2_similarity-measures.py

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from IPython import get_ipython

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#<h1> SIT 720 - Python Intro </h1>

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# ### Distances
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# ![](cosine.png)
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# #### Manhattan Similarity

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from math import*
 
def square_rooted(x):
 
    return round(sqrt(sum([a*a for a in x])),3)
 
def cosine_similarity(x,y):
 
    numerator = sum(a*b for a,b in zip(x,y))
    denominator = square_rooted(x)*square_rooted(y)
    return round(numerator/float(denominator),3)
 
print (cosine_similarity([1,1,1], [3,3,3]))

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# #### Jaccard Similarity

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from math import*
 
def jaccard_similarity(x,y):
 
    intersection_cardinality = len(set.intersection(*[set(x), set(y)]))
    union_cardinality = len(set.union(*[set(x), set(y)]))
    return intersection_cardinality/float(union_cardinality)
 
print (jaccard_similarity(["banana","orange","grapes"],["apple", "banana", "grapes", "pear"]))

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# #### Eucldean Distance

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from math import *


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v1 = [1,1,1]
v2 = [3,3,3]
v3 = [3,0,2,6]


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def eclu_dist(x,y):
    return sqrt(sum(pow(a-b, 2) for a,b in zip(x,y)))


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print(eclu_dist(v1,v2))

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# #### Manhatan Distance

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def man_dist(x,y):
    return sum(abs(a-b) for a,b in zip(x,y))


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print(man_dist(v1,v2))
print(man_dist(v1,v3))


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c1 = [1,2]
c2 = [4,3]
c3 = [4,1]


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pt = [2,2]


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print(man_dist(pt, c1))
print(man_dist(pt, c2))
print(man_dist(pt, c3))