def test_continuous_y():
# for inference_method in ["lp", "ad3"]:
for inference_method in ["lp"]:
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 1, 0, -4, 0])
crf = GridCRF(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]))
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)
