scipy.spatial.distance.seuclidean

scipy.spatial.distance.seuclidean(u, v, V)

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

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

\[\sqrt{\sum\limits_i \frac{1}{V_i} \left(u_i-v_i \right)^2}\]

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 an 1-D array of component variances. It is usually computed among a larger collection vectors.

Returns:

seuclideandouble

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])
4.4721359549995796
>>> distance.seuclidean([1, 0, 0], [0, 1, 0], [1, 0.1, 0.1])
3.3166247903553998
>>> distance.seuclidean([1, 0, 0], [0, 1, 0], [10, 0.1, 0.1])
3.1780497164141406