def calc_trigrams(brown_tags):
ngrams, corpus_size, sentence_count = n_gramer.make_ngrams_for_corpus(brown_tags, 3, START_SYMBOL, STOP_SYMBOL)
n_gramer.calculate_ngram_probabilities(ngrams, corpus_size, sentence_count)
# use only trigrams
q_values = ngrams[2]
return q_values
def calc_trigrams(brown_tags):
ngrams, corpus_size, sentence_count = n_gramer.make_ngrams_for_corpus(
brown_tags, 3, START_SYMBOL, STOP_SYMBOL)
n_gramer.calculate_ngram_probabilities(ngrams, corpus_size, sentence_count)
# use only trigrams
q_values = ngrams[2]
return q_values
def calc_probabilities(training_corpus):
# Calculate the probabilies using our external module
grams, corpus_size, sentence_count = ngramer.make_ngrams_for_corpus(training_corpus, 3, START_SYMBOL, STOP_SYMBOL)
ngramer.calculate_ngram_probabilities(grams, corpus_size, sentence_count)
# Transfer back to HW output needed
unigram_p = grams[0]
bigram_p = grams[1]
trigram_p = grams[2]
return unigram_p, bigram_p, trigram_p
def calc_probabilities(training_corpus):
# Calculate the probabilies using our external module
grams, corpus_size, sentence_count = ngramer.make_ngrams_for_corpus(
training_corpus, 3, START_SYMBOL, STOP_SYMBOL)
ngramer.calculate_ngram_probabilities(grams, corpus_size, sentence_count)
# Transfer back to HW output needed
unigram_p = grams[0]
bigram_p = grams[1]
trigram_p = grams[2]
return unigram_p, bigram_p, trigram_p
def calc_probabilities(training_corpus):
# Calculate the probabilies using our external module
grams, corpus_size, sentence_count = ngramer.make_ngrams_for_corpus(
training_corpus, 3, START_SYMBOL, STOP_SYMBOL)
ngramer.calculate_ngram_probabilities(grams, corpus_size, sentence_count)
# Transfer back to HW output needed
unigram_p = grams[0]
bigram_p = grams[1]
trigram_p = grams[2]
# TODO: Comment out when submit
if ALLOW_TESTS:
a_tests.test_grams(unigram_p, bigram_p, trigram_p)
return unigram_p, bigram_p, trigram_p
