def test_multinomial_checker_cutting_plane(): X, Y = toy.generate_checker_multinomial(n_samples=10, noise=.1) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels) clf = NSlackSSVM(model=crf, max_iter=20, C=100000, check_constraints=True) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_multinomial_checker_subgradient(): X, Y = toy.generate_checker_multinomial(n_samples=10, noise=0.0) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels) clf = SubgradientStructuredSVM(problem=crf, max_iter=50, C=10, verbose=0, momentum=.98, learning_rate=0.01, n_jobs=-1) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)
def test_multinomial_checker_cutting_plane(): X, Y = toy.generate_checker_multinomial(n_samples=10, noise=.1) n_labels = len(np.unique(Y)) crf = GridCRF(n_states=n_labels) clf = StructuredSVM(problem=crf, max_iter=20, C=100000, verbose=0, check_constraints=True, n_jobs=-1) clf.fit(X, Y) Y_pred = clf.predict(X) assert_array_equal(Y, Y_pred)