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Fixed filtering out of duplicate constraints.

Changed duplicate test and added __eq__ methods to constraints.
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1 parent 4f20b83 commit 4b4bda0d9d60542bdc92b32fe7ba91593049bb83 @presleyp committed Apr 13, 2012
Showing with 29 additions and 32 deletions.
  1. +29 −32
@@ -1,37 +1,25 @@
#!/usr/bin/env python
import csv, copy, numpy, random, cPickle, datetime
from mapping import Mapping
+from featuredict import FeatureDict
# delete the saved input file if you change the input making code
# implement tableau making if you give it a file with ungrammatical mappings
-from featuredict import FeatureDict
-#TODO get numpy working with python3
-#TODO implicational constraints
-#TODO consider constraining GEN
#TODO graphs - error rate
-#TODO think about using lexical and tier constraints smartly, not based
- #on frequency in the constraint set. You need a tier constraint when the
- #gw has similar things on a tier and the cw doesn't. You need a lexical constraint
- # when your constraints worked on a really similar word but don't work
- # on this one (harder, because constraint set keeps changing).
-#TODO consider adding lexically conditioned constraints
#TODO switch from unigram to bigram if no constraints possible
#TODO make non_boundaries dict in FeatureDict and make it read "boundary" from feature names
#TODO make sure there's a faithful mapping?
-#TODO Faithfulness: delete a change that's being reversed? make dontcares in major features and make violations notice.
-#TODO random to numpy.random - did random.random and randint but not choice or
-#sample (which will both be choice, with an optional size arg, have to take an
- #array)
-#TODO save output to file; consider tracking SSE
-#TODO work on get ngrams (copy problem in diff ngrams), get violations
+#TODO Faithfulness: delete a change that's being reversed?
+#TODO time random.sample vs numpy.random.sample
+#TODO copy problem in diff_ngrams
#TODO figure out why features are getting deleted from winners when they shouldn't be. maybe a copy is needed somewhere.
#FIXME tiers aren't working and avoiding duplicate constraints isn't working. thought i fixed tiers but instead of being wrong, they're now just not happening.
# not sure i'm getting duplicate constraints anymore, but duplicate winners
# happen occasionally. getting a runtime warning that i don't understand.
# remember tiers make winners shorter, could affect whether ngram is too large.
# then position finding might not work, and then everything gets messed up.
+#fixed a problem with avoiding duplicated constraints.
+#if the same operation happens to a candidate more than once, that will be lost bc of set representation.
#TODO think about using losers to help guide constraint induction. if you make a constraint, you want it to privilege cw over gw, but you
# also want cw to win over losers. could this help?
#TODO look at Jason Riggle's GEN and think about using CON to make GEN.
@@ -44,8 +32,6 @@
#"learner.Learn('feature_chart4.csv', ['input5.csv'], processes = '[self.change_feature_value]')", 'learnerprofile.txt')
# p = pstats.Stats('learnerprofile.txt')
# p.sort_stats('cumulative').print_stats(30)
-# with input, .414 on input5
-# without input, .142 on input5
class Input:
"""Give input in the form of a csv file where each line is a mapping, with 0
@@ -248,42 +234,44 @@ def induce(self, winners):
"""Makes one new markedness and one new faithfulness constraint
and initializes their weights, unless appropriate constraints
cannot be found within 15 tries."""
- #print 'calling induce'
#print self.i
assert len(self.weights) == len(self.constraints) + 1
self.make_constraint(Faithfulness, winners, self.feature_dict)
- #print 'made F'
if self.aligned:
self.make_constraint(MarkednessAligned, self.feature_dict, self.tier_freq, winners)
- #print 'made M'
self.make_constraint(Markedness, self.feature_dict, self.tier_freq, winners)
- new_weights = numpy.random.random(self.num_needed(self.weights))
- self.weights = numpy.append(self.weights, new_weights)
- assert len(self.weights) == len(self.constraints) + 1
+ #new_weights = numpy.random.random(self.num_needed(self.weights))
+ #self.weights = numpy.append(self.weights, new_weights)
+ #assert len(self.weights) == len(self.constraints) + 1
self.i += 1
def make_constraint(self, constraint_type, *args):
i = 0
- while i < 15:
+ while i < 10:
new_constraint = constraint_type(*args)
i += 1
if new_constraint.constraint == None:
- elif new_constraint not in self.constraints:
+ duplicate = False
+ for constraint in self.constraints:
+ if new_constraint == constraint:
+ duplicate = True
+ break
+ if not duplicate:
+ self.weights = numpy.append(self.weights, numpy.random.random(1))
def get_violations(self, mapping):
- #print 'evaluating'
new_constraints = -self.num_needed(mapping.violations)
if new_constraints < 0:
new_violations = numpy.array([constraint.get_violation(mapping)
for constraint in self.constraints[new_constraints:]])
mapping.violations = numpy.append(mapping.violations, new_violations)
assert len(mapping.violations) == len(self.constraints) + 1
- def num_needed(self, array):
+ def num_needed(self, array): # merge with get violations if adding to weights in make_constraint works
"""Find number of constraints added since the array was updated,
keeping in mind that the array has an intercept not included in
the constraint set."""
@@ -425,11 +413,11 @@ def pick_unique_pattern(self, winners):
position = position_in_ngram
assert self.constraint != None
- print 'base', base, 'constraint', self.constraint, 'protected segment', protected_segment, 'position', position
+ #print 'base', base, 'constraint', self.constraint, 'protected segment', protected_segment, 'position', position
for i in range(len(pattern)):
if len(pattern[i]) > 1:
pattern[i] = set(random.sample(pattern[i], numpy.random.randint(1, len(pattern[i]))))
- print pattern[position]
+ #print pattern[position]
assert type(pattern[position]) == set
self.constraint = pattern
@@ -484,6 +472,12 @@ def polarity(self, input):
return '+'
+ def __eq__(self, other):
+ if len(self.constraint) != len(other.constraint):
+ return False
+ else:
+ return (numpy.equal(self.constraint, other.constraint)).all()
def __str__(self):
polarity = '+' if self.violation else '-'
segments = []
@@ -526,6 +520,9 @@ def get_violation(self, mapping):
violation -= 1
return violation
+ def __eq__(self, other):
+ self.constraint == other.constraint
def __str__(self):
#segment_type = []
value = None

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