test_conditionals.py 文件源码

def test_whiten(self):
        """
        make sure that predicting using the whitened representation is the
        sameas the non-whitened one.
        """
        with self.test_context() as sess:
            rng = np.random.RandomState(0)
            Xs, X, F, k, num_data, feed_dict = self.prepare()
            k.compile(session=sess)

            F_sqrt = tf.placeholder(settings.float_type, [num_data, 1])
            F_sqrt_data = rng.rand(num_data, 1)
            feed_dict[F_sqrt] = F_sqrt_data

            K = k.K(X)
            L = tf.cholesky(K)
            V = tf.matrix_triangular_solve(L, F, lower=True)
            V_chol = tf.matrix_triangular_solve(L, tf.diag(F_sqrt[:, 0]), lower=True)
            V_sqrt = tf.expand_dims(V_chol, 2)

            Fstar_mean, Fstar_var = gpflow.conditionals.conditional(
                Xs, X, k, F, q_sqrt=F_sqrt)
            Fstar_w_mean, Fstar_w_var = gpflow.conditionals.conditional(
                Xs, X, k, V, q_sqrt=V_sqrt, white=True)

            mean_difference = sess.run(Fstar_w_mean - Fstar_mean, feed_dict=feed_dict)
            var_difference = sess.run(Fstar_w_var - Fstar_var, feed_dict=feed_dict)

            assert_allclose(mean_difference, 0, atol=4)
            assert_allclose(var_difference, 0, atol=4)