f990d4612c19b6f87f7d3539d0e4de7845dda3f7,testing/test_conditionals.py,DiagsTest,test_whiten,#DiagsTest#,53

Before Change

            Fstar_mean_1, Fstar_var_1 = GPflow.conditionals.gaussian_gp_predict_whitened(self.Xs, self.X, self.k, self.mu, self.sqrt, self.num_latent)
            Fstar_mean_2, Fstar_var_2 = GPflow.conditionals.gaussian_gp_predict_whitened(self.Xs, self.X, self.k, self.mu, self.chol, self.num_latent)

        mean_diff = tf.Session().run(Fstar_mean_1 - Fstar_mean_2, feed_dict=self.feed_dict)
        var_diff = tf.Session().run(Fstar_var_1 - Fstar_var_2, feed_dict=self.feed_dict)

        self.assertTrue(np.allclose(mean_diff, 0))
        self.assertTrue(np.allclose(var_diff, 0))

After Change

            self.chol = tf.transpose(self.chol, perm=[1,2,0])

    def test_whiten(self):
        with self.test_session() as sess, self.k.tf_mode():
            Fstar_mean_1, Fstar_var_1 = GPflow.conditionals.gaussian_gp_predict_whitened(
                self.Xs, self.X, self.k, self.mu, self.sqrt, self.num_latent)
            Fstar_mean_2, Fstar_var_2 = GPflow.conditionals.gaussian_gp_predict_whitened(
                self.Xs, self.X, self.k, self.mu, self.chol, self.num_latent)

            mean_diff = sess.run(Fstar_mean_1 - Fstar_mean_2, feed_dict=self.feed_dict)
            self.assertTrue(np.allclose(mean_diff, 0))

            var_diff = sess.run(Fstar_var_1 - Fstar_var_2, feed_dict=self.feed_dict)
            self.assertTrue(np.allclose(var_diff, 0))

    def test_nonwhiten(self):
        with self.test_session() as sess, self.k.tf_mode():
            Fstar_mean_1, Fstar_var_1 = GPflow.conditionals.gaussian_gp_predict(self.Xs, self.X, self.k, self.mu, self.sqrt, self.num_latent)
            Fstar_mean_2, Fstar_var_2 = GPflow.conditionals.gaussian_gp_predict(self.Xs, self.X, self.k, self.mu, self.chol, self.num_latent)

Instances