`scipy.spatial.distance:seuclidean`

source: /scipy/spatial/distance.py :900

Signature

def seuclidean ( u , v , V )

Summary

Return the standardized Euclidean distance between two 1-D arrays.

Extended Summary

The standardized Euclidean distance between two n-vectors u and v is

i \sum \frac{1}{V _{i}} (u_{i} - v_{i})^{2}

V is the variance vector; V[I] is the variance computed over all the i-th components of the points. If not passed, it is automatically computed.

Parameters

u : (N,) array_like: Input array.
v : (N,) array_like: Input array.
V : (N,) array_like: V is a 1-D array of component variances. It is usually computed among a larger collection of vectors.

Returns

seuclidean : double: The standardized Euclidean distance between vectors u and v.

Examples

from scipy.spatial import distance

✓

distance.seuclidean([1, 0, 0], [0, 1, 0], [0.1, 0.1, 0.1])
distance.seuclidean([1, 0, 0], [0, 1, 0], [1, 0.1, 0.1])
distance.seuclidean([1, 0, 0], [0, 1, 0], [10, 0.1, 0.1])

✗

Aliases

scipy.spatial.distance.seuclidean