10 def datafile(name
, sep
='\t'):
11 """Read key,value pairs from file.
13 with
open(os
.path
.join(os
.path
.dirname(os
.path
.realpath(__file__
)), name
), 'r') as f
:
15 splits
= line
.split(sep
)
16 yield [splits
[0], int(splits
[1])]
18 english_counts
= collections
.Counter(dict(datafile('count_1l.txt')))
19 normalised_english_counts
= support
.norms
.normalise(english_counts
)
21 english_bigram_counts
= collections
.Counter(dict(datafile('count_2l.txt')))
22 normalised_english_bigram_counts
= support
.norms
.normalise(english_bigram_counts
)
24 english_trigram_counts
= collections
.Counter(dict(datafile('count_3l.txt')))
25 normalised_english_trigram_counts
= support
.norms
.normalise(english_trigram_counts
)
27 with
open(os
.path
.join(os
.path
.dirname(os
.path
.realpath(__file__
)), 'words.txt'), 'r') as f
:
28 keywords
= [line
.rstrip() for line
in f
]
31 def weighted_choice(d
):
32 """Generate random item from a dictionary of item counts
34 target
= random
.uniform(0, sum(d
.values()))
36 for (l
, p
) in d
.items():
42 def random_english_letter():
43 """Generate a random letter based on English letter counts
45 return weighted_choice(normalised_english_counts
)
49 """Returns all n-grams of a text
51 >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
52 ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
54 >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
55 ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
56 'rown', 'ownf', 'wnfo', 'nfox']
58 return [text
[i
:i
+n
] for i
in range(len(text
)-n
+1)]
62 """A probability distribution estimated from counts in datafile.
63 Values are stored and returned as log probabilities.
65 def __init__(self
, data
=[], estimate_of_missing
=None):
66 data1
, data2
= itertools
.tee(data
)
67 self
.total
= sum([d
[1] for d
in data1
])
68 for key
, count
in data2
:
69 self
[key
] = log10(count
/ self
.total
)
70 self
.estimate_of_missing
= estimate_of_missing
or (lambda k
, N
: 1./N
)
71 def __missing__(self
, key
):
72 return self
.estimate_of_missing(key
, self
.total
)
74 def log_probability_of_unknown_word(key
, N
):
75 """Estimate the probability of an unknown word.
77 return -log10(N
* 10**((len(key
) - 2) * 1.4))
79 Pw
= Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word
)
80 Pl
= Pdist(datafile('count_1l.txt'), lambda _k
, _N
: 0)
81 P2l
= Pdist(datafile('count_2l.txt'), lambda _k
, _N
: 0)
82 P3l
= Pdist(datafile('count_3l.txt'), lambda _k
, _N
: 0)
85 """The Naive Bayes log probability of a sequence of words.
87 return sum(Pw
[w
.lower()] for w
in words
)
89 def Pletters(letters
):
90 """The Naive Bayes log probability of a sequence of letters.
92 return sum(Pl
[l
.lower()] for l
in letters
)
94 def Pbigrams(letters
):
95 """The Naive Bayes log probability of the bigrams formed from a sequence
98 return sum(P2l
[p
] for p
in ngrams(letters
, 2))
100 def Ptrigrams(letters
):
101 """The Naive Bayes log probability of the trigrams formed from a sequence
104 return sum(P3l
[p
] for p
in ngrams(letters
, 3))
107 def cosine_distance_score(text
):
108 """Finds the dissimilarity of a text to English, using the cosine distance
109 of the frequency distribution.
111 >>> cosine_distance_score('abcabc') # doctest: +ELLIPSIS
114 # return support.norms.cosine_distance(english_counts,
115 # collections.Counter(sanitise(text)))
116 return 1 - support
.norms
.cosine_similarity(english_counts
,
117 collections
.Counter(sanitise(text
)))
120 if __name__
== "__main__":