def test_next_word_is(self):
'''Test next_word_probability for is'''
next_words = next_word_probability(TEST_TEXT1, 'is')
self.assertEqual(2, len(next_words), 2)
self.assertEqual(2, next_words['a'], 2)
self.assertEqual(1, next_words['not'], 1)
next_words = probability_from_count(next_words)
self.assertEqual(TWO_THIRDS, next_words['a'])
self.assertEqual(ONE_THIRD, next_words['not'])
def test_next_word_a(self):
'''Test next_word_probability for a'''
next_words = next_word_probability(TEST_TEXT1, 'a')
self.assertEqual(2, len(next_words))
self.assertEqual(2, next_words['test'])
self.assertEqual(1, next_words['mess'])
next_words = probability_from_count(next_words)
self.assertEqual(TWO_THIRDS, next_words['test'])
self.assertEqual(ONE_THIRD, next_words['mess'])
def later_words_probabilities(sample, word, distance):
'''
@param sample: a sample of text to draw from
@param word: a word occuring before a corrupted sequence
@param distance: how many words later to estimate (i.e. 1 for the next word, 2 for the word
after that)
@returns: a single word which is the most likely possibility
'''
# Given a word, collect the relative probabilities of possible following words
# from @sample. You may want to import your code from the maximum likelihood exercise.
next_words = next_word_probability(sample, word)
next_words = probability_from_count(next_words)
node = []
node.append(next_words)
# Repeat the above process--for each distance beyond 1, evaluate the words that
# might come after each word, and combine them weighting by relative probability
# into an estimate of what might appear next.
if distance > 1:
children = dict()
for a_word in next_words.iterkeys():
children[a_word] = later_words_probabilities(sample, a_word, distance - 1)
node.append(children)
return node
