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:
            Xs, X, F, k, num_data, feed_dict = self.prepare()
            k.compile(session=sess)

            K = k.K(X) + tf.eye(num_data, dtype=settings.float_type) * 1e-6
            L = tf.cholesky(K)
            V = tf.matrix_triangular_solve(L, F, lower=True)
            Fstar_mean, Fstar_var = gpflow.conditionals.conditional(Xs, X, k, F)
            Fstar_w_mean, Fstar_w_var = gpflow.conditionals.conditional(Xs, X, k, V, white=True)

            mean1, var1 = sess.run([Fstar_w_mean, Fstar_w_var], feed_dict=feed_dict)
            mean2, var2 = sess.run([Fstar_mean, Fstar_var], feed_dict=feed_dict)

             # TODO: should tolerance be type dependent?
            assert_allclose(mean1, mean2)
            assert_allclose(var1, var2)