def test_multinomial_checker_subgradient():
X, Y = generate_checker_multinomial(n_samples=10, noise=0.4)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels, inference_method=inference_method)
clf = SubgradientSSVM(model=crf, max_iter=50)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_multinomial_checker_subgradient():
X, Y = generate_checker_multinomial(n_samples=10, noise=0.4)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels, inference_method=inference_method)
clf = SubgradientSSVM(model=crf, max_iter=50)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_multinomial_checker_cutting_plane():
X, Y = generate_checker_multinomial(n_samples=10, noise=.1)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels, inference_method=inference_method)
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)