Done Cadenus ciphers, but not automated breaking

[cipher-training.git] / norms.py

diff --git a/norms.py b/norms.py
index 3d6d37df7f2e4f9f576c9cd4ef1a2341aa48d016..66452940c376658e446bb704c7198ee1ace2be22 100644 (file)
--- a/norms.py
+++ b/norms.py
@@ -13,7 +13,7 @@ def normalise(frequencies):
     >>> sorted(normalise({1: 1, 2: 2, 3: 1}).items())
     [(1, 0.25), (2, 0.5), (3, 0.25)]
     """
     >>> sorted(normalise({1: 1, 2: 2, 3: 1}).items())
     [(1, 0.25), (2, 0.5), (3, 0.25)]
     """

-    length = sum([f for f in frequencies.values()])
+    length = sum(f for f in frequencies.values())

     return collections.defaultdict(int, ((k, v / length) 
         for (k, v) in frequencies.items()))
 
     return collections.defaultdict(int, ((k, v / length) 
         for (k, v) in frequencies.items()))
 


@@ -159,17 +159,17 @@ def harmonic_mean(frequencies1, frequencies2):
     return len(frequencies1) / total
 
 
     return len(frequencies1) / total
 
 

-def cosine_distance(frequencies1, frequencies2):
+def cosine_similarity(frequencies1, frequencies2):

     """Finds the distances between two frequency profiles, expressed as dictionaries.
     Assumes every key in frequencies1 is also in frequencies2
 
     """Finds the distances between two frequency profiles, expressed as dictionaries.
     Assumes every key in frequencies1 is also in frequencies2
 

-    >>> cosine_distance({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    >>> cosine_similarity({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS

     1.0000000000...
     1.0000000000...

-    >>> cosine_distance({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    >>> cosine_similarity({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS

     1.0000000000...
     1.0000000000...

-    >>> cosine_distance({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    >>> cosine_similarity({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS

     0.5773502691...
     0.5773502691...

-    >>> cosine_distance({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
+    >>> cosine_similarity({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS

     0.7071067811...
     """
     numerator = 0
     0.7071067811...
     """
     numerator = 0


@@ -179,7 +179,7 @@ def cosine_distance(frequencies1, frequencies2):
         numerator += frequencies1[k] * frequencies2[k]
         length1 += frequencies1[k]**2
     for k in frequencies2.keys():
         numerator += frequencies1[k] * frequencies2[k]
         length1 += frequencies1[k]**2
     for k in frequencies2.keys():

-        length2 += frequencies2[k]
+        length2 += frequencies2[k]**2

     return numerator / (length1 ** 0.5 * length2 ** 0.5)
 
 
     return numerator / (length1 ** 0.5 * length2 ** 0.5)