def test_blocks_crf(): 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]) crf = LatentGridCRF(n_labels=2, n_states_per_label=2) h_hat = crf.inference(x, w) assert_array_equal(y, h_hat / 2) h = crf.latent(x, y, w) assert_equal(crf.loss(h, h_hat), 0)
def test_continuous_y(): for inference_method in ["lp", "ad3"]: X, Y = toy.generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1, 0, # unary 0, 1, 0, # pairwise -4, 0]) crf = LatentGridCRF(n_labels=2, n_states_per_label=1, inference_method=inference_method) psi = crf.psi(x, y) y_cont = np.zeros_like(x) gx, gy = np.indices(x.shape[:-1]) y_cont[gx, gy, y] = 1 # need to generate edge marginals vert = np.dot(y_cont[1:, :, :].reshape(-1, 2).T, y_cont[:-1, :, :].reshape(-1, 2)) # horizontal edges horz = np.dot(y_cont[:, 1:, :].reshape(-1, 2).T, y_cont[:, :-1, :].reshape(-1, 2)) pw = vert + horz psi_cont = crf.psi(x, (y_cont, pw)) assert_array_almost_equal(psi, psi_cont) const = find_constraint(crf, x, y, w, relaxed=False) const_cont = find_constraint(crf, x, y, w, relaxed=True) # dpsi and loss are equal: assert_array_almost_equal(const[1], const_cont[1]) assert_almost_equal(const[2], const_cont[2]) # returned y_hat is one-hot version of other assert_array_equal(const[0], np.argmax(const_cont[0][0], axis=-1)) # test loss: assert_equal(crf.loss(y, const[0]), crf.continuous_loss(y, const_cont[0][0]))