def test_blocks_crf_directional(): # test latent directional CRF on blocks # test that all results are the same as equivalent LatentGridCRF X, Y = generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) pw_directional = np.array([ 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0 ]) w_directional = np.hstack([unary_weights.ravel(), pw_directional]) crf = LatentGridCRF(n_states_per_label=2, inference_method='lp') crf.initialize(X, Y) directional_crf = LatentDirectionalGridCRF(n_states_per_label=2, inference_method='lp') directional_crf.initialize(X, Y) h_hat = crf.inference(x, w) h_hat_d = directional_crf.inference(x, w_directional) assert_array_equal(h_hat, h_hat_d) h = crf.latent(x, y, w) h_d = directional_crf.latent(x, y, w_directional) assert_array_equal(h, h_d) h_hat = crf.loss_augmented_inference(x, y, w) h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional) assert_array_equal(h_hat, h_hat_d) joint_feature = crf.joint_feature(x, h_hat) joint_feature_d = directional_crf.joint_feature(x, h_hat) assert_array_equal(np.dot(joint_feature, w), np.dot(joint_feature_d, w_directional))
def test_blocks_crf_directional(): # test latent directional CRF on blocks # test that all results are the same as equivalent LatentGridCRF X, Y = toy.generate_blocks(n_samples=1) x, y = X[0], Y[0] pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0]) unary_weights = np.repeat(np.eye(2), 2, axis=0) w = np.hstack([unary_weights.ravel(), pairwise_weights]) pw_directional = np.array( [0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0] ) w_directional = np.hstack([unary_weights.ravel(), pw_directional]) crf = LatentGridCRF(n_labels=2, n_states_per_label=2) directional_crf = LatentDirectionalGridCRF(n_labels=2, n_states_per_label=2) h_hat = crf.inference(x, w) h_hat_d = directional_crf.inference(x, w_directional) assert_array_equal(h_hat, h_hat_d) h = crf.latent(x, y, w) h_d = directional_crf.latent(x, y, w_directional) assert_array_equal(h, h_d) h_hat = crf.loss_augmented_inference(x, y, w) h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional) assert_array_equal(h_hat, h_hat_d) psi = crf.psi(x, h_hat) psi_d = directional_crf.psi(x, h_hat) assert_array_equal(np.dot(psi, w), np.dot(psi_d, w_directional))
def test_loss_augmented_inference_exhaustive_grid(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2) for i in range(10): w = np.random.normal(size=18) y = np.random.randint(2, size=(2, 2)) x = np.random.normal(size=(2, 2, 2)) h_hat = crf.loss_augmented_inference(x, y * 2, w) h = exhaustive_loss_augmented_inference(crf, x, y * 2, w) assert_array_equal(h, h_hat)
def test_loss_augmented_inference_exhaustive_grid(): crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2) for i in xrange(10): w = np.random.normal(size=18) y = np.random.randint(2, size=(2, 2)) x = np.random.normal(size=(2, 2, 2)) h_hat = crf.loss_augmented_inference(x, y * 2, w) h = exhaustive_loss_augmented_inference(crf, x, y * 2, w) assert_array_equal(h, h_hat)