def test_binary_checker_subgradient():
#testing subgradient ssvm on non-submodular binary dataset
X, Y = toy.generate_checker(n_samples=10)
crf = GridCRF()
clf = SubgradientStructuredSVM(problem=crf, max_iter=100, C=100,
verbose=0, momentum=.9, learningrate=0.1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_subgradient():
#testing subgradient ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
clf = SubgradientStructuredSVM(problem=crf, max_iter=200, C=100,
verbose=0, momentum=.0, learningrate=0.1)
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=10, momentum=.98, learningrate=0.01,
plot=False)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_multinomial_blocks_subgradient():
#testing cutting plane ssvm on easy multinomial dataset
X, Y = toy.generate_blocks_multinomial(n_samples=10, noise=0.3,
seed=1)
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, learningrate=0.001,
plot=False)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)