def test_trigram_viterbi(self):
dt = DynamicTable()
viterbi = ViterbiMath(self._obsT, self._transmBi, self._transmTri, self._tags)
col = 0
dt.update(viterbi.get_next_column(dt, 3, col, self._wordSeq[col]))
col = col + 1
dt.update(viterbi.get_next_column(dt, 3, col, self._wordSeq[col]))
expected_table = [{'VB': 0.1, 'NN': 0.1}, {'VB': 0.004000000000000001, 'NN': 0.027999999999999997}]
actual_table = dt.probs
self.assertListEqual(expected_table, actual_table, "do_trigram() probability computation test")
def predict(self, word_seq, n):
"""
Predicts the tag sequence of a word sequence 'word_seq' using 'n'-gram model.
----
input
word_seq: a list of word strings to predict the tag sequence
n: either 2 (bigram) or 3 (trigram)
returns a list of tag strings of the tag sequence predicted
"""
seq_size = len(word_seq)
if seq_size <= 0:
return []
dynamic_table = DynamicTable()
for c in xrange(0,seq_size):
next_col = self.get_next_column(dynamic_table, n, c, word_seq[c])
dynamic_table.update(next_col)
last_c = seq_size - 1
last_col = dynamic_table.probs[last_c]
last_best_tag = max(last_col, key=last_col.get)
return dynamic_table.full_path(last_c, last_best_tag)
