Transform data by adding two synthetic feature(s).

        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
        -------
        X_transformed: array-like, shape (n_samples, n_features + 1) or (n_samples, n_features + 1 + n_classes) for classifier with predict_proba attribute
            The transformed feature set.

def transform(self, X):
        """Transform data by adding two synthetic feature(s).

        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
        -------
        X_transformed: array-like, shape (n_samples, n_features + 1) or (n_samples, n_features + 1 + n_classes) for classifier with predict_proba attribute
            The transformed feature set.
        """
        X = check_array(X)
        X_transformed = np.copy(X)
        # add class probabilities as a synthetic feature
        if issubclass(self.estimator.__class__, ClassifierMixin) and hasattr(self.estimator, 'predict_proba'):
            X_transformed = np.hstack((self.estimator.predict_proba(X), X))

        # add class prodiction as a synthetic feature
        X_transformed = np.hstack((np.reshape(self.estimator.predict(X), (-1, 1)), X_transformed))

        return X_transformed