def test_directional_bars():
X, Y = toy.generate_easy(n_samples=10, noise=5, box_size=2,
total_size=6, seed=1)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4])
clf = LatentSSVM(OneSlackSSVM(model=crf, max_iter=500, C=10.,
inference_cache=50, tol=.01))
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_directional_bars():
for inference_method in ['lp']:
X, Y = toy.generate_easy(n_samples=10, noise=5, box_size=2,
total_size=6, seed=1)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4],
inference_method=inference_method)
clf = LatentSubgradientSSVM(model=crf, max_iter=500, C=10. ** 5,
verbose=2)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_directional_bars():
# this test is very fragile :-/
X, Y = toy.generate_easy(n_samples=20, noise=2, box_size=2,
total_size=6, seed=2)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4])
clf = LatentSubgradientSSVM(model=crf, max_iter=75, C=10.,
learning_rate=1, momentum=0,
decay_exponent=0.5, decay_t0=10)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_directional_bars():
for inference_method in ['lp']:
X, Y = toy.generate_easy(n_samples=10, noise=5, box_size=2,
total_size=6, seed=1)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4],
inference_method=inference_method)
clf = LatentSSVM(problem=crf, max_iter=500, C=10. ** 5, verbose=2,
check_constraints=True, n_jobs=-1, break_on_bad=True,
base_svm='1-slack')
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
