Rearranged files, added import paths

[cipher-tools.git] / language_models.py

diff --git a/language_models.py b/language_models.py
deleted file mode 100644 (file)
index d975787..0000000
--- a/language_models.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import string
-import norms
-import random
-import collections
-import unicodedata
-import itertools
-from math import log10
-import os 
-
-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()