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Minkowski distance in Python

Basically, Minkowski distance is used in machine learning to determine the similarity of distances.

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 the same, so they will be in the same near region.

# Python3 program for finding Minkowski distance

 
# import math library

from math import *

from decimal i mport Decimal

 
# Function of the distance between two points
# and calculate the value of the distance to the given
# root value (p - root value)

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):

 

# pass the function p_root to calculate

# all vector value in parallel

return (p_root ( sum ( pow ( abs (a - b), p_value)

for a, b in zip (x, y)), p_value))

 
Driver code

vector1 = [ 0 , 2 , 3 , 4 ]

vector2 = [ 2 , 4 , 3 , 7 ]

p = 3

print   (minkowski_distance (vector1, vector2, p))

Output:

 3.503 

Link:
https://en.wikipedia.org/wiki/Minkowski_distance

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