Method predict

mne/decoding/receptive_field.py:329–366

Generate predictions with a receptive field. Parameters ---------- X : array, shape (n_times[, n_epochs], n_channels) The input features for the model. Returns ------- y_pred : array, shape (n_times[, n_epochs][, n_outputs]) T

(self, X)

        return self

    def predict(self, X):
        """Generate predictions with a receptive field.

        Parameters
        ----------
        X : array, shape (n_times[, n_epochs], n_channels)
            The input features for the model.

        Returns
        -------
        y_pred : array, shape (n_times[, n_epochs][, n_outputs])
            The output predictions. "Note that valid samples (those
            unaffected by edge artifacts during the time delaying step) can
            be obtained using ``y_pred[rf.valid_samples_]``.
        """
        if not hasattr(self, "delays_"):
            raise NotFittedError("Estimator has not been fit yet.")

        X, _ = self._check_data(X)
        X, _, X_dim = self._check_dimensions(X, None, predict=True)[:3]

        del _
        # convert to sklearn and back
        pred_shape = X.shape[:-1]
        if self._y_dim > 1:
            pred_shape = pred_shape + (self.coef_.shape[0],)
        X, _ = self._delay_and_reshape(X)
        y_pred = self.estimator_.predict(X)
        y_pred = y_pred.reshape(pred_shape, order="F")
        shape = list(y_pred.shape)
        if X_dim <= 2:
            shape.pop(1)  # epochs
            extra = 0
        else:
            extra = 1
        shape = shape[: self._y_dim + extra]
        y_pred = _reshape_view(y_pred, shape)
        return y_pred

    def score(self, X, y):
        """Score predictions generated with a receptive field.