def testEquations(classifier, labeled_data):
correctly_found = 0
incorrectly_found = 1
for (i, iIndex, wordproblem, equationTemplate, solution) in labeled_data:
foundIndex = classifier.classify(extractFeatures(wordproblem))
#print foundIndex, i
if foundIndex == i:
correctly_found += 1
else:
incorrectly_found += 1
return (correctly_found, incorrectly_found)
def trainClassifier(labeled_word_problems, algorithm):
featuresets = [(extractFeatures(wordproblem), i) for (i, iIndex, wordproblem, equationTemplate, lSolutions) in labeled_word_problems]
train_set = featuresets
if algorithm == 'DecisionTree':
classifier = nltk.DecisionTreeClassifier.train(train_set)
elif algorithm == 'NaiveBayes':
classifier = nltk.NaiveBayesClassifier.train(train_set)
elif algorithm == 'MaxEntMegam':
classifier = nltk.classify.MaxentClassifier.train(train_set, 'MEGAM', trace=0, max_iter=1)
elif algorithm == 'MaxEnt':
classifier = nltk.MaxentClassifier.train(train_set)
return classifier
#chosen_template = classifier.classify(extractFeature('Marc sold 563 tickets for the school play. Student tickets cost 4 dollars and adult tickets cost 6 dollars. Marc \'s sales totaled 2840 dollars. How many adult tickets and student tickets did Marc sell? '))
#return chosen_template
from FeatureExtractor import extractFeatures
extractFeatures("./1_fulldoc.txt", "./1_fulldocwithoutstemming.txt")
