scipy.spatial.distance.

seuclidean#

scipy.spatial.distance.seuclidean(u, v, V)[source]#

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 of 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