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)
