Challenge 3a done

[cipher-tools.git] / norms.py

diff --git a/norms.py b/norms.py
index 36af6068a93100512aac1119339b312d5ab552e5..eb436c3b8163141a3ada1f1f02f8be741d6f47fb 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()))
 


@@ -159,18 +159,18 @@ 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
-    -2.22044604...e-16
-    >>> cosine_distance({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
-    -2.22044604...e-16
-    >>> cosine_distance({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
-    0.4226497308...
-    >>> cosine_distance({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
-    0.29289321881...
+    >>> cosine_similarity({'a':1, 'b':1, 'c':1}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    1.0000000000...
+    >>> cosine_similarity({'a':2, 'b':2, 'c':2}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    1.0000000000...
+    >>> cosine_similarity({'a':0, 'b':2, 'c':0}, {'a':1, 'b':1, 'c':1}) # doctest: +ELLIPSIS
+    0.5773502691...
+    >>> cosine_similarity({'a':0, 'b':1}, {'a':1, 'b':1}) # doctest: +ELLIPSIS
+    0.7071067811...

     """
     numerator = 0
     length1 = 0
     """
     numerator = 0
     length1 = 0


@@ -178,22 +178,11 @@ def cosine_distance(frequencies1, frequencies2):
     for k in frequencies1:
         numerator += frequencies1[k] * frequencies2[k]
         length1 += frequencies1[k]**2
     for k in frequencies1:
         numerator += frequencies1[k] * frequencies2[k]
         length1 += frequencies1[k]**2

-    for k in frequencies2.keys():
-        length2 += frequencies2[k]
-    return 1 - (numerator / (length1 ** 0.5 * length2 ** 0.5))
+    for k in frequencies2:
+        length2 += frequencies2[k]**2
+    return numerator / (length1 ** 0.5 * length2 ** 0.5)

 
 
 
 

-def log_pl(frequencies1, frequencies2):
-    return sum([frequencies2[l] * log10(frequencies1[l]) for l in frequencies1])
-
-def inverse_log_pl(frequencies1, frequencies2):
-    return -log_pl(frequencies1, frequencies2)
-
-def index_of_coincidence(frequencies):
-    """Finds the (expected) index of coincidence given a set of frequencies
-    """
-    return sum([f ** 2 for f in frequencies.values()]) * len(frequencies.keys())
-

 
 if __name__ == "__main__":
     import doctest
 
 if __name__ == "__main__":
     import doctest