45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
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"""
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Created on Mon Apr 17 09:34:40 2017
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functions to calculate the similarity measure of two real vectors
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@author: nlp
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"""
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# The cosine measure definition
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def cos_sim(vect1, vect2):
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if (len(vect1) == len(vect2)):
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vect3 = []
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for x in range(0, len(vect1)):
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vect3.append(0)
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for x in range(0, len(vect1)):
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vect3[x] = vect1[x] * vect2[x]
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n1 = norm(vect1)
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n2 = norm(vect2)
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return sum(vect3)/(n1*n2)
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else:
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return 0
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# Norm of vector
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def norm(vect):
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import math as mth
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vect1 = []
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for x in range(0, len(vect)):
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vect1.append(0)
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for x in range(0, len(vect)):
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vect1[x] = vect[x] * vect[x]
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return mth.sqrt(sum(vect1))
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# Jacard similarity
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def jac_sim(set_A,set_B):
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if (str(type(set_A)) and str(type(set_B))) == "<class 'set'>":
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if set_A == set_B:
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return len(set_A & set_B)/len(set_A | set_B)
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else:
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return len(set_A & set_B)/len((set_A | set_B) - (set_A & set_B))
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else:
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print('One of the inputs not of type set')
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