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 = _3x_plus_10(x)
m = VariationalGaussianProcess(_vgp(x, y))
num_data = m.model.num_data
x_new = tf.concat([x, [[10.0], [11.0]]], 0)
y_new = _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 = _2sin_x_over_3(x_observed)
model = VariationalGaussianProcess(_vgp_matern(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_variational_gaussian_process_predict() -> None:
x_observed = tf.constant(np.arange(100).reshape((-1, 1)), dtype=gpflow.default_float())
y_observed = _3x_plus_gaussian_noise(x_observed)
model = VariationalGaussianProcess(_vgp(x_observed, y_observed))
internal_model = model.model
gpflow.optimizers.Scipy().minimize(
internal_model.training_loss_closure(),
internal_model.trainable_variables,
)
x_predict = tf.constant([[50.5]], gpflow.default_float())
mean, variance = model.predict(x_predict)
reference_model = _reference_gpr(x_observed, y_observed)
gpflow.optimizers.Scipy().minimize(
reference_model.training_loss_closure(),
reference_model.trainable_variables,
)
reference_mean, reference_variance = reference_model.predict_f(x_predict)
npt.assert_allclose(mean, reference_mean)
npt.assert_allclose(variance, reference_variance, atol=1e-3)