`scipy.spatial.distance:minkowski`

source: /scipy/spatial/distance.py :433

Signature

def minkowski ( u , v , p = 2 , w = None )

Summary

Compute the Minkowski distance between two 1-D arrays.

Extended Summary

The Minkowski distance between 1-D arrays u and v, is defined as

∥ u - v ∥_{p} = (\sum ∣ u_{i} - v_{i} ∣^{p})^{1/ p} . (\sum w_{i} (∣ (u_{i} - v_{i}) ∣^{p}))^{1/ p} .

Parameters

u : (N,) array_like: Input array.
v : (N,) array_like: Input array.
p : scalar: The order of the norm of the difference $∥ u - v ∥_{p}$ . Note that for $0 < p < 1$ , the triangle inequality only holds with an additional multiplicative factor, i.e. it is only a quasi-metric.
w : (N,) array_like, optional: The weights for each value in u and v. Default is None, which gives each value a weight of 1.0

Returns

minkowski : double: The Minkowski distance between vectors u and v.

Examples

from scipy.spatial import distance

✓

distance.minkowski([1, 0, 0], [0, 1, 0], 1)
distance.minkowski([1, 0, 0], [0, 1, 0], 2)
distance.minkowski([1, 0, 0], [0, 1, 0], 3)
distance.minkowski([1, 1, 0], [0, 1, 0], 1)
distance.minkowski([1, 1, 0], [0, 1, 0], 2)
distance.minkowski([1, 1, 0], [0, 1, 0], 3)

`scipy.spatial.distance:minkowski`

Signature

Summary

Extended Summary

Parameters

Returns

Examples

Aliases

Referenced by

This package