def test_dmdc_for_highdim_system(data_drss): Y, U, A, B, C = data_drss DMDc = regression.DMDc(svd_rank=7, svd_output_rank=5) model = Koopman(regressor=DMDc) model.fit(Y, U) # Check spectrum Aest = model.state_transition_matrix W, V = np.linalg.eig(A) West, Vest = np.linalg.eig(Aest) idxTrue = np.argsort(W) idxEst = np.argsort(West) assert_allclose(W[idxTrue], West[idxEst], 1e-07, 1e-12) # Check eigenvector reconstruction r = 5 Uc, sc, Vch = np.linalg.svd(C, full_matrices=False) Sc = np.diag(sc[:r]) Cinv = np.dot(Vch[:, :r].T, np.dot(np.linalg.inv(Sc), Uc[:, :r].T)) P = model.projection_matrix_output Atilde = np.dot(Cinv, np.dot(np.dot(P, np.dot(Aest, P.T)), C)) Wtilde, Vtilde = np.linalg.eig(Atilde) idxTilde = np.argsort(Wtilde) assert_allclose(W[idxTrue], Wtilde[idxTilde], 1e-07, 1e-12) # Evecs may be accurate up to a sign; ensured by seeding random generator # when producing the data set assert_allclose(V[:, idxTrue], Vtilde[:, idxTilde], 1e-07, 1e-12)
def test_simulate_accuracy(data_2D_superposition): x = data_2D_superposition model = Koopman() model.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_if_dmdc_model_is_accurate_with_unknown_controlmatrix( data_2D_linear_control_system, ): X, C, A, B = data_2D_linear_control_system model = Koopman() DMDc = regression.DMDc(svd_rank=3) model = Koopman(regressor=DMDc) model.fit(X, C) Aest = model.state_transition_matrix Best = model.control_matrix assert_allclose(Aest, A, 1e-07, 1e-12) assert_allclose(Best, B, 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)
def test_fit(data_random): x = data_random model = Koopman() model.fit(x) check_is_fitted(model)
def test_koopman_matrix_shape(data_random): x = data_random model = Koopman() model.fit(x) assert model.koopman_matrix.shape[0] == model.n_output_features_
def test_predict_shape(data_random): x = data_random model = Koopman() model.fit(x) assert x.shape == model.predict(x).shape