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)