Breaking affine ciphers

diff --git a/cipherbreak.py b/cipherbreak.py
index aba309db80df7568d5b9637695104aad1498da19..7b004cfacb3a6bc1b421b89aff0a297c81832c19 100644 (file)
--- a/cipherbreak.py
+++ b/cipherbreak.py
@@ -5,7 +5,6 @@ import string
 import collections
 import norms
 import logging
-from segment import segment
 
 import matplotlib.pyplot as plt
 

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.
     """

diff --git a/segment.py b/segment.py
deleted file mode 100644 (file)
index a64ea5d..0000000
--- a/segment.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""Segment a collection of letters into words"""
-
-import language_models
-import sys
-from functools import lru_cache
-sys.setrecursionlimit(1000000)
-
-@lru_cache()
-def segment(text):
-    """Return a list of words that is the best segmentation of text.
-    """
-    if not text: return []
-    candidates = ([first]+segment(rest) for first, rest in splits(text))
-    return max(candidates, key=language_models.Pwords)
-
-@lru_cache()
-def segment_wrong(text):
-    """Return a list of words that is the best segmentation of text.
-    """
-    if not text: return []
-    candidates = ([first]+segment(rest) for first, rest in splits(text))
-    return max(candidates, key=language_models.Pwords_wrong)
-
-
-def splits(text, L=20):
-    """Return a list of all possible (first, rest) pairs, len(first)<=L.
-    """
-    return [(text[:i+1], text[i+1:])
-            for i in range(min(len(text), L))]
-