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 of- V.- 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