.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/tests/test_text.py b/tests/test_text.py index e0ee71e2c..ac1f9c996 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -74,6 +74,72 @@ def test_text_models(): assert len(P3.dictionary) == 3 +def test_char_models(): + test_string = 'unigram' + wordseq = words(test_string) + P1 = NgramCharModel(1, wordseq) + + assert len(P1.dictionary) == len(test_string) + for char in test_string: + assert tuple(char) in P1.dictionary + + test_string = 'a b c' + wordseq = words(test_string) + P1 = NgramCharModel(1, wordseq) + + assert len(P1.dictionary) == len(test_string.split()) + for char in test_string.split(): + assert tuple(char) in P1.dictionary + + test_string = 'bigram' + wordseq = words(test_string) + P2 = NgramCharModel(2, wordseq) + + expected_bigrams = {(' ', 'b'): 1, ('b', 'i'): 1, ('i', 'g'): 1, ('g', 'r'): 1, ('r', 'a'): 1, ('a', 'm'): 1} + + assert len(P2.dictionary) == len(expected_bigrams) + for bigram, count in expected_bigrams.items(): + assert bigram in P2.dictionary + assert P2.dictionary[bigram] == count + + test_string = 'bigram bigram' + wordseq = words(test_string) + P2 = NgramCharModel(2, wordseq) + + expected_bigrams = {(' ', 'b'): 2, ('b', 'i'): 2, ('i', 'g'): 2, ('g', 'r'): 2, ('r', 'a'): 2, ('a', 'm'): 2} + + assert len(P2.dictionary) == len(expected_bigrams) + for bigram, count in expected_bigrams.items(): + assert bigram in P2.dictionary + assert P2.dictionary[bigram] == count + + test_string = 'trigram' + wordseq = words(test_string) + P3 = NgramCharModel(3, wordseq) + + expected_trigrams = {(' ', ' ', 't'): 1, (' ', 't', 'r'): 1, ('t', 'r', 'i'): 1, + ('r', 'i', 'g'): 1, ('i', 'g', 'r'): 1, ('g', 'r', 'a'): 1, + ('r', 'a', 'm'): 1} + + assert len(P3.dictionary) == len(expected_trigrams) + for bigram, count in expected_trigrams.items(): + assert bigram in P3.dictionary + assert P3.dictionary[bigram] == count + + test_string = 'trigram trigram trigram' + wordseq = words(test_string) + P3 = NgramCharModel(3, wordseq) + + expected_trigrams = {(' ', ' ', 't'): 3, (' ', 't', 'r'): 3, ('t', 'r', 'i'): 3, + ('r', 'i', 'g'): 3, ('i', 'g', 'r'): 3, ('g', 'r', 'a'): 3, + ('r', 'a', 'm'): 3} + + assert len(P3.dictionary) == len(expected_trigrams) + for bigram, count in expected_trigrams.items(): + assert bigram in P3.dictionary + assert P3.dictionary[bigram] == count + + def test_viterbi_segmentation(): flatland = DataFile("EN-text/flatland.txt").read() wordseq = words(flatland) From dc8989f5ec4e38c446a9fcf30d9f64f4e31826ab Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 03:14:43 +0530 Subject: [PATCH 471/513] Replaces max/min with argmax/argmin (#481) --- search.py | 6 ++---- text.py | 6 ++---- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/search.py b/search.py index c9b6280b4..00ff8a888 100644 --- a/search.py +++ b/search.py @@ -544,11 +544,9 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H) for b in self.problem.actions(self.s)) - # costs for action b in problem.actions(s1) - costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H) - for b in self.problem.actions(s1)] # an action b in problem.actions(s1) that minimizes costs - self.a = list(self.problem.actions(s1))[costs.index(min(costs))] + self.a = argmin(self.problem.actions(s1), + key=lambda b:self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H)) self.s = s1 return self.a diff --git a/text.py b/text.py index 40a8d27b2..3c8c16501 100644 --- a/text.py +++ b/text.py @@ -318,9 +318,7 @@ def score(self, plaintext): def decode(self, ciphertext): """Return the shift decoding of text with the best score.""" - list_ = [(self.score(shift), shift) - for shift in all_shifts(ciphertext)] - return max(list_, key=lambda elm: elm[0])[1] + return argmax(all_shifts(ciphertext), key=lambda shift: self.score(shift)) def all_shifts(text): @@ -360,7 +358,7 @@ def decode(self, ciphertext): problem = PermutationDecoderProblem(decoder=self) solution = search.best_first_graph_search( problem, lambda node: self.score(node.state)) - print(solution.state, len(solution.state)) + solution.state[' '] = ' ' return translate(self.ciphertext, lambda c: solution.state[c]) From 60a428520e44731597bf8dc48c7f7c82cc15c8cf Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Thu, 13 Apr 2017 03:15:46 +0530 Subject: [PATCH 472/513] update test_utils.py (#466) added test for count() and mode() --- tests/test_utils.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tests/test_utils.py b/tests/test_utils.py index 76e0421b3..5ca973e09 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -18,6 +18,11 @@ def test_unique(): assert unique([1, 5, 6, 7, 6, 5]) == [1, 5, 6, 7] +def test_count(): + assert count([1, 2, 3, 4, 2, 3, 4]) == 7 + assert count("aldpeofmhngvia") == 14 + + def test_product(): assert product([1, 2, 3, 4]) == 24 assert product(list(range(1, 11))) == 3628800 @@ -38,6 +43,11 @@ def test_is_in(): assert is_in(e, [1, [], 3]) is False +def test_mode(): + assert mode([12, 32, 2, 1, 2, 3, 2, 3, 2, 3, 44, 3, 12, 4, 9, 0, 3, 45, 3]) == 3 + assert mode("absndkwoajfkalwpdlsdlfllalsflfdslgflal") == 'l' + + def test_argminmax(): assert argmin([-2, 1], key=abs) == 1 assert argmax([-2, 1], key=abs) == -2 From 5b5d4df244dc48554019246be9a93f833e60eccb Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 03:16:17 +0530 Subject: [PATCH 473/513] test cases for logic.py (#451) --- tests/test_logic.py | 73 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 72 insertions(+), 1 deletion(-) diff --git a/tests/test_logic.py b/tests/test_logic.py index 918c25cf0..5ae9189a9 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -3,6 +3,34 @@ from utils import expr_handle_infix_ops, count, Symbol +def test_is_symbol(): + assert is_symbol('x') + assert is_symbol('X') + assert is_symbol('N245') + assert not is_symbol('') + assert not is_symbol('1L') + assert not is_symbol([1, 2, 3]) + + +def test_is_var_symbol(): + assert is_var_symbol('xt') + assert not is_var_symbol('Txt') + assert not is_var_symbol('') + assert not is_var_symbol('52') + + +def test_is_prop_symbol(): + assert not is_prop_symbol('xt') + assert is_prop_symbol('Txt') + assert not is_prop_symbol('') + assert not is_prop_symbol('52') + + +def test_variables(): + assert variables(expr('F(x, x) & G(x, y) & H(y, z) & R(A, z, 2)')) == {x, y, z} + assert variables(expr('(x ==> y) & B(x, y) & A')) == {x, y} + + def test_expr(): assert repr(expr('P <=> Q(1)')) == '(P <=> Q(1))' assert repr(expr('P & Q | ~R(x, F(x))')) == '((P & Q) | ~R(x, F(x)))' @@ -14,6 +42,10 @@ def test_extend(): assert extend({x: 1}, y, 2) == {x: 1, y: 2} +def test_subst(): + assert subst({x: 42, y:0}, F(x) + y) == (F(42) + 0) + + def test_PropKB(): kb = PropKB() assert count(kb.ask(expr) for expr in [A, C, D, E, Q]) is 0 @@ -68,7 +100,7 @@ def test_KB_wumpus(): assert kb_wumpus.ask(P[2, 2] | P[3, 1]) == {} -def test_definite_clause(): +def test_is_definite_clause(): assert is_definite_clause(expr('A & B & C & D ==> E')) assert is_definite_clause(expr('Farmer(Mac)')) assert not is_definite_clause(expr('~Farmer(Mac)')) @@ -77,6 +109,12 @@ def test_definite_clause(): assert not is_definite_clause(expr('(Farmer(f) | Rabbit(r)) ==> Hates(f, r)')) +def test_parse_definite_clause(): + assert parse_definite_clause(expr('A & B & C & D ==> E')) == ([A, B, C, D], E) + assert parse_definite_clause(expr('Farmer(Mac)')) == ([], expr('Farmer(Mac)')) + assert parse_definite_clause(expr('(Farmer(f) & Rabbit(r)) ==> Hates(f, r)')) == ([expr('Farmer(f)'), expr('Rabbit(r)')], expr('Hates(f, r)')) + + def test_pl_true(): assert pl_true(P, {}) is None assert pl_true(P, {P: False}) is False @@ -115,6 +153,22 @@ def test_dpll(): assert dpll_satisfiable(P & ~P) is False +def test_find_pure_symbol(): + assert find_pure_symbol([A, B, C], [A|~B,~B|~C,C|A]) == (A, True) + assert find_pure_symbol([A, B, C], [~A|~B,~B|~C,C|A]) == (B, False) + assert find_pure_symbol([A, B, C], [~A|B,~B|~C,C|A]) == (None, None) + + +def test_unit_clause_assign(): + assert unit_clause_assign(A|B|C, {A:True}) == (None, None) + assert unit_clause_assign(B|C, {A:True}) == (None, None) + assert unit_clause_assign(B|~A, {A:True}) == (B, True) + + +def test_find_unit_clause(): + assert find_unit_clause([A|B|C, B|~C, ~A|~B], {A:True}) == (B, False) + + def test_unify(): assert unify(x, x, {}) == {} assert unify(x, 3, {}) == {x: 3} @@ -131,6 +185,11 @@ def test_tt_entails(): assert tt_entails(A & (B | C) & E & F & ~(P | Q), A & E & F & ~P & ~Q) +def test_prop_symbols(): + assert set(prop_symbols(expr('x & y & z | A'))) == {A} + assert set(prop_symbols(expr('(x & B(z)) ==> Farmer(y) | A'))) == {A, expr('Farmer(y)'), expr('B(z)')} + + def test_eliminate_implications(): assert repr(eliminate_implications('A ==> (~B <== C)')) == '((~B | ~C) | ~A)' assert repr(eliminate_implications(A ^ B)) == '((A & ~B) | (~A & B))' @@ -156,6 +215,18 @@ def test_move_not_inwards(): assert repr(move_not_inwards(~(~(A | ~B) | ~~C))) == '((A | ~B) & ~C)' +def test_distribute_and_over_or(): + def test_enatilment(s, has_and = False): + result = distribute_and_over_or(s) + if has_and: + assert result.op == '&' + assert tt_entails(s, result) + assert tt_entails(result, s) + test_enatilment((A & B) | C, True) + test_enatilment((A | B) & C, True) + test_enatilment((A | B) | C, False) + test_enatilment((A & B) | (C | D), True) + def test_to_cnf(): assert (repr(to_cnf(wumpus_world_inference & ~expr('~P12'))) == "((~P12 | B11) & (~P21 | B11) & (P12 | P21 | ~B11) & ~B11 & P12)") From 4c2918cf532653e1ac757166f2c02f00f788cae9 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 03:17:10 +0530 Subject: [PATCH 474/513] Changed normalize() (#439) * Fixed normalize() * Update test for normalize() --- nlp.py | 4 ++-- tests/test_nlp.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/nlp.py b/nlp.py index bf0b6a6aa..622a7bb40 100644 --- a/nlp.py +++ b/nlp.py @@ -318,8 +318,8 @@ def normalize(pages): summed_hub = sum(page.hub**2 for _, page in pages.items()) summed_auth = sum(page.authority**2 for _, page in pages.items()) for _, page in pages.items(): - page.hub /= summed_hub - page.authority /= summed_auth + page.hub /= summed_hub**0.5 + page.authority /= summed_auth**0.5 class ConvergenceDetector(object): diff --git a/tests/test_nlp.py b/tests/test_nlp.py index 43f71f163..3dc5a57aa 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -95,7 +95,7 @@ def test_relevant_pages(): def test_normalize(): normalize(pageDict) print(page.hub for addr, page in nlp.pagesIndex.items()) - expected_hub = [1/91, 2/91, 3/91, 4/91, 5/91, 6/91] # Works only for sample data above + expected_hub = [1/91**0.5, 2/91**0.5, 3/91**0.5, 4/91**0.5, 5/91**0.5, 6/91**0.5] # Works only for sample data above expected_auth = list(reversed(expected_hub)) assert len(expected_hub) == len(expected_auth) == len(nlp.pagesIndex) assert expected_hub == [page.hub for addr, page in sorted(nlp.pagesIndex.items())] From 1d278f6f416550765ab2c4bacb4b7f269deea103 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Thu, 13 Apr 2017 03:17:41 +0530 Subject: [PATCH 475/513] Fix errors in HITS() (#440) --- nlp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nlp.py b/nlp.py index 622a7bb40..365d726c2 100644 --- a/nlp.py +++ b/nlp.py @@ -385,11 +385,11 @@ def __init__(self, address, hub=0, authority=0, inlinks=None, outlinks=None): def HITS(query): """The HITS algorithm for computing hubs and authorities with respect to a query.""" pages = expand_pages(relevant_pages(query)) # in order to 'map' faithfully to pseudocode we - for p in pages: # won't pass the list of pages as an argument + for p in pages.values(): # won't pass the list of pages as an argument p.authority = 1 p.hub = 1 while True: # repeat until... convergence - for p in pages: + for p in pages.values(): p.authority = sum(x.hub for x in getInlinks(p)) # p.authority ← ∑i Inlinki(p).Hub p.hub = sum(x.authority for x in getOutlinks(p)) # p.hub ← ∑i Outlinki(p).Authority normalize(pages) From e3ce769c4c26ee2ebac06715cfbf0ad28b36e509 Mon Sep 17 00:00:00 2001 From: Darius Bacon Date: Wed, 12 Apr 2017 15:54:38 -0700 Subject: [PATCH 476/513] Allow tests to run without IPython. Closes #226. --- canvas.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/canvas.py b/canvas.py index 318155bea..f78556cce 100644 --- a/canvas.py +++ b/canvas.py @@ -1,5 +1,3 @@ -from IPython.display import HTML, display - _canvas = """

    @@ -25,7 +23,7 @@ def __init__(self, varname, id=None, width=800, height=600): self.height = height self.html = _canvas.format(self.id, self.width, self.height, self.name) self.exec_list = [] - display(HTML(self.html)) + display_html(self.html) def mouse_click(self, x, y): "Override this method to handle mouse click at position (x, y)" @@ -115,10 +113,14 @@ def text_n(self, txt, xn, yn, fill=True): def alert(self, message): "Immediately display an alert" - display(HTML(''.format(message))) + display_html(''.format(message)) def update(self): "Execute the JS code to execute the commands queued by execute()" exec_code = "" self.exec_list = [] - display(HTML(exec_code)) + display_html(exec_code) + +def display_html(html_string): + from IPython.display import HTML, display + display(HTML(html_string)) From 34409d9136caf08464156d2dfc3b8f6b37c2d74e Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Thu, 13 Apr 2017 22:21:27 +0300 Subject: [PATCH 477/513] Expand count tests (#494) --- tests/test_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test_utils.py b/tests/test_utils.py index 5ca973e09..ae39cf50e 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -21,6 +21,8 @@ def test_unique(): def test_count(): assert count([1, 2, 3, 4, 2, 3, 4]) == 7 assert count("aldpeofmhngvia") == 14 + assert count([True, False, True, True, False]) == 3 + assert count([5 > 1, len("abc") == 3, 3+1 == 5]) == 2 def test_product(): From fb503e66a95ece6c198c9eece2025acd0a0e004d Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Thu, 13 Apr 2017 18:32:33 -0300 Subject: [PATCH 478/513] Use mock to remove network requirement from tests (#495) --- tests/test_nlp.py | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/tests/test_nlp.py b/tests/test_nlp.py index 3dc5a57aa..d9dc18851 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -1,5 +1,6 @@ import pytest import nlp + from nlp import loadPageHTML, stripRawHTML, findOutlinks, onlyWikipediaURLS from nlp import expand_pages, relevant_pages, normalize, ConvergenceDetector, getInlinks from nlp import getOutlinks, Page @@ -7,6 +8,9 @@ # Clumsy imports because we want to access certain nlp.py globals explicitly, because # they are accessed by function's within nlp.py +from unittest.mock import patch +from io import BytesIO + def test_rules(): assert Rules(A="B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} @@ -27,6 +31,19 @@ def test_lexicon(): < href="/wiki/TestThing" > href="/wiki/TestBoy" href="/wiki/TestLiving" href="/wiki/TestMan" >""" testHTML2 = "Nothing" +testHTML3 = """ + + + + Page Title + + + +

    AIMA book

    + + + + """ pA = Page("A", 1, 6, ["B", "C", "E"], ["D"]) pB = Page("B", 2, 5, ["E"], ["A", "C", "D"]) @@ -52,12 +69,14 @@ def test_lexicon(): # assert all(loadedPages.get(key,"") != "" for key in addresses) -def test_stripRawHTML(): +@patch('urllib.request.urlopen', return_value=BytesIO(testHTML3.encode())) +def test_stripRawHTML(html_mock): addr = "https://en.wikipedia.org/wiki/Ethics" aPage = loadPageHTML([addr]) someHTML = aPage[addr] strippedHTML = stripRawHTML(someHTML) assert "" not in strippedHTML and "" not in strippedHTML + assert "AIMA book" in someHTML and "AIMA book" in strippedHTML def test_determineInlinks(): From 17fac54ab3ca57f8f869fae2b6b0572ff960981d Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Fri, 14 Apr 2017 08:48:20 +0300 Subject: [PATCH 479/513] Learning: Grade Learner (#496) * Add grade_learner * Update test_learning.py --- learning.py | 10 +++++++++ tests/test_learning.py | 48 ++++++++++++++++++------------------------ 2 files changed, 31 insertions(+), 27 deletions(-) diff --git a/learning.py b/learning.py index 8347fbbef..fffbccf83 100644 --- a/learning.py +++ b/learning.py @@ -908,6 +908,16 @@ def score(learner, size): return [(size, mean([score(learner, size) for t in range(trials)])) for size in sizes] + +def grade_learner(predict, tests): + """Grades the given learner based on how many tests it passes. + tests is a list with each element in the form: (values, output).""" + correct = 0 + for t in tests: + if predict(t[0]) == t[1]: + correct += 1 + return correct + # ______________________________________________________________________________ # The rest of this file gives datasets for machine learning problems. diff --git a/tests/test_learning.py b/tests/test_learning.py index 1b4b825c1..1bac9a4cc 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,19 +1,19 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \ - euclidean_distance + euclidean_distance, grade_learner from utils import DataFile def test_euclidean(): - distance = euclidean_distance([1,2], [3,4]) + distance = euclidean_distance([1, 2], [3, 4]) assert round(distance, 2) == 2.83 - distance = euclidean_distance([1,2,3], [4,5,6]) + distance = euclidean_distance([1, 2, 3], [4, 5, 6]) assert round(distance, 2) == 5.2 - distance = euclidean_distance([0,0,0], [0,0,0]) + distance = euclidean_distance([0, 0, 0], [0, 0, 0]) assert distance == 0 @@ -24,7 +24,7 @@ def test_exclude(): def test_parse_csv(): Iris = DataFile('iris.csv').read() - assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa'] + assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2,'setosa'] def test_weighted_mode(): @@ -47,25 +47,25 @@ def test_naive_bayes(): # Discrete nBD = NaiveBayesLearner(iris) - assert nBD([5,3,1,0.1]) == "setosa" + assert nBD([5, 3, 1, 0.1]) == "setosa" def test_k_nearest_neighbors(): iris = DataSet(name="iris") kNN = NearestNeighborLearner(iris,k=3) - assert kNN([5,3,1,0.1]) == "setosa" - assert kNN([6,5,3,1.5]) == "versicolor" - assert kNN([7.5,4,6,2]) == "virginica" + assert kNN([5, 3, 1, 0.1]) == "setosa" + assert kNN([6, 5, 3, 1.5]) == "versicolor" + assert kNN([7.5, 4, 6, 2]) == "virginica" def test_decision_tree_learner(): iris = DataSet(name="iris") dTL = DecisionTreeLearner(iris) - assert dTL([5,3,1,0.1]) == "setosa" - assert dTL([6,5,3,1.5]) == "versicolor" - assert dTL([7.5,4,6,2]) == "virginica" + assert dTL([5, 3, 1, 0.1]) == "setosa" + assert dTL([6, 5, 3, 1.5]) == "versicolor" + assert dTL([7.5, 4, 6, 2]) == "virginica" def test_neural_network_learner(): @@ -75,14 +75,11 @@ def test_neural_network_learner(): iris.classes_to_numbers(classes) nNL = NeuralNetLearner(iris, [5], 0.15, 75) - pred1 = nNL([5,3,1,0.1]) - pred2 = nNL([6,3,3,1.5]) - pred3 = nNL([7.5,4,6,2]) + tests = [([5, 3, 1, 0.1], 0), + ([6, 3, 3, 1.5], 1), + ([7.5, 4, 6, 2], 2)] - # NeuralNetLearner might be wrong. If it is, check if prediction is in range. - assert pred1 == 0 or pred1 in range(len(classes)) - assert pred2 == 1 or pred2 in range(len(classes)) - assert pred3 == 2 or pred3 in range(len(classes)) + assert grade_learner(nNL, tests) >= 2 def test_perceptron(): @@ -92,11 +89,8 @@ def test_perceptron(): classes_number = len(iris.values[iris.target]) perceptron = PerceptronLearner(iris) - pred1 = perceptron([5,3,1,0.1]) - pred2 = perceptron([6,3,4,1]) - pred3 = perceptron([7.5,4,6,2]) - - # PerceptronLearner might be wrong. If it is, check if prediction is in range. - assert pred1 == 0 or pred1 in range(classes_number) - assert pred2 == 1 or pred2 in range(classes_number) - assert pred3 == 2 or pred3 in range(classes_number) + tests = [([5, 3, 1, 0.1], 0), + ([6, 3, 4, 1.1], 1), + ([7.5, 4, 6, 2], 2)] + + assert grade_learner(perceptron, tests) >= 2 From c0c97bf89a1c13491082279f09cb459ac080058c Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Fri, 14 Apr 2017 11:21:19 +0530 Subject: [PATCH 480/513] Implementation of GA in notebook search.ipyinb (#489) * Update learning.py converted method sample(self) to propety and removed the call in the statement return self.sampler * Update search.py minor code formatting. * Implmented GA Implemented Genetic Algoritm in search.py and search.ipynb * implemented Genetic Algorithm in search.py and made modifications according to flake8. Demonstrated the working of GA iin search.ipynb. * implemented Genetic Algorithm in search.py and made modifications according to flake8. Demonstrated the working of GA iin search.ipynb. * Updated GA in search.ipynb Removed the image links and included the images used in the image folder. Reduced the file size from 2.4 MB to 183 KB. For every 21 print statements that we previously had, now we have 2, only printing the fittest individual in each generation. 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Install python-Levenshtein to remove this warning\n", - " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" - ] - } - ], + "outputs": [], "source": [ - "from search import *" + "from search import *\n", + "\n", + "# Needed to hide warnings in the matplotlib sections\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -249,7 +244,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'Arad': (91, 492), 'Bucharest': (400, 327), 'Craiova': (253, 288), 'Drobeta': (165, 299), 'Eforie': (562, 293), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Hirsova': (534, 350), 'Iasi': (473, 506), 'Lugoj': (165, 379), 'Mehadia': (168, 339), 'Neamt': (406, 537), 'Oradea': (131, 571), 'Pitesti': (320, 368), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Vaslui': (509, 444), 'Zerind': (108, 531)}\n" + "{'Vaslui': (509, 444), 'Sibiu': (207, 457), 'Arad': (91, 492), 'Giurgiu': (375, 270), 'Mehadia': (168, 339), 'Eforie': (562, 293), 'Iasi': (473, 506), 'Oradea': (131, 571), 'Craiova': (253, 288), 'Urziceni': (456, 350), 'Fagaras': (305, 449), 'Pitesti': (320, 368), 'Neamt': (406, 537), 'Rimnicu': (233, 410), 'Zerind': (108, 531), 'Timisoara': (94, 410), 'Hirsova': (534, 350), 'Lugoj': (165, 379), 'Bucharest': (400, 327), 'Drobeta': (165, 299)}\n" ] } ], @@ -407,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, @@ -455,38 +450,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", - " Future behavior will be consistent with the long-time default:\n", - " plot commands add elements without first clearing the\n", - " Axes and/or Figure.\n", - " b = plt.ishold()\n", - "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", - " Future behavior will be consistent with the long-time default:\n", - " plot commands add elements without first clearing the\n", - " Axes and/or Figure.\n", - " plt.hold(b)\n", - "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\matplotlib\\__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n", - " warnings.warn(self.msg_depr_set % key)\n", - "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\matplotlib\\rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n", - " warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n" - ] - }, { "data": { - "image/png": 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kVKtWTU2aNNGnn35qMb5WrVrmolOSvL295enpqd9//z3XWffu3asrV64oJCRE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\nT1588UWdOXNG69evt3YUAABgw6ZNm6Y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ChQszF9/66+Obb74B4JtvvsHCwoK4uLgcy9G9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PSLbnh4uKKiohQaGqquXbuqffv26tChgyQl+2UYwKNxd3fXvHnz1L9/f506dcp0HABA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\nPn36KDk5We3atdP69evVqlUr64z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kVKtWTU2aNNGnn35qMb5WrVrmolOSvL295enpqd9//z3XWffu3asrV64oJCRE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\nl1JTU9W2bVutXbtWrVq1ss4oPX/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -540,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, @@ -595,8 +570,13 @@ " if user_input == True:\n", " node_colors = dict(initial_node_colors)\n", " if algorithm == None:\n", - " algorithms = {\"Breadth First Tree Search\": breadth_first_tree_search, \"Breadth First Search\": breadth_first_search, \"Uniform Cost Search\": uniform_cost_search, \"A-star Search\": astar_search}\n", - " algo_dropdown = widgets.Dropdown(description = \"Search algorithm: \", options = sorted(list(algorithms.keys())), value = \"Breadth First Tree Search\")\n", + " algorithms = {\"Breadth First Tree Search\": breadth_first_tree_search,\n", + " \"Breadth First Search\": breadth_first_search,\n", + " \"Uniform Cost Search\": uniform_cost_search,\n", + " \"A-star Search\": astar_search}\n", + " algo_dropdown = widgets.Dropdown(description = \"Search algorithm: \",\n", + " options = sorted(list(algorithms.keys())),\n", + " value = \"Breadth First Tree Search\")\n", " display(algo_dropdown)\n", " \n", " def slider_callback(iteration):\n", @@ -629,10 +609,12 @@ " slider.value = i\n", "# time.sleep(.5)\n", " \n", - " start_dropdown = widgets.Dropdown(description = \"Start city: \", options = sorted(list(node_colors.keys())), value = \"Arad\")\n", + " start_dropdown = widgets.Dropdown(description = \"Start city: \",\n", + " options = sorted(list(node_colors.keys())), value = \"Arad\")\n", " display(start_dropdown)\n", "\n", - " end_dropdown = widgets.Dropdown(description = \"Goal city: \", options = sorted(list(node_colors.keys())), value = \"Fagaras\")\n", + " end_dropdown = widgets.Dropdown(description = \"Goal city: \",\n", + " options = sorted(list(node_colors.keys())), value = \"Fagaras\")\n", " display(end_dropdown)\n", " \n", " button = widgets.ToggleButton(value = False)\n", @@ -660,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, @@ -734,26 +716,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed or enabled properly.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Fagaras', romania_map)\n", @@ -775,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, @@ -841,26 +810,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed or enabled properly.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -881,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, @@ -965,48 +921,35 @@ ] }, { - "cell_type": "code", - "execution_count": 18, + "cell_type": "markdown", "metadata": { - "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed or enabled properly.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "all_node_colors = []\n", - "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", - "display_visual(user_input = False, algorithm = uniform_cost_search, problem = romania_problem)" + "## A* search\n", + "\n", + "Let's change all the node_colors to starting position and define a different problem statement." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { + "collapsed": false, "deletable": true, "editable": true }, + "outputs": [], "source": [ - "## A* search\n", - "\n", - "Let's change all the node_colors to starting position and define a different problem statement." + "all_node_colors = []\n", + "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", + "display_visual(user_input = False, algorithm = uniform_cost_search, problem = romania_problem)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": { "collapsed": true, "deletable": true, @@ -1094,26 +1037,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed or enabled properly.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "romania_problem = GraphProblem('Arad', 'Bucharest', romania_map)\n", @@ -1122,27 +1052,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Widget Javascript not detected. It may not be installed or enabled properly.\n" - ] - }, - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "all_node_colors = []\n", "# display_visual(user_input = True, algorithm = breadth_first_tree_search)\n", @@ -1152,12 +1069,39 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, "deletable": true, "editable": true }, "source": [ - "# Genetic Algorithm\n" + "## Genetic Algorithm\n", + "\n", + "Genetic algorithms (or GA) are inspired by natural evolution and are particularly useful in optimization and search problems with large state spaces.\n", + "\n", + "Given a problem, algorithms in the domain make use of a *population* of solutions (also called *states*), where each solution/state represents a feasible solution. At each iteration (often called *generation*), the population gets updated using methods inspired by biology and evolution, like *crossover*, *mutation* and *selection*." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Overview\n", + "\n", + "A genetic algorithm works in the following way:\n", + "\n", + "1) Initialize random population.\n", + "\n", + "2) Calculate population fitness.\n", + "\n", + "3) Select individuals for mating.\n", + "\n", + "4) Mate selected individuals to produce new population.\n", + "\n", + " * Random chance to mutate individuals.\n", + "\n", + "5) Repeat from step 2) until an individual is fit enough or the maximum number of iterations was reached." ] }, { @@ -1167,10 +1111,19 @@ "editable": true }, "source": [ - "Genetic algorithms are\n", + "### Glossary\n", + "\n", + "Before we continue, we will lay the basic terminology of the algorithm.\n", + "\n", + "* Individual/State: A string of chars (called *genes*) that represent possible solutions.\n", "\n", - "- A method of search, often applied to optimization or learning.\n", - "- Genetic algorithms are a part of evolutionary computing, they use an evolutionary analogy, “survival of the fittest”.\n" + "* Population: The list of all the individuals/states.\n", + "\n", + "* Gene pool: The alphabet of possible values for an individual's genes.\n", + "\n", + "* Generation/Iteration: The number of times the population will be updated.\n", + "\n", + "* Fitness: An individual's score, calculated by a function specific to the problem." ] }, { @@ -1180,12 +1133,17 @@ "editable": true }, "source": [ - "## Search Space\n", - "- If we are solving some problem, we are usually looking for some solution, which will be the best among others.\n", - "- The space of all feasible solutions is called search space (also state space).\n", - "- Each point in the search space represents one feasible solution.\n", - "- Each feasible solution can be evaluated by its fitness value for the problem.\n", - "- Usually we only know a few points from the search space and we are generating other points as the process of finding solution continues." + "### Crossover\n", + "\n", + "Two individuals/states can \"mate\" and produce one child. This offspring bears characteristics from both of its parents. There are many ways we can implement this crossover. Here we will take a look at the most common ones. Most other methods are variations of those below.\n", + "\n", + "* Point Crossover: The crossover occurs around one (or more) point. The parents get \"split\" at the chosen point or points and then get merged. In the example below we see two parents get split and merged at the 3rd digit, producing the following offspring after the crossover.\n", + "\n", + "![point crossover](images/point_crossover.png)\n", + "\n", + "* Uniform Crossover: This type of crossover chooses randomly the genes to get merged. Here the genes 1, 2 and 5 where chosen from the first parent, so the genes 3, 4 will be added by the second parent.\n", + "\n", + "![uniform crossover](images/uniform_crossover.png)" ] }, { @@ -1195,14 +1153,11 @@ "editable": true }, "source": [ - "## Methodology\n", - "- In a genetic algorithm, a population of individual solutions is evolved toward better solutions.\n", - "- Each individual solution has a set of properties (its chromosomes or genes) which mate and mutate.\n", - "- The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation.\n", - "- In each generation, the fitness of every individual in the population is evaluated.\n", - "- The more fit individuals are stochastically selected from the current population, and each individual's gene is modified (recombined and possibly randomly mutated) to form a new generation.\n", - "- The new generation of individual solutions is then used in the next iteration of the algorithm.\n", - "- Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached." + "### Mutation\n", + "\n", + "When an offspring is produced, there is a chance it will mutate, having one (or more, depending on the implementation) of its genes altered.\n", + "\n", + "For example, let's say the new individual to undergo mutation is \"abcde\". Randomly we pick to change its third gene to 'z'. The individual now becomes \"abzde\" and is added to the population." ] }, { @@ -1212,25 +1167,17 @@ "editable": true }, "source": [ - "## Basic Genetic Operations\n", - " ● Selection\n", - " ● Mutation\n", - " ● Crossover\n", - " \n", - " \n", - " ### Selection\n", - "- Individuals are selected from the population to crossover.\n", - "- How do we select the individuals? Traditionally, parents are chosen to mate with probability proportional to their fitness.\n", + "### Selection\n", "\n", - "### Crossover\n", - "- Operates on two individuals (parents).\n", - "- Give rise to offsprings.\n", - "- Crossover can occur at 1, 2 or many points.\n", + "At each iteration, the fittest individuals are picked randomly to mate and produce offsprings. We measure an individual's fitness with a *fitness function*. That function depends on the given problem and it is used to score an individual. Usually the higher the better.\n", "\n", + "The selection process is this:\n", "\n", - "### Mutation\n", - "- Operates on one individual.\n", - "- Produces offspring with some changes.\n" + "1) Individuals are scored by the fitness function.\n", + "\n", + "2) Individuals are picked randomly, according to their score (higher score means higher chance to get picked). Usually the formula to calculate the chance to pick an individual is the following (for population *P* and individual *i*):\n", + "\n", + "$$ chance(i) = \\dfrac{fitness(i)}{\\sum\\limits_{k \\, in \\, P}{fitness(k)}} $$" ] }, { @@ -1240,13 +1187,16 @@ "editable": true }, "source": [ - "Now let us try to implement GA.\n", - "We will start with importing necessary packages" + "### Implementation\n", + "\n", + "Below we look over the implementation of the algorithm in the `search` module.\n", + "\n", + "First the implementation of the main core of the algorithm:" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, @@ -1254,25 +1204,48 @@ }, "outputs": [], "source": [ - "from fuzzywuzzy import fuzz\n", - "import random\n", - "import string" + "%psource genetic_algorithm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The algorithm takes the following input:\n", + "\n", + "* `population`: The initial population.\n", + "\n", + "* `fitness_fn`: The problem's fitness function.\n", + "\n", + "* `gene_pool`: The gene pool of the states/individuals. Genes need to be chars. By default '0' and '1'.\n", + "\n", + "* `f_thres`: The fitness threshold. If an individual reaches that score, iteration stops. By default 'None', which means the algorithm will try and find the optimal solution.\n", + "\n", + "* `ngen`: The number of iterations/generations.\n", + "\n", + "* `pmut`: The probability of mutation.\n", + "\n", + "The algorithm gives as output the state with the largest score." ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true, "deletable": true, "editable": true }, "source": [ - "Here we define a class GAState." + "For each generation, the algorithm updates the population. First it calculates the fitnesses of the individuals, then it selects the most fit ones and finally crosses them over to produce offsprings. There is a chance that the offspring will be mutated, given by `pmut`. If at the end of the generation an individual meets the fitness threshold, the algorithm halts and returns that individual.\n", + "\n", + "The function of mating is accomplished by the method `reproduce`:" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 17, "metadata": { "collapsed": true, "deletable": true, @@ -1280,24 +1253,42 @@ }, "outputs": [], "source": [ - "\"\"\"\n", - "Naming convention:\n", - "Instead of gene or chromosome, the name individual has been used.\n", - "What makes an individual unique from the set of individuals is\n", - "the genes\\chromosomes. Thus, considering that individuals crossover and\n", - "individuals mutate.\n", - "\"\"\"\n", - "\n", + "def reproduce(x, y):\n", + " n = len(x)\n", + " c = random.randrange(0, n)\n", + " return x[:c] + y[c:]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The method picks at random a point and merges the parents (`x` and `y`) around it.\n", "\n", - "class GAState:\n", - " def __init__(self, length):\n", - " self.string = ''.join(random.choice(string.ascii_letters)\n", - " for _ in range(length))\n", - " self.fitness = -1\n", + "The mutation is done in the method `mutate`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def mutate(x, gene_pool):\n", + " n = len(x)\n", + " g = len(gene_pool)\n", + " c = random.randrange(0, n)\n", + " r = random.randrange(0, g)\n", "\n", - " def __str__(self):\n", - " return 'Individual: ' + str(self.string) + ' fitness: ' \\\n", - " + str(self.fitness)" + " new_gene = gene_pool[r]\n", + " return x[:c] + new_gene + x[c+1:]" ] }, { @@ -1307,13 +1298,14 @@ "editable": true }, "source": [ - "Here is the main logic of our GA. There are four major operations involved. Fitness check, selection, crossover and mutation.\n", - "We assume the search to be complete if the fitness of an individual is greater than or equal to 90%. If the fitness criteria is not met and sufficient number of generations have passed, we return the fittest individual from the population." + "We pick a gene in `x` to mutate and a gene from the gene pool to replace it with.\n", + "\n", + "To help initializing the population we have the helper function `init_population`\":" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 19, "metadata": { "collapsed": true, "deletable": true, @@ -1321,45 +1313,62 @@ }, "outputs": [], "source": [ - "def ga(in_str=None, population=20, generations=10000):\n", - " in_str_len = len(in_str)\n", - " individuals = init_individual(population, in_str_len)\n", + "def init_population(pop_number, gene_pool, state_length):\n", + " g = len(gene_pool)\n", + " population = []\n", + " for i in range(pop_number):\n", + " new_individual = ''.join([gene_pool[random.randrange(0, g)]\n", + " for j in range(state_length)])\n", + " population.append(new_individual)\n", + "\n", + " return population" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The function takes as input the number of individuals in the population, the gene pool and the length of each individual/state. It creates individuals with random genes and returns the population when done." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Usage\n", "\n", - " for generation in range(generations):\n", + "Below we give two example usages for the genetic algorithm, for a graph coloring problem and the 8 queens problem.\n", "\n", - " print('Generation: ' + str(generation))\n", + "#### Graph Coloring\n", "\n", - " individuals = fitness(individuals, in_str)\n", - " individuals = selection(individuals)\n", - " individuals = crossover(individuals, population, in_str_len)\n", + "First we will take on the simpler problem of coloring a small graph with two colors. Before we do anything, let's imagine how a solution might look. First, we have only two colors, so we can represent them with a binary notation: 0 for one color and 1 for the other. These make up our gene pool. What of the individual solutions though? For that, we will look at our problem. We stated we have a graph. A graph has nodes and edges, and we want to color the nodes. Naturally, we want to store each node's color. If we have four nodes, we can store their colors in a string of genes, one for each node. A possible solution will then look like this: \"1100\". In the general case, we will represent each solution with a string of 1s and 0s, with length the number of nodes.\n", "\n", - " if any(individual.fitness >= 90 for individual in individuals):\n", - " \"\"\"\n", - " individuals[0] is the individual with the highest fitness,\n", - " because individuals is sorted in the selection function.\n", - " Thus we return the individual with the highest fitness value,\n", - " among the individuals whose fitness is equal to or greater\n", - " than 90%.\n", - " \"\"\"\n", - " print('Threshold met :)')\n", - " return individuals[0]\n", + "Next we need to come up with a fitness function that appropriately scores individuals. Again, we will look at the problem definition at hand. We want to color a graph. For a solution to be optimal, no edge should connect two nodes of the same color. How can we use this information to score a solution? A naive (and ineffective) approach would be to count the different colors in the string. So \"1111\" has a score of 1 and \"1100\" has a score of 2. Why that fitness function is not ideal though? Why, we forgot the information about the edges! The edges are pivotal to the problem and the above function only deals with node colors. We didn't use all the information at hand and ended up with an ineffective answer. How, then, can we use that information to our advantage?\n", "\n", - " individuals = mutation(individuals, in_str_len)\n", - " print('fittest individual: ' + individuals[0].string)\n", + "We said that the optimal solution will have all the edges connecting nodes of different color. So, to score a solution we can count how many edges are valid (aka connecting nodes of different color). That is a great fitness function!\n", "\n", - " \"\"\"\n", - " sufficient number of generations have passed and the individuals\n", - " could not evolve to match the desired fitness value.\n", - " thus we return the fittest individual among the individuals.\n", - " Since individuals are sorted according to their fitness\n", - " individuals[0] is the fittest.\n", - " \"\"\"\n", - " return individuals[0]" + "Let's jump into solving this problem using the `genetic_algorithm` function." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "First we need to represent the graph. Since we mostly need information about edges, we will just store the edges. We will denote edges with capital letters and nodes with integers:" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, @@ -1367,56 +1376,63 @@ }, "outputs": [], "source": [ - "def init_individual(population, length):\n", - " return [GAState(length) for _ in range(population)]" + "edges = {\n", + " 'A': [0, 1],\n", + " 'B': [0, 3],\n", + " 'C': [1, 2],\n", + " 'D': [2, 3]\n", + "}" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true, "deletable": true, "editable": true }, "source": [ - "### Fitness\n", - "We will evaluate the fitness of the every individual, by comparing every individual in the list with the threshold." + "Edge 'A' connects nodes 0 and 1, edge 'B' connects nodes 0 and 3 etc.\n", + "\n", + "We already said our gene pool is 0 and 1, so we can jump right into initializing our population. Since we have only four nodes, `state_length` should be 4. For the number of individuals, we will try 8. We can increase this number if we need higher accuracy, but be careful! Larger populations need more computating power and take longer. You need to strike that sweet balance between accuracy and cost (the ultimate dilemma of the programmer!)." ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 21, "metadata": { - "collapsed": true, + "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0011', '1111', '0000', '1010', '0111', '1010', '0111', '0011']\n" + ] + } + ], "source": [ - "def fitness(individuals, in_str):\n", - " for individual in individuals:\n", - " individual.fitness = fuzz.ratio(individual.string, in_str)\n", - "\n", - " return individuals" + "population = init_population(8, ['0', '1'], 4)\n", + "print(population)" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true, "deletable": true, "editable": true }, "source": [ - "### Selection\n", - "Now we will sort the individuals according to fitness and select the top 20% of the population\n", + "We created and printed the population. You can see that the genes in the individuals are random and there are 8 individuals each with 4 genes.\n", "\n", - "To check the entire population of individuals in each generation in the final output, uncomment the print statement in the cell below. Note that it will create a large output." + "Next we need to write our fitness function. We previously said we want the function to count how many edges are valid. So, given a coloring/individual `c`, we will do just that:" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 22, "metadata": { "collapsed": true, "deletable": true, @@ -1424,12 +1440,8 @@ }, "outputs": [], "source": [ - "def selection(individuals):\n", - " individuals = sorted(\n", - " individuals, key=lambda individual: individual.fitness, reverse=True)\n", - " # print('\\n'.join(map(str, individuals)))\n", - " individuals = individuals[:int(0.2 * len(individuals))]\n", - " return individuals" + "def fitness(c):\n", + " return sum(c[n1] != c[n2] for (n1, n2) in edges.values())" ] }, { @@ -1439,40 +1451,72 @@ "editable": true }, "source": [ - "### Crossover\n", - "\n", - "\n", - "\n", - "Here, we define our crossover function. Two individuals mate and give rise to two offsprings. The individuals that mate are among the top 20 percentile and are randomly chosen for mating. In this particular case we perform one point crossover.\n" + "Great! Now we will run the genetic algorithm and see what solution it gives." ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 23, "metadata": { - "collapsed": true, + "collapsed": false, "deletable": true, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1010\n" + ] + } + ], "source": [ - "def crossover(individuals, population, in_str_len):\n", - " offspring = []\n", - " for _ in range(int((population - len(individuals)) / 2)):\n", - " parent1 = random.choice(individuals)\n", - " parent2 = random.choice(individuals)\n", - " child1 = GAState(in_str_len)\n", - " child2 = GAState(in_str_len)\n", - " split = random.randint(0, in_str_len)\n", - " child1.string = parent1.string[0:split] + parent2.string[\n", - " split:in_str_len]\n", - " child2.string = parent2.string[0:split] + parent1.string[\n", - " split:in_str_len]\n", - " offspring.append(child1)\n", - " offspring.append(child2)\n", + "solution = genetic_algorithm(population, fitness)\n", + "print(solution)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The algorithm converged to a solution. Let's check its score:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], + "source": [ + "print(fitness(solution))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "The solution has a score of 4. Which means it is optimal, since we have exactly 4 edges in our graph, meaning all are valid!\n", "\n", - " individuals.extend(offspring)\n", - " return individuals" + "*NOTE: Because the algorithm is non-deterministic, there is a chance a different solution is given. It might even be wrong, if we are very unlucky!*" ] }, { @@ -1482,7 +1526,41 @@ "editable": true }, "source": [ - "### Mutation" + "#### Eight Queens\n", + "\n", + "Let's take a look at a more complicated problem.\n", + "\n", + "In the *Eight Queens* problem, we are tasked with placing eight queens on an 8x8 chessboard without any queen threatening the others (aka queens should not be in the same row, column or diagonal). In its general form the problem is defined as placing *N* queens in an NxN chessboard without any conflicts.\n", + "\n", + "First we need to think about the representation of each solution. We can go the naive route of representing the whole chessboard with the queens' placements on it. That is definitely one way to go about it, but for the purpose of this tutorial we will do something different. We have eight queens, so we will have a gene for each of them. The gene pool will be numbers from 0 to 7, for the different columns. The *position* of the gene in the state will denote the row the particular queen is placed in.\n", + "\n", + "For example, we can have the state \"03304577\". Here the first gene with a value of 0 means \"the queen at row 0 is placed at column 0\", for the second gene \"the queen at row 1 is placed at column 3\" and so forth.\n", + "\n", + "We now need to think about the fitness function. On the graph coloring problem we counted the valid edges. The same thought process can be applied here. Instead of edges though, we have positioning between queens. If two queens are not threatening each other, we say they are at a \"non-attacking\" positioning. We can, therefore, count how many such positionings are there.\n", + "\n", + "Let's dive right in and initialize our population:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['16144650', '15257744', '25105035', '45153531', '02333213']\n" + ] + } + ], + "source": [ + "population = init_population(100, [str(i) for i in range(8)], 8)\n", + "print(population[:5])" ] }, { @@ -1492,14 +1570,18 @@ "editable": true }, "source": [ - "We define the mutation function here. Consider each character to be the property of the string. If the string is an individual, each character is its gene. In mutation we alter some of the gene (property) of the individual (string). Not every individual has to undergo mutation. Here, in our example we have possibility of 10% that any individual will undergo mutation.\n", + "We have a population of 100 and each individual has 8 genes. The gene pool is the integers from 0 to 7, in string form. Above you can see the first five individuals.\n", + "\n", + "Next we need to write our fitness function. Remember, queens threaten each other if they are at the same row, column or diagonal.\n", "\n", - "" + "Since positionings are mutual, we must take care not to count them twice. Therefore for each queen, we will only check for conflicts for the queens after her.\n", + "\n", + "A gene's value in an individual `q` denotes the queen's column, and the position of the gene denotes its row. We can check if the aforementioned values between two genes are the same. We also need to check for diagonals. A queen *a* is in the diagonal of another queen, *b*, if the difference of the rows between them is equal to either their difference in columns (for the diagonal on the right of *a*) or equal to the negative difference of their columns (for the left diagonal of *a*). Below is given the fitness function." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 26, "metadata": { "collapsed": true, "deletable": true, @@ -1507,16 +1589,19 @@ }, "outputs": [], "source": [ - "def mutation(individuals, in_str_len):\n", - " for individual in individuals:\n", + "def fitness(q):\n", + " non_attacking = 0\n", + " for row1 in range(len(q)):\n", + " for row2 in range(row1+1, len(q)):\n", + " col1 = int(q[row1])\n", + " col2 = int(q[row2])\n", + " row_diff = row1 - row2\n", + " col_diff = col1 - col2\n", "\n", - " for idx, param in enumerate(individual.string):\n", - " if random.uniform(0.0, 1.0) <= 0.1:\n", - " individual.string = individual.string[0:idx] \\\n", - " + random.choice(string.ascii_letters) \\\n", - " + individual.string[idx + 1:in_str_len]\n", + " if col1 != col2 and row_diff != col_diff and row_diff != -col_diff:\n", + " non_attacking += 1\n", "\n", - " return individuals" + " return non_attacking" ] }, { @@ -1526,39 +1611,53 @@ "editable": true }, "source": [ - "### Calling GA\n", - "Now check out the GA. Wait for 5 to 6 seconds for the program to produce the output." + "Note that the best score achievable is 28. That is because for each queen we only check for the queens after her. For the first queen we check 7 other queens, for the second queen 6 others and so on. In short, the number of checks we make is the sum 7+6+5+...+1. Which is equal to 7\\*(7+1)/2 = 28.\n", + "\n", + "Because it is very hard and will take long to find a perfect solution, we will set the fitness threshold at 25. If we find an individual with a score greater or equal to that, we will halt. Let's see how the genetic algorithm will fare." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43506172\n", + "26\n" + ] + } + ], "source": [ - "individual = ga('aima', 20, 10000)\n", - "print(individual.string)\n", - "print(individual.fitness)" + "solution = genetic_algorithm(population, fitness, f_thres=25)\n", + "print(solution)\n", + "print(fitness(solution))" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": true, "deletable": true, "editable": true }, "source": [ - "Execute the previous cell few times with the same arguments. Compare the different outputs, realise the uncertainty involved in the process (algorithm). Below is a comparative analysis of four executions of the program, producing different outputs (individuals) still converging to the same result. \n", - "\n", - "\n", - "\n", - "Each case represents corresponding execution of the algorithm. Carefully observe the generation numbers for each case in which our desired result was found. Every time the result is displayed at the top because the list of individuals are sorted according to fitness level. Also observe the least fit individual for each run in final generation, there is difference in fitness value.\n", - "\n", - "\n", - "Now change the string, modify the values in the program, try different arguments, observe how the strings (individuals) evolve with generations and converge to the desired result. Develop an intuition about GA. Play around with the code… More importantly have fun while learning… :)\n" + "Above you can see the solution and its fitness score, which should be no less than 25." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "With that this tutorial on the genetic algorithm comes to an end. Hope you found this guide helpful!" ] } ], @@ -1571,14 +1670,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3.0 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.5.2" }, "widgets": { "state": { @@ -1594,14 +1693,14 @@ "052ea3e7259346a4b022ec4fef1fda28": { "views": [ { - "cell_index": 32.0 + "cell_index": 32 } ] }, "0ade4328785545c2b66d77e599a3e9da": { "views": [ { - "cell_index": 29.0 + "cell_index": 29 } ] }, @@ -1614,7 +1713,7 @@ "0d91be53b6474cdeac3239fdffeab908": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1627,7 +1726,7 @@ "1193eaa60bb64cb790236d95bf11f358": { "views": [ { - "cell_index": 38.0 + "cell_index": 38 } ] }, @@ -1640,7 +1739,7 @@ "16a9167ec7b4479e864b2a32e40825a1": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1674,7 +1773,7 @@ "2ab8bf4795ac4240b70e1a94e14d1dd6": { "views": [ { - "cell_index": 30.0 + "cell_index": 30 } ] }, @@ -1687,7 +1786,7 @@ "2dc962f16fd143c1851aaed0909f3963": { "views": [ { - "cell_index": 35.0 + "cell_index": 35 } ] }, @@ -1712,7 +1811,7 @@ "34658e2de2894f01b16cf89905760f14": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1737,7 +1836,7 @@ "43e48664a76342c991caeeb2d5b17a49": { "views": [ { - "cell_index": 35.0 + "cell_index": 35 } ] }, @@ -1750,14 +1849,14 @@ "49c49d665ba44746a1e1e9dc598bc411": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, "4a1c43b035f644699fd905d5155ad61f": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1773,7 +1872,7 @@ "53eccc8fc0ad461cb8277596b666f32a": { "views": [ { - "cell_index": 29.0 + "cell_index": 29 } ] }, @@ -1789,7 +1888,7 @@ "636caa7780614389a7f52ad89ea1c6e8": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1811,7 +1910,7 @@ "743219b9d37e4f47a5f777bb41ad0a96": { "views": [ { - "cell_index": 29.0 + "cell_index": 29 } ] }, @@ -1830,7 +1929,7 @@ "86e8f92c1d584cdeb13b36af1b6ad695": { "views": [ { - "cell_index": 35.0 + "cell_index": 35 } ] }, @@ -1882,7 +1981,7 @@ "a29b90d050f3442a89895fc7615ccfee": { "views": [ { - "cell_index": 29.0 + "cell_index": 29 } ] }, @@ -1907,7 +2006,7 @@ "badc9fd7b56346d6b6aea68bfa6d2699": { "views": [ { - "cell_index": 38.0 + "cell_index": 38 } ] }, @@ -1917,7 +2016,7 @@ "c2399056ef4a4aa7aa4e23a0f381d64a": { "views": [ { - "cell_index": 38.0 + "cell_index": 38 } ] }, @@ -1927,7 +2026,7 @@ "ce3f28a8aeee4be28362d068426a71f6": { "views": [ { - "cell_index": 32.0 + "cell_index": 32 } ] }, @@ -1949,7 +2048,7 @@ "e7bffb1fed664dea90f749ea79dcc4f1": { "views": [ { - "cell_index": 39.0 + "cell_index": 39 } ] }, @@ -1980,7 +2079,7 @@ "f435b108c59c42989bf209a625a3a5b5": { "views": [ { - "cell_index": 32.0 + "cell_index": 32 } ] }, @@ -1996,4 +2095,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From b009d1fff3b8f9de5c782b06d40da8a634d7a30c Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Tue, 18 Apr 2017 02:32:36 +0530 Subject: [PATCH 486/513] Planning implementations - 11.1 and 11.5 (#505) * define HLA, Problem and implement 11.1 * add demonstration of job_shop_problem * implementing 11.5 * adding test for refinement --- planning.py | 312 ++++++++++++++++++++++++++++++++++++++++- tests/test_planning.py | 48 +++++++ 2 files changed, 359 insertions(+), 1 deletion(-) diff --git a/planning.py b/planning.py index 30b8a79f6..edfb39f19 100644 --- a/planning.py +++ b/planning.py @@ -2,7 +2,8 @@ """ import itertools -from utils import Expr, expr, first +from search import Node +from utils import Expr, expr, first, FIFOQueue from logic import FolKB @@ -574,3 +575,312 @@ def goal_test(kb): go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDDL(init, [hit, go], goal_test) + + +class HLA(Action): + """ + Define Actions for the real-world (that may be refined further), and satisfy resource + constraints. + """ + unique_group = 1 + + def __init__(self, action, precond=[None, None], effect=[None, None], duration=0, + consume={}, use={}): + """ + As opposed to actions, to define HLA, we have added constraints. + duration holds the amount of time required to execute the task + consumes holds a dictionary representing the resources the task consumes + uses holds a dictionary representing the resources the task uses + """ + super().__init__(action, precond, effect) + self.duration = duration + self.consumes = consume + self.uses = use + self.completed = False + # self.priority = -1 # must be assigned in relation to other HLAs + # self.job_group = -1 # must be assigned in relation to other HLAs + + def do_action(self, job_order, available_resources, kb, args): + """ + An HLA based version of act - along with knowledge base updation, it handles + resource checks, and ensures the actions are executed in the correct order. + """ + # print(self.name) + if not self.has_usable_resource(available_resources): + raise Exception('Not enough usable resources to execute {}'.format(self.name)) + if not self.has_consumable_resource(available_resources): + raise Exception('Not enough consumable resources to execute {}'.format(self.name)) + if not self.inorder(job_order): + raise Exception("Can't execute {} - execute prerequisite actions first". + format(self.name)) + super().act(kb, args) # update knowledge base + for resource in self.consumes: # remove consumed resources + available_resources[resource] -= self.consumes[resource] + self.completed = True # set the task status to complete + + def has_consumable_resource(self, available_resources): + """ + Ensure there are enough consumable resources for this action to execute. + """ + for resource in self.consumes: + if available_resources.get(resource) is None: + return False + if available_resources[resource] < self.consumes[resource]: + return False + return True + + def has_usable_resource(self, available_resources): + """ + Ensure there are enough usable resources for this action to execute. + """ + for resource in self.uses: + if available_resources.get(resource) is None: + return False + if available_resources[resource] < self.uses[resource]: + return False + return True + + def inorder(self, job_order): + """ + Ensure that all the jobs that had to be executed before the current one have been + successfully executed. + """ + for jobs in job_order: + if self in jobs: + for job in jobs: + if job is self: + return True + if not job.completed: + return False + return True + + +class Problem(PDDL): + """ + Define real-world problems by aggregating resources as numerical quantities instead of + named entities. + + This class is identical to PDLL, except that it overloads the act function to handle + resource and ordering conditions imposed by HLA as opposed to Action. + """ + def __init__(self, initial_state, actions, goal_test, jobs=None, resources={}): + super().__init__(initial_state, actions, goal_test) + self.jobs = jobs + self.resources = resources + + def act(self, action): + """ + Performs the HLA given as argument. + + Note that this is different from the superclass action - where the parameter was an + Expression. For real world problems, an Expr object isn't enough to capture all the + detail required for executing the action - resources, preconditions, etc need to be + checked for too. + """ + args = action.args + list_action = first(a for a in self.actions if a.name == action.name) + if list_action is None: + raise Exception("Action '{}' not found".format(action.name)) + list_action.do_action(self.jobs, self.resources, self.kb, args) + # print(self.resources) + + def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA themselves ... + """ + state is a Problem, containing the current state kb + library is a dictionary containing details for every possible refinement. eg: + { + "HLA": [ + "Go(Home,SFO)", + "Go(Home,SFO)", + "Drive(Home, SFOLongTermParking)", + "Shuttle(SFOLongTermParking, SFO)", + "Taxi(Home, SFO)" + ], + "steps": [ + ["Drive(Home, SFOLongTermParking)", "Shuttle(SFOLongTermParking, SFO)"], + ["Taxi(Home, SFO)"], + [], # empty refinements ie primitive action + [], + [] + ], + "precond_pos": [ + ["At(Home), Have(Car)"], + ["At(Home)"], + ["At(Home)", "Have(Car)"] + ["At(SFOLongTermParking)"] + ["At(Home)"] + ], + "precond_neg": [[],[],[],[],[]], + "effect_pos": [ + ["At(SFO)"], + ["At(SFO)"], + ["At(SFOLongTermParking)"], + ["At(SFO)"], + ["At(SFO)"] + ], + "effect_neg": [ + ["At(Home)"], + ["At(Home)"], + ["At(Home)"], + ["At(SFOLongTermParking)"], + ["At(Home)"] + ] + } + """ + e = Expr(hla.name, hla.args) + indices = [i for i,x in enumerate(library["HLA"]) if expr(x).op == hla.name] + for i in indices: + action = HLA(expr(library["steps"][i][0]), [ # TODO multiple refinements + [expr(x) for x in library["precond_pos"][i]], + [expr(x) for x in library["precond_neg"][i]] + ], + [ + [expr(x) for x in library["effect_pos"][i]], + [expr(x) for x in library["effect_neg"][i]] + ]) + if action.check_precond(state.kb, action.args): + yield action + + def hierarchical_search(problem, hierarchy): + """ + [Figure 11.5] 'Hierarchical Search, a Breadth First Search implementation of Hierarchical + Forward Planning Search' + + The problem is a real-world prodlem defined by the problem class, and the hierarchy is + a dictionary of HLA - refinements (see refinements generator for details) + """ + act = Node(problem.actions[0]) + frontier = FIFOQueue() + frontier.append(act) + while(True): + if not frontier: #(len(frontier)==0): + return None + plan = frontier.pop() + print(plan.state.name) + hla = plan.state #first_or_null(plan) + prefix = None + if plan.parent: + prefix = plan.parent.state.action #prefix, suffix = subseq(plan.state, hla) + outcome = Problem.result(problem, prefix) + if hla is None: + if outcome.goal_test(): + return plan.path() + else: + print("else") + for sequence in Problem.refinements(hla, outcome, hierarchy): + print("...") + frontier.append(Node(plan.state, plan.parent, sequence)) + + def result(problem, action): + """The outcome of applying an action to the current problem""" + if action is not None: + problem.act(action) + return problem + else: + return problem + + +def job_shop_problem(): + """ + [figure 11.1] JOB-SHOP-PROBLEM + + A job-shop scheduling problem for assembling two cars, + with resource and ordering constraints. + + Example: + >>> from planning import * + >>> p = job_shop_problem() + >>> p.goal_test() + False + >>> p.act(p.jobs[1][0]) + >>> p.act(p.jobs[1][1]) + >>> p.act(p.jobs[1][2]) + >>> p.act(p.jobs[0][0]) + >>> p.act(p.jobs[0][1]) + >>> p.goal_test() + False + >>> p.act(p.jobs[0][2]) + >>> p.goal_test() + True + >>> + """ + init = [expr('Car(C1)'), + expr('Car(C2)'), + expr('Wheels(W1)'), + expr('Wheels(W2)'), + expr('Engine(E2)'), + expr('Engine(E2)')] + + def goal_test(kb): + # print(kb.clauses) + required = [expr('Has(C1, W1)'), expr('Has(C1, E1)'), expr('Inspected(C1)'), + expr('Has(C2, W2)'), expr('Has(C2, E2)'), expr('Inspected(C2)')] + for q in required: + # print(q) + # print(kb.ask(q)) + if kb.ask(q) is False: + return False + return True + + resources = {'EngineHoists': 1, 'WheelStations': 2, 'Inspectors': 2, 'LugNuts': 500} + + # AddEngine1 + precond_pos = [] + precond_neg = [expr("Has(C1,E1)")] + effect_add = [expr("Has(C1,E1)")] + effect_rem = [] + add_engine1 = HLA(expr("AddEngine1"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=30, use={'EngineHoists': 1}) + + # AddEngine2 + precond_pos = [] + precond_neg = [expr("Has(C2,E2)")] + effect_add = [expr("Has(C2,E2)")] + effect_rem = [] + add_engine2 = HLA(expr("AddEngine2"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=60, use={'EngineHoists': 1}) + + # AddWheels1 + precond_pos = [] + precond_neg = [expr("Has(C1,W1)")] + effect_add = [expr("Has(C1,W1)")] + effect_rem = [] + add_wheels1 = HLA(expr("AddWheels1"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=30, consume={'LugNuts': 20}, use={'WheelStations': 1}) + + # AddWheels2 + precond_pos = [] + precond_neg = [expr("Has(C2,W2)")] + effect_add = [expr("Has(C2,W2)")] + effect_rem = [] + add_wheels2 = HLA(expr("AddWheels2"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=15, consume={'LugNuts': 20}, use={'WheelStations': 1}) + + # Inspect1 + precond_pos = [] + precond_neg = [expr("Inspected(C1)")] + effect_add = [expr("Inspected(C1)")] + effect_rem = [] + inspect1 = HLA(expr("Inspect1"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=10, use={'Inspectors': 1}) + + # Inspect2 + precond_pos = [] + precond_neg = [expr("Inspected(C2)")] + effect_add = [expr("Inspected(C2)")] + effect_rem = [] + inspect2 = HLA(expr("Inspect2"), + [precond_pos, precond_neg], [effect_add, effect_rem], + duration=10, use={'Inspectors': 1}) + + job_group1 = [add_engine1, add_wheels1, inspect1] + job_group2 = [add_engine2, add_wheels2, inspect2] + + return Problem(init, [add_engine1, add_engine2, add_wheels1, add_wheels2, inspect1, inspect2], + goal_test, [job_group1, job_group2], resources) + diff --git a/tests/test_planning.py b/tests/test_planning.py index e13bcfd92..0e57ffca6 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -81,3 +81,51 @@ def test_graph_call(): graph() assert levels_size == len(graph.levels) - 1 + + +def test_job_shop_problem(): + p = job_shop_problem() + assert p.goal_test() is False + + solution = [p.jobs[1][0], + p.jobs[0][0], + p.jobs[0][1], + p.jobs[0][2], + p.jobs[1][1], + p.jobs[1][2]] + + for action in solution: + p.act(action) + + assert p.goal_test() + +def test_refinements() : + init = [expr('At(Home)')] + def goal_test(kb): + return kb.ask(expr('At(SFO)')) + + library = {"HLA": ["Go(Home,SFO)","Taxi(Home, SFO)"], + "steps": [["Taxi(Home, SFO)"],[]], + "precond_pos": [["At(Home)"],["At(Home)"]], + "precond_neg": [[],[]], + "effect_pos": [["At(SFO)"],["At(SFO)"]], + "effect_neg": [["At(Home)"],["At(Home)"],]} + # Go SFO + precond_pos = [expr("At(Home)")] + precond_neg = [] + effect_add = [expr("At(SFO)")] + effect_rem = [expr("At(Home)")] + go_SFO = HLA(expr("Go(Home,SFO)"), + [precond_pos, precond_neg], [effect_add, effect_rem]) + # Taxi SFO + precond_pos = [expr("At(Home)")] + precond_neg = [] + effect_add = [expr("At(SFO)")] + effect_rem = [expr("At(Home)")] + taxi_SFO = HLA(expr("Go(Home,SFO)"), + [precond_pos, precond_neg], [effect_add, effect_rem]) + prob = Problem(init, [go_SFO, taxi_SFO], goal_test) + result = [i for i in Problem.refinements(go_SFO, prob, library)] + assert(len(result) == 1) + assert(result[0].name == "Taxi") + assert(result[0].args == (expr("Home"), expr("SFO"))) From 0879c4bf3a9d6faf5cf7f4a591f6a5668dcbda5c Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 18 Apr 2017 00:05:49 +0300 Subject: [PATCH 487/513] Learning: Grading Learners (#499) * Update learning.py * Update test_learning.py * Update test_learning.py --- learning.py | 23 ++++++++++------------- tests/test_learning.py | 20 ++++++++++++++------ 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/learning.py b/learning.py index fffbccf83..3625f6ebc 100644 --- a/learning.py +++ b/learning.py @@ -806,8 +806,9 @@ def flatten(seqs): return sum(seqs, []) # Functions for testing learners on examples -def test(predict, dataset, examples=None, verbose=0): +def err_ratio(predict, dataset, examples=None, verbose=0): """Return the proportion of the examples that are NOT correctly predicted.""" + """verbose - 0: No output; 1: Output wrong; 2 (or greater): Output correct""" if examples is None: examples = dataset.examples if len(examples) == 0: @@ -826,6 +827,12 @@ def test(predict, dataset, examples=None, verbose=0): return 1 - (right / len(examples)) +def grade_learner(predict, tests): + """Grades the given learner based on how many tests it passes. + tests is a list with each element in the form: (values, output).""" + return mean(int(predict(X) == y) for X, y in tests) + + def train_and_test(dataset, start, end): """Reserve dataset.examples[start:end] for test; train on the remainder.""" start = int(start) @@ -863,8 +870,8 @@ def cross_validation(learner, size, dataset, k=10, trials=1): (fold + 1) * (n / k)) dataset.examples = train_data h = learner(dataset, size) - fold_errT += test(h, dataset, train_data) - fold_errV += test(h, dataset, val_data) + fold_errT += err_ratio(h, dataset, train_data) + fold_errV += err_ratio(h, dataset, val_data) # Reverting back to original once test is completed dataset.examples = examples return fold_errT / k, fold_errV / k @@ -908,16 +915,6 @@ def score(learner, size): return [(size, mean([score(learner, size) for t in range(trials)])) for size in sizes] - -def grade_learner(predict, tests): - """Grades the given learner based on how many tests it passes. - tests is a list with each element in the form: (values, output).""" - correct = 0 - for t in tests: - if predict(t[0]) == t[1]: - correct += 1 - return correct - # ______________________________________________________________________________ # The rest of this file gives datasets for machine learning problems. diff --git a/tests/test_learning.py b/tests/test_learning.py index 1bac9a4cc..348dd2f0f 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,7 +1,7 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \ - euclidean_distance, grade_learner + euclidean_distance, grade_learner, err_ratio from utils import DataFile @@ -76,10 +76,14 @@ def test_neural_network_learner(): nNL = NeuralNetLearner(iris, [5], 0.15, 75) tests = [([5, 3, 1, 0.1], 0), - ([6, 3, 3, 1.5], 1), - ([7.5, 4, 6, 2], 2)] + ([5, 3.5, 1, 0], 0), + ([6, 3, 4, 1.1], 1), + ([6, 2, 3.5, 1], 1), + ([7.5, 4, 6, 2], 2), + ([7, 3, 6, 2.5], 2)] - assert grade_learner(nNL, tests) >= 2 + assert grade_learner(nNL, tests) >= 2/3 + assert err_ratio(nNL, iris) < 0.25 def test_perceptron(): @@ -90,7 +94,11 @@ def test_perceptron(): perceptron = PerceptronLearner(iris) tests = [([5, 3, 1, 0.1], 0), + ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1), - ([7.5, 4, 6, 2], 2)] + ([6, 2, 3.5, 1], 1), + ([7.5, 4, 6, 2], 2), + ([7, 3, 6, 2.5], 2)] - assert grade_learner(perceptron, tests) >= 2 + assert grade_learner(perceptron, tests) > 1/2 + assert err_ratio(perceptron, iris) < 0.4 From 8ca5ab1c2e50f23da841209eded1778cda75807d Mon Sep 17 00:00:00 2001 From: Luke Schoen Date: Tue, 18 Apr 2017 07:15:39 +1000 Subject: [PATCH 488/513] Update intro.ipynb fixing single minor typo (#470) --- intro.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/intro.ipynb b/intro.ipynb index dec3a2c12..27d4fe99f 100644 --- a/intro.ipynb +++ b/intro.ipynb @@ -71,7 +71,7 @@ "source": [ "From there, the notebook alternates explanations with examples of use. You can run the examples as they are, and you can modify the code cells (or add new cells) and run your own examples. If you have some really good examples to add, you can make a github pull request.\n", "\n", - "If you want to see the source code of a function, you can open a browser or editor and see it in another window, or from within the notebook you can use the IPython magic funtion `%psource` (for \"print source\"):" + "If you want to see the source code of a function, you can open a browser or editor and see it in another window, or from within the notebook you can use the IPython magic function `%psource` (for \"print source\"):" ] }, { From 28d7996883878a955d76e5aa75897ec744a92587 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 18 Apr 2017 02:47:01 +0530 Subject: [PATCH 489/513] Changes to planning.py (#452) * Removed redundant condition * moved gola_test inside function * refactor goal_test() --- planning.py | 37 +++++++++---------------------------- 1 file changed, 9 insertions(+), 28 deletions(-) diff --git a/planning.py b/planning.py index edfb39f19..89c963c01 100644 --- a/planning.py +++ b/planning.py @@ -110,10 +110,7 @@ def air_cargo(): def goal_test(kb): required = [expr('At(C1 , JFK)'), expr('At(C2 ,SFO)')] - for q in required: - if kb.ask(q) is False: - return False - return True + return all([kb.ask(q) is not False for q in required]) # Actions @@ -151,11 +148,8 @@ def spare_tire(): expr('At(Spare, Trunk)')] def goal_test(kb): - required = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] - for q in required: - if kb.ask(q) is False: - return False - return True + required = [expr('At(Spare, Axle)')] + return all(kb.ask(q) is not False for q in required) # Actions @@ -197,10 +191,7 @@ def three_block_tower(): def goal_test(kb): required = [expr('On(A, B)'), expr('On(B, C)')] - for q in required: - if kb.ask(q) is False: - return False - return True + return all(kb.ask(q) is not False for q in required) # Actions @@ -228,10 +219,7 @@ def have_cake_and_eat_cake_too(): def goal_test(kb): required = [expr('Have(Cake)'), expr('Eaten(Cake)')] - for q in required: - if kb.ask(q) is False: - return False - return True + return all(kb.ask(q) is not False for q in required) # Actions @@ -517,18 +505,14 @@ def extract_solution(self, goals_pos, goals_neg, index): return solution -def goal_test(kb, goals): - for q in goals: - if kb.ask(q) is False: - return False - return True - - def spare_tire_graphplan(): pddl = spare_tire() negkb = FolKB([expr('At(Flat, Trunk)')]) graphplan = GraphPlan(pddl, negkb) + def goal_test(kb, goals): + return all(kb.ask(q) is not False for q in goals) + # Not sure goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] goals_neg = [] @@ -553,10 +537,7 @@ def double_tennis_problem(): def goal_test(kb): required = [expr('Goal(Returned(Ball))'), expr('At(a, RightNet)'), expr('At(a, LeftNet)')] - for q in required: - if kb.ask(q) is False: - return False - return True + return all(kb.ask(q) is not False for q in required) # Actions From 6b64e77c375e233596c91f16c807be2ef0ff431b Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 18 Apr 2017 00:19:03 +0300 Subject: [PATCH 490/513] Tests: RL.py (#450) * Added test_rl.py * Update test_rl.py Accidentally left "agent.U == 0" in. It was there for some testing of mine. --- tests/test_rl.py | 55 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 tests/test_rl.py diff --git a/tests/test_rl.py b/tests/test_rl.py new file mode 100644 index 000000000..05f071266 --- /dev/null +++ b/tests/test_rl.py @@ -0,0 +1,55 @@ +import pytest + +from rl import * +from mdp import sequential_decision_environment + + +north = (0, 1) +south = (0,-1) +west = (-1, 0) +east = (1, 0) + +policy = { + (0, 2): east, (1, 2): east, (2, 2): east, (3, 2): None, + (0, 1): north, (2, 1): north, (3, 1): None, + (0, 0): north, (1, 0): west, (2, 0): west, (3, 0): west, +} + + + +def test_PassiveADPAgent(): + agent = PassiveADPAgent(policy, sequential_decision_environment) + for i in range(75): + run_single_trial(agent,sequential_decision_environment) + + # Agent does not always produce same results. + # Check if results are good enough. + assert agent.U[(0, 0)] > 0.15 # In reality around 0.3 + assert agent.U[(0, 1)] > 0.15 # In reality around 0.4 + assert agent.U[(1, 0)] > 0 # In reality around 0.2 + + + +def test_PassiveTDAgent(): + agent = PassiveTDAgent(policy, sequential_decision_environment, alpha=lambda n: 60./(59+n)) + for i in range(200): + run_single_trial(agent,sequential_decision_environment) + + # Agent does not always produce same results. + # Check if results are good enough. + assert agent.U[(0, 0)] > 0.15 # In reality around 0.3 + assert agent.U[(0, 1)] > 0.15 # In reality around 0.35 + assert agent.U[(1, 0)] > 0.15 # In reality around 0.25 + + +def test_QLearning(): + q_agent = QLearningAgent(sequential_decision_environment, Ne=5, Rplus=2, + alpha=lambda n: 60./(59+n)) + + for i in range(200): + run_single_trial(q_agent,sequential_decision_environment) + + # Agent does not always produce same results. + # Check if results are good enough. + assert q_agent.Q[((0, 1), (0, 1))] >= -0.5 # In reality around 0.1 + assert q_agent.Q[((1, 0), (0, -1))] <= 0.5 # In reality around -0.1 From d3155eba40bd8bfe975b8ad8e0aa08995faf302c Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 18 Apr 2017 00:21:48 +0300 Subject: [PATCH 491/513] Update test_grid.py (#448) --- tests/test_grid.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/tests/test_grid.py b/tests/test_grid.py index 928218150..aad9ebc91 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -18,5 +18,24 @@ def test_vector_clip(): assert vector_clip((-1, 10), (0, 0), (9, 9)) == (0, 9) +def test_turn_heading(): + assert turn_heading((0, 1), 1) == (-1, 0) + assert turn_heading((0, 1), -1) == (1, 0) + assert turn_heading((1, 0), 1) == (0, 1) + assert turn_heading((1, 0), -1) == (0, -1) + assert turn_heading((0, -1), 1) == (1, 0) + assert turn_heading((0, -1), -1) == (-1, 0) + assert turn_heading((-1, 0), 1) == (0, -1) + assert turn_heading((-1, 0), -1) == (0, 1) + + +def test_turn_left(): + assert turn_left((0, 1)) == (-1, 0) + + +def test_turn_right(): + assert turn_right((0, 1)) == (1, 0) + + if __name__ == '__main__': pytest.main() From 2c29a9005ea83e0327ce19e7715580f4aeb59b63 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 18 Apr 2017 02:53:02 +0530 Subject: [PATCH 492/513] Fixed mistake in HITS and add test to NLP (#441) * Add test for determineInlinks() * Add test for HITS() * fixed premature updation * Refactor code to match pseudocode --- nlp.py | 12 +++++++----- tests/test_nlp.py | 19 +++++++++++-------- 2 files changed, 18 insertions(+), 13 deletions(-) diff --git a/nlp.py b/nlp.py index 365d726c2..bd26d0a7b 100644 --- a/nlp.py +++ b/nlp.py @@ -356,13 +356,13 @@ def detect(self): def getInlinks(page): if not page.inlinks: page.inlinks = determineInlinks(page) - return [p for addr, p in pagesIndex.items() if addr in page.inlinks] + return [addr for addr, p in pagesIndex.items() if addr in page.inlinks] def getOutlinks(page): if not page.outlinks: page.outlinks = findOutlinks(page) - return [p for addr, p in pagesIndex.items() if addr in page.outlinks] + return [addr for addr, p in pagesIndex.items() if addr in page.outlinks] # ______________________________________________________________________________ @@ -389,9 +389,11 @@ def HITS(query): p.authority = 1 p.hub = 1 while True: # repeat until... convergence - for p in pages.values(): - p.authority = sum(x.hub for x in getInlinks(p)) # p.authority ← ∑i Inlinki(p).Hub - p.hub = sum(x.authority for x in getOutlinks(p)) # p.hub ← ∑i Outlinki(p).Authority + authority = {p: pages[p].authority for p in pages} + hub = {p: pages[p].hub for p in pages} + for p in pages: + pages[p].authority = sum(hub[x] for x in getInlinks(pages[p])) # p.authority ← ∑i Inlinki(p).Hub + pages[p].hub = sum(authority[x] for x in getOutlinks(pages[p])) # p.hub ← ∑i Outlinki(p).Authority normalize(pages) if convergence(): break diff --git a/tests/test_nlp.py b/tests/test_nlp.py index d9dc18851..81eef882d 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -3,7 +3,7 @@ from nlp import loadPageHTML, stripRawHTML, findOutlinks, onlyWikipediaURLS from nlp import expand_pages, relevant_pages, normalize, ConvergenceDetector, getInlinks -from nlp import getOutlinks, Page +from nlp import getOutlinks, Page, determineInlinks, HITS from nlp import Rules, Lexicon # Clumsy imports because we want to access certain nlp.py globals explicitly, because # they are accessed by function's within nlp.py @@ -80,9 +80,9 @@ def test_stripRawHTML(html_mock): def test_determineInlinks(): - # TODO - assert True - + assert set(determineInlinks(pA)) == set(['B', 'C', 'E']) + assert set(determineInlinks(pE)) == set([]) + assert set(determineInlinks(pF)) == set(['E']) def test_findOutlinks_wiki(): testPage = pageDict[pA.address] @@ -141,17 +141,20 @@ def test_detectConvergence(): def test_getInlinks(): inlnks = getInlinks(pageDict['A']) - assert sorted([page.address for page in inlnks]) == pageDict['A'].inlinks + assert sorted(inlnks) == pageDict['A'].inlinks def test_getOutlinks(): outlnks = getOutlinks(pageDict['A']) - assert sorted([page.address for page in outlnks]) == pageDict['A'].outlinks + assert sorted(outlnks) == pageDict['A'].outlinks def test_HITS(): - # TODO - assert True # leave for now + HITS('inherit') + auth_list = [pA.authority, pB.authority, pC.authority, pD.authority, pE.authority, pF.authority] + hub_list = [pA.hub, pB.hub, pC.hub, pD.hub, pE.hub, pF.hub] + assert max(auth_list) == pD.authority + assert max(hub_list) == pE.hub if __name__ == '__main__': From 4d9bea0194e2d7aa3db346f7fbad0dc53c52d4d1 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Tue, 18 Apr 2017 02:54:27 +0530 Subject: [PATCH 493/513] Moved asserts from main code to unit tests (#396) * replace assert with if test in add_thing * removed inline assert * added unit test to check edit * improve user interface --- agents.py | 18 ++++++++++-------- tests/test_agents.py | 7 +++++++ 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/agents.py b/agents.py index 403bfbddc..bca09f3e7 100644 --- a/agents.py +++ b/agents.py @@ -85,10 +85,10 @@ def __init__(self, program=None): self.bump = False self.holding = [] self.performance = 0 - if program is None: + if program is None or not isinstance(program, collections.Callable): + print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__)) def program(percept): return eval(input('Percept={}; action? '.format(percept))) - assert isinstance(program, collections.Callable) self.program = program def can_grab(self, thing): @@ -298,12 +298,14 @@ def add_thing(self, thing, location=None): for it. (Shouldn't need to override this.""" if not isinstance(thing, Thing): thing = Agent(thing) - assert thing not in self.things, "Don't add the same thing twice" - thing.location = location if location is not None else self.default_location(thing) - self.things.append(thing) - if isinstance(thing, Agent): - thing.performance = 0 - self.agents.append(thing) + if thing in self.things: + print("Can't add the same thing twice") + else: + thing.location = location if location is not None else self.default_location(thing) + self.things.append(thing) + if isinstance(thing, Agent): + thing.performance = 0 + self.agents.append(thing) def delete_thing(self, thing): """Remove a thing from the environment.""" diff --git a/tests/test_agents.py b/tests/test_agents.py index 0162a78b8..699e317f7 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -1,4 +1,5 @@ from agents import Direction +from agents import Agent from agents import ReflexVacuumAgent, ModelBasedVacuumAgent, TrivialVacuumEnvironment @@ -65,3 +66,9 @@ def test_ModelBasedVacuumAgent() : # check final status of the environment assert environment.status == {(1,0):'Clean' , (0,0) : 'Clean'} +def test_Agent(): + def constant_prog(percept): + return percept + agent = Agent(constant_prog) + result = agent.program(5) + assert result == 5 From 80dbdf8eeada04db2fc9a03e258e33359961f4a0 Mon Sep 17 00:00:00 2001 From: Angira Sharma Date: Tue, 18 Apr 2017 03:00:39 +0530 Subject: [PATCH 494/513] added another test for air_cargo_problem (#465) --- tests/test_planning.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/tests/test_planning.py b/tests/test_planning.py index 0e57ffca6..e9c639c95 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -18,17 +18,33 @@ def test_action(): assert not a.check_precond(test_kb, args) -def test_air_cargo(): +def test_air_cargo_1(): p = air_cargo() assert p.goal_test() is False - solution = [expr("Load(C1 , P1, SFO)"), + solution_1 = [expr("Load(C1 , P1, SFO)"), expr("Fly(P1, SFO, JFK)"), expr("Unload(C1, P1, JFK)"), expr("Load(C2, P2, JFK)"), expr("Fly(P2, JFK, SFO)"), - expr("Unload (C2, P2, SFO)")] + expr("Unload (C2, P2, SFO)")] - for action in solution: + for action in solution_1: + p.act(action) + + assert p.goal_test() + + +def test_air_cargo_2(): + p = air_cargo() + assert p.goal_test() is False + solution_2 = [expr("Load(C2, P2, JFK)"), + expr("Fly(P2, JFK, SFO)"), + expr("Unload (C2, P2, SFO)"), + expr("Load(C1 , P1, SFO)"), + expr("Fly(P1, SFO, JFK)"), + expr("Unload(C1, P1, JFK)")] + + for action in solution_2: p.act(action) assert p.goal_test() From 085f10e28efa6727e4347cae1510457acdb2a8cb Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Mon, 17 Apr 2017 18:31:17 -0300 Subject: [PATCH 495/513] Add new tests to test_csp.py (#447) --- tests/test_csp.py | 64 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) diff --git a/tests/test_csp.py b/tests/test_csp.py index 803dede74..301fd643d 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -254,6 +254,70 @@ def test_mrv(): assert mrv(assignment, csp) == 'C' +def test_unordered_domain_values(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + assignment = None + assert unordered_domain_values('A', assignment, map_coloring_test) == ['1', '2', '3'] + + +def test_lcv(): + neighbors = parse_neighbors('A: B; B: C; C: ') + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4, 5], 'C': [0, 1, 2, 3, 4]} + constraints = lambda X, x, Y, y: x % 2 == 0 and (x+y) == 4 + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + assignment = {'A': 0} + + var = 'B' + + assert lcv(var, assignment, csp) == [4, 0, 1, 2, 3, 5] + assignment = {'A': 1, 'C': 3} + + constraints = lambda X, x, Y, y: (x + y) % 2 == 0 and (x + y) < 5 + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + + assert lcv(var, assignment, csp) == [1, 3, 0, 2, 4, 5] + + +def test_forward_checking(): + neighbors = parse_neighbors('A: B; B: C; C: ') + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4, 5], 'C': [0, 1, 2, 3, 4]} + constraints = lambda X, x, Y, y: (x + y) % 2 == 0 and (x + y) < 8 + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + + csp.support_pruning() + A_curr_domains = csp.curr_domains['A'] + C_curr_domains = csp.curr_domains['C'] + + var = 'B' + value = 3 + assignment = {'A': 1, 'C': '3'} + assert forward_checking(csp, var, value, assignment, None) == True + assert csp.curr_domains['A'] == A_curr_domains + assert csp.curr_domains['C'] == C_curr_domains + + assignment = {'C': 3} + + assert forward_checking(csp, var, value, assignment, None) == True + assert csp.curr_domains['A'] == [1, 3] + + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + csp.support_pruning() + + assignment = {} + assert forward_checking(csp, var, value, assignment, None) == True + assert csp.curr_domains['A'] == [1, 3] + assert csp.curr_domains['C'] == [1, 3] + + csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints) + domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4, 7], 'C': [0, 1, 2, 3, 4]} + csp.support_pruning() + + value = 7 + assignment = {} + assert forward_checking(csp, var, value, assignment, None) == False + assert (csp.curr_domains['A'] == [] or csp.curr_domains['C'] == []) + + def test_backtracking_search(): assert backtracking_search(australia) assert backtracking_search(australia, select_unassigned_variable=mrv) From 072f6853da5e4e86462d8923b7b5d1ef8e145c44 Mon Sep 17 00:00:00 2001 From: Azizur Rahman Date: Mon, 17 Apr 2017 17:31:43 -0400 Subject: [PATCH 496/513] Typo: 'logic_test.py' -> 'test_logic.py' in README.md fixed (#425) (#426) * 'test_logic.py typo fixed(#425) * typo 'logic_test.py' fixed(#425) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7cb796b02..5f85c4eb1 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ When complete, this project will have Python code for all the pseudocode algorit - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. - `logic.ipynb`: A Jupyter (IPython) notebook that explains and gives examples of how to use the code. -- `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. +- `tests/test_logic.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. # Index of Algorithms From e9c2d070883a58654aa2ae10e7ce163c124d484f Mon Sep 17 00:00:00 2001 From: articuno12 Date: Tue, 18 Apr 2017 03:03:23 +0530 Subject: [PATCH 497/513] Updated implementation of FIFOQueue (#403) * Added test for FIFOQueue * Updated FIFOQueue * Updated FIFOQueue * FIFOQueue using deque * fixed flake8 warnings --- tests/test_utils.py | 49 ++++++++++++++++++++++++++++++++++++++++++++- utils.py | 35 +++++++++++++++++--------------- 2 files changed, 67 insertions(+), 17 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index ae39cf50e..0b77390eb 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,6 +1,6 @@ import pytest from utils import * # noqa - +import random def test_removeall_list(): assert removeall(4, []) == [] @@ -189,6 +189,53 @@ def test_expr(): assert (expr('GP(x, z) <== P(x, y) & P(y, z)') == Expr('<==', GP(x, z), P(x, y) & P(y, z))) +def test_FIFOQueue() : + # Create an object + queue = FIFOQueue() + # Generate an array of number to be used for testing + test_data = [ random.choice(range(100)) for i in range(100) ] + # Index of the element to be added in the queue + front_head = 0 + # Index of the element to be removed from the queue + back_head = 0 + while front_head < 100 or back_head < 100 : + if front_head == 100 : # only possible to remove + # check for pop and append method + assert queue.pop() == test_data[back_head] + back_head += 1 + elif back_head == front_head : # only possible to push element into queue + queue.append(test_data[front_head]) + front_head += 1 + # else do it in a random manner + elif random.random() < 0.5 : + assert queue.pop() == test_data[back_head] + back_head += 1 + else : + queue.append(test_data[front_head]) + front_head += 1 + # check for __len__ method + assert len(queue) == front_head - back_head + # chek for __contains__ method + if front_head - back_head > 0 : + assert random.choice(test_data[back_head:front_head]) in queue + + # check extend method + test_data1 = [ random.choice(range(100)) for i in range(50) ] + test_data2 = [ random.choice(range(100)) for i in range(50) ] + # append elements of test data 1 + queue.extend(test_data1) + # append elements of test data 2 + queue.extend(test_data2) + # reset front_head + front_head = 0 + + while front_head < 50 : + assert test_data1[front_head] == queue.pop() + front_head += 1 + + while front_head < 100 : + assert test_data2[front_head - 50] == queue.pop() + front_head += 1 if __name__ == '__main__': pytest.main() diff --git a/utils.py b/utils.py index d738f62e6..411ceda51 100644 --- a/utils.py +++ b/utils.py @@ -598,7 +598,7 @@ def __ge__(self, odict): # ______________________________________________________________________________ # Queues: Stack, FIFOQueue, PriorityQueue -# TODO: Possibly use queue.Queue, queue.PriorityQueue +# TODO: queue.PriorityQueue # TODO: Priority queues may not belong here -- see treatment in search.py @@ -634,29 +634,32 @@ class FIFOQueue(Queue): """A First-In-First-Out Queue.""" - def __init__(self): - self.A = [] - self.start = 0 + def __init__(self, maxlen=None, items=[]): + self.queue = collections.deque(items, maxlen) def append(self, item): - self.A.append(item) - - def __len__(self): - return len(self.A) - self.start + if not self.queue.maxlen or len(self.queue) < self.queue.maxlen: + self.queue.append(item) + else: + raise Exception('FIFOQueue is full') def extend(self, items): - self.A.extend(items) + if not self.queue.maxlen or len(self.queue) + len(items) <= self.queue.maxlen: + self.queue.extend(items) + else: + raise Exception('FIFOQueue max length exceeded') def pop(self): - e = self.A[self.start] - self.start += 1 - if self.start > 5 and self.start > len(self.A) / 2: - self.A = self.A[self.start:] - self.start = 0 - return e + if len(self.queue) > 0: + return self.queue.popleft() + else : + raise Exception('FIFOQueue is empty') + + def __len__(self): + return len(self.queue) def __contains__(self, item): - return item in self.A[self.start:] + return item in self.queue class PriorityQueue(Queue): From 856e8d99fd6d323c900c9e1c94cc48a248cc49ec Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Tue, 18 Apr 2017 00:38:33 +0300 Subject: [PATCH 498/513] Implementation: Continuous Naive Bayes (#435) * Add Gaussian Function * Added Tests Add tests for Continuous Naive Bayes + Means/Standard Deviation * Update learning.py * Commenting Fix * Add test for gaussian * test for every class * Update test_learning.py * Round float results to make sure test passes --- learning.py | 82 ++++++++++++++++++++++++++++++++++++++---- tests/test_learning.py | 22 ++++++++++++ tests/test_utils.py | 6 ++++ utils.py | 4 +++ 4 files changed, 107 insertions(+), 7 deletions(-) diff --git a/learning.py b/learning.py index 3625f6ebc..06a719745 100644 --- a/learning.py +++ b/learning.py @@ -1,7 +1,7 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" from utils import ( - removeall, unique, product, mode, argmax, argmax_random_tie, isclose, + removeall, unique, product, mode, argmax, argmax_random_tie, isclose, gaussian, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile ) @@ -11,7 +11,7 @@ import math import random -from statistics import mean +from statistics import mean, stdev from collections import defaultdict # ______________________________________________________________________________ @@ -178,6 +178,45 @@ def remove_examples(self, value=""): self.examples = [x for x in self.examples if value not in x] self.update_values() + def split_values_by_classes(self): + """Split values into buckets according to their class.""" + buckets = defaultdict(lambda: []) + target_names = self.values[self.target] + + for v in self.examples: + item = [a for a in v if a not in target_names] # Remove target from item + buckets[v[self.target]].append(item) # Add item to bucket of its class + + return buckets + + def find_means_and_deviations(self): + """Finds the means and standard deviations of self.dataset. + means : A dictionary for each class/target. Holds a list of the means + of the features for the class. + deviations: A dictionary for each class/target. Holds a list of the sample + standard deviations of the features for the class.""" + target_names = self.values[self.target] + feature_numbers = len(self.inputs) + + item_buckets = self.split_values_by_classes() + + means = defaultdict(lambda: [0 for i in range(feature_numbers)]) + deviations = defaultdict(lambda: [0 for i in range(feature_numbers)]) + + for t in target_names: + # Find all the item feature values for item in class t + features = [[] for i in range(feature_numbers)] + for item in item_buckets[t]: + features = [features[i] + [item[i]] for i in range(feature_numbers)] + + # Calculate means and deviations fo the class + for i in range(feature_numbers): + means[t][i] = mean(features[i]) + deviations[t][i] = stdev(features[i]) + + return means, deviations + + def __repr__(self): return ''.format( self.name, len(self.examples), len(self.attrs)) @@ -267,15 +306,22 @@ def predict(example): # ______________________________________________________________________________ -def NaiveBayesLearner(dataset): +def NaiveBayesLearner(dataset, continuous=True): + if(continuous): + return NaiveBayesContinuous(dataset) + else: + return NaiveBayesDiscrete(dataset) + + +def NaiveBayesDiscrete(dataset): """Just count how many times each value of each input attribute occurs, conditional on the target value. Count the different target values too.""" - targetvals = dataset.values[dataset.target] - target_dist = CountingProbDist(targetvals) + target_vals = dataset.values[dataset.target] + target_dist = CountingProbDist(target_vals) attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr]) - for gv in targetvals + for gv in target_vals for attr in dataset.inputs} for example in dataset.examples: targetval = example[dataset.target] @@ -290,7 +336,29 @@ def class_probability(targetval): return (target_dist[targetval] * product(attr_dists[targetval, attr][example[attr]] for attr in dataset.inputs)) - return argmax(targetvals, key=class_probability) + return argmax(target_vals, key=class_probability) + + return predict + + +def NaiveBayesContinuous(dataset): + """Count how many times each target value occurs. + Also, find the means and deviations of input attribute values for each target value.""" + means, deviations = dataset.find_means_and_deviations() + + target_vals = dataset.values[dataset.target] + target_dist = CountingProbDist(target_vals) + + def predict(example): + """Predict the target value for example. Consider each possible value, + and pick the most likely by looking at each attribute independently.""" + def class_probability(targetval): + prob = target_dist[targetval] + for attr in dataset.inputs: + prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr]) + return prob + + return argmax(target_vals, key=class_probability) return predict diff --git a/tests/test_learning.py b/tests/test_learning.py index 348dd2f0f..ec2cf18bd 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -35,6 +35,20 @@ def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] +def test_means_and_deviation(): + iris = DataSet(name="iris") + + means, deviations = iris.find_means_and_deviations() + + assert round(means["setosa"][0], 3) == 5.006 + assert round(means["versicolor"][0], 3) == 5.936 + assert round(means["virginica"][0], 3) == 6.588 + + assert round(deviations["setosa"][0], 3) == 0.352 + assert round(deviations["versicolor"][0], 3) == 0.516 + assert round(deviations["virginica"][0], 3) == 0.636 + + def test_plurality_learner(): zoo = DataSet(name="zoo") @@ -48,6 +62,14 @@ def test_naive_bayes(): # Discrete nBD = NaiveBayesLearner(iris) assert nBD([5, 3, 1, 0.1]) == "setosa" + assert nBD([6, 5, 3, 1.5]) == "versicolor" + assert nBD([7, 3, 6.5, 2]) == "virginica" + + # Continuous + nBC = NaiveBayesLearner(iris, continuous=True) + assert nBC([5, 3, 1, 0.1]) == "setosa" + assert nBC([6, 5, 3, 1.5]) == "versicolor" + assert nBC([7, 3, 6.5, 2]) == "virginica" def test_k_nearest_neighbors(): diff --git a/tests/test_utils.py b/tests/test_utils.py index 0b77390eb..d158833d0 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -148,6 +148,12 @@ def test_sigmoid(): assert isclose(0.2689414213699951, sigmoid(-1)) +def test_gaussian(): + assert gaussian(1,0.5,0.7) == 0.6664492057835993 + assert gaussian(5,2,4.5) == 0.19333405840142462 + assert gaussian(3,1,3) == 0.3989422804014327 + + def test_step(): assert step(1) == step(0.5) == 1 assert step(0) == 1 diff --git a/utils.py b/utils.py index 411ceda51..5afa43760 100644 --- a/utils.py +++ b/utils.py @@ -258,6 +258,10 @@ def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 +def gaussian(mean, st_dev, x): + """Given the mean and standard deviation of a distribution, it returns the probability of x.""" + return 1/(math.sqrt(2*math.pi)*st_dev)*math.e**(-0.5*(float(x-mean)/st_dev)**2) + try: # math.isclose was added in Python 3.5; but we might be in 3.4 from math import isclose From cd08becf67c32e933e8e8549affdfa23b1a88937 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 24 May 2017 08:12:10 +0300 Subject: [PATCH 499/513] Notebook + Implementation: Perceptron (#512) * Update learning.ipynb * Delete perceptron.png * Add new Perceptron image * Update Perceptron Implementation --- images/perceptron.png | Bin 21245 -> 19756 bytes learning.ipynb | 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zbQLtDV9WYu972htwbIJ}fh2o(%j|Jl!|#z~Wh&fSzi=8pJw5u(L}b|{*b~a^!36oY zpTk2!;?nVR_Sn+T9NLgOH#2)GeXXBgB5xS9L+gxs?X%X|1SW`IiXd?gvn#C#2OFk_ z$5X#Bqkbs|n4~1SL5ZA}f$V4a3FiWG<+clR#*wOa?Q((az1AY%jx_IH1qtj?fl zo%c73T6sUTUnFrr)r*oo#`b^%%m4Wc@U)yi#Jh>@ye2_zB;CL*C-5@cIScSw{Y57H fzu*}Q24<{>cRKP-%WV|sWE0O#KJUogB7{E!!?WX` diff --git a/learning.ipynb b/learning.ipynb index d31a708ef..13d184e34 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, @@ -582,7 +582,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Distance Functions\n", "\n", @@ -597,7 +600,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -619,7 +624,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Euclidean Distance (`euclidean_distance`)\n", "\n", @@ -630,7 +638,9 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -652,7 +662,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Hamming Distance (`hamming_distance`)\n", "\n", @@ -663,7 +676,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -685,7 +700,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Mean Boolean Error (`mean_boolean_error`)\n", "\n", @@ -696,7 +714,9 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -718,7 +738,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Mean Error (`mean_error`)\n", "\n", @@ -729,7 +752,9 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -751,7 +776,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Mean Square Error (`ms_error`)\n", "\n", @@ -762,7 +790,9 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -784,7 +814,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "### Root of Mean Square Error (`rms_error`)\n", "\n", @@ -795,7 +828,9 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { @@ -1062,8 +1097,17 @@ "\n", "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n", "\n", - "You can think of it as a single neuron. It has *n* synapses, each with its own weight. Each synapse corresponds to one item feature. Perceptron multiplies each item feature with the corresponding synapse weight and then adds them together (aka, the dot product) and checks whether this value is greater than the threshold. If yes, it returns 1. It returns 0 otherwise.\n", + "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n", + "\n", + "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n", "\n", + "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "![perceptron](images/perceptron.png)" ] }, @@ -1076,14 +1120,12 @@ "source": [ "### Implementation\n", "\n", - "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights. If the result is greater than a predefined threshold (usually 0.5, 0 or 1), it returns 1. If it is less than the threshold, it returns 0.\n", - "\n", - "NOTE: The current implementation of the algorithm classifies an item into one of two classes. It is a binary classifier and will not work well for multi-class datasets." + "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights for each node in the outer layer. Then it picks the greatest value and classifies the item in the corresponding class." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, @@ -1091,35 +1133,7 @@ }, "outputs": [], "source": [ - "def PerceptronLearner(dataset, learning_rate=0.01, epochs=100):\n", - " \"\"\"Logistic Regression, NO hidden layer\"\"\"\n", - " i_units = len(dataset.inputs)\n", - " o_units = 1 # As of now, dataset.target gives only one index.\n", - " hidden_layer_sizes = []\n", - " raw_net = network(i_units, hidden_layer_sizes, o_units)\n", - " learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs)\n", - "\n", - " def predict(example):\n", - " # Input nodes\n", - " i_nodes = learned_net[0]\n", - "\n", - " # Activate input layer\n", - " for v, n in zip(example, i_nodes):\n", - " n.value = v\n", - "\n", - " # Forward pass\n", - " for layer in learned_net[1:]:\n", - " for node in layer:\n", - " inc = [n.value for n in node.inputs]\n", - " in_val = dotproduct(inc, node.weights)\n", - " node.value = node.activation(in_val)\n", - "\n", - " # Hypothesis\n", - " o_nodes = learned_net[-1]\n", - " pred = [o_nodes[i].value for i in range(o_units)]\n", - " return 1 if pred[0] >= 0.5 else 0\n", - "\n", - " return predict" + "%psource PerceptronLearner" ] }, { @@ -1129,11 +1143,9 @@ "editable": true }, "source": [ - "The weights are trained from the `BackPropagationLearner`. Note that the perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one node, with the weights calculated.\n", - "\n", - "`PerceptronLearner` returns `predict`, a function that can be used to classify a new item.\n", + "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n", "\n", - "That function passes the input/example through the network, calculating the dot product of the input and the weights. If that value is greater than or equal to 0.5, it returns 1. Otherwise it returns 0." + "That function `predict` passes the input/example through the network, calculating the dot product of the input and the weights for each node and returns the class with the max dot product." ] }, { @@ -1145,14 +1157,12 @@ "source": [ "### Example\n", "\n", - "We will train the Perceptron on the iris dataset. Because, though, the algorithm is a binary classifier (which means it classifies an item in one of two classes) and the iris dataset has three classes, we need to transform the dataset into a proper form, with only two classes. Therefore, we will remove the third and final class of the dataset, *Virginica*.\n", - "\n", - "Then, we will try and classify the item/flower with measurements of 5,3,1,0.1." + "We will train the Perceptron on the iris dataset. Because though the `BackPropagationLearner` works with integer indexes and not strings, we need to convert class names to integers. Then, we will try and classify the item/flower with measurements of 5, 3, 1, 0.1." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, @@ -1169,11 +1179,10 @@ ], "source": [ "iris = DataSet(name=\"iris\")\n", - "iris.remove_examples(\"virginica\")\n", "iris.classes_to_numbers()\n", "\n", "perceptron = PerceptronLearner(iris)\n", - "print(perceptron([5,3,1,0.1]))" + "print(perceptron([5, 3, 1, 0.1]))" ] }, { @@ -1183,7 +1192,7 @@ "editable": true }, "source": [ - "The output is 0, which means the item is classified in the first class, *setosa*. This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications." + "The output is 0, which means the item is classified in the first class, \"Setosa\". This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications." ] }, { diff --git a/learning.py b/learning.py index 06a719745..918f17447 100644 --- a/learning.py +++ b/learning.py @@ -653,24 +653,15 @@ def PerceptronLearner(dataset, learning_rate=0.01, epochs=100): learned_net = BackPropagationLearner(dataset, raw_net, learning_rate, epochs) def predict(example): - # Input nodes - i_nodes = learned_net[0] - - # Activate input layer - for v, n in zip(example, i_nodes): - n.value = v + o_nodes = learned_net[1] # Forward pass - for layer in learned_net[1:]: - for node in layer: - inc = [n.value for n in node.inputs] - in_val = dotproduct(inc, node.weights) - node.value = node.activation(in_val) + for node in o_nodes: + in_val = dotproduct(example, node.weights) + node.value = node.activation(in_val) # Hypothesis - o_nodes = learned_net[-1] - prediction = find_max_node(o_nodes) - return prediction + return find_max_node(o_nodes) return predict From e6d5fcfc4779dcf6e17b084476f553de725b82df Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Wed, 24 May 2017 10:43:26 +0530 Subject: [PATCH 500/513] Intersection query for relevant_pages (#509) * Modified relevant_pages() * Additional tests for relevant_pages() --- nlp.py | 20 +++++++++++--------- tests/test_nlp.py | 10 +++++++--- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/nlp.py b/nlp.py index bd26d0a7b..268a2b155 100644 --- a/nlp.py +++ b/nlp.py @@ -301,15 +301,17 @@ def expand_pages(pages): def relevant_pages(query): - """Relevant pages are pages that contain the query in its entireity. - If a page's content contains the query it is returned by the function.""" - relevant = {} - print("pagesContent in function: ", pagesContent) - for addr, page in pagesIndex.items(): - if query.lower() in pagesContent[addr].lower(): - relevant[addr] = page - return relevant - + """Relevant pages are pages that contain all of the query words. They are obtained by + intersecting the hit lists of the query words.""" + hit_intersection = {addr for addr in pagesIndex} + query_words = query.split() + for query_word in query_words: + hit_list = set() + for addr in pagesIndex: + if query_word.lower() in pagesContent[addr].lower(): + hit_list.add(addr) + hit_intersection = hit_intersection.intersection(hit_list) + return {addr: pagesIndex[addr] for addr in hit_intersection} def normalize(pages): """From the pseudocode: Normalize divides each page's score by the sum of diff --git a/tests/test_nlp.py b/tests/test_nlp.py index 81eef882d..d0ce46fbc 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -30,7 +30,7 @@ def test_lexicon(): href="https://google.com.au" < href="/wiki/TestThing" > href="/wiki/TestBoy" href="/wiki/TestLiving" href="/wiki/TestMan" >""" -testHTML2 = "Nothing" +testHTML2 = "a mom and a dad" testHTML3 = """ @@ -106,9 +106,13 @@ def test_expand_pages(): def test_relevant_pages(): - pages = relevant_pages("male") - assert all((x in pages.keys()) for x in ['A', 'C', 'E']) + pages = relevant_pages("his dad") + assert all((x in pages) for x in ['A', 'C', 'E']) assert all((x not in pages) for x in ['B', 'D', 'F']) + pages = relevant_pages("mom and dad") + assert all((x in pages) for x in ['A', 'B', 'C', 'D', 'E', 'F']) + pages = relevant_pages("philosophy") + assert all((x not in pages) for x in ['A', 'B', 'C', 'D', 'E', 'F']) def test_normalize(): From 4caca950e51e2ad916c31f1caa3f71eef98c4c79 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Wed, 24 May 2017 10:44:31 +0530 Subject: [PATCH 501/513] Fix flake8 warnings (#508) * Fix flake8 warnings * Remove unnecessary #noqa * Fix doctest --- .flake8 | 2 +- agents.py | 5 ++++- canvas.py | 1 + csp.py | 13 ++++++++----- learning.py | 8 +++----- logic.py | 38 +++++++++++--------------------------- mdp.py | 4 +++- nlp.py | 18 ++++++++++-------- planning.py | 21 +++++++++------------ search.py | 19 +++++++++---------- tests/test_csp.py | 2 +- tests/test_games.py | 2 +- tests/test_grid.py | 2 +- tests/test_logic.py | 2 +- tests/test_mdp.py | 2 +- tests/test_planning.py | 2 +- tests/test_probability.py | 4 ++-- tests/test_search.py | 2 +- tests/test_text.py | 4 ++-- tests/test_utils.py | 2 +- text.py | 6 ++---- utils.py | 4 +++- 22 files changed, 76 insertions(+), 87 deletions(-) diff --git a/.flake8 b/.flake8 index c944f27ed..688024601 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max-line-length = 100 -ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405 +ignore = E121,E123,E126,E221,E222,E225,E226,E242,E701,E702,E704,E731,W503,F405,F841 exclude = tests diff --git a/agents.py b/agents.py index bca09f3e7..edab6891c 100644 --- a/agents.py +++ b/agents.py @@ -86,9 +86,12 @@ def __init__(self, program=None): self.holding = [] self.performance = 0 if program is None or not isinstance(program, collections.Callable): - print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__)) + print("Can't find a valid program for {}, falling back to default.".format( + self.__class__.__name__)) + def program(percept): return eval(input('Percept={}; action? '.format(percept))) + self.program = program def can_grab(self, thing): diff --git a/canvas.py b/canvas.py index f78556cce..faabef6dd 100644 --- a/canvas.py +++ b/canvas.py @@ -121,6 +121,7 @@ def update(self): self.exec_list = [] display_html(exec_code) + def display_html(html_string): from IPython.display import HTML, display display(HTML(html_string)) diff --git a/csp.py b/csp.py index deb1efc12..d410b1428 100644 --- a/csp.py +++ b/csp.py @@ -20,7 +20,7 @@ class CSP(search.Problem): the other variables that participate in constraints. constraints A function f(A, a, B, b) that returns true if neighbors A, B satisfy the constraint when they have values A=a, B=b - + In the textbook and in most mathematical definitions, the constraints are specified as explicit pairs of allowable values, but the formulation here is easier to express and more compact for @@ -347,6 +347,7 @@ def topological_sort(X, root): build_topological(root, None, neighbors, visited, stack, parents) return stack, parents + def build_topological(node, parent, neighbors, visited, stack, parents): """Builds the topological sort and the parents of each node in the graph""" visited[node] = True @@ -356,7 +357,7 @@ def build_topological(node, parent, neighbors, visited, stack, parents): build_topological(n, node, neighbors, visited, stack, parents) parents[node] = parent - stack.insert(0,node) + stack.insert(0, node) def make_arc_consistent(Xj, Xk, csp): @@ -533,10 +534,12 @@ def display(self, assignment): # Sudoku -def flatten(seqs): return sum(seqs, []) +def flatten(seqs): + return sum(seqs, []) + -easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' # noqa -harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' # noqa +easy1 = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..' +harder1 = '4173698.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......' _R3 = list(range(3)) _CELL = itertools.count().__next__ diff --git a/learning.py b/learning.py index 918f17447..e4b986c0d 100644 --- a/learning.py +++ b/learning.py @@ -184,8 +184,8 @@ def split_values_by_classes(self): target_names = self.values[self.target] for v in self.examples: - item = [a for a in v if a not in target_names] # Remove target from item - buckets[v[self.target]].append(item) # Add item to bucket of its class + item = [a for a in v if a not in target_names] # Remove target from item + buckets[v[self.target]].append(item) # Add item to bucket of its class return buckets @@ -199,7 +199,7 @@ def find_means_and_deviations(self): feature_numbers = len(self.inputs) item_buckets = self.split_values_by_classes() - + means = defaultdict(lambda: [0 for i in range(feature_numbers)]) deviations = defaultdict(lambda: [0 for i in range(feature_numbers)]) @@ -216,7 +216,6 @@ def find_means_and_deviations(self): return means, deviations - def __repr__(self): return ''.format( self.name, len(self.examples), len(self.attrs)) @@ -760,7 +759,6 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): for i in range(len(w)): w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples) - def predict(example): x = [1] + example return dotproduct(w, x) diff --git a/logic.py b/logic.py index c5aaa64ba..3ba1857bc 100644 --- a/logic.py +++ b/logic.py @@ -845,23 +845,8 @@ def subst(s, x): return Expr(x.op, *[subst(s, arg) for arg in x.args]) -def fol_fc_ask(KB, alpha): - """A simple forward-chaining algorithm. [Figure 9.3]""" - while new is not None: - new = [] - for rule in KB: - p, q = parse_definite_clause(standardize_variables(rule)) - for p_ in random.KB.clauses: - if p != p_: - for theta in (subst(theta, p) == subst(theta, p_)): - q_ = subst(theta, q) - if not unify(q_,KB.sentence in KB) or not unify(q_, new): - new.append(q_) - phi = unify(q_,alpha) - if phi is not None: - return phi - KB.tell(new) - return None +def fol_fc_ask(KB, alpha): # TODO + raise NotImplementedError def standardize_variables(sentence, dic=None): @@ -936,16 +921,15 @@ def fetch_rules_for_goal(self, goal): ])) crime_kb = FolKB( - map(expr, - ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', # noqa - 'Owns(Nono, M1)', - 'Missile(M1)', - '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)', - 'Missile(x) ==> Weapon(x)', - 'Enemy(x, America) ==> Hostile(x)', - 'American(West)', - 'Enemy(Nono, America)' - ])) + map(expr, ['(American(x) & Weapon(y) & Sells(x, y, z) & Hostile(z)) ==> Criminal(x)', + 'Owns(Nono, M1)', + 'Missile(M1)', + '(Missile(x) & Owns(Nono, x)) ==> Sells(West, x, Nono)', + 'Missile(x) ==> Weapon(x)', + 'Enemy(x, America) ==> Hostile(x)', + 'American(West)', + 'Enemy(Nono, America)' + ])) def fol_bc_ask(KB, query): diff --git a/mdp.py b/mdp.py index 902582b19..aaf1d10a5 100644 --- a/mdp.py +++ b/mdp.py @@ -6,7 +6,7 @@ dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" -from utils import argmax, vector_add, print_table # noqa +from utils import argmax, vector_add from grid import orientations, turn_right, turn_left import random @@ -173,6 +173,8 @@ def policy_evaluation(pi, U, mdp, k=20): >>> sequential_decision_environment.to_arrows(pi) [['>', '>', '>', '.'], ['^', None, '^', '.'], ['^', '>', '^', '<']] +>>> from utils import print_table + >>> print_table(sequential_decision_environment.to_arrows(pi)) > > > . ^ None ^ . diff --git a/nlp.py b/nlp.py index 268a2b155..2de5caf8c 100644 --- a/nlp.py +++ b/nlp.py @@ -58,16 +58,16 @@ def __repr__(self): E0 = Grammar('E0', Rules( # Grammar for E_0 [Figure 22.4] S='NP VP | S Conjunction S', - NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', # noqa + NP='Pronoun | Name | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', VP='Verb | VP NP | VP Adjective | VP PP | VP Adverb', PP='Preposition NP', RelClause='That VP'), Lexicon( # Lexicon for E_0 [Figure 22.3] - Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", # noqa + Noun="stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", Verb="is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", # noqa Adjective="right | left | east | south | back | smelly", - Adverb="here | there | nearby | ahead | right | left | east | south | back", # noqa + Adverb="here | there | nearby | ahead | right | left | east | south | back", Pronoun="me | you | I | it", Name="John | Mary | Boston | Aristotle", Article="the | a | an", @@ -166,7 +166,7 @@ def add_edge(self, edge): self.predictor(edge) def scanner(self, j, word): - "For each edge expecting a word of this category here, extend the edge." # noqa + "For each edge expecting a word of this category here, extend the edge." for (i, j, A, alpha, Bb) in self.chart[j]: if Bb and self.grammar.isa(word, Bb[0]): self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]]) @@ -386,16 +386,18 @@ def __init__(self, address, hub=0, authority=0, inlinks=None, outlinks=None): def HITS(query): """The HITS algorithm for computing hubs and authorities with respect to a query.""" - pages = expand_pages(relevant_pages(query)) # in order to 'map' faithfully to pseudocode we - for p in pages.values(): # won't pass the list of pages as an argument + pages = expand_pages(relevant_pages(query)) + for p in pages.values(): p.authority = 1 p.hub = 1 while True: # repeat until... convergence authority = {p: pages[p].authority for p in pages} hub = {p: pages[p].hub for p in pages} for p in pages: - pages[p].authority = sum(hub[x] for x in getInlinks(pages[p])) # p.authority ← ∑i Inlinki(p).Hub - pages[p].hub = sum(authority[x] for x in getOutlinks(pages[p])) # p.hub ← ∑i Outlinki(p).Authority + # p.authority ← ∑i Inlinki(p).Hub + pages[p].authority = sum(hub[x] for x in getInlinks(pages[p])) + # p.hub ← ∑i Outlinki(p).Authority + pages[p].hub = sum(authority[x] for x in getOutlinks(pages[p])) normalize(pages) if convergence(): break diff --git a/planning.py b/planning.py index 89c963c01..da00ee5d5 100644 --- a/planning.py +++ b/planning.py @@ -663,9 +663,8 @@ def act(self, action): if list_action is None: raise Exception("Action '{}' not found".format(action.name)) list_action.do_action(self.jobs, self.resources, self.kb, args) - # print(self.resources) - - def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA themselves ... + + def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA themselves ... """ state is a Problem, containing the current state kb library is a dictionary containing details for every possible refinement. eg: @@ -709,24 +708,23 @@ def refinements(hla, state, library): # TODO - refinements may be (multiple) HLA } """ e = Expr(hla.name, hla.args) - indices = [i for i,x in enumerate(library["HLA"]) if expr(x).op == hla.name] + indices = [i for i, x in enumerate(library["HLA"]) if expr(x).op == hla.name] for i in indices: - action = HLA(expr(library["steps"][i][0]), [ # TODO multiple refinements + action = HLA(expr(library["steps"][i][0]), [ # TODO multiple refinements [expr(x) for x in library["precond_pos"][i]], [expr(x) for x in library["precond_neg"][i]] - ], + ], [ [expr(x) for x in library["effect_pos"][i]], [expr(x) for x in library["effect_neg"][i]] ]) if action.check_precond(state.kb, action.args): yield action - + def hierarchical_search(problem, hierarchy): """ [Figure 11.5] 'Hierarchical Search, a Breadth First Search implementation of Hierarchical Forward Planning Search' - The problem is a real-world prodlem defined by the problem class, and the hierarchy is a dictionary of HLA - refinements (see refinements generator for details) """ @@ -734,14 +732,14 @@ def hierarchical_search(problem, hierarchy): frontier = FIFOQueue() frontier.append(act) while(True): - if not frontier: #(len(frontier)==0): + if not frontier: return None plan = frontier.pop() print(plan.state.name) - hla = plan.state #first_or_null(plan) + hla = plan.state # first_or_null(plan) prefix = None if plan.parent: - prefix = plan.parent.state.action #prefix, suffix = subseq(plan.state, hla) + prefix = plan.parent.state.action # prefix, suffix = subseq(plan.state, hla) outcome = Problem.result(problem, prefix) if hla is None: if outcome.goal_test(): @@ -864,4 +862,3 @@ def goal_test(kb): return Problem(init, [add_engine1, add_engine2, add_wheels1, add_wheels2, inspect1, inspect2], goal_test, [job_group1, job_group2], resources) - diff --git a/search.py b/search.py index 428648614..d104d7793 100644 --- a/search.py +++ b/search.py @@ -6,8 +6,7 @@ from utils import ( is_in, argmin, argmax, argmax_random_tie, probability, weighted_sampler, - weighted_sample_with_replacement, memoize, print_table, DataFile, Stack, - FIFOQueue, PriorityQueue, name + memoize, print_table, DataFile, Stack, FIFOQueue, PriorityQueue, name ) from grid import distance @@ -419,7 +418,7 @@ def or_search(state, problem, path): return [action, plan] def and_search(states, problem, path): - """Returns plan in form of dictionary where we take action plan[s] if we reach state s.""" # noqa + """Returns plan in form of dictionary where we take action plan[s] if we reach state s.""" plan = {} for s in states: plan[s] = or_search(s, problem, path) @@ -461,8 +460,8 @@ def __call__(self, percept): if len(self.unbacktracked[s1]) == 0: self.a = None else: - # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) # noqa - unbacktracked_pop = self.unbacktracked[s1].pop(0) # noqa + # else a <- an action b such that result[s', b] = POP(unbacktracked[s']) + unbacktracked_pop = self.unbacktracked[s1].pop(0) for (s, b) in self.result.keys(): if self.result[(s, b)] == unbacktracked_pop: self.a = b @@ -546,7 +545,7 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept # an action b in problem.actions(s1) that minimizes costs self.a = argmin(self.problem.actions(s1), - key=lambda b:self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H)) + key=lambda b: self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H)) self.s = s1 return self.a @@ -573,17 +572,17 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): """Call genetic_algorithm on the appropriate parts of a problem. This requires the problem to have states that can mate and mutate, plus a value method that scores states.""" - + # NOTE: This is not tested and might not work. # TODO: Use this function to make Problems work with genetic_algorithm. - + s = problem.initial_state states = [problem.result(s, a) for a in problem.actions(s)] random.shuffle(states) return genetic_algorithm(states[:n], problem.value, ngen, pmut) -def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1): +def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1): # noqa """[Figure 4.8]""" for i in range(ngen): new_population = [] @@ -954,7 +953,7 @@ def print_boggle(board): print() -def boggle_neighbors(n2, cache={}): # noqa +def boggle_neighbors(n2, cache={}): """Return a list of lists, where the i-th element is the list of indexes for the neighbors of square i.""" if cache.get(n2): diff --git a/tests/test_csp.py b/tests/test_csp.py index 301fd643d..9c4804c3d 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -1,5 +1,5 @@ import pytest -from csp import * # noqa +from csp import * def test_csp_assign(): diff --git a/tests/test_games.py b/tests/test_games.py index 35df9c827..5dcf0af07 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -5,7 +5,7 @@ import pytest -from games import * # noqa +from games import * # Creating the game instances f52 = Fig52Game() diff --git a/tests/test_grid.py b/tests/test_grid.py index aad9ebc91..6cd5f6d24 100644 --- a/tests/test_grid.py +++ b/tests/test_grid.py @@ -1,5 +1,5 @@ import pytest -from grid import * # noqa +from grid import * def compare_list(x, y): diff --git a/tests/test_logic.py b/tests/test_logic.py index 5ae9189a9..be172e664 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -1,5 +1,5 @@ import pytest -from logic import * # noqa +from logic import * from utils import expr_handle_infix_ops, count, Symbol diff --git a/tests/test_mdp.py b/tests/test_mdp.py index f5cb40510..dc975c7f1 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -1,4 +1,4 @@ -from mdp import * # noqa +from mdp import * def test_value_iteration(): diff --git a/tests/test_planning.py b/tests/test_planning.py index e9c639c95..2c355f54c 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -1,4 +1,4 @@ -from planning import * # noqa +from planning import * from utils import expr from logic import FolKB diff --git a/tests/test_probability.py b/tests/test_probability.py index 9f8ed5cd1..cfffee5bd 100644 --- a/tests/test_probability.py +++ b/tests/test_probability.py @@ -1,5 +1,5 @@ import random -from probability import * # noqa +from probability import * from utils import rounder @@ -183,7 +183,7 @@ def test_particle_filtering(): >>> P['rain'] #doctest:+ELLIPSIS 0.2... -# A Joint Probability Distribution is dealt with like this [Figure 13.3]: # noqa +# A Joint Probability Distribution is dealt with like this [Figure 13.3]: >>> P = JointProbDist(['Toothache', 'Cavity', 'Catch']) >>> T, F = True, False >>> P[T, T, T] = 0.108; P[T, T, F] = 0.012; P[F, T, T] = 0.072; P[F, T, F] = 0.008 diff --git a/tests/test_search.py b/tests/test_search.py index d50eacfe1..ebc02b5ab 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -1,5 +1,5 @@ import pytest -from search import * # noqa +from search import * romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) diff --git a/tests/test_text.py b/tests/test_text.py index ac1f9c996..757e6fe17 100644 --- a/tests/test_text.py +++ b/tests/test_text.py @@ -2,7 +2,7 @@ import os import random -from text import * # noqa +from text import * from utils import isclose, DataFile @@ -304,7 +304,7 @@ def test_bigrams(): >>> P3.samples(20) 'flatland by edwin a abbott 1884 to the wake of a certificate from nature herself proving the equal sided triangle' -""" # noqa +""" if __name__ == '__main__': pytest.main() diff --git a/tests/test_utils.py b/tests/test_utils.py index d158833d0..90548069b 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,5 +1,5 @@ import pytest -from utils import * # noqa +from utils import * import random def test_removeall_list(): diff --git a/text.py b/text.py index 2faac1049..3cce44e6d 100644 --- a/text.py +++ b/text.py @@ -26,7 +26,6 @@ def samples(self, n): return ' '.join(self.sample() for i in range(n)) - class NgramTextModel(CountingProbDist): """This is a discrete probability distribution over n-tuples of words. @@ -80,7 +79,7 @@ def samples(self, nwords): class NgramCharModel(NgramTextModel): def add_empty(self, words, n): - return ' ' * (n - 1) + words + return ' ' * (n - 1) + words def add_sequence(self, words): for word in words: @@ -362,14 +361,13 @@ def decode(self, ciphertext): solution.state[' '] = ' ' return translate(self.ciphertext, lambda c: solution.state[c]) - def score(self, code): """Score is product of word scores, unigram scores, and bigram scores. This can get very small, so we use logs and exp.""" # remake code dictionary to contain translation for all characters full_code = code.copy() - full_code.update({x:x for x in self.chardomain if x not in code}) + full_code.update({x: x for x in self.chardomain if x not in code}) full_code[' '] = ' ' text = translate(self.ciphertext, lambda c: full_code[c]) diff --git a/utils.py b/utils.py index 5afa43760..b67153999 100644 --- a/utils.py +++ b/utils.py @@ -7,6 +7,7 @@ import os.path import random import math +import functools # ______________________________________________________________________________ # Functions on Sequences and Iterables @@ -258,6 +259,7 @@ def step(x): """Return activation value of x with sign function""" return 1 if x >= 0 else 0 + def gaussian(mean, st_dev, x): """Given the mean and standard deviation of a distribution, it returns the probability of x.""" return 1/(math.sqrt(2*math.pi)*st_dev)*math.e**(-0.5*(float(x-mean)/st_dev)**2) @@ -656,7 +658,7 @@ def extend(self, items): def pop(self): if len(self.queue) > 0: return self.queue.popleft() - else : + else: raise Exception('FIFOQueue is empty') def __len__(self): From ff8fc03843a1699c3f089833d2a836f59d639e93 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Wed, 24 May 2017 10:46:16 +0530 Subject: [PATCH 502/513] Added PermutationDecoder to notebook (#507) --- text.ipynb | 58 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/text.ipynb b/text.ipynb index a1b059384..0edb43b05 100644 --- a/text.ipynb +++ b/text.ipynb @@ -364,6 +364,64 @@ "decoded_message = decoder.decode(ciphertext)\n", "print('The decoded message is', '\"' + decoded_message + '\"')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Permutation Decoder\n", + "Now let us try to decode messages encrypted by a general monoalphabetic substitution cipher. The letters in the alphabet can be replaced by any permutation of letters. For example if the alpahbet consisted of `{A B C}` then it can be replaced by `{A C B}`, `{B A C}`, `{B C A}`, `{C A B}`, `{C B A}` or even `{A B C}` itself. Suppose we choose the permutation `{C B A}`, then the plain text `\"CAB BA AAC\"` would become `\"ACB BC CCA\"`. We can see that Caesar cipher is also a form of permutation cipher where the permutation is a cyclic permutation. Unlike the Caesar cipher, it is infeasible to try all possible permutations. The number of possible permutations in Latin alphabet is `26!` which is of the order $10^{26}$. We use graph search algorithms to search for a 'good' permutation." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource PermutationDecoder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each state/node in the graph is represented as a letter-to-letter map. If there no mapping for a letter it means the letter is unchanged in the permutation. These maps are stored as dictionaries. Each dictionary is a 'potential' permutation. We use the word 'potential' because every dictionary doesn't necessarily represent a valid permutation since a permutation cannot have repeating elements. For example the dictionary `{'A': 'B', 'C': 'X'}` is invalid because `'A'` is replaced by `'B'`, but so is `'B'` because the dictionary doesn't have a mapping for `'B'`. Two dictionaries can also represent the same permutation e.g. `{'A': 'C', 'C': 'A'}` and `{'A': 'C', 'B': 'B', 'C': 'A'}` represent the same permutation where `'A'` and `'C'` are interchanged and all other letters remain unaltered. To ensure we get a valid permutation a goal state must map all letters in the alphabet. We also prevent repetions in the permutation by allowing only those actions which go to new state/node in which the newly added letter to the dictionary maps to previously unmapped letter. These two rules togeter ensure that the dictionary of a goal state will represent a valid permutation.\n", + "The score of a state is determined using word scores, unigram scores, and bigram scores. Experiment with different weightages for word, unigram and bigram scores and see how they affect the decoding." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"ahed world\" decodes to \"shed could\"\n", + "\"ahed woxld\" decodes to \"shew atiow\"\n" + ] + } + ], + "source": [ + "ciphertexts = ['ahed world', 'ahed woxld']\n", + "\n", + "pd = PermutationDecoder(canonicalize(flatland))\n", + "for ctext in ciphertexts:\n", + " print('\"{}\" decodes to \"{}\"'.format(ctext, pd.decode(ctext)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As evident from the above example, permutation decoding using best first search is sensitive to initial text. This is because not only the final dictionary, with substitutions for all letters, must have good score but so must the intermediate dictionaries. You could think of it as performing a local search by finding substitutons for each letter one by one. We could get very different results by changing even a single letter because that letter could be a deciding factor for selecting substitution in early stages which snowballs and affects the later stages. To make the search better we can use different definition of score in different stages and optimize on which letter to substitute first." + ] } ], "metadata": { From 7de29676c6990caae410c0bc00be1fd31c02e789 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Wed, 24 May 2017 10:47:15 +0530 Subject: [PATCH 503/513] Planning notebook (#506) * start planning notebook * reorder cell execution order * incorporating suggestions --- planning.ipynb | 315 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 312 insertions(+), 3 deletions(-) diff --git a/planning.ipynb b/planning.ipynb index d5a5eb25d..37461ee9b 100644 --- a/planning.ipynb +++ b/planning.ipynb @@ -1,16 +1,325 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Planning: planning.py; chapters 10-11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook describes the [planning.py](https://github.com/aimacode/aima-python/blob/master/planning.py) module, which covers Chapters 10 (Classical Planning) and 11 (Planning and Acting in the Real World) of *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu)*. See the [intro notebook](https://github.com/aimacode/aima-python/blob/master/intro.ipynb) for instructions.\n", + "\n", + "We'll start by looking at `PDDL` and `Action` data types for defining problems and actions. Then, we will see how to use them by trying to plan a trip from *Sibiu* to *Bucharest* across the familiar map of Romania, from [search.ipynb](https://github.com/aimacode/aima-python/blob/master/search.ipynb). Finally, we will look at the implementation of the GraphPlan algorithm.\n", + "\n", + "The first step is to load the code:" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from planning import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To be able to model a planning problem properly, it is essential to be able to represent an Action. Each action we model requires at least three things:\n", + "* preconditions that the action must meet\n", + "* the effects of executing the action\n", + "* some expression that represents the action" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Planning actions have been modelled using the `Action` class. Let's look at the source to see how the internal details of an action are implemented in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "import planning" + "%psource Action" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is interesting to see the way preconditions and effects are represented here. Instead of just being a list of expressions each, they consist of two lists - `precond_pos` and `precond_neg`. This is to work around the fact that PDDL doesn't allow for negations. Thus, for each precondition, we maintain a seperate list of those preconditions that must hold true, and those whose negations must hold true. Similarly, instead of having a single list of expressions that are the result of executing an action, we have two. The first (`effect_add`) contains all the expressions that will evaluate to true if the action is executed, and the the second (`effect_neg`) contains all those expressions that would be false if the action is executed (ie. their negations would be true).\n", + "\n", + "The constructor parameters, however combine the two precondition lists into a single `precond` parameter, and the effect lists into a single `effect` parameter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `PDDL` class is used to represent planning problems in this module. The following attributes are essential to be able to define a problem:\n", + "* a goal test\n", + "* an initial state\n", + "* a set of viable actions that can be executed in the search space of the problem\n", + "\n", + "View the source to see how the Python code tries to realise these." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%psource PDDL" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `initial_state` attribute is a list of `Expr` expressions that forms the initial knowledge base for the problem. Next, `actions` contains a list of `Action` objects that may be executed in the search space of the problem. Lastly, we pass a `goal_test` function as a parameter - this typically takes a knowledge base as a parameter, and returns whether or not the goal has been reached." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets try to define a planning problem using these tools. Since we already know about the map of Romania, lets see if we can plan a trip across a simplified map of Romania.\n", + "\n", + "Here is our simplified map definition:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from utils import *\n", + "# this imports the required expr so we can create our knowledge base\n", + "\n", + "knowledge_base = [\n", + " expr(\"Connected(Bucharest,Pitesti)\"),\n", + " expr(\"Connected(Pitesti,Rimnicu)\"),\n", + " expr(\"Connected(Rimnicu,Sibiu)\"),\n", + " expr(\"Connected(Sibiu,Fagaras)\"),\n", + " expr(\"Connected(Fagaras,Bucharest)\"),\n", + " expr(\"Connected(Pitesti,Craiova)\"),\n", + " expr(\"Connected(Craiova,Rimnicu)\")\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add some logic propositions to complete our knowledge about travelling around the map. These are the typical symmetry and transitivity properties of connections on a map. We can now be sure that our `knowledge_base` understands what it truly means for two locations to be connected in the sense usually meant by humans when we use the term.\n", + "\n", + "Let's also add our starting location - *Sibiu* to the map." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knowledge_base.extend([\n", + " expr(\"Connected(x,y) ==> Connected(y,x)\"),\n", + " expr(\"Connected(x,y) & Connected(y,z) ==> Connected(x,z)\"),\n", + " expr(\"At(Sibiu)\")\n", + " ])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a complete knowledge base, which can be seen like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Connected(Bucharest, Pitesti),\n", + " Connected(Pitesti, Rimnicu),\n", + " Connected(Rimnicu, Sibiu),\n", + " Connected(Sibiu, Fagaras),\n", + " Connected(Fagaras, Bucharest),\n", + " Connected(Pitesti, Craiova),\n", + " Connected(Craiova, Rimnicu),\n", + " (Connected(x, y) ==> Connected(y, x)),\n", + " ((Connected(x, y) & Connected(y, z)) ==> Connected(x, z)),\n", + " At(Sibiu)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knowledge_base" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define possible actions to our problem. We know that we can drive between any connected places. But, as is evident from [this](https://en.wikipedia.org/wiki/List_of_airports_in_Romania) list of Romanian airports, we can also fly directly between Sibiu, Bucharest, and Craiova.\n", + "\n", + "We can define these flight actions like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Sibiu to Bucharest\n", + "precond_pos = [expr('At(Sibiu)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Bucharest)')]\n", + "effect_rem = [expr('At(Sibiu)')]\n", + "fly_s_b = Action(expr('Fly(Sibiu, Bucharest)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n", + "\n", + "#Bucharest to Sibiu\n", + "precond_pos = [expr('At(Bucharest)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Sibiu)')]\n", + "effect_rem = [expr('At(Bucharest)')]\n", + "fly_b_s = Action(expr('Fly(Bucharest, Sibiu)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n", + "\n", + "#Sibiu to Craiova\n", + "precond_pos = [expr('At(Sibiu)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Craiova)')]\n", + "effect_rem = [expr('At(Sibiu)')]\n", + "fly_s_c = Action(expr('Fly(Sibiu, Craiova)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n", + "\n", + "#Craiova to Sibiu\n", + "precond_pos = [expr('At(Craiova)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Sibiu)')]\n", + "effect_rem = [expr('At(Craiova)')]\n", + "fly_c_s = Action(expr('Fly(Craiova, Sibiu)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n", + "\n", + "#Bucharest to Craiova\n", + "precond_pos = [expr('At(Bucharest)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Craiova)')]\n", + "effect_rem = [expr('At(Bucharest)')]\n", + "fly_b_c = Action(expr('Fly(Bucharest, Craiova)'), [precond_pos, precond_neg], [effect_add, effect_rem])\n", + "\n", + "#Craiova to Bucharest\n", + "precond_pos = [expr('At(Craiova)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(Bucharest)')]\n", + "effect_rem = [expr('At(Craiova)')]\n", + "fly_c_b = Action(expr('Fly(Craiova, Bucharest)'), [precond_pos, precond_neg], [effect_add, effect_rem])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the drive actions like this." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Drive\n", + "precond_pos = [expr('At(x)')]\n", + "precond_neg = []\n", + "effect_add = [expr('At(y)')]\n", + "effect_rem = [expr('At(x)')]\n", + "drive = Action(expr('Drive(x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can define a a function that will tell us when we have reached our destination, Bucharest." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def goal_test(kb):\n", + " return kb.ask(expr(\"At(Bucharest)\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, with all the components in place, we can define the planning problem." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "prob = PDDL(knowledge_base, [fly_s_b, fly_b_s, fly_s_c, fly_c_s, fly_b_c, fly_c_b, drive], goal_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -37,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.4.3" } }, "nbformat": 4, From 7bebc1b9bcd66957e1bde011a1e6ba1226b2fda7 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Wed, 24 May 2017 08:22:39 +0300 Subject: [PATCH 504/513] Implementation: Transition Model for MDP (#445) * Update test_mdp.py * Update mdp.py --- mdp.py | 33 +++++++++++++++++++-------------- tests/test_mdp.py | 14 ++++++++++++++ 2 files changed, 33 insertions(+), 14 deletions(-) diff --git a/mdp.py b/mdp.py index aaf1d10a5..833c4d9fd 100644 --- a/mdp.py +++ b/mdp.py @@ -1,9 +1,9 @@ """Markov Decision Processes (Chapter 17) First we define an MDP, and the special case of a GridMDP, in which -states are laid out in a 2-dimensional grid. We also represent a policy +states are laid out in a 2-dimensional grid. We also represent a policy as a dictionary of {state:action} pairs, and a Utility function as a -dictionary of {state:number} pairs. We then define the value_iteration +dictionary of {state:number} pairs. We then define the value_iteration and policy_iteration algorithms.""" from utils import argmax, vector_add @@ -17,32 +17,37 @@ class MDP: """A Markov Decision Process, defined by an initial state, transition model, and reward function. We also keep track of a gamma value, for use by algorithms. The transition model is represented somewhat differently from - the text. Instead of P(s' | s, a) being a probability number for each + the text. Instead of P(s' | s, a) being a probability number for each state/state/action triplet, we instead have T(s, a) return a - list of (p, s') pairs. We also keep track of the possible states, + list of (p, s') pairs. We also keep track of the possible states, terminal states, and actions for each state. [page 646]""" - def __init__(self, init, actlist, terminals, gamma=.9): + def __init__(self, init, actlist, terminals, transitions={}, states=set(), gamma=.9): + if not (0 <= gamma < 1): + raise ValueError("An MDP must have 0 <= gamma < 1") + self.init = init self.actlist = actlist self.terminals = terminals - if not (0 <= gamma < 1): - raise ValueError("An MDP must have 0 <= gamma < 1") + self.transitions = transitions + self.states = states self.gamma = gamma - self.states = set() self.reward = {} def R(self, state): - "Return a numeric reward for this state." + """Return a numeric reward for this state.""" return self.reward[state] def T(self, state, action): - """Transition model. From a state and an action, return a list + """Transition model. From a state and an action, return a list of (probability, result-state) pairs.""" - raise NotImplementedError + if(self.transitions == {}): + raise ValueError("Transition model is missing") + else: + return self.transitions[state][action] def actions(self, state): - """Set of actions that can be performed in this state. By default, a + """Set of actions that can be performed in this state. By default, a fixed list of actions, except for terminal states. Override this method if you need to specialize by state.""" if state in self.terminals: @@ -53,9 +58,9 @@ def actions(self, state): class GridMDP(MDP): - """A two-dimensional grid MDP, as in [Figure 17.1]. All you have to do is + """A two-dimensional grid MDP, as in [Figure 17.1]. All you have to do is specify the grid as a list of lists of rewards; use None for an obstacle - (unreachable state). Also, you should specify the terminal states. + (unreachable state). Also, you should specify the terminal states. An action is an (x, y) unit vector; e.g. (1, 0) means move east.""" def __init__(self, grid, terminals, init=(0, 0), gamma=.9): diff --git a/tests/test_mdp.py b/tests/test_mdp.py index dc975c7f1..b27c1af71 100644 --- a/tests/test_mdp.py +++ b/tests/test_mdp.py @@ -25,3 +25,17 @@ def test_best_policy(): assert sequential_decision_environment.to_arrows(pi) == [['>', '>', '>', '.'], ['^', None, '^', '.'], ['^', '>', '^', '<']] + + +def test_transition_model(): + transition_model = { + "A": {"a1": (0.3, "B"), "a2": (0.7, "C")}, + "B": {"a1": (0.5, "B"), "a2": (0.5, "A")}, + "C": {"a1": (0.9, "A"), "a2": (0.1, "B")}, + } + + mdp = MDP(init="A", actlist={"a1","a2"}, terminals={"C"}, states={"A","B","C"}, transitions=transition_model) + + assert mdp.T("A","a1") == (0.3, "B") + assert mdp.T("B","a2") == (0.5, "A") + assert mdp.T("C","a1") == (0.9, "A") From db049ce61850d272b513ae3710dbdd8622bc7c3f Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 28 May 2017 04:59:48 +0300 Subject: [PATCH 505/513] RL Fixes (Fixing Build) (#519) * Update rl.py * Update mdp.py * Minor changed to rl notebook --- mdp.py | 7 +- rl.ipynb | 210 +++++++++++++++++++++++-------------------------------- rl.py | 5 +- 3 files changed, 93 insertions(+), 129 deletions(-) diff --git a/mdp.py b/mdp.py index 833c4d9fd..cbb48e874 100644 --- a/mdp.py +++ b/mdp.py @@ -22,15 +22,18 @@ class MDP: list of (p, s') pairs. We also keep track of the possible states, terminal states, and actions for each state. [page 646]""" - def __init__(self, init, actlist, terminals, transitions={}, states=set(), gamma=.9): + def __init__(self, init, actlist, terminals, transitions={}, states=None, gamma=.9): if not (0 <= gamma < 1): raise ValueError("An MDP must have 0 <= gamma < 1") + if states: + self.states = states + else: + self.states = set() self.init = init self.actlist = actlist self.terminals = terminals self.transitions = transitions - self.states = states self.gamma = gamma self.reward = {} diff --git a/rl.ipynb b/rl.ipynb index 103c32e9e..5bff1d91d 100644 --- a/rl.ipynb +++ b/rl.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# Reinforcement Learning\n", "\n", @@ -81,35 +79,13 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "from mdp import sequential_decision_environment" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequential_decision_environment" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -119,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -147,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -165,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -183,10 +159,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -209,16 +183,14 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{(0, 1): 0.4496668011879283, (1, 2): 0.619085803445832, (3, 2): 1, (0, 0): 0.32062531035042224, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.235638474671875, (3, 1): -1, (2, 2): 0.7597530664991547, (2, 1): 0.4275522091676434, (0, 2): 0.5333144285450669}\n" + "{(0, 1): 0.3892840731173828, (1, 2): 0.6211579621949068, (3, 2): 1, (0, 0): 0.3022330060485855, (2, 0): 0.0, (3, 0): 0.0, (1, 0): 0.18020445259687815, (3, 1): -1, (2, 2): 0.822969605478094, (2, 1): -0.8456690895152308, (0, 2): 0.49454878907979766}\n" ] } ], @@ -237,9 +209,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -270,16 +242,14 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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LZOLFweMtB+cSt2XCgAF2PRIHUWyEQhzi3UolJel7aZSVmesnWQsx6Faqr7ff\nHTrETpvsW/Wp3EpBcfBupUwtB99TyuctaDmEmaDI/uIXVsYLFzb82st8UlaW2m3n8/fxx7FuJ285\nLF9u944fj5EpJSU25fRJGY/2EaJpCIU4JHMrNSQOmzcndz0lcytVVsbOBBnvVho6NPpKQEi0HLZt\ny9xy8Of2bov4AHVYCYrs1Kn2vWRJ4nTPzYUXh/j3knvLwbuUcpmp9KST8vN2NiHySajEISgIDbmV\namuTdzn04lBSEnUrpbIcvDiAvbDDs2FDrOXgxaFjx4Yth/hXWLYWcQhaDn4aaz+qvBjw+Zs/P/bV\ns8H3b6i3kQgToRAHX4H6IfiZuJUgueVQUmKC4OfR9+Kwfn20B1J8zCGYVm2tfXyswLup/Gja8nKz\nQlINZvMvsoknmxedt0SCAWnflTjVFCPNQUWFWZELF8b2nvKD4FK9n0OIlkooxCFYMYO11BuyHILH\nBfGVebt2lo53K23cGB2AFR9zgKhAeavBuxfKyqzCD07/DFGhiec734l9gY23JIrt7W35JhiQ9uJQ\nbJbDokVmwQXfqObdSk0dHxGi0IRqnIPvdlpbm7vlEBQHbzn4+EC8OLRvHz2PTyt+wr/SUlvnA8x+\n/1TjMI44InZunoberRwWkrmVis1ymDfP5kMK4t1K8RaFEC2dUFkOwYnbGiMO/o1kwZgDRFv7nTtb\npVBRkWg5NCQO/pyZvpmrNYlDXZ2VuXM29qCYLIc2bSzeEB8gD1oO8cIhREsmtOLQGLeSFwffWyle\nHHr0sPn1g2n472Aw2p8rG8shnu9+F849N7N9WzLerTR/vpXv1q3mXsrnYLfGUFFho7X9yGiPn37j\nG99IP2WLEC2NUIiDH+GaL8shWUAaot/t2sHJJ8eeJ2g5BH3SpaU2piI+5pCpOBxwADz+eGb7tmS8\nW+mjj+yay8qi7+AuBrw4pOpaW6iBeUI0F0Xy6DWOAQPgV7+KxhygsG6lYM+hhmIOjbUcWgverTRj\nhv2f7dtHJ8IrBioqYudUiqdYLBwh8kUoxKGkxLoRBqd8zqdbyVf26cTBfyezHBoTc2gteLfS7Nk2\nwWD79sVV4fr/TeIgWguhEAcwgQhOY5GPcQ6pLIdgxR58wRBEj/Vk21upteLdSitW2Gy5lZXFVeF6\n99YeeyTfnu20GUIUO6ESh23bosv5iDnEi4P/TmY5+HNv3Zo4AWBwnIPfP+yD2rLFu5VWrbLeYMVm\nOfjXeaaohB79AAATFElEQVQKOhdTXoXIB6ERBz+HkW/h5WMQ3JYtJjo+4L399la5J5vDyYvDli3R\n0dE+veAkf36/dPlrjXi3UrGKQ6rZWj3FlFch8kEoBsFB1HIoL7fWez4C0hs2WAvfp9WpU+IEaQ2J\nQ3xAOtmrMEXUreTfoldsbqWjjko95uTBB+GMM5o2P0IUmtCIg7ccKiqsAs5HQHrTpuh8SGCV1Zw5\nsfv7NNJZDhs2RMUh2dvOhJXx+vVWPu3b21QUxTRX0ejRqbf9+MdNlw8hmorQiIMPSMf3HkpGNm6l\ndu1ixzLET7XdUMwh3q0kyyE5FRUWjO7c2f7LUaOaO0dCtG5CF3NINn13PJlaDlu2WDo+raBF4MnU\nreQD0LIcklNRAZ9/Hp23SgjRvIRGHHzMwVf4jXUrBUc0N0Yc4ruyynJITnm5iYNeeiNEcRA6cciH\nW2nLltiup+nEoaGYQ1lZ7NgHiUNyKiqsDGU5CFEchEYc8uVW8iLj4wZBcUj25rhMYg4Q7Q6bLl+t\nGf9fhP1d2UK0FAotDscDc4C5wIgU+1QDHwIfATW5nsgHpPPhVoLk4pDMcvDr0lkOwf1+9zv48MP0\n19Ia8WWc6g15QoimpZDt2DLgPuAYYBnwPvAiMDuwTxUwCjgOWArkPNVaPgPSkLk4fO97MGUKTJgA\nd92VPOYQTK+qCvbfP7Nrak009IY8IUTTkk4cfha37ICVwFvAwgzSPgiYByyKLI8BTiFWHM4GnsOE\nAeCLDNJNSnxAOl/ikOyFPkEqK20a57/+FSZPTu1WSnYuEUXiIERxkc6t1AGoDHw6AAcCrwLDM0i7\nB7AksLw0si7IbkBn4A1gMnBeRrlOQnxAurFupaDIpLMcfHqbN9v5U7mVkgmLiOLLWOIgRHGQznIY\nmWJ9Z+A/wFMNpO0yOH8FcABwNNAOeBeYiMUoYjMzMpqd6upqqqurY7Y3hVspVQWfThzi0xPJ8f+F\nYg5C5E5NTQ01NTV5SSuXmMOqDPdbBgRnv+9F1H3kWYK5kjZFPhOA/WhAHJJRUmLf+ejKCrHiUFFh\ny/4cqdKT5ZA7cisJ0XjiG8433XRTzmnl0lvpSGB1BvtNxtxGfYE2wFlYQDrIP4EhWPC6HXAw0MD8\nl8mJdwflu7dSuso9KA6pYg4Sh/TIrSREcZHOcpiRZF0n4DPg/AzSrgMuA/6NVf6jsWD0JZHtD2Dd\nXF8FpgP1wF/IURx8qz5Tt1JJSXIB8ekExaGqCq68Mn16kN6tpIB0euRWEqK4SCcOJ8UtO+BLYH0W\n6b8S+QR5IG75zsinUfhKONOAdKrKOllvpYoKuPnm9OmB3EqNQZaDEMVFOnFY1FSZyAe+xe+tgoYs\nh4bEIRMLJJgeRN1KCkhnj2IOQhQXoZk+w4tDaalV1ukq9dLShsWhvNx+ZyMO9fVmOcS/JhQkDg0h\ny0GI4iI04uArdS8OjXUrlZVlLw7qypo7bdrAk09q7ikhioXQiIO3HHygubFupVwsBwWkc6ekBIZn\nMrRSCNEkhEYc4i2HdJX6DjvA0KHp0/HWRy4xB7mVhBAtndCIQ3zMIZ1bqWNHeOSR5Nvi3UqZtPj9\nuerq7E1vQSFwLnYfIYRoCYRGHLKxHNIR7PWUreWwaZOJSXAktZ/KWwghWhKhEYeg5ZBprCAZpaWW\nVjbpeHHYsiXR0qiryy0fQgjRnIROHHxAOlc3TlAQsrUctmxJ3F/iIIRoiYRGHPLlVvLH+9/ZiMPW\nrYn7y60khGiJhEYcshkEl46gIOQiDvEWi8RBCNESCY04ZDMIrqF0/LHpxkMESedWkjgIIVoioRGH\nYrAcFHMQQoSF0IlDPgLSQcshG3GorZXlIIQIB6ERh6BbaYcd7B0MuRCc0TVbyyH+N8hyEEK0TEIz\nzVnQrfTWW7mn0xjLAWQ5CCHCQSgth8amk2tXVpA4CCHCQWjEIWg5NIb4gHQ2vZVAAWkhRDgIjTh4\nUQjOa5RrOnIrCSFaO6ERh0JZDgpICyFaIxKHOPJtOdx9N7zySuPyJIQQTU1oeivlMyDdGMshfv/e\nve0jhBAtCVkOSdIJ9lZqbEBaCCFaIqERh3wGpHOdsjv+txBCtFRCIw6FiDnkw60khBAtkdCIQyFi\nDvkISAshREskNOJQCMthxx2hc+eGj8m2d5MQQhQ7oanKCiEO//hHZsd4UWjMu6uFEKKYKLTlcDww\nB5gLjEiz34FAHXBaricqREA6U7bbDk4+uXFThQshRDFRSHEoA+7DBGIvYDjQP8V+vwVeBXKu2gth\nOWRKeTk895wsByFEeCikOBwEzAMWAbXAGOCUJPv9D/AssLIxJytEQDpbFHMQQoSFQopDD2BJYHlp\nZF38PqcA90eWXa4nK8QguGyROAghwkIhq7JMKvq7gWsi+5aQxq00cuTIr39XV1dTXV0ds70QE+9l\ni2IOQojmpKamhpqamrykVUhxWAb0Ciz3wqyHIN/C3E0AOwAnYC6oF+MTC4pDMgoxZXe2yHIQQjQn\n8Q3nm266Kee0ClmVTQZ2A/oCnwJnYUHpIN8M/H4EGEsSYciEYrEcJA5CiDBQyKqsDrgM+DfWI2k0\nMBu4JLL9gXyerBCvCc0WiYMQIiwUuip7JfIJkkoULmzMiZqzK6tHMQchRFgIzfQZ6soqhBD5IzRV\nmbccGhuQ7tULamtzO1aD4IQQYSE0VVm+3Eqn5TyBhywHIUR4kFspj0gchBBhITTikC/LoTEoIC2E\nCAuhEQdZDkIIkT9CIw75Ckg3BomDECIshEYcZDkIIUT+CI04KOYghBD5Q+KQR2Q5CCHCQmjEAUwg\nmlMcNAhOCBEWQicOCkgLIUTjCZU4lJbKrSSEEPkgVOLQ3G4lBaSFEGFB4pBHKirsI4QQLZ1QOUFK\nS5s35nDnndCzZ/OdXwgh8kWoxKG5LYfdd2++cwshRD4JlVupuQPSQggRFkJVlTa35SCEEGEhVFWp\nLAchhMgPoapKm3sQnBBChIXQiYMsByGEaDyhqkrlVhJCiPwQqqpUloMQQuSHUFWlshyEECI/hKoq\nVUBaCCHyQ6jEQZaDEELkh1BVpYo5CCFEfmiKqvR4YA4wFxiRZPs5wDRgOvA2sG+uJ5I4CCFEfij0\nxHtlwH3AMcAy4H3gRWB2YJ8FwOHAV5iQPAgMzuVkcisJIUR+KHRVehAwD1gE1AJjgFPi9nkXEwaA\n94CcJ71WQFoIIfJDocWhB7AksLw0si4VFwEv53oyWQ5CCJEfCu1WclnseyTwQ+DQXE+mmIMQQuSH\nQovDMqBXYLkXZj3Esy/wFyzmsDpZQiNHjvz6d3V1NdXV1Qn7SByEEK2Zmpoaampq8pJWoT305cDH\nwNHAp8AkYDixAenewP8B5wITU6TjnGvYCNl1V3j2Wdh//8ZkWQghwkGJBWFzqucLbTnUAZcB/8Z6\nLo3GhOGSyPYHgBuATsD9kXW1WCA7axSQFkKI/NBSqtKMLIc99jDLYZ99miBHQghR5DTGcgiVh14x\nByGEyA+hqkrPOAO6d2/uXAghRMsnVG4lIYQQUeRWEkIIkVcK3VtJCCFyonPnzqxenXTYk4ijU6dO\nrFq1Kq9pyq0khChKSkpK0HOfGanKSm4lIYQQeUXiIIQQIgGJgxBCiAQkDkIIIRKQOAghRI5ce+21\n/PGPfyz4ecaOHcv3v//9gp8niMRBCCFyYOXKlTz++ONceumlAEycOJFjjz2WLl260LVrV84880yW\nL1+ecVrDhw+nR48eVFVVMWTIECZNmvT19pNOOomZM2cyY8aMglxLMiQOQgiRA48++ignnngibdu2\nBWDNmjVceumlLF68mMWLF9OhQwcuvPDCjNJav349Bx98MFOmTGH16tX84Ac/4MQTT2TDhg1f7zN8\n+HAefPDBglxLMjTOQQhRlBT7OIejjz6aiy66iLPPPjvp9ilTplBdXc3atWtzSr9jx47U1NQwcOBA\nAN555x3OPfdcFixYkLCvxjkIIUSRMGPGDPbYY4+U2ydMmMCAAQNySnvq1Kls3bqVXXfd9et1e+65\nJ4sWLWL9+vU5pZktmj5DCNFiydfLvXIxUNasWUOHDh2Sbps+fTo333wzL774Ytbprl27lvPOO4+R\nI0fGpO9/r1mzhsrKyuwznCUSByFEi6U5vU6dOnVi3bp1CevnzZvHsGHDuOeeezj00EOzSnPTpk2c\ndNJJHHLIIYwYMSJmmz9XVVVV7pnOArmVhBAiB/bdd18+/vjjmHWLFy/m2GOP5YYbbuCcc87JKr0t\nW7Zw6qmn0rt3bx544IGE7bNnz6Zv375NYjWAxEEIIXJi2LBhjB8//uvlZcuWcdRRR3HZZZdx8cUX\nJ+z/6KOPsssuuyRNq7a2ltNPP5127drx6KOPJt1n/PjxDBs2LC95zwSJgxBC5MD555/Pyy+/zObN\nmwF46KGHWLhw4dexgg4dOrD99tt/vf+SJUsYMmRI0rTeeecdXnrpJcaNG0dVVdXXx7/99ttf7zNm\nzBguueSSwl5UAHVlFUIUJcXelRXguuuuo2vXrlxxxRUN7nvcccdxzz33pO3hlIqxY8fyxBNPMGbM\nmKTbC9GVVeIghChKWoI4FAsa5yCEEKJJkDgIIYRIQOIghBAiAYmDEEKIBCQOQgghEtD0GUKIoqRT\np06+t41ogE6dOuU9zUKX/PHA3UAZ8BDw2yT73AOcAGwELgA+TLKPurIKIUSWFGtX1jLgPkwg9gKG\nA/3j9hkG7ArsBlwM3F/A/ISCmpqa5s5C0aCyiKKyiKKyyA+FFIeDgHnAIqAWGAOcErfPycBjkd/v\nAVXATgXMU4tHN34UlUUUlUUUlUV+KKQ49ACWBJaXRtY1tE/PAuZJCCFEBhRSHDINEsT7wxRcEEKI\nZqaQAenBwEgs5gBwLVBPbFD6z0AN5nICmAMcAXwel9Y8oF+B8imEEGFlPhbXLSrKsYz1BdoAU0ke\nkH458nswMLGpMieEEKL5OAH4GGv5XxtZd0nk47kvsn0acECT5k4IIYQQQggRDo7H4hBzgREN7BsG\nHsbiLTMC6zoD44BPgNew7r6ea7GymQMMbaI8NhW9gDeAmcBHwOWR9a2xPL6BdfWeCswCbousb41l\n4SnDBsyOjSy31rJYBEzHymJSZF3oy6IMczf1BSpIHrMIG4cBA4kVh98BV0d+jwBuj/zeCyuTCqyM\n5hGuubK6AftHfldi7sn+tN7yaBf5Lsdic0NovWUB8L/AE8CLkeXWWhYLMTEIEvqy+DbwamD5msgn\n7PQlVhzmEB0Y2C2yDNYCCFpTr2JB/bDyAnAMKo92wPvA3rTesugJvA4cSdRyaK1lsRDoErcuL2VR\nzKqRySC61sBORLv2fk70T++OlYknzOXTF7Oo3qP1lkcp1ur7nKi7rbWWxV3AL7Cu8Z7WWhYOE8rJ\nwI8j6/JSFsU8K6sGwyXiSF8uYSyzSuA54ApgXdy21lQe9ZibrSPwb6zVHKS1lMV3gBWYj706xT6t\npSwADgU+A3bE4gxz4rbnXBbFbDksw4KSnl7Eql5r4XPMNATYGXswILF8ekbWhYkKTBgex9xK0LrL\nA+Ar4CXgW7TOsjgEm5NtIfAUcBR2f7TGsgATBoCVwPPYnHahL4tMBtGFkb4kBqS9n/AaEoNLbYBd\nsLIK0+T3JcBfMRdCkNZYHjsQ7XGyHTABOJrWWRZBjiAac2iNZdEO6BD53R54G+uB1CrKItkgujDz\nFPApsBWLt1yI9UR4neTd0n6Jlc0c4LgmzWnhGYK5UqZiLoQPsa7NrbE89gGmYGUxHfO3Q+ssiyBH\nEO2t1BrLYhfsnpiKdff2dWRrLAshhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRoSayPfPcBhuc5\n7V/GLb+d5/SFEEIUCD8nUzXREbWZ0tD8Y/HzPQkhhGgh+Ap8IrAGG219BTa32B3YS1KmARdH9qsG\n3gT+SXQisxewmS8/Ijr75e1AXSS9xyPrvJVSEkl7Bjaq+cxA2jXAM8Bs4G+BfN6OzbY6LXKsEEKI\nAuLFITgXD5gYXBf53RZ7T0JfrAJfj7mhPJ0i39thFb5fjrcc/PL3sKkLSoCuwGJsMrRqTKC6R7a9\ng82s2YXYGTW3z/TihCgExTwrqxD5Jn6SsaHA+VjLfyI2J82ukW2TsArdcwU2h8272MyWuzVwriHA\nk9iUyCuA8cCBkeVJ2BxaLpJmH0wwNgOjge8Cm7K9OCHyicRBtHYuw14kNBDoh01YBrAhsE81Ngvq\nYOydCh9i73VOhyNRjPzc+VsC67ZhU5Nvw6ZbfhZ7Z8GrCNGMSBxEa2Id0SmOwV6a81OiQefdib6r\nOcj2wGqsZb8nsa9WrCV50PpN4CzsGdsROByzGFJNkdwemz3zFez9yPs1eDVCFJBifhOcEPnCt9in\nYS30qcAjwD1YjGEKVmmvwFw68W/PehW4FJiFTSH/bmDbg1jA+QPgvMBxz2PvQZ8WWfeLSPr9SXz7\nlsNE65+YRVICXJXz1QohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBChJn/D14FxN7T\nQhWsAAAAAElFTkSuQmCC\n", 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gn+0oYdaojEbjRRz3XDqJH5w/lmn2SPYxA61ANf2ENPc9uP6UE/jJRRMQgQsm\nhu9hdcHEgczITSczKYaLThzcqdqg8zcNd8F/z54x12lAb6uXkuOTbUVM+vk7/G1Rw8XM250Z4KnF\nu9wR+6+utO7/3t1wkE+3FXHl9KFh37dwRIQnbpzBg9dMIy7a32jAZ0cNS29odxOxgsPHW4u4bsaw\nDtVMHU5QqK5reN8+2lLovo9Nawofby1i88EKNwBV1AQ4crSOvy/b694grN9Xxvi73+az7Q0DDo8c\nraOqzhqDU1UX5Jtnd/zmrbMi9o01xgRE5NvAu4AfeMIYs0FE7gOWG2PmAz8A/ioi38fK5txojnUW\nq3ZwUihh00dRPrfbpjeV5IiP9rnpo3Dim1wEvbWK1Phod5h8uT3fj9Ng2hInAHnnw6kPGjI6OJNm\n0/KEDI3GU3REmqcP94isRGo9d00nDEhw21ZGZnUsLdVR//2lqWQmtfw+OO05OenxjdKAXt673UlD\nUt1g4NQUjlTVkxofTVl1PYfKa5nSQk58RGYit3u6iTptJWeMaUh33nPpRAAmD011x1U09ehX8wCr\nK224lFN7xLSSPvp0WzETBqe4EyS2t6bwxpoD1NSHeGXVPqYPT2NtQRn5dv//I0friI/x8/P5GwC4\ndOoQd7zGx1uKCBma9eJqy/R2do9uy1C7pjB1WBoDk2PdoPilTubmnVSpkz76bHsJN/5tmft8bZP3\n84XP95CRGMN1M4fx8ooCjtYG+PvyvVTXB7nnkonc98ZGHnx/KzX1Ieav2c+sURkEQ4bLHlrE9OFp\nfJpfzNnjspjQwXa/YxHR2zhjzFtYDcjebfd4ft4IhO9OEQHThqXxtVkncIud22x61wiNu6R600fe\n58OljxyJsY3vPrwD5FLjo3GyFxU1AXzSMGCpJT67WlNU2XgwT2sXw9Z4azltpS9a4g0mOenxjVJb\nJ2Qkcta4LBJj/e0e99BZbVX/Z+Sm88SinZwyMsPTtbjx33yr3baSHBvFuZ4cv/fveMaYTHdytclh\nUkfhzBwxgC9OzwnbDtCeEbHt7RETTkvpo+q6ICt2H+HG03PdwNH0IgbWe/Taqn3MmzaEaL8PY4w7\n5iQYMpw3YSDlNQG2F1ayKL+Y6x9b2uj1BUeq2HrIChiVtQFGZiUyKit8ii/SUuOjuf6U4Vw4eTCf\n7SjmvY2HOG1URqNZAzrCqV04c3+9bE+pcunUIfxrzf5GN0iF5TUs2FTILWeMcAN8ZW2Al5bvZUZu\nOqfaMxA0ZZCiAAAc7ElEQVSstedFc2oOC7cVsedwFQVHqggZ+LpnPrPu0K+mufD7hHsvm+w+Dlcn\nifE3dEmN8TQ0NzzvbzV9FN+kSuptGIr2i1tTqKgJkBQb1WaV2qmpNJ05MzOpk+kjT9k72y7h7V01\nNC2eYk/ZTshIICEmimtm9PwqbReeOJilPzmPgSlxlNnpuqZtClsOVZAaH83yn83B7/lbeXs3nTk2\nq8NBISEmij9cPfVYT6FTWkoffb7rMHXBEKePzqTavqh596mqC1AfMLy+Zh/3vL6BqvogXzn1BLYV\nVnLQk748e1wWq/eWsqvkaLNpTnLS4/nInjZlYEosh8prOTdMb63udP8VJwJw0vA0BiTGMnt055st\nvW0KVXUB3l5/gGvyhnH1jBwrKHjez3+utFZ3vHbGcPcmY+HWInYUHeUbZ45qljp2Op+8ZM/pFDIw\nJDXumMrbGT3d+6hHmTAJpKYNzU0DQGwb6aPE2JbjrHfG0/LqelLiW1+JzXkNNA8KGZ0MCt5aTltj\nJFqSndwQFDKTYon1BMLWzr8nOA35fn8LNYWDFYwbmEy039coQDvnkRDjd+/sR2Qmhl1UqbdpqRaw\nOL+YGL+PmbkDwqaYzn9wIVPve4+V9ih3Z2Dmx/ZFfnBqHNnJsUwcnEJ2cixFFbWs2tMwQdxl04ZQ\nUx/koy1F5KTHc7rdVfjcCT0bFByJsVHcPHuEO4FgZ3jTR+9vPERVXZArpjesm+J9z99ad4Bpw9IY\nkZno3mS8smofCTF+LpoymDR70svkuChm5KbzzoaD3PO6tVKiM3fVVXnDOtWudCx61ze4m4Xpndgo\nZRSuTSHG7ws7Z5IjoZVRwT4Rt6G5vCYQdjnMpqJaDAqdTR95g0Lnagrexmm/T5oN0OuNwrUpGGPY\ncqiCy8MMfnK+xBMGp7hTJrS3ltDTWmpTWLW3lElDU4iP8bvtLU5NwRjjjpp22goO29OSf77rMCMy\nE7l33iTqAiFEhMwkayqKsuoyxg1M5tKpg6msDVJWXc/i7cVcOX0ouRmJfLajhBkdXLa1N/M2NC/e\nXkxmUiwzcwe466/UBqxAuvdwFev2lfGTi8Y3eh1YXZqdz1d2cixzJw9iiT09zNOfWVOZ/PqKE3ll\nVUHYFQkjrV8HhXBio3xusIiJ8tE0SMdE+VrtK9xabxu/Txo1NKe00cgMDXe4TYNCWjtqGeF4A1pW\nJ2sbTXWmF0d3805X4jhQVkNFTYCxYe4cnZrCpCEpJMdGMWdCdsSnF+gqsWHaFIIhw4Z9ZW47TNMU\nk3ehqS32FCEFR6rZfLCc1XtLOWN0Jmd65rByer+FDPz4ovGcPS6bhz/Kpz5oqA8GmTkig0unDOYr\ns05otWZ9vHEu7hU19Xy8tYgLJw/C55OGmkK99X46YxMunGwtnOW9ZnhnD3jj9tmkxEdz92vr3XaY\nnPR4Th+dwewx3Zs2cvTroBB2nILf5zZAx4YJAG3dFbdWU/D7GmoKFTUBtwdIa5w8d8nROrLsKjs0\njKDtqGhPzSe9kz2YABbccabbCO68J+0Jcj3FeR+9NQVnOc9xYUbKpsZHc+HkQe5Kb499bUb3FLQL\nhGtT2FlcydG6ICfmNF78qS5o3dl6Vzpz1tr415r9bhfTpiPTvV2vnQ4F3prvySekIyIdHhzW28XF\nWO/bJ9uKqagJcO54q2txbHTjlN37Gw8xaUhK2AZtZywLNHT2uO+yycRG+3h2yR4unDyoWwaptaTv\nhPBOCNclNTba566JHC4AdCYozJkwkNvOHNmoTaGipt7tz98ab0P3SM/I487mtr15cyen2Rmjs5MZ\nafcoccqY1skulN3BZy+/6m1T2G3PIRVuRLffJzxyw8nHZerDGxQe+2QHP3l1HevsGVFPtFNgTXso\nbWwy5sAZb9HSYyelNnZgkjvNtHNTMDAlliGd7O7c28V4priJ8fs4w76bj3XbcYJU1gZYtaeUs8LM\nDhzj94VNG8fH+Ll82lB8AvOmdqz7blfrvbd23SBsl1S/361BtDSLamvCNbQ+9jWr7/lVjyxu3NDc\njgu7t5FpVHYSS+2RmV2Rx0+L75qLuNP4dm6YaRt6kyifr1FNYX9ZDTFRvk537+2tvG0Kv3pzE2B9\nXuKj/Yyyx440bYz2DkRLT4jmyulDWe1ZZazpqHOn95u3e63TccKpJfRF3vO6+YwR7vfd29C8ZHsJ\ngZBplv5Z9tM5rV4/8nIHsPLu83v85qpf1xTCzQUWE9WQPgqXC20rKLTa0OypKVTXB1vd1+Ht/eTt\n690VXzpnedJjlZOewDvfO4OfXTyh7Z17UNP1rveVVjM0Lb7PXcCcu1mn0ROsUbMTh6S4HQ1im6SY\nNu4vZ6o9MG/y0NRm7SdNP/eDU+M5aXhao7UQnJucrhp41tt9b07DYEU3yNaH+DS/mPhof7PxKFnJ\nsc0W72mqpwMC9POaQrhxClF+cYNFuK5gTWc9barVhmYRAqEQwZChPmja1UDr97QBjOriEcJdeTE8\n1vmLukOUTxqNU9hfWu0uxtOXOBco7zw8mw9UcKlnVlZvbaK4spbCilquzhvGmoIypuSkkpYQw/Kf\nzeFQeU2zsTfO61/9ZuNxpxMGJ3P5tCEtLhjV13jbS/w+IdovVNbW8+GWQmaOGHDctqf066AQLgXj\n7TYa7qLZVtqmtT7Ffp9QGzDuZFrtSQF5B1S1p2FatcyqqXl6H5XWuDnhvsS54K/3rBBXURto1Cbl\n1Cb2Hal2U0enjcpgzMAkTrcHS2UmxXZokGRCTBR/uvakYy5/b/f0TTPDvi9xUX7+ak9099OLenet\nuTX9Oig8ddNMvvCnhY1SCkJDr6Rw1/fW0kcv3Tar1d/n8wlB0zDDYvtqCp5pMo6hYVjZNQX7b10f\nDHGoouaYppPorZJjrc9J0+U2velH53P88Efb3dHKEwancNox9EjrL85sYXnZ2GgfFbUwcXAKF3Rg\n0arepl+3KYzOTuKN22c32uaThryzL0xNwf0yXT/d3faHL03ln/95WtilBb38Yi2rV2PncduT0/e2\nKThfdtU5fp80Ws/CmL5Z+4qP8RMX7Ws0NQU07mXlvblZlF9MRmLMMXVRVg1tkG0tLNXb9eugANbd\n0a4HLnZHDqYnRrttCq0FhYtOHOxumz0ms12TnPl9wrp9ZayzJ8DqaE2hqxqGH7jyRJ6+aWaXHOt4\n4m1T2G+P3u2LNQWg2Qyr0X5x1xaAxl2dD5XXckInlnZVjTmzAzftvnu86dfpI6+7L5nIdTOHk5Oe\n4KaPwrUPdGbsgsMJMt94doX9unb0PvKUwWnjSDrG+YWundnzk9X1BL+/oRa4v8wKCoP7YEMzWEHh\nQFkNo7IS2V50lBMyEhtNcdK0vcxZkEgdu5M0KPQNMVE+d3WnhppC8/3CB4X29TJoGmRi23Hn37S2\n8vI3ZpGTrnd1neEdp7C/1LqrG9KJda6PB+mJVqpxSk4a24uONmpkDucEDQpdpqemCe8q/T59FM5P\nLprA7NGZzAqzfGC4LqntrSk0DQpx7akpNJl8Ly93QKOpq1X7eccp7D1cRUZijDvwrq9x0kfOokAj\nw1yoPvjBWe7Pzip1qvNyMxIYkBjT7hXmeiutKYQxOjuJZ285Jexz4XoftfdD0CwotKOm0N3T5vZl\nVu8jq5F/W2GluzpaX+QEhUlDUvnNlSeG7TEzMiuJjMQYSo7WaU2hCyy446ywA2KPNxoUOuhYppfw\nS9Og0J42Ba3MdRWnpmCMYevBig4vEXk8cXoS5WYktNorLjU+mpKjdeRqQ/Mxa2mt8OONBoUO8qZz\n0hKiKa2qb2XvxprWKDra+0gdG2ecQsGRaipqw0+Z3VfMmzoYv4g7cV1LUhOiSY2P7hXTK6jeQYNC\nB2QmxTaaxuL9759FcZO1k1vTtKbQrhHNGhS6jN8nfLy1iD+8twW/TzgtTJtRXzE6O5nvzmk76OVm\nJLa5TrjqX/TT0AHLfzan0eOs5Ng278S8OlNTaLpGtOq8umAIY+C11fuZMyH7uO8l0hV+c+WJYWcL\nVv2XBoV2mDMhm12eycU6q+n1XRuau1dFTcD9ObuTS5H2NcfDqnmqe2lQaIeuWnWrac+E9oxvaJpy\nUp3nDQrpOo+UUmH1jeby44R3hs5ov7SrFnC893nuTSpqGjoFNJ0GQill0ZpCNwo2xIR2DVxzRPmE\n758/NgIl6l/qPWspaFBQKjwNCt3I26AX3YHxDvm/vigSxenXnGkglFKNafqoG3nXB9ZeRT1L++Ur\nFV5Eg4KIzBWRLSKSLyJ3tbDP1SKyUUQ2iMjzkSxPTwt5gkK49Z9V9wm3xKRSKoLpIxHxAw8B5wMF\nwDIRmW+M2ejZZwzwY+B0Y8wREcmOVHl6g4CnobnpRHcq8px5fhJi/AwfoNM6KBVOq0FBRO5osskA\nxcCnxpidbRx7JpBvjNlhH+tF4DJgo2ef/wAeMsYcATDGFHag7Mcdb0Ozpo+634I7zqKyNsAwDQhK\ntaitHEZyk38pQB7wtohc28ZrhwJ7PY8L7G1eY4GxIrJIRJaIyNxwBxKRW0VkuYgsLyoqauPX9l6N\nu6Rq+qi7pSfGaEBQqg2t1hSMMfeG2y4iA4AFwItd8PvHAGcDOcBCETnRGFPapByPAo8C5OXlHbdj\n8hs1NGv6SCnVC3XqdtUYcxho66q2DxjmeZxjb/MqAOYbY+rtdNRWrCDRJ3m7pOqU2Eqp3qhTVyYR\nOQc40sZuy4AxIjJCRGKAa4H5TfZ5DauWgIhkYqWTdnSmTMeDQFC7pCqlere2GprXYTUuew0A9gNf\nbe21xpiAiHwbeBfwA08YYzaIyH3AcmPMfPu5C0RkIxAEfmSMKencqfR+3ppC07WXlVKqN2irS+ol\nTR4boMQYc7Q9BzfGvAW81WTbPZ6fDXCH/a/PC3pnxNOYoJTqhdpqaN7dXQXpD7yzomr2SCnVG2lr\nZzf64zXTSI6z4rBoVUEp1QtpUOhGg1LjuMOe7VSbFJRSvZEGhW7m9DrShmalVG+kQaGb+e3xCRoT\nlFK9kQaFbubMbiEaFZRSvZAGhW7m1hR6uBxKKRWOBoVu5rQpaEVBKdUbaVDoZj5taFZK9WIaFLqZ\nW1Po4XIopVQ4GhS6mVND0IqCUqo30qDQzZxgoL2PlFK9kQaFbmbsmVI1JCileiMNCt3MmT1bKwpK\nqd5Ig0I3cybP1t5HSqneSINCN3MW2tGYoJTqjTQodDM3faStCkqpXkiDQjdz0kdaU1BK9UYaFLqZ\n2/tIo4JSqhfSoNBDonQ9TqVUL9TqGs2q682dPIjrTxnO9+0V2JRSqjfRoNDNYqP83H/FiT1dDKWU\nCkvTR0oppVwaFJRSSrk0KCillHJpUFBKKeXSoKCUUsoV0aAgInNFZIuI5IvIXa3s90URMSKSF8ny\nKKWUal3EgoKI+IGHgAuBicB1IjIxzH7JwHeBpZEqi1JKqfaJZE1hJpBvjNlhjKkDXgQuC7PfL4Hf\nAjURLItSSql2iGRQGArs9TwusLe5RGQ6MMwY82ZrBxKRW0VkuYgsLyoq6vqSKqWUAnqwoVlEfMCD\nwA/a2tcY86gxJs8Yk5eVlRX5wimlVD8VyaCwDxjmeZxjb3MkA5OBj0RkF3AqMF8bm5VSqudEMigs\nA8aIyAgRiQGuBeY7TxpjyowxmcaYXGNMLrAEmGeMWR7BMimllGpFxIKCMSYAfBt4F9gEvGSM2SAi\n94nIvEj9XqWUUp0X0VlSjTFvAW812XZPC/ueHcmyKKWUapuOaFZKKeXSoKCUUsqlQUEppZRLg4JS\nSimXBgWllFIuDQpKKaVcGhSUUkq5NCgopZRyaVBQSinl0qCglFLKpUFBKaWUS4OCUkoplwYFpZRS\nLg0KSimlXBoUlFJKuTQoKKWUcmlQUEop5dKgoJRSyqVBQSmllEuDglJKKZcGBaWUUi4NCkoppVwa\nFJRSSrk0KCillHJpUFBKKeXSoKCUUsqlQUEppZQrokFBROaKyBYRyReRu8I8f4eIbBSRtSLybxE5\nIZLlUUop1bqIBQUR8QMPARcCE4HrRGRik91WAXnGmCnAy8DvIlUepZRSbYtkTWEmkG+M2WGMqQNe\nBC7z7mCM+dAYU2U/XALkRLA8Siml2hDJoDAU2Ot5XGBva8nNwNsRLI9SSqk2RPV0AQBE5AYgDzir\nhedvBW4FGD58eDeWTCml+pdI1hT2AcM8j3PsbY2IyBzgp8A8Y0xtuAMZYx41xuQZY/KysrIiUlil\nlFKRDQrLgDEiMkJEYoBrgfneHUTkJOAvWAGhMIJlUUop1Q4RCwrGmADwbeBdYBPwkjFmg4jcJyLz\n7N1+DyQB/xCR1SIyv4XDKaWU6gYRbVMwxrwFvNVk2z2en+dE8vcrpZTqGB3RrJRSyqVBQSmllEuD\nglJKKZcGBaWUUi4NCkoppVwaFJRSSrk0KCillHJpUFBKKeXqFRPiKaVUV6uvr6egoICampqeLkq3\niouLIycnh+jo6E69XoOCUqpPKigoIDk5mdzcXESkp4vTLYwxlJSUUFBQwIgRIzp1DE0fKaX6pJqa\nGjIyMvpNQAAQETIyMo6pdqRBQSnVZ/WngOA41nPWoKCUUsqlQUEppSKkurqas846i2AwyOrVq5k1\naxaTJk1iypQp/P3vf2/z9Q8++CATJ05kypQpnHfeeezevRuAoqIi5s6dG5Eya1BQSqkIeeKJJ7jy\nyivx+/0kJCTw9NNPs2HDBt555x2+973vUVpa2urrTzrpJJYvX87atWu56qqruPPOOwHIyspi8ODB\nLFq0qMvLrL2PlFJ93r3/2sDG/eVdesyJQ1L4+aWTWt3nueee4/nnnwdg7Nix7vYhQ4aQnZ1NUVER\naWlpLb7+nHPOcX8+9dRTefbZZ93Hl19+Oc899xynn356Z08hLK0pKKVUBNTV1bFjxw5yc3ObPff5\n559TV1fHqFGj2n28xx9/nAsvvNB9nJeXxyeffNIVRW1EawpKqT6vrTv6SCguLg5bCzhw4ABf+cpX\neOqpp/D52ndf/uyzz7J8+XI+/vhjd1t2djb79+/vsvI6NCgopVQExMfHNxsvUF5ezsUXX8z999/P\nqaee2q7jLFiwgPvvv5+PP/6Y2NhYd3tNTQ3x8fFdWmbQ9JFSSkVEeno6wWDQDQx1dXVcccUVfPWr\nX+Wqq65qtO+Pf/xjXn311WbHWLVqFbfddhvz588nOzu70XNbt25l8uTJXV5uDQpKKRUhF1xwAZ9+\n+ikAL730EgsXLuTJJ59k2rRpTJs2jdWrVwOwbt06Bg0a1Oz1P/rRj6isrORLX/oS06ZNY968ee5z\nH374IRdffHGXl1nTR0opFSHf+ta3+OMf/8icOXO44YYbuOGGG8LuV19fz6xZs5ptX7BgQYvHnj9/\nPq+//nqXldWhNQWllIqQ6dOnc8455xAMBlvd79133+3QcYuKirjjjjtIT08/luKFpTUFpZSKoJtu\nuqnLj5mVlcXll1/e5ccFrSkopfowY0xPF6HbHes5a1BQSvVJcXFxlJSU9KvA4KynEBcX1+ljaPpI\nKdUn5eTkUFBQQFFRUU8XpVs5K691lgYFpVSfFB0d3enVx/qziKaPRGSuiGwRkXwRuSvM87Ei8nf7\n+aUikhvJ8iillGpdxIKCiPiBh4ALgYnAdSIyscluNwNHjDGjgT8Cv41UeZRSSrUtkjWFmUC+MWaH\nMaYOeBG4rMk+lwFP2T+/DJwn/XH9PKWU6iUi2aYwFNjreVwAnNLSPsaYgIiUARlAsXcnEbkVuNV+\nWCkiWzpZpsymx+4H9Jz7Bz3n/uFYzvmE9ux0XDQ0G2MeBR491uOIyHJjTF4XFOm4oefcP+g59w/d\ncc6RTB/tA4Z5HufY28LuIyJRQCpQEsEyKaWUakUkg8IyYIyIjBCRGOBaYH6TfeYDX7N/vgr4wPSn\nkSZKKdXLRCx9ZLcRfBt4F/ADTxhjNojIfcByY8x84HHgGRHJBw5jBY5IOuYU1HFIz7l/0HPuHyJ+\nzqI35koppRw695FSSimXBgWllFKufhEU2ppu43glIk+ISKGIrPdsGyAi74vINvv/dHu7iMj/2u/B\nWhGZ3nMl7zwRGSYiH4rIRhHZICLftbf32fMWkTgR+VxE1tjnfK+9fYQ9PUy+PV1MjL29z0wfIyJ+\nEVklIm/Yj/v0OYvILhFZJyKrRWS5va1bP9t9Pii0c7qN49WTwNwm2+4C/m2MGQP8234M1vmPsf/d\nCjzSTWXsagHgB8aYicCpwLfsv2dfPu9a4FxjzFRgGjBXRE7Fmhbmj/Y0MUewpo2BvjV9zHeBTZ7H\n/eGczzHGTPOMR+jez7Yxpk//A2YB73oe/xj4cU+XqwvPLxdY73m8BRhs/zwY2GL//BfgunD7Hc//\ngNeB8/vLeQMJwEqs2QGKgSh7u/s5x+rxN8v+OcreT3q67J041xysi+C5wBuA9INz3gVkNtnWrZ/t\nPl9TIPx0G0N7qCzdYaAx5oD980FgoP1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -300,16 +270,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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dukOkJFCHtMgZZHRKz46azTV1riGgTAAnk08W+kl+Cga50CkcpFTIOK309aav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\nCEhNX9+k9HkZRyl+6eveiHN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CUUl9LZUhd5f/tpbN8Fre+k/9//DyL3z04sy7PWid88+ih6o3bV1yTWHTSg/QZvCjt+Da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\ngVFmthyfYflE4C/U7X0mhLA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -342,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -369,14 +337,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "q_agent = QLearningAgent(sequential_decision_environment, Ne=5, Rplus=2, \n", - " alpha=lambda n: 60./(59+n))\n" + " alpha=lambda n: 60./(59+n))" ] }, { @@ -388,14 +356,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ "for i in range(200):\n", - " run_single_trial(q_agent,sequential_decision_environment)\n" + " run_single_trial(q_agent,sequential_decision_environment)" ] }, { @@ -412,56 +380,54 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(float,\n", - " {((0, 0), (-1, 0)): -0.07323076923076924,\n", - " ((0, 0), (0, -1)): -0.0759999433406361,\n", - " ((0, 0), (0, 1)): 0.2244371077466747,\n", - " ((0, 0), (1, 0)): -0.07085714285714287,\n", - " ((0, 1), (-1, 0)): -0.04883916667786259,\n", - " ((0, 1), (0, -1)): -0.05252175603090532,\n", - " ((0, 1), (0, 1)): 0.3396752416362625,\n", + " {((0, 0), (-1, 0)): -0.12953971401732597,\n", + " ((0, 0), (0, -1)): -0.12753699595470713,\n", + " ((0, 0), (0, 1)): -0.01158029172666495,\n", + " ((0, 0), (1, 0)): -0.13035841083471436,\n", + " ((0, 1), (-1, 0)): -0.04,\n", + " ((0, 1), (0, -1)): -0.1057916516323444,\n", + " ((0, 1), (0, 1)): 0.13072636267769677,\n", " ((0, 1), (1, 0)): -0.07323076923076924,\n", - " ((0, 2), (-1, 0)): -0.05158410382845185,\n", - " ((0, 2), (0, -1)): -0.04733337973118637,\n", - " ((0, 2), (0, 1)): -0.048398095611170026,\n", - " ((0, 2), (1, 0)): 0.4729172313717893,\n", - " ((1, 0), (-1, 0)): 0.14857758363326573,\n", - " ((1, 0), (0, -1)): -0.0759999433406361,\n", - " ((1, 0), (0, 1)): -0.07695450531425811,\n", - " ((1, 0), (1, 0)): -0.09719395035017139,\n", - " ((1, 2), (-1, 0)): 0.21593724199115555,\n", - " ((1, 2), (0, -1)): 0.26570820298073916,\n", - " ((1, 2), (0, 1)): 0.19612684250448048,\n", - " ((1, 2), (1, 0)): 0.6105607273543103,\n", - " ((2, 0), (-1, 0)): 0.06795076480003,\n", - " ((2, 0), (0, -1)): -0.11306695825372484,\n", - " ((2, 0), (0, 1)): -0.105596446586541,\n", - " ((2, 0), (1, 0)): -0.10409381636745853,\n", - " ((2, 1), (-1, 0)): -0.0383184014263534,\n", - " ((2, 1), (0, -1)): -0.7913059177862865,\n", - " ((2, 1), (0, 1)): -0.7672970392961057,\n", - " ((2, 1), (1, 0)): -0.8402721538112866,\n", - " ((2, 2), (-1, 0)): 0.2351847866756862,\n", - " ((2, 2), (0, -1)): 0.24909509983624728,\n", - " ((2, 2), (0, 1)): 0.25112211666264095,\n", - " ((2, 2), (1, 0)): 0.7743960998734626,\n", - " ((3, 0), (-1, 0)): -0.1037923159515085,\n", - " ((3, 0), (0, -1)): -0.07807333741195537,\n", - " ((3, 0), (0, 1)): -0.9374064176172849,\n", - " ((3, 0), (1, 0)): -0.07323076923076924,\n", - " ((3, 1), None): -1,\n", - " ((3, 2), None): 1})" + " ((0, 2), (-1, 0)): 0.12165200587479848,\n", + " ((0, 2), (0, -1)): 0.09431411803674361,\n", + " ((0, 2), (0, 1)): 0.14047883620608154,\n", + " ((0, 2), (1, 0)): 0.19224095989491635,\n", + " ((1, 0), (-1, 0)): -0.09696833851887868,\n", + " ((1, 0), (0, -1)): -0.15641263417341367,\n", + " ((1, 0), (0, 1)): -0.15340385689815017,\n", + " ((1, 0), (1, 0)): -0.15224266498911238,\n", + " ((1, 2), (-1, 0)): 0.18537063683043895,\n", + " ((1, 2), (0, -1)): 0.17757702529142774,\n", + " ((1, 2), (0, 1)): 0.17562120416256435,\n", + " ((1, 2), (1, 0)): 0.27484289408254886,\n", + " ((2, 0), (-1, 0)): -0.16785234970594098,\n", + " ((2, 0), (0, -1)): -0.1448679824723624,\n", + " ((2, 0), (0, 1)): -0.028114098214323924,\n", + " ((2, 0), (1, 0)): -0.16267477943781278,\n", + " ((2, 1), (-1, 0)): -0.2301056003129034,\n", + " ((2, 1), (0, -1)): -0.4332722098873507,\n", + " ((2, 1), (0, 1)): 0.2965645851500498,\n", + " ((2, 1), (1, 0)): -0.90815406879654,\n", + " ((2, 2), (-1, 0)): 0.1905755278897695,\n", + " ((2, 2), (0, -1)): 0.07306332481110034,\n", + " ((2, 2), (0, 1)): 0.1793881607466996,\n", + " ((2, 2), (1, 0)): 0.34260576652777697,\n", + " ((3, 0), (-1, 0)): -0.16576962655130892,\n", + " ((3, 0), (0, -1)): -0.16840120349372995,\n", + " ((3, 0), (0, 1)): -0.5090288592720464,\n", + " ((3, 0), (1, 0)): -0.88375,\n", + " ((3, 1), None): -0.6897322258069369,\n", + " ((3, 2), None): 0.388990723935834})" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -484,9 +450,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -499,29 +465,27 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(>,\n", - " {(0, 0): 0.2244371077466747,\n", - " (0, 1): 0.3396752416362625,\n", - " (0, 2): 0.4729172313717893,\n", - " (1, 0): 0.14857758363326573,\n", - " (1, 2): 0.6105607273543103,\n", - " (2, 0): 0.06795076480003,\n", - " (2, 1): -0.0383184014263534,\n", - " (2, 2): 0.7743960998734626,\n", - " (3, 0): -0.07323076923076924,\n", - " (3, 1): -1,\n", - " (3, 2): 1})" + " {(0, 0): -0.01158029172666495,\n", + " (0, 1): 0.13072636267769677,\n", + " (0, 2): 0.19224095989491635,\n", + " (1, 0): -0.09696833851887868,\n", + " (1, 2): 0.27484289408254886,\n", + " (2, 0): -0.028114098214323924,\n", + " (2, 1): 0.2965645851500498,\n", + " (2, 2): 0.34260576652777697,\n", + " (3, 0): -0.16576962655130892,\n", + " (3, 1): -0.6897322258069369,\n", + " (3, 2): 0.388990723935834})" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -539,10 +503,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -582,9 +544,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.5.2+" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/rl.py b/rl.py index 43d860935..20a392592 100644 --- a/rl.py +++ b/rl.py @@ -1,5 +1,4 @@ -"""Reinforcement Learning (Chapter 21) -""" +"""Reinforcement Learning (Chapter 21)""" from collections import defaultdict from utils import argmax @@ -61,7 +60,7 @@ def __call__(self, percept): return self.a def update_state(self, percept): - ''' To be overridden in most cases. The default case + '''To be overridden in most cases. The default case assumes the percept to be of type (state, reward)''' return percept From 86a1908ca65f9f695a358b3a453e9d79672b36b0 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 28 May 2017 21:13:22 +0300 Subject: [PATCH 506/513] Notebook: Naive Bayes (#510) * Update learning.ipynb * Update test_learning.py * Resolve conflicts --- learning.ipynb | 985 ++++++++++++++++++++++------------------- tests/test_learning.py | 6 +- 2 files changed, 527 insertions(+), 464 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index 13d184e34..0b6bfc094 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Learning\n", "\n", @@ -16,9 +13,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -27,10 +22,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Contents\n", "\n", @@ -49,10 +41,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Machine Learning Overview\n", "\n", @@ -83,10 +72,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Datasets\n", "\n", @@ -99,10 +85,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n", "\n", @@ -111,10 +94,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Intro\n", "\n", @@ -127,11 +107,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -140,10 +118,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Class Attributes\n", "\n", @@ -170,10 +145,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Class Helper Functions\n", "\n", @@ -188,10 +160,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Importing a Dataset\n", "\n", @@ -202,11 +171,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -215,22 +182,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To check that we imported the correct dataset, we can do the following:" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -248,32 +208,22 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Which correctly prints the first line in the csv file and the list of attribute indexes." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -290,10 +240,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Attributes\n", "\n", @@ -304,12 +251,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -325,22 +268,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -362,22 +298,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now we will print all the possible values for the first feature/attribute." ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -393,22 +322,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty." ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -426,28 +348,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['setosa', 'virginica', 'versicolor']\n" + "['versicolor', 'virginica', 'setosa']\n" ] } ], @@ -457,20 +372,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper Functions" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We will now take a look at the auxiliary functions found in the class.\n", "\n", @@ -483,12 +392,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -506,54 +411,42 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`." ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['setosa', 'versicolor']\n" + "['versicolor', 'setosa']\n" ] } ], "source": [ - "iris.remove_examples(\"virginica\")\n", - "print(iris.values[iris.target])" + "iris2 = DataSet(name=\"iris\")\n", + "\n", + "iris2.remove_examples(\"virginica\")\n", + "print(iris2.values[iris2.target])" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "Finally we take a look at `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers." + "We also have `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -565,27 +458,54 @@ } ], "source": [ - "print(\"Class of first example:\",iris.examples[0][iris.target])\n", - "iris.classes_to_numbers()\n", - "print(\"Class of first example:\",iris.examples[0][iris.target])" + "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n", + "iris2.classes_to_numbers()\n", + "print(\"Class of first example:\",iris2.examples[0][iris2.target])" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "As you can see \"setosa\" was mapped to 0." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "Finally, we take a look at `find_means_and_deviations`. It finds the means and standard deviations of the features for each class." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n", + "Versicolor mean for first feature: 5.936\n", + "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n", + "Virginica deviation for second feature: 0.32249663817263746\n" + ] + } + ], + "source": [ + "means, deviations = iris.find_means_and_deviations()\n", + "\n", + "print(\"Setosa feature means:\", means[\"setosa\"])\n", + "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n", + "\n", + "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n", + "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Distance Functions\n", "\n", @@ -598,12 +518,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -624,10 +540,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Euclidean Distance (`euclidean_distance`)\n", "\n", @@ -636,12 +549,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -662,10 +571,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Hamming Distance (`hamming_distance`)\n", "\n", @@ -674,12 +580,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -700,10 +602,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Mean Boolean Error (`mean_boolean_error`)\n", "\n", @@ -712,12 +611,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -738,10 +633,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Mean Error (`mean_error`)\n", "\n", @@ -750,12 +642,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -776,10 +664,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Mean Square Error (`ms_error`)\n", "\n", @@ -788,12 +673,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -814,10 +695,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Root of Mean Square Error (`rms_error`)\n", "\n", @@ -826,12 +704,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -852,10 +726,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Plurality Learner Classifier\n", "\n", @@ -872,10 +743,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Implementation\n", "\n", @@ -884,11 +752,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -905,10 +771,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n", "\n", @@ -917,10 +780,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example\n", "\n", @@ -929,12 +789,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -953,20 +809,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## k-Nearest Neighbours (kNN) Classifier\n", "\n", @@ -978,10 +828,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's see how kNN works with a simple plot shown in the above picture.\n", "\n", @@ -996,10 +843,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Implementation\n", "\n", @@ -1008,11 +852,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1028,10 +870,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n", "\n", @@ -1040,10 +879,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example\n", "\n", @@ -1052,12 +888,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1076,47 +908,371 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "## Perceptron Classifier\n", + "## Naive Bayes Learner\n", "\n", "### Overview\n", "\n", - "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n", + "#### Theory of Probabilities\n", "\n", - "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n", + "The Naive Bayes algorithm is a probabilistic classifier, making use of [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). The theorem states that the conditional probability of **A** given **B** equals the conditional probability of **B** given **A** multiplied by the probability of **A**, divided by the probability of **B**.\n", "\n", - "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n", + "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n", "\n", - "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*." + "From the theory of Probabilities we have the Multiplication Rule, if the events *X* are independent the following is true:\n", + "\n", + "$$P(X_{1} \\cap X_{2} \\cap ... \\cap X_{n}) = P(X_{1})*P(X_{2})*...*P(X_{n})$$\n", + "\n", + "For conditional probabilities this becomes:\n", + "\n", + "$$P(X_{1}, X_{2}, ..., X_{n}|Y) = P(X_{1}|Y)*P(X_{2}|Y)*...*P(X_{n}|Y)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "![perceptron](images/perceptron.png)" + "#### Classifying an Item\n", + "\n", + "How can we use the above to classify an item though?\n", + "\n", + "We have a dataset with a set of classes (**C**) and we want to classify an item with a set of features (**F**). Essentially what we want to do is predict the class of an item given the features.\n", + "\n", + "For a specific class, **Class**, we will find the conditional probability given the item features:\n", + "\n", + "$$P(Class|F) = \\dfrac{P(F|Class)*P(Class)}{P(F)}$$\n", + "\n", + "We will do this for every class and we will pick the maximum. This will be the class the item is classified in.\n", + "\n", + "The features though are a vector with many elements. We need to break the probabilities up using the multiplication rule. Thus the above equation becomes:\n", + "\n", + "$$P(Class|F) = \\dfrac{P(Class)*P(F_{1}|Class)*P(F_{2}|Class)*...*P(F_{n}|Class)}{P(F_{1})*P(F_{2})*...*P(F_{n})}$$\n", + "\n", + "The calculation of the conditional probability then depends on the calculation of the following:\n", + "\n", + "*a)* The probability of **Class** in the dataset.\n", + "\n", + "*b)* The conditional probability of each feature occuring in an item classified in **Class**.\n", + "\n", + "*c)* The probabilities of each individual feature.\n", + "\n", + "For *a)*, we will count how many times **Class** occurs in the dataset (aka how many items are classified in a particular class).\n", + "\n", + "For *b)*, if the feature values are discrete ('Blue', '3', 'Tall', etc.), we will count how many times a feature value occurs in items of each class. If the feature values are not discrete, we will go a different route. We will use a distribution function to calculate the probability of values for a given class and feature. If we know the distribution function of the dataset, then great, we will use it to compute the probabilities. If we don't know the function, we can assume the dataset follows the normal (Gaussian) distribution without much loss of accuracy. In fact, it can be proven that any distribution tends to the Gaussian the larger the population gets (see [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem)).\n", + "\n", + "*NOTE:* If the values are continuous but use the discrete approach, there might be issues if we are not lucky. For one, if we have two values, '5.0 and 5.1', with the discrete approach they will be two completely different values, despite being so close. Second, if we are trying to classify an item with a feature value of '5.15', if the value does not appear for the feature, its probability will be 0. This might lead to misclassification. Generally, the continuous approach is more accurate and more useful, despite the overhead of calculating the distribution function.\n", + "\n", + "The last one, *c)*, is tricky. If feature values are discrete, we can count how many times they occur in the dataset. But what if the feature values are continuous? Imagine a dataset with a height feature. Is it worth it to count how many times each value occurs? Most of the time it is not, since there can be miscellaneous differences in the values (for example, 1.7 meters and 1.700001 meters are practically equal, but they count as different values).\n", + "\n", + "So as we cannot calculate the feature value probabilities, what are we going to do?\n", + "\n", + "Let's take a step back and rethink exactly what we are doing. We are essentially comparing conditional probabilities of all the classes. For two classes, **A** and **B**, we want to know which one is greater:\n", + "\n", + "$$\\dfrac{P(F|A)*P(A)}{P(F)} vs. \\dfrac{P(F|B)*P(B)}{P(F)}$$\n", + "\n", + "Wait, **P(F)** is the same for both the classes! In fact, it is the same for every combination of classes. That is because **P(F)** does not depend on a class, thus being independent of the classes.\n", + "\n", + "So, for *c)*, we actually don't need to calculate it at all." ] }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Wrapping It Up\n", + "\n", + "Classifying an item to a class then becomes a matter of calculating the conditional probabilities of feature values and the probabilities of classes. This is something very desirable and computationally delicious.\n", + "\n", + "Remember though that all the above are true because we made the assumption that the features are independent. In most real-world cases that is not true though. Is that an issue here? Fret not, for the the algorithm is very efficient even with that assumption. That is why the algorithm is called **Naive** Bayes Classifier. We (naively) assume that the features are independent to make computations easier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementation\n", + "\n", + "The implementation of the Naive Bayes Classifier is split in two; Discrete and Continuous. The user can choose between them with the argument `continuous`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Discrete\n", + "\n", + "The implementation for discrete values counts how many times each feature value occurs for each class, and how many times each class occurs. The results are stored in a `CountinProbDist` object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the below code you can see the probabilities of the class \"Setosa\" appearing in the dataset and the probability of the first feature (at index 0) of the same class having a value of 5. Notice that the second probability is relatively small, even though if we observe the dataset we will find that a lot of values are around 5. The issue arises because the features in the Iris dataset are continuous, and we are assuming they are discrete. If the features were discrete (for example, \"Tall\", \"3\", etc.) this probably wouldn't have been the case and we would see a much nicer probability distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3333333333333333\n", + "0.10588235294117647\n" + ] + } + ], + "source": [ + "dataset = iris\n", + "\n", + "target_vals = dataset.values[dataset.target]\n", + "target_dist = CountingProbDist(target_vals)\n", + "attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr])\n", + " for gv in target_vals\n", + " for attr in dataset.inputs}\n", + "for example in dataset.examples:\n", + " targetval = example[dataset.target]\n", + " target_dist.add(targetval)\n", + " for attr in dataset.inputs:\n", + " attr_dists[targetval, attr].add(example[attr])\n", + "\n", + "\n", + "print(target_dist['setosa'])\n", + "print(attr_dists['setosa', 0][5.0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we found the different values for the classes (called targets here) and calculated their distribution. Next we initialized a dictionary of `CountingProbDist` objects, one for each class and feature. Finally, we iterated through the examples in the dataset and calculated the needed probabilites.\n", + "\n", + "Having calculated the different probabilities, we will move on to the predicting function. It will receive as input an item and output the most likely class. Using the above formula, it will multiply the probability of the class appearing, with the probability of each feature value appearing in the class. It will return the max result." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setosa\n" + ] + } + ], + "source": [ + "def predict(example):\n", + " def class_probability(targetval):\n", + " return (target_dist[targetval] *\n", + " product(attr_dists[targetval, attr][example[attr]]\n", + " for attr in dataset.inputs))\n", + " return argmax(target_vals, key=class_probability)\n", + "\n", + "\n", + "print(predict([5, 3, 1, 0.1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can view the complete code by executing the next line:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource NaiveBayesDiscrete" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Continuous\n", + "\n", + "In the implementation we use the Gaussian/Normal distribution function. To make it work, we need to find the means and standard deviations of features for each class. We make use of the `find_means_and_deviations` Dataset function. On top of that, we will also calculate the class probabilities as we did with the Discrete approach." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5.006, 3.418, 1.464, 0.244]\n", + "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n" + ] + } + ], + "source": [ + "means, deviations = dataset.find_means_and_deviations()\n", + "\n", + "target_vals = dataset.values[dataset.target]\n", + "target_dist = CountingProbDist(target_vals)\n", + "\n", + "\n", + "print(means[\"setosa\"])\n", + "print(deviations[\"versicolor\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the means of the features for the \"Setosa\" class and the deviations for \"Versicolor\".\n", + "\n", + "The prediction function will work similarly to the Discrete algorithm. It will multiply the probability of the class occuring with the conditional probabilities of the feature values for the class.\n", + "\n", + "Since we are using the Gaussian distribution, we will input the value for each feature into the Gaussian function, together with the mean and deviation of the feature. This will return the probability of the particular feature value for the given class. We will repeat for each class and pick the max value." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "setosa\n" + ] + } + ], + "source": [ + "def predict(example):\n", + " def class_probability(targetval):\n", + " prob = target_dist[targetval]\n", + " for attr in dataset.inputs:\n", + " prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr])\n", + " return prob\n", + "\n", + " return argmax(target_vals, key=class_probability)\n", + "\n", + "\n", + "print(predict([5, 3, 1, 0.1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The complete code of the continuous algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "metadata": { - "deletable": true, - "editable": true + "collapsed": true }, + "outputs": [], + "source": [ + "%psource NaiveBayesContinuous" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + "We will now use the Naive Bayes Classifier (Discrete and Continuous) to classify items:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Discrete Classifier\n", + "setosa\n", + "versicolor\n", + "versicolor\n", + "\n", + "Continuous Classifier\n", + "setosa\n", + "versicolor\n", + "virginica\n" + ] + } + ], + "source": [ + "nBD = NaiveBayesLearner(iris, continuous=False)\n", + "print(\"Discrete Classifier\")\n", + "print(nBD([5, 3, 1, 0.1]))\n", + "print(nBD([6, 5, 3, 1.5]))\n", + "print(nBD([7, 3, 6.5, 2]))\n", + "\n", + "\n", + "nBC = NaiveBayesLearner(iris, continuous=True)\n", + "print(\"\\nContinuous Classifier\")\n", + "print(nBC([5, 3, 1, 0.1]))\n", + "print(nBC([6, 5, 3, 1.5]))\n", + "print(nBC([7, 3, 6.5, 2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice how the Discrete Classifier misclassified the second item, while the Continuous one had no problem." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perceptron Classifier\n", + "\n", + "### Overview\n", + "\n", + "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n", + "\n", + "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n", + "\n", + "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n", + "\n", + "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![perceptron](images/perceptron.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "### Implementation\n", "\n", @@ -1125,11 +1281,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 33, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1138,10 +1292,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n", "\n", @@ -1150,10 +1301,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example\n", "\n", @@ -1162,12 +1310,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1187,20 +1331,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output is 0, which means the item is classified in the first class, \"Setosa\". This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## MNIST Handwritten Digits Classification\n", "\n", @@ -1217,10 +1355,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Loading MNIST digits data\n", "\n", @@ -1229,11 +1364,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1251,11 +1384,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1300,21 +1431,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1323,10 +1449,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n", "\n", @@ -1335,12 +1458,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1362,10 +1481,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Visualizing MNIST digits data\n", "\n", @@ -1374,11 +1490,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1412,18 +1526,14 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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1BODrr79m5syZYW3fT2We2bNnB+Dll18G4J577mH06NEA9O7dO+LtRrPk+oUX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0bjcO1F8uTJY0J5Er4MVC4fMGCACTuI35hXyZUrF2AdHwkVSLL9559/zgMPPABEnxIlSGgvMTGR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\nr9GsWTMA3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7b88++2yqV68OOMY0QJ48eYxhcuWVVwJw7NgxT8YXjP79+wNw3nnnmW2NGjUC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\nK3yek3B0e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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1437,18 +1547,14 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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DBg1SPVe/fn0AnnrqKZYsWRJFy/xJ9erVzbm6Y8cOj60Jzumnnw44LmaAdu3a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\niE//Az1/hR++BpKtAAAAAElFTkSuQmCC\n", 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PorLFQ4mKNaKwBBY5/PzzzwAmVyoQUQ6l3Lxnz54ZlChB9mvmhN9YkDu3c9uaPXt2UF/A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\n7og2hFijABPMK2rmKZlmRTpAzhEJJFV5jo2NBQJfPymQZM+ePVkgvK/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YAGDeD+K+jYqK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\nUiMjIy5oMJbGW8ibN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1462,21 +1568,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let's have a look at the average of all the images of training and testing data." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1513,12 +1614,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1541,7 +1638,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1582,10 +1679,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Testing\n", "\n", @@ -1594,12 +1688,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1619,22 +1709,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms." ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "# takes ~8 seconds to execute this\n", @@ -1643,20 +1726,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Moving forward we can use `MNIST_DataSet` to test our algorithms." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### k-Nearest Neighbors\n", "\n", @@ -1667,12 +1744,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1692,22 +1765,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To make sure that the output we got is correct, let's plot that image along with its label." ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1719,10 +1785,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -1730,7 +1796,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1744,10 +1810,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", @@ -1771,7 +1834,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.2+" } }, "nbformat": 4, diff --git a/tests/test_learning.py b/tests/test_learning.py index ec2cf18bd..5e998b6f5 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -60,10 +60,10 @@ def test_naive_bayes(): iris = DataSet(name="iris") # Discrete - nBD = NaiveBayesLearner(iris) + nBD = NaiveBayesLearner(iris, continuous=False) assert nBD([5, 3, 1, 0.1]) == "setosa" - assert nBD([6, 5, 3, 1.5]) == "versicolor" - assert nBD([7, 3, 6.5, 2]) == "virginica" + assert nBD([6, 3, 4, 1.1]) == "versicolor" + assert nBD([7.7, 3, 6, 2]) == "virginica" # Continuous nBC = NaiveBayesLearner(iris, continuous=True) From dfe938febb244ccab73359242548766caead9eb0 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 28 May 2017 21:14:53 +0300 Subject: [PATCH 507/513] Implementation: Tree CSP Solver (#434) * Update csp.py * Add test --- csp.py | 50 +++++++++++++++++++++++++++++++++++++---------- tests/test_csp.py | 9 +++++++++ 2 files changed, 49 insertions(+), 10 deletions(-) diff --git a/csp.py b/csp.py index d410b1428..d75b37fec 100644 --- a/csp.py +++ b/csp.py @@ -85,7 +85,7 @@ def display(self, assignment): # Subclasses can print in a prettier way, or display with a GUI print('CSP:', self, 'with assignment:', assignment) - # These methods are for the tree- and graph-search interface: + # These methods are for the tree and graph-search interface: def actions(self, state): """Return a list of applicable actions: nonconflicting @@ -308,15 +308,18 @@ def tree_csp_solver(csp): """[Figure 6.11]""" assignment = {} root = csp.variables[0] - root = 'NT' X, parent = topological_sort(csp, root) + + csp.support_pruning() for Xj in reversed(X[1:]): if not make_arc_consistent(parent[Xj], Xj, csp): return None - for Xi in X: - if not csp.curr_domains[Xi]: + + assignment[root] = csp.curr_domains[root][0] + for Xi in X[1:]: + assignment[Xi] = assign_value(parent[Xi], Xi, csp, assignment) + if not assignment[Xi]: return None - assignment[Xi] = csp.curr_domains[Xi][0] return assignment @@ -361,7 +364,34 @@ def build_topological(node, parent, neighbors, visited, stack, parents): def make_arc_consistent(Xj, Xk, csp): - raise NotImplementedError + """Make arc between parent (Xj) and child (Xk) consistent under the csp's constraints, + by removing the possible values of Xj that cause inconsistencies.""" + #csp.curr_domains[Xj] = [] + for val1 in csp.domains[Xj]: + keep = False # Keep or remove val1 + for val2 in csp.domains[Xk]: + if csp.constraints(Xj, val1, Xk, val2): + # Found a consistent assignment for val1, keep it + keep = True + break + + if not keep: + # Remove val1 + csp.prune(Xj, val1, None) + + return csp.curr_domains[Xj] + + +def assign_value(Xj, Xk, csp, assignment): + """Assign a value to Xk given Xj's (Xk's parent) assignment. + Return the first value that satisfies the constraints.""" + parent_assignment = assignment[Xj] + for val in csp.curr_domains[Xk]: + if csp.constraints(Xj, parent_assignment, Xk, val): + return val + + # No consistent assignment available + return None # ______________________________________________________________________________ # Map-Coloring Problems @@ -389,8 +419,8 @@ def different_values_constraint(A, a, B, b): def MapColoringCSP(colors, neighbors): """Make a CSP for the problem of coloring a map with different colors - for any two adjacent regions. Arguments are a list of colors, and a - dict of {region: [neighbor,...]} entries. This dict may also be + for any two adjacent regions. Arguments are a list of colors, and a + dict of {region: [neighbor,...]} entries. This dict may also be specified as a string of the form defined by parse_neighbors.""" if isinstance(neighbors, str): neighbors = parse_neighbors(neighbors) @@ -400,9 +430,9 @@ def MapColoringCSP(colors, neighbors): def parse_neighbors(neighbors, variables=[]): """Convert a string of the form 'X: Y Z; Y: Z' into a dict mapping - regions to neighbors. The syntax is a region name followed by a ':' + regions to neighbors. The syntax is a region name followed by a ':' followed by zero or more region names, followed by ';', repeated for - each region name. If you say 'X: Y' you don't need 'Y: X'. + each region name. If you say 'X: Y' you don't need 'Y: X'. >>> parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} True """ diff --git a/tests/test_csp.py b/tests/test_csp.py index 9c4804c3d..78afac673 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -338,6 +338,7 @@ def test_universal_dict(): def test_parse_neighbours(): assert parse_neighbors('X: Y Z; Y: Z') == {'Y': ['X', 'Z'], 'X': ['Y', 'Z'], 'Z': ['X', 'Y']} + def test_topological_sort(): root = 'NT' Sort, Parents = topological_sort(australia,root) @@ -351,5 +352,13 @@ def test_topological_sort(): assert Parents['WA'] == 'SA' +def test_tree_csp_solver(): + australia_small = MapColoringCSP(list('RB'), + 'NT: WA Q; NSW: Q V') + tcs = tree_csp_solver(australia_small) + assert (tcs['NT'] == 'R' and tcs['WA'] == 'B' and tcs['Q'] == 'B' and tcs['NSW'] == 'R' and tcs['V'] == 'B') or \ + (tcs['NT'] == 'B' and tcs['WA'] == 'R' and tcs['Q'] == 'R' and tcs['NSW'] == 'B' and tcs['V'] == 'R') + + if __name__ == "__main__": pytest.main() From b96f01b5847002b54de85db6324ca42ef2942c31 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 28 May 2017 21:19:26 +0300 Subject: [PATCH 508/513] Update csp.ipynb (#433) --- csp.ipynb | 89 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 88 insertions(+), 1 deletion(-) diff --git a/csp.ipynb b/csp.ipynb index 66c7eac6d..5255ff1d8 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, @@ -627,6 +627,93 @@ "solve_parameters.nassigns" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tree CSP Solver\n", + "\n", + "The `tree_csp_solver` function (**Figure 6.11** in the book) can be used to solve problems whose constraint graph is a tree. Given a CSP, with `neighbors` forming a tree, it returns an assignement that satisfies the given constraints. The algorithm works as follows:\n", + "\n", + "First it finds the *topological sort* of the tree. This is an ordering of the tree where each variable/node comes after its parent in the tree. The function that accomplishes this is `topological_sort`, which builds the topological sort using the recursive function `build_topological`. That function is an augmented DFS, where each newly visited node of the tree is pushed on a stack. The stack in the end holds the variables topologically sorted.\n", + "\n", + "Then the algorithm makes arcs between each parent and child consistent. *Arc-consistency* between two variables, *a* and *b*, occurs when for every possible value of *a* there is an assignment in *b* that satisfies the problem's constraints. If such an assignment cannot be found, then the problematic value is removed from *a*'s possible values. This is done with the use of the function `make_arc_consistent` which takes as arguments a variable `Xj` and its parent, and makes the arc between them consistent by removing any values from the parent which do not allow for a consistent assignment in `Xj`.\n", + "\n", + "If an arc cannot be made consistent, the solver fails. If every arc is made consistent, we move to assigning values.\n", + "\n", + "First we assign a random value to the root from its domain and then we start assigning values to the rest of the variables. Since the graph is now arc-consistent, we can simply move from variable to variable picking any remaining consistent values. At the end we are left with a valid assignment. If at any point though we find a variable where no consistent value is left in its domain, the solver fails.\n", + "\n", + "The implementation of the algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%psource tree_csp_solver" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now use the above function to solve a problem. More specifically, we will solve the problem of coloring the map of Australia. At our disposal we have two colors: Red and Blue. As a reminder, this is the graph of Australia:\n", + "\n", + "`\"SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: \"`\n", + "\n", + "Unfortunately as you can see the above is not a tree. If, though, we remove `SA`, which has arcs to `WA`, `NT`, `Q`, `NSW` and `V`, we are left with a tree (we also remove `T`, since it has no in-or-out arcs). We can now solve this using our algorithm. Let's define the map coloring problem at hand:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "australia_small = MapColoringCSP(list('RB'),\n", + " 'NT: WA Q; NSW: Q V')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will input `australia_small` to the `tree_csp_solver` and we will print the given assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'WA': 'B', 'NT': 'R', 'Q': 'B', 'V': 'B', 'NSW': 'R'}\n" + ] + } + ], + "source": [ + "assignment = tree_csp_solver(australia_small)\n", + "print(assignment)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`WA`, `Q` and `V` got painted Blue, while `NT` and `NSW` got painted Red." + ] + }, { "cell_type": "markdown", "metadata": { From 3e57e00f914822325c4d0136c953fd4b2e3ee411 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sun, 28 May 2017 15:27:34 -0300 Subject: [PATCH 509/513] Refactor backpropagation (#437) * Create function to initialize random weights * Add sigmoid derivative function --- learning.py | 29 +++++++++++++++++------------ tests/test_learning.py | 17 ++++++++++++++++- tests/test_utils.py | 8 ++++++++ utils.py | 4 ++++ 4 files changed, 45 insertions(+), 13 deletions(-) diff --git a/learning.py b/learning.py index e4b986c0d..2899bffeb 100644 --- a/learning.py +++ b/learning.py @@ -3,7 +3,8 @@ from utils import ( removeall, unique, product, mode, argmax, argmax_random_tie, isclose, gaussian, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, - weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile + weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, + DataFile, sigmoid_derivative ) import copy @@ -567,13 +568,17 @@ def predict(example): return predict +def random_weights(min_value, max_value, num_weights): + return [random.uniform(min_value, max_value) for i in range(num_weights)] + + def BackPropagationLearner(dataset, net, learning_rate, epochs): """[Figure 18.23] The back-propagation algorithm for multilayer network""" # Initialise weights for layer in net: for node in layer: - node.weights = [random.uniform(-0.5, 0.5) - for i in range(len(node.weights))] + node.weights = random_weights(min_value=-0.5, max_value=0.5, + num_weights=len(node.weights)) examples = dataset.examples ''' @@ -611,10 +616,11 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs): delta = [[] for i in range(n_layers)] # Compute outer layer delta - err = [t_val[i] - o_nodes[i].value - for i in range(o_units)] - delta[-1] = [(o_nodes[i].value) * (1 - o_nodes[i].value) * - (err[i]) for i in range(o_units)] + + # Error for the MSE cost function + err = [t_val[i] - o_nodes[i].value for i in range(o_units)] + # The activation function used is the sigmoid function + delta[-1] = [sigmoid_derivative(o_nodes[i].value) * err[i] for i in range(o_units)] # Backward pass h_layers = n_layers - 2 @@ -623,11 +629,9 @@ def BackPropagationLearner(dataset, net, learning_rate, epochs): h_units = len(layer) nx_layer = net[i+1] # weights from each ith layer node to each i + 1th layer node - w = [[node.weights[k] for node in nx_layer] - for k in range(h_units)] + w = [[node.weights[k] for node in nx_layer] for k in range(h_units)] - delta[i] = [(layer[j].value) * (1 - layer[j].value) * - dotproduct(w[j], delta[i+1]) + delta[i] = [sigmoid_derivative(layer[j].value) * dotproduct(w[j], delta[i+1]) for j in range(h_units)] # Update weights @@ -744,7 +748,8 @@ def LinearLearner(dataset, learning_rate=0.01, epochs=100): X_col = [ones] + X_col # Initialize random weigts - w = [random.uniform(-0.5, 0.5) for _ in range(len(idx_i) + 1)] + num_weights = len(idx_i) + 1 + w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights) for epoch in range(epochs): err = [] diff --git a/tests/test_learning.py b/tests/test_learning.py index 5e998b6f5..72c0350a6 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,7 +1,7 @@ from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \ NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \ - euclidean_distance, grade_learner, err_ratio + euclidean_distance, grade_learner, err_ratio, random_weights from utils import DataFile @@ -124,3 +124,18 @@ def test_perceptron(): assert grade_learner(perceptron, tests) > 1/2 assert err_ratio(perceptron, iris) < 0.4 + + +def test_random_weights(): + min_value = -0.5 + max_value = 0.5 + num_weights = 10 + + test_weights = random_weights(min_value, max_value, num_weights) + + assert len(test_weights) == num_weights + + for weight in test_weights: + assert weight >= min_value and weight <= max_value + + diff --git a/tests/test_utils.py b/tests/test_utils.py index 90548069b..f90895799 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -154,6 +154,14 @@ def test_gaussian(): assert gaussian(3,1,3) == 0.3989422804014327 +def test_sigmoid_derivative(): + value = 1 + assert sigmoid_derivative(value) == 0 + + value = 3 + assert sigmoid_derivative(value) == -6 + + def test_step(): assert step(1) == step(0.5) == 1 assert step(0) == 1 diff --git a/utils.py b/utils.py index b67153999..1757526ff 100644 --- a/utils.py +++ b/utils.py @@ -250,6 +250,10 @@ def clip(x, lowest, highest): return max(lowest, min(x, highest)) +def sigmoid_derivative(value): + return value * (1 - value) + + def sigmoid(x): """Return activation value of x with sigmoid function""" return 1/(1 + math.exp(-x)) From c25fc70da8d86106b40fd196cd370da767183d17 Mon Sep 17 00:00:00 2001 From: articuno12 Date: Mon, 29 May 2017 00:21:49 +0530 Subject: [PATCH 510/513] Removed errors to make the build pass (#418) * removed flake8 errors * fixed remaining flake8 errors * fixed loop * added space --- csp.py | 1 - logic.py | 20 ++++++++++++++++++-- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/csp.py b/csp.py index d75b37fec..9e933c266 100644 --- a/csp.py +++ b/csp.py @@ -339,7 +339,6 @@ def topological_sort(X, root): visited shows the state (visited - not visited) of nodes """ - nodes = X.variables neighbors = X.neighbors visited = defaultdict(lambda: False) diff --git a/logic.py b/logic.py index 3ba1857bc..e3d326e68 100644 --- a/logic.py +++ b/logic.py @@ -845,8 +845,24 @@ def subst(s, x): return Expr(x.op, *[subst(s, arg) for arg in x.args]) -def fol_fc_ask(KB, alpha): # TODO - raise NotImplementedError +def fol_fc_ask(KB, alpha): + """A simple forward-chaining algorithm. [Figure 9.3]""" + new = [] + while new is not None: + for rule in KB.clauses: + p, q = parse_definite_clause(standardize_variables(rule)) + for p_ in KB.clauses: + if p != p_: + for theta in KB.clauses: + if subst(theta, p) == subst(theta, p_): + q_ = subst(theta, q) + if not unify(q_, KB.sentence in KB) or not unify(q_, new): + new.append(q_) + phi = unify(q_, alpha) + if phi is not None: + return phi + KB.tell(new) + return None def standardize_variables(sentence, dic=None): From 416c152bca1c2bed87d7d110c8b476a2f4cca4f1 Mon Sep 17 00:00:00 2001 From: Allen Date: Mon, 29 May 2017 04:54:31 +1000 Subject: [PATCH 511/513] changed cross validation wrapper (#346) is supposed to return an answer when errT converges, not errV used to return size of when err_val converges but is supposed to return the size with minimum err_val --- learning.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/learning.py b/learning.py index 2899bffeb..afc0caceb 100644 --- a/learning.py +++ b/learning.py @@ -949,13 +949,20 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): err_val = [] err_train = [] size = 1 + while True: errT, errV = cross_validation(learner, size, dataset, k) # Check for convergence provided err_val is not empty - if (err_val and isclose(err_val[-1], errV, rel_tol=1e-6)): - best_size = size - return learner(dataset, best_size) - + if (err_train and isclose(err_train[-1], errT, rel_tol=1e-6)): + best_size = 0 + min_val = math.inf + + i = 0 + while i Date: Sun, 28 May 2017 22:11:36 -0700 Subject: [PATCH 512/513] Update README.md --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 5f85c4eb1..0c95aebb8 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,18 @@

    ------------------ # `aima-python` [![Build Status](https://travis-ci.org/aimacode/aima-python.svg?branch=master)](https://travis-ci.org/aimacode/aima-python) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/aimacode/aima-python) -Python code for the book *Artificial Intelligence: A Modern Approach.* You can use this in conjunction with a course on AI, or for study on your own. We're looking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. +Python code for the book *[Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu).* You can use this in conjunction with a course on AI, or for study on your own. We're looking for [solid contributors](https://github.com/aimacode/aima-python/blob/master/CONTRIBUTING.md) to help. ## Python 3.4 -This code is in Python 3.4 (Python 3.5, also works, but Python 2.x does not). You can [install the latest Python version](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +This code is in Python 3.4 (Python 3.5 and later also works, but Python 2.x does not). You can [install the latest Python version](https://www.python.org/downloads) or use a browser-based Python interpreter such as [repl.it](https://repl.it/languages/python3). +You can run the code in an IDE, or from the command line with `python -i `*filename*`.py` where the `-i` option puts you in an interactive loop where you can run Python functions. + +In addition to the *filename*`.py` files, there are also *filename*`.ipynb` files, which are Jupyter (formerly Ipython) notebooks. You can read these notebooks, and you can also run the code embedded with them. See [jupyter.org](http://jupyter.org/) for instructions on setting up a Jupyter notebook environment. ## Structure of the Project @@ -137,7 +139,7 @@ Here is a table of the implemented data structures, the figure, name of the impl # Acknowledgements -Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, and @reachtarunhere. +Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, @reachtarunhere, @MrDupin, and @Chipe1. [agents]:../master/agents.py From 01e4450a2630761736c3a3c688ef0bcae7f27948 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Mon, 29 May 2017 08:21:29 +0300 Subject: [PATCH 513/513] Show outputs (#521) --- csp.ipynb | 678 +++++++++++++++++++++++++++++------------------------- 1 file changed, 361 insertions(+), 317 deletions(-) diff --git a/csp.ipynb b/csp.ipynb index 5255ff1d8..5404e6a47 100644 --- a/csp.ipynb +++ b/csp.ipynb @@ -2,11 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Constraint Satisfaction Problems (CSPs)\n", "\n", @@ -17,21 +13,20 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ - "from csp import *" + "from csp import *\n", + "\n", + "# Needed to hide warnings in the matplotlib sections\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Review\n", "\n", @@ -42,9 +37,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -53,20 +46,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The __ _ _init_ _ __ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as dict where key specify the variables and value specify the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list its value which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Graph Coloring\n", "\n", @@ -75,12 +62,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { @@ -88,7 +71,7 @@ "['R', 'G', 'B']" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -100,10 +83,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this what we need is that the neighbors must not have the same color. This is defined in the function **different_values_constraint** of the module." ] @@ -112,9 +92,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -123,10 +101,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows to take input in the form of strings and return a Dict of the form compatible with the **CSP Class**." ] @@ -135,9 +110,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -146,10 +119,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables our the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **australia**, **usa** and **france** are three CSPs that have been created using **MapColoringCSP**. **australia** corresponds to ** Figure 6.1 ** in the book." ] @@ -158,9 +128,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -169,23 +137,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " ,\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "australia, usa, france" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## NQueens\n", "\n", @@ -196,9 +170,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -207,10 +179,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The **NQueensCSP** method implements methods that support solving the problem via **min_conflicts** which is one of the techniques for solving CSPs. Because **min_conflicts** hill climbs the number of conflicts to solve the CSP **assign** and **unassign** are modified to record conflicts. More details about the structures **rows**, **downs**, **ups** which help in recording conflicts are explained in the docstring." ] @@ -219,9 +188,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -230,21 +197,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The _ ___init___ _ method takes only one parameter **n** the size of the problem. To create an instance we just pass the required n into the constructor." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -253,10 +215,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper Functions\n", "\n", @@ -265,11 +224,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -291,46 +248,35 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next, we define **make_instru** which takes an instance of **CSP** and returns a **InstruCSP** instance. " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ "def make_instru(csp):\n", - " return InstruCSP(csp.variables, csp.domains, csp.neighbors,\n", - " csp.constraints)" + " return InstruCSP(csp.variables, csp.domains, csp.neighbors, csp.constraints)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We will now use a graph defined as a dictonary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their corresponding values are the nodes they are connected to." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -361,21 +307,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -384,11 +325,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -397,10 +336,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# Backtracking Search\n", "\n", @@ -409,11 +345,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -422,69 +356,101 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'R',\n", + " 1: 'R',\n", + " 2: 'R',\n", + " 3: 'R',\n", + " 4: 'G',\n", + " 5: 'R',\n", + " 6: 'G',\n", + " 7: 'R',\n", + " 8: 'B',\n", + " 9: 'R',\n", + " 10: 'G',\n", + " 11: 'B',\n", + " 12: 'G',\n", + " 13: 'G',\n", + " 14: 'Y',\n", + " 15: 'Y',\n", + " 16: 'B',\n", + " 17: 'B',\n", + " 18: 'B',\n", + " 19: 'G',\n", + " 20: 'B'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "result # A dictonary of assignments." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Let us also check the number of assignments made." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "coloring_problem1.nassigns" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let us check the total number of assignments and unassignments which is the length ofour assignment history." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "len(coloring_problem1.assignment_history)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let us explore the optional keyword arguments that the **backtracking_search** function takes. These optional arguments help speed up the assignment further. Along with these, we will also point out to methods in the CSP class that help make this work. \n", "\n", @@ -495,9 +461,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -508,9 +472,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -521,9 +483,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -532,10 +492,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Another ordering related parameter **order_domain_values** governs the value ordering. Here we select the Least Constraining Value which is implemented by the function **lcv**. The idea is to select the value which rules out the fewest values in the remaining variables. The intuition behind selecting the **lcv** is that it leaves a lot of freedom to assign values later. The idea behind selecting the mrc and lcv makes sense because we need to do all variables but for values, we might better try the ones that are likely. So for vars, we face the hard ones first.\n" ] @@ -544,9 +501,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -555,31 +510,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally, the third parameter **inference** can make use of one of the two techniques called Arc Consistency or Forward Checking. The details of these methods can be found in the **Section 6.3.2** of the book. In short the idea of inference is to detect the possible failure before it occurs and to look ahead to not make mistakes. **mac** and **forward_checking** implement these two techniques. The **CSP** methods **support_pruning**, **suppose**, **prune**, **choices**, **infer_assignment** and **restore** help in using these techniques. You can know more about these by looking up the source code." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let us compare the performance with these parameters enabled vs the default parameters. We will use the Graph Coloring problem instance usa for comparison. We will call the instances **solve_simple** and **solve_parameters** and solve them using backtracking and compare the number of assignments." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -589,40 +536,109 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'AL': 'B',\n", + " 'AR': 'B',\n", + " 'AZ': 'R',\n", + " 'CA': 'Y',\n", + " 'CO': 'R',\n", + " 'CT': 'R',\n", + " 'DC': 'B',\n", + " 'DE': 'B',\n", + " 'FL': 'G',\n", + " 'GA': 'R',\n", + " 'IA': 'B',\n", + " 'ID': 'R',\n", + " 'IL': 'G',\n", + " 'IN': 'R',\n", + " 'KA': 'B',\n", + " 'KY': 'B',\n", + " 'LA': 'G',\n", + " 'MA': 'G',\n", + " 'MD': 'G',\n", + " 'ME': 'R',\n", + " 'MI': 'B',\n", + " 'MN': 'G',\n", + " 'MO': 'R',\n", + " 'MS': 'R',\n", + " 'MT': 'G',\n", + " 'NC': 'B',\n", + " 'ND': 'B',\n", + " 'NE': 'G',\n", + " 'NH': 'B',\n", + " 'NJ': 'G',\n", + " 'NM': 'B',\n", + " 'NV': 'B',\n", + " 'NY': 'B',\n", + " 'OH': 'G',\n", + " 'OK': 'G',\n", + " 'OR': 'G',\n", + " 'PA': 'R',\n", + " 'RI': 'B',\n", + " 'SC': 'G',\n", + " 'SD': 'R',\n", + " 'TN': 'G',\n", + " 'TX': 'R',\n", + " 'UT': 'G',\n", + " 'VA': 'R',\n", + " 'VT': 'R',\n", + " 'WA': 'B',\n", + " 'WI': 'R',\n", + " 'WV': 'Y',\n", + " 'WY': 'B'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "backtracking_search(solve_simple)\n", - "backtracking_search(solve_parameters, order_domain_values=lcv, select_unassigned_variable=mrv, inference=mac )" + "backtracking_search(solve_parameters, order_domain_values=lcv, select_unassigned_variable=mrv, inference=mac)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "460302" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solve_simple.nassigns" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "49" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "solve_parameters.nassigns" ] @@ -648,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -670,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -689,16 +705,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'WA': 'B', 'NT': 'R', 'Q': 'B', 'V': 'B', 'NSW': 'R'}\n" + "{'Q': 'R', 'NT': 'B', 'NSW': 'B', 'WA': 'R', 'V': 'R'}\n" ] } ], @@ -711,15 +725,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`WA`, `Q` and `V` got painted Blue, while `NT` and `NSW` got painted Red." + "`WA`, `Q` and `V` got painted with the same color and `NT` and `NSW` got painted with the other." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Graph Coloring Visualization\n", "\n", @@ -728,11 +739,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -745,21 +754,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which return such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. This will be more clear with the example below. If this sounds confusing do not worry this is not the part of the core material and our only goal is to help you visualize how the process works." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -813,21 +817,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally let us plot our problem. We first use the function above to obtain a step function." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -836,21 +835,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next we set the canvas size." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -859,23 +853,43 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Finally our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the coloring change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tt92mzZs3Kzc31x2RAZ937NgxTZgwQf/85z8pNwEfRMEJrzdixAi1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\nac8nIuXFgpOIiP5GVVUVGzZsgK+vLx49eiQ6DimT7t2B6dPLV3JqaAAtWwK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mzZqJTlF6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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9d8fbb23a9f446585fac31b107eb123" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import ipywidgets as widgets\n", "from IPython.display import display\n", @@ -895,10 +909,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## NQueens Visualization\n", "\n", @@ -907,11 +918,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -972,21 +981,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let us visualize a solution obtained via backtracking. We use of the previosuly defined **make_instru** function for keeping a history of steps." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -997,11 +1001,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1010,23 +1012,43 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now finally we set some matplotlib parameters to adjust how our plot will look. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe the how queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click.The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds upto one second for each time step.\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e0cf790018f34082961a812b9bc7eb81" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n", "matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n", @@ -1046,21 +1068,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now let us finally repeat the above steps for **min_conflicts** solution." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1070,11 +1087,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1083,23 +1098,43 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The visualization has same features as the above. But here it also highlights the conflicts by labeling the conflicted queens with a red background." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a61406396a92432d9f8f40c6f7a52d3e" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assignment_history)-1, step=0, value=0)\n", "w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n", @@ -1113,6 +1148,15 @@ "a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n", "display(a)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -1131,7 +1175,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.2+" }, "widgets": { "state": {}, @@ -1139,5 +1183,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }
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Install python-Levenshtein to remove this warning\n", + " warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n" + ] + } + ], "source": [ "from search import *" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Review\n", "\n", @@ -50,7 +67,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Problem\n", "\n", @@ -61,7 +81,9 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -70,7 +92,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "The `Problem` class has six methods.\n", "\n", @@ -94,7 +119,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "We will use the abstract class `Problem` to define our real **problem** named `GraphProblem`. You can see how we define `GraphProblem` by running the next cell." ] @@ -103,7 +131,9 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -112,7 +142,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values." ] @@ -121,7 +154,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -153,7 +188,9 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "source": [ "It is pretty straightforward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n", @@ -168,7 +205,9 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -177,7 +216,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "# Romania map visualisation\n", "\n", @@ -186,7 +228,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Have a look at `romania_locations`. It is a dictionary defined in search module. We will use these location values to draw the romania graph using **networkx**." ] @@ -195,14 +240,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'Giurgiu': (375, 270), 'Neamt': (406, 537), 'Drobeta': (165, 299), 'Timisoara': (94, 410), 'Pitesti': (320, 368), 'Bucharest': (400, 327), 'Craiova': (253, 288), 'Oradea': (131, 571), 'Iasi': (473, 506), 'Urziceni': (456, 350), 'Sibiu': (207, 457), 'Fagaras': (305, 449), 'Lugoj': (165, 379), 'Rimnicu': (233, 410), 'Vaslui': (509, 444), 'Eforie': (562, 293), 'Hirsova': (534, 350), 'Mehadia': (168, 339), 'Arad': (91, 492), 'Zerind': (108, 531)}\n" + "{'Arad': (91, 492), 'Bucharest': (400, 327), 'Craiova': (253, 288), 'Drobeta': (165, 299), 'Eforie': (562, 293), 'Fagaras': (305, 449), 'Giurgiu': (375, 270), 'Hirsova': (534, 350), 'Iasi': (473, 506), 'Lugoj': (165, 379), 'Mehadia': (168, 339), 'Neamt': (406, 537), 'Oradea': (131, 571), 'Pitesti': (320, 368), 'Rimnicu': (233, 410), 'Sibiu': (207, 457), 'Timisoara': (94, 410), 'Urziceni': (456, 350), 'Vaslui': (509, 444), 'Zerind': (108, 531)}\n" ] } ], @@ -213,7 +260,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in the notebook and we use ipywidgets to interact with the map to see how the searching algorithm works." ] @@ -222,7 +272,9 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -239,7 +291,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Let's get started by initializing an empty graph. We will add nodes, place the nodes in their location as shown in the book, add edges to the graph." ] @@ -248,7 +303,57 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# initialise a graph\n", + "G = nx.Graph()\n", + "\n", + "# use this while labeling nodes in the map\n", + "node_labels = dict()\n", + "# use this to modify colors of nodes while exploring the graph.\n", + "# This is the only dict we send to `show_map(node_colors)` while drawing the map\n", + "node_colors = dict()\n", + "\n", + "for n, p in romania_locations.items():\n", + " # add nodes from romania_locations\n", + " G.add_node(n)\n", + " # add nodes to node_labels\n", + " node_labels[n] = n\n", + " # node_colors to color nodes while exploring romania map\n", + " node_colors[n] = \"white\"\n", + "\n", + "# we'll save the initial node colors to a dict to use later\n", + "initial_node_colors = dict(node_colors)\n", + " \n", + "# positions for node labels\n", + "node_label_pos = { k:[v[0],v[1]-10] for k,v in romania_locations.items() }\n", + "\n", + "# use this while labeling edges\n", + "edge_labels = dict()\n", + "\n", + "# add edges between cities in romania map - UndirectedGraph defined in search.py\n", + "for node in romania_map.nodes():\n", + " connections = romania_map.get(node)\n", + " for connection in connections.keys():\n", + " distance = connections[connection]\n", + "\n", + " # add edges to the graph\n", + " G.add_edge(node, connection)\n", + " # add distances to edge_labels\n", + " edge_labels[(node, connection)] = distance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -292,16 +397,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "We have completed building our graph based on romania_map and its locations. It's time to display it here in the notebook. This function `show_map(node_colors)` helps us do that. We will be calling this function later on to display the map at each and every interval step while searching, using variety of algorithms from the book." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -335,23 +445,48 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "We can simply call the function with node_colors dictionary object to display it." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " b = plt.ishold()\n", + "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " plt.hold(b)\n", + "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\matplotlib\\__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n", + " warnings.warn(self.msg_depr_set % key)\n", + "C:\\Users\\Eshan\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\matplotlib\\rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n", + " warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n" + ] + }, { "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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rYGRkJHYcIiIiKmEjRozA4cOHER8fL3YUIqrgWPwkIvoO2dnZ4NLJVJzK4rR3QRDg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\nj/Z+XLlyZY3rTz31VIYOHVo9a3T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kVKtWTU2aNNGnn35qMb5WrVrmolOSvL295enpqd9//z3XWffu3asrV64oJCRE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\nT1588UWdOXNG69evt3YUAABgw6ZNm6Y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ChQszF9/66+Obb74B4JtvvsHCwoK4uLgcy9G9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PSLbnh4uKKiohQaGqquXbuqffv26tChgyQl+2UYwKNxd3fXvHnz1L9/f506dcp0HABA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\nPn36KDk5We3atdP69evVqlUr64z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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -364,14 +499,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Voila! You see, the romania map as shown in the Figure[3.2] in the book. Now, see how different searching algorithms perform with our problem statements." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Searching algorithms visualisations\n", "\n", @@ -399,9 +540,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -498,13 +641,15 @@ " \n", " slider = widgets.IntSlider(min=0, max=1, step=1, value=0)\n", " slider_visual = widgets.interactive(slider_callback, iteration = slider)\n", - " display(slider_visual)\n", - " " + " display(slider_visual)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "\n", "## Breadth first tree search\n", @@ -515,9 +660,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -576,7 +723,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback functions which are called when we interact with the slider and the button.\n", "\n" @@ -584,18 +734,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nsAOsCRtjAIXz4YcfVgeHzz//fK6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- "text/plain": [ - "" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": {}, "metadata": {}, "output_type": "display_data" } @@ -609,7 +763,9 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "source": [ "## Breadth first search\n", @@ -619,9 +775,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -683,18 +841,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nzj777Bx//PEpLy9P06ZNc+aZZ+b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- "text/plain": [ - "" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": {}, "metadata": {}, "output_type": "display_data" } @@ -707,7 +869,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## Uniform cost search\n", "\n", @@ -716,9 +881,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -799,18 +966,22 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg\noCC2bduGh4cHPj4+dOjQgQIFCqTyUYgk5efnR+7cuZk+fbrRKSIiImLlgoOD+fTTTzly5Mhz3zuL\niIikNQ0/xaqdP3+ehm81JCpvFDHVY6Ao8H/flz0BwsDlqAvtmrRj5dKV2NnZvdD+4+Li+PbbbwkK\nCmLnzp3UqFEDHx8f2rdvT968eVP7cES4efMmbm5u7N+/n0qVKhmdIyIiIlbMbDZTvXp1AgICaNu2\nrdE5IiJipTT8FKsXGRnJ0mVLmTlvJtE20TxyfQROQALYP7TH9owtderUYfig4TRr1izZn1rHxMTw\n9ddfs2HDBnbt2kXdunXx8fGhXbt25MqVK3UPSqza3Llz2bZtG7t379YqCxERETHU9u3bGTlyJCdP\nnsTGRrecEBGR9Kfhp8j/Yzab+e677/jx4I/8cPAH7t+7T/d3utOpUydKliyZqt8rOjqaHTt2EBQU\nxN69e2nQoAE+Pj60bt2aHDlypOr3EusTHx9PtWrVGDduHG+//bbROSIiImLFLBYL9erVY9CgQXTu\n3NnoHBERsUIafooY7MGDB2zfvp2goCB++OEHGjVqhI+PD61atSJr1qxG50kmtX//frp3786ZM2dw\ncXExOkdERESs2J49e+jXrx9hYWEvfPkoERGR1KLhp0gGcv/+fbZu3cqGDRsICQmhcePG+Pj40KJF\nC5ydnY3Ok0zG19eX0qVLM3HiRKNTRERExIpZLBY8PT1599136dmzp9E5IiJiZTT8FMmg7t69y5Yt\nWwgKCuLo0aM0a9aMTp060axZMxwdHY3Ok0zgjz/+oEqVKhw6dIgyZcoYnSMiIiJW7ODBg3Tt2pXz\n58/j4OBgdI6IiFgRDT9FMoFbt26xefNmgoKCOHHiBC1btsTHx4cmTZrozaP8o8DAQA4ePMj27duN\nThEREREr16xZM1q1aoW/v7/RKSIiYkU0/BTJZK5fv87GjRsJCgrizJkztGnTBh8fH7y8vLC3tzc6\nTzKYuLg4PDw8mDFjBi1btjQ6R0RERKzYsWPHaNOmDRcuXMDJycnoHBERsRIafoqkklatWpEvXz5W\nrFiRbt/z6tWrBAcHExQUxMWLF2nXrh0+Pj40bNhQF5OXRN9++y39+vXj9OnTumSCiIiIGKp9+/a8\n/vrrDB482OgUERGxEjZGB4iktePHj2NnZ0eDBg2MTkl1RYsW5cMPP+TQoUMcPXqUsmXLMmLECIoU\nKYK/vz/79+8nISHB6EwxmLe3N+7u7syYMcPoFBEREbFy48ePJzAwkIcPHxqdIiIiVkLDT3nlLVu2\nLHHV27lz5/5x2/j4+HSqSn2urq4MGzaMY8eOERISQtGiRRk4cCDFihXjgw8+ICQkBLPZbHSmGGTm\nzJnMmjWL8PBwo1NERETEirm7u+Pl5cXcuXONThERESuh4ae80mJjY1m7di3vv/8+HTp0YNmyZYnP\n/f7779jY2LB+/Xq8vLxwcXFhyZIl3Lt3D19fX4oVK4azszNubm6sWrUqyX5jYmLo0aMH2bJlo1Ch\nQnzyySfpfGT/rEyZMowcOZITJ06wb98+8ubNy/vvv0+JEiUYMmQIR44cQVe8sC4lS5ZkwIABDBky\nxOgUERERsXIBAQHMnj2byMhIo1NERMQKaPgpr7Tg4GBcXV2pXLky3bp144svvnjmNPCRI0fSr18/\nzpw5Q9u2bYmNjaVGjRp8/fXXnDlzhkGDBvGf//yH77//PvE1Q4YMYe/evWzZsoW9e/dy/PhxDhw4\nkN6H90IqVKjA2LFjCQsL45tvvsHFxYVu3bpRqlQpRowYQWhoqAahVmL48OEcO3aMPXv2GJ0iIiIi\nVqxcuXK0bt2amTNnGp0iIiJWQDc8kleap6cnrVu35sMPPwSgVKlSTJ8+nfbt2/P7779TsmRJZs6c\nyaBBg/5xP126dCFbtmwsWbKE6Oho8uTJw6pVq+jcuTMA0dHRFC1alHbt2qXrDY+Sy2KxcPLkSYKC\ngtiwYQM2Njb4+PjQqVMn3N3dMZlMRidKGvnqq6/46KOPOHnyJA4ODkbniIiIiJW6cuUKNWrU4Ndf\nfyVfvnxG54iIyCtMKz/llXXhwgUOHjxIly5dEh/z9fVl+fLlSbarUaNGkq/NZjOTJ0+mSpUq5M2b\nl2zZsrFly5bEayVevHiRp0+fUrdu3cTXuLi44O7unoZHk7pMJhNVq1blk08+4cKFC6xbt464uDha\ntWpFpUqVCAgI4OzZs0ZnShpo3bo1rq6uzJs3z+gUERERsWKurq507tyZwMBAo1NEROQVZ2d0gEha\nWbZsGWazmWLFij3z3B9//JH4excXlyTPTZs2jVmzZjF37lzc3NzImjUrH3/8Mbdv307zZiOYTCZq\n1qxJzZo1+fTTTzl06BAbNmzgrbfeInfu3Pj4+ODj40PZsmWNTpVUYDKZmDNnDvXr18fX15dChQoZ\nnSQiIiJWatSoUbi5uTF48GAKFy5sdI6IiLyitPJTXkkJCQl88cUXTJ06lZMnTyb55eHhwcqVK5/7\n2pCQEFq1aoWvry8eHh6UKlWK8+fPJz5funRp7OzsOHToUOJj0dHRnD59Ok2PKT2YTCbq1avHrFmz\niIiIYMGCBdy4cYMGDRpQvXp1pk6dyuXLl43OlBQqV64c7733HiNGjDA6RURERKxY4cKF8ff35+7d\nu0aniIjIK0wrP+WVtGPHDu7evUvv3r3JlStXkud8fHxYvHgxXbt2/dvXlitXjg0bNhASEkKePHmY\nP38+ly9fTtyPi4sLvXr1YsSIEeTNm5dChQoxceJEzGZzmh9XerKxsaFBgwY0aNCAOXPmcODAAYKC\ngqhduzYlS5ZMvEbo362slYxv1KhRVKxYkYMHD/L6668bnSMiIiJWauLEiUYniIjIK04rP+WVtGLF\nCho1avTM4BOgY8eOXLlyhT179vztjX1Gjx5N7dq1ad68OW+++SZZs2Z9ZlA6ffp0PD09ad++PV5e\nXri7u/PGG2+k2fEYzdbWFk9PTxYtWsT169eZNGkSZ8+epWrVqtSvX585c+Zw7do1ozPlJWTNmpVp\n06bRv39/EhISjM4RERERK2UymXSzTRERSVO627uIJNuTJ0/Ys2cPQUFBbNu2DQ8PDzp16sTbb79N\ngQIFjM6Tf2GxWPD09KRTp074+/sbnSMiIiIiIiKS6jT8FJFUERcXx7fffktQUBA7d+6kRo0a+Pj4\n0L59e/LmzZvs/ZrNZp48eYKjo2Mq1spf/vvf/+Ll5UVYWBj58uUzOkdERETkGT///DPOzs64u7tj\nY6OTF0VE5OVo+CkiqS4mJoavv/6aDRs2sGvXLurWrYuPjw/t2rX720sR/JOzZ88yZ84cbty4QaNG\njejVqxcuLi5pVG6dBg0axOPHj1myZInRKSIiIiKJDhw4gJ+fHzdu3CBfvny8+eabfPrpp/rAVkRE\nXoo+NhORVOfk5ESHDh0ICgri2rVr+Pn5sWPHDlxdXWnZsiVffvklUVFRL7SvqKgo8ufPT/HixRk0\naBDz588nPj4+jY/AugQEBLB9+3aOHj1qdIqIiIgI8Od7wH79+uHh4cHRo0cJDAwkKiqK/v37G50m\nIiKZjFZ+iki6efjwIdu2bSMoKIgffviBRo0aERQURJYsWf71tVu3bqVv376sX7+ehg0bpkOtdVm1\nahULFy7k559/1ulkIiIiYojo6GgcHBywt7dn7969+Pn5sWHDBurUqQP8eUZQ3bp1OXXqFCVKlDC4\nVkREMgv9hCsi6SZbtmy88847bNu2jfDwcLp06YKDg8M/vubJkycArFu3jsqVK1OuXLm/3e7OnTt8\n8sknrF+/HrPZnOrtr7ru3btjY2PDqlWrjE4RERERK3Tjxg1Wr17Nb7/9BkDJkiX5448/cHNzS9zG\nyckJd3d3Hjx4YFSmiIhkQhp+ijxH586dWbdundEZr6ycOXPi4+ODyWT6x+3+Go7u3r2bpk2bJl7j\nyWw289fC9Z07dzJu3DhGjRrFkCFDOHToUNrGv4JsbGyYP38+I0eO5P79+0bniIiIiJVxcHBg+vTp\nREREAFCqVCnq16+Pv78/jx8/JioqiokTJxIREUGRIkUMrhURkcxEw0+R53ByciI2NtboDKuWkJAA\nwLZt2zCZTNStWxc7Ozvgz2GdyWRi2rRp9O/fnw4dOlCrVi3atGlDqVKlkuznjz/+ICQkRCtC/0WN\nGjVo27Yt48aNMzpFRERErEzu3LmpXbs2CxYsICYmBoCvvvqKq1ev0qBBA2rUqMHx48dZsWIFuXPn\nNrhWREQyEw0/RZ7D0dEx8Y2XGGvVqlXUrFkzyVDz6NGj9OzZk82bN/Pdd9/h7u5OeHg47u7uFCxY\nMHG7WbNm0bx5c959912cnZ3p378/Dx8+NOIwMoXJkyezbt06Tp06ZXSKiIiIWJmZM2dy9uxZOnTo\nQHBwMBs2bKBs2bL8/vvvODg44O/vT4MGDdi6dSsTJkzg6tWrRieLiEgmoOGnyHM4Ojpq5aeBLBYL\ntra2WCwWvv/++ySnvO/fv59u3bpRr149fvrpJ8qWLcvy5cvJnTs3Hh4eifvYsWMHo0aNwsvLix9/\n/JEdO3awZ88evvvuO6MOK8PLkycP48ePZ8CAAeh+eCIiIpKeChQowMqVKyldujQffPAB8+bN49y5\nc/Tq1YsDBw7Qu3dvHBwcuHv3LgcPHmTo0KFGJ4uISCZgZ3SASEal096N8/TpUwIDA3F2dsbe3h5H\nR0dee+017O3tiY+PJywsjMuXL7N48WLi4uIYMGAAe/bs4Y033qBy5crAn6e6T5w4kXbt2jFz5kwA\nChUqRO3atZk9ezYdOnQw8hAztPfff58lS5awfv16unTpYnSOiIiIWJHXXnuN1157jU8//ZQHDx5g\nZ2dHnjx5AIiPj8fOzo5evXrx2muvUb9+fX744QfefPNNY6NFRCRD08pPkefQae/GsbGxIWvWrEyd\nOpWBAwdy8+ZNtm/fzrVr17C1taV3794cPnyYpk2bsnjxYuzt7Tl48CAPHjzAyckJgNDQUH755RdG\njBgB/DlQhT8vpu/k5JT4tTzL1taW+fPnM2zYMF0iQERERAzh5OSEra1t4uAzISEBOzu7xGvCV6hQ\nAT8/PxYuXGhkpoiIZAIafoo8h1Z+GsfW1pZBgwZx69YtIiIiCAgIYOXKlfj5+XH37l0cHByoWrUq\nkydP5vTp0/znP/8hZ86cfPfddwwePBj489T4IkWK4OHhgcViwd7eHoDw8HBcXV158uSJkYeY4b32\n2mt4eXkxadIko1NERETEypjNZho3boybmxuDBg1i586dPHjwAPjzfeJfbt++TY4cORIHoiIiIn9H\nw0+R59A1PzOGIkWKMHbsWK5evcrq1avJmzfvM9ucOHGCtm3bcurUKT799FMAfvrpJ7y9vQESB50n\nTpzg7t27lChRAhcXl/Q7iEwqMDCQ5cuX8+uvvxqdIiIiIlbExsaGevXqcevWLR4/fkyvXr2oXbs2\n7777Ll9++SUhISFs2rSJzZs3U7JkySQDURERkf9Lw0+R59Bp7xnP3w0+L126RGhoKJUrV6ZQoUKJ\nQ807d+5QpkwZAOzs/ry88ZYtW3BwcKBevXoAuqHPvyhYsCCjRo3igw8+0J+ViIiIpKtx48aRJUsW\n3n33Xa5fv86ECRNwdnZm0qRJdO7cma5du+Ln58fHH39sdKqIiGRwJot+ohX5W6tXr2bXrl2sXr3a\n6BR5DovFgslk4sqVK9jb21OkSBEsFgvx8fF88MEHhIaGEhISgp2dHffv36d8+fL06NGDMWPGkDVr\n1mf2I896+vQpVatWZdKkSbRr187oHBEREbEio0aN4quvvuL06dNJHj916hRlypTB2dkZ0Hs5ERH5\nZxp+ijzHxo0bWb9+PRs3bjQ6RZLh2LFjdO/eHQ8PD8qVK0dwcDB2dnbs3buX/PnzJ9nWYrGwYMEC\nIiMj8fHxoWzZsgZVZ0z79u3Dz8+PM2fOJP6QISIiIpIeHB0dWbVqFZ07d06827uIiMjL0GnvIs+h\n094zL4vFQs2aNVm3bh2Ojo4cOHAAf39/vvrqK/Lnz4/ZbH7mNVWrVuXmzZu88cYbVK9enalTp3L5\n8mUD6jOeRo0aUadOHQIDA41OERERESszfvx49uzZA6DBp4iIJItWfoo8x969e5kyZQp79+41OkXS\nUUJCAgcOHCAoKIjNmzfj6uqKj48PHTt2pHjx4kbnGSYiIoJq1apx5MgRSpUqZXSOiIiIWJFz585R\nrlw5ndouIiLJopWfIs+hu71bJ1tbWzw9PVm0aBHXrl1j8uTJnD17lmrVqlG/fn3mzJnDtWvXjM5M\nd8WKFWPIkCEMHjzY6BQRERGxMuXLl9fgU0REkk3DT5Hn0GnvYmdnR+PGjVm2bBnXr19n9OjRiXeW\nb9iwIZ999hk3b940OjPdDB48mLCwML755hujU0REREREREReiIafIs/h5OSklZ+SyMHBgebNm/P5\n559z48YNhgwZwk8//UT58uXx8vJiyZIl3Llzx+jMNJUlSxbmzJnDwIEDiYuLMzpHRERErJDFYsFs\nNuu9iIiIvDANP0WeQys/5XmyZMlC69atWbNmDdevX6dfv37s3buX0qVL4+3tzYoVK4iMjDQ6M000\nb96cChUqMGvWLKNTRERExAqZTCb69evHJ598YnSKiIhkErrhkchzXLt2jRo1anD9+nWjUySTiI6O\nZseOHQQFBbF3714aNGhAp06daNOmDTly5DA6L9VcvHiROnXqcOLECYoWLWp0joiIiFiZS5cuUbt2\nbc6dO0eePHmMzhERkQxOw0+R54iMjKRUqVKv7Ao+SVsPHz5k27ZtBAUF8cMPP9CoUSN8fHxo1aoV\nWbNmNTovxcaOHcv58+dZv3690SkiIiJihfr27Uv27NkJDAw0OkVERDI4DT9FniMmJoZcuXLpup+S\nYvfv32fr1q1s2LCBkJAQGjdujI+PDy1atMDZ2dnovGR5/PgxlSpVYuXKlXh6ehqdIyIiIlbm6tWr\nVKlShbCwMAoWLGh0joiIZGAafoo8h9lsxtbWFrPZjMlkMjpHXhF3795ly5YtBAUFcfToUZo1a0an\nTp1o1qwZjo6ORue9lM2bNzN27FiOHz+Ovb290TkiIiJiZT788EMSEhKYO3eu0SkiIpKBafgp8g8c\nHR25f/9+phtKSeZw69YtNm/eTFBQECdOnKBly5b4+PjQpEkTHBwcjM77VxaLBW9vb5o3b86gQYOM\nzhERERErc/PmTSpVqsTx48cpXry40TkiIpJBafgp8g9y5szJ5cuXyZUrl9Ep8oq7fv06mzZtIigo\niLCwMNq0aYOPjw9eXl4ZelXlr7/+SoMGDTh9+jQFChQwOkdERESszMiRI7lz5w5LliwxOkVERDIo\nDT9F/kHBggU5fvw4hQoVMjpFrMjVq1cJDg4mKCiICxcu0K5dO/4/9u48qub8/wP4896b1kulBTEm\naorCyFp2sjOM5assoSJ7mLHTIPuWbSyDKaQxIWOylW0w9n1NFCpRUSHtdbu/P+bnHllyq1uflufj\nHId77+f9+TxvR7fu677e77eDgwPatWsHNTU1oeN9Ytq0aXj16hV8fHyEjkJERETlTGJiIiwsLHDp\n0iWYm5sLHYeIiEogFj+J8lCrVi2cOnUKtWrVEjoKlVMRERGKQuizZ8/Qr18/ODg4oFWrVpBIJELH\nA/DfzvZ169bF3r17YWdnJ3QcIiIiKmc8PT0RFhYGX19foaMQEVEJxOInUR7q1q2LgIAAWFlZCR2F\nCOHh4dizZw/27NmDly9fon///nBwcICdnR3EYrGg2fz8/ODl5YUrV66UmKIsERERlQ9JSUkwNzfH\n6dOn+Xs7ERF9Qth3y0QlnKamJtLT04WOQQQAMDc3x6xZs3Dr1i2cOnUKhoaGcHNzw7fffouff/4Z\nly9fhlCfZw0aNAja2trYtm2bINcnIiKi8qtSpUqYOnUq5s6dK3QUIiIqgdj5SZSHFi1aYOXKlWjR\nooXQUYi+6P79+/D394e/vz8yMzMxYMAAODg4wMbGBiKRqNhy3L59G507d0ZISAgMDAyK7bpERERE\nqampMDc3x+HDh2FjYyN0HCIiKkHY+UmUB01NTaSlpQkdgyhP1tbW8PT0RGhoKP766y+IxWL873//\ng4WFBWbPno07d+4US0fo999/jwEDBmDOnDlFfi0iIiKiD2lra2PWrFnw8PAQOgoREZUwLH4S5YHT\n3qk0EYlEaNiwIZYsWYLw8HDs3r0bmZmZ+OGHH2BlZYV58+YhJCSkSDN4enrir7/+wo0bN4r0OkRE\nREQfGzlyJO7evYuLFy8KHYWIiEoQFj+J8qClpcXiJ5VKIpEITZo0wYoVKxAREQEfHx+8ffsWnTt3\nRv369bFw4UKEhYWp/Lr6+vpYtGgRxo8fj5ycHJWfn4iIiOhLNDQ04OHhwVkoRESUC4ufRHngtHcq\nC0QiEWxtbbF69WpERUVh48aNiIuLQ5s2bdCoUSMsXboUT548Udn1nJ2dkZ2dDV9fX5Wdk4iIiEgZ\nw4YNQ1RUFE6dOiV0FCIiKiFY/CTKA6e9U1kjFovRunVrrF+/HtHR0Vi1ahUiIiJga2uLZs2aYeXK\nlYiKiir0NTZs2IAZM2YgMTERR44cgX03e1QzrQZdA11U+aYKmrdprpiWT0RERKQqFSpUwLx58+Dh\n4VEsa54TEVHJx93eifIwfvx41KlTB+PHjxc6ClGRys7Oxj///AN/f3/89ddfsLS0hIODA/73v//B\nxMQk3+eTy+Vo2aolbt2/BYmeBMnfJwM1AagDyAIQC1S8UxGieBHcx7ljrsdcqKmpqfppERERUTkk\nk8nQoEEDrFy5Et26dRM6DhERCYzFT6I8TJkyBVWqVMHUqVOFjkJUbDIzM3HixAn4+/sjMDAQDRo0\nwIABA9C/f39UqVLlq+NlMhlc3Fyw7/g+pHZJBaoDEH3h4FeA9kltNPumGQ4fOAxtbW2VPhciIiIq\nn/bv349Fixbh2rVrEIm+9IsIERGVByx+EuUhODgYWlpaaNOmjdBRiASRkZGB4OBg+Pv74/Dhw2jc\nuDEcHBzQt29fGBoafnbM2AljsSNoB1L/lwpoKHERGaB5SBOtq7XG0cCjkEgkqn0SREREVO7I5XI0\nbtwYc+bMQd++fYWOQ0REAmLxkygP7789+GkxEZCWloajR4/C398fQUFBsLW1hYODA/r06QN9fX0A\nwMmTJ9FrUC+kOqcCWvk4eTagvVsbXlO9MGrUqKJ5AkRERFSuHDlyBNOmTcPt27f54SoRUTnG4icR\nEeVbSkoKDh06BH9/f5w4cQKtW7eGg4MDtv+xHf+o/QM0LcBJHwO1rtbC45DH/MCBiIiICk0ul6NV\nq1YYO3YsBg8eLHQcIiISCIufRERUKO/evUNgYCC2b9+OE2dOAFOg3HT3j+UAOlt1ELw3GC1btlR1\nTCIiIiqH/vnnH7i5uSEkJAQVKlQQOg4REQlALHQAIiIq3SpWrIjBgwejW7duULdRL1jhEwDEQGq9\nVPy+43eV5iMiIqLyq3379qhZsyZ27twpdBQiIhIIi59ERKQSUdFRyKyUWahzyPXliIiOUE0gIiIi\nIgALFy6Ep6cnMjIyhI5CREQCYPGTqBCysrKQnZ0tdAyiEiE1LRVQK+RJ1IAnT57Az88PJ0+exL17\n9xAfH4+cnByVZCQiIqLyx87ODvXr18fWrVuFjkJERAIo7NtUojItODgYtra20NXVVdxiOBybAAAg\nAElEQVT34Q7w27dvR05ODnenJgJgbGgMPCjkSdIAEUQ4dOgQYmNjERcXh9jYWCQnJ8PIyAhVqlRB\n1apV8/xbX1+fGyYRERFRLp6enujZsydcXFygra0tdBwiIipGLH4S5aFbt244f/487OzsFPd9XFTZ\ntm0bhg8fDg2Ngi50SFQ2tLBrgYq7KuId3hX4HNoR2pg0ZhImTpyY6/7MzEy8fPkyV0E0Li4OT548\nwcWLF3Pdn5qaiipVqihVKNXV1S31hVK5XI6tW7fi7Nmz0NTUhL29PRwdHUv98yIiIlKlRo0aoUWL\nFti4cSOmTJkidBwiIipG3O2dKA86OjrYvXs3bG1tkZaWhvT0dKSlpSEtLQ0ZGRm4fPkyZs6ciYSE\nBOjr6wsdl0hQMpkM1b6thlfdXwHVC3CCd4Dmb5qIjY7N1W2dX+np6YiLi8tVJP3S35mZmUoVSatW\nrQqpVFriCoopKSlwd3fHxYsX0bt3b8TGxuLRo0dwdHTEhAkTAAD379/HggULcOnSJUgkEgwdOhRz\n584VODkREVHxCwkJQfv27REWFoZKlSoJHYeIiIoJi59EeahWrRri4uKgpaUF4L+uT7FYDIlEAolE\nAh0dHQDArVu3WPwkArB4yWIsDFiItB/S8j1WclaCQTUHYadP8e3GmpqaqlShNDY2FnK5/JOi6JcK\npe9fG4ra+fPn0a1bN/j4+KBfv34AgE2bNmHu3Ll4/PgxXrx4AXt7ezRr1gxTp07Fo0ePsGXLFrRt\n2xaLFy8uloxEREQliZOTEywsLODh4SF0FCIiKiYsfhLloUqVKnByckLHjh0hkUigpqaGChUq5Ppb\nJpOhQYMGUFPjKhJEiYmJqFO/DuJt4yFvkI8fLxGA9IAU1y9fh4WFRZHlK4zk5GSlukljY2MhkUiU\n6iatUqWK4sOVgtixYwdmzZqF8PBwqKurQyKRIDIyEj179oS7uzvEYjHmzZuH0NBQRUHW29sb8+fP\nx40bN2BgYKCqLw8REVGpEB4eDltbWzx69AiVK1cWOg4RERUDVmuI8iCRSNCkSRN07dpV6ChEpULl\nypXxz7F/0KJtC7yTvYPcRokCaDigfUgbB/YdKLGFTwCQSqWQSqUwMzPL8zi5XI537959tjB67dq1\nT+7X1NTMs5vUwsICFhYWn51yr6uri/T0dAQGBsLBwQEAcPToUYSGhiIpKQkSiQR6enrQ0dFBZmYm\n1NXVYWlpiYyMDJw7dw69e/cukq8VERFRSWVubo6+ffti5cqVnAVBRFROsPhJlAdnZ2eYmpp+9jG5\nXF7i1v8jKgmsra1x5fwVtO/cHu8evkNyg2TAEoDkg4PkAJ4CkksSSBOkOHzoMFq2bClQYtUSiUSo\nVKkSKlWqhO+++y7PY+VyOd6+ffvZ7tFLly4hNjYWHTp0wE8//fTZ8V27doWLiwvc3d3x+++/w9jY\nGNHR0ZDJZDAyMkK1atUQHR0NPz8/DB48GO/evcP69evx6tUrpKamFsXTLzdkMhlCQkKQkJAA4L/C\nv7W1NSQSyVdGEhGR0ObMmQMbGxtMmjQJxsbGQschIqIixmnvRIXw+vVrZGVlwdDQEGKxWOg4RCVK\nRkYG9u/fj6VeSxH+JBxqNdUgU5dBnCWGPFYOA6kB3rx6g8C/A9GmTRuh45Zab9++xb///otz584p\nNmX666+/MGHCBAwbNgweHh5YtWoVZDIZ6tati0qVKiEuLg6LFy9WrBNKynv16hW8vb2xefNmVKhQ\nAVWrVoVIJEJsbCzS09MxevRouLq68s00EVEJ5+7uDjU1NXh5eQkdhYiIihiLn0R52Lt3L8zMzNCo\nUaNc9+fk5EAsFmPfvn24evUqJkyYgBo1agiUkqjku3fvnmIqto6ODmrVqoWmTZti/fr1OHXqFA4c\nOCB0xDLD09MTBw8exJYtW2BjYwMASEpKwoMHD1CtWjVs27YNJ06cwPLly9GqVatcY2UyGYYNG/bF\nNUoNDQ3LbWejXC7H6tWr4enpiT59+mDs2LFo2rRprmOuX7+OjRs3IiAgALNmzcLUqVM5Q4CIqISK\njY2FtbU1bt++zd/jiYjKOBY/ifLQuHFj/PDDD5g3b95nH7906RLGjx+PlStXol27dsWajYjo5s2b\nyM7OVhQ5AwICMG7cOEydOhVTp05VLM/xYWd669at8e2332L9+vXQ19fPdT6ZTAY/Pz/ExcV9ds3S\n169fw8DAIM8NnN7/28DAoEx1xE+fPh2HDx/GkSNHULNmzTyPjY6ORo8ePWBvb49Vq1axAEpEVEJN\nnz4dSUlJ2LRpk9BRiIioCHHNT6I86OnpITo6GqGhoUhJSUFaWhrS0tKQmpqKzMxMPH/+HLdu3UJM\nTIzQUYmoHIqLi4OHhweSkpJgZGSEN2/ewMnJCePHj4dYLEZAQADEYjGaNm2KtLQ0zJw5E+Hh4Vix\nYsUnhU/gv03ehg4d+sXrZWdn49WrV58URaOjo3H9+vVc97/PpMyO95UrVy7RBcINGzbg4MGDOHfu\nnFI7A9eoUQNnz55Fq1atsHbtWkyaNKkYUhIRUX5NmzYNlpaWmDZtGmrVqiV0HCIiKiLs/CTKw9Ch\nQ7Fr1y6oq6sjJycHEokEampqUFNTQ4UKFVCxYkVkZWXB29sbHTt2FDouEZUzGRkZePToER4+fIiE\nhASYm5vD3t5e8bi/vz/mzp2Lp0+fwtDQEE2aNMHUqVM/me5eFDIzM/Hy5cvPdpB+fF9KSgqMjY2/\nWiStWrUqdHV1i7VQmpKSgpo1a+LSpUtf3cDqY0+ePEGTJk0QGRmJihUrFlFCIiIqjHnz5iEiIgLb\nt28XOgoRERURFj+J8jBgwACkpqZixYoVkEgkuYqfampqEIvFkMlk0NfXh4aGhtBxiYgUU90/lJ6e\njsTERGhqairVuVjc0tPTv1go/fjvjIwMxfT6rxVKK1asWOhC6e+//46///4bgYGBBRrft29fdO7c\nGaNHjy5UDiIiKhpv376Fubk5/v33X9SpU0foOEREVARY/CTKw7BhwwAAO3bsEDgJUenRvn171K9f\nH+vWrQMA1KpVCxMmTMBPP/30xTHKHEMEAGlpaUoVSePi4pCdna1UN2mVKlUglUo/uZZcLkeTJk2w\naNEidO3atUB5T5w4gcmTJ+POnTslemo/EVF5tnTpUty6dQt//vmn0FGIiKgIsPhJlIfg4GBkZGSg\nV69eAHJ3VMlkMgCAWCzmG1oqV+Lj4/HLL7/g6NGjiImJgZ6eHurXr48ZM2bA3t4eb968QYUKFaCj\nowNAucJmQkICdHR0oKmpWVxPg8qBlJQUpQqlsbGxEIvFn3ST6unpYd26dXj37l2BN2/KyclB5cqV\nER4eDkNDQxU/QyIiUoWUlBSYm5sjODgYDRo0EDoOERGpGDc8IspDly5dct3+sMgpkUiKOw5RidC3\nb1+kp6fDx8cHZmZmePnyJc6cOYOEhAQA/20Ull8GBgaqjkkEHR0d1K5dG7Vr187zOLlcjuTk5E+K\nog8ePEDFihULtWu9WCyGoaEhXr9+zeInEVEJpaOjgxkzZsDDwwN///230HGIiEjF2PlJ9BUymQwP\nHjxAeHg4TE1N0bBhQ6Snp+PGjRtITU1FvXr1ULVqVaFjEhWLt2/fQl9fHydOnECHDh0+e8znpr0P\nHz4c4eHhOHDgAKRSKaZMmYKff/5ZMebj7lCxWIx9+/ahb9++XzyGqKg9e/YMdnZ2iI6OLtR5TE1N\n8c8//3AnYSKiEiw9PR3fffcdAgIC0KxZM6HjEBGRChW8lYGonFi2bBkaNGgAR0dH/PDDD/Dx8YG/\nvz969OiB//3vf5gxYwbi4uKEjklULKRSKaRSKQIDA5GRkaH0uNWrV8Pa2ho3b96Ep6cnZs2ahQMH\nDhRhUqLCMzAwQGJiIlJTUwt8jvT0dMTHx7O7mYiohNPU1MScOXPg4eGBmzdvws3NDY0aNYKZmRms\nra3RpUsX7Nq1K1+//xARUcnA4idRHs6ePQs/Pz8sXboU6enpWLNmDVatWoWtW7fi119/xY4dO/Dg\nwQP89ttvQkclKhYSiQQ7duzArl27oKenhxYtWmDq1Km4cuVKnuOaN2+OGTNmwNzcHCNHjsTQoUPh\n5eVVTKmJCkZbWxv29vbw9/cv8Dn27t2LVq1aoVKlSipMRkRERaFatWq4fv06fvjhB5iammLLli0I\nDg6Gv78/Ro4cCV9fX9SsWROzZ89Genq60HGJiEhJLH4S5SE6OhqVKlVSTM/t168funTpAnV1dQwe\nPBi9evXCjz/+iMuXLwuclKj49OnTBy9evMChQ4fQvXt3XLx4Eba2tli6dOkXx9jZ2X1yOyQkpKij\nEhXa2LFjsXHjxgKP37hxI8aOHavCREREVBTWrFmDsWPHYtu2bYiMjMSsWbPQpEkTmJubo169eujf\nvz+Cg4Nx7tw5PHz4EJ06dUJiYqLQsYmISAksfhLlQU1NDampqbk2N6pQoQKSk5MVtzMzM5GZmSlE\nPCLBqKurw97eHnPmzMG5c+fg6uqKefPmITs7WyXnF4lE+HhJ6qysLJWcmyg/unTpgsTERAQFBeV7\n7IkTJ/D8+XP06NGjCJIREZGqbNu2Db/++isuXLiAH3/8Mc+NTb/77jvs2bMHNjY26N27NztAiYhK\nARY/ifLwzTffAAD8/PwAAJcuXcLFixchkUiwbds2BAQE4OjRo2jfvr2QMYkEV7duXWRnZ3/xDcCl\nS5dy3b548SLq1q37xfMZGRkhJiZGcTsuLi7XbaLiIhaL4e3tjaFDh+LmzZtKj7t79y4GDx4MHx+f\nPN9EExGRsJ4+fYoZM2bgyJEjqFmzplJjxGIx1qxZAyMjIyxatKiIExIRUWGx+EmUh4YNG6JHjx5w\ndnZGp06d4OTkBGNjY8yfPx/Tp0+Hu7s7qlatipEjRwodlahYJCYmwt7eHn5+frh79y4iIiKwd+9e\nrFixAh07doRUKv3suEuXLmHZsmUIDw/H1q1bsWvXrjx3be/QoQM2bNiA69ev4+bNm3B2doaWllZR\nPS2iPLVt2xabN29Gly5dEBAQgJycnC8em5OTg7///hsdOnTA+vXrYW9vX4xJiYgov3777TcMGzYM\nFhYW+RonFouxePFibN26lbPAiIhKODWhAxCVZFpaWpg/fz6aN2+OkydPonfv3hg9ejTU1NRw+/Zt\nhIWFwc7ODpqamkJHJSoWUqkUdnZ2WLduHcLDw5GRkYHq1atjyJAhmD17NoD/pqx/SCQS4aeffsKd\nO3ewcOFCSKVSLFiwAH369Ml1zIdWrVqFESNGoH379qhSpQqWL1+O0NDQon+CRF/Qt29fGBsbY8KE\nCZgxYwbGjBmDQYMGwdjYGADw6tUr7N69G5s2bYJMJoO6ujq6d+8ucGoiIspLRkYGfHx8cO7cuQKN\nr1OnDqytrbF//344OjqqOB0REamKSP7xompERERE9FlyuRyXL1/Gxo0bcfDgQSQlJUEkEkEqlaJn\nz54YO3Ys7Ozs4OzsDE1NTWzevFnoyERE9AWBgYFYs2YNTp06VeBz/Pnnn/D19cXhw4dVmIyIiFSJ\nnZ9ESnr/OcGHHWpyufyTjjUiIiq7RCIRbG1tYWtrCwCKTb7U1HL/SrV27Vp8//33OHz4MDc8IiIq\noZ4/f57v6e4fs7CwwIsXL1SUiIiIigKLn0RK+lyRk4VPIqLy7eOi53u6urqIiIgo3jBERJQv6enp\nhV6+SlNTE2lpaSpKRERERYEbHhEREREREVG5o6uri9evXxfqHG/evIGenp6KEhERUVFg8ZOIiIiI\niIjKnaZNm+LkyZPIysoq8DmCgoLQpEkTFaYiIiJVY/GT6Cuys7M5lYWIiIiIqIypX78+atWqhYMH\nDxZofGZmJrZu3YoxY8aoOBkREakSi59EX3H48GE4OjoKHYOIiIiIiFRs7Nix+PXXXxWbm+bHX3/9\nBUtLS1hbWxdBMiIiUhUWP4m+gouYE5UMERERMDAwQGJiotBRqBRwdnaGWCyGRCKBWCxW/PvOnTtC\nRyMiohKkX79+iI+Ph5eXV77GPX78GJMmTYKHh0cRJSMiIlVh8ZPoKzQ1NZGeni50DKJyz9TUFD/+\n+CPWrl0rdBQqJTp16oTY2FjFn5iYGNSrV0+wPIVZU46IiIqGuro6Dh8+jHXr1mHFihVKdYDev38f\n9vb2mDt3Luzt7YshJRERFQaLn0RfoaWlxeInUQkxa9YsbNiwAW/evBE6CpUCGhoaMDIygrGxseKP\nWCzG0aNH0bp1a+jr68PAwADdu3fHo0ePco29cOECbGxsoKWlhebNmyMoKAhisRgXLlwA8N960K6u\nrqhduza0tbVhaWmJVatW5TqHk5MT+vTpgyVLlqBGjRowNTUFAOzcuRNNmzZFpUqVULVqVTg6OiI2\nNlYxLisrC+PHj4eJiQk0NTXx7bffsrOIiKgIffPNNzh37hx8fX3RokUL7Nmz57MfWN27dw/jxo1D\nmzZtsHDhQowePVqAtERElF9qQgcgKuk47Z2o5DAzM0OPHj2wfv16FoOowFJTUzFlyhTUr18fKSkp\n8PT0RK9evXD//n1IJBK8e/cOvXr1Qs+ePbF79248e/YMkyZNgkgkUpxDJpPh22+/xb59+2BoaIhL\nly7Bzc0NxsbGcHJyUhx38uRJ6Orq4vjx44puouzsbCxcuBCWlpZ49eoVpk2bhkGDBuHUqVMAAC8v\nLxw+fBj79u3DN998g+joaISFhRXvF4mIqJz55ptvcPLkSZiZmcHLywuTJk1C+/btoauri/T0dDx8\n+BBPnz6Fm5sb7ty5g+rVqwsdmYiIlCSSF2RlZ6Jy5NGjR+jRowffeBKVEA8fPsSAAQNw7do1VKhQ\nQeg4VEI5Oztj165d0NTUVNzXpk0bHD58+JNjk5KSoK+vj4sXL6JZs2bYsGED5s+fj+joaKirqwMA\nfH19MXz4cPz7779o0aLFZ685depU3L9/H0eOHAHwX+fnyZMnERUVBTW1L3/efO/ePTRo0ACxsbEw\nNjbGuHHj8PjxYwQFBRXmS0BERPm0YMEChIWFYefOnQgJCcGNGzfw5s0baGlpwcTEBB07duTvHkRE\npRA7P4m+gtPeiUoWS0tL3Lp1S+gYVAq0bdsWW7duVXRcamlpAQDCw8Pxyy+/4PLly4iPj0dOTg4A\nICoqCs2aNcPDhw/RoEEDReETAJo3b/7JOnAbNmzA9u3bERkZibS0NGRlZcHc3DzXMfXr1/+k8Hnt\n2jUsWLAAt2/fRmJiInJyciASiRAVFQVjY2M4OzujS5cusLS0RJcuXdC9e3d06dIlV+cpERGp3oez\nSqysrGBlZSVgGiIiUhWu+Un0FZz2TlTyiEQiFoLoq7S1tVGrVi3Url0btWvXRrVq1QAA3bt3x+vX\nr7Ft2zZcuXIFN27cgEgkQmZmptLn9vPzw9SpUzFixAgcO3YMt2/fxqhRoz45h46OTq7bycnJ6Nq1\nK3R1deHn54dr164pOkXfj23SpAkiIyOxaNEiZGdnY8iQIejevXthvhREREREROUWOz+JvoK7vROV\nPjk5ORCL+fkeferly5cIDw+Hj48PWrZsCQC4cuWKovsTAOrUqQN/f39kZWUppjdevnw5V8H9/Pnz\naNmyJUaNGqW4T5nlUUJCQvD69WssWbJEsV7c5zqZpVIp+vfvj/79+2PIkCFo1aoVIiIiFJsmERER\nERGRcvjOkOgrOO2dqPTIycnBvn374ODggOnTp+PixYtCR6ISxtDQEJUrV8aWLVvw+PFjnD59GuPH\nj4dEIlEc4+TkBJlMhpEjRyI0NBTHjx/HsmXLAEBRALWwsMC1a9dw7NgxhIeHY/78+Yqd4PNiamoK\ndXV1rFu3DhERETh06BDmzZuX65hVq1bB398fDx8+RFhYGP744w/o6enBxMREdV8IIiIiIqJygsVP\noq94v1ZbVlaWwEmI6EveTxe+ceMGpk2bBolEgqtXr8LV1RVv374VOB2VJGKxGHv27MGNGzdQv359\nTJw4EUuXLs21gUXFihVx6NAh3LlzBzY2Npg5cybmz58PuVyu2EBp7Nix6Nu3LxwdHdG8eXO8ePEC\nkydP/ur1jY2NsX37dgQEBMDKygqLFy/G6tWrcx0jlUqxbNkyNG3aFM2aNUNISAiCg4NzrUFKRETC\nkclkEIvFCAwMLNIxRESkGtztnUgJUqkUMTExqFixotBRiOgDqampmDNnDo4ePQozMzPUq1cPMTEx\n2L59OwCgS5cuMDc3x8aNG4UNSqVeQEAAHB0dER8fD11dXaHjEBHRF/Tu3RspKSk4ceLEJ489ePAA\n1tbWOHbsGDp27Fjga8hkMlSoUAEHDhxAr169lB738uVL6Ovrc8d4IqJixs5PIiVw6jtRySOXy+Ho\n6IgrV65g8eLFaNSoEY4ePYq0tDTFhkgTJ07Ev//+i4yMDKHjUimzfft2nD9/HpGRkTh48CB+/vln\n9OnTh4VPIqISztXVFadPn0ZUVNQnj/3+++8wNTUtVOGzMIyNjVn4JCISAIufRErgju9EJc+jR48Q\nFhaGIUOGoE+fPvD09ISXlxcCAgIQERGBlJQUBAYGwsjIiN+/lG+xsbEYPHgw6tSpg4kTJ6J3796K\njmIiIiq5evToAWNjY/j4+OS6Pzs7G7t27YKrqysAYOrUqbC0tIS2tjZq166NmTNn5lrmKioqCr17\n94aBgQF0dHRgbW2NgICAz17z8ePHEIvFuHPnjuK+j6e5c9o7EZFwuNs7kRK44ztRySOVSpGWlobW\nrVsr7mvatCm+++47jBw5Ei9evICamhqGDBkCPT09AZNSaTRjxgzMmDFD6BhERJRPEokEw4YNw/bt\n2zF37lzF/YGBgUhISICzszMAQFdXFzt37kS1atVw//59jBo1Ctra2vDw8AAAjBo1CiKRCGfPnoVU\nKkVoaGiuzfE+9n5DPCIiKnnY+UmkBE57Jyp5qlevDisrK6xevRoymQzAf29s3r17h0WLFsHd3R0u\nLi5wcXEB8N9O8ERERFT2ubq6IjIyMte6n97e3ujcuTNMTEwAAHPmzEHz5s1Rs2ZNdOvWDdOnT8fu\n3bsVx0dFRaF169awtrbGt99+iy5duuQ5XZ5baRARlVzs/CRSAqe9E5VMK1euRP/+/dGhQwc0bNgQ\n58+fR69evdCsWTM0a9ZMcVxGRgY0NDQETEpERETFxdzcHG3btoW3tzc6duyIFy9eIDg4GHv27FEc\n4+/vj/Xr1+Px48dITk5GdnZ2rs7OiRMnYvz48Th06BDs7e3Rt29fNGzYUIinQ0REhcTOTyIlsPOT\nqGSysrLC+vXrUa9ePdy5cwcNGzbE/PnzAQDx8fE4ePAgHBwc4OLigtWrV+PBgwcCJyYiIqLi4Orq\nigMHDuDNmzfYvn07DAwMFDuznzt3DkOGDEHPnj1x6NAh3Lp1C56ensjMzFSMd3Nzw9OnTzF8+HA8\nfPgQtra2WLx48WevJRb/97b6w+7PD9cPJSIiYbH4SaQErvlJVHLZ29tjw4YNOHToELZt2wZjY2N4\ne3ujTZs26Nu3L16/fo2srCz4+PjA0dER2dnZQkcm+qpXr17BxMQEZ8+eFToKEVGp1L9/f2hqasLX\n1xc+Pj4YNmyYorPzwoULMDU1xYwZM9C4cWOYmZnh6dOnn5yjevXqGDlyJPz9/fHLL79gy5Ytn72W\nkZERACAmJkZx382bN4vgWRERUUGw+EmkBE57JyrZZDIZdHR0EB0djY4dO2L06NFo06YNHj58iKNH\nj8Lf3x9XrlyBhoYGFi5cKHRcoq8yMjLCli1bMGzYMCQlJQkdh4io1NHU1MTAgQMxb948PHnyRLEG\nOABYWFggKioKf/75J548eYJff/0Ve/fuzTXe3d0dx44dw9OnT3Hz5k0EBwfD2tr6s9eSSqVo0qQJ\nli5digcPHuDcuXOYPn06N0EiIiohWPwkUgKnvROVbO87OdatW4f4+HicOHECmzdvRu3atQH8twOr\npqYmGjdujIcPHwoZlUhpPXv2RKdOnTB58mShoxARlUojRozAmzdv0LJlS1haWiru//HHHzF58mRM\nnDgRNjY2OHv2LDw9PXONlclkGD9+PKytrdGtWzd888038Pb2Vjz+cWFzx44dyM7ORtOmTTF+/Hgs\nWrTokzwshhIRCUMk57Z0RF81fPhwtGvXDsOHDxc6ChF9wfPnz9GxY0cMGjQIHh4eit3d36/D9e7d\nO9StWxfTp0/HhAkThIxKpLTk5GR8//338PLyQu/evYWOQ0RERERU6rDzk0gJnPZOVPJlZGQgOTkZ\nAwcOBPBf0VMsFiM1NRV79uxBhw4dYGxsDEdHR4GTEilPKpVi586dGD16NOLi4oSOQ0RERERU6rD4\nSaQETnsnKvlq166N6tWrw9PTE2FhYUhLS4Ovry/c3d2xatUq1KhRA2vXrlVsSkBUWrRs2RLOzs4Y\nOXIkOGGHiIiIiCh/WPwkUgJ3eycqHTZt2oSoqCg0b94choaG8PLywuPHj9G9e3esXbsWrVu3Fjoi\nUYHMmzcPz549y7XeHBERERERfZ2a0AGISgNOeycqHWxsbHDkyBGcPHkSGhoakMlk+P7772FiYiJ0\nNKJCUVdXh6+vL9q3b4/27dsrNvMiIiIiIqK8sfhJpAQtLS3Ex8cLHYOIlKCtrY0ffvhB6BhEKlev\nXj3MnDkTQ4cOxZkzZyCRSISORERERERU4nHaO5ESOO2diIhKgkmTJkFdXR0rVqwQOgoRERERUanA\n4ieREjjtnYiISgKxWIzt27fDy8sLt27dEjoOEVGJ9urVKxgYGCAqKkroKEREJCAWP4mUwN3eiUo3\nuVzOXbKpzKhZsyZWrlwJJycn/mwiIsrDypUr4eDggJo1awodhYiIBMTiJ5ESOO2dqPSSy+XYu3cv\ngoKChI5CpDJOTk6wtLTEnDlzhI5CRFQivXr1Clu3bsXMmTOFjkJERAJj8ZNICacD+FkAACAASURB\nVJz2TlR6iUQiiEQizJs3j92fVGaIRCJs3rwZu3fvxunTp4WOQ0RU4qxYsQKOjo745ptvhI5CREQC\nY/GTSAmc9k5UuvXr1w/Jyck4duyY0FGIVMbQ0BBbt27F8OHD8fbtW6HjEBGVGC9fvsS2bdvY9UlE\nRABY/CRSCjs/iUo3sViMOXPmYP78+ez+pDKle/fu6Nq1KyZOnCh0FCKiEmPFihUYOHAguz6JiAgA\ni59ESuGan0Sl34ABA5CQkIBTp04JHYVIpVauXInz589j//79QkchIhLcy5cv8fvvv7Prk4iIFFj8\nJFICp70TlX4SiQRz5syBp6en0FGIVEoqlcLX1xdjx45FbGys0HGIiAS1fPlyDBo0CDVq1BA6ChER\nlRAsfhIpgdPeicqGgQMH4vnz5zhz5ozQUYhUytbWFiNHjsSIESO4tAMRlVtxcXHw9vZm1ycREeXC\n4ieREjjtnahsUFNTw+zZs9n9SWXSL7/8gpiYGGzdulXoKEREgli+fDkGDx6M6tWrCx2FiIhKEJGc\n7QFEX5WYmAhzc3MkJiYKHYWICikrKwsWFhbw9fVFq1athI5DpFIhISFo06YNLl26BHNzc6HjEBEV\nm9jYWFhZWeHu3bssfhIRUS7s/CRSAqe9E5UdFSpUwKxZs7BgwQKhoxCpnJWVFTw8PDB06FBkZ2cL\nHYeIqNgsX74cQ4YMYeGTiIg+wc5PIiXk5ORATU0NMpkMIpFI6DhEVEiZmZn47rvv4O/vD1tbW6Hj\nEKlUTk4OOnfujA4dOmDWrFlCxyEiKnLvuz7v3bsHExMToeMQEVEJw+InkZI0NDSQlJQEDQ0NoaMQ\nkQps2rQJhw4dwuHDh4WOQqRyz549Q+PGjREUFIRGjRoJHYeIqEj99NNPkMlkWLt2rdBRiIioBGLx\nk0hJurq6iIyMhJ6entBRiEgFMjIyYGZmhgMHDqBJkyZCxyFSOT8/PyxevBjXrl2DlpaW0HGIiIpE\nTEwMrK2tcf/+fVSrVk3oOEREVAJxzU8iJXHHd6KyRUNDA9OnT+fan1RmDRo0CPXq1ePUdyIq05Yv\nX46hQ4ey8ElERF/Ezk8iJZmamuL06dMwNTUVOgoRqUhaWhrMzMxw+PBh2NjYCB2HSOUSExPRoEED\n7Ny5Ex06dBA6DhGRSrHrk4iIlMHOTyIlccd3orJHS0sLU6dOxcKFC4WOQlQkKleujG3btsHZ2Rlv\n3rwROg4RkUotW7YMw4YNY+GTiIjyxM5PIiU1bNgQPj4+7A4jKmNSU1NRu3ZtHD9+HPXr1xc6DlGR\nGDduHJKSkuDr6yt0FCIilXjx4gXq1auHkJAQVK1aVeg4RERUgrHzk0hJWlpaXPOTqAzS1tbGzz//\nzO5PKtOWL1+Oy5cvY+/evUJHISJSiWXLlmH48OEsfBIR0VepCR2AqLTgtHeismvMmDEwMzNDSEgI\nrKyshI5DpHI6Ojrw9fVFr1690KpVK04RJaJS7fnz5/D19UVISIjQUYiIqBRg5yeRkrjbO1HZJZVK\nMXnyZHZ/UpnWvHlzjB49Gi4uLuCqR0RUmi1btgzOzs7s+iQiIqWw+EmkJE57Jyrbxo0bh+PHjyM0\nNFToKERFZs6cOYiPj8fmzZuFjkJEVCDPnz/Hrl27MG3aNKGjEBFRKcHiJ5GSOO2dqGyrWLEiJk6c\niMWLFwsdhajIVKhQAb6+vvjll18QFhYmdBwionxbunQpXFxcUKVKFaGjEBFRKcE1P4mUxGnvRGXf\nhAkTYGZmhvDwcJibmwsdh6hI1KlTB7/88gucnJxw7tw5qKnx10EiKh2io6Ph5+fHWRpERJQv7Pwk\nUhKnvROVfbq6uhg/fjy7P6nMGzduHCpVqoQlS5YIHYWISGlLly6Fq6srjI2NhY5CRESlCD/qJ1IS\np70TlQ8TJ06Eubk5nj59ilq1agkdh6hIiMVi+Pj4wMbGBt26dUOTJk2EjkRElKdnz57hjz/+YNcn\nERHlGzs/iZTEae9E5YO+vj7GjBnDjjgq86pXr45169bBycmJH+4RUYm3dOlSjBgxgl2fRESUbyx+\nEimJ096Jyo/Jkydj3759iIyMFDoKUZFydHREw4YNMWPGDKGjEBF90bNnz7B7925MmTJF6ChERFQK\nsfhJpIT09HSkp6fjxYsXiIuLg0wmEzoSERUhAwMDuLm5YdmyZQCAnJwcvHz5EmFhYXj27Bm75KhM\n2bBhA/bv34/jx48LHYWI6LOWLFmCkSNHsuuTiIgKRCSXy+VChyAqqa5fv46NGzdi79690NTUhIaG\nBtLT06Gurg43NzeMHDkSJiYmQsckoiLw8uVLWFhYwG20G3x8fZCcnAw1bTXkZOUgOzUbPX7ogSkT\np8DOzg4ikUjouESFcvz4cbi4uODOnTvQ19cXOg4RkUJUVBRsbGwQGhoKIyMjoeMQEVEpxOIn0WdE\nRkZi0KBBePHiBUaPHg0XF5dcv2zdvXsXmzZtwp9//on+/ftj/fr10NDQEDAxEalSdnY23H9yx5at\nW4C6gKypDPjwc440QHRLBO3b2jAxMMHBgIOwtLQULC+RKri7uyM+Ph5//PGH0FGIiBTGjBkDXV1d\nLF26VOgoRERUSrH4SfSRkJAQdOrUCVOmTIG7uzskEskXj01KSoKLiwsSEhJw+PBhaGtrF2NSIioK\nmZmZ6NarGy5FXkJqr1Qgr2/rHEB0UwTpeSlOBZ/ijtlUqqWmpqJRo0aYP38+HBwchI5DRITIyEg0\natQIDx8+hKGhodBxiIiolGLxk+gDMTExsLOzw4IFC+Dk5KTUGJlMhuHDhyM5ORkBAQEQi7mULlFp\nJZfL4TjEEQfvHERanzTgy5995BYK6J3Qw40rN1CrVq0izUhUlK5evYqePXvixo0bqF69utBxiKic\nGz16NPT19bFkyRKhoxARUSnG4ifRByZMmAB1dXWsWrUqX+MyMzPRtGlTLFmyBN27dy+idERU1C5c\nuIDOfTsjxTUFUM/fWPFZMX40+hEBfwYUTTiiYuLp6Ynz588jKCiI69kSkWDY9UlERKrC4ifR/0tO\nTkbNmjVx584d1KhRI9/jvb29sX//fhw6dKgI0hFRcejr0BcH3h6A3K4APxpTAc2Nmoh6EsUNGahU\ny87ORsuWLTF06FCMGzdO6DhEVE6NGjUKBgYGWLx4sdBRiIiolGPxk+j//fbbbwgODsb+/fsLND41\nNRU1a9bE1atXOe2VqBR6+fIlatauiYxxGXmv85kHrcNamNNnDmbNnKXacETF7NGjR2jRogXOnz/P\nzbyIqNi97/p89OgRDAwMhI5DRESlHBcnJPp/hw4dwqBBgwo8XltbG71798aRI0dUmIqIisuJEydQ\nwbxCgQufAJBWNw279+9WXSgigVhYWMDT0xNOTk7IysoSOg4RlTOLFi3C6NGjWfgkIiKVYPGT6P8l\nJCSgWrVqhTpHtWrVkJiYqKJERFScEhISkKVdyCKPFHid+Fo1gYgENmbMGFSuXBmLFi0SOgoRlSMR\nEREICAjATz/9JHQUIiIqI1j8JCIiIqJPiEQieHt7Y9OmTbhy5YrQcYionFi0aBHGjBnDrk8iIlIZ\nFj+J/p+BgQFiYmIKdY6YmBhUrlxZRYmIqDgZGBigQmqFwp0kGdCvrK+aQEQlgImJCdavXw8nJyek\npqYKHYeIyrinT59i//797PokIiKVYvGT6P/17NkTf/zxR4HHp6am4u+//0b37t1VmIqIikvHjh2R\nFZ4FFKK+o/VACwP7DlRdKKISYMCAAWjatCmmTZsmdBQiKuMWLVqEsWPHspmAiIhUisVPov83ePBg\nnD59GtHR0QUa/+eff8LQ0BDq6uoqTkZExcHY2Bjde3SH6LaoYCdIBbLvZcPVxVW1wYhKgF9//RWB\ngYEIDg4WOgoRlVFPnjzBgQMHMHnyZKGjEBFRGcPiJ9H/k0qlGDx4MFavXp3vsZmZmVizZg3q1q2L\n+vXrY9y4cYiKiiqClERUlKZMnALtW9pAZv7Hiq+KoSPVQY8ePXDy5EnVhyMSkJ6eHnx8fODq6sqN\n/YioSLDrk4iIigqLn0QfmD17NgICArBz506lx8hkMri6usLMzAwBAQEIDQ1FxYoVYWNjAzc3Nzx9\n+rQIExORKtnZ2aGHfQ9oBWoBsnwMfABUulsJ1y5ew9SpU+Hm5oauXbvi9u3bRZaVqLjZ29ujf//+\nGDNmDORyudBxiKgMefLkCf7++292fRIRUZFg8ZPoA1WrVsWRI0cwc+ZMeHl5QSbLu/qRlJSEAQMG\nIDo6Gn5+fhCLxTA2NsbSpUvx6NEjVKlSBU2aNIGzszPCwsKK6VkQUUGJRCL4+viiRY0W0N6r/fX1\nP3MA0XURKh2vhONHj8PMzAwODg548OABevTogc6dO8PJyQmRkZHFkp+oqC1ZsgR3797F7t27hY5C\nRGXIwoULMW7cOOjrc9NAIiJSPRY/iT5iZWWFCxcuICAgAGZmZli6dClevnyZ65i7d+9izJgxMDU1\nhaGhIYKCgqCtrZ3rGAMDAyxYsACPHz9GrVq10KJFCwwZMgQPHjwozqdDRPmkrq6OoINBGNZpGDQ3\nakLriBbw4qODUgHRRRF0tujA/Ik5rly4giZNmuQ6x4QJExAWFgZTU1PY2Njg559/RkJCQvE+GSIV\n09LSwq5duzBp0iQ8e/ZM6DhEVAY8fvwYgYGBmDRpktBRiIiojBLJOW+J6IuuX7+OTZs2Yc+ePdDR\n0YGOjg7evn0LDQ0NuLm5YcSIETAxMVHqXElJSdiwYQPWrFmDdu3aYc6cOahfv34RPwMiKoxXr15h\n67atWP3rarx79w4VdCogPTkd8kw5evfpjSkTp8DW1hYiUd6bJMXExGD+/PkICAjAlClT4O7uDi0t\nrWJ6FkSqt3DhQpw+fRrHjh2DWMzP0omo4JydnfHtt99i3rx5QkchIqIyisVPIiVkZGQgPj4eqamp\n0NXVhYGBASQSSYHOlZycjM2bN2PVqlWws7ODh4cHbGxsVJyYiFQpJycHCQkJePPmDfbs2YMnT57g\n999/z/d5QkNDMWvWLFy9ehWenp4YOnRogV9LiISUnZ2N1q1bY+DAgXB3dxc6DhGVUuHh4bC1tUV4\neDj09PSEjkNERGUUi59ERERElG/h4eGws7PD2bNnUbduXaHjEFEptH79eiQkJLDrk4iIihSLn0RE\nRERUIL/99hu2bt2KixcvokKFCkLHIaJS5P3bULlczuUziIioSPGnDBEREREViJubG6pUqYIFCxYI\nHYWIShmRSASRSMTCJxERFTl2fhIRERFRgcXExMDGxgYHDhyAra2t0HGIiIiIiHLhx2xUpojFYuzf\nv79Q59ixYwcqVaqkokREVFLUqlULXl5eRX4dvoZQeVOtWjVs2LABTk5OSElJEToOEREREVEu7Pyk\nUkEsFkMkEuFz/11FIhGGDRsGb29vvHz5Evr6+oVadywjIwPv3r2DoaFhYSITUTFydnbGjh07FNPn\nTExM0KNHDyxevFixe2xCQgJ0dHSgqalZpFn4GkLl1bBhw6CtrY1NmzYJHYWIShi5XA6RSCR0DCIi\nKqdY/KRS4eXLl4p/Hzx4EG5uboiNjVUUQ7W0tFCxYkWh4qlcVlYWN44gygdnZ2e8ePECu3btQlZW\nFkJCQuDi4oLWrVvDz89P6HgqxTeQVFK9ffsWDRo0wObNm9GtWzeh4xBRCZSTk8M1PomIqNjxJw+V\nCsbGxoo/77u4jIyMFPe9L3x+OO09MjISYrEY/v7+aNeuHbS1tdGoUSPcvXsX9+/fR8uWLSGVStG6\ndWtERkYqrrVjx45chdTo6Gj8+OOPMDAwgI6ODqysrLBnzx7F4/fu3UOnTp2gra0NAwMDODs7Iykp\nSfH4tWvX0KVLFxgZGUFXVxetW7fGpUuXcj0/sViMjRs3ol+/fpBKpZg9ezZycnIwYsQI1K5dG9ra\n2rCwsMCKFStU/8UlKiM0NDRgZGQEExMTdOzYEQMGDMCxY8cUj3887V0sFmPz5s348ccfoaOjA0tL\nS5w+fRrPnz9H165dIZVKYWNjg5s3byrGvH99OHXqFOrXrw+pVIoOHTrk+RoCAEeOHIGtrS20tbVh\naGiI3r17IzMz87O5AKB9+/Zwd3f/7PO0tbXFmTNnCv6FIioiurq62L59O0aMGIH4+Hih4xCRwGQy\nGS5fvoxx48Zh1qxZePfuHQufREQkCP70oTJv3rx5mDlzJm7dugU9PT0MHDgQ7u7uWLJkCa5evYr0\n9PRPigwfdlWNGTMGaWlpOHPmDEJCQrBmzRpFATY1NRVdunRBpUqVcO3aNRw4cAAXLlyAq6urYvy7\nd+8wdOhQnD9/HlevXoWNjQ169OiB169f57qmp6cnevTogXv37mHcuHHIyclBjRo1sG/fPoSGhmLx\n4sVYsmQJfHx8Pvs8d+3ahezsbFV92YhKtSdPniAoKOirHdSLFi3CoEGDcOfOHTRt2hSOjo4YMWIE\nxo0bh1u3bsHExATOzs65xmRkZGDp0qXYvn07Ll26hDdv3mD06NG5jvnwNSQoKAi9e/dGly5dcOPG\nDZw9exbt27dHTk5OgZ7bhAkTMGzYMPTs2RP37t0r0DmIikr79u3h6OiIMWPGfHapGiIqP1atWoWR\nI0fiypUrCAgIwHfffYeLFy8KHYuIiMojOVEps2/fPrlYLP7sYyKRSB4QECCXy+XyiIgIuUgkkm/d\nulXx+KFDh+QikUh+4MABxX3bt2+XV6xY8Yu3GzRoIPf09Pzs9bZs2SLX09OTp6SkKO47ffq0XCQS\nyR8/fvzZMTk5OfJq1arJ/fz8cuWeOHFiXk9bLpfL5TNmzJB36tTps4+1bt1abm5uLvf29pZnZmZ+\n9VxEZcnw4cPlampqcqlUKtfS0pKLRCK5WCyWr127VnGMqampfNWqVYrbIpFIPnv2bMXte/fuyUUi\nkXzNmjWK+06fPi0Xi8XyhIQEuVz+3+uDWCyWh4WFKY7x8/OTa2pqKm5//BrSsmVL+aBBg76Y/eNc\ncrlc3q5dO/mECRO+OCY9PV3u5eUlNzIykjs7O8ufPXv2xWOJiltaWprc2tpa7uvrK3QUIhJIUlKS\nvGLFivKDBw/KExIS5AkJCfIOHTrIx44dK5fL5fKsrCyBExIRUXnCzk8q8+rXr6/4d5UqVSASiVCv\nXr1c96WkpCA9Pf2z4ydOnIgFCxagRYsW8PDwwI0bNxSPhYaGokGDBtDW1lbc16JFC4jFYoSEhAAA\nXr16hVGjRsHS0hJ6enqoVKkSXr16haioqFzXady48SfX3rx5M5o2baqY2r969epPxr139uxZbNu2\nDbt27YKFhQW2bNmimFZLVB60bdsWd+7cwdWrV+Hu7o7u3btjwoQJeY75+PUBwCevD0DudYc1NDRg\nbm6uuG1iYoLMzEy8efPms9e4efMmOnTokP8nlAcNDQ1MnjwZjx49QpUqVdCgQQNMnz79ixmIipOm\npiZ8fX3x008/ffFnFhGVbatXr0bz5s3Rs2dPVK5cGZUrV8aMGTMQGBiI+Ph4qKmpAfhvqZgPf7cm\nIiIqCix+Upn34bTX91NRP3ffl6aguri4ICIiAi4uLggLC0OLFi3g6en51eu+P+/QoUNx/fp1rF27\nFhcvXsTt27dRvXr1TwqTOjo6uW77+/tj8uTJcHFxwbFjx3D79m2MHTs2z4Jm27ZtcfLkSezatQv7\n9++Hubk5NmzY8MXC7pdkZ2fj9u3bePv2bb7GEQlJW1sbtWrVgrW1NdasWYOUlJSvfq8q8/ogl8tz\nvT68f8P28biCTmMXi8WfTA/OyspSaqyenh6WLFmCO3fuID4+HhYWFli1alW+v+eJVM3GxgaTJ0/G\n8OHDC/y9QUSlk0wmQ2RkJCwsLBRLMslkMrRq1Qq6urrYu3cvAODFixdwdnbmJn5ERFTkWPwkUoKJ\niQlGjBiBP//8E56entiyZQsAoG7durh79y5SUlIUx54/fx5yuRxWVlaK2xMmTEDXrl1Rt25d6Ojo\nICYm5qvXPH/+PGxtbTFmzBg0bNgQtWvXRnh4uFJ5W7ZsiaCgIOzbtw9BQUEwMzPDmjVrkJqaqtT4\n+/fvY/ny5WjVqhVGjBiBhIQEpcYRlSRz587FsmXLEBsbW6jzFPZNmY2NDU6ePPnFx42MjHK9JqSn\npyM0NDRf16hRowZ+//13/PPPPzhz5gzq1KkDX19fFp1IUNOmTUNGRgbWrl0rdBQiKkYSiQQDBgyA\npaWl4gNDiUQCLS0ttGvXDkeOHAEAzJkzB23btoWNjY2QcYmIqBxg8ZPKnY87rL5m0qRJCA4OxtOn\nT3Hr1i0EBQXB2toaADB48GBoa2tj6NChuHfvHs6ePYvRo0ejX79+qFWrFgDAwsICu3btwoMHD3D1\n6lUMHDgQGhoaX72uhYUFbty4gaCgIISHh2PBggU4e/ZsvrI3a9YMBw8exMGDB3H27FmYmZlh5cqV\nXy2I1KxZE0OHDsW4cePg7e2NjRs3IiMjI1/XJhJa27ZtYWVlhYULFxbqPMq8ZuR1zOzZs7F37154\neHjgwYMHuH//PtasWaPozuzQoQP8/Pxw5swZ3L9/H66urpDJZAXKam1tjcDAQPj6+mLjxo1o1KgR\ngoODufEMCUIikWDnzp1YvHgx7t//P/buO6zK+v/j+PMcEAXBvbcQJM7UXKmplebIrblx7xylODAH\n7txb0jAHamaO1AxTc++BmhP3pDQVEZF5zu+PfvLNshIFbsbrcV3nuvKc+7553QTn5rzv9+fzOWN0\nHBFJRO+//z49e/YEnr9Gtm3bltOnT3P27FlWrFjB1KlTjYooIiKpiIqfkqL8tUPrRR1bce3islgs\n9O3bl2LFivHhhx+SK1cuFi9eDIC9vT1btmwhJCSEChUq0LhxYypXroyvr2/s/l9//TWhoaG8/fbb\ntG7dms6dO1OoUKH/zNS9e3c+/vhj2rRpQ/ny5blx4wYDBw6MU/ZnypQpw9q1a9myZQs2Njb/+T3I\nnDkzH374Ib/99htubm58+OGHzxVsNZeoJBcDBgzA19eXmzdvvvL7w8u8Z/zbNnXq1GHdunX4+/tT\npkwZatSowc6dOzGb/7gEDx06lPfee49GjRpRu3Ztqlat+tpdMFWrVmX//v2MGDGCvn378sEHH3Ds\n2LHXOqbIq3BxcWH8+PG0bdtW1w6RVODZ3NO2trakSZMGq9Uae42MiIjg7bffJl++fLz99tu89957\nlClTxsi4IiKSSpisagcRSXX+/IfoP70WExND7ty56dKlC8OGDYudk/TatWusWrWK0NBQPDw8cHV1\nTczoIhJHUVFR+Pr6Mnr0aKpVq8a4ceNwdnY2OpakIlarlQYNGlCyZEnGjRtndBwRSSCPHz+mc+fO\n1K5dm+rVq//jtaZXr174+Phw+vTp2GmiREREEpI6P0VSoX/rUns23HbSpEmkS5eORo0aPbcYU3Bw\nMMHBwZw8eZI333yTqVOnal5BkSQsTZo09OjRg8DAQNzd3SlXrhz9+vXj3r17RkeTVMJkMvHVV1/h\n6+vL/v37jY4jIglk2bJlfPfdd8yePRtPT0+WLVvGtWvXAFi4cGHs35ijR49mzZo1KnyKiEiiUeen\niLxQrly5aN++PcOHD8fR0fG516xWK4cOHeKdd95h8eLFtG3bNnYIr4gkbXfv3mXMmDGsXLmSTz/9\nlP79+z93g0Mkoaxbtw5PT09OnDjxt+uKiCR/x44do1evXrRp04bNmzdz+vRpatSoQfr06Vm6dCm3\nb98mc+bMwL+PQhIREYlvqlaISKxnHZxTpkzB1taWRo0a/e0DakxMDCaTKXYxlXr16v2t8BkaGppo\nmUUkbnLkyMHs2bM5ePAgp06dws3NjQULFhAdHW10NEnhGjduTNWqVRkwYIDRUUQkAZQtW5YqVarw\n6NEj/P39mTNnDkFBQSxatAgXFxd++uknLl++DMR9Dn4REZHXoc5PEcFqtbJt2zYcHR2pVKkS+fPn\np0WLFowcORInJ6e/3Z2/evUqrq6ufP3117Rr1y72GCaTiYsXL7Jw4ULCwsJo27YtFStWNOq0ROQl\nHDlyhEGDBvHrr78yYcIEGjZsqA+lkmBCQkIoVaoUs2fP5qOPPjI6jojEs1u3btGuXTt8fX1xdnbm\n22+/pVu3bhQvXpxr165RpkwZli9fjpOTk9FRRUQkFVHnp4hgtVrZsWMHlStXxtnZmdDQUBo2bBj7\nh+mzQsizztCxY8dStGhRateuHXuMZ9s8efIEJycnfv31V9555x28vb0T+WxEJC7KlSvHzz//zNSp\nUxk+fDhVqlRh3759RseSFCpDhgwsWbKEzz//XN3GIilMTEwM+fLlo2DBgowcORIAT09PvL292bt3\nL1OnTuXtt99W4VNERBKdOj9FJNaVK1eYMGECvr6+VKxYkZkzZ1K2bNnnhrXfvHkTZ2dnFixYQMeO\nHV94HIvFwvbt26lduzabNm2iTp06iXUKIvIaYmJi8PPzY/jw4ZQpU4YJEybg7u5udCxJgSwWCyaT\nSV3GIinEn0cJXb58mb59+5IvXz7WrVvHyZMnyZ07t8EJRUQkNVPnp4jEcnZ2ZuHChVy/fp1ChQox\nb948LBYLwcHBREREADBu3Djc3NyoW7fu3/Z/di/l2cq+5cuXV+FTUrRHjx7h6OhISrmPaGNjQ/v2\n7blw4QKVK1fm3XffpVu3bty5c8foaJLCmM3mfy18hoeHM27cOL799ttETCUicRUWFgY8P0rIxcWF\nKlWqsGjRIry8vGILn89GEImIiCQ2FT9F5G/y58/PihUr+PLLL7GxsWHcuHFUrVqVJUuW4Ofnx4AB\nA8iZM+ff9nv2h++RI0dYu3Ytw4YNS+zoIokqY8aMpE+fnqCgIKOjxCt7e3s8PT25cOECGTNmpESJ\nEnz++eeEhIQYHU1SiVu3bnH79m1GjBjBpk2bjI4jIi8QEhLCiBEj2L595HtbAgAAIABJREFUO8HB\nwQCxo4U6dOiAr68vHTp0AP64Qf7XBTJFREQSi65AIvKP7OzsMJlMeHl54eLiQvfu3QkLC8NqtRIV\nFfXCfSwWCzNnzqRUqVJazEJSBVdXVy5evGh0jASRJUsWJk+eTEBAALdu3cLV1ZVZs2YRGRn50sdI\nKV2xknisVitvvPEG06ZNo1u3bnTt2jW2u0xEkg4vLy+mTZtGhw4d8PLyYteuXbFF0Ny5c+Ph4UGm\nTJmIiIjQFBciImIoFT9F5D9lzpyZlStXcvfuXfr370/Xrl3p27cvDx8+/Nu2J0+eZPXq1er6lFTD\nzc2NwMBAo2MkqAIFCrB48WK2bt2Kv78/RYoUYeXKlS81hDEyMpLff/+dAwcOJEJSSc6sVutziyDZ\n2dnRv39/XFxcWLhwoYHJROSvQkND2b9/Pz4+PgwbNgx/f3+aN2+Ol5cXO3fu5MGDBwCcO3eO7t27\n8/jxY4MTi4hIaqbip4i8tAwZMjBt2jRCQkJo0qQJGTJkAODGjRuxc4LOmDGDokWL0rhxYyOjiiSa\nlNz5+VclS5Zk8+bN+Pr6Mm3aNMqXL8/Vq1f/dZ9u3brx7rvv0qtXL/Lnz68iljzHYrFw+/ZtoqKi\nMJlM2NraxnaImc1mzGYzoaGhODo6GpxURP7s1q1blC1blpw5c9KjRw+uXLnCmDFj8Pf35+OPP2b4\n8OHs2rWLvn37cvfuXa3wLiIihrI1OoCIJD+Ojo7UrFkT+GO+p/Hjx7Nr1y5at27NmjVrWLp0qcEJ\nRRKPq6sry5cvNzpGoqpRowaHDh1izZo15M+f/x+3mzFjBuvWrWPKlCnUrFmT3bt3M3bsWAoUKMCH\nH36YiIklKYqKiqJgwYL8+uuvVK1aFXt7e8qWLUvp0qXJnTs3WbJkYcmSJZw6dYpChQoZHVdE/sTN\nzY3BgweTLVu22Oe6d+9O9+7d8fHxYdKkSaxYsYJHjx5x9uxZA5OKiIiAyarJuETkNUVHRzNkyBAW\nLVpEcHAwPj4+tGrVSnf5JVU4deoUrVq14syZM0ZHMYTVav3HudyKFStG7dq1mTp1auxzPXr04Lff\nfmPdunXAH1NllCpVKlGyStIzbdo0Bg4cyNq1azl69CiHDh3i0aNH3Lx5k8jISDJkyICXlxddu3Y1\nOqqI/Ifo6Ghsbf/XW/Pmm29Srlw5/Pz8DEwlIiKizk8RiQe2trZMmTKFyZMnM2HCBHr06EFAQABf\nfPFF7ND4Z6xWK2FhYTg4OGjye0kR3njjDa5cuYLFYkmVK9n+0+9xZGQkrq6uf1sh3mq1ki5dOuCP\nwnHp0qWpUaMG8+fPx83NLcHzStLy2WefsXTpUjZv3syCBQtii+mhoaFcu3aNIkWKPPczdv36dQAK\nFixoVGQR+QfPCp8Wi4UjR45w8eJF1q9fb3AqERERzfkpIvHo2crwFouFnj17kj59+hdu16VLF955\n5x1+/PFHrQQtyZ6DgwNZs2bl5s2bRkdJUuzs7KhWrRrffvstq1atwmKxsH79evbt24eTkxMWi4WS\nJUty69YtChYsiLu7Oy1btnzhQmqSsm3YsIElS5bw3XffYTKZiImJwdHRkeLFi2Nra4uNjQ0Av//+\nO35+fgwePJgrV64YnFpE/onZbObJkycMGjQId3d3o+OIiIio+CkiCaNkyZKxH1j/zGQy4efnR//+\n/fH09KR8+fJs2LBBRVBJ1lLDiu9x8ez3+dNPP2Xy5Mn06dOHihUrMnDgQM6ePUvNmjUxm81ER0eT\nJ08eFi1axOnTp3nw4AFZs2ZlwYIFBp+BJKYCBQowadIkOnfuTEhIyAuvHQDZsmWjatWqmEwmmjVr\nlsgpRSQuatSowfjx442OISIiAqj4KSIGsLGxoUWLFpw6dYqhQ4cyYsQISpcuzZo1a7BYLEbHE4mz\n1LTi+3+Jjo5m+/btBAUFAX+s9n737l169+5NsWLFqFy5Ms2bNwf+eC+Ijo4G/uigLVu2LCaTidu3\nb8c+L6lDv379GDx4MBcuXHjh6zExMQBUrlwZs9nMiRMn+OmnnxIzooi8gNVqfeENbJPJlCqnghER\nkaRJVyQRMYzZbKZJkyYEBAQwZswYJk6cSMmSJfnmm29iP+iKJAcqfv7P/fv3WblyJd7e3jx69Ijg\n4GAiIyNZvXo1t2/fZsiQIcAfc4KaTCZsbW25e/cuTZo0YdWqVSxfvhxvb+/nFs2Q1GHo0KGUK1fu\nueeeFVVsbGw4cuQIpUqVYufOnXz99deUL1/eiJgi8v8CAgJo2rSpRu+IiEiSp+KniBjOZDJRv359\nDh8+zJQpU5g1axbFihXDz89P3V+SLGjY+//kzJmTnj17cvDgQYoWLUrDhg3Jly8ft27dYtSoUdSr\nVw/438IY3333HXXq1CEiIgJfX19atmxpZHwx0LOFjQIDA2M7h589N2bMGCpVqoSLiwtbtmzBw8OD\nTJkyGZZVRMDb25tq1aqpw1NERJI8k1W36kQkibFarfz88894e3tz584dhg0bRtu2bUmTJo3R0URe\n6Ny5czRs2FAF0L/w9/fn8uXLFC1alNKlSz9XrIqIiGDTpk10796dcuXK4ePjE7uC97MVvyV1mj9/\nPr6+vhw5coTLly/j4eHBmTNn8Pb2pkOHDs/9HFksFhVeRAwQEBDARx99xKVLl7C3tzc6joiIyL9S\n8VNEkrRdu3YxevRorly5wtChQ2nfvj1p06Y1OpbIcyIiIsiYMSOPHz9Wkf4fxMTEPLeQzZAhQ/D1\n9aVJkyYMHz6cfPnyqZAlsbJkyULx4sU5efIkpUqVYvLkybz99tv/uBhSaGgojo6OiZxSJPVq2LAh\n77//Pn379jU6ioiIyH/SJwwRSdKqVavG9u3b8fPzY+3atbi6ujJ37lzCw8ONjiYSK23atOTJk4dr\n164ZHSXJela0unHjBo0aNWLOnDl06dKFL7/8knz58gGo8CmxNm/ezN69e6lXrx7r16+nQoUKLyx8\nhoaGMmfOHCZNmqTrgkgiOX78OEePHqVr165GRxEREXkp+pQhIslC5cqV8ff357vvvsPf3x8XFxdm\nzJhBWFiY0dFEAC169LLy5MnDG2+8wZIlSxg7diyAFjiTv6lYsSKfffYZ27dv/9efD0dHR7Jmzcqe\nPXtUiBFJJKNGjWLIkCEa7i4iIsmGip8ikqyUL1+ejRs3snHjRnbv3o2zszOTJ08mNDTU6GiSyrm5\nuan4+RJsbW2ZMmUKTZs2je3k+6ehzFarlZCQkMSMJ0nIlClTKF68ODt37vzX7Zo2bUq9evVYvnw5\nGzduTJxwIqnUsWPHOH78uG42iIhIsqLip4gkS2XKlGHt2rVs3bqVo0eP4uLiwvjx41UoEcO4urpq\nwaMEUKdOHT766CNOnz5tdBQxwJo1a6hevfo/vv7w4UMmTJjAiBEjaNiwIWXLlk28cCKp0LOuz3Tp\n0hkdRURE5KWp+CkiyVqJEiVYtWoVO3fu5OzZs7i4uDB69GiCg4ONjiapjIa9xz+TycTPP//M+++/\nz3vvvUenTp24deuW0bEkEWXKlIns2bPz5MkTnjx58txrx48fp379+kyePJlp06axbt068uTJY1BS\nkZTv6NGjBAQE0KVLF6OjiIiIxImKnyKSIri7u+Pn58f+/fu5evUqb7zxBsOHD+f+/ftGR5NUws3N\nTZ2fCSBt2rR8+umnBAYGkitXLkqVKsXgwYN1gyOV+fbbbxk6dCjR0dGEhYUxY8YMqlWrhtls5vjx\n4/To0cPoiCIp3qhRoxg6dKi6PkVEJNkxWa1Wq9EhRETi25UrV5g4cSJr1qyha9eufPbZZ+TIkcPo\nWJKCRUdH4+joSHBwsD4YJqDbt28zcuRINmzYwODBg+ndu7e+36lAUFAQefPmxcvLizNnzvDDDz8w\nYsQIvLy8MJt1L18koR05coQmTZpw8eJFveeKiEiyo78WRSRFcnZ2ZsGCBQQEBPD48WOKFCnCgAED\nCAoKMjqapFC2trYULFiQK1euGB0lRcubNy9fffUVO3bsYNeuXRQpUoRly5ZhsViMjiYJKHfu3Cxa\ntIjx48dz7tw5Dhw4wOeff67Cp0giUdeniIgkZ+r8FJFU4fbt20yaNIlly5bRtm1bBg0aRL58+eJ0\njPDwcL777jv27NlDcHAwadKkIVeuXLRs2ZK33347gZJLclK/fn06d+5Mo0aNjI6SauzZs4dBgwbx\n9OlTvvjiC2rVqoXJZDI6liSQFi1acO3aNfbt24etra3RcURShcOHD9O0aVMuXbpE2rRpjY4jIiIS\nZ7pdLiKpQt68eZk5cyZnz57Fzs6OkiVL0rNnT65fv/6f+965c4chQ4ZQoEAB/Pz8KFWqFI0bN6ZW\nrVo4OTnRvHlzypcvz+LFi4mJiUmEs5GkSoseJb6qVauyf/9+RowYQd++ffnggw84duyY0bEkgSxa\ntIgzZ86wdu1ao6OIpBrPuj5V+BQRkeRKnZ8ikirdu3ePadOmsWDBAho3bszQoUNxcXH523bHjx+n\nQYMGNG3alE8++QRXV9e/bRMTE4O/vz9jx44ld+7c+Pn54eDgkBinIUnM/PnzCQgIYMGCBUZHSZWi\noqLw9fVl9OjRVKtWjXHjxuHs7Gx0LIln586dIzo6mhIlShgdRSTFO3ToEM2aNVPXp4iIJGvq/BSR\nVCl79uxMmDCBwMBA8uTJQ4UKFWjfvv1zq3WfPn2a2rVrM2vWLGbOnPnCwieAjY0N9erVY+fOnaRL\nl45mzZoRHR2dWKciSYhWfDdWmjRp6NGjB4GBgbi7u1OuXDn69evHvXv3jI4m8cjd3V2FT5FEMmrU\nKLy8vFT4FBGRZE3FTxFJ1bJmzcro0aO5dOkSb7zxBpUrV6Z169acOHGCBg0aMH36dJo0afJSx0qb\nNi1LlizBYrHg7e2dwMklKdKw96TB0dGRESNGcO7cOSwWC+7u7owbN44nT54YHU0SkAYzicSvgwcP\ncubMGTp16mR0FBERkdeiYe8iIn8SEhLCvHnzmDBhAkWLFuXAgQNxPsbly5epWLEiN27cwN7ePgFS\nSlJlsVhwdHTk7t27ODo6Gh1H/t+lS5cYNmwYe/fuZeTIkXTq1EmL5aQwVquV9evX06BBA2xsbIyO\nI5Ii1K5dm0aNGtGjRw+jo4iIiLwWdX6KiPxJhgwZGDJkCCVLlmTAgAGvdAwXFxfKlSvHt99+G8/p\nJKkzm824uLhw6dIlo6PIn7zxxhusWrWK9evXs3LlSkqUKMH69evVKZiCWK1WZs+ezaRJk4yOIpIi\nHDhwgHPnzqnrU0REUgQVP0VE/iIwMJDLly/TsGHDVz5Gz549WbhwYTymkuRCQ9+TrnLlyvHzzz8z\ndepUhg8fTpUqVdi3b5/RsSQemM1mFi9ezLRp0wgICDA6jkiy92yuTzs7O6OjiIiIvDYVP0VE/uLS\npUuULFmSNGnSvPIxypYtq+6/VMrNzU3FzyTMZDJRt25dTpw4Qbdu3WjVqhWNGzfm/PnzRkeT11Sg\nQAGmTZtG27ZtCQ8PNzqOSLK1f/9+zp8/T8eOHY2OIiIiEi9U/BQR+YvQ0FCcnJxe6xhOTk48fvw4\nnhJJcuLq6qoV35MBGxsb2rdvz4ULF3jnnXeoWrUq3bt3JygoyOho8hratm1L0aJFGTZsmNFRRJKt\nUaNGMWzYMHV9iohIiqHip4jIX8RH4fLx48dkyJAhnhJJcqJh78mLvb09np6eXLhwgQwZMlC8eHE+\n//xzQkJCjI4mr8BkMuHj48M333zDjh07jI4jkuzs27ePwMBAOnToYHQUERGReKPip4jIX7i5uREQ\nEEBERMQrH+PQoUO4ubnFYypJLtzc3NT5mQxlyZKFyZMnExAQwK1bt3Bzc2PWrFlERkYaHU3iKGvW\nrHz11Vd06NCBR48eGR1HJFnx9vZW16eIiKQ4Kn6KiPyFi4sLxYsXZ+3ata98jHnz5tGtW7d4TCXJ\nRc6cOQkPDyc4ONjoKPIKChQowOLFi/npp5/w9/fH3d2db775BovFYnQ0iYM6depQt25d+vbta3QU\nkWRj3759XLx4kfbt2xsdRUREJF6p+Cki8gK9e/dm3rx5r7TvhQsXOHXqFM2aNYvnVJIcmEwmDX1P\nAUqWLMnmzZv56quvmDp1KuXLl2f79u1Gx5I4mDJlCvv372fNmjVGRxFJFjTXp4iIpFQqfoqIvECD\nBg347bff8PX1jdN+ERER9OjRg08++YS0adMmUDpJ6jT0PeWoUaMGhw4dwtPTk27dulG7dm1Onjxp\ndCx5CenTp2fZsmX07t1bC1mJ/Ie9e/dy6dIldX2KiEiKpOKniMgL2NrasmnTJoYNG8by5ctfap+n\nT5/SsmVLMmXKhJeXVwInlKRMnZ8pi9lspkWLFpw7d46PPvqIDz/8EA8PD65fv250NPkPFStWpGvX\nrnTu3Bmr1Wp0HJEka9SoUXz++eekSZPG6CgiIiLxTsVPEZF/4Obmxvbt2xk2bBhdunT5x26vyMhI\nVq1axTvvvIODgwPffPMNNjY2iZxWkhIVP1MmOzs7PvnkEwIDAylUqBBlypRh4MCBPHjwwOho8i9G\njBjB3bt3WbBggdFRRJKkPXv2cOXKFTw8PIyOIiIikiBMVt0GFxH5V/fu3cPHx4cvv/ySQoUK0aBB\nA7JmzUpkZCRXr15l2bJlFClShF69etG0aVPMZt1XSu0OHjxInz59OHLkiNFRJAEFBQXh7e3NmjVr\nGDhwIH379sXe3t7oWPIC586do2rVqhw4cABXV1ej44gkKe+//z5t2rShU6dORkcRERFJECp+ioi8\npOjoaDZs2MDevXsJCgpiy5Yt9OnThxYtWlC0aFGj40kScv/+fVxcXHj48CEmk8noOJLALly4gJeX\nF0eOHMHb2xsPDw91fydBs2bNYuXKlezZswdbW1uj44gkCbt376Zjx46cP39eQ95FRCTFUvFTREQk\nAWTJkoULFy6QPXt2o6NIIjlw4ACDBg0iODiYiRMnUrduXRW/kxCLxUKtWrWoUaMGw4YNMzqOSJLw\n3nvv0a5dOzp27Gh0FBERkQSjsZkiIiIJQCu+pz6VKlVi9+7djBs3Dk9Pz9iV4iVpMJvNLF68mJkz\nZ3Ls2DGj44gYbteuXdy4cYN27doZHUVERCRBqfgpIiKSALToUepkMplo0KABp06dom3btjRt2pTm\nzZvrZyGJyJcvHzNmzKBdu3Y8ffrU6Dgihnq2wrumgRARkZROxU8REZEEoOJn6mZra0uXLl0IDAyk\nTJkyVKpUid69e/Pbb78ZHS3Va9WqFSVKlGDo0KFGRxExzM6dO7l58yZt27Y1OoqIiEiCU/FTREQk\nAWjYuwA4ODgwdOhQzp8/j52dHUWLFsXb25vQ0NCXPsadO3cYMWoElapXwv0td0qWL0m9xvVYv349\n0dHRCZg+ZTKZTMyfP5/vvvuO7du3Gx1HxBCjRo1i+PDh6voUEZFUQcVPEREDeHt7U7JkSaNjSAJS\n56f8WbZs2Zg+fTpHjx4lMDAQV1dX5s2bR1RU1D/uc/LkSeo1qofzm85M3jKZg3kPcv7t8/xS7Bc2\nWzbTbmA7cubLifcYb8LDwxPxbJK/LFmy4OvrS8eOHQkODjY6jkii2rFjB7dv36ZNmzZGRxEREUkU\nWu1dRFKdjh07cv/+fTZs2GBYhrCwMCIiIsicObNhGSRhhYSEkCdPHh4/fqwVv+Vvjh8/zuDBg7l+\n/Trjx4+nadOmz/2cbNiwgVYerXha6SnWt6yQ7h8OFAT2e+1xd3Jn2+Ztek+Jo08++YTg4GD8/PyM\njiKSKKxWK9WrV6dz5854eHgYHUdERCRRqPNTRMQADg4OKlKkcBkyZMDR0ZE7d+4YHUWSoDJlyrB1\n61bmzp3LuHHjYleKB9i+fTst27ck7OMwrBX/pfAJkBueNn3KadNpanxYQ4v4xNGkSZM4cuQI3377\nrdFRRBLFjh07CAoKonXr1kZHERERSTQqfoqI/InZbGbt2rXPPVe4cGGmTZsW+++LFy9SrVo17O3t\nKVasGFu2bMHJyYmlS5fGbnP69Glq1qyJg4MDWbNmpWPHjoSEhMS+7u3tTYkSJRL+hMRQGvou/6Vm\nzZocO3aMPn360L59e2rXrk2DJg142ugp5H3Jg5ghsmYkFyIvMGjooATNm9I4ODiwbNky+vTpoxsV\nkuJZrVbN9SkiIqmSip8iInFgtVpp1KgRdnZ2HD58mEWLFjFy5EgiIyNjtwkLC+PDDz8kQ4YMHD16\nlPXr17N//346d+783LE0FDrl06JH8jLMZjNt2rTh/PnzODg4EJYzDArF9SAQXiOcRV8v4smTJwkR\nM8UqX748PXv2pFOnTmg2KEnJfv75Z3799VdatWpldBQREZFEpeKniEgc/PTTT1y8eJFly5ZRokQJ\nKlSowPTp059btGT58uWEhYWxbNkyihYtStWqVVmwYAFr1qzhypUrBqaXxKbOT4kLOzs7jv1yDN55\nxQNkAlNBEytWrIjXXKnBsGHDuH//PvPnzzc6ikiCeNb1OWLECHV9iohIqqPip4hIHFy4cIE8efKQ\nK1eu2OfKlSuH2fy/t9Pz589TsmRJHBwcYp975513MJvNnD17NlHzirFU/JS4OHr0KA+ePIh71+ef\nPCnxhFlfzoq3TKlFmjRp8PPzY8SIEerWlhRp+/bt3L17l5YtWxodRUREJNGp+Cki8icmk+lvwx7/\n3NUZH8eX1EPD3iUubty4gTmHGV7nbSIH3L51O94ypSZvvvkmo0aNol27dkRHRxsdRyTeqOtTRERS\nOxU/RUT+JHv27AQFBcX++7fffnvu30WKFOHOnTv8+uuvsc8dOXIEi8US+293d3d++eWX5+bd27dv\nH1arFXd39wQ+A0lKXFxcuHr1KjExMUZHkWTgyZMnWGwt/73hv0kDEU8j4idQKtSrVy8yZcrE+PHj\njY4iEm+2bdvG77//rq5PERFJtVT8FJFUKSQkhJMnTz73uH79Ou+99x5z587l2LFjBAQE0LFjR+zt\n7WP3q1mzJm5ubnh4eHDq1CkOHjzIgAEDSJMmTWxXZ5s2bXBwcMDDw4PTp0+ze/duevToQdOmTXF2\ndjbqlMUADg4OZMuWjZs3bxodRZKBTJkyYY58zT/NIiC9U/r4CZQKmc1mFi1axJw5czhy5IjRcURe\n25+7Pm1sbIyOIyIiYggVP0UkVdqzZw9lypR57uHp6cm0adMoXLgwNWrU4OOPP6Zr167kyJEjdj+T\nycT69euJjIykQoUKdOzYkWHDhgGQLl06AOzt7dmyZQshISFUqFCBxo0bU7lyZXx9fQ05VzGWhr7L\nyypRogSR1yPhdWbauAqlSpWKt0ypUd68eZk9ezbt2rUjLCzM6Dgir2Xbtm08ePCAFi1aGB1FRETE\nMCbrXye3ExGRODl58iSlS5fm2LFjlC5d+qX28fLyYufOnezfvz+B04nRevToQYkSJejdu7fRUSQZ\nqPJ+FfZl2AdvvcLOVnD82pE1C9dQq1ateM+W2rRu3ZqsWbMye/Zso6OIvBKr1UrlypXp06cPrVq1\nMjqOiIiIYdT5KSISR+vXr2fr1q1cu3aNHTt20LFjR0qXLv3Shc/Lly+zfft2ihcvnsBJJSnQiu8S\nF4P7D8bppBO8yq3pWxBxP4KMGTPGe67UaO7cuXz//fds3brV6Cgir2Tr1q0EBwfz8ccfGx1FRETE\nUCp+iojE0ePHj/nkk08oVqwY7dq1o1ixYvj7+7/Uvo8ePaJYsWKkS5eO4cOHJ3BSSQo07F3iom7d\nuuRyyIXtwTiuyPwUHH50oE3zNjRu3JgOHTo8t1ibxF3mzJlZtGgRnTp14sGDB0bHEYkTq9XKyJEj\nNdeniIgIGvYuIiKSoM6fP0/9+vXV/Skv7datW5QuX5oHJR5gqWQB03/sEAoOqx3o0KADc2fNJSQk\nhPHjx/PVV18xYMAAPv3009g5iSXu+vbty71791i5cqXRUURe2pYtW/j000/55ZdfVPwUEZFUT52f\nIiIiCcjZ2ZmbN28SFfU6q9hIapIvXz4WL1wMu8FhlQNcBCwv2PAJmPeacfjagX7t+jFn5hwAMmTI\nwMSJEzl06BCHDx+maNGirF27Ft3vfjUTJ07kxIkTKn5KsvGs63PkyJEqfIqIiKDOTxERkQTn4uLC\njz/+iJubm9FRJBkICQmhbNmyjBgxgujoaCZOm8jte7eJdo4mwi4CG4sN6R6nI+ZSDI0bN2ZAvwGU\nLVv2H4+3fft2+vfvT7Zs2ZgxY4ZWg38FR48epW7duhw/fpx8+fIZHUfkX/n7+zNgwABOnTql4qeI\niAgqfoqIiCS42rVr06dPH+rVq2d0FEnirFYrrVq1IlOmTPj4+MQ+f/jwYfbv38/Dhw9Jly4duXLl\nomHDhmTJkuWljhsdHc3ChQsZNWoUjRs3ZsyYMWTPnj2hTiNFGjNmDHv27MHf3x+zWYOnJGmyWq1U\nrFiRAQMGaKEjERGR/6fip4iISALr27cvhQsX5tNPPzU6ioi8oujoaKpUqUKbNm3o06eP0XFEXujH\nH3/E09OTU6dOqUgvIiLy/3RFFBFJIOHh4UybNs3oGJIEuLq6asEjkWTO1taWpUuX4u3tzfnz542O\nI/I3f57rU4VPERGR/9FVUUQknvy1kT4qKoqBAwfy+PFjgxJJUqHip0jK4ObmxpgxY2jXrp0WMZMk\n58cff+Tp06c0bdrU6CgiIiJJioqfIiKvaO3atVy4cIFHjx4BYDKZAIiJiSEmJgYHBwfSpk1LcHCw\nkTElCXBzcyMwMNDoGCISD3r06EG2bNkYO3as0VFEYqnrU0RE5J9pzk8RkVfk7u7OjRs3+OCDD6hd\nuzbFixenePHiZM6cOXabzJkzs2PHDt566y0Dk4rRoqOjcXR0JDg4mHTp0hkdR+SlREdHY2tra3SM\nJOnOnTuULl2aDRs2UKFCBaPjiPDDDz8wZMgQTp48qeKniIjIX+jOMHLCAAAgAElEQVTKKCLyinbv\n3s3s2bMJCwtj1KhReHh40KJFC7y8vPjhhx8AyJIlC3fv3jU4qRjN1taWQoUKcfnyZaOjSBJy/fp1\nzGYzx48fT5Jfu3Tp0mzfvj0RUyUfefLkYc6cObRr144nT54YHUdSOavVyqhRo9T1KSIi8g90dRQR\neUXZs2enU6dObN26lRMnTjBo0CAyZcrExo0b6dq1K1WqVOHq1as8ffrU6KiSBGjoe+rUsWNHzGYz\nNjY22NnZ4eLigqenJ2FhYRQoUIBff/01tjN8165dmM1mHjx4EK8ZatSoQd++fZ977q9f+0W8vb3p\n2rUrjRs3VuH+BZo3b06FChUYNGiQ0VEklfvhhx+IiIigSZMmRkcRERFJklT8FBF5TdHR0eTOnZue\nPXvy7bff8v333zNx4kTKli1L3rx5iY6ONjqiJAFa9Cj1qlmzJr/++itXr15l3LhxzJs3j0GDBmEy\nmciRI0dsp5bVasVkMv1t8bSE8Nev/SJNmjTh7NmzlC9fngoVKjB48GBCQkISPFtyMnv2bDZu3Ii/\nv7/RUSSVUteniIjIf9MVUkTkNf15TrzIyEicnZ3x8PBg5syZ/Pzzz9SoUcPAdJJUqPiZeqVNm5bs\n2bOTN29eWrZsSdu2bVm/fv1zQ8+vX7/Oe++9B/zRVW5jY0OnTp1ijzFp0iTeeOMNHBwcKFWqFMuX\nL3/ua4wePZpChQqRLl06cufOTYcOHYA/Ok937drF3LlzYztQb9y48dJD7tOlS8fQoUM5deoUv/32\nG0WKFGHRokVYLJb4/SYlU5kyZWLx4sV06dKF+/fvGx1HUqFNmzYRFRVF48aNjY4iIiKSZGkWexGR\n13Tr1i0OHjzIsWPHuHnzJmFhYaRJk4ZKlSrRrVs3HBwcYju6JPVyc3Nj5cqVRseQJCBt2rREREQ8\n91yBAgVYs2YNzZo149y5c2TOnBl7e3sAhg0bxtq1a5k/fz5ubm4cOHCArl27kiVLFurUqcOaNWuY\nOnUqq1atonjx4ty9e5eDBw8CMHPmTAIDA3F3d2fChAlYrVayZ8/OjRs34vSelCdPHhYvXsyRI0fo\n168f8+bNY8aMGVSpUiX+vjHJ1HvvvUfz5s3p2bMnq1at0nu9JBp1fYqIiLwcFT9FRF7D3r17+fTT\nT7l27Rr58uUjV65cODo6EhYWxuzZs/H392fmzJm8+eabRkcVg6nzUwAOHz7MihUrqFWr1nPPm0wm\nsmTJAvzR+fnsv8PCwpg+fTpbt26lcuXKABQsWJBDhw4xd+5c6tSpw40bN8iTJw81a9bExsaGfPny\nUaZMGQAyZMiAnZ0dDg4OZM+e/bmv+SrD68uVK8e+fftYuXIlrVq1okqVKnzxxRcUKFAgzsdKScaP\nH0/ZsmVZsWIFbdq0MTqOpBIbN24kJiaGRo0aGR1FREQkSdMtQhGRV3Tp0iU8PT3JkiULu3fvJiAg\ngB9//JHVq1ezbt06vvzyS6Kjo5k5c6bRUSUJyJs3L8HBwYSGhhodRRLZjz/+iJOTE/b29lSuXJka\nNWowa9asl9r37NmzhIeHU7t2bZycnGIfPj4+XLlyBfhj4Z2nT59SqFAhunTpwnfffUdkZGSCnY/J\nZKJ169acP38eNzc3SpcuzciRI1P1quf29vb4+fnx6aefcvPmTaPjSCqgrk8REZGXpyuliMgrunLl\nCvfu3WPNmjW4u7tjsViIiYkhJiYGW1tbPvjgA1q2bMm+ffuMjipJgNls5smTJ6RPn97oKJLIqlWr\nxqlTpwgMDCQ8PJzVq1eTLVu2l9r32dyamzZt4uTJk7GPM2fOsGXLFgDy5ctHYGAgCxYsIGPGjAwc\nOJCyZcvy9OnTBDsngPTp0+Pt7U1AQEDs0PoVK1YkyoJNSVGZMmXo168fHTp00JyokuA2bNiA1WpV\n16eIiMhLUPFTROQVZcyYkcePH/P48WOA2MVEbGxsYrfZt28fuXPnNiqiJDEmk0nzAaZCDg4OFC5c\nmPz58z/3/vBXdnZ2AMTExMQ+V7RoUdKmTcu1a9dwdnZ+7pE/f/7n9q1Tpw5Tp07l8OHDnDlzJvbG\ni52d3XPHjG8FChRg5cqVrFixgqlTp1KlShWOHDmSYF8vKRs8eDBPnz5l9uzZRkeRFOzPXZ+6poiI\niPw3zfkpIvKKnJ2dcXd3p0uXLnz++eekSZMGi8VCSEgI165dY+3atQQEBLBu3Tqjo4pIMlCwYEFM\nJhM//PADH330Efb29jg6OjJw4EAGDhyIxWLh3XffJTQ0lIMHD2JjY0OXLl1YsmQJ0dHRVKhQAUdH\nR7755hvs7OxwdXUFoFChQhw+fJjr16/j6OhI1qxZEyT/s6Ln4sWLadiwIbVq1WLChAmp6gaQra0t\nS5cupWLFitSsWZOiRYsaHUlSoO+//x6Ahg0bGpxEREQkeVDnp4jIK8qePTvz58/nzp07NGjQgF69\netGvXz+GDh3Kl19+idlsZtGiRVSsWNHoqCKSRP25aytPnjx4e3szbNgwcuXKRZ8+fQAYM2YMo0aN\nYurUqRQvXpxatWqxdu1aChcuDECmTJnw9fXl3XffpUSJEqxbt45169ZRsGBBAAYOHIidnR1FixYl\nR44c3Lhx429fO76YzWY6derE+fPnyZUrFyVKlGDChAmEh4fH+9dKqt544w3Gjx9Pu3btEnTuVUmd\nrFYr3t7ejBo1Sl2fIiIiL8lkTa0TM4mIxKO9e/fyyy+/EBERQcaMGSlQoAAlSpQgR44cRkcTETHM\n5cuXGThwICdPnmTKlCk0btw4VRRsrFYr9evX56233mLs2LFGx5EUZN26dYwZM4Zjx46lit8lERGR\n+KDip4jIa7JarfoAIvEiPDwci8WCg4OD0VFE4tX27dvp378/2bJlY8aMGZQqVcroSAnu119/5a23\n3mLdunVUqlTJ6DiSAlgsFsqUKcPo0aNp0KCB0XFERESSDc35KSLymp4VPv96L0kFUYmrRYsWce/e\nPT7//PN/XRhHJLl5//33CQgIYMGCBdSqVYvGjRszZswYsmfPbnS0BJMrVy7mzZuHh4cHAQEBODo6\nGh1JkokrV65w7tw5QkJCSJ8+Pc7OzhQvXpz169djY2ND/fr1jY4oSVhYWBgHDx7k/v37AGTNmpVK\nlSphb29vcDIREeOo81NERCSR+Pr6UqVKFVxdXWOL5X8ucm7atImhQ4eydu3a2MVqRFKahw8f4u3t\nzfLly/Hy8qJ3796xK92nRO3bt8fe3h4fHx+jo0gSFh0dzQ8//MC8efMICAjg7bffxsnJiSdPnvDL\nL7+QK1cu7ty5w/Tp02nWrJnRcSUJunjxIj4+PixZsoQiRYqQK1curFYrQUFBXLx4kY4dO9K9e3dc\nXFyMjioikui04JGIiEgiGTJkCDt27MBsNmNjYxNb+AwJCeH06dNcvXqVM2fOcOLECYOTiiSczJkz\nM2PGDHbv3s2WLVsoUaIEmzdvNjpWgpk1axb+/v4p+hzl9Vy9epW33nqLiRMn0q5dO27evMnmzZtZ\ntWoVmzZt4sqVKwwfPhwXFxf69evHkSNHjI4sSYjFYsHT05MqVapgZ2fH0aNH2bt3L9999x1r1qxh\n//79HDx4EICKFSvi5eWFxWIxOLWISOJS56eIiEgiadiwIaGhoVSvXp1Tp05x8eJF7ty5Q2hoKDY2\nNuTMmZP06dMzfvx46tWrZ3RckQRntVrZvHkzn332Gc7OzkybNg13d/eX3j8qKoo0adIkYML4sXPn\nTlq3bs2pU6fIli2b0XEkCbl06RLVqlVjyJAh9OnT5z+337BhA507d2bNmjW8++67iZBQkjKLxULH\njh25evUq69evJ0uWLP+6/e+//06DBg0oWrQoCxcu1BRNIpJqqPNTROQ1Wa1Wbt269bc5P0X+6p13\n3mHHjh1s2LCBiIgI3n33XYYMGcKSJUvYtGkT33//PevXr6datWpGR5VXEBkZSYUKFZg6darRUZIN\nk8lEvXr1+OWXX6hVqxbvvvsu/fv35+HDh/+577PCaffu3Vm+fHkipH111atXp3Xr1nTv3l3XCon1\n6NEj6tSpw8iRI1+q8AnQoEEDVq5cSfPmzbl8+XICJ0waQkND6d+/P4UKFcLBwYEqVapw9OjR2Nef\nPHlCnz59yJ8/Pw4ODhQpUoQZM2YYmDjxjB49mosXL7Jly5b/LHwCZMuWja1bt3Ly5EkmTJiQCAlF\nRJIGdX6KiMQDR0dHgoKCcHJyMjqKJGGrVq2iV69eHDx4kCxZspA2bVocHBwwm3UvMiUYOHAgFy5c\nYMOGDeqmeUX37t1j+PDhrFu3jmPHjpE3b95//F5GRUWxevVqDh06xKJFiyhbtiyrV69OsosohYeH\nU65cOTw9PfHw8DA6jiQB06dP59ChQ3zzzTdx3nfEiBHcu3eP+fPnJ0CypKVFixacPn0aHx8f8ubN\ny7Jly5g+fTrnzp0jd+7cdOvWjZ9//plFixZRqFAhdu/eTZcuXfD19aVNmzZGx08wDx8+xNnZmbNn\nz5I7d+447Xvz5k1KlSrFtWvXyJAhQwIlFBFJOlT8FBGJB/nz52ffvn0UKFDA6CiShJ0+fZpatWoR\nGBj4t5WfLRYLJpNJRbNkatOmTfTu3Zvjx4+TNWtWo+MkexcuXMDNze2lfh8sFgslSpSgcOHCzJ49\nm8KFCydCwldz4sQJatasydGjRylYsKDRccRAFouFIkWKsHjxYt55550473/nzh2KFSvG9evXU3Tx\nKjw8HCcnJ9atW8dHH30U+/zbb79N3bp1GT16NCVKlKBZs2aMHDky9vXq1atTsmRJZs2aZUTsRDF9\n+nSOHz/OsmXLXmn/5s2bU6NGDXr16hXPyUREkh61moiIxIPMmTO/1DBNSd3c3d0ZNmwYFouF0NBQ\nVq9ezS+//ILVasVsNqvwmUzdvHmTzp07s3LlShU+48mbb775n9tERkYCsHjxYoKCgvjkk09iC59J\ndTGPt956iwEDBtChQ4ckm1ESx/bt23FwcKBSpUqvtH+ePHmoWbMmS5cujedkSUt0dDQxMTGkTZv2\nueft7e3Zu3cvAFWqVGHjxo3cunULgP3793Py5Enq1KmT6HkTi9VqZf78+a9VuOzVqxfz5s3TVBwi\nkiqo+CkiEg9U/JSXYWNjQ+/evcmQIQPh4eGMGzeOqlWr0rNnT06dOhW7nYoiyUdUVBQtW7bks88+\ne6XuLfln/3YzwGKxYGdnR3R0NMOGDaNt27ZUqFAh9vXw8HBOnz6Nr68v69evT4y4L83T05OoqKhU\nMyehvNi+ffuoX7/+a930ql+/Pvv27YvHVEmPo6MjlSpVYuzYsdy5cweLxYKfnx8HDhwgKCgIgFmz\nZlGyZEkKFCiAnZ0dNWrU4IsvvkjRxc+7d+/y4MEDKlas+MrHqF69OtevX+fRo0fxmExEJGlS8VNE\nJB6o+Ckv61lhM3369AQHB/PFF19QrFgxmjVrxsCBA9m/f7/mAE1Ghg8fTsaMGfH09DQ6Sqry7Pdo\nyJAhODg40KZNGzJnzhz7ep8+ffjwww+ZPXs2vXv3pnz58ly5csWouM+xsbFh6dKlTJgwgdOnTxsd\nRwzy8OHDl1qg5t9kyZKF4ODgeEqUdPn5+WE2m8mXLx/p0qVjzpw5tG7dOvZaOWvWLA4cOMCmTZs4\nfvw406dPZ8CAAfz0008GJ084z35+Xqd4bjKZyJIli/5+FZFUQZ+uRETigYqf8rJMJhMWi4W0adOS\nP39+7t27R58+fdi/fz82NjbMmzePsWPHcv78eaOjyn/w9/dn+fLlLFmyRAXrRGSxWLC1teXq1av4\n+PjQo0cPSpQoAfwxFNTb25vVq1czYcIEtm3bxpkzZ7C3t3+lRWUSirOzMxMmTKBt27axw/cldbGz\ns3vt//eRkZHs378/dr7o5Pz4t+9F4cKF2bFjB0+ePOHmzZscPHiQyMhInJ2dCQ8Px8vLi8mTJ1O3\nbl2KFy9Or169aNmyJVOmTPnbsSwWC3PnzjX8fF/34e7uzoMHD17r5+fZz9BfpxQQEUmJ9Je6iEg8\nyJw5c7z8ESopn8lkwmw2YzabKVu2LGfOnAH++ADSuXNncuTIwYgRIxg9erTBSeXf3L59m44dO7J8\n+fIku7p4SnTq1CkuXrwIQL9+/ShVqhQNGjTAwcEBgAMHDjBhwgS++OILPDw8yJYtG5kyZaJatWos\nXryYmJgYI+M/p3PnzhQoUIBRo0YZHUUMkCtXLq5evfpax7h69SotWrTAarUm+4ednd1/nq+9vT05\nc+bk4cOHbNmyhUaNGhEVFUVUVNTfbkDZ2Ni8cAoZs9lM7969DT/f132EhIQQHh7OkydPXvnn59Gj\nRzx69Oi1O5BFRJIDW6MDiIikBBo2JC/r8ePHrF69mqCgIPbs2cOFCxcoUqQIjx8/BiBHjhy8//77\n5MqVy+Ck8k+io6Np3bo1vXv35t133zU6TqrxbK6/KVOm0KJFC3bu3MnChQtxdXWN3WbSpEm89dZb\n9OzZ87l9r127RqFChbCxsQEgNDSUH374gfz58xs2V6vJZGLhwoW89dZb1KtXj8qVKxuSQ4zRrFkz\nypQpw9SpU0mfPn2c97darfj6+jJnzpwESJe0/PTTT1gsFooUKcLFixcZNGgQRYsWpUOHDtjY2FCt\nWjWGDBlC+vTpKViwIDt37mTp0qUv7PxMKZycnHj//fdZuXIlXbp0eaVjLFu2jI8++oh06dLFczoR\nkaRHxU8RkXiQOXNm7ty5Y3QMSQYePXqEl5cXrq6upE2bFovFQrdu3ciQIQO5cuUiW7ZsZMyYkWzZ\nshkdVf6Bt7c3dnZ2DB061OgoqYrZbGbSpEmUL1+e4cOHExoa+tz77tWrV9m4cSMbN24EICYmBhsb\nG86cOcOtW7coW7Zs7HMBAQH4+/tz6NAhMmbMyOLFi19qhfn4ljNnTubPn4+HhwcnTpzAyckp0TNI\n4rt+/TrTp0+PLeh37949zsfYvXs3FouF6tWrx3/AJObRo0cMHTqU27dvkyVLFpo1a8bYsWNjb2as\nWrWKoUOH0rZtWx48eEDBggUZN27ca62Enhz06tWLIUOG0Llz5zjP/Wm1Wpk3bx7z5s1LoHQiIkmL\nyWq1Wo0OISKS3K1YsYKNGzeycuVKo6NIMrBv3z6yZs3Kb7/9xgcffMDjx4/VeZFMbNu2jfbt23P8\n+HFy5sxpdJxUbfz48Xh7e/PZZ58xYcIEfHx8mDVrFlu3biVv3ryx240ePZr169czZswY6tWrF/t8\nYGAgx44do02bNkyYMIHBgwcbcRoAdOrUCRsbGxYuXGhYBkl4J0+eZPLkyfz444906dKF0qVLM3Lk\nSA4fPkzGjBlf+jjR0dF8+OGHNGrUiD59+iRgYknKLBYLb775JpMnT6ZRo0Zx2nfVqlWMHj2a06dP\nv9aiSSIiyYXm/BQRiQda8EjionLlyhQpUoSqVaty5syZFxY+XzRXmRgrKCgIDw8Pli1bpsJnEuDl\n5cXvv/9OnTp1AMibNy9BQUE8ffo0dptNmzaxbds2ypQpE1v4fDbvp5ubG/v378fZ2dnwDrEZM2aw\nbdu22K5VSTmsVis///wztWvXpm7dupQqVYorV67wxRdf0KJFCz744AOaNm1KWFjYSx0vJiaGHj16\nkCZNGnr06JHA6SUpM5vN+Pn50bVrV/bv3//S++3atYtPPvmEZcuWqfApIqmGip8iIvFAxU+Ji2eF\nTbPZjJubG4GBgWzZsoV169axcuVKLl++rNXDk5iYmBjatGlDt27deO+994yOI//Pyckpdt7VIkWK\nULhwYdavX8+tW7fYuXMnffr0IVu2bPTv3x/431B4gEOHDrFgwQJGjRpl+HDzDBkysGTJErp37869\ne/cMzSLxIyYmhtWrV1O+fHl69+7Nxx9/zJUrV/D09Izt8jSZTMycOZO8efNSvXp1Tp069a/HvHr1\nKk2aNOHKlSusXr2aNGnSJMapSBJWoUIF/Pz8aNiwIV999RURERH/uG14eDg+Pj40b96cb775hjJl\nyiRiUhERY2nYu4hIPLhw4QL169cnMDDQ6CiSTISHhzN//nzmzp3LrVu3iIyMBODNN98kW7ZsNG3a\nNLZgI8YbPXo0O3bsYNu2bbHFM0l6vv/+e7p37469vT1RUVGUK1eOiRMn/m0+z4iICBo3bkxISAh7\n9+41KO3fDRo0iIsXL7J27Vp1ZCVTT58+ZfHixUyZMoXcuXMzaNAgPvroo3+9oWW1WpkxYwZTpkyh\ncOHC9OrViypVqpAxY0ZCQ0M5ceIE8+fP58CBA3Tt2pXRo0e/1OroknoEBATg6enJ6dOn6dy5M61a\ntSJ37txYrVaCgoJYtmwZX375JeXLl2fq1KmULFnS6MgiIolKxU8RkXhw9+5dihUrpo4deWlz5sxh\n0qRJ1KtXD1dXV3bu3MnTp0/p168fN2/exM/PjzZt2hg+HFdg586dtGrVimPHjpEnTx6j48hL2LZt\nG25ubuTPnz+2iGi1WmP/e/Xq1bRs2ZJ9+/ZRsWJFI6M+JyIignLlyvHZZ5/RoUMHo+NIHNy/f595\n8+YxZ84cKlWqhKenJ5UrV47TMaKioti4cSM+Pj6cO3eOR48e4ejoSOHChencuTMtW7bEwcEhgc5A\nUoLz58/j4+PDpk2bePDgAQBZs2alfv367NmzB09PTz7++GODU4qIJD4VP0VE4kFUVBQODg5ERkaq\nW0f+0+XLl2nZsiUNGzZk4MCBpEuXjvDwcGbMmMH27dvZunUr8+bNY/bs2Zw7d87ouKna3bt3KVOm\nDIsWLaJWrVpGx5E4slgsmM1mIiIiCA8PJ2PGjNy/f5+qVatSvnx5Fi9ebHTEvzl16hTvv/8+R44c\noVChQkbHkf9w7do1pk+fzrJl/8fenYfVmP//A3+eU9pLqSxJaVFCWSLbGGuMfZsJ2UqyjaX4IGOZ\nkm0IGXuWYjDJOhgMQsg2ydpiaB2UNSntdf/+8HO+c8YylepueT6u61yce3nfz3Paznmd9/ILBg0a\nhBkzZsDKykrsWEQfOHToEFasWFGk+UGJiCoLFj+JiEqIhoYGkpKSRJ87jsq/hIQENGvWDH///Tc0\nNDRk28+cOYMxY8YgMTER9+/fR6tWrfDmzRsRk1ZtBQUF6NmzJ1q2bInFixeLHYe+QEhICObOnYu+\nffsiNzcXPj4+uHfvHgwNDcWO9lErVqzA0aNHce7cOU6zQERERPSFuJoCEVEJ4aJHVFjGxsZQVFRE\naGio3PZ9+/ahXbt2yMvLQ2pqKrS1tfHy5UuRUtKyZcuQmZkJLy8vsaPQF+rYsSNGjx6NZcuWYcGC\nBejVq1e5LXwCwPTp0wEAq1atEjkJERERUcXHnp9ERCXExsYGO3fuRLNmzcSOQhXAkiVL4OfnhzZt\n2sDU1BQ3b97E+fPncfjwYfTo0QMJCQlISEhA69atoaysLHbcKufixYv47rvvEBYWVq6LZFR0Cxcu\nhKenJ3r27ImAgADo6+uLHemj4uLiYGdnh+DgYC5OQkRERPQFFDw9PT3FDkFEVJHl5OTg2LFjOH78\nOJ4/f44nT54gJycHhoaGnP+TPqldu3ZQUVFBXFwcoqKiUKNGDWzYsAGdO3cGAGhra8t6iFLZevHi\nBbp3746tW7fC1tZW7DhUwjp27AgnJyc8efIEpqamqFmzptx+QRCQnZ2NtLQ0qKqqipTy3WgCfX19\nzJo1C2PGjOHvAiIiIqJiYs9PIqJiSkxMxObNm7Ft2zY0bNgQFhYW0NLSQlpaGs6dOwcVFRVMmjQJ\nI0aMkJvXkeifUlNTkZubCz09PbGjEN7N89m3b180btwYy5cvFzsOiUAQBGzatAmenp7w9PSEq6ur\naIVHQRAwcOBAWFpa4qeffhIlQ0UmCEKxPoR8+fIl1q9fjwULFpRCqk/bsWMHpkyZUqZzPYeEhKBL\nly54/vw5atSoUWbXpcJJSEiAiYkJwsLC0KJFC7HjEBFVWJzzk4ioGAIDA9GiRQukp6fj3LlzOH/+\nPPz8/ODj44PNmzcjOjoaq1atwh9//IEmTZogMjJS7MhUTlWvXp2Fz3Jk5cqVSElJ4QJHVZhEIsHE\niRNx6tQpBAUFoXnz5ggODhYti5+fH3bu3ImLFy+KkqGievv2bZELn/Hx8Zg2bRoaNGiAxMTETx7X\nuXNnTJ069YPtO3bs+KJFD4cOHYrY2Nhin18c7du3R1JSEgufInB2dka/fv0+2H7jxg1IpVIkJibC\nyMgIycnJnFKJiOgLsfhJRFRE/v7+mDVrFs6ePYs1a9bAysrqg2OkUim6deuGQ4cOwdvbG507d0ZE\nRIQIaYmosK5cuQIfHx8EBgaiWrVqYschkTVt2hRnz56Fl5cXXF1dMXDgQMTExJR5jpo1a8LPzw+j\nRo0q0x6BFVVMTAy+++47mJmZ4ebNm4U659atWxg+fDhsbW2hqqqKe/fuYevWrcW6/qcKrrm5uf95\nrrKycpl/GKaoqPjB1A8kvvffRxKJBDVr1oRU+um37Xl5eWUVi4iowmLxk4ioCEJDQ+Hh4YHTp08X\negGKkSNHYtWqVejduzdSU1NLOSERFcerV68wbNgwbNmyBUZGRmLHoXJCIpFg0KBBiIyMhJ2dHVq3\nbg0PDw+kpaWVaY6+ffuiW7ducHd3L9PrViT37t1D165dYWVlhezsbPzxxx9o3rz5Z88pKChAjx49\n0Lt3bzRr1gyxsbFYtmwZDAwMvjiPs7Mz+vbti+XLl6NevXqoV68eduzYAalUCgUFBUilUtltzJgx\nAICAgIAPeo4eP34cbdq0gZqaGvT09NC/f3/k5OQAeFdQnT17NurVqwd1dXW0bt0ap06dkp0bEhIC\nqVSKs2fPok2bNlBXV0erVq3kisLvj3n16tUXP2YqeQkJCZBKpQgPDwfwf1+vEydOoHXr1lBRUcGp\nU6fw6NEj9O/fH7q6ulBXV0ejRo0QFBQka+fevXuwt7eHmsQEsRIAACAASURBVJoadHV14ezsLPsw\n5fTp01BWVkZKSorctX/44QdZj9NXr17B0dER9erVg5qaGpo0aYKAgICyeRKIiEoAi59EREWwdOlS\nLFmyBJaWlkU6b/jw4WjdujV27txZSsmIqLgEQYCzszMGDRr00SGIRCoqKpgzZw7u3LmD5ORkWFpa\nwt/fHwUFBWWWYdWqVTh//jx+++23MrtmRZGYmIhRo0bh3r17SExMxJEjR9C0adP/PE8ikWDx4sWI\njY3FzJkzUb169RLNFRISgrt37+KPP/5AcHAwhg4diuTkZCQlJSE5ORl//PEHlJWV0alTJ1mef/Yc\nPXnyJPr3748ePXogPDwcFy5cQOfOnWXfd05OTrh48SICAwMRERGB0aNHo1+/frh7965cjh9++AHL\nly/HzZs3oaurixEjRnzwPFD58e8lOT729fHw8MDixYsRHR0NOzs7TJo0CVlZWQgJCUFkZCR8fX2h\nra0NAMjIyECPHj2gpaWFsLAwHD58GJcvX4aLiwsAoGvXrtDX18e+ffvkrvHrr79i5MiRAICsrCzY\n2tri+PHjiIyMhJubGyZMmIBz586VxlNARFTyBCIiKpTY2FhBV1dXePv2bbHODwkJERo2bCgUFBSU\ncDKqyLKysoT09HSxY1Rpq1evFlq1aiVkZ2eLHYUqiGvXrglt27YVbG1thUuXLpXZdS9duiTUrl1b\nSE5OLrNrllf/fg7mzp0rdO3aVYiMjBRCQ0MFV1dXwdPTU9i/f3+JX7tTp07ClClTPtgeEBAgaGpq\nCoIgCE5OTkLNmjWF3Nzcj7bx9OlToX79+sL06dM/er4gCEL79u0FR0fHj54fExMjSKVS4e+//5bb\nPmDAAOH7778XBEEQzp8/L0gkEuH06dOy/aGhoYJUKhUeP34sO0YqlQovX74szEOnEuTk5CQoKioK\nGhoacjc1NTVBKpUKCQkJQnx8vCCRSIQbN24IgvB/X9NDhw7JtWVjYyMsXLjwo9fx8/MTtLW15V6/\nvm8nJiZGEARBmD59uvD111/L9l+8eFFQVFSUfZ98zNChQwVXV9diP34iorLEnp9ERIX0fs41NTW1\nYp3foUMHKCgo8FNykjNr1ixs3rxZ7BhV1p9//oklS5Zg7969UFJSEjsOVRB2dnYIDQ3F9OnTMXTo\nUAwbNuyzC+SUlPbt28PJyQmurq4f9A6rKpYsWYLGjRvju+++w6xZs2S9HL/55hukpaWhXbt2GDFi\nBARBwKlTp/Ddd9/B29sbr1+/LvOsTZo0gaKi4gfbc3NzMWjQIDRu3Bg+Pj6fPP/mzZvo0qXLR/eF\nh4dDEAQ0atQImpqastvx48fl5qaVSCSwtraW3TcwMIAgCHj27NkXPDIqKR07dsSdO3dw+/Zt2W3P\nnj2fPUcikcDW1lZu27Rp0+Dt7Y127dph/vz5smHyABAdHQ0bGxu516/t2rWDVCqVLcg5YsQIhIaG\n4u+//wYA7NmzBx07dpRNAVFQUIDFixejadOm0NPTg6amJg4dOlQmv/eIiEoCi59ERIUUHh6Obt26\nFft8iUQCe3v7Qi/AQFVDgwYN8ODBA7FjVEmvX7/GkCFDsGnTJpiYmIgdhyoYiUQCR0dHREdHw8LC\nAs2bN4enpycyMjJK9bpeXl5ITEzE9u3bS/U65U1iYiLs7e1x4MABeHh4oFevXjh58iTWrl0LAPjq\nq69gb2+PcePGITg4GH5+fggNDYWvry/8/f1x4cKFEsuipaX10Tm8X79+LTd0Xl1d/aPnjxs3Dqmp\nqQgMDCz2kPOCggJIpVKEhYXJFc6ioqI++N745wJu769XllM20KepqanBxMQEpqamspuhoeF/nvfv\n760xY8YgPj4eY8aMwYMHD9CuXTssXLjwP9t5//3QvHlzWFpaYs+ePcjLy8O+fftkQ94BYMWKFVi9\nejVmz56Ns2fP4vbt23LzzxIRlXcsfhIRFVJqaqps/qTiql69Ohc9IjksfopDEAS4uLigd+/eGDRo\nkNhxqAJTV1eHl5cXwsPDER0djYYNG+LXX38ttZ6ZSkpK2LVrFzw8PBAbG1sq1yiPLl++jAcPHuDo\n0aMYOXIkPDw8YGlpidzcXGRmZgIAxo4di2nTpsHExERW1Jk6dSpycnJkPdxKgqWlpVzPuvdu3Ljx\nn3OC+/j44Pjx4/j999+hoaHx2WObN2+O4ODgT+4TBAFJSUlyhTNTU1PUqVOn8A+GKg0DAwOMHTsW\ngYGBWLhwIfz8/AAAVlZWuHv3Lt6+fSs7NjQ0FIIgwMrKSrZtxIgR2L17N06ePImMjAwMHjxY7vi+\nffvC0dERNjY2MDU1xV9//VV2D46I6Aux+ElEVEiqqqqyN1jFlZmZCVVV1RJKRJWBhYUF30CIYP36\n9YiPj//skFOiojA2NkZgYCD27NkDHx8ffPXVVwgLCyuVazVp0gQeHh4YNWoU8vPzS+Ua5U18fDzq\n1asn93c4NzcXvXr1kv1drV+/vmyYriAIKCgoQG5uLgDg5cuXJZZl4sSJiI2NxdSpU3Hnzh389ddf\nWL16Nfbu3YtZs2Z98rwzZ85g7ty52LBhA5SVlfH06VM8ffpUtur2v82dOxf79u3D/PnzERUVhYiI\nCPj6+iIrKwsNGjSAo6MjnJyccODAAcTFxeHGjRtYuXIlDh8+LGujMEX4qjqFQnn2ua/Jx/a5ubnh\njz/+QFxcHG7duoWTJ0+icePGAN4tuqmmpiZbFOzChQuYMGECBg8eDFNTU1kbw4cPR0REBObPn4++\nffvKFectLCwQHByM0NBQREdHY/LkyYiLiyvBR0xEVLpY/CQiKiRDQ0NER0d/URvR0dGFGs5EVYeR\nkRGeP3/+xYV1Krzw8HAsXLgQe/fuhbKysthxqJL56quv8Oeff8LFxQX9+vWDs7MzkpKSSvw67u7u\nqFatWpUp4H/77bdIT0/H2LFjMX78eGhpaeHy5cvw8PDAhAkTcP/+fbnjJRIJpFIpdu7cCV1dXYwd\nO7bEspiYmODChQt48OABevTogdatWyMoKAj79+9H9+7dP3leaGgo8vLy4ODgAAMDA9nNzc3to8f3\n7NkThw4dwsmTJ9GiRQt07twZ58+fh1T67i1cQEAAnJ2dMXv2bFhZWaFv3764ePEijI2N5Z6Hf/v3\nNq72Xv7882tSmK9XQUEBpk6disaNG6NHjx6oXbs2AgICALz78P6PP/7Amzdv0Lp1awwcOBDt27fH\ntm3b5NowMjLCV199hTt37sgNeQeAefPmwc7ODr169UKnTp2goaGBESNGlNCjJSIqfRKBH/URERXK\nmTNnMGPGDNy6datYbxQePXoEGxsbJCQkQFNTsxQSUkVlZWWFffv2oUmTJmJHqfTevHmDFi1aYMmS\nJXBwcBA7DlVyb968weLFi7Ft2zbMmDED7u7uUFFRKbH2ExIS0LJlS5w+fRrNmjUrsXbLq/j4eBw5\ncgTr1q2Dp6cnevbsiRMnTmDbtm1QVVXFsWPHkJmZiT179kBRURE7d+5EREQEZs+ejalTp0IqlbLQ\nR0REVAWx5ycRUSF16dIFWVlZuHz5crHO37JlCxwdHVn4pA9w6HvZEAQBrq6u6NatGwufVCa0tLTw\n008/4erVq7h27RoaNWqEQ4cOldgwY2NjY6xcuRIjR45EVlZWibRZntWvXx+RkZFo06YNHB0doaOj\nA0dHR/Tu3RuJiYl49uwZVFVVERcXh6VLl8La2hqRkZFwd3eHgoICC59ERERVFIufRESFJJVKMXny\nZMyZM6fIq1vGxsZi06ZNmDRpUimlo4qMix6VDT8/P0RHR2P16tViR6EqxtzcHIcPH8aWLVuwYMEC\ndO3aFXfu3CmRtkeOHAkLCwvMmzevRNorzwRBQHh4ONq2bSu3/fr166hbt65sjsLZs2cjKioKvr6+\nqFGjhhhRiYiIqBxh8ZOIqAgmTZoEXV1djBw5stAF0EePHqFnz55YsGABGjVqVMoJqSJi8bP03b59\nG/PmzUNQUBAXHSPRdO3aFTdv3sS3334Le3t7TJw4Ec+fP/+iNiUSCTZv3ow9e/bg/PnzJRO0nPh3\nD1mJRAJnZ2f4+flhzZo1iI2NxY8//ohbt25hxIgRUFNTAwBoamqylycRERHJsPhJRFQECgoK2LNn\nD7Kzs9GjRw/8+eefnzw2Ly8PBw4cQLt27eDq6orvv/++DJNSRcJh76UrLS0NDg4O8PX1haWlpdhx\nqIpTVFTEpEmTEB0dDWVlZTRq1Ai+vr6yVcmLQ09PD1u2bIGTkxNSU1NLMG3ZEwQBwcHB6N69O6Ki\noj4ogI4dOxYNGjTAxo0b0a1bN/z+++9YvXo1hg8fLlJiIiIiKu+44BERUTHk5+djzZo1WLduHXR1\ndTF+/Hg0btwY6urqSE1Nxblz5+Dn5wcTExPMmTMHvXr1EjsylWOPHj1Cq1atSmVF6KpOEARMnjwZ\n2dnZ2Lp1q9hxiD4QFRUFd3d3xMfHY9WqVV/092L8+PHIzs6WrfJckbz/wHD58uXIysrCzJkz4ejo\nCCUlpY8ef//+fUilUjRo0KCMkxIREVFFw+InEdEXyM/Pxx9//AF/f3+EhoZCXV0dtWrVgo2NDSZM\nmAAbGxuxI1IFUFBQAE1NTSQnJ3NBrBImCAIKCgqQm5tboqtsE5UkQRBw/PhxTJ8+HWZmZli1ahUa\nNmxY5HbS09PRrFkzLF++HIMGDSqFpCUvIyMD/v7+WLlyJQwNDTFr1iz06tULUikHqBEREVHJYPGT\niIioHGjatCn8/f3RokULsaNUOoIgcP4/qhBycnKwfv16LFmyBMOHD8ePP/4IHR2dIrVx5coVDBw4\nELdu3ULt2rVLKemXe/nyJdavX4/169ejXbt2mDVr1gcLGRFR2QsODsa0adNw9+5d/u0kokqDH6kS\nERGVA1z0qPTwzRtVFEpKSnB3d0dkZCSysrLQsGFDbNy4EXl5eYVuo23bthg7dizGjh37wXyZ5UF8\nfDymTp2KBg0a4O+//0ZISAgOHTrEwidROdGlSxdIJBIEBweLHYWIqMSw+ElERFQOWFhYsPhJRAAA\nfX19bNq0CadOnUJQUBBatGiBs2fPFvr8BQsW4MmTJ9iyZUsppiyamzdvwtHRES1btoS6ujoiIiKw\nZcuWYg3vJ6LSI5FI4ObmBl9fX7GjEBGVGA57JyIiKgf8/f1x7tw57Ny5U+woFcrDhw8RGRkJHR0d\nmJqaom7dumJHIipRgiDg4MGDmDlzJpo2bQofHx+YmZn953mRkZH4+uuvcfXqVZibm5dB0g+9X7l9\n+fLliIyMhLu7O1xdXaGlpSVKHiIqnMzMTNSvXx8XL16EhYWF2HGIiL4Ye34SERGVAxz2XnTnz5/H\noEGDMGHCBAwYMAB+fn5y+/n5LlUGEokEgwcPRmRkJOzs7NC6dWt4eHggLS3ts+c1atQI8+bNw6hR\no4o0bL4k5OXlITAwELa2tpg2bRqGDx+O2NhYzJgxg4VPogpAVVUV48aNw88//yx2FCKiEsHiJxFR\nEUilUhw8eLDE2125ciVMTExk9728vLhSfBVjYWGBv/76S+wYFUZGRgaGDBmCb7/9Fnfv3oW3tzc2\nbtyIV69eAQCys7M51ydVKioqKpgzZw7u3LmD5ORkWFpawt/fHwUFBZ88Z+rUqVBVVcXy5cvLJGNG\nRgbWr18PCwsLbNiwAQsXLsTdu3cxevRoKCkplUkGIioZEydOxJ49e5CSkiJ2FCKiL8biJxFVak5O\nTpBKpXB1df1g3+zZsyGVStGvXz8Rkn3on4WamTNnIiQkRMQ0VNb09fWRl5cnK97R561YsQI2NjZY\nsGABdHV14erqigYNGmDatGlo3bo1Jk2ahGvXrokdk6jEGRgYICAgAIcPH8aWLVtgZ2eH0NDQjx4r\nlUrh7+8PX19f3Lx5U7Y9IiICP//8Mzw9PbFo0SJs3rwZSUlJxc704sULeHl5wcTEBMHBwdi9ezcu\nXLiAPn36QCrl2w2iisjAwAC9e/fGtm3bxI5CRPTF+GqEiCo1iUQCIyMjBAUFITMzU7Y9Pz8fv/zy\nC4yNjUVM92lqamrQ0dEROwaVIYlEwqHvRaCqqors7Gw8f/4cALBo0SLcu3cP1tbW6NatGx4+fAg/\nPz+5n3uiyuR90XP69OkYOnQohg0bhsTExA+OMzIywqpVqzB8+HDs2rULtm1t0apDK8z+dTa8znvh\nx9M/YvrW6TCxMEHvAb1x/vz5Qk8ZERcXhylTpsDCwgKPHj3ChQsXcPDgQa7cTlRJuLm5Ye3atWU+\ndQYRUUlj8ZOIKj1ra2s0aNAAQUFBsm2///47VFVV0alTJ7lj/f390bhxY6iqqqJhw4bw9fX94E3g\ny5cv4eDgAA0NDZiZmWH37t1y++fMmYOGDRtCTU0NJiYmmD17NnJycuSOWb58OerUqQMtLS04OTkh\nPT1dbr+Xlxesra1l98PCwtCjRw/o6+ujevXq6NChA65evfolTwuVQxz6Xnh6enq4efMmZs+ejYkT\nJ8Lb2xsHDhzArFmzsHjxYgwfPhy7d+/+aDGIqLKQSCRwdHREdHQ0LCws0KJFC3h6eiIjI0PuuJ49\neyLpZRLGzBmD8HrhyJyciaxvsoDOQEGXAmT0yUD25GycyD2BPsP6YLTL6M8WO27evIlhw4ahVatW\n0NDQkK3cbmlpWdoPmYjKkK2tLYyMjHD48GGxoxARfREWP4mo0pNIJHBxcZEbtrN9+3Y4OzvLHbdl\nyxbMmzcPixYtQnR0NFauXInly5dj48aNcsd5e3tj4MCBuHPnDoYMGYIxY8bg0aNHsv0aGhoICAhA\ndHQ0Nm7ciL1792Lx4sWy/UFBQZg/fz68vb0RHh4OCwsLrFq16qO530tLS8OoUaMQGhqKP//8E82b\nN0fv3r05D1Mlw56fhTdmzBh4e3vj1atXMDY2hrW1NRo2bIj8/HwAQLt27dCoUSP2/KQqQV1dHV5e\nXrhx4waio6PRsGFD/PrrrxAEAa9fv4bdV3Z4a/EWuWNygcYAFD7SiAog2Al46/wWB64ewECHgXLz\niQqCgDNnzqB79+7o27cvWrZsidjYWCxduhR16tQps8dKRGXLzc0Na9asETsGEdEXkQhcCpWIKjFn\nZ2e8fPkSO3fuhIGBAe7evQt1dXWYmJjgwYMHmD9/Pl6+fIkjR47A2NgYS5YswfDhw2Xnr1mzBn5+\nfoiIiADwbv60H374AYsWLQLwbvi8lpYWtmzZAkdHx49m2Lx5M1auXCnr0de+fXtYW1tj06ZNsmPs\n7e0RExOD2NhYAO96fh44cAB37tz5aJuCIKBu3brw8fH55HWp4tm1axd+//13/Prrr2JHKZdyc3OR\nmpoKPT092bb8/Hw8e/YM33zzDQ4cOABzc3MA7xZquHnzJntIU5V08eJFuLm5QUVFBVn5WYiQRiC7\nezZQ2DXAcgG1vWpwG+YGrwVe2L9/P5YvX47s7GzMmjULw4YN4wJGRFVEXl4ezM3NsX//frRs2VLs\nOERExcKen0RUJWhra2PgwIHYtm0bdu7ciU6dOsHQ0FC2/8WLF/j7778xfvx4aGpqym4eHh6Ii4uT\na+ufw9EVFBSgr6+PZ8+eybbt378fHTp0QJ06daCpqQl3d3e5obdRUVFo06aNXJv/NT/a8+fPMX78\neFhaWkJbWxtaWlp4/vw5h/RWMhz2/ml79uzBiBEjYGpqijFjxiAtLQ3Au5/B2rVrQ09PD23btsWk\nSZMwaNAgHD16VG6qC6KqpEOHDrh+/Trs7e0Rfjcc2d2KUPgEgGpARp8M+Kz0gZmZGVduJ6rCFBUV\nMWXKFPb+JKIKjcVPIqoyxowZg507d2L79u1wcXGR2/d+aN/mzZtx+/Zt2S0iIgL37t2TO7ZatWpy\n9yUSiez8q1evYtiwYejZsyeOHTuGW7duYdGiRcjNzf2i7KNGjcKNGzewZs0aXLlyBbdv30bdunU/\nmEuUKrb3w945KEPe5cuXMWXKFJiYmMDHxwe7du3C+vXrZfslEgl+++03jBw5EhcvXkT9+vURGBgI\nIyMjEVMTiUtBQQGxCbFQaKvw8WHu/0UbyDfIh6OjI1duJ6riXFxc8Pvvv+PJkydiRyEiKhZFsQMQ\nEZWVrl27QklJCa9evUL//v3l9tWsWRMGBgZ4+PCh3LD3orp8+TIMDQ3xww8/yLbFx8fLHWNlZYWr\nV6/CyclJtu3KlSufbTc0NBRr167FN998AwB4+vQpkpKSip2TyicdHR0oKSnh2bNnqFWrlthxyoW8\nvDyMGjUK7u7umDdvHgAgOTkZeXl5WLZsGbS1tWFmZgZ7e3usWrUKmZmZUFVVFTk1kfjevHmDffv3\nIX98frHbyG+TjwNHD2Dp0qUlmIyIKhptbW0MHz4cGzduhLe3t9hxiIiKjMVPIqpS7t69C0EQPui9\nCbybZ3Pq1KmoXr06evXqhdzcXISHh+Px48fw8PAoVPsWFhZ4/Pgx9uzZg7Zt2+LkyZMIDAyUO2ba\ntGkYPXo0WrZsiU6dOmHfvn24fv06dHV1P9vurl27YGdnh/T0dMyePRvKyspFe/BUIbwf+s7i5zt+\nfn6wsrLCxIkTZdvOnDmDhIQEmJiY4MmTJ9DR0UGtWrVgY2PDwifR/xcTEwMlXSVkaWYVv5H6QGxg\nLARBkFuEj4iqHjc3N1y5coW/D4ioQuLYFSKqUtTV1aGhofHRfS4uLti+fTt27dqFZs2a4euvv8aW\nLVtgamoqO+ZjL/b+ua1Pnz6YOXMm3N3d0bRpUwQHB3/wCbmDgwM8PT0xb948tGjRAhEREZgxY8Zn\nc/v7+yM9PR0tW7aEo6MjXFxcUL9+/SI8cqoouOK7vNatW8PR0RGampoAgJ9//hnh4eE4fPgwzp8/\nj7CwMMTFxcHf31/kpETlS2pqKiTKX1igUAQkUgkyMzNLJhQRVVhmZmYYPnw4C59EVCFxtXciIqJy\nZNGiRXj79i2Hmf5Dbm4uqlWrhry8PBw/fhw1a9ZEmzZtUFBQAKlUihEjRsDMzAxeXl5iRyUqN65f\nvw77ofZ4M/pN8RspACSLJMjLzeN8n0RERFRh8VUMERFROcIV3995/fq17P+Kioqyf/v06YM2bdoA\nAKRSKTIzMxEbGwttbW1RchKVV4aGhsh5kQN8yXp7zwEdfR0WPomIiKhC4ysZIiKicoTD3gF3d3cs\nWbIEsbGxAN5NLfF+oMo/izCCIGD27Nl4/fo13N3dRclKVF4ZGBigRcsWQETx21C+pYxxLuNKLhQR\nVVppaWk4efIkrl+/jvT0dLHjEBHJ4YJHRERE5UiDBg3w8OFD2ZDuqiYgIABr1qyBqqoqHj58iP/9\n739o1arVB4uURUREwNfXFydPnkRwcLBIaYnKt9luszHCfQTSmqUV/eRsAHeB74O+L/FcRFS5vHjx\nAkOGDMGrV6+QlJSEnj17ci5uIipXqt67KiIionJMQ0MD2traePz4sdhRylxKSgr279+PxYsX4+TJ\nk7h37x5cXFywb98+pKSkyB1br149NGvWDH5+frCwsBApMVH51rt3b2jkaQD3in6u0kUldO3WFYaG\nhiUfjIgqtIKCAhw5cgS9evXCwoULcerUKTx9+hQrV67EwYMHcfXqVWzfvl3smEREMix+EhERlTNV\ndei7VCpF9+7dYW1tjQ4dOiAyMhLW1taYOHEifHx8EBMTAwB4+/YtDh48CGdnZ/Ts2VPk1ETll4KC\nAk4cOQH1M+pAYX+lCIBCqAJqPqmJX7b9Uqr5iKhiGj16NGbNmoV27drhypUr8PT0RNeuXdGlSxe0\na9cO48ePx7p168SOSUQkw+InERFROVNVFz2qXr06xo0bhz59+gB4t8BRUFAQFi9ejDVr1sDNzQ0X\nLlzA+PHj8fPPP0NNTU3kxETlX9OmTXH6+GlondCCNEQKfG4qvheA0jElGCUa4fL5y6hRo0aZ5SSi\niuH+/fu4fv06XF1dMW/ePJw4cQKTJ09GUFCQ7BhdXV2oqqri2bNnIiYlIvo/LH4SERGVM1W15ycA\nqKioyP6fn58PAJg8eTIuXbqEuLg49O3bF4GBgfjlF/ZIIyqstm3bIvx6OIYYDoH0ZymUDioBUQAS\nAcQDuANoBGpAc7cmJneejJvXbqJevXrihiaicik3Nxf5+flwcHCQbRsyZAhSUlLw/fffw9PTEytX\nrkSTJk1Qs2ZN2YKFRERiYvGTiIionKnKxc9/UlBQgCAIKCgoQLNmzbBjxw6kpaUhICAAjRs3Fjse\nUYViZmaGnxb/BC01LXgO9UT75+1hFW6FJveaoFtWN2yatwnPk55j5YqVqF69uthxiaicatKkCSQS\nCY4ePSrbFhISAjMzMxgZGeHs2bOoV68eRo8eDQCQSCRiRSUikpEI/CiGiIioXImIiMDgwYMRHR0t\ndpRyIyUlBW3atEGDBg1w7NgxseMQERFVWdu3b4evry86d+6Mli1bYu/evahduza2bt2KpKQkVK9e\nnVPTEFG5wuInEVER5OfnQ0FBQXZfEAR+ok0lLisrC9ra2khPT4eioqLYccqFly9fYu3atfD09BQ7\nChERUZXn6+uLX375BampqdDV1cWGDRtga2sr25+cnIzatWuLmJCI6P+w+ElE9IWysrKQkZEBDQ0N\nKCkpiR2HKgljY2OcO3cOpqamYkcpM1lZWVBWVv7kBwr8sIGIiKj8eP78OVJTU2Fubg7g3SiNgwcP\nYv369VBVVYWOjg4GDBiAb7/9Ftra2iKnJaKqjHN+EhEVUk5ODhYsWIC8vDzZtr1792LSpEmYMmUK\nFi5ciISEBBETUmVS1VZ8T0pKgqmpKZKSkj55DAufRERE5Yeenh7Mzc2RnZ0NLy8vNGjQAK6urkhJ\nScGwYcPQvHlz7Nu3D05OTmJHJaIqjj0/iYgK6e+//4alpSXevn2L/Px87NixA5MnT0abNm2gqamJ\n69evQ1lZGTdu3ICenp7YcamCmzRpEqysrDBlyhSxd/FkawAAIABJREFUo5S6/Px82Nvb4+uvv+aw\ndiIiogpEEAT8+OOP2L59O9q2bYsaNWrg2bNnKCgowG+//YaEhAS0bdsWGzZswIABA8SOS0RVFHt+\nEhEV0osXL6CgoACJRIKEhAT8/PPP8PDwwLlz53DkyBHcvXsXderUwYoVK8SOSpVAVVrxfdGiRQCA\n+fPni5yEqHLx8vKCtbW12DGIqBILDw+Hj48P3N3dsWHDBmzevBmbNm3CixcvsGjRIhgbG2PkyJFY\ntWqV2FGJqApj8ZOIqJBevHgBXV1dAJD1/nRzcwPwrueavr4+Ro8ejStXrogZkyqJqjLs/dy5c9i8\neTN2794tt5gYUWXn7OwMqVQqu+nr66Nv3764f/9+iV6nvE4XERISAqlUilevXokdhYi+wPXr19Gx\nY0e4ublBX18fAFCrVi107twZDx8+BAB069YNdnZ2yMjIEDMqEVVhLH4SERXS69ev8ejRI+zfvx9+\nfn6oVq2a7E3l+6JNbm4usrOzxYxJlURV6Pn57NkzjBgxAjt27ECdOnXEjkNU5uzt7fH06VMkJyfj\n9OnTyMzMxKBBg8SO9Z9yc3O/uI33C5hxBi6iiq127dq4d++e3Ovfv/76C1u3boWVlRUAoFWrVliw\nYAHU1NTEiklEVRyLn0REhaSqqopatWph3bp1OHv2LOrUqYO///5btj8jIwNRUVFVanVuKj0mJiZ4\n/PgxcnJyxI5SKgoKCjBy5Eg4OTnB3t5e7DhEolBWVoa+vj5q1qyJZs2awd3dHdHR0cjOzkZCQgKk\nUinCw8PlzpFKpTh48KDsflJSEoYPHw49PT2oq6ujRYsWCAkJkTtn7969MDc3h5aWFgYOHCjX2zIs\nLAw9evSAvr4+qlevjg4dOuDq1asfXHPDhg0YPHgwNDQ0MHfuXABAZGQk+vTpAy0tLdSqVQuOjo54\n+vSp7Lx79+6hW7duqF69OjQ1NdG8eXOEhIQgISEBXbp0AQDo6+tDQUEBY8aMKZknlYjK1MCBA6Gh\noYHZs2dj06ZN2LJlC+bOnQtLS0s4ODgAALS1taGlpSVyUiKqyhTFDkBEVFF0794dFy9exNOnT/Hq\n1SsoKChAW1tbtv/+/ftITk5Gz549RUxJlUW1atVQr149xMbGomHDhmLHKXHLli1DZmYmvLy8xI5C\nVC6kpaUhMDAQNjY2UFZWBvDfQ9YzMjLw9ddfo3bt2jhy5AgMDAxw9+5duWPi4uIQFBSE3377Denp\n6RgyZAjmzp2LjRs3yq47atQorF27FgCwbt069O7dGw8fPoSOjo6snYULF2LJkiVYuXIlJBIJkpOT\n0bFjR7i6umLVqlXIycnB3Llz0b9/f1nx1NHREc2aNUNYWBgUFBRw9+5dqKiowMjICAcOHMC3336L\nqKgo6OjoQFVVtcSeSyIqWzt27MDatWuxbNkyVK9eHXp6epg9ezZMTEzEjkZEBIDFTyKiQrtw4QLS\n09M/WKny/dC95s2b49ChQyKlo8ro/dD3ylb8vHjxIn7++WeEhYVBUZEvRajqOnHiBDQ1NQG8m0va\nyMgIx48fl+3/ryHhu3fvxrNnz3D9+nVZobJ+/fpyx+Tn52PHjh3Q0NAAAIwbNw4BAQGy/Z07d5Y7\nfs2aNdi/fz9OnDgBR0dH2fahQ4fK9c788ccf0axZMyxZskS2LSAgALq6uggLC0PLli2RkJCAmTNn\nokGDBgAgNzKiRo0aAN71/Hz/fyKqmOzs7LBjxw5ZB4HGjRuLHYmISA6HvRMRFdLBgwcxaNAg9OzZ\nEwEBAXj58iWA8ruYBFV8lXHRoxcvXsDR0RH+/v4wNDQUOw6RqDp27Ig7d+7g9u3b+PPPP9G1a1fY\n29vj8ePHhTr/1q1bsLGxkeuh+W/GxsaywicAGBgY4NmzZ7L7z58/x/jx42FpaSkbmvr8+XMkJibK\ntWNrayt3/8aNGwgJCYGmpqbsZmRkBIlEgpiYGADA9OnT4eLigq5du2LJkiUlvpgTEZUfUqkUderU\nYeGTiMolFj+JiAopMjISPXr0gKamJubPnw8nJyfs2rWr0G9SiYqqsi16VFBQgFGjRsHR0ZHTQxAB\nUFNTg4mJCUxNTWFra4stW7bgzZs38PPzg1T67mX6P3t/5uXlFfka1apVk7svkUhQUFAguz9q1Cjc\nuHEDa9aswZUrV3D79m3UrVv3g/mG1dXV5e4XFBSgT58+suLt+9uDBw/Qp08fAO96h0ZFRWHgwIG4\nfPkybGxs5HqdEhEREZUFFj+JiArp6dOncHZ2xs6dO7FkyRLk5ubCw8MDTk5OCAoKkutJQ1QSKlvx\nc+XKlXj9+jUWLVokdhSicksikSAzMxP6+voA3i1o9N7Nmzfljm3evDnu3Lkjt4BRUYWGhmLKlCn4\n5ptvYGVlBXV1dblrfkqLFi0QEREBIyMjmJqayt3+WSg1MzPD5MmTcezYMbi4uGDr1q0AACUlJQDv\nhuUTUeXzX9N2EBGVJRY/iYgKKS0tDSoqKlBRUcHIkSNx/PhxrFmzRrZKbb9+/eDv74/s7Gyxo1Il\nUZmGvV+5cgU+Pj4IDAz8oCcaUVWVnZ2Np0+f4unTp4iOjsaUKVOQkZGBvn37QkVFBW3atMFPP/2E\nyMhIXL58GTNnzpSbasXR0RE1a9ZE//79cenSJcTFxeHo0aMfrPb+ORYWFti1axeioqLw559/Ytiw\nYbIFlz7n+++/R2pqKhwcHHD9+nXExcXhzJkzGD9+PN6+fYusrCxMnjxZtrr7tWvXcOnSJdmQWGNj\nY0gkEvz+++948eIF3r59W/QnkIjKJUEQcPbs2WL1ViciKg0sfhIRFVJ6erqsJ05eXh6kUikGDx6M\nkydP4sSJEzA0NISLi0uheswQFUa9evXw4sULZGRkiB3li7x69QrDhg3Dli1bYGRkJHYconLjzJkz\nMDAwgIGBAdq0aYMbN25g//796NChAwDA398fwLvFRCZOnIjFixfLna+mpoaQkBAYGhqiX79+sLa2\nhqenZ5Hmovb390d6ejpatmwJR0dHuLi4fLBo0sfaq1OnDkJDQ6GgoICePXuiSZMmmDJlClRUVKCs\nrAwFBQWkpKTA2dkZDRs2xODBg9G+fXusXLkSwLu5R728vDB37lzUrl0bU6ZMKcpTR0TlmEQiwYIF\nC3DkyBGxoxARAQAkAvujExEVirKyMm7dugUrKyvZtoKCAkgkEtkbw7t378LKyoorWFOJadSoEfbu\n3Qtra2uxoxSLIAgYMGAAzMzMsGrVKrHjEBERURnYt28f1q1bV6Se6EREpYU9P4mICik5ORmWlpZy\n26RSKSQSCQRBQEFBAaytrVn4pBJV0Ye++/r6Ijk5GcuWLRM7ChEREZWRgQMHIj4+HuHh4WJHISJi\n8ZOIqLB0dHRkq+/+m0Qi+eQ+oi9RkRc9un79OpYuXYrAwEDZ4iZERERU+SkqKmLy5MlYs2aN2FGI\niFj8JCIiKs8qavHz9evXGDJkCDZt2gQTExOx4xAREVEZGzt2LI4ePYrk5GSxoxBRFcfiJxHRF8jL\nywOnTqbSVBGHvQuCABcXF/Tp0weDBg0SOw4RERGJQEdHB8OGDcPGjRvFjkJEVRyLn0REX8DCwgIx\nMTFix6BKrCL2/Fy/fj3i4+Ph4+MjdhQiIiIS0dSpU7Fp0yZkZWWJHYWIqjAWP4mIvkBKSgpq1Kgh\ndgyqxAwMDJCWloY3b96IHaVQwsPDsXDhQuzduxfKyspixyEiIiIRWVpawtbWFr/++qvYUYioCmPx\nk4iomAoKCpCWlobq1auLHYUqMYlEUmF6f7558wYODg5Yt24dzM3NxY5DVKUsXboUrq6uYscgIvqA\nm5sbfH19OVUUEYmGxU8iomJKTU2FhoYGFBQUxI5ClVxFKH4KggBXV1fY29vDwcFB7DhEVUpBQQG2\nbduGsWPHih2FiOgD9vb2yM3Nxfnz58WOQkRVFIufRETFlJKSAh0dHbFjUBXQoEGDcr/o0ebNm3H/\n/n2sXr1a7ChEVU5ISAhUVVVhZ2cndhQiog9IJBJZ708iIjGw+ElEVEwsflJZsbCwKNc9P2/fvo35\n8+cjKCgIKioqYschqnK2bt2KsWPHQiKRiB2FiOijRowYgcuXL+Phw4diRyGiKojFTyKiYmLxk8pK\neR72npaWBgcHB/j6+sLCwkLsOERVzqtXr3Ds2DGMGDFC7ChERJ+kpqYGV1dXrF27VuwoRFQFsfhJ\nRFRMLH5SWbGwsCiXw94FQcDEiRPRoUMHDB8+XOw4RFXS7t270atXL+jq6oodhYjosyZNmoRffvkF\nqampYkchoiqGxU8iomJi8ZPKip6eHgoKCvDy5Uuxo8jZvn07bt++jZ9//lnsKERVkiAIsiHvRETl\nnaGhIb755hts375d7ChEVMWw+ElEVEwsflJZkUgk5W7o+7179+Dh4YGgoCCoqamJHYeoSrpx4wbS\n0tLQuXNnsaMQERWKm5sb1q5di/z8fLGjEFEVwuInEVExsfhJZak8DX1/+/YtHBwc4OPjAysrK7Hj\nEFVZW7duhYuLC6RSvqQnoorBzs4OtWvXxtGjR8WOQkRVCF8pEREV06tXr1CjRg2xY1AVUZ56fk6e\nPBl2dnYYPXq02FGIqqy3b98iKCgITk5OYkchIioSNzc3+Pr6ih2DiKoQFj+JiIqJPT+pLJWX4ufO\nnTtx9epVrFu3TuwoRFXavn370L59e9StW1fsKERERTJo0CDExsbi5s2bYkchoiqCxU8iomJi8ZPK\nUnkY9h4VFYUZM2YgKCgIGhoaomYhquq40BERVVSKioqYPHky1qxZI3YUIqoiFMUOQERUUbH4SWXp\nfc9PQRAgkUjK/PoZGRlwcHDA0qVLYW1tXebXJ6L/ExUVhZiYGPTq1UvsKERExTJ27FiYm5sjOTkZ\ntWvXFjsOEVVy7PlJRFRMLH5SWdLW1oaKigqePn0qyvWnTZsGGxsbuLi4iHJ9Ivo/27Ztg5OTE6pV\nqyZ2FCKiYqlRowaGDh2KTZs2iR2FiKoAiSAIgtghiIgqIh0dHcTExHDRIyoz7du3x9KlS/H111+X\n6XX37NkDLy8vhIWFQVNTs0yvTUTyBEFAbm4usrOz+fNIRBVadHQ0OnXqhPj4eKioqIgdh4gqMfb8\nJCIqhoKCAqSlpaF69epiR6EqRIxFj/766y9MmzYNe/fuZaGFqByQSCRQUlLizyMRVXgNGzZE8+bN\nERgYKHYUIqrkWPwkIiqCzMxMhIeH4+jRo1BRUUFMTAzYgZ7KSlkXP7OysuDg4ICFCxeiWbNmZXZd\nIiIiqhrc3Nzg6+vL19NEVKpY/CQiKoSHDx/if//7H4yMjODs7IxVq1bBxMQEXbp0ga2tLbZu3Yq3\nb9+KHZMqubJe8X369OmwsLDAhAkTyuyaREREVHV0794dOTk5CAkJETsKEVViLH4SEX1GTk4OXF1d\n0bZtWygoKODatWu4ffs2QkJCcPfuXSQmJmLJkiU4cuQIjI2NceTIEbEjUyVWlj0/g4KCcOrUKWzZ\nskWU1eWJiIio8pNIJJg2bRp8fX3FjkJElRgXPCIi+oScnBz0798fioqK+PXXX6GhofHZ469fv44B\nAwZg2bJlGDVqVBmlpKokPT0dNWvWRHp6OqTS0vv8MiYmBm3btsWJEydga2tbatchIiIiysjIgLGx\nMa5evQozMzOx4xBRJcTiJxHRJ4wZMwYvX77EgQMHoKioWKhz3q9auXv3bnTt2rWUE1JVVLduXVy5\ncgVGRkal0n52djbatWsHJycnTJkypVSuQUSf9/5vT15eHgRBgLW1Nb7++muxYxERlZo5c+YgMzOT\nPUCJqFSw+ElE9BF3797FN998gwcPHkBNTa1I5x46dAhLlizBn3/+WUrpqCrr1KkT5s+fX2rF9alT\np+Lx48fYv38/h7sTieD48eNYsmQJIiMjoaamhrp16yI3Nxf16tXDd999hwEDBvznSAQioorm0aNH\nsLGxQXx8PLS0tMSOQ0SVDOf8JCL6iA0bNmDcuHFFLnwCQL9+/fDixQsWP6lUlOaiR4cOHcLRo0ex\nbds2Fj6JROLh4QFbW1s8ePAAjx49wurVq+Ho6AipVIqVK1di06ZNYkckIipxhoaG6NGjB7Zv3y52\nFCKqhNjzk4joX968eQNjY2NERETAwMCgWG389NNPiIqKQkBAQMmGoypvxYoVSEpKwqpVq0q03fj4\neNjZ2eHo0aNo3bp1ibZNRIXz6NEjtGzZElevXkX9+vXl9j158gT+/v6YP38+/P39MXr0aHFCEhGV\nkmvXrmHYsGF48OABFBQUxI5DRJUIe34SEf1LWFgYrK2ti134BIDBgwfj3LlzJZiK6J3SWPE9JycH\nQ4YMgYeHBwufRCISBAG1atXCxo0bZffz8/MhCAIMDAwwd+5cjBs3DsHBwcjJyRE5LRFRyWrdujVq\n1aqFY8eOiR2FiCoZFj+JiP7l1atX0NPT+6I29PX1kZKSUkKJiP5PaQx7nzNnDmrVqgV3d/cSbZeI\niqZevXoYOnQoDhw4gF9++QWCIEBBQUFuGgpzc3NERERASUlJxKRERKXDzc2Nix4RUYlj8ZOI6F8U\nFRWRn5//RW3k5eUBAM6cOYP4+Pgvbo/oPVNTUyQkJMi+x77U0aNHsX//fgQEBHCeTyIRvZ+Javz4\n8ejXrx/Gjh0LKysr+Pj4IDo6Gg8ePEBQUBB27tyJIUOGiJyWiKh0DBo0CA8fPsStW7fEjkJElQjn\n/CQi+pfQ0FBMnjwZN2/eLHYbt27dQo8ePdC4cWM8fPgQz549Q/369WFubv7BzdjYGNWqVSvBR0CV\nXf369REcHAwzM7MvaicxMRGtWrXCoUOH0K5duxJKR0TFlZKSgvT0dBQUFCA1NRUHDhzAnj17EBsb\nCxMTE6SmpuK7776Dr68ve34SUaX1008/ITo6Gv7+/mJHIaJKgsVPIqJ/ycvLg4mJCY4dO4amTZsW\nqw03Nzeoq6tj8eLFAIDMzEzExcXh4cOHH9yePHkCQ0PDjxZGTUxMoKysXJIPjyqB7t27w93dHT17\n9ix2G7m5uejYsSMGDBiAWbNmlWA6IiqqN2/eYOvWrVi4cCHq1KmD/Px86Ovro2vXrhg0aBBUVVUR\nHh6Opk2bwsrKir20iahSe/XqFczNzREVFYVatWqJHYeIKgEWP4mIPsLb2xuPHz/Gpk2binzu27dv\nYWRkhPDwcBgbG//n8Tk5OYiPj/9oYTQxMRG1atX6aGHUzMwMampqxXl4VMF9//33sLS0xNSpU4vd\nhoeHB+7cuYNjx45BKuUsOERi8vDwwPnz5zFjxgzo6elh3bp1OHToEGxtbaGqqooVK1ZwMTIiqlIm\nTJgATU1N1KhRAxcuXEBKSgqUlJRQq1YtODg4YMCAARw5RUSFxuInEdFHJCUloVGjRggPD4eJiUmR\nzv3pp58QGhqKI0eOfHGOvLw8JCYmIiYm5oPCaGxsLGrUqPHJwqiWltYXX784MjIysG/fPty5cwca\nGhr45ptv0KpVKygqKoqSpzLy9fVFTEwM1q5dW6zzT5w4gXHjxiE8PBz6+volnI6IiqpevXpYv349\n+vXrB+BdrydHR0d06NABISEhiI2Nxe+//w5LS0uRkxIRlb7IyEjMnj0bwcHBGDZsGAYMGABdXV3k\n5uYiPj4e27dvx4MHD+Dq6opZs2ZBXV1d7MhEVM7xnSgR0UfUqVMH3t7e6NmzJ0JCQgo95ObgwYNY\ns2YNLl26VCI5FBUVYWpqClNTU9jb28vtKygowOPHj+UKooGBgbL/a2hofLIwWqNGjRLJ9zEvXrzA\ntWvXkJGRgdWrVyMsLAz+/v6oWbMmAODatWs4ffo0srKyYG5ujrZt28LCwkJuGKcgCBzW+RkWFhY4\nceJEsc59/PgxnJ2dERQUxMInUTkQGxsLfX19aGpqyrbVqFEDN2/exLp16zB37lw0btwYR48ehaWl\nJX8/ElGldvr0aQwfPhwzZ87Ezp07oaOjI7e/Y8eOGD16NO7duwcvLy906dIFR48elb3OJCL6GPb8\nJCL6DG9vbwQEBCAwMBCtWrX65HHZ2dnYsGEDVqxYgaNHj8LW1rYMU35IEAQkJyd/dCj9w4cPoaCg\n8NHCqLm5OfT19b/ojXV+fj6ePHmCevXqoXnz5ujatSu8vb2hqqoKABg1ahRSUlKgrKyMR48eISMj\nA97e3ujfvz+Ad0VdqVSKV69e4cmTJ6hduzb09PRK5HmpLB48eIAePXogNja2SOfl5eWhS5cu6NGj\nB+bOnVtK6YiosARBgCAIGDx4MFRUVLB9+3a8ffsWe/bsgbe3N549ewaJRAIPDw/89ddf2Lt3L4d5\nElGldfnyZQwYMAAHDhxAhw4d/vN4QRDwww8/4NSpUwgJCYGGhkYZpCSiiojFTyKi//DLL79g3rx5\nMDAwwKRJk9CvXz9oaWkhPz8fCQkJ2LZtG7Zt2wYbGxts3rwZpqamYkf+LEEQ8PLly08WRnNycj5Z\nGK1Tp06RCqM1a9bEnDlzMG3aNNm8kg8ePIC6ujoMDAwgCAJmzJiBgIAA3Lp1C0ZGRgDeDXdasGAB\nwsLC8PTpUzRv3hw7d+6Eubl5qTwnFU1ubi40NDTw5s2bIi2INW/ePFy/fh0nT57kPJ9E5ciePXsw\nfvx41KhRA1paWnjz5g28vLzg5OQEAJg1axYiIyNx7NgxcYMSEZWSzMxMmJmZwd/fHz169Cj0eYIg\nwMXFBUpKSsWaq5+IqgYWP4mICiE/Px/Hjx/H+vXrcenSJWRlZQEA9PT0MGzYMEyYMKHSzMWWkpLy\n0TlGHz58iLS0NJiZmWHfvn0fDFX/t7S0NNSuXRv+/v5wcHD45HEvX75EzZo1ce3aNbRs2RIA0KZN\nG+Tm5mLz5s2oW7cuxowZg6ysLBw/flzWg7Sqs7CwwG+//QYrK6tCHX/69Gk4OTkhPDycK6cSlUMp\nKSnYtm0bkpOTMXr0aFhbWwMA7t+/j44dO2LTpk0YMGCAyCmJiErHjh07sHfvXhw/frzI5z59+hSW\nlpaIi4v7YJg8ERHAOT+JiApFQUEBffv2Rd++fQG863mnoKBQKXvP6ejooGXLlrJC5D+lpaUhJiYG\nxsbGnyx8vp+PLj4+HlKp9KNzMP1zzrrDhw9DWVkZDRo0AABcunQJ169fx507d9CkSRMAwKpVq9C4\ncWPExcWhUaNGJfVQK7QGDRrgwYMHhSp+JiUlYfTo0di9ezcLn0TllI6ODv73v//JbUtLS8OlS5fQ\npUsXFj6JqFLbsGED5s+fX6xza9WqhV69emHH/2vvzsO0Luv9gb9nRhh2FcETqMCwhaloKuLBLTcO\nappKCymZkDtqx9Q6prkvlbsoaCIuF6SehBIlQTuY5FIKEoJIOCiCoGiiKRKyzPz+6OdcToqyj37n\n9bquuS6e73Pf9/fzPCI8vJ97ufPO/Pd///d6rgwoguL9qx1gI2jQoEEhg8/P0rx58+y0005p1KjR\nKttUVVUlSV544YW0aNHiY4crVVVV1QSfd9xxRy666KKceeaZ2XTTTbN06dI8/PDDadeuXbbffvus\nWLEiSdKiRYu0adMm06ZN20Cv7Iuna9eumTVr1me2W7lyZY4++uiccMIJ2XfffTdCZcD60rx583z9\n61/PNddcU9elAGwwM2bMyGuvvZaDDjporcc46aSTcvvtt6/HqoAiMfMTgA1ixowZ2XLLLbPZZpsl\n+ddsz6qqqpSVlWXx4sU5//zz87vf/S6nnXZazj777CTJsmXL8sILL9TMAv0wSF24cGFatWqVd999\nt2as+n7acZcuXTJ16tTPbHfppZcmyVrPpgDqltnaQNHNnTs33bp1S1lZ2VqPsd1222XevHnrsSqg\nSISfAKw31dXVeeedd7LFFlvkxRdfTIcOHbLpppsmSU3w+de//jU//OEP89577+WWW27JgQceWCvM\nfOONN2qWtn+4LfXcuXNTVlZmH6eP6NKlS+67775PbfPoo4/mlltuyeTJk9fpHxTAxuGLHaA+WrJk\nSZo0abJOYzRp0iTvv//+eqoIKBrhJwDrzfz589O7d+8sXbo0c+bMSUVFRW6++ebss88+2X333XPX\nXXfl6quvzt57753LL788zZs3T5KUlJSkuro6LVq0yJIlS9KsWbMkqQnspk6dmsaNG6eioqKm/Yeq\nq6tz7bXXZsmSJTWn0nfq1KnwQWmTJk0yderUDB8+POXl5Wnbtm322muvbLLJv/5qX7hwYfr37587\n77wzbdq0qeNqgdXx9NNPp0ePHvVyWxWg/tp0001rVvesrX/84x81q40A/p3wE2ANDBgwIG+99VbG\njBlT16V8Lm211Va55557MmXKlLz22muZPHlybrnlljzzzDO5/vrrc8YZZ+Ttt99OmzZtcsUVV+TL\nX/5yunbtmh133DGNGjVKSUlJtt122zz55JOZP39+ttpqqyT/OhSpR48e6dq16yfet1WrVpk5c2ZG\njx5dczJ9w4YNa4LQD0PRD39atWr1hZxdVVVVlfHjx2fIkCF56qmnsuOOO2bixIn54IMP8uKLL+aN\nN97IiSeemIEDB+b73/9+BgwYkAMPPLCuywZWw/z589OnT5/Mmzev5gsggPpgu+22y1//+te89957\nNV+Mr6lHH3003bt3X8+VAUVRUv3hmkKAAhgwYEDuvPPOlJSU1CyT3m677fLNb34zJ5xwQs2suHUZ\nf13Dz1deeSUVFRWZNGlSdt5553Wq54tm1qxZefHFF/OnP/0p06ZNS2VlZV555ZVcc801Oemkk1Ja\nWpqpU6fmqKOOSu/evdOnT5/ceuutefTRR/PHP/4xO+yww2rdp7q6Om+++WYqKysze/bsmkD0w58V\nK1Z8LBD98OdLX/rS5zIY/fvf/57DDz88S5YsyaBBg/Ld7373Y0vEnn322QwdOjT33ntv2rZtm+nT\np6/z73lg47j88svzyiuv5JZbbqnrUgA2um8ngMK4AAAfb0lEQVR961vZb7/9cvLJJ69V/7322itn\nnHFGjjzyyPVcGVAEwk+gUAYMGJAFCxZkxIgRWbFiRd58881MmDAhl112WTp37pwJEyakcePGH+u3\nfPnyNGjQYLXGX9fwc86cOenUqVOeeeaZehd+rsq/73N3//3356qrrkplZWV69OiRiy++ODvttNN6\nu9+iRYs+MRStrKzM+++//4mzRTt37pytttqqTpajvvnmm9lrr71y5JFH5tJLL/3MGqZNm5aDDz44\n5513Xk488cSNVCWwtqqqqtKlS5fcc8896dGjR12XA7DRPfrooznttNMybdq0Nf4S+rnnnsvBBx+c\nOXPm+NIX+ETCT6BQVhVOPv/889l5553z05/+NBdccEEqKipy7LHHZu7cuRk9enR69+6de++9N9Om\nTcuPfvSjPPHEE2ncuHEOO+ywXH/99WnRokWt8Xv27JnBgwfn/fffz7e+9a0MHTo05eXlNff75S9/\nmV/96ldZsGBBunTpkh//+Mc5+uijkySlpaU1e1wmyde+9rVMmDAhkyZNyrnnnptnn302y5YtS/fu\n3XPllVdm991330jvHkny7rvvrjIYXbRoUSoqKj4xGG3Xrt0G+cC9cuXK7LXXXvna176Wyy+/fLX7\nVVZWZq+99spdd91l6Tt8zk2YMCFnnHFG/vrXv34uZ54DbGjV1dXZc889s//+++fiiy9e7X7vvfde\n9t577wwYMCCnn376BqwQ+CLztQhQL2y33Xbp06dPRo0alQsuuCBJcu211+a8887L5MmTU11dnSVL\nlqRPnz7ZfffdM2nSpLz11ls57rjj8oMf/CC/+c1vasb64x//mMaNG2fChAmZP39+BgwYkJ/85Ce5\n7rrrkiTnnntuRo8enaFDh6Zr16556qmncvzxx6dly5Y56KCD8vTTT2e33XbLww8/nO7du6dhw4ZJ\n/vXh7ZhjjsngwYOTJDfeeGMOOeSQVFZWFv7wns+TFi1a5Ktf/Wq++tWvfuy5JUuW5KWXXqoJQ597\n7rmafUZff/31tGvX7hOD0Q4dOtT8d15TDz30UJYvX57LLrtsjfp17tw5gwcPzoUXXij8hM+5YcOG\n5bjjjhN8AvVWSUlJfvvb36ZXr15p0KBBzjvvvM/8M3HRokX5xje+kd122y2nnXbaRqoU+CIy8xMo\nlE9bln7OOedk8ODBWbx4cSoqKtK9e/fcf//9Nc/feuut+fGPf5z58+fX7KX42GOPZd99901lZWU6\nduyYAQMG5P7778/8+fNrls+PHDkyxx13XBYtWpTq6uq0atUqjzzySPbYY4+asc8444y8+OKLefDB\nB1d7z8/q6upstdVWueqqq3LUUUetr7eIDeSDDz7Iyy+//IkzRl999dW0bdv2Y6Fop06d0rFjx0/c\niuFDBx98cL7zne/k+9///hrXtGLFinTo0CFjx47NjjvuuC4vD9hA3nrrrXTq1CkvvfRSWrZsWdfl\nANSp1157LV//+tez+eab5/TTT88hhxySsrKyWm0WLVqU22+/PTfccEO+/e1v5xe/+EWdbEsEfHGY\n+QnUG/++r+Suu+5a6/mZM2eme/futQ6R6dWrV0pLSzNjxox07NgxSdK9e/daYdV//ud/ZtmyZZk9\ne3aWLl2apUuXpk+fPrXGXrFiRSoqKj61vjfffDPnnXde/vjHP2bhwoVZuXJlli5dmrlz5671a2bj\nKS8vT7du3dKtW7ePPbd8+fK88sorNWHo7Nmz8+ijj6aysjIvv/xyWrdu/YkzRktLS/PMM89k1KhR\na1XTJptskhNPPDFDhgxxiAp8To0cOTKHHHKI4BMgSZs2bfLkk0/mN7/5TX7+85/ntNNOy6GHHpqW\nLVtm+fLlmTNnTsaNG5dDDz009957r+2hgNUi/ATqjY8GmEnStGnT1e77WctuPpxEX1VVlSR58MEH\ns80229Rq81kHKh1zzDF58803c/3116d9+/YpLy/Pfvvtl2XLlq12nXw+NWjQoCbQ/HcrV67Mq6++\nWmum6J///OdUVlbmb3/7W/bbb79PnRn6WQ455JAMHDhwXcoHNpDq6urceuutueGGG+q6FIDPjfLy\n8vTv3z/9+/fPlClTMnHixLz99ttp3rx59t9//wwePDitWrWq6zKBLxDhJ1AvTJ8+PePGjcv555+/\nyjbbbrttbr/99rz//vs1wegTTzyR6urqbLvttjXtpk2bln/+8581gdRTTz2V8vLydOrUKStXrkx5\neXnmzJmTffbZ5xPv8+HejytXrqx1/YknnsjgwYNrZo0uXLgwr7322tq/aL4QysrK0r59+7Rv3z77\n779/reeGDBmSKVOmrNP4m2++ed555511GgPYMJ555pn885//XOXfFwD13ar2YQdYEzbGAArngw8+\nqAkOn3vuuVxzzTXZd99906NHj5x55pmr7Hf00UenSZMmOeaYYzJ9+vRMnDgxJ510Uvr27VtrxuiK\nFSsycODAzJgxI4888kjOOeecnHDCCWncuHGaNWuWs846K2eddVZuv/32zJ49O1OnTs0tt9ySYcOG\nJUm23HLLNG7cOOPHj88bb7yRd99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- "text/plain": [ - "" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": {}, "metadata": {}, "output_type": "display_data" } @@ -823,7 +994,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true + }, "source": [ "## A* search\n", "\n", @@ -832,9 +1006,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { - "collapsed": true + "collapsed": true, + "deletable": true, + "editable": true }, "outputs": [], "source": [ @@ -918,18 +1094,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { - "collapsed": false + "collapsed": false, + "deletable": true, + "editable": true }, "outputs": [ { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nyXzKlCnD6tWr2bt3LyEhIZQtW5aFCxfy5MkTo9NEREQyHA0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gTZhMpr/d7MmTJ+zZs4eg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\nJUm23HLLNG7cOOPHj88bb7yRd99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- "text/plain": [ - "" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": {}, "metadata": {}, "output_type": "display_data" } @@ -942,19 +1122,23 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": false, + "deletable": true, + "editable": true, "scrolled": false }, "outputs": [ { - "data": { - "image/png": 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whYSEEHwCBQQjPwED+/bbbxUWFqZVq1bp8ccfz3Z+9erVeuqpp7Rr1y4NHz5c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\nzUb+Vb06aVi+YYctAZvX6NGjc/z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- "text/plain": [ - "" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "Widget Javascript not detected. It may not be installed or enabled properly.\n" + ] + }, + { + "data": {}, "metadata": {}, "output_type": "display_data" } @@ -965,6 +1149,387 @@ "display_visual(user_input = True)" ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "# Genetic Algorithm\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Genetic algorithms are\n", + "\n", + "- A method of search, often applied to optimization or learning.\n", + "- Genetic algorithms are a part of evolutionary computing, they use an evolutionary analogy, “survival of the fittest”.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Search Space\n", + "- If we are solving some problem, we are usually looking for some solution, which will be the best among others.\n", + "- The space of all feasible solutions is called search space (also state space).\n", + "- Each point in the search space represents one feasible solution.\n", + "- Each feasible solution can be evaluated by its fitness value for the problem.\n", + "- Usually we only know a few points from the search space and we are generating other points as the process of finding solution continues." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Methodology\n", + "- In a genetic algorithm, a population of individual solutions is evolved toward better solutions.\n", + "- Each individual solution has a set of properties (its chromosomes or genes) which mate and mutate.\n", + "- The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation.\n", + "- In each generation, the fitness of every individual in the population is evaluated.\n", + "- The more fit individuals are stochastically selected from the current population, and each individual's gene is modified (recombined and possibly randomly mutated) to form a new generation.\n", + "- The new generation of individual solutions is then used in the next iteration of the algorithm.\n", + "- Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Basic Genetic Operations\n", + " ● Selection\n", + " ● Mutation\n", + " ● Crossover\n", + " \n", + " \n", + " ### Selection\n", + "- Individuals are selected from the population to crossover.\n", + "- How do we select the individuals? Traditionally, parents are chosen to mate with probability proportional to their fitness.\n", + "\n", + "### Crossover\n", + "- Operates on two individuals (parents).\n", + "- Give rise to offsprings.\n", + "- Crossover can occur at 1, 2 or many points.\n", + "\n", + "\n", + "### Mutation\n", + "- Operates on one individual.\n", + "- Produces offspring with some changes.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now let us try to implement GA.\n", + "We will start with importing necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from fuzzywuzzy import fuzz\n", + "import random\n", + "import string" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "Here we define a class GAState." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Naming convention:\n", + "Instead of gene or chromosome, the name individual has been used.\n", + "What makes an individual unique from the set of individuals is\n", + "the genes\\chromosomes. Thus, considering that individuals crossover and\n", + "individuals mutate.\n", + "\"\"\"\n", + "\n", + "\n", + "class GAState:\n", + " def __init__(self, length):\n", + " self.string = ''.join(random.choice(string.ascii_letters)\n", + " for _ in range(length))\n", + " self.fitness = -1\n", + "\n", + " def __str__(self):\n", + " return 'Individual: ' + str(self.string) + ' fitness: ' \\\n", + " + str(self.fitness)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here is the main logic of our GA. There are four major operations involved. Fitness check, selection, crossover and mutation.\n", + "We assume the search to be complete if the fitness of an individual is greater than or equal to 90%. If the fitness criteria is not met and sufficient number of generations have passed, we return the fittest individual from the population." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def ga(in_str=None, population=20, generations=10000):\n", + " in_str_len = len(in_str)\n", + " individuals = init_individual(population, in_str_len)\n", + "\n", + " for generation in range(generations):\n", + "\n", + " print('Generation: ' + str(generation))\n", + "\n", + " individuals = fitness(individuals, in_str)\n", + " individuals = selection(individuals)\n", + " individuals = crossover(individuals, population, in_str_len)\n", + "\n", + " if any(individual.fitness >= 90 for individual in individuals):\n", + " \"\"\"\n", + " individuals[0] is the individual with the highest fitness,\n", + " because individuals is sorted in the selection function.\n", + " Thus we return the individual with the highest fitness value,\n", + " among the individuals whose fitness is equal to or greater\n", + " than 90%.\n", + " \"\"\"\n", + " print('Threshold met :)')\n", + " return individuals[0]\n", + "\n", + " individuals = mutation(individuals, in_str_len)\n", + " print('fittest individual: ' + individuals[0].string)\n", + "\n", + " \"\"\"\n", + " sufficient number of generations have passed and the individuals\n", + " could not evolve to match the desired fitness value.\n", + " thus we return the fittest individual among the individuals.\n", + " Since individuals are sorted according to their fitness\n", + " individuals[0] is the fittest.\n", + " \"\"\"\n", + " return individuals[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def init_individual(population, length):\n", + " return [GAState(length) for _ in range(population)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "### Fitness\n", + "We will evaluate the fitness of the every individual, by comparing every individual in the list with the threshold." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def fitness(individuals, in_str):\n", + " for individual in individuals:\n", + " individual.fitness = fuzz.ratio(individual.string, in_str)\n", + "\n", + " return individuals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "### Selection\n", + "Now we will sort the individuals according to fitness and select the top 20% of the population\n", + "\n", + "To check the entire population of individuals in each generation in the final output, uncomment the print statement in the cell below. Note that it will create a large output." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def selection(individuals):\n", + " individuals = sorted(\n", + " individuals, key=lambda individual: individual.fitness, reverse=True)\n", + " # print('\\n'.join(map(str, individuals)))\n", + " individuals = individuals[:int(0.2 * len(individuals))]\n", + " return individuals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Crossover\n", + "\n", + "\n", + "\n", + "Here, we define our crossover function. Two individuals mate and give rise to two offsprings. The individuals that mate are among the top 20 percentile and are randomly chosen for mating. In this particular case we perform one point crossover.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def crossover(individuals, population, in_str_len):\n", + " offspring = []\n", + " for _ in range(int((population - len(individuals)) / 2)):\n", + " parent1 = random.choice(individuals)\n", + " parent2 = random.choice(individuals)\n", + " child1 = GAState(in_str_len)\n", + " child2 = GAState(in_str_len)\n", + " split = random.randint(0, in_str_len)\n", + " child1.string = parent1.string[0:split] + parent2.string[\n", + " split:in_str_len]\n", + " child2.string = parent2.string[0:split] + parent1.string[\n", + " split:in_str_len]\n", + " offspring.append(child1)\n", + " offspring.append(child2)\n", + "\n", + " individuals.extend(offspring)\n", + " return individuals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Mutation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We define the mutation function here. Consider each character to be the property of the string. If the string is an individual, each character is its gene. In mutation we alter some of the gene (property) of the individual (string). Not every individual has to undergo mutation. Here, in our example we have possibility of 10% that any individual will undergo mutation.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def mutation(individuals, in_str_len):\n", + " for individual in individuals:\n", + "\n", + " for idx, param in enumerate(individual.string):\n", + " if random.uniform(0.0, 1.0) <= 0.1:\n", + " individual.string = individual.string[0:idx] \\\n", + " + random.choice(string.ascii_letters) \\\n", + " + individual.string[idx + 1:in_str_len]\n", + "\n", + " return individuals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Calling GA\n", + "Now check out the GA. Wait for 5 to 6 seconds for the program to produce the output." + ] + }, { "cell_type": "code", "execution_count": null, @@ -972,7 +1537,29 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "individual = ga('aima', 20, 10000)\n", + "print(individual.string)\n", + "print(individual.fitness)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "source": [ + "Execute the previous cell few times with the same arguments. Compare the different outputs, realise the uncertainty involved in the process (algorithm). Below is a comparative analysis of four executions of the program, producing different outputs (individuals) still converging to the same result. \n", + "\n", + "\n", + "\n", + "Each case represents corresponding execution of the algorithm. Carefully observe the generation numbers for each case in which our desired result was found. Every time the result is displayed at the top because the list of individuals are sorted according to fitness level. Also observe the least fit individual for each run in final generation, there is difference in fitness value.\n", + "\n", + "\n", + "Now change the string, modify the values in the program, try different arguments, observe how the strings (individuals) evolve with generations and converge to the desired result. Develop an intuition about GA. Play around with the code… More importantly have fun while learning… :)\n" + ] } ], "metadata": { @@ -984,14 +1571,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.0" }, "widgets": { "state": { @@ -1007,14 +1594,14 @@ "052ea3e7259346a4b022ec4fef1fda28": { "views": [ { - "cell_index": 32 + "cell_index": 32.0 } ] }, "0ade4328785545c2b66d77e599a3e9da": { "views": [ { - "cell_index": 29 + "cell_index": 29.0 } ] }, @@ -1027,7 +1614,7 @@ "0d91be53b6474cdeac3239fdffeab908": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1040,7 +1627,7 @@ "1193eaa60bb64cb790236d95bf11f358": { "views": [ { - "cell_index": 38 + "cell_index": 38.0 } ] }, @@ -1053,7 +1640,7 @@ "16a9167ec7b4479e864b2a32e40825a1": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1087,7 +1674,7 @@ "2ab8bf4795ac4240b70e1a94e14d1dd6": { "views": [ { - "cell_index": 30 + "cell_index": 30.0 } ] }, @@ -1100,7 +1687,7 @@ "2dc962f16fd143c1851aaed0909f3963": { "views": [ { - "cell_index": 35 + "cell_index": 35.0 } ] }, @@ -1125,7 +1712,7 @@ "34658e2de2894f01b16cf89905760f14": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1150,7 +1737,7 @@ "43e48664a76342c991caeeb2d5b17a49": { "views": [ { - "cell_index": 35 + "cell_index": 35.0 } ] }, @@ -1163,14 +1750,14 @@ "49c49d665ba44746a1e1e9dc598bc411": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, "4a1c43b035f644699fd905d5155ad61f": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1186,7 +1773,7 @@ "53eccc8fc0ad461cb8277596b666f32a": { "views": [ { - "cell_index": 29 + "cell_index": 29.0 } ] }, @@ -1202,7 +1789,7 @@ "636caa7780614389a7f52ad89ea1c6e8": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1224,7 +1811,7 @@ "743219b9d37e4f47a5f777bb41ad0a96": { "views": [ { - "cell_index": 29 + "cell_index": 29.0 } ] }, @@ -1243,7 +1830,7 @@ "86e8f92c1d584cdeb13b36af1b6ad695": { "views": [ { - "cell_index": 35 + "cell_index": 35.0 } ] }, @@ -1295,7 +1882,7 @@ "a29b90d050f3442a89895fc7615ccfee": { "views": [ { - "cell_index": 29 + "cell_index": 29.0 } ] }, @@ -1320,7 +1907,7 @@ "badc9fd7b56346d6b6aea68bfa6d2699": { "views": [ { - "cell_index": 38 + "cell_index": 38.0 } ] }, @@ -1330,7 +1917,7 @@ "c2399056ef4a4aa7aa4e23a0f381d64a": { "views": [ { - "cell_index": 38 + "cell_index": 38.0 } ] }, @@ -1340,7 +1927,7 @@ "ce3f28a8aeee4be28362d068426a71f6": { "views": [ { - "cell_index": 32 + "cell_index": 32.0 } ] }, @@ -1362,7 +1949,7 @@ "e7bffb1fed664dea90f749ea79dcc4f1": { "views": [ { - "cell_index": 39 + "cell_index": 39.0 } ] }, @@ -1393,7 +1980,7 @@ "f435b108c59c42989bf209a625a3a5b5": { "views": [ { - "cell_index": 32 + "cell_index": 32.0 } ] }, @@ -1409,4 +1996,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 4edce2a21fc317551f1db6701f020722329d8610 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Fri, 14 Apr 2017 08:51:49 +0300 Subject: [PATCH 481/513] Update text.py (#492) --- text.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/text.py b/text.py index 3c8c16501..2faac1049 100644 --- a/text.py +++ b/text.py @@ -19,10 +19,10 @@ class UnigramTextModel(CountingProbDist): """This is a discrete probability distribution over words, so you can add, sample, or get P[word], just like with CountingProbDist. You can - also generate a random text n words long with P.samples(n)""" + also generate a random text n words long with P.samples(n).""" def samples(self, n): - "Return a string of n words, random according to the model." + """Return a string of n words, random according to the model.""" return ' '.join(self.sample() for i in range(n)) @@ -97,12 +97,13 @@ def viterbi_segment(text, P): n = len(text) words = [''] + list(text) best = [1.0] + [0.0] * n - # Fill in the vectors best, words via dynamic programming + # Fill in the vectors best words via dynamic programming for i in range(n+1): for j in range(0, i): w = text[j:i] - if P[w] * best[i - len(w)] >= best[i]: - best[i] = P[w] * best[i - len(w)] + curr_score = P[w] * best[i - len(w)] + if curr_score >= best[i]: + best[i] = curr_score words[i] = w # Now recover the sequence of best words sequence = [] @@ -124,7 +125,7 @@ class IRSystem: The constructor s = IRSystem('the a') builds an empty system with two stopwords. Next, index several documents with s.index_document(text, url). Then ask queries with s.query('query words', n) to retrieve the top n - matching documents. Queries are literal words from the document, + matching documents. Queries are literal words from the document, except that stopwords are ignored, and there is one special syntax: The query "learn: man cat", for example, runs "man cat" and indexes it.""" @@ -137,14 +138,14 @@ def __init__(self, stopwords='the a of'): self.documents = [] def index_collection(self, filenames): - "Index a whole collection of files." + """Index a whole collection of files.""" prefix = os.path.dirname(__file__) for filename in filenames: self.index_document(open(filename).read(), os.path.relpath(filename, prefix)) def index_document(self, text, url): - "Index the text of a document." + """Index the text of a document.""" # For now, use first line for title title = text[:text.index('\n')].strip() docwords = words(text) @@ -278,7 +279,7 @@ def maketrans(from_, to_): def encode(plaintext, code): - """Encodes text, using a code which is a permutation of the alphabet.""" + """Encode text using a code which is a permutation of the alphabet.""" trans = maketrans(alphabet + alphabet.upper(), code + code.upper()) return translate(plaintext, trans) @@ -331,19 +332,18 @@ def all_shifts(text): class PermutationDecoder: - """This is a much harder problem than the shift decoder. There are 26! - permutations, so we can't try them all. Instead we have to search. + """This is a much harder problem than the shift decoder. There are 26! + permutations, so we can't try them all. Instead we have to search. We want to search well, but there are many things to consider: Unigram probabilities (E is the most common letter); Bigram probabilities (TH is the most common bigram); word probabilities (I and A are the most common one-letter words, etc.); etc. - We could represent a search state as a permutation of the 26 letters, - and alter the solution through hill climbing. With an initial guess + We could represent a search state as a permutation of the 26 letters, + and alter the solution through hill climbing. With an initial guess based on unigram probabilities, this would probably fare well. However, I chose instead to have an incremental representation. A state is represented as a letter-to-letter map; for example {'z': 'e'} to - represent that 'z' will be translated to 'e'. - """ + represent that 'z' will be translated to 'e'.""" def __init__(self, training_text, ciphertext=None): self.Pwords = UnigramTextModel(words(training_text)) From 1cd64285fee66375f9d2bb6ea42b9088fd169199 Mon Sep 17 00:00:00 2001 From: ESHAN PANDEY Date: Fri, 14 Apr 2017 11:41:50 +0530 Subject: [PATCH 482/513] Update search.py (#480) Implemented Genetic Algorithm. Because #477 had unnecessary edits, --- search.py | 135 +++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 93 insertions(+), 42 deletions(-) diff --git a/search.py b/search.py index 00ff8a888..b073ab2c8 100644 --- a/search.py +++ b/search.py @@ -4,18 +4,21 @@ then create problem instances and solve them with calls to the various search functions.""" -from utils import ( - is_in, argmin, argmax, argmax_random_tie, probability, - weighted_sample_with_replacement, memoize, print_table, DataFile, Stack, - FIFOQueue, PriorityQueue, name -) -from grid import distance - -from collections import defaultdict +import bisect import math import random +import string import sys -import bisect +from collections import defaultdict + +from fuzzywuzzy import fuzz + +from grid import distance +from utils import ( + is_in, argmin, argmax_random_tie, probability, + memoize, print_table, DataFile, Stack, + FIFOQueue, PriorityQueue, name +) infinity = float('inf') @@ -569,46 +572,94 @@ def LRTA_cost(self, s, a, s1, H): # Genetic Algorithm -def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): - """Call genetic_algorithm on the appropriate parts of a problem. - This requires the problem to have states that can mate and mutate, - plus a value method that scores states.""" - s = problem.initial_state - states = [problem.result(s, a) for a in problem.actions(s)] - random.shuffle(states) - return genetic_algorithm(states[:n], problem.value, ngen, pmut) +class GAState: + def __init__(self, length): + self.string = ''.join(random.choice(string.ascii_letters) + for _ in range(length)) + self.fitness = -1 -def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): - """[Figure 4.8]""" - for i in range(ngen): - new_population = [] - for i in range(len(population)): - fitnesses = map(fitness_fn, population) - p1, p2 = weighted_sample_with_replacement(2, population, fitnesses) - child = p1.mate(p2) - if random.uniform(0, 1) < pmut: - child.mutate() - new_population.append(child) - population = new_population - return argmax(population, key=fitness_fn) +def ga(in_str=None, population=20, generations=10000): + in_str_len = len(in_str) + individuals = init_individual(population, in_str_len) + for generation in range(generations): -class GAState: + individuals = fitness(individuals, in_str) + individuals = selection(individuals) + individuals = crossover(individuals, population, in_str_len) - """Abstract class for individuals in a genetic search.""" + if any(individual.fitness >= 90 for individual in individuals): + """ + individuals[0] is the individual with the highest fitness, + because individuals is sorted in the selection function. + Thus we return the individual with the highest fitness value, + among the individuals whose fitness is equal to or greater + than 90. + """ - def __init__(self, genes): - self.genes = genes + return individuals[0] - def mate(self, other): - """Return a new individual crossing self and other.""" - c = random.randrange(len(self.genes)) - return self.__class__(self.genes[:c] + other.genes[c:]) + individuals = mutation(individuals, in_str_len) - def mutate(self): - """Change a few of my genes.""" - raise NotImplementedError + """ + sufficient number of generations have passed and the individuals + could not evolve to match the desired fitness value. + thus we return the fittest individual among the individuals. + Since individuals are sorted according to their fitness + individuals[0] is the fittest. + """ + return individuals[0] + + +def init_individual(population, length): + return [GAState(length) for _ in range(population)] + + +def fitness(individuals, in_str): + for individual in individuals: + individual.fitness = fuzz.ratio(individual.string, in_str) # noqa + + return individuals + + +def selection(individuals): + individuals = sorted( + individuals, key=lambda individual: individual.fitness, reverse=True) + + individuals = individuals[:int(0.2 * len(individuals))] + return individuals + + +def crossover(individuals, population, in_str_len): + offspring = [] + for _ in range(int((population - len(individuals)) / 2)): + parent1 = random.choice(individuals) + parent2 = random.choice(individuals) + child1 = GAState(in_str_len) + child2 = GAState(in_str_len) + split = random.randint(0, in_str_len) + child1.string = parent1.string[0:split] + parent2.string[ + split:in_str_len] + child2.string = parent2.string[0:split] + parent1.string[ + split:in_str_len] + offspring.append(child1) + offspring.append(child2) + + individuals.extend(offspring) + return individuals + + +def mutation(individuals, in_str_len): + for individual in individuals: + + for idx, param in enumerate(individual.string): + if random.uniform(0.0, 1.0) <= 0.1: + individual.string = individual.string[0:idx] \ + + random.choice(string.ascii_letters) \ + + individual.string[idx + 1:in_str_len] + + return individuals # _____________________________________________________________________________ # The remainder of this file implements examples for the search algorithms. @@ -926,7 +977,7 @@ def print_boggle(board): print() -def boggle_neighbors(n2, cache={}): +def boggle_neighbors(n2, cache={}): # noqa """Return a list of lists, where the i-th element is the list of indexes for the neighbors of square i.""" if cache.get(n2): From 38a384499e90f5a6937fe42791ff61a2f045f6cb Mon Sep 17 00:00:00 2001 From: Luke Schoen Date: Fri, 14 Apr 2017 16:12:18 +1000 Subject: [PATCH 483/513] Fix incorrect abbreviation from PDLL to PDDL (Planning Domain Definition Language) (#475) --- planning.py | 32 ++++++++++++++++---------------- tests/test_planning.py | 4 ++-- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/planning.py b/planning.py index b92cb6eaa..30b8a79f6 100644 --- a/planning.py +++ b/planning.py @@ -6,9 +6,9 @@ from logic import FolKB -class PDLL: +class PDDL: """ - PDLL used to define a search problem. + Planning Domain Definition Language (PDDL) used to define a search problem. It stores states in a knowledge base consisting of first order logic statements. The conjunction of these logical statements completely defines a state. """ @@ -140,7 +140,7 @@ def goal_test(kb): effect_rem = [expr("At(p, f)")] fly = Action(expr("Fly(p, f, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - return PDLL(init, [load, unload, fly], goal_test) + return PDDL(init, [load, unload, fly], goal_test) def spare_tire(): @@ -181,7 +181,7 @@ def goal_test(kb): leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], [effect_add, effect_rem]) - return PDLL(init, [remove, put_on, leave_overnight], goal_test) + return PDDL(init, [remove, put_on, leave_overnight], goal_test) def three_block_tower(): @@ -219,7 +219,7 @@ def goal_test(kb): moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg], [effect_add, effect_rem]) - return PDLL(init, [move, moveToTable], goal_test) + return PDDL(init, [move, moveToTable], goal_test) def have_cake_and_eat_cake_too(): @@ -248,7 +248,7 @@ def goal_test(kb): effect_rem = [] bake_cake = Action(expr('Bake(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem]) - return PDLL(init, [eat_cake, bake_cake], goal_test) + return PDDL(init, [eat_cake, bake_cake], goal_test) class Level(): @@ -408,17 +408,17 @@ class Graph: Used in graph planning algorithm to extract a solution """ - def __init__(self, pdll, negkb): - self.pdll = pdll - self.levels = [Level(pdll.kb, negkb)] - self.objects = set(arg for clause in pdll.kb.clauses + negkb.clauses for arg in clause.args) + def __init__(self, pddl, negkb): + self.pddl = pddl + self.levels = [Level(pddl.kb, negkb)] + self.objects = set(arg for clause in pddl.kb.clauses + negkb.clauses for arg in clause.args) def __call__(self): self.expand_graph() def expand_graph(self): last_level = self.levels[-1] - last_level(self.pdll.actions, self.objects) + last_level(self.pddl.actions, self.objects) self.levels.append(last_level.perform_actions()) def non_mutex_goals(self, goals, index): @@ -436,8 +436,8 @@ class GraphPlan: Returns solution for the planning problem """ - def __init__(self, pdll, negkb): - self.graph = Graph(pdll, negkb) + def __init__(self, pddl, negkb): + self.graph = Graph(pddl, negkb) self.nogoods = [] self.solution = [] @@ -524,9 +524,9 @@ def goal_test(kb, goals): def spare_tire_graphplan(): - pdll = spare_tire() + pddl = spare_tire() negkb = FolKB([expr('At(Flat, Trunk)')]) - graphplan = GraphPlan(pdll, negkb) + graphplan = GraphPlan(pddl, negkb) # Not sure goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] @@ -573,4 +573,4 @@ def goal_test(kb): effect_rem = [expr("At(actor, loc)")] go = Action(expr("Go(actor, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - return PDLL(init, [hit, go], goal_test) + return PDDL(init, [hit, go], goal_test) diff --git a/tests/test_planning.py b/tests/test_planning.py index 461cdcdbb..e13bcfd92 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -73,9 +73,9 @@ def test_have_cake_and_eat_cake_too(): def test_graph_call(): - pdll = spare_tire() + pddl = spare_tire() negkb = FolKB([expr('At(Flat, Trunk)')]) - graph = Graph(pdll, negkb) + graph = Graph(pddl, negkb) levels_size = len(graph.levels) graph() From a77b947ed4ed3329b3f6827461d61d74d3e477b0 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Mon, 17 Apr 2017 22:39:40 +0300 Subject: [PATCH 484/513] Implementation: Genetic Algorithm (Fixing Build) (#501) * Update search.py * Update test_search.py * minor edits and notes * removed fuzzywuzzy * Add 8-Queens Test * Optimization without veering from pseudocode * Variable renaming * Update search.py * Optimization * Update test_search.py * Fairer reproduction --- search.py | 177 +++++++++++++++++++------------------------ tests/test_search.py | 39 ++++++++++ 2 files changed, 116 insertions(+), 100 deletions(-) diff --git a/search.py b/search.py index b073ab2c8..428648614 100644 --- a/search.py +++ b/search.py @@ -4,21 +4,18 @@ then create problem instances and solve them with calls to the various search functions.""" -import bisect -import math -import random -import string -import sys -from collections import defaultdict - -from fuzzywuzzy import fuzz - -from grid import distance from utils import ( - is_in, argmin, argmax_random_tie, probability, - memoize, print_table, DataFile, Stack, + is_in, argmin, argmax, argmax_random_tie, probability, weighted_sampler, + weighted_sample_with_replacement, memoize, print_table, DataFile, Stack, FIFOQueue, PriorityQueue, name ) +from grid import distance + +from collections import defaultdict +import math +import random +import sys +import bisect infinity = float('inf') @@ -572,94 +569,74 @@ def LRTA_cost(self, s, a, s1, H): # Genetic Algorithm -class GAState: - def __init__(self, length): - self.string = ''.join(random.choice(string.ascii_letters) - for _ in range(length)) - self.fitness = -1 - - -def ga(in_str=None, population=20, generations=10000): - in_str_len = len(in_str) - individuals = init_individual(population, in_str_len) - - for generation in range(generations): - - individuals = fitness(individuals, in_str) - individuals = selection(individuals) - individuals = crossover(individuals, population, in_str_len) - - if any(individual.fitness >= 90 for individual in individuals): - """ - individuals[0] is the individual with the highest fitness, - because individuals is sorted in the selection function. - Thus we return the individual with the highest fitness value, - among the individuals whose fitness is equal to or greater - than 90. - """ - - return individuals[0] - - individuals = mutation(individuals, in_str_len) - - """ - sufficient number of generations have passed and the individuals - could not evolve to match the desired fitness value. - thus we return the fittest individual among the individuals. - Since individuals are sorted according to their fitness - individuals[0] is the fittest. - """ - return individuals[0] - - -def init_individual(population, length): - return [GAState(length) for _ in range(population)] - - -def fitness(individuals, in_str): - for individual in individuals: - individual.fitness = fuzz.ratio(individual.string, in_str) # noqa - - return individuals - - -def selection(individuals): - individuals = sorted( - individuals, key=lambda individual: individual.fitness, reverse=True) - - individuals = individuals[:int(0.2 * len(individuals))] - return individuals - - -def crossover(individuals, population, in_str_len): - offspring = [] - for _ in range(int((population - len(individuals)) / 2)): - parent1 = random.choice(individuals) - parent2 = random.choice(individuals) - child1 = GAState(in_str_len) - child2 = GAState(in_str_len) - split = random.randint(0, in_str_len) - child1.string = parent1.string[0:split] + parent2.string[ - split:in_str_len] - child2.string = parent2.string[0:split] + parent1.string[ - split:in_str_len] - offspring.append(child1) - offspring.append(child2) - - individuals.extend(offspring) - return individuals - - -def mutation(individuals, in_str_len): - for individual in individuals: - - for idx, param in enumerate(individual.string): - if random.uniform(0.0, 1.0) <= 0.1: - individual.string = individual.string[0:idx] \ - + random.choice(string.ascii_letters) \ - + individual.string[idx + 1:in_str_len] - - return individuals +def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20): + """Call genetic_algorithm on the appropriate parts of a problem. + This requires the problem to have states that can mate and mutate, + plus a value method that scores states.""" + + # NOTE: This is not tested and might not work. + # TODO: Use this function to make Problems work with genetic_algorithm. + + s = problem.initial_state + states = [problem.result(s, a) for a in problem.actions(s)] + random.shuffle(states) + return genetic_algorithm(states[:n], problem.value, ngen, pmut) + + +def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1): + """[Figure 4.8]""" + for i in range(ngen): + new_population = [] + fitnesses = map(fitness_fn, population) + random_selection = weighted_sampler(population, fitnesses) + for j in range(len(population)): + x = random_selection() + y = random_selection() + child = reproduce(x, y) + if random.uniform(0, 1) < pmut: + child = mutate(child, gene_pool) + new_population.append(child) + + population = new_population + + if f_thres: + fittest_individual = argmax(population, key=fitness_fn) + if fitness_fn(fittest_individual) >= f_thres: + return fittest_individual + + return argmax(population, key=fitness_fn) + + +def init_population(pop_number, gene_pool, state_length): + """Initializes population for genetic algorithm + pop_number : Number of individuals in population + gene_pool : List of possible values for individuals + (char only) + state_length: The length of each individual""" + g = len(gene_pool) + population = [] + for i in range(pop_number): + new_individual = ''.join([gene_pool[random.randrange(0, g)] + for j in range(state_length)]) + population.append(new_individual) + + return population + + +def reproduce(x, y): + n = len(x) + c = random.randrange(1, n) + return x[:c] + y[c:] + + +def mutate(x, gene_pool): + n = len(x) + g = len(gene_pool) + c = random.randrange(0, n) + r = random.randrange(0, g) + + new_gene = gene_pool[r] + return x[:c] + new_gene + x[c+1:] # _____________________________________________________________________________ # The remainder of this file implements examples for the search algorithms. diff --git a/tests/test_search.py b/tests/test_search.py index 11d522e94..d50eacfe1 100644 --- a/tests/test_search.py +++ b/tests/test_search.py @@ -87,6 +87,45 @@ def test_LRTAStarAgent(): assert my_agent('State_5') is None +def test_genetic_algorithm(): + # Graph coloring + edges = { + 'A': [0, 1], + 'B': [0, 3], + 'C': [1, 2], + 'D': [2, 3] + } + + population = init_population(8, ['0', '1'], 4) + + def fitness(c): + return sum(c[n1] != c[n2] for (n1, n2) in edges.values()) + + solution = genetic_algorithm(population, fitness) + assert solution == "0101" or solution == "1010" + + # Queens Problem + population = init_population(100, [str(i) for i in range(8)], 8) + + def fitness(q): + non_attacking = 0 + for row1 in range(len(q)): + for row2 in range(row1+1, len(q)): + col1 = int(q[row1]) + col2 = int(q[row2]) + row_diff = row1 - row2 + col_diff = col1 - col2 + + if col1 != col2 and row_diff != col_diff and row_diff != -col_diff: + non_attacking += 1 + + return non_attacking + + + solution = genetic_algorithm(population, fitness, f_thres=25) + assert fitness(solution) >= 25 + + # TODO: for .ipynb: """ >>> compare_graph_searchers() From 5ea1fb6931e1537fa7b8643abd70d2f05ae98471 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Mon, 17 Apr 2017 22:41:17 +0300 Subject: [PATCH 485/513] Notebook: Genetic Algorithms (#503) * Update search.ipynb * Delete comparision.PNG * Delete mutation.png * Delete Crossover.png * Add images * Update search.ipynb --- 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    • \n", "\n", - "This topic of Bayes nets can be confusing, because there are many different concepts to keep track of:\n", + "We implement this with the help of seven Python classes:\n", + "\n", + "\n", + "## `BayesNet()`\n", + "\n", + "A `BayesNet` is a graph (as in the diagram above) where each node represents a random variable, and the edges are parent→child links. You can construct an empty graph with `BayesNet()`, then add variables one at a time with the method call `.add(`*variable_name, parent_names, cpt*`)`, where the names are strings, and each of the `parent_names` must already have been `.add`ed.\n", + "\n", + "## `Variable(`*name, cpt, parents*`)`\n", + "\n", + "A random variable; the ovals in the diagram above. The value of a variable depends on the value of the parents, in a probabilistic way specified by the variable's conditional probability table (CPT). Given the parents, the variable is independent of all the other variables. For example, if I know whether *Alarm* is true or false, then I know the probability of *JohnCalls*, and evidence about the other variables won't give me any more information about *JohnCalls*. Each row of the CPT uses the same order of variables as the list of parents.\n", + "We will only allow variables with a finite discrete domain; not continuous values. \n", + "\n", + "## `ProbDist(`*mapping*`)`
    `Factor(`*mapping*`)`\n", "\n", - "* `BayesNet`: A graph, where each node represents a variable, and is pointed to by zero or more *parents*. (See diagram above.)\n", + "A probability distribution is a mapping of `{outcome: probability}` for every outcome of a random variable. \n", + "You can give `ProbDist` the same arguments that you would give to the `dict` initializer, for example\n", + "`ProbDist(sun=0.6, rain=0.1, cloudy=0.3)`.\n", + "As a shortcut for Boolean Variables, you can say `ProbDist(0.95)` instead of `ProbDist({T: 0.95, F: 0.05})`. \n", + "In a probability distribution, every value is between 0 and 1, and the values sum to 1.\n", + "A `Factor` is similar to a probability distribution, except that the values need not sum to 1. Factors\n", + "are used in the variable elimination inference method.\n", "\n", - "* `Variable`: A random variable; the ovals in the diagram above. We will only allow variables with a finite discrete domain of possible values; in the diagram all the variables are Boolean, meaning their domain is the set $\\{t, f\\}$. The value of a variable depends on the value of the parents, in a probabilistic way specified by the variable's conditional probability table. Given the parents, the variable is independent of all the other variables. For example, if I know whether *Alarm* is true or false, then I know the probability of *JohnCalls*, and evidence about the other variables won't give me any more information about *JohnCalls*.\n", + "## `Evidence(`*mapping*`)`\n", "\n", - "* `ProbDist`: A probability distribution enumerates each possible value in the domain of a variable,\n", - "and the probability of that value. For example, `{True: 0.95, False: 0.05}` is a probability distribution for a Boolean variable.\n", + "A mapping of `{Variable: value, ...}` pairs, describing the exact values for a set of variables—the things we know for sure.\n", "\n", - "* `CPTable`: A conditional probability table is a a mapping, `{tuple: ProbDist, ...}`, where each tuple lists the values of each of the parent variables, in order, and the probability distribution says what the possible outcomes are for the variable, given those values of the parents. For example, for the variable *Alarm*, the top row of the `CPTable` says \"*t, t*, .95\", which means that when *Burglary* is true and *Earthquake* is true, the probability of *Alarm* being true is .95. Think of this row entry as an abbreviation that makes sense for Boolean variables, but to accomodate non-Boolean variables, we will represent this in the more general format: `{(True, True): {True: 0.95, False: 0.05}}`.\n", + "## `CPTable(`*rows, parents*`)`\n", "\n", - "* `Evidence`: A mapping, `{Variable: value, ...}`, which denotes which variables we have observed known values for.\n", + "A conditional probability table (or *CPT*) describes the probability of each possible outcome value of a random variable, given the values of the parent variables. A `CPTable` is a a mapping, `{tuple: probdist, ...}`, where each tuple lists the values of each of the parent variables, in order, and each probability distribution says what the possible outcomes are, given those values of the parents. The `CPTable` for *Alarm* in the diagram above would be represented as follows:\n", "\n", - "We will introduce implementations of these concepts:\n", + " CPTable({(T, T): .95,\n", + " (T, F): .94,\n", + " (F, T): .29,\n", + " (F, F): .001},\n", + " [Burglary, Earthquake])\n", + " \n", + "How do you read this? Take the second row, \"`(T, F): .94`\". This means that when the first parent (`Burglary`) is true, and the second parent (`Earthquake`) is fale, then the probability of `Alarm` being true is .94. Note that the .94 is an abbreviation for `ProbDist({T: .94, F: .06})`.\n", + " \n", + "## `T = Bool(True); F = Bool(False)`\n", "\n", - "# `BayesNet`\n", + "When I used `bool` values (`True` and `False`), it became hard to read rows in CPTables, because the columns didn't line up:\n", + "\n", + " (True, True, False, False, False)\n", + " (False, False, False, False, True)\n", + " (True, False, False, True, True)\n", + " \n", + "Therefore, I created the `Bool` class, with constants `T` and `F` such that `T == True` and `F == False`, and now rows are easier to read:\n", "\n", - "A `BayesNet` is a graph of variables, where each variable is specified by a triple of `(name, parentnames, cpt)`, where the name is a string, the `parentnames` is a sequence of strings, and the CPT is in a format we will explain soon." + " (T, T, F, F, F)\n", + " (F, F, F, F, T)\n", + " (T, F, F, T, T)\n", + " \n", + "Here is the code for these classes:" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": { "button": false, "collapsed": true, @@ -68,403 +88,431 @@ }, "outputs": [], "source": [ + "from collections import defaultdict, Counter\n", + "import itertools\n", + "import math\n", + "import random\n", + "\n", "class BayesNet(object):\n", - " \"Bayesian network: a graph with an ordered list of variables.\"\n", + " \"Bayesian network: a graph of variables connected by parent links.\"\n", " \n", " def __init__(self): \n", - " self.variables = [] # List of variables, in parent-first topological order\n", + " self.variables = [] # List of variables, in parent-first topological sort order\n", " self.lookup = {} # Mapping of {variable_name: variable} pairs\n", " \n", " def add(self, name, parentnames, cpt):\n", - " \"Add a new Variable to the BayesNet. Parentnames must already have been added.\"\n", + " \"Add a new Variable to the BayesNet. Parentnames must have been added previously.\"\n", " parents = [self.lookup[name] for name in parentnames]\n", - " var = Variable(name, parents, cpt)\n", + " var = Variable(name, cpt, parents)\n", " self.variables.append(var)\n", " self.lookup[name] = var\n", - " return self" + " return self\n", + " \n", + "class Variable(object):\n", + " \"A discrete random variable; conditional on zero or more parent Variables.\"\n", + " \n", + " def __init__(self, name, cpt, parents=()):\n", + " \"A variable has a name, list of parent variables, and a Conditional Probability Table.\"\n", + " self.__name__ = name\n", + " self.parents = parents\n", + " self.cpt = CPTable(cpt, parents)\n", + " self.domain = set(itertools.chain(*self.cpt.values())) # All the outcomes in the CPT\n", + " \n", + " def __repr__(self): return self.__name__\n", + " \n", + "class Factor(dict): \"An {outcome: frequency} mapping.\"\n", + "\n", + "class ProbDist(Factor):\n", + " \"\"\"A Probability Distribution is an {outcome: probability} mapping. \n", + " The values are normalized to sum to 1.\n", + " ProbDist(0.75) is an abbreviation for ProbDist({T: 0.75, F: 0.25}).\"\"\"\n", + " def __init__(self, mapping=(), **kwargs):\n", + " if isinstance(mapping, float):\n", + " mapping = {T: mapping, F: 1 - mapping}\n", + " self.update(mapping, **kwargs)\n", + " normalize(self)\n", + " \n", + "class Evidence(dict): \n", + " \"A {variable: value} mapping, describing what we know for sure.\"\n", + " \n", + "class CPTable(dict):\n", + " \"A mapping of {row: ProbDist, ...} where each row is a tuple of values of the parent variables.\"\n", + " \n", + " def __init__(self, mapping, parents=()):\n", + " \"\"\"Provides two shortcuts for writing a Conditional Probability Table. \n", + " With no parents, CPTable(dist) means CPTable({(): dist}).\n", + " With one parent, CPTable({val: dist,...}) means CPTable({(val,): dist,...}).\"\"\"\n", + " if len(parents) == 0 and not (isinstance(mapping, dict) and set(mapping.keys()) == {()}):\n", + " mapping = {(): mapping}\n", + " for (row, dist) in mapping.items():\n", + " if len(parents) == 1 and not isinstance(row, tuple): \n", + " row = (row,)\n", + " self[row] = ProbDist(dist)\n", + "\n", + "class Bool(int):\n", + " \"Just like `bool`, except values display as 'T' and 'F' instead of 'True' and 'False'\"\n", + " __str__ = __repr__ = lambda self: 'T' if self else 'F'\n", + " \n", + "T = Bool(True)\n", + "F = Bool(False)" ] }, { "cell_type": "markdown", - "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, + "metadata": {}, "source": [ - "# `Variable` \n", - "\n", - "The `Variable` data structure holds a name, a list of parents (which are actual variables, not names), and a conditional probability table. The order of the parent variables is important, because you will have to use the same order in the CPT. For convenience, we also store the* domain* of the variable: the set of possible values (all our variables are discrete). " + "And here are some associated functions:" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 2, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": true }, "outputs": [], "source": [ - "class Variable(object):\n", - " \"A discrete random variable in a BayesNet.\"\n", - " \n", - " def __init__(self, name, parents, cpt):\n", - " \"A variable has a name, list of parent variables, and a CPT.\"\n", - " self.name = name\n", - " self.parents = parents\n", - " self.cpt = CPT(cpt, parents)\n", - " self.domain = set(v for row in self.cpt for v in self.cpt[row])\n", - " \n", - " def P(self, evidence):\n", - " \"The full probability distribution for P(variable | evidence).\"\n", - " return self.cpt[tuple(evidence[var] for var in self.parents)]\n", + "def P(var, evidence={}):\n", + " \"The probability distribution for P(variable | evidence), when all parent variables are known (in evidence).\"\n", + " row = tuple(evidence[parent] for parent in var.parents)\n", + " return var.cpt[row]\n", "\n", - " def __repr__(self): return self.name" + "def normalize(dist):\n", + " \"Normalize a {key: value} distribution so values sum to 1.0. Mutates dist and returns it.\"\n", + " total = sum(dist.values())\n", + " for key in dist:\n", + " dist[key] = dist[key] / total\n", + " assert 0 <= dist[key] <= 1, \"Probabilities must be between 0 and 1.\"\n", + " return dist\n", + "\n", + "def sample(probdist):\n", + " \"Randomly sample an outcome from a probability distribution.\"\n", + " r = random.random() # r is a random point in the probability distribution\n", + " c = 0.0 # c is the cumulative probability of outcomes seen so far\n", + " for outcome in probdist:\n", + " c += probdist[outcome]\n", + " if r <= c:\n", + " return outcome\n", + " \n", + "def globalize(mapping):\n", + " \"Given a {name: value} mapping, export all the names to the `globals()` namespace.\"\n", + " globals().update(mapping)" ] }, { "cell_type": "markdown", - "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, + "metadata": {}, "source": [ - "# `ProbDist` and `Evidence`\n", - "\n", - "A `ProbDist` is a mapping of `{outcome: probability}` for every outcome of a random variable. You can give it the same arguments that you would give to the `dict` constructor. As a shortcut for Boolean random variables, you can say `ProbDist(0.2)` instead of `ProbDist({False: 0.8, True: 0.2})`.\n", + "# Sample Usage\n", "\n", - "`Evidence` is just a dict of `{variable: value}` pairs, describing the exact values for a set of variables." + "Here are some examples of using the classes:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [], "source": [ - "class ProbDist(dict):\n", - " \"A Probability Distribution; an {outcome: probability} mapping.\"\n", - " def __init__(self, mapping=(), **kwargs):\n", - " if isinstance(mapping, float):\n", - " mapping = {True: mapping, False: 1 - mapping}\n", - " self.update(mapping, **kwargs)\n", - " total = sum(self.values())\n", - " normalize(self)\n", - " \n", - "def normalize(dic):\n", - " \"Make sum to values of dic sum to 1.0; assert no negative values.\"\n", - " total = sum(dic.values())\n", - " for key in dic:\n", - " dic[key] = dic[key] / total\n", - " assert dic[key] >= 0\n", - " \n", - "class Evidence(dict): pass" + "# Example random variable: Earthquake:\n", + "# An earthquake occurs on 0.002 of days, independent of any other variables.\n", + "Earthquake = Variable('Earthquake', 0.002)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{'heads': 0.6, 'tails': 0.4}" + "{F: 0.998, T: 0.002}" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# An example ProbDist\n", - "ProbDist(heads=6, tails=4)" + "# The probability distribution for Earthquake\n", + "P(Earthquake)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.75, True: 0.25}" + "0.002" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# A Boolean ProbDist\n", - "ProbDist(0.25) " + "# Get the probability of a specific outcome by subscripting the probability distribution\n", + "P(Earthquake)[T]" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 6, "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, + "outputs": [ + { + "data": { + "text/plain": [ + "F" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# `CPT`: Conditional Probability Table\n", - "\n", - "A `CPT` is a mapping from tuples of parent values to probability distributions. Every possible tuple must be represented in the table. We allow shortcuts for the case of `CPT`s with zeron or one parent." + "# Randomly sample from the distribution:\n", + "sample(P(Earthquake))" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({F: 99793, T: 207})" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# Randomly sample 100,000 times, and count up the results:\n", + "Counter(sample(P(Earthquake)) for i in range(100000))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false }, "outputs": [], "source": [ - "class CPT(dict):\n", - " \"\"\"A mapping of {row: ProbDist, ...} where each row is a tuple\n", - " of possible values of the parent variables.\"\"\"\n", - " \n", - " def __init__(self, data, parents=None):\n", - " \"\"\"Provides two shortcuts for writing a Conditional Probability Table. \n", - " With no parents, CPT(dist) => CPT({(): dist}).\n", - " With one parent, CPT({val: dist,...}) => CPT({(val,): dist,...}).\"\"\"\n", - " def Tuple(row): return row if isinstance(row, tuple) else (row,)\n", - " if not parents and (not isinstance(data, dict) or set(data.keys()) != {()}):\n", - " data = {(): data}\n", - " for row in data:\n", - " self[Tuple(row)] = ProbDist(data[row])\n", - " if parents:\n", - " assert set(self) == set(expected_tuples(parents)), (\n", - " \"CPT must handle all possibile tuples of parent values\")\n", - "\n", - "def expected_tuples(parents):\n", - " \"The set of tuples of one value from each parent (in order).\"\n", - " return set(itertools.product(*[p.domain for p in parents]))" + "# Two equivalent ways of specifying the same Boolean probability distribution:\n", + "assert ProbDist(0.75) == ProbDist({T: 0.75, F: 0.25})" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{(): {False: 0.75, True: 0.25}}" + "{'lose': 0.15, 'tie': 0.1, 'win': 0.75}" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# An example of a CPT with no parents, and thus one row with an empty tuple\n", - "CPT({(): 0.25})" + "# Two equivalent ways of specifying the same non-Boolean probability distribution:\n", + "assert ProbDist(win=15, lose=3, tie=2) == ProbDist({'win': 15, 'lose': 3, 'tie': 2})\n", + "ProbDist(win=15, lose=3, tie=2)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 10, "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 1, 'b': 2, 'c': 3, 'd': 4}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# The difference between a Factor and a ProbDist--the ProbDist is normalized:\n", + "Factor(a=1, b=2, c=3, d=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 0.1, 'b': 0.2, 'c': 0.3, 'd': 0.4}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ProbDist(a=1, b=2, c=3, d=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "# An Example Bayes Net\n", + "# Example: Alarm Bayes Net\n", "\n", - "Now we are ready to define the network from the burglary alarm scenario:" + "Here is how we define the Bayes net from the diagram above:" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 12, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [], "source": [ - "T = True\n", - "F = False\n", - "\n", "alarm_net = (BayesNet()\n", " .add('Burglary', [], 0.001)\n", " .add('Earthquake', [], 0.002)\n", - " .add('Alarm', ['Burglary', 'Earthquake'],\n", - " {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001})\n", + " .add('Alarm', ['Burglary', 'Earthquake'], {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001})\n", " .add('JohnCalls', ['Alarm'], {T: 0.90, F: 0.05})\n", - " .add('MaryCalls', ['Alarm'], {T: 0.70, F:0.01}))" + " .add('MaryCalls', ['Alarm'], {T: 0.70, F: 0.01})) " ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": { - "button": false, - "collapsed": true, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Burglary, Earthquake, Alarm, JohnCalls, MaryCalls]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "globals().update(alarm_net.lookup)" + "# Make Burglary, Earthquake, etc. be global variables\n", + "globalize(alarm_net.lookup) \n", + "alarm_net.variables" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{(False, False): {False: 0.999, True: 0.001},\n", - " (False, True): {False: 0.71, True: 0.29},\n", - " (True, False): {False: 0.06000000000000005, True: 0.94},\n", - " (True, True): {False: 0.050000000000000044, True: 0.95}}" + "{F: 0.999, T: 0.001}" ] }, - "execution_count": 35, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Alarm.cpt" + "# Probability distribution of a Burglary\n", + "P(Burglary)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.999, True: 0.001}" + "{F: 0.06000000000000005, T: 0.94}" ] }, - "execution_count": 36, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Alarm.P({Burglary:False, Earthquake:False})" + "# Probability of Alarm going off, given a Burglary and not an Earthquake:\n", + "P(Alarm, {Burglary: T, Earthquake: F})" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 16, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "0.001" + "{(F, F): {F: 0.999, T: 0.001},\n", + " (F, T): {F: 0.71, T: 0.29},\n", + " (T, F): {F: 0.06000000000000005, T: 0.94},\n", + " (T, T): {F: 0.050000000000000044, T: 0.95}}" ] }, - "execution_count": 38, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Alarm.P({Burglary:False, Earthquake:False})[True]" + "# Where that came from: the (T, F) row of Alarm's CPT:\n", + "Alarm.cpt" ] }, { @@ -478,254 +526,418 @@ } }, "source": [ - "# Inference in Bayes Nets" + "# Bayes Nets as Joint Probability Distributions\n", + "\n", + "A Bayes net is a compact way of specifying a full joint distribution over all the variables in the network. Given a set of variables {*X*1, ..., *X**n*}, the full joint distribution is:\n", + "\n", + "P(*X*1=*x*1, ..., *X**n*=*x**n*) = Π*i* P(*X**i* = *x**i* | parents(*X**i*))\n", + "\n", + "For a network with *n* variables, each of which has *b* values, there are *bn* rows in the joint distribution (for example, a billion rows for 30 Boolean variables), making it impractical to explicitly create the joint distribution for large networks. But for small networks, the function `joint_distribution` creates the distribution, which can be instructive to look at, and can be used to do inference. " ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 17, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": true }, "outputs": [], "source": [ - "def enumeration_ask(X, e, bn):\n", - " \"Given evidence e, ask what the probability distribution is for X in bn.\"\n", - " assert X not in e, \"Query variable must be distinct from evidence\"\n", - " Q = {}\n", - " for xi in X.domain:\n", - " Q[xi] = enumerate_all_vars(bn.variables, extend(e, X, xi), bn)\n", - " return ProbDist(Q)\n", + "def joint_distribution(net):\n", + " \"Given a Bayes net, create the joint distribution over all variables.\"\n", + " return ProbDist({row: prod(P_xi_given_parents(var, row, net)\n", + " for var in net.variables)\n", + " for row in all_rows(net)})\n", "\n", - "def enumerate_all_vars(vars, e, bn):\n", - " \"\"\"Return the sum of those entries in P(vars | e_{others})\n", - " consistent with e, where P is the joint distribution represented\n", - " by bn, and e_{others} means e restricted to bn's other variables\n", - " (the ones other than vars). Parents must precede children in vars.\"\"\"\n", - " if not vars:\n", - " return 1.0\n", - " Y, rest = vars[0], vars[1:]\n", - " if Y in e:\n", - " y = e[Y]\n", - " return P(Y, e, y) * enumerate_all_vars(rest, e, bn)\n", - " else:\n", - " return sum(P(Y, e, y) * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", - " for y in Y.domain)\n", - " \n", - "def extend(dic, var, val):\n", - " \"\"\"Return a copy of the dict, with {var: val} added.\"\"\"\n", - " dic2 = dic.copy()\n", - " dic2[var] = val\n", - " return dic2" + "def all_rows(net): return itertools.product(*[var.domain for var in net.variables])\n", + "\n", + "def P_xi_given_parents(var, row, net):\n", + " \"The probability that var = xi, given the values in this row.\"\n", + " dist = P(var, Evidence(zip(net.variables, row)))\n", + " xi = row[net.variables.index(var)]\n", + " return dist[xi]\n", + "\n", + "def prod(numbers):\n", + " \"The product of numbers: prod([2, 3, 5]) == 30. Analogous to `sum([2, 3, 5]) == 10`.\"\n", + " result = 1\n", + " for x in numbers:\n", + " result *= x\n", + " return result" ] }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 18, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.7158281646356071, True: 0.2841718353643929}" + "{(F, F, F, F, F),\n", + " (F, F, F, F, T),\n", + " (F, F, F, T, F),\n", + " (F, F, F, T, T),\n", + " (F, F, T, F, F),\n", + " (F, F, T, F, T),\n", + " (F, F, T, T, F),\n", + " (F, F, T, T, T),\n", + " (F, T, F, F, F),\n", + " (F, T, F, F, T),\n", + " (F, T, F, T, F),\n", + " (F, T, F, T, T),\n", + " (F, T, T, F, F),\n", + " (F, T, T, F, T),\n", + " (F, T, T, T, F),\n", + " (F, T, T, T, T),\n", + " (T, F, F, F, F),\n", + " (T, F, F, F, T),\n", + " (T, F, F, T, F),\n", + " (T, F, F, T, T),\n", + " (T, F, T, F, F),\n", + " (T, F, T, F, T),\n", + " (T, F, T, T, F),\n", + " (T, F, T, T, T),\n", + " (T, T, F, F, F),\n", + " (T, T, F, F, T),\n", + " (T, T, F, T, F),\n", + " (T, T, F, T, T),\n", + " (T, T, T, F, F),\n", + " (T, T, T, F, T),\n", + " (T, T, T, T, F),\n", + " (T, T, T, T, T)}" ] }, - "execution_count": 185, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {JohnCalls:T, MaryCalls:T}, alarm_net)" + "# All rows in the joint distribution (2**5 == 32 rows)\n", + "set(all_rows(alarm_net))" ] }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 19, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false + }, + "outputs": [], + "source": [ + "# Let's work through just one row of the table:\n", + "row = (F, F, F, F, F)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.9438825459610851, True: 0.056117454038914924}" + "{F: 0.999, T: 0.001}" ] }, - "execution_count": 189, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {MaryCalls:T}, alarm_net)" + "# This is the probability distribution for Alarm\n", + "P(Alarm, {Burglary: F, Earthquake: F})" ] }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 21, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.8499098822502404, True: 0.15009011774975956}" + "0.999" ] }, - "execution_count": 190, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Alarm, {MaryCalls:T}, alarm_net)" + "# Here's the probability that Alarm is false, given the parent values in this row:\n", + "P_xi_given_parents(Alarm, row, alarm_net)" ] }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 22, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{(F, F, F, F, F): 0.9367427006190001,\n", + " (F, F, F, F, T): 0.009462047481000001,\n", + " (F, F, F, T, F): 0.04930224740100002,\n", + " (F, F, F, T, T): 0.0004980024990000002,\n", + " (F, F, T, F, F): 2.9910060000000004e-05,\n", + " (F, F, T, F, T): 6.979013999999999e-05,\n", + " (F, F, T, T, F): 0.00026919054000000005,\n", + " (F, F, T, T, T): 0.00062811126,\n", + " (F, T, F, F, F): 0.0013341744900000002,\n", + " (F, T, F, F, T): 1.3476510000000005e-05,\n", + " (F, T, F, T, F): 7.021971000000001e-05,\n", + " (F, T, F, T, T): 7.092900000000001e-07,\n", + " (F, T, T, F, F): 1.7382600000000002e-05,\n", + " (F, T, T, F, T): 4.0559399999999997e-05,\n", + " (F, T, T, T, F): 0.00015644340000000006,\n", + " (F, T, T, T, T): 0.00036503460000000007,\n", + " (T, F, F, F, F): 5.631714000000006e-05,\n", + " (T, F, F, F, T): 5.688600000000006e-07,\n", + " (T, F, F, T, F): 2.9640600000000033e-06,\n", + " (T, F, F, T, T): 2.9940000000000035e-08,\n", + " (T, F, T, F, F): 2.8143600000000003e-05,\n", + " (T, F, T, F, T): 6.56684e-05,\n", + " (T, F, T, T, F): 0.0002532924000000001,\n", + " (T, F, T, T, T): 0.0005910156000000001,\n", + " (T, T, F, F, F): 9.40500000000001e-08,\n", + " (T, T, F, F, T): 9.50000000000001e-10,\n", + " (T, T, F, T, F): 4.9500000000000054e-09,\n", + " (T, T, F, T, T): 5.0000000000000066e-11,\n", + " (T, T, T, F, F): 5.7e-08,\n", + " (T, T, T, F, T): 1.3299999999999996e-07,\n", + " (T, T, T, T, F): 5.130000000000002e-07,\n", + " (T, T, T, T, T): 1.1970000000000001e-06}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# The full joint distribution:\n", + "joint_distribution(alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Burglary, Earthquake, Alarm, JohnCalls, MaryCalls]\n" + ] + }, { "data": { "text/plain": [ - "{False: 0.9641190847135443, True: 0.03588091528645573}" + "0.00062811126" ] }, - "execution_count": 191, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Earthquake, {MaryCalls:T}, alarm_net)" + "# Probability that \"the alarm has sounded, but neither a burglary nor an earthquake has occurred, \n", + "# and both John and Mary call\" (page 514 says it should be 0.000628)\n", + "\n", + "print(alarm_net.variables)\n", + "joint_distribution(alarm_net)[F, F, T, T, T]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inference by Querying the Joint Distribution\n", + "\n", + "We can use `P(variable, evidence)` to get the probability of aa variable, if we know the vaues of all the parent variables. But what if we don't know? Bayes nets allow us to calculate the probability, but the calculation is not just a lookup in the CPT; it is a global calculation across the whole net. One inefficient but straightforward way of doing the calculation is to create the joint probability distribution, then pick out just the rows that\n", + "match the evidence variables, and for each row check what the value of the query variable is, and increment the probability for that value accordningly:" ] }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 24, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false + "collapsed": false + }, + "outputs": [], + "source": [ + "def enumeration_ask(X, evidence, net):\n", + " \"The probability distribution for query variable X in a belief net, given evidence.\"\n", + " i = net.variables.index(X) # The index of the query variable X in the row\n", + " dist = defaultdict(float) # The resulting probability distribution over X\n", + " for (row, p) in joint_distribution(net).items():\n", + " if matches_evidence(row, evidence, net):\n", + " dist[row[i]] += p\n", + " return ProbDist(dist)\n", + "\n", + "def matches_evidence(row, evidence, net):\n", + " \"Does the tuple of values for this row agree with the evidence?\"\n", + " return all(evidence[v] == row[net.variables.index(v)]\n", + " for v in evidence)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{F: 0.9931237539265789, T: 0.006876246073421024}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# The probability of a Burgalry, given that John calls but Mary does not: \n", + "enumeration_ask(Burglary, {JohnCalls: F, MaryCalls: T}, alarm_net)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.7029390000000001, True: 0.29706099999999996}" + "{F: 0.03368899586522123, T: 0.9663110041347788}" ] }, - "execution_count": 193, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(JohnCalls, {Earthquake:T}, alarm_net)" + "# The probability of an Alarm, given that there is an Earthquake and Mary calls:\n", + "enumeration_ask(Alarm, {MaryCalls: T, Earthquake: T}, alarm_net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable Elimination\n", + "\n", + "The `enumeration_ask` algorithm takes time and space that is exponential in the number of variables. That is, first it creates the joint distribution, of size *bn*, and then it sums out the values for the rows that match the evidence. We can do better than that if we interleave the joining of variables with the summing out of values.\n", + "This approach is called *variable elimination*. The key insight is that\n", + "when we compute\n", + "\n", + "P(*X*1=*x*1, ..., *X**n*=*x**n*) = Π*i* P(*X**i* = *x**i* | parents(*X**i*))\n", + "\n", + "we are repeating the calculation of, say, P(*X**3* = *x**4* | parents(*X**3*))\n", + "multiple times, across multiple rows of the joint distribution.\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# TODO: Copy over and update Variable Elimination algorithm. Also, sampling algorithms." + ] + }, + { + "cell_type": "markdown", "metadata": { "button": false, - "collapsed": true, "deletable": true, "new_sheet": false, "run_control": { "read_only": false } }, - "outputs": [], "source": [ - "def enumeration_ask(X, e, bn):\n", - " \"Given evidence e, ask what the probability distribution is for X in bn.\"\n", - " assert X not in e, \"Query variable must be distinct from evidence\"\n", - " Q = {}\n", - " for xi in X.domain:\n", - " Q[xi] = enumerate_all_vars(bn.variables, extend(e, X, xi), bn)\n", - " return ProbDist(Q)\n", + "# Example: Flu Net\n", "\n", - "def enumerate_all_vars(vars, e, bn):\n", - " \"\"\"Return the sum of those entries in P(vars | e_{others})\n", - " consistent with e, where P is the joint distribution represented\n", - " by bn, and e_{others} means e restricted to bn's other variables\n", - " (the ones other than vars). Parents must precede children in vars.\"\"\"\n", - " if not vars:\n", - " return 1.0\n", - " Y, rest = vars[0], vars[1:]\n", - " if Y in e:\n", - " y = e[Y]\n", - " return P(Y, e, y) * enumerate_all_vars(rest, e, bn)\n", - " else:\n", - " return sum(P(Y, e, y) * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", - " for y in Y.domain)\n", - " \n", - "def extend(dic, var, val):\n", - " \"\"\"Return a copy of the dict, with {var: val} added.\"\"\"\n", - " dic2 = dic.copy()\n", - " dic2[var] = val\n", - " return dic2" + "In this net, whether a patient gets the flu is dependent on whether they were vaccinated, and having the flu influences whether they get a fever or headache. Here `Fever` is a non-Boolean variable, with three values, `no`, `mild`, and `high`." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 28, "metadata": { "button": false, + "collapsed": false, "deletable": true, "new_sheet": false, "run_control": { "read_only": false } }, + "outputs": [], + "source": [ + "flu_net = (BayesNet()\n", + " .add('Vaccinated', [], 0.60)\n", + " .add('Flu', ['Vaccinated'], {T: 0.002, F: 0.02})\n", + " .add('Fever', ['Flu'], {T: ProbDist(no=25, mild=25, high=50),\n", + " F: ProbDist(no=97, mild=2, high=1)})\n", + " .add('Headache', ['Flu'], {T: 0.5, F: 0.03}))\n", + "\n", + "globalize(flu_net.lookup)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{F: 0.9616440110625343, T: 0.03835598893746573}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Full Joint ???" + "# If you just have a headache, you probably don't have the Flu.\n", + "enumeration_ask(Flu, {Headache: T, Fever: 'no'}, flu_net)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": { "button": false, "collapsed": false, @@ -739,72 +951,22 @@ { "data": { "text/plain": [ - "({(False, False, False, False, False): 0.9367427006189999,\n", - " (False, False, False, False, True): 0.009462047481,\n", - " (False, False, False, True, False): 0.049302247401000004,\n", - " (False, False, False, True, True): 0.0004980024990000001,\n", - " (False, False, True, False, False): 2.9910059999999997e-05,\n", - " (False, False, True, False, True): 6.979013999999998e-05,\n", - " (False, False, True, True, False): 0.00026919054,\n", - " (False, False, True, True, True): 0.0006281112599999999,\n", - " (False, True, False, False, False): 0.00133417449,\n", - " (False, True, False, False, True): 1.3476510000000001e-05,\n", - " (False, True, False, True, False): 7.021971e-05,\n", - " (False, True, False, True, True): 7.0929e-07,\n", - " (False, True, True, False, False): 1.73826e-05,\n", - " (False, True, True, False, True): 4.055939999999999e-05,\n", - " (False, True, True, True, False): 0.00015644340000000003,\n", - " (False, True, True, True, True): 0.0003650346,\n", - " (True, False, False, False, False): 5.631714000000005e-05,\n", - " (True, False, False, False, True): 5.688600000000004e-07,\n", - " (True, False, False, True, False): 2.9640600000000024e-06,\n", - " (True, False, False, True, True): 2.994000000000003e-08,\n", - " (True, False, True, False, False): 2.8143599999999996e-05,\n", - " (True, False, True, False, True): 6.566839999999998e-05,\n", - " (True, False, True, True, False): 0.00025329240000000004,\n", - " (True, False, True, True, True): 0.0005910156,\n", - " (True, True, False, False, False): 9.405000000000008e-08,\n", - " (True, True, False, False, True): 9.500000000000009e-10,\n", - " (True, True, False, True, False): 4.950000000000005e-09,\n", - " (True, True, False, True, True): 5.0000000000000054e-11,\n", - " (True, True, True, False, False): 5.699999999999999e-08,\n", - " (True, True, True, False, True): 1.3299999999999993e-07,\n", - " (True, True, True, True, False): 5.130000000000001e-07,\n", - " (True, True, True, True, True): 1.197e-06},\n", - " 32,\n", - " 0.9999999999999999)" + "{F: 0.9914651882096696, T: 0.008534811790330398}" ] }, - "execution_count": 51, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def full_joint(net):\n", - " rows = itertools.product(*[var.domain for var in net.variables])\n", - " return {row: joint_probability(row, net)\n", - " for row in rows}\n", - "\n", - "def joint_probability(row, net):\n", - " evidence = dict(zip(net.variables, row))\n", - " def Pvar(var): \n", - " return var.cpt[tuple(evidence[v] for v in var.parents)][evidence[var]]\n", - " return prod(Pvar(v) for v in net.variables)\n", - " \n", - "def prod(numbers):\n", - " product = 1\n", - " for x in numbers:\n", - " product *= x\n", - " return product\n", - "\n", - "j = full_joint(alarm_net)\n", - "j, len(j), sum(j.values())" + "# Even more so if you were vaccinated.\n", + "enumeration_ask(Flu, {Headache: T, Fever: 'no', Vaccinated: T}, flu_net)" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -812,50 +974,22 @@ { "data": { "text/plain": [ - "({(False, False, False, True, True): 0.23895323731595236,\n", - " (False, False, True, True, True): 0.3013824614795795,\n", - " (False, True, False, True, True): 0.0003403339180750413,\n", - " (False, True, True, True, True): 0.17515213192200013,\n", - " (True, False, False, True, True): 1.4365911696438334e-05,\n", - " (True, False, True, True, True): 0.2835830968876924,\n", - " (True, True, False, True, True): 2.399116849772601e-08,\n", - " (True, True, True, True, True): 0.00057434857383556},\n", - " 8,\n", - " 1.0)" + "{F: 0.9194016377587207, T: 0.08059836224127925}" ] }, - "execution_count": 50, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "def joint_distribution(net, evidence={}):\n", - " \"Given a Bayes net and some evidence variables, return the joint distribution over all variables.\"\n", - " values = [({evidence[var]} if var in evidence else var.domain)\n", - " for var in net.variables]\n", - " return ProbDist({row: joint_probability(row, net)\n", - " for row in itertools.product(*values)})\n", - "\n", - "def joint_probability(row, net):\n", - " evidence = dict(zip(net.variables, row))\n", - " def Pvar(var): \n", - " return var.cpt[tuple(evidence[v] for v in var.parents)][evidence[var]]\n", - " return prod(Pvar(v) for v in net.variables)\n", - " \n", - "def prod(numbers):\n", - " product = 1\n", - " for x in numbers:\n", - " product *= x\n", - " return product\n", - "\n", - "j = joint_distribution(alarm_net, {JohnCalls:True, MaryCalls:True})\n", - "j, len(j), sum(j.values())" + "source": [ + "# But if you were not vaccinated, there is a higher chance you have the flu.\n", + "enumeration_ask(Flu, {Headache: T, Fever: 'no', Vaccinated: F}, flu_net)" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 32, "metadata": { "collapsed": false }, @@ -863,475 +997,363 @@ { "data": { "text/plain": [ - "[Burglary, Earthquake, Alarm, JohnCalls, MaryCalls]" + "{F: 0.1904145077720207, T: 0.8095854922279793}" ] }, - "execution_count": 52, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "alarm_net.variables" + "# And if you have both headache and fever, and were not vaccinated, \n", + "# then the flu is very likely, especially if it is a high fever.\n", + "enumeration_ask(Flu, {Headache: T, Fever: 'mild', Vaccinated: F}, flu_net)" ] }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 33, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "'tests pass'" + "{F: 0.055534567434831886, T: 0.9444654325651682}" ] }, - "execution_count": 146, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def tests():\n", - " ProbDist({'heads': 1, 'tails': 1}) == ProbDist(heads=2, tails=2) == {'heads': 0.5, 'tails': 0.5}\n", - " ProbDist(0.2) == ProbDist({False: 0.8, True: 0.2})\n", - " \n", - " CPT(0.2, []) == CPT({(): {False: 0.8, True: 0.2}}, [])\n", - " \n", - " return 'tests pass'\n", - " \n", - "tests()\n" + "enumeration_ask(Flu, {Headache: T, Fever: 'high', Vaccinated: F}, flu_net)" ] }, { "cell_type": "markdown", - "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, + "metadata": {}, "source": [ - "The entries in a `CPTable` are all of the form `{(parent_value, ...): ProbDist}`. You could create such a table yourself, but we provide the function `CPT` to make it slightly easier. We provide functions to verify CPTs and ProbDists." + "# Entropy\n", + "\n", + "We can compute the entropy of a probability distribution:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { - "button": false, - "collapsed": true, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [], "source": [ - "The one method, `P`, gives the probability distribution for the variable, given evidence that specifies the values of all the parents.\n", - "(If you don't know the values for all the parents, later we will see that `enumeration_ask` can still give you an answer.)" + "def entropy(probdist):\n", + " \"The entropy of a probability distribution.\"\n", + " return - sum(p * math.log(p, 2)\n", + " for p in probdist.values())" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 35, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.7, True: 0.3}" + "1.0" ] }, - "execution_count": 102, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ProbDist(.3)" + "entropy(ProbDist(heads=0.5, tails=0.5))" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 36, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.011397802630112312" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "T = True \n", - "F = False\n", - "\n", - "def CPT(data, \n", - "\n" + "entropy(ProbDist(yes=1000, no=1))" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 37, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false + "outputs": [ + { + "data": { + "text/plain": [ + "0.8687212463394045" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" } - }, + ], "source": [ - "Now name the variables and ask for **P**(*Alarm* | *Burglary*=*f*, *Earthquake*=*t*):" + "entropy(P(Alarm, {Earthquake: T, Burglary: F}))" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 38, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.71, True: 0.29}" + "0.011407757737461138" ] }, - "execution_count": 86, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Alarm.P({Burglary:F, Earthquake:T})" + "entropy(P(Alarm, {Earthquake: F, Burglary: F}))" ] }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "For non-Boolean variables, the entropy can be greater than 1 bit:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.5" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Asia\n", - "https://www.norsys.com/tutorials/netica/secA/tut_A1.htm\n", - " \n", - "Asia = (BayesNet()\n", - " .add('VisitAsia', [], 0.01)\n", - " .add('Smoker', [], 0.30)\n", - " .add('TB', ['VisitAsia'], {T: " + "entropy(P(Fever, {Flu: T}))" ] }, { "cell_type": "markdown", "metadata": { - "button": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "source": [ - "# Flu Net" + "# Unknown Outcomes: Smoothing\n", + "\n", + "So far we have dealt with discrete distributions where we know all the possible outcomes in advance. For Boolean variables, the only outcomes are `T` and `F`. For `Fever`, we modeled exactly three outcomes. However, in some applications we will encounter new, previously unknown outcomes over time. For example, we could train a model on the distribution of words in English, and then somebody could coin a brand new word. To deal with this, we introduce\n", + "the `DefaultProbDist` distribution, which uses the key `None` to stand as a placeholder for any unknown outcome(s)." ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 40, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": true }, "outputs": [], "source": [ - "sick = (BayesNet()\n", - " .add('Vaccinated', [], {(): 0.35})\n", - " .add('Flu', ['Vaccinated'], {T: 0.075, F: 0.45})\n", - " .add('Fever', ['Flu'], {T: 0.75, F: 0.25})\n", - " .add('Headache', ['Flu'], {T: 0.7, F: 0.4}))" + "class DefaultProbDist(ProbDist):\n", + " \"\"\"A Probability Distribution that supports smoothing for unknown outcomes (keys).\n", + " The default_value represents the probability of an unknown (previously unseen) key. \n", + " The key `None` stands for unknown outcomes.\"\"\"\n", + " def __init__(self, default_value, mapping=(), **kwargs):\n", + " self[None] = default_value\n", + " self.update(mapping, **kwargs)\n", + " normalize(self)\n", + " \n", + " def __missing__(self, key): return self[None] " ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 41, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "{False: 0.6, True: 0.39999999999999997}" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "globals().update(sick)\n", + "import re\n", + "\n", + "def words(text): return re.findall(r'\\w+', text.lower())\n", "\n", - "enumeration_ask(Headache, {Flu: False}, sick)" + "english = words('''This is a sample corpus of English prose. To get a better model, we would train on much\n", + "more text. But this should give you an idea of the process. So far we have dealt with discrete \n", + "distributions where we know all the possible outcomes in advance. For Boolean variables, the only \n", + "outcomes are T and F. For Fever, we modeled exactly three outcomes. However, in some applications we \n", + "will encounter new, previously unknown outcomes over time. For example, when we could train a model on the \n", + "words in this text, we get a distribution, but somebody could coin a brand new word. To deal with this, \n", + "we introduce the DefaultProbDist distribution, which uses the key `None` to stand as a placeholder for any \n", + "unknown outcomes. Probability theory allows us to compute the likelihood of certain events, given \n", + "assumptions about the components of the event. A Bayesian network, or Bayes net for short, is a data \n", + "structure to represent a joint probability distribution over several random variables, and do inference on it.''')\n", + "\n", + "E = DefaultProbDist(0.1, Counter(english))" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 42, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.386842105263158, True: 0.613157894736842}" + "0.052295177222545036" ] }, - "execution_count": 75, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Headache, {Vaccinated: False, Fever: True}, sick)" + "# 'the' is a common word:\n", + "E['the']" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 43, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.7158281646356071, True: 0.2841718353643929}" + "0.005810575246949448" ] }, - "execution_count": 38, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {JohnCalls: True, MaryCalls: True}, alarm_net)" + "# 'possible' is a less-common word:\n", + "E['possible']" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 44, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.9999098156062451, True: 9.018439375484353e-05}" + "0.0005810575246949449" ] }, - "execution_count": 39, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {JohnCalls: False, MaryCalls: False}, alarm_net)" + "# 'impossible' was not seen in the training data, but still gets a non-zero probability ...\n", + "E['impossible']" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 45, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.993123753926579, True: 0.0068762460734210235}" + "0.0005810575246949449" ] }, - "execution_count": 40, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {JohnCalls: False, MaryCalls: True}, alarm_net)" + "# ... as do other rare, previously unseen words:\n", + "E['llanfairpwllgwyngyll']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this does not mean that 'impossible' and 'llanfairpwllgwyngyll' and all the other unknown words\n", + "*each* have probability 0.004.\n", + "Rather, it means that together, all the unknown words total probability 0.004. With that\n", + "interpretation, the sum of all the probabilities is still 1, as it should be. In the `DefaultProbDist`, the\n", + "unknown words are all represented by the key `None`:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 46, "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "{False: 0.9948701418665987, True: 0.005129858133401302}" + "0.0005810575246949449" ] }, - "execution_count": 41, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "enumeration_ask(Burglary, {JohnCalls: True, MaryCalls: False}, alarm_net)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, - "outputs": [], - "source": [ - "# Not executable yet\n", - "weather = (BayesNet()\n", - " .add('Yesterday', [], {(): {'rain': 0.2, 'sun': 0.8}})\n", - " .add('Pressure', [], {(): {'lo': 0.3, 'hi': 0.7}})\n", - " .add('Today', ['Yesterday', 'Pressure'], \n", - " {('rain', 'lo'): {'rain': 0.7, 'sun': 0.3},\n", - " ('rain', 'hi'): {'rain': 0.5, 'sun': 0.5},\n", - " ('sun', 'lo'): {'rain': 0.2, 'sun': 0.8},\n", - " ('sun', 'hi'): {'rain': 0.1, 'sun': 0.9}}))\n", - " \n", - "globals().update(weather)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "button": false, - "collapsed": false, - "deletable": true, - "new_sheet": false, - "run_control": { - "read_only": false - } - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "True", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0menumeration_ask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYesterday\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mToday\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'rain'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweather\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36menumeration_ask\u001b[0;34m(X, e, bn)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mxi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menumerate_all_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mProbDist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36menumerate_all_vars\u001b[0;34m(vars, e, bn)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mY\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0menumerate_all_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36menumerate_all_vars\u001b[0;34m(vars, e, bn)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n\u001b[0;32m---> 22\u001b[0;31m for y in (True, False))\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m return sum(Y.P(e)[y] * enumerate_all_vars(rest, extend(e, Y, y), bn)\n\u001b[0;32m---> 22\u001b[0;31m for y in (True, False))\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: True" - ] - } - ], - "source": [ - "enumeration_ask(Yesterday, {Today: 'rain'}, weather)" + "E[None]" ] } ], From fdac4c14ee3d5b1d02e3e173a85c0782cf95227c Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 2 Aug 2016 12:11:54 +0530 Subject: [PATCH 363/513] modifies gramatical errors in learning notebook --- learning.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index ee5ab418e..f372399ab 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -38,7 +38,7 @@ "\n", "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n", "\n", - "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the aggent. The agent then learns a function that maps from an input image to one of those strings.\n", + "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n", "\n", "* **Unsupervised Learning**:\n", "\n", @@ -48,7 +48,7 @@ "\n", "* **Reinforcement Learning**:\n", "\n", - "In Reinforcement Learning the agent from a series of reinforcements—rewards or punishments.\n", + "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n", "\n", "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." ] From b771b9730520e1495d344156d2f11eb5e4d83162 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Tue, 2 Aug 2016 12:27:12 +0530 Subject: [PATCH 364/513] adds contents table in learning notebook --- learning.ipynb | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/learning.ipynb b/learning.ipynb index f372399ab..f1b3a50aa 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -50,7 +50,20 @@ "\n", "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n", "\n", - "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." + "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it.\n", + "\n", + "## Contents\n", + "\n", + "* Explanations of learning module\n", + "* Practical Machine Learning Task\n", + " * MNIST handwritten digits classification\n", + " * Loading and Visualising digits data\n", + " * Naive kNN classifier\n", + " * Overfitting and how to avoid it\n", + " * Train-Test split\n", + " * Crossvalidation\n", + " * Regularisation\n", + " * Email spam detector" ] }, { From bbe9f3d17b5eb2b40005a8bd794da7c08ac4befe Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sat, 6 Aug 2016 20:31:21 +0530 Subject: [PATCH 365/513] Updated normalize to support dict --- utils.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/utils.py b/utils.py index 9b7c47707..1db43fcc5 100644 --- a/utils.py +++ b/utils.py @@ -228,10 +228,16 @@ def num_or_str(x): return str(x).strip() -def normalize(numbers): +def normalize(dist): """Multiply each number by a constant such that the sum is 1.0""" - total = float(sum(numbers)) - return [(n / total) for n in numbers] + if isinstance(dist, dict): + total = sum(dist.values()) + for key in dist: + dist[key] = dist[key] / total + assert 0 <= dist[key] <= 1, "Probabilities must be between 0 and 1." + return dist + total = sum(dist) + return [(n / total) for n in dist] def clip(x, lowest, highest): From 35b787cd01b55f0b76dd0164967ff71d2f963063 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Sat, 6 Aug 2016 20:37:48 +0530 Subject: [PATCH 366/513] Shorthand for True False --- utils.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/utils.py b/utils.py index 1db43fcc5..4ef7e0c08 100644 --- a/utils.py +++ b/utils.py @@ -606,3 +606,14 @@ def __delitem__(self, key): for i, (value, item) in enumerate(self.A): if item == key: self.A.pop(i) + +# ______________________________________________________________________________ +# Useful Shorthands + + +class Bool(int): + """Just like `bool`, except values display as 'T' and 'F' instead of 'True' and 'False'""" + __str__ = __repr__ = lambda self: 'T' if self else 'F' + +T = Bool(True) +F = Bool(False) From c631aa07efdd4f4d6dfe852b1e1cde2715f184e2 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Thu, 11 Aug 2016 23:10:24 +0530 Subject: [PATCH 367/513] updates contents table and modifies notebook accordingly --- learning.ipynb | 100 +++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 81 insertions(+), 19 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index f1b3a50aa..dd1cb91d4 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -22,6 +22,30 @@ "from learning import *" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Contents\n", + "\n", + "* Review\n", + "* Explanations of learning module\n", + "* Practical Machine Learning Task\n", + " * MNIST handwritten digits classification\n", + " * Loading and Visualising digits data\n", + " * kNN classifier\n", + " * Review\n", + " * Native implementation from Learning module\n", + " * Faster implementation using NumPy\n", + " * Overfitting and how to avoid it\n", + " * Train-Test split\n", + " * Crossvalidation\n", + " * Regularisation\n", + " * Sub-sampling\n", + " * Fine tuning parameters to get better results\n", + " * Email spam detector" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,20 +74,7 @@ "\n", "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n", "\n", - "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it.\n", - "\n", - "## Contents\n", - "\n", - "* Explanations of learning module\n", - "* Practical Machine Learning Task\n", - " * MNIST handwritten digits classification\n", - " * Loading and Visualising digits data\n", - " * Naive kNN classifier\n", - " * Overfitting and how to avoid it\n", - " * Train-Test split\n", - " * Crossvalidation\n", - " * Regularisation\n", - " * Email spam detector" + "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it." ] }, { @@ -92,7 +103,14 @@ "* Single-hidden-layer Neural Network classifier\n", "* SVMs (Support Vector Machines)\n", "\n", - "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n", + "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading MNIST digits data\n", "\n", "Let's start by loading MNIST data into numpy arrays." ] @@ -220,6 +238,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Visualizing MNIST digits data\n", + "\n", "To get a better understanding of the dataset, let's visualize some random images for each class from training & testing datasets." ] }, @@ -442,11 +462,18 @@ "\n", "Similarly if we put **k = 5**, you can observe that there are 4 yellow points, which is majority. So, we classify our test point as **yellow- Class A**.\n", "\n", - "In practical tasks, we iterate through a bunch of values for k (like [1, 2, 5, 10, 20, 50, 100]) and see how it performs and select the best one.\n", + "In practical tasks, we iterate through a bunch of values for k (like [1, 2, 5, 10, 20, 50, 100]) and see how it performs and select the best one. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Native implementations from Learning module\n", "\n", "Let's classify MNIST data in this method. Similar to these points, our images in MNIST data also have **features**. These points have two features as (2, 3) which represents co-ordinates of the point in 2-dimentional plane. Our images have 28x28 pixel values and we treat them as **features** for this particular task. \n", "\n", - "Next couple of cells help you understand some useful definitions from learning module. " + "Next couple of cells help you understand some useful definitions from learning module." ] }, { @@ -629,14 +656,16 @@ "source": [ "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n", "\n", - "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset. We will have an optimised version below in NumPy which is nearly ~50-100 times faster than this implementation." + "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset. We will have an optimised version below in NumPy which is nearly ~50-100 times faster than our native implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Faster kNN classifier implementation" + "### Faster implementation using NumPy\n", + "\n", + "Here we calculate manhattan distance between two images faster than our native implementation. Which in turn make predicting labels for test images far efficient." ] }, { @@ -682,6 +711,39 @@ " " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's print the shapes of data to make sure everything's on track." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'train_img' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training images size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training labels size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_lbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Testing images size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training labels size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_lbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'train_img' is not defined" + ] + } + ], + "source": [ + "print(\"Training images size:\", train_img.shape)\n", + "print(\"Training labels size:\", train_lbl.shape)\n", + "print(\"Testing images size:\", test_img.shape)\n", + "print(\"Training labels size:\", test_lbl.shape)" + ] + }, { "cell_type": "code", "execution_count": 21, From 2701794b0352735fe591291e2f678cfcadda2386 Mon Sep 17 00:00:00 2001 From: Rahul Patel Date: Thu, 25 Aug 2016 21:17:27 +0530 Subject: [PATCH 368/513] Edited a link in CONTRIBUTING.md (#249) The link to "Pseudocode algorithm (pdf)" was pointing to https://github.com/aimacode/pseudocode/blob/master/algorithms.pdf, which was causing the 404 error. The name of the file algorithms.pdf was changed to aima3e-algorithms.pdf by @ctjoreilly in commit 80286be. Edited the link to reflect that change and point to a valid URL. --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9cf485e54..9e1013fa1 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -11,7 +11,7 @@ Thanks for considering contributing to `aima-python`! Here is some of the work t ## New and Improved Algorithms -- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](https://github.com/aimacode/pseudocode/blob/master/algorithms.pdf) to see what's missing. +- Implement functions that were in the third edition of the book but were not yet implemented in the code. Check the [list of pseudocode algorithms (pdf)](https://github.com/aimacode/pseudocode/blob/master/aima3e-algorithms.pdf) to see what's missing. - As we finish chapters for the new fourth edition, we will share the new pseudocode in the [`aima-pseudocode`](https://github.com/aimacode/aima-pseudocode) repository, and describe what changes are necessary. We hope to have a `algorithm-name.md` file for each algorithm, eventually; it would be great if contributors could add some for the existing algorithms. - Give examples of how to use the code in the `.ipynb` file. From 8ec601285ca1b80dd593d07fd8c3c9f96d4bd993 Mon Sep 17 00:00:00 2001 From: Surya Teja Cheedella Date: Mon, 29 Aug 2016 16:10:53 +0530 Subject: [PATCH 369/513] adds SVM classifier on MNIST in SkLearn --- learning.ipynb | 135 ++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 112 insertions(+), 23 deletions(-) diff --git a/learning.ipynb b/learning.ipynb index dd1cb91d4..f6b4460d6 100644 --- a/learning.ipynb +++ b/learning.ipynb @@ -43,6 +43,7 @@ " * Regularisation\n", " * Sub-sampling\n", " * Fine tuning parameters to get better results\n", + " * Introduction to Scikit-Learn\n", " * Email spam detector" ] }, @@ -288,9 +289,9 @@ "outputs": [ { "data": { - "image/png": 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URVFiwEr0jiLVM+4h9eeY2/n99ddflClTJrP3l3EA8N9//wGOYnXVVVcBsGHD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QDfoFdz0vecDiiCopY0AB9++CHg2VIsXLgwJeOKlWXLlgGe+h3emks2MIVB5268c/sCs3iS+6sc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\nh2irHjOJ0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VUf1N9erVAVdJFcVRxgn418caNWrw22+/AbBnzx4AihcvzhlnnJHpfRdffDEA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\n2y4r2tatW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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -311,9 +312,9 @@ "outputs": [ { "data": { - "image/png": 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4UhRFURRFiQJdPCmKoiiKokSBLp4URVEURVGiQBdPiqIoiqIoUaCLJ0VRFEVR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nsB0K9fPwBatmxJhw4dANiyZUvc67t37w7As88+C8Aff/yRhVEaBhx88MEcdthhUY/t\nsssuORqNYYQbU54MwzAMwzACUKDYdsZ+QRa9Hpo0aQL4KlODBg0ixwHArrvuCsDnn3+e0meGwc9i\n8+bNGgvt2rUD4LXXXkvb5+fSd6VZs2bccMMNACxfvhyAqVOnxr3u77//BuD3338v0e/JxhylMh16\n6KEAtG/fnjZt2gCwww47RP4ujSnROADo2bMnAA8//HBKvzsMx2kqfPHFF+48HT16dNTP4siXOZaG\nXJ6LCxcu5JBDDkn4XIUK6QtSmM9Tdue4//77A3D99dcDuLy2SO69914Azj777JSiMmGbYyYobo5l\nJmxXr149nnjiCSB60ZTodZD64ikM3H333QD07duXI488Ekjv4ikX1K5dG4ChQ4dywgknALifV199\nddzrP/roIwBuvPFGAKZPn54w7JULrrnmGsC78ABUq1YN8BZCyTYnr776KgC77bYb4P9NyiKdO3cG\nvA1Opjds2WLfffcFYNtttwX8m1KVKlXca7bbbjsA9ttvP1cQ8fHHHwNw1llnAbm/Fmmx36FDB3dO\nKVyszWa+0LRpUwAOP/xwwPu762fr1q2B6M2LvosrrrgCSH2zki/06tWL//3vf4BXEACJN22nnXYa\nAA0bNnR/uzAQm/YwYMAAatSoAUSnPSxbtgyACy64AICXXnop7rMaNWoEwKxZs2jbtm2px2ZhO8Mw\nDMMwjADkvfIkJemZZ55hjz32ABKvrMWoUaMAOPbYYzM/uDTx559/5noIpaZixYqAlzAN8OCDDwLe\nTkdonj/99JN7TOEulUrfddddgBfKnDFjRoZHXTSSwufOncs222wDRCsOsTzwwAMA/PDDDy6srKTw\ncePGATBkyBC+//57AJ588snMDDxNaEfYo0cPHn30UQDefvvtuNeVL18egIsvvjh7g8sgCpuffPLJ\nDB06FIBy5YLtQXfccUcAFixYAPhqSa64/PLLAW8nr2unjr9Vq1blbFxBadSoEW+++SaAUyci0dwi\n7w9SfaXuf/bZZ0DiYzmfkIo/bdo0d+2VyiYFqk6dOlSvXj3qfQceeKBTGz/99NNsDTeO008/HfCj\nEJFpD1KcIr9HKYy6vyRSnn755RcALrvssrSM0ZQnwzAMwzCMAOSt8qSkU+U5mdC6WdcAABFVSURB\nVIlbeKlYsaLbQUTaR4CnNi1atAjwFZiFCxe652UtoedUOj1y5Eiee+45AKfWZJMBAwYA3m63qNyr\ndevWuVyCSCVJybcyIOzatat77uabbwbCqzZqZyf1rHLlygwZMgSA+vXrx71eeSbt27fP0ggzw0kn\nnQTAiSeeCMBXX33FyJEjAT/n6d1333Wv32effQD49ddfAXjllVfiPlM74VyhHBCNNZJZs2Zlezil\nplKlSnGKk3K3nn32WaeiffXVV4CfQA3wzTffAPDee+9lYaSZRzYvlStXdo8NHDgQgJdffhnw8oNU\nrCOqVq0a9Z5cce211wKJryklRcdC5P2lNOTl4qlJkyZJnW8l2SmMp9BevlNQUOCSHfMBycVXX311\nwkUTeOE7JQMm4rHHHgP8i9pTTz0FQPPmzTnnnHMA3E0sm2jBsM8++7ikYC3ilLw4efLkOOl7++23\n5/bbbwegS5cugC8/33nnnaEP11155ZVA9EW5bt26Rb5+xIgRcY+tXr0a8L/bsLPvvvu6pNutt94a\n8KpEv/jii1wOq9ToGI4sVLjzzjsBmD9/fk7GVBo2bNjAzz//DPgLpMGDBwPRYThdlyIXT0raV2Vz\nvlKnTh0A59dVUFDAypUrAX/RpA3NJZdc4u4n+nnFFVdEbQJyweuvv+6uqbEpOMuWLXNeeFOmTAGi\nN5/ZxMJ2hmEYhmEYAchL5emJJ55IqDgpmfjcc88F4KGHHgLgqKOOyt7gMkhhYWFelXlrF6QQVyRK\n2hs/fnxKn6Vdvt4n1/Vc07ZtW2eNoRBjnz59ALjnnnt46623AD/c1aBBAxo3bgz4u6oXXngBgOHD\nh7Nx48bsDT4A/fv3BxIXWiikGonK35W4KgoKCpg5cyYQ3qRcJYAr9HHrrbc6xUml7PmUSB2LlNrY\nJP6CggLn8h/W4zAZq1atcqXqUs4UNo2katWqcY+peCPfUbirVq1agHeN0Xeq1InzzjsP8P4Ouga9\n/vrrQOrX40wg64/mzZu7c/Cvv/4C/K4T3bt3Z/369YA/16OPPtp9xuzZswFcIrxemwlMeTIMwzAM\nwwhAXilPPXr0ALxcplgF5uGHH2bQoEHuefBWsLFEPqfSzbBzxhln5HoIJUK780mTJrlS0ttuuw3A\nKTJBUa5UWAwywTfYU3mtcioKCgpo1apVse8/6KCDAE8JkOFmmOjcuTOTJk0C4nMQfvvtN1fqHklR\nrwc/HyWs6PvUz0i0O3700Ud55JFHAM+wFXwX/DBTqVIlZ+YZ+91s3Lgx50nspUVO2cmQXU1ZRLYp\nkRxzzDGAn9um733Lli3OQmX48OGAlzeWKxQx2nrrrd31XdYTnTp1inv9ihUrAM9SQYVEn3zySdRz\nM2fOzJiaZsqTYRiGYRhGAEKrPLVo0cKZyMVW85QrV44vv/wS8M3NInfsMu2TnYHeE/nYY4895gzS\nwo4qmwoLC3NSll9arrrqqrR9lsqr1eoiDKjyU8qniKyMVJnsr7/+6qwKVFGiHIyrrrqKCy+8EMD1\nGHvnnXcyOPLkKJdg1qxZca0dlBNz2WWX8c8//0S9r127dm4nGMu8efNSUgdygVofJaoQFM2aNXM/\ntRs+4ogjAK99EvjfdRg5+OCDXSVWLGeffXbayrjDTKLqrHxQDVNB0ZlIVTFWjfrtt98AL29UFcth\noKhrRlHI/iSyHZuupbLfUGVhJgjd4kk3nGeeecYlHMfKy1u2bHGJm4nCHImcZCUDyr06TAdNUcjF\nOZJ8Ke/OFLl2Y06EkqkThUEkIyvBfdy4ce4Enzx5MoBrHrzLLru4xGRdSHKxeNJiQGNOlGCr5qFb\nbbWV6+mnc/f8888vcnF79dVXs2bNmrSPOR3ceuutgG9HIFauXOmS3CdMmAB4Cy053B9//PGAHxYJ\n8+JJG8tIZK0xbdo0tt9+e8AvzNACuqCgwNkYhDG0nApKok50PCvMo0KHl19+mU2bNmVvcGkgsgAg\nkaWNkuJ1vIbNS07HYeSCR50cvv76a/eY5pZKZwd9JuCO7f79+6flGLawnWEYhmEYRgBCpzyJRx55\nhDPPPDPhc48//njgUNDjjz8OwJgxY4CSJyxnE+0QFHKcPXu2M4H7t6F+YJHJ8+pDlWuUMK3Se+1q\n2rZt63a0kU65UiZkDiqFdc6cOa53mvqmLVy4MOtJvBpDtWrVinyNjGdvueUW91hkt/qiiO2lFSYU\nRpVRogxOjz766Lgk95kzZ7priULJSvyXRUqYUHl69erV474fzaN58+ZOmYoNoRQUFLhrrvoV6n1h\nVmh07axatar7XnQtiUTdCsSnn37qohV33HEHABMnTgxVoYpo0aIFQNT9Ut/xP//84777e+65Bwif\n4iQmTpwIeJYv6nmqdIHIHqjJrjN6TkqVFGPwj9vOnTub8mQYhmEYhpFtQqc8aTWZzEZg/PjxgfMK\ntBLNB8UJvH5Z6kWk3U6+5DupnHunnXZyj8lSX3kh06ZNA7z+XuptlwzlqClv4fPPP3eJx//X3t2E\nRNWFcQD/u4gBwaCNm4hAYQilFqEJhSSS1KKioUVEVquoqJWQFdkXKPSJ0qIggiKTtEAhsEJpYR8W\nBUFhERGtikIIIitmMXLfxfA/93rnqnNtPs71/f9A5n3Hqe6ZuTNz7nOe8zwsSVGs0hOMUDAfhlc7\nX758MY+ZqVgbI0snTpzAhQsXALjJkG1tbWbLfD4LvnlxU4I3byKbtkDZPMbbHd0GzJeIx+OoqakB\n4ObEzNamgoVbGXlavnw5ADsjT+QttPv8+XMAwLt37wCkz+OgfFHvnwXcfCiWImG7IRvw82Xbtm0A\n3MK1QVvdZxKPx81/s99kZWWlVbmy27dvB+B+lgb1pLt58yauXLlS0OOaK36Orlu3DgcPHgTgfg4G\n5f/OFOFmS5qgYra5ih5aM3nilyKrM3uXA4hfstN92TJ8yb/L+2HOCqVRUVVVZb5ouIzgTZqzzYIF\nC8yHKD+wgmqOEPu6/fnzxyyJsH/Yq1evAKRfs6NHjwKASUqmlpYW09SSX1qNjY25GMqccUI/14Th\nkZGRjF5TTGAtJFbqZ20u7y7CbCrcz/QY9rUrNibpc6zl5eXm/ZZNde3q6mrz2nBSG5ULM+Ju42yq\naw8ODmbsUuP/2zR54mcA64zNhhekTOvwNgbmzmy+vtXV1bk6zH/CyRB3d7KuXBDWIouSjx8/mkkq\nl/mDnntOqFg7sNC0bCciIiISgjWRJ9ZyYjVQ79Ure6Oxnk6QqqoqUxfKX+Kgvb3dlDaICu/yBmuv\njI6OFutwpsWljt7eXlRUVABwExK9yXqMBvJKggmpixcvNlcVFy9eBOBWuf327ZvpA+ff+n7jxg2z\nvFSoiFNZWZk51u/fvwPIbbXsPXv2mGgPz93Ozs6CLdcRI52M+vF1AdyrcY57yZIlGcm2gLsUefr0\naQBu5d+gxxYDl1jZI7O7uztUPzdWZAZg6lxFLbrNJS7eeg0ODgIADhw4AACBSz/enmK2YAeJbFy6\ndAktLS0AgqOl/kgiv1+KgYnT3d3dppYT8b1YVlZmvvsYuY5yD0bAjfq9ePGiyEeSSZEnERERkRCs\niDzV1NTg/PnzGfc/ePAAQLoqMeAW5vPiem9ra6uJXvEqgpEJViGPAibIedfsOzo6inU4szp58iQA\noKKiwkTIdu3aBcBN2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SYFwmQlkh2+3Zs8fU1kvE8QciCSnxRN12iqIoiqIoEeAb5UnkRnHb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\nLVeunLkui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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -610,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -625,10 +626,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -636,7 +637,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -670,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -720,20 +721,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'train_img' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training images size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training labels size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_lbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Testing images size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_img\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training labels size:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_lbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'train_img' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Training images size: (60000, 784)\n", + "Training labels size: (60000,)\n", + "Testing images size: (10000, 784)\n", + "Training labels size: (10000,)\n" ] } ], @@ -746,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -765,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "collapsed": false }, @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -809,6 +809,95 @@ "print(\"Accuracy of predictions:\", num_accuracy, \"%\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Scikit-Learn\n", + "\n", + "In this section we will solve this MNIST problem using Scikit-Learn. Learn more about Scikit-Learn [here](http://scikit-learn.org/stable/index.html). As we are using this library, we don't need to define our own functions (kNN or Support Vector Machines aka SVMs) to classify digits.\n", + "\n", + "Let's start by importing necessary modules for kNN and SVM." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import NearestNeighbors\n", + "from sklearn import svm" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n", + " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", + " multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n", + " verbose=0)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# takes ~3 mins to execute the cell\n", + "SVMclf = svm.LinearSVC()\n", + "SVMclf.fit(train_img, train_lbl)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "predictions = SVMclf.predict(test_img)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of predictions: 88.25 %\n" + ] + } + ], + "source": [ + "num_correct = np.sum(predictions == test_lbl)\n", + "num_accuracy = (float(num_correct)/len(test_lbl)) * 100\n", + "print(\"Accuracy of predictions:\", num_accuracy, \"%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might observe that this accuracy is far less than what we got using native kNN implementation. But we can tweak the parameters to get higher accuracy on this problem which we are going to explain in coming sections." + ] + }, { "cell_type": "code", "execution_count": null, From 5574d77c9e670a3fb6905eb50d7fcaf499e9ac78 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Wed, 7 Sep 2016 13:30:46 +0530 Subject: [PATCH 370/513] Added Default Parameter to Support Smoothing (#246) --- text.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/text.py b/text.py index 39bbb921f..57a19d2ab 100644 --- a/text.py +++ b/text.py @@ -32,10 +32,10 @@ class NgramTextModel(CountingProbDist): You can add, sample or get P[(word1, ..., wordn)]. The method P.samples(n) builds up an n-word sequence; P.add and P.add_sequence add data.""" - def __init__(self, n, observation_sequence=[]): + def __init__(self, n, observation_sequence=[], default=0): # In addition to the dictionary of n-tuples, cond_prob is a # mapping from (w1, ..., wn-1) to P(wn | w1, ... wn-1) - CountingProbDist.__init__(self) + CountingProbDist.__init__(self, default=default) self.n = n self.cond_prob = defaultdict() self.add_sequence(observation_sequence) From 61ef26763d7e1b5e117f2d5e22346d9d873abd37 Mon Sep 17 00:00:00 2001 From: opensourceware Date: Wed, 7 Sep 2016 16:02:23 +0800 Subject: [PATCH 371/513] Planning (#253) * Minor docstring changes * Added Spare Tire Problem * Fixed a bug in substitute method of class Action * Fixed minor typo in comment --- planning.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 47 insertions(+), 4 deletions(-) diff --git a/planning.py b/planning.py index 60247a7bc..92f4f773e 100644 --- a/planning.py +++ b/planning.py @@ -7,7 +7,7 @@ class PDLL: """ - PDLL used to deine a search problem + PDLL used to define a search problem It stores states in a knowledge base consisting of first order logic statements The conjunction of these logical statements completely define a state """ @@ -61,7 +61,11 @@ def __call__(self, kb, args): def substitute(self, e, args): """Replaces variables in expression with their respective Propostional symbol""" - new_args = [args[i] for x in e.args for i in range(len(self.args)) if self.args[i] == x] + new_args = list(e.args) + for num, x in enumerate(e.args): + for i in range(len(self.args)): + if self.args[i] == x: + new_args[num] = args[i] return Expr(e.op, *new_args) def check_precond(self, kb, args): @@ -123,8 +127,8 @@ def goal_test(kb): effect_rem = [expr("In(c, p)")] unload = Action(expr("Unload(c, p, a)"), [precond_pos, precond_neg], [effect_add, effect_rem]) - # Load - # Used used 'f' instead of 'from' because 'from' is a python keyword and expr uses eval() function + # Fly + # Used 'f' instead of 'from' because 'from' is a python keyword and expr uses eval() function precond_pos = [expr("At(p, f)"), expr("Plane(p)"), expr("Airport(f)"), expr("Airport(to)")] precond_neg = [] effect_add = [expr("At(p, to)")] @@ -132,3 +136,42 @@ def goal_test(kb): fly = Action(expr("Fly(p, f, to)"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [load, unload, fly], goal_test) + + +def spare_tire(): + init = [expr('Tire(Flat)'), + expr('Tire(Spare)'), + expr('At(Flat, Axle)'), + expr('At(Spare, Trunk)')] + + def goal_test(kb): + required = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ##Actions + #Remove + precond_pos = [expr("At(obj, loc)")] + precond_neg = [] + effect_add = [expr("At(obj, Ground)")] + effect_rem = [expr("At(obj, loc)")] + remove = Action(expr("Remove(obj, loc)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + #PutOn + precond_pos = [expr("Tire(t)"), expr("At(t, Ground)")] + precond_neg = [expr("At(Flat, Axle)")] + effect_add = [expr("At(t, Axle)")] + effect_rem = [expr("At(t, Ground)")] + put_on = Action(expr("PutOn(t, Axle)"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + #LeaveOvernight + precond_pos = [] + precond_neg = [] + effect_add = [] + effect_rem = [expr("At(Spare, Ground)"), expr("At(Spare, Axle)"), expr("At(Spare, Trunk)"), + expr("At(Flat, Ground)"), expr("At(Flat, Axle)"), expr("At(Flat, Trunk)")] + leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [remove, put_on, leave_overnight], goal_test) From 04c7d51c8df8a127459e8b4f2b7cecae3ba206d3 Mon Sep 17 00:00:00 2001 From: Jonathon Belotti Date: Wed, 7 Sep 2016 18:03:05 +1000 Subject: [PATCH 372/513] Implementing HITS algorithm (#244) * Implementing HITS algorithm * Moving HITS work to nlp.py and test_nlp.py --- nlp.py | 177 ++++++++++++++++++++++++++++++++++++++++++++++ tests/test_nlp.py | 121 ++++++++++++++++++++++++++++++- 2 files changed, 296 insertions(+), 2 deletions(-) diff --git a/nlp.py b/nlp.py index 83686170f..7273b98da 100644 --- a/nlp.py +++ b/nlp.py @@ -4,6 +4,8 @@ # from the third edition until this gets reviewed.) from collections import defaultdict +import urllib.request +import re # ______________________________________________________________________________ # Grammars and Lexicons @@ -206,3 +208,178 @@ def CYK_parse(words, grammar): P[X, start, length] = max(P[X, start, length], P[Y, start, len1] * P[Z, start+len1, len2] * p) return P + + +# ______________________________________________________________________________ +# Page Ranking + +# First entry in list is the base URL, and then following are relative URL pages +examplePagesSet = ["https://en.wikipedia.org/wiki/", "Aesthetics", "Analytic_philosophy", + "Ancient_Greek", "Aristotle", "Astrology","Atheism", "Baruch_Spinoza", + "Belief", "Betrand Russell", "Confucius", "Consciousness", + "Continental Philosophy", "Dialectic", "Eastern_Philosophy", + "Epistemology", "Ethics", "Existentialism", "Friedrich_Nietzsche", + "Idealism", "Immanuel_Kant", "List_of_political_philosophers", "Logic", + "Metaphysics", "Philosophers", "Philosophy", "Philosophy_of_mind", "Physics", + "Plato", "Political_philosophy", "Pythagoras", "Rationalism","Social_philosophy", + "Socrates", "Subjectivity", "Theology", "Truth", "Western_philosophy"] + + +def loadPageHTML( addressList ): + """Download HTML page content for every URL address passed as argument""" + contentDict = {} + for addr in addressList: + with urllib.request.urlopen(addr) as response: + raw_html = response.read().decode('utf-8') + # Strip raw html of unnessecary content. Basically everything that isn't link or text + html = stripRawHTML(raw_html) + contentDict[addr] = html + return contentDict + +def initPages( addressList ): + """Create a dictionary of pages from a list of URL addresses""" + pages = {} + for addr in addressList: + pages[addr] = Page(addr) + return pages + +def stripRawHTML( raw_html ): + """Remove the section of the HTML which contains links to stylesheets etc., + and remove all other unnessecary HTML""" + # TODO: Strip more out of the raw html + return re.sub(".*?", "", raw_html, flags=re.DOTALL) # remove section + +def determineInlinks( page ): + """Given a set of pages that have their outlinks determined, we can fill + out a page's inlinks by looking through all other page's outlinks""" + inlinks = [] + for addr, indexPage in pagesIndex.items(): + if page.address == indexPage.address: + continue + elif page.address in indexPage.outlinks: + inlinks.append(addr) + return inlinks + +def findOutlinks( page, handleURLs=None ): + """Search a page's HTML content for URL links to other pages""" + urls = re.findall(r'href=[\'"]?([^\'" >]+)', pagesContent[page.address]) + if handleURLs: + urls = handleURLs(urls) + return urls + +def onlyWikipediaURLS( urls ): + """Some example HTML page data is from wikipedia. This function converts + relative wikipedia links to full wikipedia URLs""" + wikiURLs = [url for url in urls if url.startswith('/wiki/')] + return ["https://en.wikipedia.org"+url for url in wikiURLs] + + +# ______________________________________________________________________________ +# HITS Helper Functions + +def expand_pages( pages ): + """From Textbook: adds in every page that links to or is linked from one of + the relevant pages.""" + expanded = {} + for addr,page in pages.items(): + if addr not in expanded: + expanded[addr] = page + for inlink in page.inlinks: + if inlink not in expanded: + expanded[inlink] = pagesIndex[inlink] + for outlink in page.outlinks: + if outlink not in expanded: + expanded[outlink] = pagesIndex[outlink] + return expanded + +def relevant_pages(query): + """relevant pages are pages that contain the query in its entireity. + If a page's content contains the query it is returned by the function""" + relevant = {} + print("pagesContent in function: ", pagesContent) + for addr, page in pagesIndex.items(): + if query.lower() in pagesContent[addr].lower(): + relevant[addr] = page + return relevant + +def normalize( pages ): + """From the pseudocode: Normalize divides each page's score by the sum of + the squares of all pages' scores (separately for both the authority and hubs scores). + """ + summed_hub = sum(page.hub**2 for _,page in pages.items()) + summed_auth = sum(page.authority**2 for _,page in pages.items()) + for _, page in pages.items(): + page.hub /= summed_hub + page.authority /= summed_auth + +class ConvergenceDetector(object): + """If the hub and authority values of the pages are no longer changing, we have + reached a convergence and further iterations will have no effect. This detects convergence + so that we can stop the HITS algorithm as early as possible.""" + def __init__(self): + self.hub_history = None + self.auth_history = None + + def __call__(self): + return self.detect() + + def detect(self): + curr_hubs = [page.hub for addr, page in pagesIndex.items()] + curr_auths = [page.authority for addr, page in pagesIndex.items()] + if self.hub_history == None: + self.hub_history, self.auth_history = [],[] + else: + diffsHub = [abs(x-y) for x, y in zip(curr_hubs,self.hub_history[-1])] + diffsAuth = [abs(x-y) for x, y in zip(curr_auths,self.auth_history[-1])] + aveDeltaHub = sum(diffsHub)/float(len(pagesIndex)) + aveDeltaAuth = sum(diffsAuth)/float(len(pagesIndex)) + if aveDeltaHub < 0.01 and aveDeltaAuth < 0.01: # may need tweaking + return True + if len(self.hub_history) > 2: # prevent list from getting long + del self.hub_history[0] + del self.auth_history[0] + self.hub_history.append([x for x in curr_hubs]) + self.auth_history.append([x for x in curr_auths]) + return False + + +def getInlinks( page ): + if not page.inlinks: + page.inlinks = determineInlinks(page) + return [p for addr, p in pagesIndex.items() if addr in page.inlinks ] + +def getOutlinks( page ): + if not page.outlinks: + page.outlinks = findOutlinks(page) + return [p for addr, p in pagesIndex.items() if addr in page.outlinks] + + +# ______________________________________________________________________________ +# HITS Algorithm + +class Page(object): + def __init__(self, address, hub=0, authority=0, inlinks=None, outlinks=None): + self.address = address + self.hub = hub + self.authority = authority + self.inlinks = inlinks + self.outlinks = outlinks + +pagesContent = {} # maps Page relative or absolute URL/location to page's HTML content +pagesIndex = {} +convergence = ConvergenceDetector() # assign function to variable to mimic pseudocode's syntax + +def HITS(query): + """The HITS algorithm for computing hubs and authorities with respect to a query.""" + pages = expand_pages(relevant_pages(query)) # in order to 'map' faithfully to pseudocode we + for p in pages: # won't pass the list of pages as an argument + p.authority = 1 + p.hub = 1 + while True: # repeat until... convergence + for p in pages: + p.authority = sum(x.hub for x in getInlinks(p)) # p.authority ← ∑i Inlinki(p).Hub + p.hub = sum(x.authority for x in getOutlinks(p)) # p.hub ← ∑i Outlinki(p).Authority + normalize(pages) + if convergence(): + break + return pages diff --git a/tests/test_nlp.py b/tests/test_nlp.py index 4e7bebeae..d51ac539d 100644 --- a/tests/test_nlp.py +++ b/tests/test_nlp.py @@ -1,6 +1,11 @@ import pytest -from nlp import * - +import nlp +from nlp import loadPageHTML, stripRawHTML, determineInlinks, findOutlinks, onlyWikipediaURLS +from nlp import expand_pages, relevant_pages, normalize, ConvergenceDetector, getInlinks +from nlp import getOutlinks, Page, HITS +from nlp import Rules, Lexicon +# Clumsy imports because we want to access certain nlp.py globals explicitly, because +# they are accessed by function's within nlp.py def test_rules(): assert Rules(A="B C | D E") == {'A': [['B', 'C'], ['D', 'E']]} @@ -8,3 +13,115 @@ def test_rules(): def test_lexicon(): assert Lexicon(Art="the | a | an") == {'Art': ['the', 'a', 'an']} + + +# ______________________________________________________________________________ +# Data Setup + +testHTML = """Keyword String 1: A man is a male human. + Keyword String 2: Like most other male mammals, a man inherits an + X from his mom and a Y from his dad. + Links: + href="https://google.com.au" + < href="/wiki/TestThing" > href="/wiki/TestBoy" + href="/wiki/TestLiving" href="/wiki/TestMan" >""" +testHTML2 = "Nothing" + +pA = Page("A", 1, 6, ["B","C","E"],["D"]) +pB = Page("B", 2, 5, ["E"],["A","C","D"]) +pC = Page("C", 3, 4, ["B","E"],["A","D"]) +pD = Page("D", 4, 3, ["A","B","C","E"],[]) +pE = Page("E", 5, 2, [],["A","B","C","D","F"]) +pF = Page("F", 6, 1, ["E"],[]) +pageDict = {pA.address:pA,pB.address:pB,pC.address:pC, + pD.address:pD,pE.address:pE,pF.address:pF} +nlp.pagesIndex = pageDict +nlp.pagesContent ={pA.address:testHTML,pB.address:testHTML2, + pC.address:testHTML,pD.address:testHTML2, + pE.address:testHTML,pF.address:testHTML2} + +# This test takes a long time (> 60 secs) +# def test_loadPageHTML(): +# # first format all the relative URLs with the base URL +# addresses = [examplePagesSet[0] + x for x in examplePagesSet[1:]] +# loadedPages = loadPageHTML(addresses) +# relURLs = ['Ancient_Greek','Ethics','Plato','Theology'] +# fullURLs = ["https://en.wikipedia.org/wiki/"+x for x in relURLs] +# assert all(x in loadedPages for x in fullURLs) +# assert all(loadedPages.get(key,"") != "" for key in addresses) + +def test_stripRawHTML(): + addr = "https://en.wikipedia.org/wiki/Ethics" + aPage = loadPageHTML([addr]) + someHTML = aPage[addr] + strippedHTML = stripRawHTML(someHTML) + assert "" not in strippedHTML and "" not in strippedHTML + +def test_determineInlinks(): + # TODO + assert True + +def test_findOutlinks_wiki(): + testPage = pageDict[pA.address] + outlinks = findOutlinks(testPage, handleURLs=onlyWikipediaURLS) + assert "https://en.wikipedia.org/wiki/TestThing" in outlinks + assert "https://en.wikipedia.org/wiki/TestThing" in outlinks + assert "https://google.com.au" not in outlinks +# ______________________________________________________________________________ +# HITS Helper Functions + +def test_expand_pages(): + pages = {k: pageDict[k] for k in ('F')} + pagesTwo = {k: pageDict[k] for k in ('A','E')} + expanded_pages = expand_pages(pages) + assert all(x in expanded_pages for x in ['F','E']) + assert all(x not in expanded_pages for x in ['A','B','C','D']) + expanded_pages = expand_pages(pagesTwo) + print(expanded_pages) + assert all(x in expanded_pages for x in ['A','B','C','D','E','F']) + +def test_relevant_pages(): + pages = relevant_pages("male") + assert all((x in pages.keys()) for x in ['A','C','E']) + assert all((x not in pages) for x in ['B','D','F']) + +def test_normalize(): + normalize( pageDict ) + print(page.hub for addr,page in nlp.pagesIndex.items()) + expected_hub = [1/91,2/91,3/91,4/91,5/91,6/91] # Works only for sample data above + expected_auth = list(reversed(expected_hub)) + assert len(expected_hub) == len(expected_auth) == len(nlp.pagesIndex) + assert expected_hub == [page.hub for addr,page in sorted(nlp.pagesIndex.items())] + assert expected_auth == [page.authority for addr,page in sorted(nlp.pagesIndex.items())] + +def test_detectConvergence(): + # run detectConvergence once to initialise history + convergence = ConvergenceDetector() + convergence() + assert convergence() # values haven't changed so should return True + # make tiny increase/decrease to all values + for _, page in nlp.pagesIndex.items(): + page.hub += 0.0003 + page.authority += 0.0004 + # retest function with values. Should still return True + assert convergence() + for _, page in nlp.pagesIndex.items(): + page.hub += 3000000 + page.authority += 3000000 + # retest function with values. Should now return false + assert not convergence() + +def test_getInlinks(): + inlnks = getInlinks(pageDict['A']) + assert sorted([page.address for page in inlnks]) == pageDict['A'].inlinks + +def test_getOutlinks(): + outlnks = getOutlinks(pageDict['A']) + assert sorted([page.address for page in outlnks]) == pageDict['A'].outlinks + +def test_HITS(): + # TODO + assert True # leave for now + +if __name__ == '__main__': + pytest.main() From 5fd9c6a0bb703057d6e09493d00b91a4200f4fdc Mon Sep 17 00:00:00 2001 From: Rahul Patel Date: Wed, 14 Sep 2016 14:46:38 +0530 Subject: [PATCH 373/513] Added test in test_planning.py (#261) Added test for spare_tire problem of planning module. --- tests/test_planning.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tests/test_planning.py b/tests/test_planning.py index e90601a6f..739324256 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -34,3 +34,15 @@ def test_air_cargo(): p.act(action) assert p.goal_test() + +def test_spare_tire(): + p = spare_tire() + assert p.goal_test() is False + solution = [expr("Remove(Flat, Axle)"), + expr("Remove(Spare, Trunk)"), + expr("PutOn(Spare, Axle)")] + + for action in solution: + p.act(action) + + assert p.goal_test() From 62f2fc01147afa3236c7c6b03361f01c02b9d0d9 Mon Sep 17 00:00:00 2001 From: Rahul Patel Date: Thu, 22 Sep 2016 23:00:32 +0530 Subject: [PATCH 374/513] Added implementation of Three Block Tower (#263) In the precondition positive list, I am not sure whether b!=x is the best way to represent the condition. IsNot(b, x) can be used instead. --- planning.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/planning.py b/planning.py index 92f4f773e..c4ebe1181 100644 --- a/planning.py +++ b/planning.py @@ -175,3 +175,38 @@ def goal_test(kb): leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [remove, put_on, leave_overnight], goal_test) + +def three_block_tower(): + init = [expr('On(A, Table)'), + expr('On(B, Table)'), + expr('On(C, A)'), + expr('Block(A)'), + expr('Block(B)'), + expr('Block(C)'), + expr('Clear(B)'), + expr('Clear(C)')] + + def goal_test(kb): + required = [expr('On(A, B)'), expr('On(B, C)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ## Actions + # Move + precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'), + expr('Block(y)'), expr('b != x'), expr('b != y'), expr('x != y')] + precond_neg = [] + effect_add = [expr('On(b, y)'), expr('Clear(x)')] + effect_rem = [expr('On(b, x)'), expr('Clear(y)')] + move = Action(expr('Move(b, x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem]) + + # MoveToTable + precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)'), expr('b != x')] + precond_neg = [] + effect_add = [expr('On(b, Table)'), expr('Clear(x)')] + effect_rem = [expr('On(b, x)')] + moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [move, moveToTable], goal_test) From 4c9ef4eb5201909dd4997d671a6c705092dbabcf Mon Sep 17 00:00:00 2001 From: opensourceware Date: Wed, 28 Sep 2016 02:00:57 +0800 Subject: [PATCH 375/513] Added implementation of the cake problem, tests for cake and three towers problem (#265) * Added implementation of the cake problem * Added test for three_block_tower and fixed a bug in three_block_tower code --- planning.py | 34 ++++++++++++++++++++++++++++++---- tests/test_planning.py | 23 +++++++++++++++++++++++ 2 files changed, 53 insertions(+), 4 deletions(-) diff --git a/planning.py b/planning.py index c4ebe1181..2dd57787a 100644 --- a/planning.py +++ b/planning.py @@ -175,7 +175,7 @@ def goal_test(kb): leave_overnight = Action(expr("LeaveOvernight"), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [remove, put_on, leave_overnight], goal_test) - + def three_block_tower(): init = [expr('On(A, Table)'), expr('On(B, Table)'), @@ -195,18 +195,44 @@ def goal_test(kb): ## Actions # Move - precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'), - expr('Block(y)'), expr('b != x'), expr('b != y'), expr('x != y')] + precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Clear(y)'), expr('Block(b)'), expr('Block(y)')] precond_neg = [] effect_add = [expr('On(b, y)'), expr('Clear(x)')] effect_rem = [expr('On(b, x)'), expr('Clear(y)')] move = Action(expr('Move(b, x, y)'), [precond_pos, precond_neg], [effect_add, effect_rem]) # MoveToTable - precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)'), expr('b != x')] + precond_pos = [expr('On(b, x)'), expr('Clear(b)'), expr('Block(b)')] precond_neg = [] effect_add = [expr('On(b, Table)'), expr('Clear(x)')] effect_rem = [expr('On(b, x)')] moveToTable = Action(expr('MoveToTable(b, x)'), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [move, moveToTable], goal_test) + +def have_cake_and_eat_cake_too(): + init = [expr('Have(Cake)')] + + def goal_test(kb): + required = [expr('Have(Cake)'), expr('Eaten(Cake)')] + for q in required: + if kb.ask(q) is False: + return False + return True + + ##Actions + # Eat cake + precond_pos = [expr('Have(Cake)')] + precond_neg = [] + effect_add = [expr('Eaten(Cake)')] + effect_rem = [expr('Have(Cake)')] + eat_cake = Action(expr('Eat(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem]) + + #Bake Cake + precond_pos = [] + precond_neg = [expr('Have(Cake)')] + effect_add = [expr('Have(Cake)')] + effect_rem = [] + bake_cake = Action(expr('Bake(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem]) + + return PDLL(init, [eat_cake, bake_cake], goal_test) diff --git a/tests/test_planning.py b/tests/test_planning.py index 739324256..3567ab445 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -46,3 +46,26 @@ def test_spare_tire(): p.act(action) assert p.goal_test() + +def test_three_block_tower(): + p = three_block_tower() + assert p.goal_test() is False + solution = [expr("MoveToTable(C, A)"), + expr("Move(B, Table, C)"), + expr("Move(A, Table, B)")] + + for action in solution: + p.act(action) + + assert p.goal_test() + +def test_have_cake_and_eat_cake_too(): + p = have_cake_and_eat_cake_too() + assert p.goal_test() is False + solution = [expr("Eat(Cake)"), + expr("Bake(Cake)")] + + for action in solution: + p.act(action) + + assert p.goal_test() From 9f7f4df8f716a62fc1a2fee4b54efd217b834201 Mon Sep 17 00:00:00 2001 From: Jonathon Belotti Date: Tue, 17 Jan 2017 02:37:23 +1100 Subject: [PATCH 376/513] Adding HITS algorithm to completed table (#277) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 23f32e851..ef56f3655 100644 --- a/README.md +++ b/README.md @@ -114,7 +114,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | | 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | | 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | -| 22.1 | HITS | | | +| 22.1 | HITS | `HITS` | [`nlp.py`](../master/nlp.py) | | 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | | 23.5 | CYK-Parse | `CYK_parse` | [`nlp.py`](../master/nlp.py) | | 25.9 | Monte-Carlo-Localization| | From 0e46096cdd1c87f36a1b8eec24d3c507cc46c04a Mon Sep 17 00:00:00 2001 From: Senthil Kumaran Date: Mon, 23 Jan 2017 13:23:56 -0800 Subject: [PATCH 377/513] Import turn_heading from grids.py since it is used in agents.py (#281) --- agents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.py b/agents.py index cd5f0b865..21dedaa15 100644 --- a/agents.py +++ b/agents.py @@ -35,7 +35,7 @@ # # Speed control in GUI does not have any effect -- fix it. -from grid import distance2 +from grid import distance2, turn_heading from statistics import mean import random From 123571e44b6f81e13d1c2bf3c183cbd8c479a7dc Mon Sep 17 00:00:00 2001 From: Rishabh Agarwal Date: Wed, 1 Mar 2017 09:13:52 +0530 Subject: [PATCH 378/513] Used learning_rate in gradient update for w (#284) --- learning.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/learning.py b/learning.py index 0894b2190..5db41efa5 100644 --- a/learning.py +++ b/learning.py @@ -647,15 +647,14 @@ def Linearlearner(dataset, learning_rate=0.01, epochs=100): err = [] # Pass over all examples for example in examples: - x = [example[i] for i in range(idx_i)] - x = [1] + x + x = [1] + example y = dotproduct(w, x) t = example[idx_t] err.append(t - y) # update weights for i in range(len(w)): - w[i] = w[i] - dotproduct(err, X_col[i]) + w[i] = w[i] - learning_rate * dotproduct(err, X_col[i]) def predict(example): x = [1] + example From fc73e8f01e2ce237d886eab3f0fd71306a339b25 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 2 Mar 2017 00:06:10 -0800 Subject: [PATCH 379/513] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ef56f3655..4e88494f0 100644 --- a/README.md +++ b/README.md @@ -18,11 +18,11 @@ When complete, this project will have Python code for all the pseudocode algorit - `logic.py`: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. - `logic.ipynb`: A Jupyter (IPython) notebook that explains and gives examples of how to use the code. -- `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/). +- `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. # Index of Code -Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. +Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. You can see a [pdf file of all the algorithms](http://aima.cs.berkeley.edu/algorithms.pdf) from the book. | **Figure** | **Name (in 3rd edition)** | **Name (in repository)** | **File** From 1ff10729859c67f24685ad10b8f77f41d82ab5b2 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 2 Mar 2017 00:09:58 -0800 Subject: [PATCH 380/513] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4e88494f0..c6cf16d19 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ When complete, this project will have Python code for all the pseudocode algorit # Index of Code -Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. You can see a [pdf file of all the algorithms](http://aima.cs.berkeley.edu/algorithms.pdf) from the book. +Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The [aima-pseudocode](https://github.com/aimacode/aima-pseudocode) project describes all the algorithms from the book. | **Figure** | **Name (in 3rd edition)** | **Name (in repository)** | **File** From 53ca00316a3f0b2525fa791c11532c479e645983 Mon Sep 17 00:00:00 2001 From: Tarun Kumar Vangani Date: Thu, 2 Mar 2017 16:39:21 +0530 Subject: [PATCH 381/513] added six to pip installs (#297) --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 5af22b933..e6563f0fe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -8,6 +8,7 @@ before_install: - git submodule update --remote install: + - pip install six - pip install flake8 - pip install jupyter - pip install -r requirements.txt From fc287e277ec4c84ffc8a5504bff5d5438b2d161e Mon Sep 17 00:00:00 2001 From: Sampad Kumar Saha Date: Thu, 2 Mar 2017 17:12:16 +0530 Subject: [PATCH 382/513] Typo in bold formatting. (#298) * Corrected the bad bold formatting. * Changed Python version locally. * Reverted back the local environment changes. --- agents.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.ipynb b/agents.ipynb index db42f8d33..7976b12b2 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -8,7 +8,7 @@ "\n", "An agent, as defined in 2.1 is anything that can perceive its environment through sensors, and act upon that environment through actuators based on its agent program. This can be a dog, robot, or even you. As long as you can perceive the environment and act on it, you are an agent. This notebook will explain how to implement a simple agent, create an environment, and create a program that helps the agent act on the environment based on its percepts.\n", "\n", - "Before moving on, review the     Agent     and Environment classes in [agents.py](https://github.com/aimacode/aima-python/blob/master/agents.py).\n", + "Before moving on, review the Agent and Environment classes in [agents.py](https://github.com/aimacode/aima-python/blob/master/agents.py).\n", "\n", "Let's begin by importing all the functions from the agents.py module and creating our first agent - a blind dog." ] From 493fd13ad8d1f392f5512273214b34c5f18b135d Mon Sep 17 00:00:00 2001 From: Yagnesh Date: Thu, 2 Mar 2017 04:28:39 -0800 Subject: [PATCH 383/513] Fixed genetic_algorithm() population iterator (#296) Seems like a typo, results in error: TypeError: 'int' object is not iterable. Fixing it: for i in len(population) -> for i in range(len(population)) --- search.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/search.py b/search.py index 12a723662..2596c4ca7 100644 --- a/search.py +++ b/search.py @@ -584,7 +584,7 @@ def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1): "[Figure 4.8]" for i in range(ngen): new_population = [] - for i in len(population): + for i in range(len(population)): fitnesses = map(fitness_fn, population) p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2) child = p1.mate(p2) From 9054eefdf8fc45726221299ea44364b0b8b96df2 Mon Sep 17 00:00:00 2001 From: Agnishom Chattopadhyay Date: Fri, 3 Mar 2017 09:07:37 +0530 Subject: [PATCH 384/513] ModelBasedReflexAgent should have model. Code updated (#300) --- agents.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/agents.py b/agents.py index 21dedaa15..90ee8a20d 100644 --- a/agents.py +++ b/agents.py @@ -145,10 +145,10 @@ def program(percept): return program -def ModelBasedReflexAgentProgram(rules, update_state): - "This agent takes action based on the percept and state. [Figure 2.12]" +def ModelBasedReflexAgentProgram(rules, update_state, model): + "This agent takes action based on the percept and state. [Figure 2.8]" def program(percept): - program.state = update_state(program.state, program.action, percept) + program.state = update_state(program.state, program.action, percept, model) rule = rule_match(program.state, rules) action = rule.action return action From a6e319246158a85d095ad9ae1a33567ebee5bb62 Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Fri, 3 Mar 2017 05:45:52 +0200 Subject: [PATCH 385/513] Commenting Fixes (#294) * Update search.py Commenting issues fixed (spacing and punctuation was off sometimes). * Update agents.py * Update canvas.py Grammar * Update grid.py * Update learning.py Added period * Update logic.py Fix quoting * Update mdp.py Fixed quoting * Update nlp.py Capitalization and punctuation fixes * Update planning.py * Update probability.py * Update rl.py 'th' to 'the' * Update search.py * Update text.py * Update utils.py * Update utils.py * Update utils.py * Update learning.py Typo * Update utils.py --- agents.py | 20 ++++++++++---------- canvas.py | 4 ++-- grid.py | 2 +- learning.py | 8 ++++---- logic.py | 12 ++++++------ mdp.py | 8 ++++---- nlp.py | 10 +++++----- planning.py | 16 ++++++++-------- probability.py | 18 +++++++++--------- rl.py | 8 ++++---- search.py | 49 +++++++++++++++++++++++++------------------------ text.py | 20 ++++++++++---------- utils.py | 37 ++++++++++++++++++------------------- 13 files changed, 106 insertions(+), 106 deletions(-) diff --git a/agents.py b/agents.py index 90ee8a20d..8ef811978 100644 --- a/agents.py +++ b/agents.py @@ -329,7 +329,7 @@ class Direction(): To change directions: d = d + "right" or d = d + Direction.R #Both do the same thing Note that the argument to __add__ must be a string and not a Direction object. - Also, it (the argument) can only be right or left. ''' + Also, it (the argument) can only be right or left.''' R = "right" L = "left" @@ -428,8 +428,8 @@ def default_location(self, thing): return (random.choice(self.width), random.choice(self.height)) def move_to(self, thing, destination): - '''Move a thing to a new location. Returns True on success or False if there is an Obstacle - If thing is grabbing anything, they move with him ''' + '''Move a thing to a new location. Returns True on success or False if there is an Obstacle. + If thing is holding anything, they move with him.''' thing.bump = self.some_things_at(destination, Obstacle) if not thing.bump: thing.location = destination @@ -451,7 +451,7 @@ def move_to(self, thing, destination): def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): '''Adds things to the world. If (exclude_duplicate_class_items) then the item won't be added if the location - has at least one item of the same class''' + has at least one item of the same class.''' if (self.is_inbounds(location)): if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): @@ -526,7 +526,7 @@ class Wall(Obstacle): # Continuous environment class ContinuousWorld(Environment): - """ Model for Continuous World. """ + """ Model for Continuous World.""" def __init__(self, width=10, height=10): super(ContinuousWorld, self).__init__() self.width = width @@ -538,7 +538,7 @@ def add_obstacle(self, coordinates): class PolygonObstacle(Obstacle): def __init__(self, coordinates): - """ Coordinates is a list of tuples. """ + """ Coordinates is a list of tuples.""" super(PolygonObstacle, self).__init__() self.coordinates = coordinates @@ -715,7 +715,7 @@ def init_world(self, program): self.add_thing(Explorer(program), (1, 1), True) def get_world(self, show_walls=True): - '''returns the items in the world''' + '''Returns the items in the world''' result = [] x_start, y_start = (0, 0) if show_walls else (1, 1) x_end, y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) @@ -765,8 +765,8 @@ def percept(self, agent): return result def execute_action(self, agent, action): - '''Modify the state of the environment based on the agent's actions - Performance score taken directly out of the book''' + '''Modify the state of the environment based on the agent's actions. + Performance score taken directly out of the book.''' if isinstance(agent, Explorer) and self.in_danger(agent): return @@ -818,7 +818,7 @@ def in_danger(self, agent): def is_done(self): '''The game is over when the Explorer is killed - or if he climbs out of the cave only at (1,1)''' + or if he climbs out of the cave only at (1,1).''' explorer = [agent for agent in self.agents if isinstance(agent, Explorer) ] if len(explorer): if explorer[0].alive: diff --git a/canvas.py b/canvas.py index 4ad780380..213e38cc9 100644 --- a/canvas.py +++ b/canvas.py @@ -12,8 +12,8 @@ class Canvas: """Inherit from this class to manage the HTML canvas element in jupyter notebooks. To create an object of this class any_name_xyz = Canvas("any_name_xyz") - The first argument given must be the name of the object being create - IPython must be able to refernce the variable name that is being passed + The first argument given must be the name of the object being created. + IPython must be able to refernce the variable name that is being passed. """ def __init__(self, varname, id=None, width=800, height=600): diff --git a/grid.py b/grid.py index 0fb0efe9d..4400d217b 100644 --- a/grid.py +++ b/grid.py @@ -1,6 +1,6 @@ # OK, the following are not as widely useful utilities as some of the other # functions here, but they do show up wherever we have 2D grids: Wumpus and -# Vacuum worlds, TicTacToe and Checkers, and markov decision Processes. +# Vacuum worlds, TicTacToe and Checkers, and Markov Decision Processes. # __________________________________________________________________________ import math diff --git a/learning.py b/learning.py index 5db41efa5..ce8871300 100644 --- a/learning.py +++ b/learning.py @@ -499,9 +499,9 @@ def __init__(self, weights=None, inputs=None): def network(input_units, hidden_layer_sizes, output_units): """ - Create of Directed Acyclic Network of given number layers + Create Directed Acyclic Network of given number layers. hidden_layers_sizes : list number of neuron units in each hidden layer - excluding input and output layers. + excluding input and output layers """ # Check for PerceptronLearner if hidden_layer_sizes: @@ -523,7 +523,7 @@ def network(input_units, hidden_layer_sizes, output_units): def BackPropagationLearner(dataset, net, learning_rate, epoches): - "[Figure 18.23] The back-propagation algorithm for multilayer network" + """[Figure 18.23] The back-propagation algorithm for multilayer network""" # Initialise weights for layer in net: for node in layer: @@ -826,7 +826,7 @@ def cross_validation_wrapper(learner, dataset, k=10, trials=1): """ Fig 18.8 Return the optimal value of size having minimum error - on validataion set + on validataion set. err_train: a training error array, indexed by size err_val: a validataion error array, indexed by size """ diff --git a/logic.py b/logic.py index 8b5e8bf8e..338e5aac2 100644 --- a/logic.py +++ b/logic.py @@ -670,7 +670,7 @@ def sat_count(sym): class HybridWumpusAgent(agents.Agent): - "An agent for the wumpus world that does logical inference. [Figure 7.20]""" + """An agent for the wumpus world that does logical inference. [Figure 7.20]""" def __init__(self): raise NotImplementedError @@ -789,7 +789,7 @@ def unify(x, y, s): def is_variable(x): - "A variable is an Expr with no args and a lowercase symbol as the op." + """A variable is an Expr with no args and a lowercase symbol as the op.""" return isinstance(x, Expr) and not x.args and x.op[0].islower() @@ -819,7 +819,7 @@ def occur_check(var, x, s): def extend(s, var, val): - "Copy the substitution s and extend it by setting var to val; return copy." + """Copy the substitution s and extend it by setting var to val; return copy.""" s2 = s.copy() s2[var] = val return s2 @@ -932,7 +932,7 @@ def fetch_rules_for_goal(self, goal): def fol_bc_ask(KB, query): """A simple backward-chaining algorithm for first-order logic. [Figure 9.6] - KB should be an instance of FolKB, and query an atomic sentence. """ + KB should be an instance of FolKB, and query an atomic sentence.""" return fol_bc_or(KB, query, {}) @@ -995,7 +995,7 @@ def diff(y, x): def simp(x): - "Simplify the expression x." + """Simplify the expression x.""" if isnumber(x) or not x.args: return x args = list(map(simp, x.args)) @@ -1058,5 +1058,5 @@ def simp(x): def d(y, x): - "Differentiate and then simplify." + """Differentiate and then simplify.""" return simp(diff(y, x)) diff --git a/mdp.py b/mdp.py index 8b0714da9..2854d0616 100644 --- a/mdp.py +++ b/mdp.py @@ -80,7 +80,7 @@ def T(self, state, action): (0.1, self.go(state, turn_left(action)))] def go(self, state, direction): - "Return the state that results from going in this direction." + """Return the state that results from going in this direction.""" state1 = vector_add(state, direction) return state1 if state1 in self.states else state @@ -110,7 +110,7 @@ def to_arrows(self, policy): def value_iteration(mdp, epsilon=0.001): - "Solving an MDP by value iteration. [Figure 17.4]" + """Solving an MDP by value iteration. [Figure 17.4]""" U1 = {s: 0 for s in mdp.states} R, T, gamma = mdp.R, mdp.T, mdp.gamma while True: @@ -134,14 +134,14 @@ def best_policy(mdp, U): def expected_utility(a, s, U, mdp): - "The expected utility of doing a in state s, according to the MDP and U." + """The expected utility of doing a in state s, according to the MDP and U.""" return sum([p * U[s1] for (p, s1) in mdp.T(s, a)]) # ______________________________________________________________________________ def policy_iteration(mdp): - "Solve an MDP by policy iteration [Figure 17.7]" + """Solve an MDP by policy iteration [Figure 17.7]""" U = {s: 0 for s in mdp.states} pi = {s: random.choice(mdp.actions(s)) for s in mdp.states} while True: diff --git a/nlp.py b/nlp.py index 7273b98da..3c95e961d 100644 --- a/nlp.py +++ b/nlp.py @@ -34,7 +34,7 @@ def Lexicon(**rules): class Grammar: def __init__(self, name, rules, lexicon): - "A grammar has a set of rules and a lexicon." + """A grammar has a set of rules and a lexicon.""" self.name = name self.rules = rules self.lexicon = lexicon @@ -44,11 +44,11 @@ def __init__(self, name, rules, lexicon): self.categories[word].append(lhs) def rewrites_for(self, cat): - "Return a sequence of possible rhs's that cat can be rewritten as." + """Return a sequence of possible rhs's that cat can be rewritten as.""" return self.rules.get(cat, ()) def isa(self, word, cat): - "Return True iff word is of category cat" + """Return True iff word is of category cat""" return cat in self.categories[word] def __repr__(self): @@ -293,8 +293,8 @@ def expand_pages( pages ): return expanded def relevant_pages(query): - """relevant pages are pages that contain the query in its entireity. - If a page's content contains the query it is returned by the function""" + """Relevant pages are pages that contain the query in its entireity. + If a page's content contains the query it is returned by the function.""" relevant = {} print("pagesContent in function: ", pagesContent) for addr, page in pagesIndex.items(): diff --git a/planning.py b/planning.py index 2dd57787a..3899c4534 100644 --- a/planning.py +++ b/planning.py @@ -7,9 +7,9 @@ class PDLL: """ - PDLL used to define a search problem - It stores states in a knowledge base consisting of first order logic statements - The conjunction of these logical statements completely define a state + PDLL used to define a search problem. + It stores states in a knowledge base consisting of first order logic statements. + The conjunction of these logical statements completely defines a state. """ def __init__(self, initial_state, actions, goal_test): @@ -22,7 +22,7 @@ def goal_test(self): def act(self, action): """ - Performs the action given as argument + Performs the action given as argument. Note that action is an Expr like expr('Remove(Glass, Table)') or expr('Eat(Sandwich)') """ action_name = action.op @@ -36,10 +36,10 @@ def act(self, action): class Action: """ - Defines an action schema using preconditions and effects - Use this to describe actions in PDDL - action is an Expr where variables are given as arguments(args) - Precondition and effect are both lists with positive and negated literals + Defines an action schema using preconditions and effects. + Use this to describe actions in PDDL. + action is an Expr where variables are given as arguments(args). + Precondition and effect are both lists with positive and negated literals. Example: precond_pos = [expr("Human(person)"), expr("Hungry(Person)")] precond_neg = [expr("Eaten(food)")] diff --git a/probability.py b/probability.py index ed3aa5243..8a7fc4779 100644 --- a/probability.py +++ b/probability.py @@ -357,7 +357,7 @@ def pointwise_product(factors, bn): def sum_out(var, factors, bn): - "Eliminate var from all factors by summing over its values." + """Eliminate var from all factors by summing over its values.""" result, var_factors = [], [] for f in factors: (var_factors if var in f.variables else result).append(f) @@ -367,21 +367,21 @@ def sum_out(var, factors, bn): class Factor: - "A factor in a joint distribution." + """A factor in a joint distribution.""" def __init__(self, variables, cpt): self.variables = variables self.cpt = cpt def pointwise_product(self, other, bn): - "Multiply two factors, combining their variables." + """Multiply two factors, combining their variables.""" variables = list(set(self.variables) | set(other.variables)) cpt = {event_values(e, variables): self.p(e) * other.p(e) for e in all_events(variables, bn, {})} return Factor(variables, cpt) def sum_out(self, var, bn): - "Make a factor eliminating var by summing over its values." + """Make a factor eliminating var by summing over its values.""" variables = [X for X in self.variables if X != var] cpt = {event_values(e, variables): sum(self.p(extend(e, var, val)) for val in bn.variable_values(var)) @@ -389,18 +389,18 @@ def sum_out(self, var, bn): return Factor(variables, cpt) def normalize(self): - "Return my probabilities; must be down to one variable." + """Return my probabilities; must be down to one variable.""" assert len(self.variables) == 1 return ProbDist(self.variables[0], {k: v for ((k,), v) in self.cpt.items()}) def p(self, e): - "Look up my value tabulated for e." + """Look up my value tabulated for e.""" return self.cpt[event_values(e, self.variables)] def all_events(variables, bn, e): - "Yield every way of extending e with values for all variables." + """Yield every way of extending e with values for all variables.""" if not variables: yield e else: @@ -453,7 +453,7 @@ def rejection_sampling(X, e, bn, N): def consistent_with(event, evidence): - "Is event consistent with the given evidence?" + """Is event consistent with the given evidence?""" return all(evidence.get(k, v) == v for k, v in event.items()) @@ -527,7 +527,7 @@ def markov_blanket_sample(X, e, bn): class HiddenMarkovModel: - """ A Hidden markov model which takes Transition model and Sensor model as inputs""" + """A Hidden markov model which takes Transition model and Sensor model as inputs""" def __init__(self, transition_model, sensor_model, prior=[0.5, 0.5]): self.transition_model = transition_model diff --git a/rl.py b/rl.py index 97bb313a0..5241710fe 100644 --- a/rl.py +++ b/rl.py @@ -24,7 +24,7 @@ def __init__(self, init, actlist, terminals, gamma, states): def T(self, s, a): """Returns a list of tuples with probabilities for states - based on the learnt model P. """ + based on the learnt model P.""" return [(prob, res) for (res, prob) in self.P[(s, a)].items()] def __init__(self, pi, mdp): @@ -62,7 +62,7 @@ def __call__(self, percept): def update_state(self, percept): ''' To be overridden in most cases. The default case - assumes th percept to be of type (state, reward)''' + assumes the percept to be of type (state, reward)''' return percept @@ -70,7 +70,7 @@ class PassiveTDAgent: """The abstract class for a Passive (non-learning) agent that uses temporal differences to learn utility estimates. Override update_state method to convert percept to state and reward. The mdp being provided - should be an instance of a subclass of the MDP Class.[Figure 21.4] + should be an instance of a subclass of the MDP Class. [Figure 21.4] """ def __init__(self, pi, mdp, alpha=None): @@ -106,7 +106,7 @@ def __call__(self, percept): def update_state(self, percept): ''' To be overridden in most cases. The default case - assumes th percept to be of type (state, reward)''' + assumes the percept to be of type (state, reward)''' return percept diff --git a/search.py b/search.py index 2596c4ca7..3bc9c5412 100644 --- a/search.py +++ b/search.py @@ -389,13 +389,14 @@ def simulated_annealing(problem, schedule=exp_schedule()): def and_or_graph_search(problem): - """Used when the environment is nondeterministic and completely observable - Contains OR nodes where the agent is free to choose any action + """Used when the environment is nondeterministic and completely observable. + Contains OR nodes where the agent is free to choose any action. After every action there is an AND node which contains all possible states - the agent may reach due to stochastic nature of environment - The agent must be able to handle all possible states of the AND node(as it - may end up in any of them) returns a conditional plan to reach goal state, - or failure if the former is not possible""" + the agent may reach due to stochastic nature of environment. + The agent must be able to handle all possible states of the AND node (as it + may end up in any of them). + Returns a conditional plan to reach goal state, + or failure if the former is not possible.""" "[Figure 4.11]" # functions used by and_or_search @@ -411,7 +412,7 @@ def or_search(state, problem, path): return [action, plan] def and_search(states, problem, path): - "returns plan in form of dictionary where we take action plan[s] if we reach state s" # noqa + "Returns plan in form of dictionary where we take action plan[s] if we reach state s." # noqa plan = {} for s in states: plan[s] = or_search(s, problem, path) @@ -497,7 +498,7 @@ def h(self, state): def c(self, s, a, s1): """ - Returns a cost estimate for an agent to move from state 's' to state 's1' + Returns a cost estimate for an agent to move from state 's' to state 's1'. """ return 1 @@ -516,7 +517,7 @@ class LRTAStarAgent: Abstract class for LRTA*-Agent. A problem needs to be provided which is an instanace of a subclass of Problem Class. - Takes a OnlineSearchProblem [Figure 4.23] as a problem + Takes a OnlineSearchProblem [Figure 4.23] as a problem. """ def __init__(self, problem): @@ -552,7 +553,7 @@ def __call__(self, s1): # as of now s1 is a state rather than a percept def LRTA_cost(self, s, a, s1, H): """ Returns cost to move from state 's' to state 's1' plus - estimated cost to get to goal from s1 + estimated cost to get to goal from s1. """ print(s, a, s1) if s1 is None: @@ -788,25 +789,25 @@ def distance_to_node(n): class GraphProblem(Problem): - "The problem of searching a graph from one node to another." + """The problem of searching a graph from one node to another.""" def __init__(self, initial, goal, graph): Problem.__init__(self, initial, goal) self.graph = graph def actions(self, A): - "The actions at a graph node are just its neighbors." + """The actions at a graph node are just its neighbors.""" return list(self.graph.get(A).keys()) def result(self, state, action): - "The result of going to a neighbor is just that neighbor." + """The result of going to a neighbor is just that neighbor.""" return action def path_cost(self, cost_so_far, A, action, B): return cost_so_far + (self.graph.get(A, B) or infinity) def h(self, node): - "h function is straight-line distance from a node's state to goal." + """h function is straight-line distance from a node's state to goal.""" locs = getattr(self.graph, 'locations', None) if locs: return int(distance(locs[node.state], locs[self.goal])) @@ -817,10 +818,10 @@ def h(self, node): class GraphProblemStochastic(GraphProblem): """ A version of GraphProblem where an action can lead to - nondeterministic output i.e. multiple possible states + nondeterministic output i.e. multiple possible states. Define the graph as dict(A = dict(Action = [[, , ...], ], ...), ...) - A the dictionary format is different, make sure the graph is created as a directed graph + A the dictionary format is different, make sure the graph is created as a directed graph. """ def result(self, state, action): @@ -849,7 +850,7 @@ def __init__(self, N): self.initial = [None] * N def actions(self, state): - "In the leftmost empty column, try all non-conflicting rows." + """In the leftmost empty column, try all non-conflicting rows.""" if state[-1] is not None: return [] # All columns filled; no successors else: @@ -858,26 +859,26 @@ def actions(self, state): if not self.conflicted(state, row, col)] def result(self, state, row): - "Place the next queen at the given row." + """Place the next queen at the given row.""" col = state.index(None) new = state[:] new[col] = row return new def conflicted(self, state, row, col): - "Would placing a queen at (row, col) conflict with anything?" + """Would placing a queen at (row, col) conflict with anything?""" return any(self.conflict(row, col, state[c], c) for c in range(col)) def conflict(self, row1, col1, row2, col2): - "Would putting two queens in (row1, col1) and (row2, col2) conflict?" + """Would putting two queens in (row1, col1) and (row2, col2) conflict?""" return (row1 == row2 or # same row col1 == col2 or # same column row1 - col1 == row2 - col2 or # same \ diagonal row1 + col1 == row2 + col2) # same / diagonal def goal_test(self, state): - "Check if all columns filled, no conflicts." + """Check if all columns filled, no conflicts.""" if state[-1] is None: return False return not any(self.conflicted(state, state[col], col) @@ -909,7 +910,7 @@ def random_boggle(n=4): def print_boggle(board): - "Print the board in a 2-d array." + """Print the board in a 2-d array.""" n2 = len(board) n = exact_sqrt(n2) for i in range(n2): @@ -957,7 +958,7 @@ def boggle_neighbors(n2, cache={}): def exact_sqrt(n2): - "If n2 is a perfect square, return its square root, else raise error." + """If n2 is a perfect square, return its square root, else raise error.""" n = int(math.sqrt(n2)) assert n * n == n2 return n @@ -1006,7 +1007,7 @@ def __len__(self): class BoggleFinder: - """A class that allows you to find all the words in a Boggle board. """ + """A class that allows you to find all the words in a Boggle board.""" wordlist = None # A class variable, holding a wordlist diff --git a/text.py b/text.py index 57a19d2ab..855e89aaf 100644 --- a/text.py +++ b/text.py @@ -153,17 +153,17 @@ def query(self, query_text, n=10): return heapq.nlargest(n, ((self.total_score(qwords, docid), docid) for docid in docids)) def score(self, word, docid): - "Compute a score for this word on the document with this docid." + """Compute a score for this word on the document with this docid.""" # There are many options; here we take a very simple approach return (log(1 + self.index[word][docid]) / log(1 + self.documents[docid].nwords)) def total_score(self, words, docid): - "Compute the sum of the scores of these words on the document with this docid." + """Compute the sum of the scores of these words on the document with this docid.""" return sum(self.score(word, docid) for word in words) def present(self, results): - "Present the results as a list." + """Present the results as a list.""" for (score, docid) in results: doc = self.documents[docid] print( @@ -171,7 +171,7 @@ def present(self, results): doc.title[:45].expandtabs()))) def present_results(self, query_text, n=10): - "Get results for the query and present them." + """Get results for the query and present them.""" self.present(self.query(query_text, n)) @@ -264,7 +264,7 @@ def maketrans(from_, to_): def encode(plaintext, code): - "Encodes text, using a code which is a permutation of the alphabet." + """Encodes text, using a code which is a permutation of the alphabet.""" trans = maketrans(alphabet + alphabet.upper(), code + code.upper()) return translate(plaintext, trans) @@ -293,7 +293,7 @@ def __init__(self, training_text): self.P2 = CountingProbDist(bigrams(training_text), default=1) def score(self, plaintext): - "Return a score for text based on how common letters pairs are." + """Return a score for text based on how common letters pairs are.""" s = 1.0 for bi in bigrams(plaintext): @@ -302,7 +302,7 @@ def score(self, plaintext): return s def decode(self, ciphertext): - "Return the shift decoding of text with the best score." + """Return the shift decoding of text with the best score.""" list_ = [(self.score(shift), shift) for shift in all_shifts(ciphertext)] @@ -310,7 +310,7 @@ def decode(self, ciphertext): def all_shifts(text): - "Return a list of all 26 possible encodings of text by a shift cipher." + """Return a list of all 26 possible encodings of text by a shift cipher.""" yield from (shift_encode(text, i) for i, _ in enumerate(alphabet)) @@ -339,7 +339,7 @@ def __init__(self, training_text, ciphertext=None): self.P2 = NgramTextModel(2, training_text) # By letter pair def decode(self, ciphertext): - "Search for a decoding of the ciphertext." + """Search for a decoding of the ciphertext.""" self.ciphertext = ciphertext problem = PermutationDecoderProblem(decoder=self) return search.best_first_tree_search( @@ -368,5 +368,5 @@ def actions(self, state): succs = [extend(state, plainchar, cipherchar)] # ???? # noqa def goal_test(self, state): - "We're done when we get all 26 letters assigned." + """We're done when we get all 26 letters assigned.""" return len(state) >= 26 diff --git a/utils.py b/utils.py index 4ef7e0c08..a6c5d0bd5 100644 --- a/utils.py +++ b/utils.py @@ -14,7 +14,7 @@ def sequence(iterable): - "Coerce iterable to sequence, if it is not already one." + """Coerce iterable to sequence, if it is not already one.""" return (iterable if isinstance(iterable, collections.abc.Sequence) else tuple(iterable)) @@ -46,7 +46,7 @@ def product(numbers): def first(iterable, default=None): - "Return the first element of an iterable or the next element of a generator; or default." + """Return the first element of an iterable or the next element of a generator; or default.""" try: return iterable[0] except IndexError: @@ -74,12 +74,12 @@ def argmin_random_tie(seq, key=identity): def argmax_random_tie(seq, key=identity): - "Return an element with highest fn(seq[i]) score; break ties at random." + """Return an element with highest fn(seq[i]) score; break ties at random.""" return argmax(shuffled(seq), key=key) def shuffled(iterable): - "Randomly shuffle a copy of iterable." + """Randomly shuffle a copy of iterable.""" items = list(iterable) random.shuffle(items) return items @@ -184,7 +184,7 @@ def inverse_matrix(X): def probability(p): - "Return true with probability p." + """Return true with probability p.""" return p > random.uniform(0.0, 1.0) @@ -198,7 +198,7 @@ def weighted_sample_with_replacement(seq, weights, n): def weighted_sampler(seq, weights): - "Return a random-sample function that picks from seq weighted by weights." + """Return a random-sample function that picks from seq weighted by weights.""" totals = [] for w in weights: totals.append(w + totals[-1] if totals else w) @@ -207,7 +207,7 @@ def weighted_sampler(seq, weights): def rounder(numbers, d=4): - "Round a single number, or sequence of numbers, to d decimal places." + """Round a single number, or sequence of numbers, to d decimal places.""" if isinstance(numbers, (int, float)): return round(numbers, d) else: @@ -217,8 +217,7 @@ def rounder(numbers, d=4): def num_or_str(x): """The argument is a string; convert to a number if - possible, or strip it. - """ + possible, or strip it.""" try: return int(x) except ValueError: @@ -258,7 +257,7 @@ def step(x): from math import isclose except ImportError: def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): - "Return true if numbers a and b are close to each other." + """Return true if numbers a and b are close to each other.""" return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) # ______________________________________________________________________________ @@ -292,19 +291,19 @@ def memoized_fn(*args): def name(obj): - "Try to find some reasonable name for the object." + """Try to find some reasonable name for the object.""" return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) or getattr(getattr(obj, '__class__', 0), '__name__', 0) or str(obj)) def isnumber(x): - "Is x a number?" + """Is x a number?""" return hasattr(x, '__int__') def issequence(x): - "Is x a sequence?" + """Is x a sequence?""" return isinstance(x, collections.abc.Sequence) @@ -332,7 +331,7 @@ def print_table(table, header=None, sep=' ', numfmt='%g'): def AIMAFile(components, mode='r'): - "Open a file based at the AIMA root directory." + """Open a file based at the AIMA root directory.""" aima_root = os.path.dirname(__file__) aima_file = os.path.join(aima_root, *components) @@ -379,7 +378,7 @@ def __floordiv__(self, rhs): return Expr('//', self, rhs) def __matmul__(self, rhs): return Expr('@', self, rhs) def __or__(self, rhs): - "Allow both P | Q, and P |'==>'| Q." + """Allow both P | Q, and P |'==>'| Q.""" if isinstance(rhs, Expression): return Expr('|', self, rhs) else: @@ -436,17 +435,17 @@ def __repr__(self): def Symbol(name): - "A Symbol is just an Expr with no args." + """A Symbol is just an Expr with no args.""" return Expr(name) def symbols(names): - "Return a tuple of Symbols; names is a comma/whitespace delimited str." + """Return a tuple of Symbols; names is a comma/whitespace delimited str.""" return tuple(Symbol(name) for name in names.replace(',', ' ').split()) def subexpressions(x): - "Yield the subexpressions of an Expression (including x itself)." + """Yield the subexpressions of an Expression (including x itself).""" yield x if isinstance(x, Expr): for arg in x.args: @@ -454,7 +453,7 @@ def subexpressions(x): def arity(expression): - "The number of sub-expressions in this expression." + """The number of sub-expressions in this expression.""" if isinstance(expression, Expr): return len(expression.args) else: # expression is a number From 93e7fdc6d5953578fd3469a6ce93314484dd1310 Mon Sep 17 00:00:00 2001 From: Jacob Kalakal Joseph Date: Thu, 2 Mar 2017 19:47:01 -0800 Subject: [PATCH 386/513] Corrects a typo in tests/test_games.py (#278) --- tests/test_games.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_games.py b/tests/test_games.py index fc8733dc9..28644fbc5 100644 --- a/tests/test_games.py +++ b/tests/test_games.py @@ -1,6 +1,6 @@ """A lightweight test suite for games.py""" -# You can run this test suite by doing: py.test tests/games.py +# You can run this test suite by doing: py.test tests/test_games.py # Of course you need to have py.test installed to do this. import pytest From 38e30019b9d7b6ed5450b2341028c91accd9e986 Mon Sep 17 00:00:00 2001 From: Manpreet Kaur Date: Thu, 2 Mar 2017 23:13:01 -0500 Subject: [PATCH 387/513] GraphPlan Algorithm (#274) --- planning.py | 292 +++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 291 insertions(+), 1 deletion(-) diff --git a/planning.py b/planning.py index 3899c4534..9d3c01bff 100644 --- a/planning.py +++ b/planning.py @@ -1,10 +1,10 @@ """Planning (Chapters 10-11) """ +import itertools from utils import Expr, expr, first from logic import FolKB - class PDLL: """ PDLL used to define a search problem. @@ -236,3 +236,293 @@ def goal_test(kb): bake_cake = Action(expr('Bake(Cake)'), [precond_pos, precond_neg], [effect_add, effect_rem]) return PDLL(init, [eat_cake, bake_cake], goal_test) + +class Level(): + """ + Contains the state of the planning problem + and exhaustive list of actions which use the + states as pre-condition. + """ + + def __init__(self, poskb, negkb): + self.poskb = poskb + #Current state + self.current_state_pos = poskb.clauses + self.current_state_neg = negkb.clauses + #Current action to current state link + self.current_action_links_pos = {} + self.current_action_links_neg = {} + #Current state to action link + self.current_state_links_pos = {} + self.current_state_links_neg = {} + #Current action to next state link + self.next_action_links = {} + #Next state to current action link + self.next_state_links_pos = {} + self.next_state_links_neg = {} + self.mutex = [] + + + def __call__(self, actions, objects): + self.build(actions, objects) + self.find_mutex() + + + def find_mutex(self): + #Inconsistent effects + for poseff in self.next_state_links_pos: + #negeff = Expr('not'+poseff.op, poseff.args) + negeff = poseff + if negeff in self.next_state_links_neg: + for a in self.next_state_links_pos[poseff]: + for b in self.next_state_links_neg[negeff]: + if set([a,b]) not in self.mutex: + self.mutex.append(set([a,b])) + + #Interference + for posprecond in self.current_state_links_pos: + #negeff = Expr('not'+posprecond.op, posprecond.args) + negeff = posprecond + if negeff in self.next_state_links_neg: + for a in self.current_state_links_pos[posprecond]: + for b in self.next_state_links_neg[negeff]: + if set([a,b]) not in self.mutex: + self.mutex.append(set([a,b])) + + for negprecond in self.current_state_links_neg: + #poseff = Expr(negprecond.op[3:], negprecond.args) + poseff = negprecond + if poseff in self.next_state_links_pos: + for a in self.next_state_links_pos[poseff]: + for b in self.current_state_links_neg[negprecond]: + if set([a,b]) not in self.mutex: + self.mutex.append(set([a,b])) + + #Competing needs + for posprecond in self.current_state_links_pos: + #negprecond = Expr('not'+posprecond.op, posprecond.args) + negprecond = posprecond + if negprecond in self.current_state_links_neg: + for a in self.current_state_links_pos[posprecond]: + for b in self.current_state_links_neg[negprecond]: + if set([a,b]) not in self.mutex: + self.mutex.append(set([a,b])) + + #Inconsistent support + state_mutex = [] + for pair in self.mutex: + next_state_0 = self.next_action_links[list(pair)[0]] + if len(pair) == 2: + next_state_1 = self.next_action_links[list(pair)[1]] + else: + next_state_1 = self.next_action_links[list(pair)[0]] + if (len(next_state_0) == 1) and (len(next_state_1) == 1): + state_mutex.append(set([next_state_0[0], next_state_1[0]])) + + self.mutex = self.mutex+state_mutex + + + def build(self, actions, objects): + + #Add persistence actions for positive states + for clause in self.current_state_pos: + self.current_action_links_pos[Expr('Persistence', clause)] = [clause] + self.next_action_links[Expr('Persistence', clause)] = [clause] + self.current_state_links_pos[clause] = [Expr('Persistence', clause)] + self.next_state_links_pos[clause] = [Expr('Persistence', clause)] + + #Add persistence actions for negative states + for clause in self.current_state_neg: + self.current_action_links_neg[Expr('Persistence', Expr('not'+clause.op, clause.args))] = [clause] + self.next_action_links[Expr('Persistence', Expr('not'+clause.op, clause.args))] = [clause] + self.current_state_links_neg[clause] = [Expr('Persistence', Expr('not'+clause.op, clause.args))] + self.next_state_links_neg[clause] = [Expr('Persistence', Expr('not'+clause.op, clause.args))] + + for a in actions: + num_args = len(a.args) + possible_args = tuple(itertools.permutations(objects, num_args)) + + for arg in possible_args: + if a.check_precond(self.poskb, arg): + for num, symbol in enumerate(a.args): + if not symbol.op.islower(): + arg = list(arg) + arg[num] = symbol + arg = tuple(arg) + + new_action = a.substitute(Expr(a.name, *a.args), arg) + self.current_action_links_pos[new_action] = [] + self.current_action_links_neg[new_action] = [] + + for clause in a.precond_pos: + new_clause = a.substitute(clause, arg) + self.current_action_links_pos[new_action].append(new_clause) + if new_clause in self.current_state_links_pos: + self.current_state_links_pos[new_clause].append(new_action) + else: + self.current_state_links_pos[new_clause] = [new_action] + + for clause in a.precond_neg: + new_clause = a.substitute(clause, arg) + #new_clause = Expr('not'+new_clause.op, new_clause.arg) + self.current_action_links_neg[new_action].append(new_clause) + if new_clause in self.current_state_links_neg: + self.current_state_links_neg[new_clause].append(new_action) + else: + self.current_state_links_neg[new_clause] = [new_action] + + self.next_action_links[new_action] = [] + for clause in a.effect_add: + new_clause = a.substitute(clause, arg) + self.next_action_links[new_action].append(new_clause) + if new_clause in self.next_state_links_pos: + self.next_state_links_pos[new_clause].append(new_action) + else: + self.next_state_links_pos[new_clause] = [new_action] + + for clause in a.effect_rem: + new_clause = a.substitute(clause, arg) + self.next_action_links[new_action].append(new_clause) + if new_clause in self.next_state_links_neg: + self.next_state_links_neg[new_clause].append(new_action) + else: + self.next_state_links_neg[new_clause] = [new_action] + + + def perform_actions(self): + new_kb_pos, new_kb_neg = FolKB(list(set(self.next_state_links_pos.keys()))), FolKB(list(set(self.next_state_links_neg.keys()))) + return Level(new_kb_pos, new_kb_neg) + + +class Graph: + """ + Contains levels of state and actions + Used in graph planning algorithm to extract a solution + """ + + def __init__(self, pdll, negkb): + self.pdll = pdll + self.levels = [Level(pdll.kb, negkb)] + self.objects = set(arg for clause in pdll.kb.clauses + negkb.clauses for arg in clause.args) + + def __call__(): + expand_graph() + + def expand_graph(self): + last_level = self.levels[-1] + last_level(self.pdll.actions, self.objects) + self.levels.append(last_level.perform_actions()) + + def non_mutex_goals(self, goals, index): + goal_perm = itertools.combinations(goals, 2) + for g in goal_perm: + if set(g) in self.levels[index].mutex: + return False + return True + + +class GraphPlan: + """ + Class for formulation GraphPlan algorithm + Constructs a graph of state and action space + Returns solution for the planning problem + """ + + def __init__(self, pdll, negkb): + self.graph = Graph(pdll, negkb) + self.nogoods = [] + self.solution = [] + + def check_leveloff(self): + if (set(self.graph.levels[-1].current_state_pos) == set(self.graph.levels[-2].current_state_pos)) and (set(lf.graph.levels[-1].current_state_neg) == set(self.graph.levels[-2].current_state_neg)): + return True + + def extract_solution(self, goals_pos, goals_neg, index): + level = self.graph.levels[index] + if not self.graph.non_mutex_goals(goals_pos+goals_neg, index): + self.nogoods.append((level, goals_pos, goals_neg)) + return + + level = self.graph.levels[index-1] + + #Create all combinations of actions that satisfy the goal + actions = [] + for goal in goals_pos: + actions.append(level.next_state_links_pos[goal]) + + for goal in goals_neg: + actions.append(level.next_state_links_neg[goal]) + + all_actions = list(itertools.product(*actions)) + + #Filter out the action combinations which contain mutexes + non_mutex_actions = [] + for action_tuple in all_actions: + action_pairs = itertools.combinations(list(set(action_tuple)), 2) + non_mutex_actions.append(list(set(action_tuple))) + for pair in action_pairs: + if set(pair) in level.mutex: + non_mutex_actions.pop(-1) + break + + #Recursion + for action_list in non_mutex_actions: + if [action_list, index] not in self.solution: + self.solution.append([action_list, index]) + + new_goals_pos = [] + new_goals_neg = [] + for act in set(action_list): + if act in level.current_action_links_pos: + new_goals_pos = new_goals_pos + level.current_action_links_pos[act] + + for act in set(action_list): + if act in level.current_action_links_neg: + new_goals_neg = new_goals_neg + level.current_action_links_neg[act] + + if abs(index)+1 == len(self.graph.levels): + return + elif (level, new_goals_pos, new_goals_neg) in self.nogoods: + return + else: + self.extract_solution(new_goals_pos, new_goals_neg, index-1) + + #Level-Order multiple solutions + solution = [] + for item in self.solution: + if item[1] == -1: + solution.append([]) + solution[-1].append(item[0]) + else: + solution[-1].append(item[0]) + + for num, item in enumerate(solution): + item.reverse() + solution[num] = item + + return solution + + +def goal_test(kb, goals): + for q in goals: + if kb.ask(q) is False: + return False + return True + + +def spare_tire_graphplan(): + pdll = spare_tire() + negkb = FolKB([expr('At(Flat, Trunk)')]) + graphplan = GraphPlan(pdll, negkb) + ##Not sure + goals_pos = [expr('At(Spare, Axle)'), expr('At(Flat, Ground)')] + goals_neg = [] + + while True: + if goal_test(graphplan.graph.levels[-1].poskb, goals_pos) and graphplan.graph.non_mutex_goals(goals_pos+goals_neg, -1): + solution = graphplan.extract_solution(goals_pos, goals_neg, -1) + if solution: + return solution + graphplan.graph.expand_graph() + if len(graphplan.graph.levels)>=2 and graphplan.check_leveloff(): + return None From e8a5e07343460281f69de689c127f0d758b59285 Mon Sep 17 00:00:00 2001 From: Vidur Satija Date: Fri, 3 Mar 2017 10:41:15 +0530 Subject: [PATCH 388/513] Fixed Bug #295 (#301) Syntax error. Used the function len instead of count. --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index ce8871300..b944e4b5f 100644 --- a/learning.py +++ b/learning.py @@ -370,7 +370,7 @@ def plurality_value(examples): def count(attr, val, examples): "Count the number of examples that have attr = val." - return count(e[attr] == val for e in examples) + return len(e[attr] == val for e in examples) #count(e[attr] == val for e in examples) def all_same_class(examples): "Are all these examples in the same target class?" From be8543fa1926dc264f2448a3455421fb54131076 Mon Sep 17 00:00:00 2001 From: Vladimir Date: Fri, 3 Mar 2017 08:12:37 +0300 Subject: [PATCH 389/513] Correct a typo in usage (#279) --- agents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.py b/agents.py index 8ef811978..650dfe97b 100644 --- a/agents.py +++ b/agents.py @@ -325,7 +325,7 @@ def delete_thing(self, thing): class Direction(): '''A direction class for agents that want to move in a 2D plane Usage: - d = Direction("Down") + d = Direction("down") To change directions: d = d + "right" or d = d + Direction.R #Both do the same thing Note that the argument to __add__ must be a string and not a Direction object. From e59faf6e0f83753465968493ef7f039013c88767 Mon Sep 17 00:00:00 2001 From: Pranjal Bhansali Date: Fri, 3 Mar 2017 17:07:13 +0530 Subject: [PATCH 390/513] Fixed typo in comments (#302) --- planning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/planning.py b/planning.py index 9d3c01bff..4bfa1d71a 100644 --- a/planning.py +++ b/planning.py @@ -60,7 +60,7 @@ def __call__(self, kb, args): return self.act(kb, args) def substitute(self, e, args): - """Replaces variables in expression with their respective Propostional symbol""" + """Replaces variables in expression with their respective Propositional symbol""" new_args = list(e.args) for num, x in enumerate(e.args): for i in range(len(self.args)): From 82d78c61ededf24fdceed008fff066194fe70b8d Mon Sep 17 00:00:00 2001 From: Chinmaya Pancholi Date: Sat, 4 Mar 2017 15:27:38 +0530 Subject: [PATCH 391/513] Fixes typos in the search.ipynb (#307) --- search.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/search.ipynb b/search.ipynb index 77bbc91bf..7f4fe7473 100644 --- a/search.ipynb +++ b/search.ipynb @@ -28,9 +28,9 @@ "source": [ "## Review\n", "\n", - "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular navigation problem / route finding problem. First, we will start the problem solving by precicly defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", + "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular navigation problem / route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n", "\n", - "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information aboout the problem other than its definition.\n", + "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information about the problem other than its definition.\n", "* Examples:\n", " 1. Breadth First Search\n", " 2. Depth First Search\n", @@ -96,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the abstract class `Problem` to define out real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell." + "We will use the abstract class `Problem` to define our real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell." ] }, { @@ -156,7 +156,7 @@ "collapsed": true }, "source": [ - "It is pretty straight forward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n", + "It is pretty straightforward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n", "\n", "And `romania_map.locations` contains the positions of each of the nodes. We will use the straight line distance (which is different from the one provided in `romania_map`) between two cities in algorithms like A\\*-search and Recursive Best First Search.\n", "\n", @@ -392,7 +392,7 @@ "* Currently exploring node - red\n", "* Already explored nodes - gray\n", "\n", - "Now, we will define some helper methods to display interactive buttons ans sliders when visualising search algorithms." + "Now, we will define some helper methods to display interactive buttons and sliders when visualising search algorithms." ] }, { From 1356ab9e078f442edabab2fe95cfaf2b8a0ef023 Mon Sep 17 00:00:00 2001 From: Sampad Kumar Saha Date: Sat, 4 Mar 2017 15:30:07 +0530 Subject: [PATCH 392/513] Modern String Formatting in Code (#292) * Modern string formatting in csp.py. * Modern string formatting in games.py. * Modern string formatting in learning.py. * Modern string formatting in nlp.py. * Modern string formatting in probability.py. * Modern string formatting in search.py. * Replaced {0\!r} by {} if %s. * Corrected a typo. --- csp.py | 2 +- games.py | 2 +- learning.py | 14 +++++++------- nlp.py | 4 ++-- probability.py | 8 ++++---- search.py | 4 ++-- 6 files changed, 17 insertions(+), 17 deletions(-) diff --git a/csp.py b/csp.py index f300cb816..207576928 100644 --- a/csp.py +++ b/csp.py @@ -344,7 +344,7 @@ def __init__(self, value): self.value = value def __getitem__(self, key): return self.value - def __repr__(self): return '{Any: %r}' % self.value + def __repr__(self): return '{{Any: {0!r}}}'.format(self.value) def different_values_constraint(A, a, B, b): diff --git a/games.py b/games.py index 90604bf69..9b98c5638 100644 --- a/games.py +++ b/games.py @@ -203,7 +203,7 @@ def display(self, state): print(state) def __repr__(self): - return '<%s>' % self.__class__.__name__ + return '<{}>'.format(self.__class__.__name__) class Fig52Game(Game): diff --git a/learning.py b/learning.py index b944e4b5f..24554ff22 100644 --- a/learning.py +++ b/learning.py @@ -139,8 +139,8 @@ def check_example(self, example): if self.values: for a in self.attrs: if example[a] not in self.values[a]: - raise ValueError('Bad value %s for attribute %s in %s' % - (example[a], self.attrnames[a], example)) + raise ValueError('Bad value {} for attribute {} in {}' + .format(example[a], self.attrnames[a], example)) def attrnum(self, attr): "Returns the number used for attr, which can be a name, or -n .. n-1." @@ -157,7 +157,7 @@ def sanitize(self, example): for i, attr_i in enumerate(example)] def __repr__(self): - return '' % ( + return ''.format( self.name, len(self.examples), len(self.attrs)) # ______________________________________________________________________________ @@ -317,8 +317,8 @@ def display(self, indent=0): subtree.display(indent + 1) def __repr__(self): - return ('DecisionFork(%r, %r, %r)' - % (self.attr, self.attrname, self.branches)) + return ('DecisionFork({0!r}, {1!r}, {2!r})' + .format(self.attr, self.attrname, self.branches)) class DecisionLeaf: @@ -771,9 +771,9 @@ def test(predict, dataset, examples=None, verbose=0): if output == desired: right += 1 if verbose >= 2: - print(' OK: got %s for %s' % (desired, example)) + print(' OK: got {} for {}'.format(desired, example)) elif verbose: - print('WRONG: got %s, expected %s for %s' % ( + print('WRONG: got {}, expected {} for {}'.format( output, desired, example)) return 1 - (right / len(examples)) diff --git a/nlp.py b/nlp.py index 3c95e961d..f136cb035 100644 --- a/nlp.py +++ b/nlp.py @@ -52,7 +52,7 @@ def isa(self, word, cat): return cat in self.categories[word] def __repr__(self): - return '' % self.name + return ''.format(self.name) E0 = Grammar('E0', Rules( # Grammar for E_0 [Figure 22.4] @@ -158,7 +158,7 @@ def add_edge(self, edge): if edge not in self.chart[end]: self.chart[end].append(edge) if self.trace: - print('Chart: added %s' % (edge,)) + print('Chart: added {}'.format(edge)) if not expects: self.extender(edge) else: diff --git a/probability.py b/probability.py index 8a7fc4779..abbc07791 100644 --- a/probability.py +++ b/probability.py @@ -80,7 +80,7 @@ def show_approx(self, numfmt='%.3g'): for (v, p) in sorted(self.prob.items())]) def __repr__(self): - return "P(%s)" % self.varname + return "P({})".format(self.varname) class JointProbDist(ProbDist): @@ -117,7 +117,7 @@ def values(self, var): return self.vals[var] def __repr__(self): - return "P(%s)" % self.variables + return "P({})".format(self.variables) def event_values(event, variables): @@ -192,14 +192,14 @@ def variable_node(self, var): for n in self.nodes: if n.variable == var: return n - raise Exception("No such variable: %s" % var) + raise Exception("No such variable: {}".format(var)) def variable_values(self, var): "Return the domain of var." return [True, False] def __repr__(self): - return 'BayesNet(%r)' % self.nodes + return 'BayesNet({0!r})'.format(self.nodes) class BayesNode: diff --git a/search.py b/search.py index 3bc9c5412..04d5b6c51 100644 --- a/search.py +++ b/search.py @@ -96,7 +96,7 @@ def __init__(self, state, parent=None, action=None, path_cost=0): self.depth = parent.depth + 1 def __repr__(self): - return "" % (self.state,) + return "".format(self.state) def __lt__(self, node): return self.state < node.state @@ -1133,7 +1133,7 @@ def __getattr__(self, attr): return getattr(self.problem, attr) def __repr__(self): - return '<%4d/%4d/%4d/%s>' % (self.succs, self.goal_tests, + return '<{:4d}/{:4d}/{:4d}/{}>'.format(self.succs, self.goal_tests, self.states, str(self.found)[:4]) From ce7aa26cf7e6894d78a70c4630812226158b3a0c Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 5 Mar 2017 11:07:20 +0200 Subject: [PATCH 393/513] Add new tests (#314) - Added tests for PluralityLearner, NaiveBayes and kNN algorithms - Replace parse_csv with non-trivial input --- tests/test_learning.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/tests/test_learning.py b/tests/test_learning.py index 31fb671bc..d36a1146d 100644 --- a/tests/test_learning.py +++ b/tests/test_learning.py @@ -1,9 +1,12 @@ import pytest -from learning import parse_csv, weighted_mode, weighted_replicate +from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \ + PluralityLearner, NaiveBayesLearner, NearestNeighborLearner +from utils import DataFile def test_parse_csv(): - assert parse_csv('1, 2, 3 \n 0, 2, na') == [[1, 2, 3], [0, 2, 'na']] + Iris = DataFile('iris.csv').read() + assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa'] def test_weighted_mode(): @@ -12,3 +15,21 @@ def test_weighted_mode(): def test_weighted_replicate(): assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C'] + +def test_plurality_learner(): + zoo = DataSet(name="zoo") + + pL = PluralityLearner(zoo) + assert pL([]) == "mammal" + +def test_naive_bayes(): + iris = DataSet(name="iris") + + nB = NaiveBayesLearner(iris) + assert nB([5,3,1,0.1]) == "setosa" + +def test_k_nearest_neighbors(): + iris = DataSet(name="iris") + + kNN = NearestNeighborLearner(iris,k=3) + assert kNN([5,3,1,0.1]) == "setosa" From ee5068e96e6d894c45d6d34113154f64eb8c185b Mon Sep 17 00:00:00 2001 From: Antonis Maronikolakis Date: Sun, 5 Mar 2017 11:09:14 +0200 Subject: [PATCH 394/513] Image Mistake Fix (#311) * Delete knn_plot.png * Add files via upload --- images/knn_plot.png | Bin 53541 -> 35268 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/images/knn_plot.png b/images/knn_plot.png index 6a5b0f036f413a5e265f0b0441c9d842e7495ff6..1cee33e9e56eb385e15e063f248ad0e83c87833e 100644 GIT binary patch literal 35268 zcmaHSWmFtp(=8Gp!Gi_}?oM!bcXxNU;O-jS-Q9igV8Mf17$6M6-5qW}@BRPHTD^MB zOrJid`;_de+7+#=D20NEj|c??g(4#@t_lSOeFGexA7OxBz==#Xz=_06T2%oG%7+pP zDku~R>hT>oA3#B=euRR?)`o)O&47Z!bz0FSP6V#~RJf+5zkFR z+fB{M(#_Mv)dI@d#KzH$$1YrWk5=CMC zUqjtGK$Fo!z&(fIq$mHcn_dN1|L^AWuK$~B_$MUbs+9d1n5opFboc+yq%zW5R8&-7 zkJ^9K1zar6{zVB~#Z_KiZToa{n3k3{C6N0z*bipo@l zh-QyCM5fhCa&n=^?29;{M!6c0<)B%wc^>S 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z9ejdc0fHb#3OZ@dxvvp0Gf%XWN+F4rHWLF}e!u`i*LSfIx7^;8FD4t9n7pN(_ZWU{ zbdde=nPYJzasL>oB zFLQ$B;$O}YAcR1%-%Gk!)RC=V4cIr_EwM1d1WpZYR9+QMc@2yL}Pt79hMj$uS8tV5N zKvu$y687A7q0xB3C7evN5pA;7>gP*w&0HzaGB1F5BfB|#l0SI5&g{+njL*d0wdFlY zy_X}g^oG6%i${bd!M=;>&bbt&i4NKe)q4ULWn6oNy%{DVR?QZ3<##E^W9&Tyo^X}{ zHN^>;K}G3s15^6Dd6q4P=1{{t-2ljhp*58 From ca893d7f3ada7dadf4f11492b5572fc406408a4a Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sun, 5 Mar 2017 06:10:01 -0300 Subject: [PATCH 395/513] Fix __call__ command for Graph class (#306) --- planning.py | 4 ++-- tests/test_planning.py | 10 ++++++++++ 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/planning.py b/planning.py index 4bfa1d71a..a17677460 100644 --- a/planning.py +++ b/planning.py @@ -405,8 +405,8 @@ def __init__(self, pdll, negkb): self.levels = [Level(pdll.kb, negkb)] self.objects = set(arg for clause in pdll.kb.clauses + negkb.clauses for arg in clause.args) - def __call__(): - expand_graph() + def __call__(self): + self.expand_graph() def expand_graph(self): last_level = self.levels[-1] diff --git a/tests/test_planning.py b/tests/test_planning.py index 3567ab445..4e012b207 100644 --- a/tests/test_planning.py +++ b/tests/test_planning.py @@ -69,3 +69,13 @@ def test_have_cake_and_eat_cake_too(): p.act(action) assert p.goal_test() + +def test_graph_call(): + pdll = spare_tire() + negkb = FolKB([expr('At(Flat, Trunk)')]) + graph = Graph(pdll, negkb) + + levels_size = len(graph.levels) + graph() + + assert levels_size == len(graph.levels) - 1 From c9eab0f6c55bf850b31ee9bed20da91a61dd7412 Mon Sep 17 00:00:00 2001 From: lucasmoura Date: Sun, 5 Mar 2017 06:23:29 -0300 Subject: [PATCH 396/513] Add tests to CSP class (#299) * Add tests to CSP class Add test for the following methods on CSP class: * assign * unassigns * nconflits * actions * Refactor some asserts in test_csp.py Some asserts were being done in the following way: assert (X is not None) == True Now, they are handled in the following way: assert X --- tests/test_csp.py | 64 ++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 55 insertions(+), 9 deletions(-) diff --git a/tests/test_csp.py b/tests/test_csp.py index 358d6fe07..7eae4b0c4 100644 --- a/tests/test_csp.py +++ b/tests/test_csp.py @@ -2,16 +2,62 @@ from csp import * #noqa +def test_csp_assign(): + var = 10 + val = 5 + assignment = {} + australia.assign(var, val, assignment) + + assert australia.nassigns == 1 + assert assignment[var] == val + + +def test_csp_unassign(): + var = 10 + assignment = {var: 5} + australia.unassign(var, assignment) + + assert var not in assignment + + +def test_csp_nconflits(): + map_coloring_test = MapColoringCSP(list('RGB'), 'A: B C; B: C; C: ') + assignment = {'A': 'R', 'B': 'G'} + var = 'C' + val = 'R' + assert map_coloring_test.nconflicts(var, val, assignment) == 1 + + val = 'B' + assert map_coloring_test.nconflicts(var, val, assignment) == 0 + + +def test_csp_actions(): + map_coloring_test = MapColoringCSP(list('123'), 'A: B C; B: C; C: ') + + state = {'A': '1', 'B': '2', 'C': '3'} + assert map_coloring_test.actions(state) == [] + + state = {'A': '1', 'B': '3'} + assert map_coloring_test.actions(state) == [('C', '2')] + + state = {'A': '1', 'C': '2'} + assert map_coloring_test.actions(state) == [('B', '3')] + + state = {'A': '1'} + assert (map_coloring_test.actions(state) == [('C', '2'), ('C', '3')] or + map_coloring_test.actions(state) == [('B', '2'), ('B', '3')]) + + def test_backtracking_search(): - assert (backtracking_search(australia) is not None) == True - assert (backtracking_search(australia, select_unassigned_variable=mrv) is not None) == True - assert (backtracking_search(australia, order_domain_values=lcv) is not None) == True - assert (backtracking_search(australia, select_unassigned_variable=mrv, - order_domain_values=lcv) is not None) == True - assert (backtracking_search(australia, inference=forward_checking) is not None) == True - assert (backtracking_search(australia, inference=mac) is not None) == True - assert (backtracking_search(usa, select_unassigned_variable=mrv, - order_domain_values=lcv, inference=mac) is not None) == True + assert backtracking_search(australia) + assert backtracking_search(australia, select_unassigned_variable=mrv) + assert backtracking_search(australia, order_domain_values=lcv) + assert backtracking_search(australia, select_unassigned_variable=mrv, + order_domain_values=lcv) + assert backtracking_search(australia, inference=forward_checking) + assert backtracking_search(australia, inference=mac) + assert backtracking_search(usa, select_unassigned_variable=mrv, + order_domain_values=lcv, inference=mac) def test_universal_dict(): From 0ff4b6e5b4fba07ccfb35fbd3887f36b1f6b647f Mon Sep 17 00:00:00 2001 From: Zulfikar Date: Sun, 5 Mar 2017 16:24:08 +0700 Subject: [PATCH 397/513] Change Link's Style (#271) --- README.md | 177 +++++++++++++++++++++++++++++------------------------- 1 file changed, 96 insertions(+), 81 deletions(-) diff --git a/README.md b/README.md index c6cf16d19..c59ac4b0c 100644 --- a/README.md +++ b/README.md @@ -24,58 +24,57 @@ When complete, this project will have Python code for all the pseudocode algorit Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The [aima-pseudocode](https://github.com/aimacode/aima-pseudocode) project describes all the algorithms from the book. - | **Figure** | **Name (in 3rd edition)** | **Name (in repository)** | **File** |:--------|:-------------------|:---------|:-----------| -| 2.1 | Environment | `Environment` | [`agents.py`](../master/agents.py) | -| 2.1 | Agent | `Agent` | [`agents.py`](../master/agents.py) | -| 2.3 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.7 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`](../master/agents.py) | -| 2.8 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`](../master/agents.py) | -| 2.10 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`](../master/agents.py) | -| 2.12 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`](../master/agents.py) | -| 3 | Problem | `Problem` | [`search.py`](../master/search.py) | -| 3 | Node | `Node` | [`search.py`](../master/search.py) | -| 3 | Queue | `Queue` | [`utils.py`](../master/utils.py) | -| 3.1 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`](../master/search.py) | -| 3.2 | Romania | `romania` | [`search.py`](../master/search.py) | -| 3.7 | Tree-Search | `tree_search` | [`search.py`](../master/search.py) | -| 3.7 | Graph-Search | `graph_search` | [`search.py`](../master/search.py) | -| 3.11 | Breadth-First-Search | `breadth_first_search` | [`search.py`](../master/search.py) | -| 3.14 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`](../master/search.py) | -| 3.17 | Depth-Limited-Search | `depth_limited_search` | [`search.py`](../master/search.py) | -| 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`](../master/search.py) | -| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`](../master/search.py) | -| 3.24 | A\*-Search | `astar_search` | [`search.py`](../master/search.py) | -| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`](../master/search.py) | -| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`](../master/search.py) | -| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`](../master/search.py) | -| 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`](../master/search.py) | -| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`](../master/search.py) | -| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`](../master/search.py) | -| 4.24 | LRTA\*-Agent | `LRTAStarAgent` | [`search.py`](../master/search.py) | -| 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`](../master/games.py) | -| 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`](../master/games.py) | -| 6 | CSP | `CSP` | [`csp.py`](../master/csp.py) | -| 6.3 | AC-3 | `AC3` | [`csp.py`](../master/csp.py) | -| 6.5 | Backtracking-Search | `backtracking_search` | [`csp.py`](../master/csp.py) | -| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`](../master/csp.py) | -| 6.11 | Tree-CSP-Solver | `tree_csp_solver` | [`csp.py`](../master/csp.py) | -| 7 | KB | `KB` | [`logic.py`](../master/logic.py) | -| 7.1 | KB-Agent | `KB_Agent` | [`logic.py`](../master/logic.py) | -| 7.7 | Propositional Logic Sentence | `Expr` | [`logic.py`](../master/logic.py) | -| 7.10 | TT-Entails | `tt_entials` | [`logic.py`](../master/logic.py) | -| 7.12 | PL-Resolution | `pl_resolution` | [`logic.py`](../master/logic.py) | -| 7.14 | Convert to CNF | `to_cnf` | [`logic.py`](../master/logic.py) | -| 7.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`](../master/logic.py) | -| 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`](../master/logic.py) | -| 7.18 | WalkSAT | `WalkSAT` | [`logic.py`](../master/logic.py) | +| 2.1 | Environment | `Environment` | [`agents.py`][agents] | +| 2.1 | Agent | `Agent` | [`agents.py`][agents] | +| 2.3 | Table-Driven-Vacuum-Agent | `TableDrivenVacuumAgent` | [`agents.py`][agents] | +| 2.7 | Table-Driven-Agent | `TableDrivenAgent` | [`agents.py`][agents] | +| 2.8 | Reflex-Vacuum-Agent | `ReflexVacuumAgent` | [`agents.py`][agents] | +| 2.10 | Simple-Reflex-Agent | `SimpleReflexAgent` | [`agents.py`][agents] | +| 2.12 | Model-Based-Reflex-Agent | `ReflexAgentWithState` | [`agents.py`][agents] | +| 3 | Problem | `Problem` | [`search.py`][search] | +| 3 | Node | `Node` | [`search.py`][search] | +| 3 | Queue | `Queue` | [`utils.py`][utils] | +| 3.1 | Simple-Problem-Solving-Agent | `SimpleProblemSolvingAgent` | [`search.py`][search] | +| 3.2 | Romania | `romania` | [`search.py`][search] | +| 3.7 | Tree-Search | `tree_search` | [`search.py`][search] | +| 3.7 | Graph-Search | `graph_search` | [`search.py`][search] | +| 3.11 | Breadth-First-Search | `breadth_first_search` | [`search.py`][search] | +| 3.14 | Uniform-Cost-Search | `uniform_cost_search` | [`search.py`][search] | +| 3.17 | Depth-Limited-Search | `depth_limited_search` | [`search.py`][search] | +| 3.18 | Iterative-Deepening-Search | `iterative_deepening_search` | [`search.py`][search] | +| 3.22 | Best-First-Search | `best_first_graph_search` | [`search.py`][search] | +| 3.24 | A\*-Search | `astar_search` | [`search.py`][search] | +| 3.26 | Recursive-Best-First-Search | `recursive_best_first_search` | [`search.py`][search] | +| 4.2 | Hill-Climbing | `hill_climbing` | [`search.py`][search] | +| 4.5 | Simulated-Annealing | `simulated_annealing` | [`search.py`][search] | +| 4.8 | Genetic-Algorithm | `genetic_algorithm` | [`search.py`][search] | +| 4.11 | And-Or-Graph-Search | `and_or_graph_search` | [`search.py`][search] | +| 4.21 | Online-DFS-Agent | `online_dfs_agent` | [`search.py`][search] | +| 4.24 | LRTA\*-Agent | `LRTAStarAgent` | [`search.py`][search] | +| 5.3 | Minimax-Decision | `minimax_decision` | [`games.py`][games] | +| 5.7 | Alpha-Beta-Search | `alphabeta_search` | [`games.py`][games] | +| 6 | CSP | `CSP` | [`csp.py`][csp] | +| 6.3 | AC-3 | `AC3` | [`csp.py`][csp] | +| 6.5 | Backtracking-Search | `backtracking_search` | [`csp.py`][csp] | +| 6.8 | Min-Conflicts | `min_conflicts` | [`csp.py`][csp] | +| 6.11 | Tree-CSP-Solver | `tree_csp_solver` | [`csp.py`][csp] | +| 7 | KB | `KB` | [`logic.py`][logic] | +| 7.1 | KB-Agent | `KB_Agent` | [`logic.py`][logic] | +| 7.7 | Propositional Logic Sentence | `Expr` | [`logic.py`][logic] | +| 7.10 | TT-Entails | `tt_entials` | [`logic.py`][logic] | +| 7.12 | PL-Resolution | `pl_resolution` | [`logic.py`][logic] | +| 7.14 | Convert to CNF | `to_cnf` | [`logic.py`][logic] | +| 7.15 | PL-FC-Entails? | `pl_fc_resolution` | [`logic.py`][logic] | +| 7.17 | DPLL-Satisfiable? | `dpll_satisfiable` | [`logic.py`][logic] | +| 7.18 | WalkSAT | `WalkSAT` | [`logic.py`][logic] | | 7.20 | Hybrid-Wumpus-Agent | | | -| 7.22 | SATPlan | `SAT_plan` | [`logic.py`](../master/logic.py) | -| 9 | Subst | `subst` | [`logic.py`](../master/logic.py) | -| 9.1 | Unify | `unify` | [`logic.py`](../master/logic.py) | -| 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`](../master/logic.py) | -| 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`](../master/logic.py) | +| 7.22 | SATPlan | `SAT_plan` | [`logic.py`][logic] | +| 9 | Subst | `subst` | [`logic.py`][logic] | +| 9.1 | Unify | `unify` | [`logic.py`][logic] | +| 9.3 | FOL-FC-Ask | `fol_fc_ask` | [`logic.py`][logic] | +| 9.6 | FOL-BC-Ask | `fol_bc_ask` | [`logic.py`][logic] | | 9.8 | Append | | | | 10.1 | Air-Cargo-problem | | | 10.2 | Spare-Tire-Problem | | @@ -87,36 +86,36 @@ Here is a table of algorithms, the figure, name of the code in the book and in t | 11.5 | Hierarchical-Search | | | 11.8 | Angelic-Search | | | 11.10 | Doubles-tennis | | -| 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`](../master/probability.py) | -| 13.1 | DT-Agent | `DTAgent` | [`probability.py`](../master/probability.py) | -| 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`](../master/probability.py) | -| 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`](../master/probability.py) | -| 14.13 | Prior-Sample | `prior_sample` | [`probability.py`](../master/probability.py) | -| 14.14 | Rejection-Sampling | `rejection_sampling` | [`probability.py`](../master/probability.py) | -| 14.15 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`](../master/probability.py) | -| 14.16 | Gibbs-Ask | `gibbs_ask` | [`probability.py`](../master/probability.py) | -| 15.4 | Forward-Backward | `forward_backward` | [`probability.py`](../master/probability.py) | -| 15.6 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`](../master/probability.py) | -| 15.17 | Particle-Filtering | `particle_filtering` | [`probability.py`](../master/probability.py) | +| 13 | Discrete Probability Distribution | `ProbDist` | [`probability.py`][probability] | +| 13.1 | DT-Agent | `DTAgent` | [`probability.py`][probability] | +| 14.9 | Enumeration-Ask | `enumeration_ask` | [`probability.py`][probability] | +| 14.11 | Elimination-Ask | `elimination_ask` | [`probability.py`][probability] | +| 14.13 | Prior-Sample | `prior_sample` | [`probability.py`][probability] | +| 14.14 | Rejection-Sampling | `rejection_sampling` | [`probability.py`][probability] | +| 14.15 | Likelihood-Weighting | `likelihood_weighting` | [`probability.py`][probability] | +| 14.16 | Gibbs-Ask | `gibbs_ask` | [`probability.py`][probability] | +| 15.4 | Forward-Backward | `forward_backward` | [`probability.py`][probability] | +| 15.6 | Fixed-Lag-Smoothing | `fixed_lag_smoothing` | [`probability.py`][probability] | +| 15.17 | Particle-Filtering | `particle_filtering` | [`probability.py`][probability] | | 16.9 | Information-Gathering-Agent | | -| 17.4 | Value-Iteration | `value_iteration` | [`mdp.py`](../master/mdp.py) | -| 17.7 | Policy-Iteration | `policy_iteration` | [`mdp.py`](../master/mdp.py) | +| 17.4 | Value-Iteration | `value_iteration` | [`mdp.py`][mdp] | +| 17.7 | Policy-Iteration | `policy_iteration` | [`mdp.py`][mdp] | | 17.7 | POMDP-Value-Iteration | | | -| 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`](../master/learning.py) | -| 18.8 | Cross-Validation | `cross_validation` | [`learning.py`](../master/learning.py) | -| 18.11 | Decision-List-Learning | `DecisionListLearner` | [`learning.py`](../master/learning.py) | -| 18.24 | Back-Prop-Learning | `BackPropagationLearner` | [`learning.py`](../master/learning.py) | -| 18.34 | AdaBoost | `AdaBoost` | [`learning.py`](../master/learning.py) | +| 18.5 | Decision-Tree-Learning | `DecisionTreeLearner` | [`learning.py`][learning] | +| 18.8 | Cross-Validation | `cross_validation` | [`learning.py`][learning] | +| 18.11 | Decision-List-Learning | `DecisionListLearner` | [`learning.py`][learning] | +| 18.24 | Back-Prop-Learning | `BackPropagationLearner` | [`learning.py`][learning] | +| 18.34 | AdaBoost | `AdaBoost` | [`learning.py`][learning] | | 19.2 | Current-Best-Learning | | | 19.3 | Version-Space-Learning | | | 19.8 | Minimal-Consistent-Det | | | 19.12 | FOIL | | -| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`](../master/rl.py) | -| 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`](../master/rl.py) | -| 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`](../master/rl.py) | -| 22.1 | HITS | `HITS` | [`nlp.py`](../master/nlp.py) | -| 23 | Chart-Parse | `Chart` | [`nlp.py`](../master/nlp.py) | -| 23.5 | CYK-Parse | `CYK_parse` | [`nlp.py`](../master/nlp.py) | +| 21.2 | Passive-ADP-Agent | `PassiveADPAgent` | [`rl.py`][rl] | +| 21.4 | Passive-TD-Agent | `PassiveTDAgent` | [`rl.py`][rl] | +| 21.8 | Q-Learning-Agent | `QLearningAgent` | [`rl.py`][rl] | +| 22.1 | HITS | `HITS` | [`nlp.py`][nlp] | +| 23 | Chart-Parse | `Chart` | [`nlp.py`][nlp] | +| 23.5 | CYK-Parse | `CYK_parse` | [`nlp.py`][nlp] | | 25.9 | Monte-Carlo-Localization| | @@ -126,16 +125,32 @@ Here is a table of the implemented data structures, the figure, name of the impl | **Figure** | **Name (in repository)** | **File** | |:-----------|:-------------------------|:---------| -| 3.2 | romania_map | [`search.py`](../master/search.py) | -| 4.9 | vacumm_world | [`search.py`](../master/search.py) | -| 4.23 | one_dim_state_space | [`search.py`](../master/search.py) | -| 6.1 | australia_map | [`search.py`](../master/search.py) | -| 7.13 | wumpus_world_inference | [`logic.py`](../master/login.py) | -| 7.16 | horn_clauses_KB | [`logic.py`](../master/logic.py) | -| 17.1 | sequential_decision_environment | [`mdp.py`](../master/mdp.py) | -| 18.2 | waiting_decision_tree | [`learning.py`](../master/learning.py) | +| 3.2 | romania_map | [`search.py`][search] | +| 4.9 | vacumm_world | [`search.py`][search] | +| 4.23 | one_dim_state_space | [`search.py`][search] | +| 6.1 | australia_map | [`search.py`][search] | +| 7.13 | wumpus_world_inference | [`logic.py`][logic] | +| 7.16 | horn_clauses_KB | [`logic.py`][logic] | +| 17.1 | sequential_decision_environment | [`mdp.py`][mdp] | +| 18.2 | waiting_decision_tree | [`learning.py`][learning] | # Acknowledgements Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the [contributors](https://github.com/aimacode/aima-python/graphs/contributors) who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, and @reachtarunhere. + + +[agents]:../master/agents.py +[csp]:../master/csp.py +[games]:../master/games.py +[grid]:../master/grid.py +[learning]:../master/learning.py +[logic]:../master/logic.py +[mdp]:../master/mdp.py +[nlp]:../master/nlp.py +[planning]:../master/planning.py +[probability]:../master/probability.py +[rl]:../master/rl.py +[search]:../master/search.py +[utils]:../master/utils.py +[text]:../master/text.py From 413139d3245ef127a3fd255064bf59e894ae84b2 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 5 Mar 2017 11:02:09 -0800 Subject: [PATCH 398/513] Update CONTRIBUTING.md --- CONTRIBUTING.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9e1013fa1..eec7aaf1a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -31,7 +31,8 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) - I have set `--max-line-length 100`, not 79. - You don't need two spaces after a sentence-ending period. - Strunk and White is [not a good guide for English](http://chronicle.com/article/50-Years-of-Stupid-Grammar/25497). -- I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). +- I prefer more concise docstrings; I don't follow [Pep 257](https://www.python.org/dev/peps/pep-0257/). In most cases, +a one-line docstring suffices. It is rarely necessary to list what each argument does; the name of the argument usually is enough. - Not all constants have to be UPPERCASE. - At some point I may add [Pep 484](https://www.python.org/dev/peps/pep-0484/) type annotations, but I think I'll hold off for now; I want to get more experience with them, and some people may still be in Python 3.4. @@ -40,7 +41,7 @@ Beyond the above rules, we use [Pep 8](https://www.python.org/dev/peps/pep-0008) Contributing a Patch ==================== -1. Submit an issue describing your proposed change to the repo in question. +1. Submit an issue describing your proposed change to the repo in question (or work on an existing issue). 1. The repo owner will respond to your issue promptly. 1. Fork the desired repo, develop and test your code changes. 1. Submit a pull request. @@ -88,15 +89,15 @@ Then you can run the testsuite with:: # Choice of Programming Languages Are we right to concentrate on Java and Python versions of the code? I think so; both languages are popular; Java is -fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top five most popular languages are: +fast enough for our purposes, and has reasonable type declarations (but can be verbose); Python is popular and has a very direct mapping to the pseudocode in the book (but lacks type declarations and can be slow). The [TIOBE Index](http://www.tiobe.com/tiobe_index) says the top seven most popular languages, in order, are: - Java, C, C++, C#, Python + Java, C, C++, C#, Python, PHP, Javascript -So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. And finally, Javascript is the language of the browser; it would be nice to have code that runs in the browser, in Javascript or a variant such as Typescript. +So it might be reasonable to also support C++/C# at some point in the future. It might also be reasonable to support a language that combines the terse readability of Python with the type safety and speed of Java; perhaps Go or Julia. I see no reason to support PHP. Javascript is the language of the browser; it would be nice to have code that runs in the browser without need for any downloads; this would be in Javascript or a variant such as Typescript. -There is also a `aima-lisp` project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular. +There is also a `aima-lisp` project; in 1995 when we wrote the first edition of the book, Lisp was the right choice, but today it is less popular (currently #31 on the TIOBE index). -What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [norvig russell "Modern Approach"](https://www.google.com/webhp#q=russell%20norvig%20%22modern%20approach%22%20java) along with the names of various languages and looked at the estimated counts of results on +What languages are instructors recommending for their AI class? To get an approximate idea, I gave the query [\[norvig russell "Modern Approach"\]](https://www.google.com/webhp#q=russell%20norvig%20%22modern%20approach%22%20java) along with the names of various languages and looked at the estimated counts of results on various dates. However, I don't have much confidence in these figures... |Language |2004 |2005 |2007 |2010 |2016 | From bd6ec0d1bd0886ff937ba223523534016afc030a Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 5 Mar 2017 11:14:21 -0800 Subject: [PATCH 399/513] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c59ac4b0c..08e5e23fd 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ When complete, this project will have Python code for all the pseudocode algorit - `logic.ipynb`: A Jupyter (IPython) notebook that explains and gives examples of how to use the code. - `tests/logic_test.py`: A lightweight test suite, using `assert` statements, designed for use with [`py.test`](http://pytest.org/latest/), but also usable on their own. -# Index of Code +# Index of Algorithms Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The [aima-pseudocode](https://github.com/aimacode/aima-pseudocode) project describes all the algorithms from the book. From 48f079e43dc5c3ef42b61d147b117a798b050b22 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 5 Mar 2017 11:15:45 -0800 Subject: [PATCH 400/513] Update CONTRIBUTING.md --- CONTRIBUTING.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index eec7aaf1a..892b64d24 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,7 +1,13 @@ How to Contribute to aima-python ========================== -Thanks for considering contributing to `aima-python`! Here is some of the work that needs to be done: +Thanks for considering contributing to `aima-python`! Whether you are an aspiring [Google Summer of Code](https://summerofcode.withgoogle.com/organizations/5663121491361792/) student, or an independent contributor, here is a guide to how you can help: + +## Read the Code and Start on an Issue + +- First, read and understand the code to get a feel for the extent and the style. +- Look at the [issues](https://github.com/aimacode/aima-python/issues) and pick one to work on. +- One of the issues is that some algorithms are missing from the [list of algorithms](https://github.com/aimacode/aima-python/blob/master/README.md#index-of-algorithms). ## Port to Python 3; Pythonic Idioms; py.test From efb3324ea38353890eee8fcf5c08af74e31ff274 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 5 Mar 2017 18:59:10 -0800 Subject: [PATCH 401/513] Stop using statistics.mode. Stop using statistics.mode, because it throws an error when there are ties. --- learning.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/learning.py b/learning.py index 24554ff22..678604b1b 100644 --- a/learning.py +++ b/learning.py @@ -11,14 +11,15 @@ import math import random -# XXX statistics.mode is not quite the same as the old utils.mode: -# it insists on there being a unique most-frequent value. Code using mode -# needs to be revisited, or we need to restore utils.mode. -from statistics import mean, mode -from collections import defaultdict +from statistics import mean +from collections import defaultdict, Counter # ______________________________________________________________________________ +def mode(data): + """Return the most common data item. If there are ties, return any one of them.""" + (item, count) = Counter(data).most_common(1) + return item def rms_error(predictions, targets): return math.sqrt(ms_error(predictions, targets)) From af9808098e9ee8a068c03c6f2b1aee9a80e3da99 Mon Sep 17 00:00:00 2001 From: Kaivalya Rawal Date: Tue, 7 Mar 2017 10:54:52 +0530 Subject: [PATCH 402/513] minor edits in agents (#327) * fix bold look on github's ipython view * standardise docstrings --- agents.ipynb | 2 +- agents.py | 121 +++++++++++++++++++++++++-------------------------- 2 files changed, 60 insertions(+), 63 deletions(-) diff --git a/agents.ipynb b/agents.ipynb index 7976b12b2..8eba9f07e 100644 --- a/agents.ipynb +++ b/agents.ipynb @@ -8,7 +8,7 @@ "\n", "An agent, as defined in 2.1 is anything that can perceive its environment through sensors, and act upon that environment through actuators based on its agent program. This can be a dog, robot, or even you. As long as you can perceive the environment and act on it, you are an agent. This notebook will explain how to implement a simple agent, create an environment, and create a program that helps the agent act on the environment based on its percepts.\n", "\n", - "Before moving on, review the Agent and Environment classes in [agents.py](https://github.com/aimacode/aima-python/blob/master/agents.py).\n", + "Before moving on, review the Agent and Environment classes in [agents.py](https://github.com/aimacode/aima-python/blob/master/agents.py).\n", "\n", "Let's begin by importing all the functions from the agents.py module and creating our first agent - a blind dog." ] diff --git a/agents.py b/agents.py index 650dfe97b..abd7b9f7a 100644 --- a/agents.py +++ b/agents.py @@ -55,16 +55,16 @@ def __repr__(self): return '<{}>'.format(getattr(self, '__name__', self.__class__.__name__)) def is_alive(self): - "Things that are 'alive' should return true." + """Things that are 'alive' should return true.""" return hasattr(self, 'alive') and self.alive def show_state(self): - "Display the agent's internal state. Subclasses should override." + """Display the agent's internal state. Subclasses should override.""" print("I don't know how to show_state.") def display(self, canvas, x, y, width, height): + """Display an image of this Thing on the canvas.""" # Do we need this? - "Display an image of this Thing on the canvas." pass @@ -89,7 +89,7 @@ def __init__(self, program=None): self.performance = 0 if program is None: def program(percept): - return eval(input('Percept={}; action? ' .format(percept))) + return eval(input('Percept={}; action? '.format(percept))) assert isinstance(program, collections.Callable) self.program = program @@ -129,14 +129,14 @@ def program(percept): def RandomAgentProgram(actions): - "An agent that chooses an action at random, ignoring all percepts." + """An agent that chooses an action at random, ignoring all percepts.""" return lambda percept: random.choice(actions) # ______________________________________________________________________________ def SimpleReflexAgentProgram(rules, interpret_input): - "This agent takes action based solely on the percept. [Figure 2.10]" + """This agent takes action based solely on the percept. [Figure 2.10]""" def program(percept): state = interpret_input(percept) rule = rule_match(state, rules) @@ -146,7 +146,7 @@ def program(percept): def ModelBasedReflexAgentProgram(rules, update_state, model): - "This agent takes action based on the percept and state. [Figure 2.8]" + """This agent takes action based on the percept and state. [Figure 2.8]""" def program(percept): program.state = update_state(program.state, program.action, percept, model) rule = rule_match(program.state, rules) @@ -157,7 +157,7 @@ def program(percept): def rule_match(state, rules): - "Find the first rule that matches state." + """Find the first rule that matches state.""" for rule in rules: if rule.matches(state): return rule @@ -168,12 +168,12 @@ def rule_match(state, rules): def RandomVacuumAgent(): - "Randomly choose one of the actions from the vacuum environment." + """Randomly choose one of the actions from the vacuum environment.""" return Agent(RandomAgentProgram(['Right', 'Left', 'Suck', 'NoOp'])) def TableDrivenVacuumAgent(): - "[Figure 2.3]" + """[Figure 2.3]""" table = {((loc_A, 'Clean'),): 'Right', ((loc_A, 'Dirty'),): 'Suck', ((loc_B, 'Clean'),): 'Left', @@ -189,7 +189,7 @@ def TableDrivenVacuumAgent(): def ReflexVacuumAgent(): - "A reflex agent for the two-state vacuum environment. [Figure 2.8]" + """A reflex agent for the two-state vacuum environment. [Figure 2.8]""" def program(percept): location, status = percept if status == 'Dirty': @@ -202,11 +202,11 @@ def program(percept): def ModelBasedVacuumAgent(): - "An agent that keeps track of what locations are clean or dirty." + """An agent that keeps track of what locations are clean or dirty.""" model = {loc_A: None, loc_B: None} def program(percept): - "Same as ReflexVacuumAgent, except if everything is clean, do NoOp." + """Same as ReflexVacuumAgent, except if everything is clean, do NoOp.""" location, status = percept model[location] = status # Update the model here if model[loc_A] == model[loc_B] == 'Clean': @@ -242,32 +242,29 @@ def thing_classes(self): return [] # List of classes that can go into environment def percept(self, agent): - ''' - Return the percept that the agent sees at this point. - (Implement this.) - ''' + """Return the percept that the agent sees at this point. (Implement this.)""" raise NotImplementedError def execute_action(self, agent, action): - "Change the world to reflect this action. (Implement this.)" + """Change the world to reflect this action. (Implement this.)""" raise NotImplementedError def default_location(self, thing): - "Default location to place a new thing with unspecified location." + """Default location to place a new thing with unspecified location.""" return None def exogenous_change(self): - "If there is spontaneous change in the world, override this." + """If there is spontaneous change in the world, override this.""" pass def is_done(self): - "By default, we're done when we can't find a live agent." + """By default, we're done when we can't find a live agent.""" return not any(agent.is_alive() for agent in self.agents) def step(self): """Run the environment for one time step. If the actions and exogenous changes are independent, this method will - do. If there are interactions between them, you'll need to + do. If there are interactions between them, you'll need to override this method.""" if not self.is_done(): actions = [] @@ -281,14 +278,14 @@ def step(self): self.exogenous_change() def run(self, steps=1000): - "Run the Environment for given number of time steps." + """Run the Environment for given number of time steps.""" for step in range(steps): if self.is_done(): return self.step() def list_things_at(self, location, tclass=Thing): - "Return all things exactly at a given location." + """Return all things exactly at a given location.""" return [thing for thing in self.things if thing.location == location and isinstance(thing, tclass)] @@ -317,19 +314,19 @@ def delete_thing(self, thing): except ValueError as e: print(e) print(" in Environment delete_thing") - print(" Thing to be removed: {} at {}" .format(thing, thing.location)) - print(" from list: {}" .format([(thing, thing.location) for thing in self.things])) + print(" Thing to be removed: {} at {}".format(thing, thing.location)) + print(" from list: {}".format([(thing, thing.location) for thing in self.things])) if thing in self.agents: self.agents.remove(thing) class Direction(): - '''A direction class for agents that want to move in a 2D plane + """A direction class for agents that want to move in a 2D plane Usage: d = Direction("down") To change directions: d = d + "right" or d = d + Direction.R #Both do the same thing Note that the argument to __add__ must be a string and not a Direction object. - Also, it (the argument) can only be right or left.''' + Also, it (the argument) can only be right or left.""" R = "right" L = "left" @@ -396,7 +393,7 @@ def __init__(self, width=10, height=10): perceptible_distance = 1 def things_near(self, location, radius=None): - "Return all things within radius of location." + """Return all things within radius of location.""" if radius is None: radius = self.perceptible_distance radius2 = radius * radius @@ -404,7 +401,7 @@ def things_near(self, location, radius=None): if distance2(location, thing.location) <= radius2] def percept(self, agent): - '''By default, agent perceives things within a default radius.''' + """By default, agent perceives things within a default radius.""" return self.things_near(agent.location) def execute_action(self, agent, action): @@ -428,8 +425,8 @@ def default_location(self, thing): return (random.choice(self.width), random.choice(self.height)) def move_to(self, thing, destination): - '''Move a thing to a new location. Returns True on success or False if there is an Obstacle. - If thing is holding anything, they move with him.''' + """Move a thing to a new location. Returns True on success or False if there is an Obstacle. + If thing is holding anything, they move with him.""" thing.bump = self.some_things_at(destination, Obstacle) if not thing.bump: thing.location = destination @@ -449,9 +446,8 @@ def move_to(self, thing, destination): # obs.thing_added(thing) def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False): - '''Adds things to the world. - If (exclude_duplicate_class_items) then the item won't be added if the location - has at least one item of the same class.''' + """Adds things to the world. If (exclude_duplicate_class_items) then the item won't be + added if the location has at least one item of the same class.""" if (self.is_inbounds(location)): if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): @@ -459,12 +455,12 @@ def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False) super(XYEnvironment, self).add_thing(thing, location) def is_inbounds(self, location): - '''Checks to make sure that the location is inbounds (within walls if we have walls)''' + """Checks to make sure that the location is inbounds (within walls if we have walls)""" x,y = location return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end) def random_location_inbounds(self, exclude=None): - '''Returns a random location that is inbounds (within walls if we have walls)''' + """Returns a random location that is inbounds (within walls if we have walls)""" location = (random.randint(self.x_start, self.x_end), random.randint(self.y_start, self.y_end)) if exclude is not None: while(location == exclude): @@ -472,7 +468,7 @@ def random_location_inbounds(self, exclude=None): return location def delete_thing(self, thing): - '''Deletes thing, and everything it is holding (if thing is an agent)''' + """Deletes thing, and everything it is holding (if thing is an agent)""" if isinstance(thing, Agent): for obj in thing.holding: super(XYEnvironment, self).delete_thing(obj) @@ -484,7 +480,7 @@ def delete_thing(self, thing): obs.thing_deleted(thing) def add_walls(self): - '''Put walls around the entire perimeter of the grid.''' + """Put walls around the entire perimeter of the grid.""" for x in range(self.width): self.add_thing(Wall(), (x, 0)) self.add_thing(Wall(), (x, self.height - 1)) @@ -506,7 +502,7 @@ def add_observer(self, observer): self.observers.append(observer) def turn_heading(self, heading, inc): - "Return the heading to the left (inc=+1) or right (inc=-1) of heading." + """Return the heading to the left (inc=+1) or right (inc=-1) of heading.""" return turn_heading(heading, inc) @@ -526,7 +522,8 @@ class Wall(Obstacle): # Continuous environment class ContinuousWorld(Environment): - """ Model for Continuous World.""" + """Model for Continuous World.""" + def __init__(self, width=10, height=10): super(ContinuousWorld, self).__init__() self.width = width @@ -537,8 +534,9 @@ def add_obstacle(self, coordinates): class PolygonObstacle(Obstacle): + def __init__(self, coordinates): - """ Coordinates is a list of tuples.""" + """Coordinates is a list of tuples.""" super(PolygonObstacle, self).__init__() self.coordinates = coordinates @@ -604,7 +602,7 @@ def thing_classes(self): TableDrivenVacuumAgent, ModelBasedVacuumAgent] def percept(self, agent): - "Returns the agent's location, and the location status (Dirty/Clean)." + """Returns the agent's location, and the location status (Dirty/Clean).""" return (agent.location, self.status[agent.location]) def execute_action(self, agent, action): @@ -622,7 +620,7 @@ def execute_action(self, agent, action): self.status[agent.location] = 'Clean' def default_location(self, thing): - "Agents start in either location at random." + """Agents start in either location at random.""" return random.choice([loc_A, loc_B]) # ______________________________________________________________________________ @@ -632,7 +630,7 @@ def default_location(self, thing): class Gold(Thing): def __eq__(self, rhs): - '''All Gold are equal''' + """All Gold are equal""" return rhs.__class__ == Gold pass @@ -648,7 +646,6 @@ class Pit(Thing): class Breeze(Thing): pass - class Arrow(Thing): pass @@ -671,7 +668,7 @@ class Explorer(Agent): direction = Direction("right") def can_grab(self, thing): - '''Explorer can only grab gold''' + """Explorer can only grab gold""" return thing.__class__ == Gold @@ -684,7 +681,7 @@ def __init__(self, agent_program, width=6, height=6): self.init_world(agent_program) def init_world(self, program): - '''Spawn items to the world based on probabilities from the book''' + """Spawn items to the world based on probabilities from the book""" "WALLS" self.add_walls() @@ -715,7 +712,7 @@ def init_world(self, program): self.add_thing(Explorer(program), (1, 1), True) def get_world(self, show_walls=True): - '''Returns the items in the world''' + """Returns the items in the world""" result = [] x_start, y_start = (0, 0) if show_walls else (1, 1) x_end, y_end = (self.width, self.height) if show_walls else (self.width - 1, self.height - 1) @@ -727,16 +724,16 @@ def get_world(self, show_walls=True): return result def percepts_from(self, agent, location, tclass=Thing): - '''Returns percepts from a given location, and replaces some items with percepts from chapter 7.''' + """Returns percepts from a given location, and replaces some items with percepts from chapter 7.""" thing_percepts = { Gold: Glitter(), Wall: Bump(), Wumpus: Stench(), Pit: Breeze()} - '''Agents don't need to get their percepts''' + """Agents don't need to get their percepts""" thing_percepts[agent.__class__] = None - '''Gold only glitters in its cell''' + """Gold only glitters in its cell""" if location != agent.location: thing_percepts[Gold] = None @@ -746,8 +743,8 @@ def percepts_from(self, agent, location, tclass=Thing): return result if len(result) else [None] def percept(self, agent): - '''Returns things in adjacent (not diagonal) cells of the agent. - Result format: [Left, Right, Up, Down, Center / Current location]''' + """Returns things in adjacent (not diagonal) cells of the agent. + Result format: [Left, Right, Up, Down, Center / Current location]""" x, y = agent.location result = [] result.append(self.percepts_from(agent, (x - 1, y))) @@ -756,7 +753,7 @@ def percept(self, agent): result.append(self.percepts_from(agent, (x, y + 1))) result.append(self.percepts_from(agent, (x, y))) - '''The wumpus gives out a a loud scream once it's killed.''' + """The wumpus gives out a a loud scream once it's killed.""" wumpus = [thing for thing in self.things if isinstance(thing, Wumpus)] if len(wumpus) and not wumpus[0].alive and not wumpus[0].screamed: result[-1].append(Scream()) @@ -765,8 +762,8 @@ def percept(self, agent): return result def execute_action(self, agent, action): - '''Modify the state of the environment based on the agent's actions. - Performance score taken directly out of the book.''' + """Modify the state of the environment based on the agent's actions. + Performance score taken directly out of the book.""" if isinstance(agent, Explorer) and self.in_danger(agent): return @@ -794,7 +791,7 @@ def execute_action(self, agent, action): agent.performance += 1000 if Gold() in agent.holding else 0 self.delete_thing(agent) elif action == 'Shoot': - '''The arrow travels straight down the path the agent is facing''' + """The arrow travels straight down the path the agent is facing""" if agent.has_arrow: arrow_travel = agent.direction.move_forward(agent.location) while(self.is_inbounds(arrow_travel)): @@ -807,7 +804,7 @@ def execute_action(self, agent, action): agent.has_arrow = False def in_danger(self, agent): - '''Checks if Explorer is in danger (Pit or Wumpus), if he is, kill him''' + """Checks if Explorer is in danger (Pit or Wumpus), if he is, kill him""" for thing in self.list_things_at(agent.location): if isinstance(thing, Pit) or (isinstance(thing, Wumpus) and thing.alive): agent.alive = False @@ -817,8 +814,8 @@ def in_danger(self, agent): return False def is_done(self): - '''The game is over when the Explorer is killed - or if he climbs out of the cave only at (1,1).''' + """The game is over when the Explorer is killed + or if he climbs out of the cave only at (1,1).""" explorer = [agent for agent in self.agents if isinstance(agent, Explorer) ] if len(explorer): if explorer[0].alive: @@ -845,7 +842,7 @@ def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000): def test_agent(AgentFactory, steps, envs): - "Return the mean score of running an agent in each of the envs, for steps" + """Return the mean score of running an agent in each of the envs, for steps""" def score(env): agent = AgentFactory() env.add_thing(agent) From 651416e78d7786bcf8bdbe8f3b0a90deace9945d Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 6 Mar 2017 21:26:04 -0800 Subject: [PATCH 403/513] Update learning.py --- learning.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/learning.py b/learning.py index 678604b1b..aa0dc509d 100644 --- a/learning.py +++ b/learning.py @@ -1,7 +1,7 @@ """Learn to estimate functions from examples. (Chapters 18-20)""" from utils import ( - removeall, unique, product, argmax, argmax_random_tie, isclose, + removeall, unique, product, mode, argmax, argmax_random_tie, isclose, dotproduct, vector_add, scalar_vector_product, weighted_sample_with_replacement, weighted_sampler, num_or_str, normalize, clip, sigmoid, print_table, DataFile ) @@ -16,11 +16,6 @@ # ______________________________________________________________________________ -def mode(data): - """Return the most common data item. If there are ties, return any one of them.""" - (item, count) = Counter(data).most_common(1) - return item - def rms_error(predictions, targets): return math.sqrt(ms_error(predictions, targets)) From b2458ca6ab7c62245f2eb54a00a9204e8eb8a76c Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Mon, 6 Mar 2017 21:27:34 -0800 Subject: [PATCH 404/513] Update utils.py --- utils.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/utils.py b/utils.py index a6c5d0bd5..124b04132 100644 --- a/utils.py +++ b/utils.py @@ -59,6 +59,11 @@ def is_in(elt, seq): """Similar to (elt in seq), but compares with 'is', not '=='.""" return any(x is elt for x in seq) +def mode(data): + """Return the most common data item. If there are ties, return any one of them.""" + [(item, count)] = Counter(data).most_common(1) + return item + # ______________________________________________________________________________ # argmin and argmax From 94e63cd7f21410c61d4437c9347c541a3270ea0d Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 7 Mar 2017 11:02:53 +0530 Subject: [PATCH 405/513] changed mean boolean error (#325) * changed mean boolean error * Update learning.py --- learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learning.py b/learning.py index aa0dc509d..3790a2b02 100644 --- a/learning.py +++ b/learning.py @@ -33,7 +33,7 @@ def manhattan_distance(predictions, targets): def mean_boolean_error(predictions, targets): - return mean([(p != t) for p, t in zip(predictions, targets)]) + return mean(int(p != t) for p, t in zip(predictions, targets)) # ______________________________________________________________________________ From 69d93e6bba049d86a0df24f6e7f07e27253f85ac Mon Sep 17 00:00:00 2001 From: articuno12 Date: Tue, 7 Mar 2017 11:08:09 +0530 Subject: [PATCH 406/513] Update agents.py (#322) --- agents.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/agents.py b/agents.py index abd7b9f7a..a5bf376ca 100644 --- a/agents.py +++ b/agents.py @@ -381,7 +381,7 @@ class XYEnvironment(Environment): that are held.""" def __init__(self, width=10, height=10): - super(XYEnvironment, self).__init__() + super().__init__() self.width = width self.height = height @@ -452,7 +452,7 @@ def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False) if (exclude_duplicate_class_items and any(isinstance(t, thing.__class__) for t in self.list_things_at(location))): return - super(XYEnvironment, self).add_thing(thing, location) + super().add_thing(thing, location) def is_inbounds(self, location): """Checks to make sure that the location is inbounds (within walls if we have walls)""" @@ -471,11 +471,11 @@ def delete_thing(self, thing): """Deletes thing, and everything it is holding (if thing is an agent)""" if isinstance(thing, Agent): for obj in thing.holding: - super(XYEnvironment, self).delete_thing(obj) + super().delete_thing(obj) for obs in self.observers: obs.thing_deleted(obj) - super(XYEnvironment, self).delete_thing(thing) + super().delete_thing(thing) for obs in self.observers: obs.thing_deleted(thing) @@ -525,7 +525,7 @@ class ContinuousWorld(Environment): """Model for Continuous World.""" def __init__(self, width=10, height=10): - super(ContinuousWorld, self).__init__() + super().__init__() self.width = width self.height = height @@ -536,8 +536,8 @@ def add_obstacle(self, coordinates): class PolygonObstacle(Obstacle): def __init__(self, coordinates): - """Coordinates is a list of tuples.""" - super(PolygonObstacle, self).__init__() + """ Coordinates is a list of tuples.""" + super().__init__() self.coordinates = coordinates # ______________________________________________________________________________ @@ -556,7 +556,7 @@ class VacuumEnvironment(XYEnvironment): each turn taken.""" def __init__(self, width=10, height=10): - super(VacuumEnvironment, self).__init__(width, height) + super().__init__(width, height) self.add_walls() def thing_classes(self): @@ -579,7 +579,7 @@ def execute_action(self, agent, action): agent.performance += 100 self.delete_thing(dirt) else: - super(VacuumEnvironment, self).execute_action(agent, action) + super().execute_action(agent, action) if action != 'NoOp': agent.performance -= 1 @@ -593,7 +593,7 @@ class TrivialVacuumEnvironment(Environment): Environment.""" def __init__(self): - super(TrivialVacuumEnvironment, self).__init__() + super().__init__() self.status = {loc_A: random.choice(['Clean', 'Dirty']), loc_B: random.choice(['Clean', 'Dirty'])} @@ -677,7 +677,7 @@ class WumpusEnvironment(XYEnvironment): # Room should be 4x4 grid of rooms. The extra 2 for walls def __init__(self, agent_program, width=6, height=6): - super(WumpusEnvironment, self).__init__(width, height) + super().__init__(width, height) self.init_world(agent_program) def init_world(self, program): From 9689bbe7bd00290075e232d086ffd4474cd517e3 Mon Sep 17 00:00:00 2001 From: "C.G.Vedant" Date: Tue, 7 Mar 2017 11:08:33 +0530 Subject: [PATCH 407/513] Fixed knn_plot (#317) --- images/knn_plot.png | Bin 35268 -> 45256 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/images/knn_plot.png b/images/knn_plot.png index 1cee33e9e56eb385e15e063f248ad0e83c87833e..58b316fdd08a80ee60cc98476a420b14f5d904b7 100644 GIT binary patch literal 45256 zcmaI8WmFwa6D^FphT!fH+}(o(mq3EMy9Rf6cXxMp2=4Cg9^By@p7-AM{ky|D>%gp; zIeogTx_0f_MToqtI3gSl90&*qqNK!kMGz3sTj1pk0|lH>x^8&|-k|iP#J_`leEwv2 z6vY8&U~MFR+k=2$qJF+WM<{LtfRoS;k}{&u+u)E0WJJ02Q`R6LL?Dvig_QrSoMkwt z|D11m=yJPMI*_&=lP_chN9YI*)~tqr|8^q9RQQ;>#sMYuC44c~jxD!ELb`Jq#7e5^8(p8sb|>o|p`J2y{^GD`Cx|MwJPW6JvJ-F1<` zTgbt|LGs;y_c2va1gnHy%U{$^eP_^PA%;jTyc*}&WORSDVYgiQI}}5TS|~W10ILOj zf8xNg)jKi%HTHlwNO9}(sLPHAtw-m*p_o#wZ&Xyb!nFg6Ytvuk;vx!Bt}hGe zA4@BS|uQpX(y8~vnYAOzhmg_P3tEWI}A@w0J z_W_wQ@+MRDuuhXfGv>O+pl4WAUd|YT$H>`wsKGjWoS?yKE#I9HT){^^U{;}fCVZ+b zQQ`$qep zl)yWdH?c^dyr$-Ow-31ff_ZA7yb?2DJFYkapvqm~`$;~D6p|XFP_HQ8e70O+JgB+U zCv#QZ;8uI-f5G=8l{@Zd7kGOyL8R$FDEWJWZAMVTcI4*zQfB@E^9F}T0+H9!Lh8V!>?PBghxA_xjxfZl!0M~A`i7cd`hyGSL#JgDii4Kf7DSv$=d?jr>G zlV6Ap`EOHTQ&&^1EBt8i(@So(;@bEG1YkB8%JpFC6)#p3gav;4BUKxYVr05sORiaQ zBqSs-y(lXyN5{wiRg&ZDUsR8`Pxti|jxq(=uX4tP*npx#E`~-LNfhuXY|tK3ESTQb zY-7T9h7#8LXIam=N~myxv$aXYBbnhLBYG4>Y0o1+b~0ZEIy63xIB4CpedjtnG6JSE zKUijvbJR-W_&FnpZjV#yU=-f)5|Fd-S4~11nGIP43h66X--XR-CfcG;y6$E$P#bY6 zJ3C~>PVZJwFZQXX6u}ODoW{#6j6j8*Z9;gtSQJU3K8FYy8lp$>-D82cJzQAY6S}>L zAoG0}6%8DyJLm7?qg8gU znLzTWv1R@8;pTqTT1x4!?%2`vUWdgpGkxn3$(mKuf9qgHH$|Sq(C{b*UIQOaYg6dB z5x^=lvAkhV$x0XA@IvrMY~m44)V2l$$`dj~zoPjXsy@DFBTIO6Mx><%)}$Q)>J8jM z&fu*FGyA%=(Yg#{Y9pwgTH&P2F94$y{G&+WzzP4~5{I5kYZd5?+7w#rocU!8g>zbS zllA5VnjCMBHz@d0o$;h@)N-3MLrY7`>(iu{(Q2b5)1d+oypA&A)^QCT*=c`r4JlSS zWY(5@;-+Hy+9Mo2Wn=pCE7KQ3J%cb__h9L+2YN6BDqp``#u;2-K|Tw*qz97OfG@*_ zOt`B&Fez(LPU3bZiV)~aSLc^HIZT=~JrFZMWHuqKLVxu>bX$-`Ow?a9M!9xuSzO(;mnW)^Xb-&f`9`SUwd z>kRaMw3{vW7rk1m#vp&U+flVsALf=O%m5>bdI7r7S3h%hEaMR-^E&p@xSgPruHO$A zAf?^3P####oya@zF5xivtNIP`!_%-8f*WO-kXMK559g2-$PgSNqx7OjlyHEfzMflq zfs=&%iiB|M>UzJc!sGoT&jtlV!F7#oh`hNLD_ff%<;Tx|wi%#5zD&hNOPk5ujCi6k zXF}J(9fjd}MK-prLa3RoCxzVpg`UX@@a+W_Q~M*Z6zmK_-6;Vhax}GbbB2F;uQT(`}JB%C2^LcVOz=_R{fm;p)=s zBh7YqV0vv0FEh2#>%$WV7q<`8U}tw1n^DL6j-L$<9{%Zil;L-i6@HS2T?mf&XMh`k z>zF2ylC!9@j?*{pZklZsW8Q+OBZ&0`KTi5#@49Pe^2Bq*Ww}n2>=^UHjN8oz|2!6( zfuzcN`@)n=#|(nxy$tbd&;-`bo&T)}ML@YyDg|K}jl{K~@4V?8rK!&@w&`eE0i^P) zjfRn<#?Ic2Ym<|2f(Yuf?y$z9V`^&Zq#d_;q zv`$?*Ck-a%z=nz$wtrsd(kNPjbne?@Ic#_6kJ+vfT^%OSDqAT3oS= zh|tsI*5S+u30ct|zNXNyzFz}#wsg2K1B5Z5 z4MO-@C)8t?k31jNumif>1HtkB#@yWBz2PDj#}j3Ll-MJ-jA(4VV+LOKbvG_ z<9HnByNJ-{g=b%G&X?cmrGD%n!mC@|?n5px>C1_f?sK-0(o-*fPl!_>uH18P2y5WP zyKs_^z2R`lv&3vvRok1;Ig1sm_1q{Eo-@7MSYK0Ii$HsviRI~FJNhzjL#KGOoGWgN ze*gYGmi7?F{!mg}ymj8RNx{MrcXDDj5JfmC!?8X`uPe^8{D~MGXpXKhxSq(i&Af{f z3T;Sh>??H}zyAgmPAqbnYr!90;9~`o3?j(l#3@~i&2D(lKJ)sGhP4FUW}Xmv#J~(+dX{LZe%_;=!j=VQe3p(#2YUj;*U~VQ>%U z(73w(iZe(!GE%bt{b6+wU1C5&2FtWdJ{r7LJrRT4=_PcGQml?1LSn3r!E7YF#tLI8 zCT4%*y@C176Q<1zyoX!z06(>WjN~sNKBL%U4<}}NN}}SWKGVT_aDSeml!2G%(dM^0 z!Zbh%wY2FX9Cau`&Vz2u?Q^65>PO#)q#?$2{uM7E9{*RP977`7Dk=7Kjzi zfyb-Flw8wwzdD9rB17)$SJ}=sw+CVe+xqz*kpbrCjP`CL>S51v0B{D94rlRTR6-NK ziWI}2n~J*@1c5{2eRx%pllSkJ>Zlh^QenP{G|tolnC(0y5OfgD_QW5*C7#Lse|WQI zL*S8S>w+rtVmiL{p-6CMOT#~6{BB!R-`y*ae|K(CZl@nVUowP!4BfZY+;?>5>96?A zS*B|46`j3L5yB>SNb* z_DqD{%+w=>MU%HA0@B@x-e4IYK!%A%3idlai68Hs1yNRRCZ&4HLFv$`l5Xz6tAytt z_7pw%f*C4~?dMf~J#+MEul$7I9$A(8{L)3e!O)A;7|Uc}=(Y3Ku0pOR&QPe>unKT% z;>|+bXg;7227*>mq*=x522Ut2AQNX z&wx+S$OmyR%f9G?L4`#XEj^gl9ErhB7#~+`^LdLo_i+t7UBhE`BIwlQ)1yP7_yZ+0 zf|w}OV|s^KO1iC)r&P0|zXa^1JLo(<{MU*`(C(0D>R0Dcsh-B?4DC9JL!uUSm4@Q& zVR2rK@eOa%|0fCLj+g`8f%YRM{2o%Eg=;vf?ZNOzidL_FY)My-4@Y>i8knIw}@sIKtnDa`N--Z|EF6^-yEUU5DRoMw$|Bb#IG z9s6wfIz0+Lw@OaaRZD-MZ9ue5UV#&fHyn-ksA~1$hw!{Pb5E!>A)Y(xx6NP_J#iF{ zA-!0?*g41Sck5W2Q+~g~mmUdG3S-l}S=4f$S}>{(1z8371)*~|lgG z`@!cfHd_pe#4q@bk5ifnS+>Jq-KV1GwI#z8_D+2365d{k|!@#4sL@?j`w3B@j@(?^|Z+wVJ?o8 zZyWrMnPfv!tcu(Lok48-`)_ZHFHIp0bRLGAK zii%(8li<5H73jiA@YOTUXbqkCzi``a?E z-K*#>o%(u>d+P%8CzMk(j!fhwhc0;@lMmn2H5=L23fk4F-eO*vn8j6$ARjqR=&g={)j-T8`n)&6l&76mb}PXdkV^@iIelWs4>7c?|mPCNuGMsMA| zzy`&4wXv$|UQ0NodR=09LVXV1TZ!G2J58>r){}u^5Vg08O;m4OVQt|f!S4}a-g~l^ zBKi0jK1;LU`I(3^$b#n?6}f+ZOO?rA$LP=wnd-QvvnHi!tI;%lkBsUfc>Y)+F&^E? 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