Method fit_transform

mne/decoding/csp.py:333–358

Fit CSP to data, then transform it. Fits transformer to ``X`` and ``y`` with optional parameters ``fit_params``, and returns a transformed version of ``X``. Parameters ---------- X : array, shape (n_epochs, n_channels, n_times) The data on which

(self, X, y=None, **fit_params)

        return X[:, np.newaxis, :] * self.patterns_[: self.n_components].T

    def fit_transform(self, X, y=None, **fit_params):
        """Fit CSP to data, then transform it.

        Fits transformer to ``X`` and ``y`` with optional parameters ``fit_params``, and
        returns a transformed version of ``X``.

        Parameters
        ----------
        X : array, shape (n_epochs, n_channels, n_times)
            The data on which to estimate the CSP.
        y : array, shape (n_epochs,)
            The class for each epoch.
        **fit_params : dict
            Additional fitting parameters passed to the :meth:`mne.decoding.CSP.fit`
            method. Not used for this class.

        Returns
        -------
        X_csp : array, shape (n_epochs, n_components[, n_times])
            If ``self.transform_into == 'average_power'`` then returns the power of CSP
            features averaged over time and shape is ``(n_epochs, n_components)``. If
            ``self.transform_into == 'csp_space'`` then returns the data in CSP space
            and shape is ``(n_epochs, n_components, n_times)``.
        """
        # use parent TransformerMixin method but with custom docstring
        return super().fit_transform(X, y=y, **fit_params)

    @legacy(alt="get_spatial_filter_from_estimator(clf, info=info).plot_patterns()")
    @fill_doc