Method transform

mne/decoding/search_light.py:200–220

Transform each data slice/task with a series of independent estimators. The number of tasks in X should match the number of tasks/estimators given at fit time. Parameters ---------- X : array, shape (n_samples, nd_features, n_tasks) The input sam

(self, X)

        return y_pred

    def transform(self, X):
        """Transform each data slice/task with a series of independent estimators.

        The number of tasks in X should match the number of tasks/estimators
        given at fit time.

        Parameters
        ----------
        X : array, shape (n_samples, nd_features, n_tasks)
            The input samples. For each data slice/task, the corresponding
            estimator makes a transformation of the data, e.g.
            ``[estimators[ii].transform(X[..., ii]) for ii in range(n_estimators)]``.
            The feature dimension can be multidimensional e.g.
            X.shape = (n_samples, n_features_1, n_features_2, n_tasks).

        Returns
        -------
        Xt : array, shape (n_samples, n_estimators)
            The transformed values generated by each estimator.
        """  # noqa: E501
        return self._transform(X, "transform")

    def predict(self, X):
        """Predict each data slice/task with a series of independent estimators.