pyriemann.estimation.HankelCovariances¶
- class pyriemann.estimation.HankelCovariances(delays=4, estimator='scm', **kwds)¶
Estimation of covariance matrix with time delayed Hankel matrices.
Hankel covariance matrices [1] are useful to catch spectral dynamics of the signal, similarly to the CSSP method [2]. It is done by concatenating time delayed version of the signal before covariance estimation.
- Parameters:
- delaysint | list of int, default=4
The delays to apply for the Hankel matrices. If int, it use a range of delays up to the given value. A list of int can be given.
- estimatorstring, default=’scm’
Covariance matrix estimator, see
pyriemann.utils.covariance.covariances()
.- **kwdsdict
Any further parameters are passed directly to the covariance estimator.
See also
References
[2]Spatio-spectral filters for improving the classification of single trial EEG S. Lemm, B. Blankertz, B. Curio, K-R. Muller. IEEE Transactions on Biomedical Engineering 52(9), 1541-1548, 2005.
- __init__(delays=4, 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:
- selfHankelCovariances instance
The HankelCovariances 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_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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_output(*, transform=None)¶
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
- 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 the Hankel covariance matrices.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- Returns:
- covmatsndarray, shape (n_matrices, n_delays x n_channels, n_delays x n_channels)
Hankel covariance matrices, where n_delays is equal to delays when it is a int, and to 1 + len(delays) when it is a list.