193608aed0bec118ce431f8febfe3807ccd16090,testing/test_variational.py,VariationalUnivariateTest,testUnivariateConditionals,#VariationalUnivariateTest#,125

Before Change

            for is_diagonal in [True, False]:
                for is_whitened in [True, False]:
                    m = self.get_model(is_diagonal, is_whitened)
                    free_vars = tf.placeholder(tf.float64)
                    m.make_tf_array(free_vars)
                    with m.tf_mode():
                        if is_whitened:
                            fmean_func, fvar_func = gpflow.conditionals.conditional(
                                self.X, self.Z, m.kern, m.q_mu, q_sqrt=m.q_sqrt)
                        else:
                            fmean_func, fvar_func = gpflow.conditionals.conditional(
                                self.X, self.Z, m.kern, m.q_mu, q_sqrt=m.q_sqrt, whiten=True)
                    mean_value = fmean_func.eval(
                        session=sess, feed_dict={free_vars: m.get_free_state()})[0, 0]
                    var_value = fvar_func.eval(
                        session=sess, feed_dict={free_vars: m.get_free_state()})[0, 0]
                    self.assertTrue(np.abs(mean_value - self.posteriorMean) < 1e-4)
                    self.assertTrue(np.abs(var_value - self.posteriorVariance) < 1e-4)

After Change

                    mean_value = fmean_func.eval(session=sess)[0, 0]
                    var_value = fvar_func.eval(session=sess)[0, 0]
                    assert_allclose(mean_value - self.posteriorMean, 0, atol=4)
                    assert_allclose(var_value - self.posteriorVariance, 0, atol=4)


class VariationalMultivariateTest(GPflowTestCase):
    def setUp(self):

Instances