Rearranged files, added import paths

[cipher-tools.git] / support / language_models.py

diff --git a/support/language_models.py b/support/language_models.py
new file mode 100644 (file)
index 0000000..53a383d
--- /dev/null
+++ b/support/language_models.py
@@ -0,0 +1,123 @@
+import string
+import random
+import collections
+import unicodedata
+import itertools
+from math import log10
+import os 
+
+import norms
+
+def datafile(name, sep='\t'):
+    """Read key,value pairs from file.
+    """
+    with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), name), 'r') as f:
+        for line in f:
+            splits = line.split(sep)
+            yield [splits[0], int(splits[1])]
+
+english_counts = collections.Counter(dict(datafile('count_1l.txt')))
+normalised_english_counts = norms.normalise(english_counts)
+
+english_bigram_counts = collections.Counter(dict(datafile('count_2l.txt')))
+normalised_english_bigram_counts = norms.normalise(english_bigram_counts)
+
+english_trigram_counts = collections.Counter(dict(datafile('count_3l.txt')))
+normalised_english_trigram_counts = norms.normalise(english_trigram_counts)
+
+with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'words.txt'), 'r') as f:
+    keywords = [line.rstrip() for line in f]
+
+
+def weighted_choice(d):
+    """Generate random item from a dictionary of item counts
+    """
+    target = random.uniform(0, sum(d.values()))
+    cuml = 0.0
+    for (l, p) in d.items():
+        cuml += p
+        if cuml > target:
+            return l
+    return None
+
+def random_english_letter():
+    """Generate a random letter based on English letter counts
+    """
+    return weighted_choice(normalised_english_counts)
+
+
+def ngrams(text, n):
+    """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', 
+     'nf', 'fo', 'ox']
+    >>> ngrams(sanitise('the quick brown fox'), 4) # doctest: +NORMALIZE_WHITESPACE
+    ['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)]
+
+
+class Pdist(dict):
+    """A probability distribution estimated from counts in datafile.
+    Values are stored and returned as log probabilities.
+    """
+    def __init__(self, data=[], estimate_of_missing=None):
+        data1, data2 = itertools.tee(data)
+        self.total = sum([d[1] for d in data1])
+        for key, count in data2:
+            self[key] = log10(count / self.total)
+        self.estimate_of_missing = estimate_of_missing or (lambda k, N: 1./N)
+    def __missing__(self, key):
+        return self.estimate_of_missing(key, self.total)
+
+def log_probability_of_unknown_word(key, N):
+    """Estimate the probability of an unknown word.
+    """
+    return -log10(N * 10**((len(key) - 2) * 1.4))
+
+Pw = Pdist(datafile('count_1w.txt'), log_probability_of_unknown_word)
+Pl = Pdist(datafile('count_1l.txt'), lambda _k, _N: 0)
+P2l = Pdist(datafile('count_2l.txt'), lambda _k, _N: 0)
+P3l = Pdist(datafile('count_3l.txt'), lambda _k, _N: 0)
+
+def Pwords(words): 
+    """The Naive Bayes log probability of a sequence of words.
+    """
+    return sum(Pw[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 
+    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
+    of letters.
+    """
+    return sum(P3l[p] for p in ngrams(letters, 3))
+
+
+def cosine_distance_score(text):
+    """Finds the dissimilarity of a text to English, using the cosine distance
+    of the frequency distribution.
+
+    >>> cosine_distance_score('abcabc') # doctest: +ELLIPSIS
+    0.73777...
+    """
+    # return norms.cosine_distance(english_counts, 
+    #     collections.Counter(sanitise(text)))
+    return 1 - norms.cosine_similarity(english_counts, 
+        collections.Counter(sanitise(text)))
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()