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)
with pytest.raises(NotFittedError):
model.score(x)
def test_score_complex_data(data_random_complex):
x = data_random_complex
model = Koopman().fit(x)
with pytest.raises(ValueError):
model.score(x, cast_as_real=False)
def test_score_with_target(data_2D_superposition):
x = data_2D_superposition
model = Koopman().fit(x)
# Test with a target
assert model.score(x[::2], y=x[1::2]) > 0.8
def test_score_without_target(data_2D_superposition):
x = data_2D_superposition
model = Koopman().fit(x)
# Test without a target
assert model.score(x) > 0.8
def test_observables_integration_accuracy(data_1D_cosine, observables):
x = data_1D_cosine
model = Koopman(observables=observables, quiet=True).fit(x)
assert model.score(x) > 0.95