Added pocket engima enciphering and deciphering
[cipher-training.git] / language_models.py
1 import string
2 import norms
3 import random
4 import collections
5 import unicodedata
6 import itertools
7 from math import log10
8
9 def letters(text):
10 """Remove all non-alphabetic characters from a text
11 >>> letters('The Quick')
12 'TheQuick'
13 >>> letters('The Quick BROWN fox jumped! over... the (9lazy) DOG')
14 'TheQuickBROWNfoxjumpedoverthelazyDOG'
15 """
16 return ''.join([c for c in text if c in string.ascii_letters])
17
18 def unaccent(text):
19 """Remove all accents from letters.
20 It does this by converting the unicode string to decomposed compatability
21 form, dropping all the combining accents, then re-encoding the bytes.
22
23 >>> unaccent('hello')
24 'hello'
25 >>> unaccent('HELLO')
26 'HELLO'
27 >>> unaccent('héllo')
28 'hello'
29 >>> unaccent('héllö')
30 'hello'
31 >>> unaccent('HÉLLÖ')
32 'HELLO'
33 """
34 return unicodedata.normalize('NFKD', text).\
35 encode('ascii', 'ignore').\
36 decode('utf-8')
37
38 def sanitise(text):
39 """Remove all non-alphabetic characters and convert the text to lowercase
40
41 >>> sanitise('The Quick')
42 'thequick'
43 >>> sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')
44 'thequickbrownfoxjumpedoverthelazydog'
45 >>> sanitise('HÉLLÖ')
46 'hello'
47 """
48 # sanitised = [c.lower() for c in text if c in string.ascii_letters]
49 # return ''.join(sanitised)
50 return letters(unaccent(text)).lower()
51
52
53 def datafile(name, sep='\t'):
54 """Read key,value pairs from file.
55 """
56 with open(name, 'r') as f:
57 for line in f:
58 splits = line.split(sep)
59 yield [splits[0], int(splits[1])]
60
61 english_counts = collections.Counter(dict(datafile('count_1l.txt')))
62 normalised_english_counts = norms.normalise(english_counts)
63
64 english_bigram_counts = collections.Counter(dict(datafile('count_2l.txt')))
65 normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
66
67 english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
68 normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
69
70 with open('words.txt', 'r') as f:
71 keywords = [line.rstrip() for line in f]
72
73
74 def weighted_choice(d):
75 """Generate random item from a dictionary of item counts
76 """
77 target = random.uniform(0, sum(d.values()))
78 cuml = 0.0
79 for (l, p) in d.items():
80 cuml += p
81 if cuml > target:
82 return l
83 return None
84
85 def random_english_letter():
86 """Generate a random letter based on English letter counts
87 """
88 return weighted_choice(normalised_english_counts)
89
90
91 def ngrams(text, n):
92 """Returns all n-grams of a text
93
94 >>> ngrams(sanitise('the quick brown fox'), 2) # doctest: +NORMALIZE_WHITESPACE
95 ['th', 'he', 'eq', 'qu', 'ui', 'ic', 'ck', 'kb', 'br', 'ro', 'ow', 'wn',
96 'nf', 'fo', 'ox']
97 >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
98 ['theq', 'hequ', 'equi', 'quic', 'uick', 'ickb', 'ckbr', 'kbro', 'brow',
99 'rown', 'ownf', 'wnfo', 'nfox']
100 """
101 return [text[i:i+n] for i in range(len(text)-n+1)]
102
103
104 class Pdist(dict):
105 """A probability distribution estimated from counts in datafile.
106 Values are stored and returned as log probabilities.
107 """
108 def __init__(self, data=[], estimate_of_missing=None):
109 data1, data2 = itertools.tee(data)
110 self.total = sum([d[1] for d in data1])
111 for key, count in data2:
112 self[key] = log10(count / self.total)
113 self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
114 def __missing__(self, key):
115 return self.estimate_of_missing(key, self.total)
116
117 def log_probability_of_unknown_word(key, N):
118 """Estimate the probability of an unknown word.
119 """
120 return -log10(N * 10**((len(key) - 2) * 1.4))
121
122 Pw = Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word)
123 Pw_wrong = Pdist(datafile('count_1w.txt'), lambda _k, N: log10(1/N))
124 Pl = Pdist(datafile('count_1l.txt'), lambda _k, _N: 0)
125 P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
126 P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
127
128 def Pwords(words):
129 """The Naive Bayes log probability of a sequence of words.
130 """
131 return sum(Pw[w.lower()] for w in words)
132
133 def Pwords_wrong(words):
134 """The Naive Bayes log probability of a sequence of words.
135 """
136 return sum(Pw_wrong[w.lower()] for w in words)
137
138
139 def Pletters(letters):
140 """The Naive Bayes log probability of a sequence of letters.
141 """
142 return sum(Pl[l.lower()] for l in letters)
143
144 def Pbigrams(letters):
145 """The Naive Bayes log probability of the bigrams formed from a sequence
146 of letters.
147 """
148 return sum(P2l[p] for p in ngrams(letters, 2))
149
150 def Ptrigrams(letters):
151 """The Naive Bayes log probability of the trigrams formed from a sequence
152 of letters.
153 """
154 return sum(P3l[p] for p in ngrams(letters, 3))
155
156
157 def cosine_similarity_score(text):
158 """Finds the dissimilarity of a text to English, using the cosine distance
159 of the frequency distribution.
160
161 >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
162 0.26228882...
163 """
164 return norms.cosine_similarity(english_counts,
165 collections.Counter(sanitise(text)))
166
167
168 if __name__ == "__main__":
169 import doctest
170 doctest.testmod()