pyriemann.estimation.Kernels¶

class pyriemann.estimation.Kernels(metric='linear', n_jobs=None, **kwds)¶

Estimation of kernel matrices 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”: Metric to use when computing kernel function between channels [2]: “linear”, “poly”, “polynomial”, “rbf”, “laplacian”, “cosine”.
n_jobsint, default=None: 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.
**kwdsdict: Any further parameters are passed directly to the kernel function [2].

See also

Covariances

Notes

Added 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 and transform in a single function.

Estimate kernel matrices from time series.

Parameters:

Xndarray, shape (n_matrices, n_channels, n_times): Multi-channel time-series.
yNone: Not used, here for compatibility with sklearn API.

Returns:

X_newndarray, shape (n_matrices, n_channels, n_channels): Kernel matrices.

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 Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

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: