源网址http://dataconomy.com/2015/04/implementing-the-five-most-popular-similarity-measures-in-python/
- Euclidean distance
- Manhattan distance
- Minkowski distance
- Cosine similaraity
- Jaccard similarity
代码：similaritymeasures.py
#!/usr/bin/env python
from math import*
from decimal import Decimal
class Similarity():
""" Five similarity measures function """
def euclidean_distance(self,x,y):
""" return euclidean distance between two lists """
return sqrt(sum(pow(a-b,2) for a, b in zip(x, y)))
def manhattan_distance(self,x,y):
""" return manhattan distance between two lists """
return sum(abs(a-b) for a,b in zip(x,y))
def minkowski_distance(self,x,y,p_value):
""" return minkowski distance between two lists """
return self.nth_root(sum(pow(abs(a-b),p_value) for a,b in zip(x, y)),p_value)
def nth_root(self,value, n_root):
""" returns the n_root of an value """
root_value = 1/float(n_root)
return round (Decimal(value) ** Decimal(root_value),3)
def cosine_similarity(self,x,y):
""" return cosine similarity between two lists """
numerator = sum(a*b for a,b in zip(x,y))
denominator = self.square_rooted(x)*self.square_rooted(y)
return round(numerator/float(denominator),3)
def square_rooted(self,x):
""" return 3 rounded square rooted value """
return round(sqrt(sum([a*a for a in x])),3)
def jaccard_similarity(self,x,y):
""" returns the jaccard similarity between two lists """
intersection_cardinality = len(set.intersection(*[set(x), set(y)]))
union_cardinality = len(set.union(*[set(x), set(y)]))
return intersection_cardinality/float(union_cardinality)
使用方法：
#!/usr/bin/env python
from similaritymeasures import Similarity
def main():
""" main function to create Similarity class instance and get use of it """
measures = Similarity()
print measures.euclidean_distance([0,3,4,5],[7,6,3,-1])
print measures.jaccard_similarity([0,1,2,5,6],[0,2,3,5,7,9])
if __name__ == "__main__":
main()
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