85 lines
2.0 KiB
Python
85 lines
2.0 KiB
Python
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# -*- coding: UTF-8 -*-
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# !/usr/bin/python
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# @time :2019/4/3 10:36
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# @author :Mo
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# @function :TS-SS distance
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# @url :https://github.com/taki0112/Vector_Similarity
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# @paper :A Hybrid Geometric Approach for Measuring Similarity Level Among Documents and Document Clustering
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import numpy as np
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import math
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zero_bit = 0.000000001
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def Cosine(vec1, vec2):
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"""
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余弦相似度
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:param vec1: arrary
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:param vec2: arrary
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:return: float
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"""
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result = InnerProduct(vec1, vec2) / (VectorSize(vec1) * VectorSize(vec2) + zero_bit)
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return result
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def VectorSize(vec):
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vec_pow = sum(math.pow(v + zero_bit, 2) for v in vec)
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if vec_pow >= 0:
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return math.sqrt(vec_pow)
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else:
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return zero_bit
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def InnerProduct(vec1, vec2):
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try:
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return sum(v1 * v2 for v1, v2 in zip(vec1, vec2))
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except:
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return zero_bit
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def Euclidean(vec1, vec2):
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vec12_pow = sum(math.pow((v1 - v2), 2) for v1, v2 in zip(vec1, vec2))
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if vec12_pow >= 0:
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return math.sqrt(vec12_pow)
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else:
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return zero_bit
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def Theta(vec1, vec2):
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cosine_vec12 = Cosine(vec1, vec2)
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if -1 <= cosine_vec12 and cosine_vec12 <= 1:
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return math.acos(cosine_vec12) + 10
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else:
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return zero_bit + 10
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def Triangle(vec1, vec2):
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theta = math.radians(Theta(vec1, vec2))
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return (VectorSize(vec1) * VectorSize(vec2) * math.sin(theta)) / 2
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def Magnitude_Difference(vec1, vec2):
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return abs(VectorSize(vec1) - VectorSize(vec2))
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def Sector(vec1, vec2):
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ED = Euclidean(vec1, vec2)
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MD = Magnitude_Difference(vec1, vec2)
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theta = Theta(vec1, vec2)
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return math.pi * math.pow((ED + MD), 2) * theta / 360
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def TS_SS(vec1, vec2):
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return Triangle(vec1, vec2) * Sector(vec1, vec2)
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if __name__ == '__main__':
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vec1_test = np.array([1, 38, 17, 32])
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vec2_test = np.array([5, 6, 8, 9])
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print(Euclidean(vec1_test, vec2_test))
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print(Cosine(vec1_test, vec2_test))
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print(TS_SS(vec1_test, vec2_test))
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