def test_k_means_initialization_grid_crf():
# with only 1 state per label, nothing happends
X, Y = toy.generate_big_checker(n_samples=10)
crf = LatentGridCRF(n_labels=2, n_states_per_label=1,
inference_method='lp')
H = crf.init_latent(X, Y)
assert_array_equal(Y, H)
def test_with_crosses_bad_init():
# use less perfect initialization
X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
total_size=8)
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
inference_method='lp')
clf = LatentSSVM(problem=crf, max_iter=50, C=10. ** 3, verbose=2,
check_constraints=True, n_jobs=-1, break_on_bad=True)
H_init = crf.init_latent(X, Y)
mask = np.random.uniform(size=H_init.shape) > .7
H_init[mask] = 2 * (H_init[mask] / 2)
clf.fit(X, Y, H_init=H_init)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
