pyriemann.estimation.CospCovariances¶
- class pyriemann.estimation.CospCovariances(window=128, overlap=0.75, fmin=None, fmax=None, fs=None)¶
Estimation of cospectral covariance matrix.
Co-spectral matrices are the real part of complex cross-spectral matrices (see
pyriemann.utils.covariance.cross_spectrum()), estimated as the spectrum covariance in the frequency domain. This method returns a 4-d array with a cospectral covariance matrix for each input and in each frequency bin of the FFT.- Parameters
- windowint, default=128
The length of the FFT window used for spectral estimation.
- overlapfloat, default=0.75
The percentage of overlap between window.
- fminfloat | None, default=None
The minimal frequency to be returned.
- fmaxfloat | None, default=None
The maximal frequency to be returned.
- fsfloat | None, default=None
The sampling frequency of the signal.
See also
- Attributes
- freqs_ndarray, shape (n_freqs,)
If transformed, the frequencies associated to cospectra. None if
fsis None.
- __init__(window=128, overlap=0.75, fmin=None, fmax=None, fs=None)¶
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
- selfCospCovariances instance
The CospCovariances 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 the cospectral covariance matrices.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- Returns
- covmatsndarray, shape (n_matrices, n_channels, n_channels, n_freqs)
Covariance matrices for each input and for each frequency bin.