pyriemann.estimation.BlockCovariances¶
- class pyriemann.estimation.BlockCovariances(block_size, estimator='scm', **kwds)¶
Estimation of block covariance matrix.
Perform a block covariance estimation for each given matrix. The resulting matrices are block diagonal matrices.
The blocks on the diagonal are calculated as individual covariance matrices for a subset of channels using the given the estimator. Varying block sized possible by passing a list to allow incorporation of different modalities with different number of channels (e.g. EEG, ECoG, LFP, EMG) with their own respective covariance matrices.
- Parameters
- block_sizeint | list of int
Sizes of individual blocks given as int for same-size block, or list for varying block sizes.
- estimatorstring, default=’scm’
Covariance matrix estimator, see
pyriemann.utils.covariance.covariances().- **kwdsoptional keyword parameters
Any further parameters are passed directly to the covariance estimator.
See also
Notes
New in version 0.3.
- __init__(block_size, 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
- selfBlockCovariances instance
The BlockCovariances 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 block 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.