krg_based.py 文件源码

def _predict_variances(self, x):

        # Initialization
        n_eval, n_features_x = x.shape
        x = (x - self.X_mean) / self.X_std
        # Get pairwise componentwise L1-distances to the input training set
        dx = manhattan_distances(x, Y=self.X_norma.copy(), sum_over_features=
                                 False)
        d = self._componentwise_distance(dx)

        # Compute the correlation function
        r = self.options['corr'](self.optimal_theta, d).reshape(n_eval,self.nt)

        C = self.optimal_par['C']
        rt = linalg.solve_triangular(self.optimal_par['C'], r.T, lower=True)

        u = linalg.solve_triangular(self.optimal_par['G'].T,np.dot(self.optimal_par['Ft'].T, rt) -
                             self.options['poly'](x).T)

        MSE = self.optimal_par['sigma2']*(1.-(rt ** 2.).sum(axis=0)+(u ** 2.).sum(axis=0))
        # Mean Squared Error might be slightly negative depending on
        # machine precision: force to zero!
        MSE[MSE < 0.] = 0.
        return MSE