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)