def test_loss_augmented_inference_exhaustive():
crf = LatentGridCRF(n_labels=2, n_states_per_label=2,
inference_method='dai')
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)
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))
