"""Returns all n-grams of a text
>>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
- ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
+ ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
'nf', 'fo', 'ox']
>>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
- ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
+ ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
'rown', 'ownf', 'wnfo', 'nfox']
"""
return [text[i:i+n] for i in range(len(text)-n+1)]
P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
-def Pwords(words):
+def Pwords(words):
"""The Naive Bayes log probability of a sequence of words.
"""
return sum(Pw[w.lower()] for w in words)
-def Pwords_wrong(words):
+def Pwords_wrong(words):
"""The Naive Bayes log probability of a sequence of words.
"""
return sum(Pw_wrong[w.lower()] for w in words)
-
def Pletters(letters):
"""The Naive Bayes log probability of a sequence of letters.
"""
return sum(Pl[l.lower()] for l in letters)
def Pbigrams(letters):
- """The Naive Bayes log probability of the bigrams formed from a sequence
+ """The Naive Bayes log probability of the bigrams formed from a sequence
of letters.
"""
return sum(P2l[p] for p in ngrams(letters, 2))
def Ptrigrams(letters):
- """The Naive Bayes log probability of the trigrams formed from a sequence
+ """The Naive Bayes log probability of the trigrams formed from a sequence
of letters.
"""
return sum(P3l[p] for p in ngrams(letters, 3))
>>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
0.26228882...
"""
- return norms.cosine_similarity(english_counts,
- collections.Counter(sanitise(text)))
+ return norms.cosine_similarity(english_counts,
+ collections.Counter(sanitise(text)))
if __name__ == "__main__":