def test_graph_crf_energy_lp_relaxed():
crf = GraphCRF(n_states=2, inference_method='lp')
for i in xrange(10):
w_ = np.random.uniform(size=w.shape)
inf_res, energy_lp = crf.inference((x_1, g_1), w_, relaxed=True,
return_energy=True)
assert_almost_equal(energy_lp,
-np.dot(w_, crf.psi((x_1, g_1), inf_res)))
# now with fractional solution
x = np.array([[0, 0], [0, 0], [0, 0]])
inf_res, energy_lp = crf.inference((x, g_1), w, relaxed=True,
return_energy=True)
assert_almost_equal(energy_lp, -np.dot(w, crf.psi((x, g_1), inf_res)))
def test_graph_crf_loss_augment():
x = (x_1, g_1)
y = y_1
crf = GraphCRF(n_states=2, inference_method='lp')
y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True)
# check that y_hat fulfulls energy + loss condition
assert_almost_equal(np.dot(w, crf.psi(x, y_hat)) + crf.loss(y, y_hat),
-energy)
def test_graph_crf_energy_lp_integral():
crf = GraphCRF(n_states=2, inference_method='lp')
inf_res, energy_lp = crf.inference((x_1, g_1), w, relaxed=True,
return_energy=True)
# integral solution
assert_array_almost_equal(np.max(inf_res[0], axis=-1), 1)
y = np.argmax(inf_res[0], axis=-1)
# energy and psi check out
assert_almost_equal(energy_lp, -np.dot(w, crf.psi((x_1, g_1), y)))
