def test_binary_crf_exhaustive_loss_augmented(): # tests graph cut inference against brute force # on random data / weights np.random.seed(0) for i in xrange(50): # generate data and weights y = np.random.randint(2, size=(3, 3)) x = np.random.uniform(-1, 1, size=(3, 3)) x = np.dstack([-x, np.zeros_like(x)]) w = np.random.uniform(-1, 1, size=5) crf = GridCRF() # check loss augmented map inference y_hat = crf.loss_augmented_inference(x, y, w) y_ex = exhausive_loss_augmented_inference_binary(crf, x, y, w) # print(y_hat) # print(y_ex) # print("++++++++++++++++++++++") assert_array_equal(y_hat, y_ex)
def test_loss_augmentation(): X, Y = toy.generate_blocks(n_samples=1) x, y = X[0], Y[0] w = np.array([1.0, 1.0, 0.0, -4.0, 0.0]) unary_params = w[:2] pairwise_flat = np.asarray(w[2:]) pairwise_params = np.zeros((2, 2)) pairwise_params[np.tri(2, dtype=np.bool)] = pairwise_flat pairwise_params = pairwise_params + pairwise_params.T - np.diag(np.diag(pairwise_params)) crf = GridCRF() x_loss_augmented = crf.loss_augment(x, y, w) y_hat = crf.inference(x_loss_augmented, w) # test that loss_augmented_inference does the same y_hat_2 = crf.loss_augmented_inference(x, y, w) assert_array_equal(y_hat_2, y_hat) energy = compute_energy(x, y_hat, unary_params, pairwise_params) energy_loss_augmented = compute_energy(x_loss_augmented, y_hat, unary_params, pairwise_params) assert_almost_equal(energy + crf.loss(y, y_hat), energy_loss_augmented) # with zero in w: unary_params[1] = 0 assert_raises(ValueError, crf.loss_augment, x, y, w)