pyriemann.estimation.Covariances

class pyriemann.estimation.Covariances(estimator='scm', **kwds)

Estimation of covariance matrix.

Perform a simple covariance matrix estimation for each given input.

Parameters
estimatorstring, default=’scm’

Covariance matrix estimator, see pyriemann.utils.covariance.covariances().

**kwdsoptional keyword parameters

Any further parameters are passed directly to the covariance estimator.

__init__(estimator='scm', **kwds)

Init.

fit(X, y=None)

Fit.

Do nothing. For compatibility purpose.

Parameters
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

yNone

Not used, here for compatibility with sklearn API.

Returns
selfCovariances instance

The Covariances instance.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)

Estimate covariance matrices.

Parameters
Xndarray, shape (n_matrices, n_channels, n_times)

Multi-channel time-series.

Returns
covmatsndarray, shape (n_matrices, n_channels, n_channels)

Covariance matrices.