Fixed typo in docstring

[cipher-tools.git] / norms.py

diff --git a/norms.py b/norms.py
index c9cafc4f718b05b697a20e4f8b5f55085336bc88..eb436c3b8163141a3ada1f1f02f8be741d6f47fb 100644 (file)
--- a/norms.py
+++ b/norms.py
@@ -1,4 +1,5 @@
 import collections
 import collections

+from math import log10

 
 def normalise(frequencies):
     """Scale a set of frequencies so they sum to one
 
 def normalise(frequencies):
     """Scale a set of frequencies so they sum to one


@@ -12,7 +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()))
 


@@ -32,23 +33,9 @@ def euclidean_scale(frequencies):
     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()))
 

-
-def scale(frequencies):
-    """Scale a set of frequencies so the largest is 1
-    
-    >>> sorted(scale({1: 1, 2: 0}).items())
-    [(1, 1.0), (2, 0.0)]
-    >>> sorted(scale({1: 1, 2: 1}).items())
-    [(1, 1.0), (2, 1.0)]
-    >>> sorted(scale({1: 1, 2: 1, 3: 1}).items())
-    [(1, 1.0), (2, 1.0), (3, 1.0)]
-    >>> sorted(scale({1: 1, 2: 2, 3: 1}).items())
-    [(1, 0.5), (2, 1.0), (3, 0.5)]
-    """
-    largest = max(frequencies.values())
-    return collections.defaultdict(int, ((k, v / largest) 
-        for (k, v) in frequencies.items()))
-    
+def identity_scale(frequencies):
+    return frequencies
+        

 
 def l2(frequencies1, frequencies2):
     """Finds the distances between two frequency profiles, expressed as dictionaries.
 
 def l2(frequencies1, frequencies2):
     """Finds the distances between two frequency profiles, expressed as dictionaries.


@@ -69,7 +56,7 @@ def l2(frequencies1, frequencies2):
     1.0
     """
     total = 0
     1.0
     """
     total = 0

-    for k in frequencies1.keys():
+    for k in frequencies1:

         total += (frequencies1[k] - frequencies2[k]) ** 2
     return total ** 0.5
 euclidean_distance = l2
         total += (frequencies1[k] - frequencies2[k]) ** 2
     return total ** 0.5
 euclidean_distance = l2


@@ -90,7 +77,7 @@ def l1(frequencies1, frequencies2):
     1
     """
     total = 0
     1
     """
     total = 0

-    for k in frequencies1.keys():
+    for k in frequencies1:

         total += abs(frequencies1[k] - frequencies2[k])
     return total
 
         total += abs(frequencies1[k] - frequencies2[k])
     return total
 


@@ -113,7 +100,7 @@ def l3(frequencies1, frequencies2):
     0.6299605249...
     """
     total = 0
     0.6299605249...
     """
     total = 0

-    for k in frequencies1.keys():
+    for k in frequencies1:

         total += abs(frequencies1[k] - frequencies2[k]) ** 3
     return total ** (1/3)
 
         total += abs(frequencies1[k] - frequencies2[k]) ** 3
     return total ** (1/3)
 


@@ -139,7 +126,7 @@ def geometric_mean(frequencies1, frequencies2):
     0.009259259...
     """
     total = 1
     0.009259259...
     """
     total = 1

-    for k in frequencies1.keys():
+    for k in frequencies1:

         total *= abs(frequencies1[k] - frequencies2[k])
     return total
 
         total *= abs(frequencies1[k] - frequencies2[k])
     return total
 


@@ -165,42 +152,37 @@ def harmonic_mean(frequencies1, frequencies2):
     0.2
     """
     total = 0
     0.2
     """
     total = 0

-    for k in frequencies1.keys():
+    for k in frequencies1:

         if abs(frequencies1[k] - frequencies2[k]) == 0:
             return 0
         total += 1 / abs(frequencies1[k] - frequencies2[k])
     return len(frequencies1) / total
 
 
         if abs(frequencies1[k] - frequencies2[k]) == 0:
             return 0
         total += 1 / abs(frequencies1[k] - frequencies2[k])
     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
     length2 = 0
     """
     numerator = 0
     length1 = 0
     length2 = 0

-    for k in frequencies1.keys():
+    for k in frequencies1:

         numerator += frequencies1[k] * frequencies2[k]
         length1 += frequencies1[k]**2
         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 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