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search.py
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"""Search (Chapters 3-4)
The way to use this code is to subclass Problem to create a class of problems,
then create problem instances and solve them with calls to the various search
functions."""
from utils import *
import math, random, sys, time, bisect, 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
of your subclass and solve them with the various search functions."""
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
def actions(self, state):
"""Return the actions that can be executed in the given
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
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
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
method if checking against a single self.goal is not enough."""
return 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 + 1
def value(self, state):
"""For optimization problems, each state has a value. Hill-climbing
and related algorithms try to maximize this value."""
abstract
#______________________________________________________________________________
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
the same state. Also includes the action that got us to this state, and
the total path_cost (also known as g) to reach the node. Other functions
may add an f and h value; see best_first_graph_search and astar_search for
an explanation of how the f and h values are handled. You will not need to
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."
self.state = state
self.parent = parent
self.action = action
self.path_cost = path_cost
self.depth = 0
if parent:
self.depth = parent.depth + 1
def __repr__(self):
return "<Node %s>" % (self.state,)
def expand(self, problem):
"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):
"Fig. 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 [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."
node, path_back = self, []
while node:
path_back.append(node)
node = node.parent
return list(reversed(path_back))
# We want for a queue of nodes in breadth_first_search or
# astar_search to have no duplicated states, so we treat nodes
# with the same state as equal. [Problem: this may not be what you
# want in other contexts.]
def __eq__(self, other):
return isinstance(other, Node) and self.state == other.state
def __hash__(self):
return hash(self.state)
#______________________________________________________________________________
class SimpleProblemSolvingAgentProgram:
"""Abstract framework for a problem-solving agent. [Fig. 3.1]"""
def __init__(self, initial_state=None):
update(self, state=initial_state, seq=[])
def __call__(self, percept):
self.state = self.update_state(self.state, percept)
if not self.seq:
goal = self.formulate_goal(self.state)
problem = self.formulate_problem(self.state, goal)
self.seq = self.search(problem)
if not self.seq: return None
return self.seq.pop(0)
def update_state(self, percept):
abstract
def formulate_goal(self, state):
abstract
def formulate_problem(self, state, goal):
abstract
def search(self, problem):
abstract
#______________________________________________________________________________
# 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.
Don't worry about repeated paths to a state. [Fig. 3.7]"""
frontier.append(Node(problem.initial))
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
return node
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.
If two paths reach a state, only use the first one. [Fig. 3.7]"""
frontier.append(Node(problem.initial))
explored = set()
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
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)
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)
if problem.goal_test(node.state):
return node
frontier = FIFOQueue()
frontier.append(node)
explored = set()
while frontier:
node = frontier.pop()
explored.add(node.state)
for child in node.expand(problem):
if child.state not in explored and child not in frontier:
if problem.goal_test(child.state):
return child
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,
if f is a heuristic estimate to the goal, then we have greedy best
first search; if f is node.depth then we have breadth-first search.
There is a subtlety: the line "f = memoize(f, 'f')" means that the f
values will be cached on the nodes as they are computed. So after doing
a best first search you can examine the f values of the path returned."""
f = memoize(f, 'f')
node = Node(problem.initial)
if problem.goal_test(node.state):
return node
frontier = PriorityQueue(min, f)
frontier.append(node)
explored = set()
while frontier:
node = frontier.pop()
if problem.goal_test(node.state):
return node
explored.add(node.state)
for child in node.expand(problem):
if child.state not in explored and child not in frontier:
frontier.append(child)
elif child in frontier:
incumbent = frontier[child]
if f(child) < f(incumbent):
del frontier[incumbent]
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):
if problem.goal_test(node.state):
return node
elif node.depth == limit:
return 'cutoff'
else:
cutoff_occurred = False
for child in node.expand(problem):
result = recursive_dls(child, problem, limit)
if result == 'cutoff':
cutoff_occurred = True
elif result is not None:
return result
return if_(cutoff_occurred, 'cutoff', None)
# Body of depth_limited_search:
return recursive_dls(Node(problem.initial), problem, limit)
def iterative_deepening_search(problem):
"[Fig. 3.18]"
for depth in xrange(sys.maxint):
result = depth_limited_search(problem, depth)
if result != 'cutoff':
return result
#______________________________________________________________________________
# Informed (Heuristic) Search
greedy_best_first_graph_search = best_first_graph_search
# 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).
You need to specify the h function when you call astar_search, or
else in your Problem subclass."""
h = memoize(h or problem.h, 'h')
return best_first_graph_search(problem, lambda n: n.path_cost + h(n))
#______________________________________________________________________________
# Other search algorithms
def recursive_best_first_search(problem, h=None):
"[Fig. 3.26]"
h = memoize(h or problem.h, 'h')
def RBFS(problem, node, flimit):
if problem.goal_test(node.state):
return node, 0 # (The second value is immaterial)
successors = node.expand(problem)
if len(successors) == 0:
return None, infinity
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
best = successors[0]
if best.f > flimit:
return None, best.f
if len(successors) > 1:
alternative = successors[1].f
else:
alternative = infinity
result, best.f = RBFS(problem, best, min(flimit, alternative))
if result is not None:
return result, best.f
node = Node(problem.initial)
node.f = h(node)
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]"""
current = Node(problem.initial)
while True:
neighbors = current.expand(problem)
if not neighbors:
break
neighbor = argmax_random_tie(neighbors,
lambda node: problem.value(node.state))
if problem.value(neighbor.state) <= problem.value(current.state):
break
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: if_(t < limit, k * math.exp(-lam * t), 0)
def simulated_annealing(problem, schedule=exp_schedule()):
"[Fig. 4.5]"
current = Node(problem.initial)
for t in xrange(sys.maxint):
T = schedule(t)
if T == 0:
return current
neighbors = current.expand(problem)
if not neighbors:
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)):
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()
#______________________________________________________________________________
# 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)
def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):
"[Fig. 4.8]"
for i in range(ngen):
new_population = []
for i in len(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:
child.mutate()
new_population.append(child)
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
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:])
def mutate(self):
"Change a few of my genes."
abstract
#_____________________________________________________________________________
# The remainder of this file implements examples for the search algorithms.
#______________________________________________________________________________
# 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})
this makes a graph with 3 nodes, A, B, and C, with an edge of length 1 from
A to B, and an edge of length 2 from A to C. You can also do:
g = Graph({'A': {'B': 1, 'C': 2}, directed=False)
This makes an undirected graph, so inverse links are also added. The graph
stays undirected; if you add more links with g.connect('B', 'C', 3), then
inverse link is also added. You can use g.nodes() to get a list of nodes,
g.get('A') to get a dict of links out of A, and g.get('A', 'B') to get the
length of the link from A to B. 'Lengths' can actually be any object at
all, and nodes can be any hashable object."""
def __init__(self, dict=None, directed=True):
self.dict = dict or {}
self.directed = directed
if not directed: self.make_undirected()
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():
self.connect1(b, a, distance)
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)
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
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)
def nodes(self):
"Return a list of nodes in the graph."
return 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,
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.
Because inverse links are added, some nodes will have more connections.
The distance between nodes is the hypotenuse times curvature(),
where curvature() defaults to a random number between 1.1 and 1.5."""
g = UndirectedGraph()
g.locations = {}
## 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.
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
return distance(g.locations[n], here)
neighbor = argmin(nodes, distance_to_node)
d = distance(g.locations[neighbor], here) * curvature()
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(
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):
"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."
return self.graph.get(A).keys()
def result(self, state, action):
"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."
locs = getattr(self.graph, 'locations', None)
if locs:
return int(distance(locs[node.state], locs[self.goal]))
else:
return infinity
#______________________________________________________________________________
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,
row r, and a value of None means that the c-th column has not been
filled in yet. We fill in columns left to right.
>>> depth_first_tree_search(NQueensProblem(8))
<Node [7, 3, 0, 2, 5, 1, 6, 4]>
"""
def __init__(self, N):
self.N = N
self.initial = [None] * 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
else:
col = state.index(None)
return [row for row in range(self.N)
if not self.conflicted(state, row, col)]
def result(self, state, 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?"
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?"
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."
if state[-1] is None:
return False
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.
ALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
cubes16 = ['FORIXB', 'MOQABJ', 'GURILW', 'SETUPL',
'CMPDAE', 'ACITAO', 'SLCRAE', 'ROMASH',
'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."""
cubes = [cubes16[i % 16] for i in range(n*n)]
random.shuffle(cubes)
return 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
boyan_best = list('RSTCSDEIAEGNLRPEATESMSSID')
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
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):
return cache.get(n2)
n = exact_sqrt(n2)
neighbors = [None] * n2
for i in range(n2):
neighbors[i] = []
on_top = i < n
on_bottom = i >= n2 - n
on_left = i % n == 0
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_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)
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))
assert n * n == n2
return n
#_____________________________________________________________________________
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]
self.words.sort()
self.bounds = {}
for c in ALPHABET:
c2 = chr(ord(c) + 1)
self.bounds[c] = (bisect.bisect(self.words, c),
bisect.bisect(self.words, c2))
def lookup(self, prefix, lo=0, hi=None):
"""See if prefix is in dictionary, as a full word or as a prefix.
Return two values: the first is the lowest i such that
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)
i = bisect.bisect_left(words, prefix, lo, hi)
if i < len(words) and words[i].startswith(prefix):
return i, (words[i] == prefix)
else:
return None, False
def __contains__(self, word):
return self.lookup(word)[1]
def __len__(self):
return len(self.words)
#_____________________________________________________________________________
class BoggleFinder:
"""A class that allows you to find all the words in a Boggle board. """
wordlist = None ## A class variable, holding a wordlist
def __init__(self, board=None):
if BoggleFinder.wordlist is None:
BoggleFinder.wordlist = Wordlist("../data/EN-text/wordlist")
self.found = {}
if board:
self.set_board(board)
def set_board(self, board=None):
"Set the board, and find all the words in it."
if board is None:
board = random_boggle()
self.board = board
self.neighbors = boggle_neighbors(len(board))
self.found = {}
for i in range(len(board)):
lo, hi = self.wordlist.bounds[board[i]]
self.find(lo, hi, i, [], '')
return self
def find(self, lo, hi, i, visited, prefix):
"""Looking in square i, find the words that continue the prefix,
considering the entries in self.wordlist.words[lo:hi], and not
revisiting the squares in visited."""
if i in visited:
return
wordpos, is_word = self.wordlist.lookup(prefix, lo, hi)
if wordpos is not None:
if is_word:
self.found[prefix] = True
visited.append(i)
c = self.board[i]
if c == 'Q': c = 'QU'
prefix += c
for j in self.neighbors[i]:
self.find(wordpos, hi, j, visited, prefix)
visited.pop()
def words(self):
"The words found."
return 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."
return sum([self.scores[len(w)] for w in self.words()])
def __len__(self):
"The number of words found."
return len(self.found)
#_____________________________________________________________________________
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."""
finder = BoggleFinder()
if board is None:
board = random_boggle()
best = len(finder.set_board(board))
for _ in range(ntimes):
i, oldc = mutate_boggle(board)
new = len(finder.set_board(board))
if new > best:
best = new
if verbose: print best, _, board
else:
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)
return i, oldc
#______________________________________________________________________________
# Code to compare searchers on various problems.
class InstrumentedProblem(Problem):
"""Delegates to a problem, and keeps statistics."""
def __init__(self, problem):
self.problem = problem
self.succs = self.goal_tests = self.states = 0
self.found = None
def actions(self, state):
self.succs += 1
return self.problem.actions(state)
def result(self, state, action):
self.states += 1
return self.problem.result(state, action)
def goal_test(self, state):
self.goal_tests += 1
result = self.problem.goal_test(state)
if result:
self.found = state
return result
def path_cost(self, c, state1, action, state2):
return self.problem.path_cost(c, state1, action, state2)
def value(self, state):
return self.problem.value(state)
def __getattr__(self, attr):
return getattr(self.problem, attr)
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,
iterative_deepening_search,
depth_limited_search,
recursive_best_first_search]):
def do(searcher, problem):
p = InstrumentedProblem(problem)
searcher(p)
return p
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()
Searcher Romania(A, B) Romania(O, N) Australia
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'])
#______________________________________________________________________________
__doc__ += """
>>> ab = GraphProblem('A', 'B', romania)
>>> breadth_first_tree_search(ab).solution()
['S', 'F', 'B']
>>> breadth_first_search(ab).solution()
['S', 'F', 'B']
>>> uniform_cost_search(ab).solution()
['S', 'R', 'P', 'B']
>>> depth_first_graph_search(ab).solution()
['T', 'L', 'M', 'D', 'C', 'P', 'B']
>>> iterative_deepening_search(ab).solution()
['S', 'F', 'B']
>>> len(depth_limited_search(ab).solution())
50
>>> astar_search(ab).solution()
['S', 'R', 'P', 'B']
>>> recursive_best_first_search(ab).solution()
['S', 'R', 'P', 'B']
>>> board = list('SARTELNID')
>>> print_boggle(board)
S A R
T E L
N I D
>>> f = BoggleFinder(board)
>>> len(f)
206
"""