X-Git-Url: https://git.njae.me.uk/?a=blobdiff_plain;f=language_models.py;h=62219efe54ab2ad20e07c1838d5ade29e6511d7d;hb=ae4400046f558cbea84662a0159d13bfa9cbb569;hp=1786ebe74c759015d842b61eda6058425971b08f;hpb=69c9038aaf5cc2f0a758435713f18f3b51bbbe4a;p=cipher-training.git

diff --git a/language_models.py b/language_models.py
index 1786ebe..62219ef 100644
--- a/language_models.py
+++ b/language_models.py
@@ -1,7 +1,26 @@
+"""Language-specific functions, including models of languages based on data of
+its use.
+"""
+
+import string
+import random
+import norms
+import collections
 import unicodedata
+import itertools
+from math import log10
+
+def letters(text):
+    """Remove all non-alphabetic characters from a text
+    >>> letters('The Quick')
+    'TheQuick'
+    >>> letters('The Quick BROWN fox jumped! over... the (9lazy) DOG')
+    'TheQuickBROWNfoxjumpedoverthelazyDOG'
+    """
+    return ''.join([c for c in text if c in string.ascii_letters])
 
 def unaccent(text):
-    """Remove all accents from letters. 
+    """Remove all accents from letters.
     It does this by converting the unicode string to decomposed compatability
     form, dropping all the combining accents, then re-encoding the bytes.
 
@@ -20,6 +39,134 @@ def unaccent(text):
         encode('ascii', 'ignore').\
         decode('utf-8')
 
+def sanitise(text):
+    """Remove all non-alphabetic characters and convert the text to lowercase
+
+    >>> sanitise('The Quick')
+    'thequick'
+    >>> sanitise('The Quick BROWN fox jumped! over... the (9lazy) DOG')
+    'thequickbrownfoxjumpedoverthelazydog'
+    >>> sanitise('HÉLLÖ')
+    'hello'
+    """
+    # sanitised = [c.lower() for c in text if c in string.ascii_letters]
+    # return ''.join(sanitised)
+    return letters(unaccent(text)).lower()
+
+
+def datafile(name, sep='\t'):
+    """Read key,value pairs from file.
+    """
+    with open(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('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)
+Pw_wrong = Pdist(datafile('count_1w.txt'), lambda _k, N: log10(1/N))
+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 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
+    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_similarity_score(text):
+    """Finds the dissimilarity of a text to English, using the cosine distance
+    of the frequency distribution.
+
+    >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
+    0.26228882...
+    """
+    return norms.cosine_similarity(english_counts,
+                                   collections.Counter(sanitise(text)))
+
 
 if __name__ == "__main__":
     import doctest