def test_graph_crf_continuous_inference():
for inference_method in ['lp', 'ad3']:
crf = GraphCRF(n_states=2, inference_method=inference_method)
assert_array_equal(np.argmax(crf.inference((x_1, g_1), w,
relaxed=True)[0], axis=-1),
y_1)
assert_array_equal(np.argmax(crf.inference((x_2, g_2), w,
relaxed=True)[0], axis=-1),
y_2)
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_inference():
# create two samples with different graphs
# two states only, pairwise smoothing
for inference_method in ['qpbo', 'lp', 'ad3', 'dai']:
crf = GraphCRF(n_states=2, inference_method=inference_method)
assert_array_equal(crf.inference((x_1, g_1), w), y_1)
assert_array_equal(crf.inference((x_2, g_2), w), y_2)
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)))
