Minkowski distance in Python

• Last Updated : 16 Sep, 2021

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

 `# Python3 program to find Minkowski distance` `# import math library``from` `math ``import` `*``from` `decimal ``import` `Decimal` `# Function distance between two points``# and calculate distance value to given``# root value(p is 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 p_root function to calculate``    ``# all the value of vector parallelly``    ``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`

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