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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 `

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