def test_if_fitted(data_random):
x = data_random
model = Koopman()
with pytest.raises(NotFittedError):
model.predict(x)
with pytest.raises(NotFittedError):
model.simulate(x)
with pytest.raises(NotFittedError):
model.koopman_matrix
with pytest.raises(NotFittedError):
model._step(x)
def test_simulate_accuracy(data_2D_superposition):
x = data_2D_superposition
model = Koopman().fit(x)
n_steps = 10
x_pred = model.simulate(x[0], n_steps=n_steps)
assert_allclose(x[1:n_steps + 1], x_pred)
def test_simulate_accuracy_dmdc(data_2D_linear_control_system):
X, C, _, _ = data_2D_linear_control_system
DMDc = regression.DMDc(svd_rank=3)
model = Koopman(regressor=DMDc).fit(X, C)
n_steps = len(C)
x_pred = model.simulate(X[0, :], C, n_steps=n_steps - 1)
assert_allclose(X[1:n_steps, :], x_pred, 1e-07, 1e-12)
def test_simulate_with_time_delay(data_2D_superposition):
x = data_2D_superposition
observables = TimeDelay()
model = Koopman(observables=observables)
model.fit(x)
n_steps = 10
n_consumed_samples = observables.n_consumed_samples
x_pred = model.simulate(x[:n_consumed_samples + 1], n_steps=n_steps)
assert_allclose(x[n_consumed_samples + 1:n_consumed_samples + n_steps + 1],
x_pred)