pyriemann.utils.covariance.covariances¶
- pyriemann.utils.covariance.covariances(X, estimator='cov', **kwds)¶
Estimation of covariance matrix.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series, real or complex-valued.
- estimator{‘corr’, ‘cov’, ‘hub’, ‘lwf’, ‘mcd’, ‘oas’, ‘sch’, ‘scm’, ‘stu’, ‘tyl’}, default=’scm’
Covariance matrix estimator [est]:
‘corr’ for correlation coefficient matrix [corr],
‘cov’ for NumPy based covariance matrix [cov],
‘hub’ for Huber’s M-estimator based covariance matrix [mest],
‘lwf’ for Ledoit-Wolf shrunk covariance matrix [lwf] only for real-valued inputs,
‘mcd’ for minimum covariance determinant matrix [mcd] only for real-valued inputs,
‘oas’ for oracle approximating shrunk covariance matrix [oas] only for real-valued inputs,
‘sch’ for Schaefer-Strimmer shrunk covariance matrix [sch] only for real-valued inputs,
‘scm’ for sample covariance matrix [scm],
‘stu’ for Student-t’s M-estimator based covariance matrix [mest],
‘tyl’ for Tyler’s M-estimator based covariance matrix [mest],
or a callable function.
For regularization, consider ‘lwf’ or ‘oas’. For robustness, consider ‘hub’, ‘mcd’, ‘stu’ or ‘tyl’.
- **kwdsdict
Any further parameters are passed directly to the covariance estimator.
- Returns:
- covmatsndarray, shape (n_matrices, n_channels, n_channels)
Covariance matrices.
References