bayesquad.py 文件源码

def _int_var_rbf_hyp(self, hyp, X, jitter=1e-8):
        """
        Posterior integral variance as a function of hyper-parameters
        :param hyp: RBF kernel hyper-parameters [s2, el_1, ..., el_d]
        :param X: sigma-points
        :param jitter: numerical jitter (for stabilizing computations)
        :return: posterior integral variance
        """
        # reshape X to SP matrix
        X = np.reshape(X, (self.n, self.d))
        # set kernel hyper-parameters
        s2, el = 1, hyp  # sig_var hyper always set to 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 = s2 * np.sqrt(det(2 * inv(L) + np.eye(self.d))) ** -1 - ks.dot(
            solve(K + jitter * np.eye(self.n), ks.T))
        return postvar