Transpositon ciphers

[cipher-training.git] / language_models.py

diff --git a/language_models.py b/language_models.py
index babbea19ceed80bd9d95fee347c64e53256b5626..02d48bd89a3cf820ebb7f62212eb8c6fa18d1ca4 100644 (file)
--- a/language_models.py
+++ b/language_models.py
@@ -65,12 +65,6 @@ def datafile(name, sep='\t'):
 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]
 
@@ -92,19 +86,6 @@ def random_english_letter():
     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.
@@ -129,34 +110,21 @@ 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): 
+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): 
+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
@@ -165,8 +133,8 @@ def cosine_similarity_score(text):
     >>> cosine_similarity_score('abcabc') # doctest: +ELLIPSIS
     0.26228882...
     """
-    return norms.cosine_similarity(english_counts, 
-        collections.Counter(sanitise(text)))
+    return norms.cosine_similarity(english_counts,
+                                   collections.Counter(sanitise(text)))
 
 
 if __name__ == "__main__":