Select continuous features and transform them using PCA.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}

def transform(self, X):
        """Select continuous features and transform them using PCA.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        _, X_sel, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No continuous feature was found!')
        else:
            pca = PCA(svd_solver=self.svd_solver, iterated_power=self.iterated_power, random_state=self.random_state)
            return pca.fit_transform(X_sel)