scipy.spatial.distance.mahalanobis¶
- scipy.spatial.distance.mahalanobis(u, v, VI)[source]¶
Compute the Mahalanobis distance between two 1-D arrays.
The Mahalanobis distance between 1-D arrays u and v, is defined as
\[\sqrt{ (u-v) V^{-1} (u-v)^T }\]where
Vis the covariance matrix. Note that the argument VI is the inverse ofV.- Parameters
- u(N,) array_like
Input array.
- v(N,) array_like
Input array.
- VIarray_like
The inverse of the covariance matrix.
- Returns
- mahalanobisdouble
The Mahalanobis distance between vectors u and v.
Examples
>>> from scipy.spatial import distance >>> iv = [[1, 0.5, 0.5], [0.5, 1, 0.5], [0.5, 0.5, 1]] >>> distance.mahalanobis([1, 0, 0], [0, 1, 0], iv) 1.0 >>> distance.mahalanobis([0, 2, 0], [0, 1, 0], iv) 1.0 >>> distance.mahalanobis([2, 0, 0], [0, 1, 0], iv) 1.7320508075688772