def main():
"""Trains all of the language models and tests them on the dev data. Change devPath if you
wish to do things like test on the training data.
"""
trainPath = '../data/micro/en_US/'
trainingCorpus = CapstoneCorpus(trainPath)
#print str(trainingCorpus)
sent = "When you breathe, I want to be the air for you. I'll be there for you, I'd live and I'd"
tokens = Tokenize(sent)
print 'Uniform Language Model: '
uniformLM = UniformLanguageModel(trainingCorpus)
print "VocSize= " + str(len(uniformLM.words))
print sent
print tokens
print "uniform score=" + str(uniformLM.score(tokens))
print 'Unigram Language Model: '
unigramLM = UnigramLanguageModel(trainingCorpus)
print "VocSize= " + str(len(unigramLM.unigramCounts))
print "unigram score=" + str(unigramLM.score(tokens))
print 'Laplace Unigram Language Model: '
laplaceUnigramLM = LaplaceUnigramLanguageModel(trainingCorpus)
laplaceUnigramLM.save("smallUnigram.LM")
print "VocSize= " + str(len(laplaceUnigramLM.f1))
print "unigram score=" + str(laplaceUnigramLM.score(tokens))
print 'Laplace Bigram Language Model: '
laplaceBigramLM = LaplaceBigramLanguageModel(trainingCorpus)
laplaceBigramLM.save("smallBigram.LM")
print "bigram score=" + str(laplaceBigramLM.score(tokens))
print 'Laplace Ngram Language Model: N=2'
laplaceN2gramLM = LaplaceNgramLanguageModel(trainingCorpus,2)
laplaceN2gramLM.save("smallN2gram.LM")
print "N=2gram score=" + str(laplaceN2gramLM.score(tokens))
print 'Laplace Ngram Language Model: N=3'
laplaceN3gramLM = LaplaceNgramLanguageModel(trainingCorpus,3)
laplaceN3gramLM.save("smallN3gram.LM")
print "N=3gram score=" + str(laplaceN2gramLM.score(tokens))
print 'Custom Language Model: '
customLM = CustomLanguageModel(trainingCorpus,N=2)
print "Custom LM score=" + str(customLM.score(tokens))
def main():
"""Trains all of the language models and tests them on the dev data. Change devPath if you
wish to do things like test on the training data.
"""
trainPath = '../data/micro/en_US/'
trainingCorpus = CapstoneCorpus(trainPath)
#print str(trainingCorpus)
sent = "When you breathe, I want to be the air for you. I'll be there for you, I'd live and I'd"
tokens = Tokenize(sent)
print 'Uniform Language Model: '
uniformLM = UniformLanguageModel(trainingCorpus)
print "VocSize= " + str(len(uniformLM.words))
print sent
print tokens
print "uniform score=" + str(uniformLM.score(tokens))
print 'Unigram Language Model: '
unigramLM = UnigramLanguageModel(trainingCorpus)
print "VocSize= " + str(len(unigramLM.unigramCounts))
print "unigram score=" + str(unigramLM.score(tokens))
print 'Laplace Unigram Language Model: '
laplaceUnigramLM = LaplaceUnigramLanguageModel(trainingCorpus)
laplaceUnigramLM.save("smallUnigram.LM")
print "VocSize= " + str(len(laplaceUnigramLM.f1))
print "unigram score=" + str(laplaceUnigramLM.score(tokens))
print 'Laplace Bigram Language Model: '
laplaceBigramLM = LaplaceBigramLanguageModel(trainingCorpus)
laplaceBigramLM.save("smallBigram.LM")
print "bigram score=" + str(laplaceBigramLM.score(tokens))
print 'Laplace Ngram Language Model: N=2'
laplaceN2gramLM = LaplaceNgramLanguageModel(trainingCorpus, 2)
laplaceN2gramLM.save("smallN2gram.LM")
print "N=2gram score=" + str(laplaceN2gramLM.score(tokens))
print 'Laplace Ngram Language Model: N=3'
laplaceN3gramLM = LaplaceNgramLanguageModel(trainingCorpus, 3)
laplaceN3gramLM.save("smallN3gram.LM")
print "N=3gram score=" + str(laplaceN2gramLM.score(tokens))
print 'Custom Language Model: '
customLM = CustomLanguageModel(trainingCorpus, N=2)
print "Custom LM score=" + str(customLM.score(tokens))
