bayesquad.py 文件源码

def _int_var_rbf(self, X, hyp, jitter=1e-8):
        """
        Posterior integral variance of the Gaussian Process quadrature.
        X - vector (1, 2*xdim**2+xdim)
        hyp - kernel hyperparameters [s2, el_1, ... el_d]
        """
        # reshape X to SP matrix
        X = np.reshape(X, (self.n, self.d))
        # set kernel hyper-parameters
        s2, el = hyp[0], hyp[1:]
        self.kern.param_array[0] = s2  # variance
        self.kern.param_array[1:] = el  # lengthscale
        K = self.kern.K(X)
        L = np.diag(el ** 2)
        # posterior variance of the integral
        ks = s2 * np.sqrt(det(L + np.eye(self.d))) * multivariate_normal(mean=np.zeros(self.d), cov=L).pdf(X)
        postvar = -ks.dot(solve(K + jitter * np.eye(self.n), ks.T))
        return postvar