Minkowski distance in Python
Minkowski distance is a metric in a normed vector space. Minkowski distance is used for distance similarity of vector. Given two or more vectors, find distance similarity of these vectors.
Mainly, Minkowski distance is applied in machine learning to find out distance similarity.
Examples :
Input : vector1 = 0 2 3 4
vector2 = 2, 4, 3, 7
p = 3
Output : distance1 = 3.5033
Input : vector1 = 1, 4, 7, 12, 23
vector2 = 2, 5, 6, 10, 20
p = 2
Output : distance2 = 4.0
Note : Here distance1 and distance2 are almost same so it will be in same near region.
Python3
from math import *
from decimal import Decimal
def p_root(value, root):
root_value = 1 / float (root)
return round (Decimal(value) * *
Decimal(root_value), 3 )
def minkowski_distance(x, y, p_value):
return (p_root( sum ( pow ( abs (a - b), p_value)
for a, b in zip (x, y)), p_value))
vector1 = [ 0 , 2 , 3 , 4 ]
vector2 = [ 2 , 4 , 3 , 7 ]
p = 3
print (minkowski_distance(vector1, vector2, p))
|
Output :
3.503
Time Complexity : O(N)
Auxiliary Space : O(N)
Reference :
https://en.wikipedia.org/wiki/Minkowski_distance
Last Updated :
17 Apr, 2023
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