Data

Similarity Measure

#!/usr/bin/env python

# ref. https://github.com/saimadhu-polamuri/DataAspirant_codes/blob/master/Similarity_measures/similaritymeasures.py 
# ref. http://hi.baidu.com/uu_master/item/0b89469b42adef9bcd80e5e8

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)