From: Neil Smith <neil.git@njae.me.uk>
Date: Tue, 24 Oct 2017 09:13:49 +0000 (+0100)
Subject: Updated letter counts and tests based on it.
X-Git-Url: https://git.njae.me.uk/?a=commitdiff_plain;h=defd4de8e665aa31bbf17487bcd5517c5c84b092;p=cipher-tools.git

Updated letter counts and tests based on it.
---

diff --git a/count_1l.txt b/count_1l.txt
index c1fc8ef..e9ac0c6 100644
--- a/count_1l.txt
+++ b/count_1l.txt
@@ -1,26 +1,26 @@
-e	756288
-t	559059
-o	503173
-a	489107
-i	420131
-n	418342
-h	415853
-s	403715
-r	372431
-d	267381
-l	258537
-u	189758
-m	171836
-w	153882
-y	142711
-c	140497
-f	134935
-g	117474
-p	100241
-b	92647
-v	65181
-k	54114
-x	7386
-j	6590
-q	5488
-z	3575
+e	758103
+t	560576
+o	504520
+a	490129
+i	421240
+n	419374
+h	416369
+s	404473
+r	373599
+d	267917
+l	259023
+u	190269
+m	172199
+w	154157
+y	143040
+c	141094
+f	135318
+g	117888
+p	100690
+b	92919
+v	65297
+k	54248
+x	7414
+j	6679
+q	5499
+z	3577
diff --git a/language_models.py b/language_models.py
index 8824bca..0fa6e85 100644
--- a/language_models.py
+++ b/language_models.py
@@ -126,6 +126,7 @@ def log_probability_of_unknown_word(key, N):
 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.
@@ -143,15 +144,29 @@ def Pbigrams(letters):
     """
     return sum(P2l[p] for p in ngrams(letters, 2))
 
+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
-    0.370847405...
+    0.73777...
     """
-    return norms.cosine_distance(english_counts, 
+    # return norms.cosine_distance(english_counts, 
+    #     collections.Counter(sanitise(text)))
+    return 1 - norms.cosine_similarity(english_counts, 
         collections.Counter(sanitise(text)))