def test_loss_augmented_inference_energy_graph():
crf = LatentGraphCRF(n_labels=2, n_states_per_label=2, inference_method="lp")
for i in xrange(10):
w = np.random.normal(size=18)
y = np.random.randint(2, size=(2))
x = np.random.normal(size=(2, 2))
e = np.array([[0, 1], [1, 2], [2, 0]], dtype=np.int)
h_hat, energy = crf.loss_augmented_inference((x, e), y * 2, w, relaxed=True, return_energy=True)
assert_almost_equal(-energy, np.dot(w, crf.psi((x, e), h_hat)) + crf.loss(y * 2, h_hat))
def test_loss_augmented_inference_energy_graph():
crf = LatentGraphCRF(n_labels=2,
n_features=2,
n_states_per_label=2,
inference_method='lp')
for i in xrange(10):
w = np.random.normal(size=18)
y = np.random.randint(2, size=(2))
x = np.random.normal(size=(2, 2))
e = np.array([[0, 1], [1, 2], [2, 0]], dtype=np.int)
h_hat, energy = crf.loss_augmented_inference((x, e),
y * 2,
w,
relaxed=True,
return_energy=True)
assert_almost_equal(
-energy,
np.dot(w, crf.psi((x, e), h_hat)) + crf.loss(y * 2, h_hat))
