pyriemann.utils.mean.mean_covariance¶
- pyriemann.utils.mean.mean_covariance(X, *args, metric='riemann', sample_weight=None, **kwargs)¶
Mean of matrices according to a metric.
Compute the mean of a set of matrices according to a metric [1].
- Parameters:
- Xndarray, shape (n_matrices, n, n)
Set of matrices.
- *argstuple
The arguments passed to the sub function.
- metricstring | callable, default=”riemann”
Metric for mean estimation, can be: “ale”, “alm”, “euclid”, “harmonic”, “identity”, “kullback_sym”, “logchol”, “logdet”, “logeuclid”, “riemann”, “wasserstein”, or a callable function. If an exponent is given in args, it can be “power”, “poweuclid”.
- sample_weightNone | ndarray, shape (n_matrices,), default=None
Weights for each matrix. If None, it uses equal weights.
- **kwargsdict
The keyword arguments passed to the sub function.
- Returns:
- Mndarray, shape (n, n)
Mean of matrices.
References
[1]Review of Riemannian distances and divergences, applied to SSVEP-based BCI S. Chevallier, E. K. Kalunga, Q. Barthélemy, E. Monacelli. Neuroinformatics, Springer, 2021, 19 (1), pp.93-106