pyriemann.estimation.Kernels¶
- class pyriemann.estimation.Kernels(metric='linear', n_jobs=None, **kwds)¶
Estimation of kernel matrix between channels of time series.
Perform a kernel matrix estimation for each given time series, evaluating a kernel function between each pair of channels (rather than between pairs of time samples) and allowing to extract nonlinear channel relationship [1].
For an input time series \(X \in \mathbb{R}^{c \times t}\), composed of \(c\) channels and \(t\) time samples, kernel function \(\kappa()\) is computed between channels \(i\) and \(j\):
\[K_{i,j} = \kappa \left( X[i], X[j] \right)\]Linear kernel is related to
pyriemann.estimation.Covariances[1], but this class allows to generalize to nonlinear relationships.- Parameters
- metricstring, default=’linear’
The metric to use when computing kernel function between channels [2]: ‘linear’, ‘poly’, ‘polynomial’, ‘rbf’, ‘laplacian’, ‘cosine’.
- n_jobsint, default=None
The number of jobs to use for the computation [2]. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel.
- **kwdsoptional keyword parameters
Any further parameters are passed directly to the kernel function [2].
See also
Notes
New in version 0.4.
References
- 1(1,2)
Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices L. Wang, J. Zhang, L. Zhou, C. Tang, W Li. ICCV, 2015.
- 2(1,2,3)
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
- __init__(metric='linear', n_jobs=None, **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
- selfKernels instance
The Kernels 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 kernel matrices from time series.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- Returns
- Kndarray, shape (n_matrices, n_channels, n_channels)
Kernel matrices.