pyriemann.utils.distance.distance_mahalanobis¶
- pyriemann.utils.distance.distance_mahalanobis(X, cov, mean=None)¶
Mahalanobis distance between vectors and a Gaussian distribution.
The Mahalanobis distance between a vector \(x\) and a Gaussian distribution \(\mathcal{N}(\mu, C)\), with mean \(\mu\) and covariance matrix \(C\), is:
\[d(x, \mathcal{N}(\mu, C)) = \sqrt{ (x - \mu)^H C^{-1} (x - \mu) }\]- Parameters
- Xndarray, shape (n_channels, n_vectors)
Multi-channel vectors.
- covndarray, shape (n_channels, n_channels)
Covariance matrix of the Gaussian distribution.
- meanNone | ndarray, shape (n_channels, 1), default=None
Mean of the Gaussian distribution. If None, distribution is considered as centered.
- Returns
- dndarray, shape (n_vectors,)
Mahalanobis distances.
Notes
New in version 0.3.1.
References