Breaking affine ciphers

[cipher-training.git] / language_models.py

diff --git a/language_models.py b/language_models.py
index d8b9da8dc8e60e7301aa6fec5dbab328e86abcef..bd09a1556f2e1c0a76741f127ab0399428dcbca5 100644 (file)
--- a/language_models.py
+++ b/language_models.py
@@ -64,43 +64,12 @@ def datafile(name, sep='\t'):
 
 english_counts = collections.Counter(dict(datafile('count_1l.txt')))
 normalised_english_counts = norms.normalise(english_counts)
+Pl = {l: log10(n) for l, n in normalised_english_counts.items()}
 
 with open('words.txt', 'r') as f:
     keywords = [line.rstrip() for line in f]
 
 
-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)
-
-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.
     """