def getUnigramPerplexity():
'''
Controller method to find the perplexity of all the test books with all the genres
'''
unigram_model = loadUnigramModels()
#Reads in the unigrams one file at a time and stores it with their bookname
book_tokens = {}
for genre in genres:
print("\nReading test files for genre {0}".format(genre))
for path in os.listdir(test_path + genre):
book_tokens[path] = getTokensForFile(test_path + genre + '/' + path)
#Computes the perplixity of every test corpus against each of the unigram models created
book_perplexity = defaultdict(dict)
for book, unigrams in book_tokens.iteritems():
print('')
for genre, model in unigram_model.iteritems():
book_perplexity[book][genre] = computeUnigramPerplexity(model, unigrams)
print("Perplexity of '{0}' book on {1} genre model: {2}".format(book,genre,book_perplexity[book][genre]))
return book_perplexity
def main():
'''
Controller method for loading the models and calling the sentence generation
methods
'''
#Generate the unigram model for all the genres or load it from memory
unigram_model = loadUnigramModels()
#Generate random sentences from the unigram model which ends as soon as sentence end character is presented
generateRandomSentenceFromUnigram(unigram_model)
#Generate the bigram model for all the genres
bigram_model = loadBigramModels('BigramSentenceModel')
#Generate random sentences from the bigram model with default seed and n=200
generateRandomSentenceFromBigram(bigram_model, n=200)
#Generate random sentences from the bigram model with custom seed and n=100
#Will consider <START> character as seed for history which has not been specified
bigram_seed = {
'children':'sjbdsabdoisabdoisbdoias',
'crime':'killed'
}
generateRandomSentenceFromBigram(bigram_model, seed = bigram_seed, n=200)
