preduct expected value of target given model and input X

        Parameters
        ---------
        X : array-like of shape (n_samples, m_features),
            containing the input dataset

        Returns
        -------
        y : np.array of shape (n_samples,)
            containing expected values under the model

def predict_mu(self, X):
        """
        preduct expected value of target given model and input X

        Parameters
        ---------
        X : array-like of shape (n_samples, m_features),
            containing the input dataset

        Returns
        -------
        y : np.array of shape (n_samples,)
            containing expected values under the model
        """
        if not self._is_fitted:
            raise AttributeError('GAM has not been fitted. Call fit first.')

        X = check_X(X, n_feats=self.statistics_['m_features'],
                    edge_knots=self.edge_knots_, dtypes=self.dtype,
                    features=self.feature, verbose=self.verbose)

        lp = self._linear_predictor(X)
        return self.link.mu(lp, self.distribution)