def test_vgp_update_updates_num_data() -> None:
x_np = np.arange(5, dtype=np.float64).reshape(-1, 1)
x = tf.convert_to_tensor(x_np, x_np.dtype)
y = fnc_3x_plus_10(x)
m = VariationalGaussianProcess(vgp_model(x, y))
num_data = m.model.num_data
x_new = tf.concat([x, [[10.0], [11.0]]], 0)
y_new = fnc_3x_plus_10(x_new)
m.update(Dataset(x_new, y_new))
new_num_data = m.model.num_data
assert new_num_data - num_data == 2
def test_vgp_update_q_mu_sqrt_unchanged() -> None:
x_observed = tf.constant(np.arange(10).reshape((-1, 1)), dtype=gpflow.default_float())
y_observed = fnc_2sin_x_over_3(x_observed)
model = VariationalGaussianProcess(vgp_matern_model(x_observed, y_observed))
old_q_mu = model.model.q_mu.numpy()
old_q_sqrt = model.model.q_sqrt.numpy()
data = Dataset(x_observed, y_observed)
model.update(data)
new_q_mu = model.model.q_mu.numpy()
new_q_sqrt = model.model.q_sqrt.numpy()
npt.assert_allclose(old_q_mu, new_q_mu, atol=1e-5)
npt.assert_allclose(old_q_sqrt, new_q_sqrt, atol=1e-5)
def test_vgp_update() -> None:
x = tf.constant(np.arange(5).reshape(-1, 1), dtype=gpflow.default_float())
data = Dataset(x, fnc_3x_plus_10(x))
m = VariationalGaussianProcess(vgp_model(data.query_points, data.observations))
reference_model = vgp_model(data.query_points, data.observations)
npt.assert_allclose(m.model.q_mu, reference_model.q_mu, atol=1e-5)
npt.assert_allclose(m.model.q_sqrt, reference_model.q_sqrt, atol=1e-5)
x_new = tf.concat([x, tf.constant([[10.0], [11.0]], dtype=gpflow.default_float())], 0)
new_data = Dataset(x_new, fnc_3x_plus_10(x_new))
m.update(new_data)
reference_model_new = vgp_model(new_data.query_points, new_data.observations)
npt.assert_allclose(m.model.q_mu, reference_model_new.q_mu, atol=1e-5)
npt.assert_allclose(m.model.q_sqrt, reference_model_new.q_sqrt, atol=1e-5)