def innovations(self, x, rep):
        # Get the variance at x with noise
        _, v = self.model.predict(x)

        # Get the variance at x without noise
        v_ = self.sn2 - v

        # Compute the variance between the test point x and
        # the representer points
        sigma_x_rep = self.model.predict_variance(rep, x)

        norm_cov = np.dot(sigma_x_rep, np.linalg.inv(v_))
        # Compute the stochastic innovation for the mean
        dm_rep = np.dot(norm_cov,
                    np.linalg.cholesky(v + 1e-10))
        dm_rep = dm_rep.dot(self.W)

        # Compute the deterministic innovation for the variance
        dv_rep = -norm_cov.dot(sigma_x_rep.T)

        return dm_rep, dv_rep
dataset/ETHPy150Open automl/RoBO/robo/acquisition/information_gain_mc.py/InformationGainMC.innovations/swapped operands of `-`