fig = pyplot.gcf()
fig.set_size_inches(20, 15)
ax1 = fig.add_subplot(211)
ax1.set_ylabel('L_m')
ax1.set_xlabel('Values of Lambda')
pyplot.plot(alpha, result)
pyplot.show()
if __name__ == "__main__":
# read all required files
from FileReader import FileReader
file = FileReader()
vocab, unigram, bigram = file.readAll()
# compute the probability of each word
from LanguageModel import LanguageModel
lm = LanguageModel()
P_w = lm.Unigram(unigram)
vocab_size = len(vocab)
P_w1_w2, words = lm.Bigram(bigram, vocab_size)
sentence = 'The sixteen officials sold fire insurance'
word_list = sentence.upper().split()
# calculate log-likelihood under unigram
result_unigram = log_likelihood_unigram(word_list, P_w, vocab)
print("Log-likelihood under unigram model is " +
str(math.log(result_unigram)))
