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Minkowski distance in Python
The Minkowski distance is a metric and in a normed vector space, the result is Minkowski inequality. Minkowski distance is used for distance similarity of vector.
scipy.spatial.distance.minkowski
>>> from scipy.spatial import distance >>> distance.minkowski([1, 0, 0], [0, 1, 0], 1) 2.0 >>> distance.minkowski([1, 0, 0], [0, 1, 0], 2) 1.4142135623730951 >>> distance.minkowski([1, 0, 0], [0, 1, 0], 3) 1.2599210498948732 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 1) 1.0 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 2) 1.0 >>> distance.minkowski([1, 1, 0], [0, 1, 0], 3) 1.0
Example code
from math import *
from decimal import Decimal
def my_p_root(value, root):
my_root_value = 1 / float(root)
return round (Decimal(value) **
Decimal(my_root_value), 3)
def my_minkowski_distance(x, y, p_value):
return (my_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]
my_position = 5
print("The Distance is::",my_minkowski_distance(vector1, vector2, my_position))
Output
The Distance is:: 3.144
Updated on: 30-Jul-2019
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