def test_inference_chain():
# same with pairwise edges:
features = np.array([-1, 1, -1, 1, -1, 1])
unary_parameters = np.array([-1, 1])
pairwise_parameters = np.array([+1,
+0, 1,
+3, 0, 0,
+0, 3, 0, 0])
w = np.hstack([unary_parameters, pairwise_parameters])
crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2)
edges = np.vstack([np.arange(5), np.arange(1, 6)]).T
# edges for latent states. Latent states named 6, 7
node_indices = np.arange(features.size)
other_edges = []
for n in node_indices[:3]:
other_edges.append([n, 6])
for n in node_indices[3:]:
other_edges.append([n, 7])
all_edges = np.vstack([edges, other_edges])
x = (features.reshape(-1, 1), all_edges, 2)
h, energy_lp = crf.inference(x, w, return_energy=True)
y = np.argmax(crf.get_unary_potentials(x, w), axis=1)[:6]
energy_psi = np.dot(w, crf.psi(x, h))
assert_almost_equal(energy_psi, -energy_lp)
assert_array_equal(y, features > 0)
assert_array_equal(h, [0, 0, 0, 1, 1, 1, 2, 3])
# continuous inference and psi:
h, energy_lp = crf.inference(x, w, return_energy=True, relaxed=True)
energy_psi = np.dot(w, crf.psi(x, h))
assert_almost_equal(energy_psi, -energy_lp)
def test_inference_trivial_features():
# size 6 chain graph
# first three and last three have a latent variable
# last two features are for latent variables
features = np.array([-1, 1, -1, 1, -1, 1, 0, 0])
unary_parameters = np.array([-1, 1, 0, 0])
pairwise_parameters = np.array([+0,
+0, 0,
+3, 0, 0,
+0, 3, 0, 0])
w = np.hstack([unary_parameters, pairwise_parameters])
crf = LatentNodeCRF(n_labels=2, n_features=1, n_hidden_states=2,
latent_node_features=True)
# edges for latent states. Latent states named 6, 7
node_indices = np.arange(6)
other_edges = []
for n in node_indices[:3]:
other_edges.append([n, 6])
for n in node_indices[3:]:
other_edges.append([n, 7])
all_edges = np.vstack(other_edges)
x = (features.reshape(-1, 1), all_edges, 2)
# test inference
h, energy_lp = crf.inference(x, w, return_energy=True)
assert_array_equal(h, [0, 0, 0, 1, 1, 1, 2, 3])
y = crf.label_from_latent(h)
assert_array_equal(y, [0, 0, 0, 1, 1, 1])
y_unaries = np.argmax(crf.get_unary_potentials(x, w), axis=1)[:6]
assert_array_equal(y_unaries, features[:6] > 0)
# test psi
energy_psi = np.dot(w, crf.psi(x, h))
assert_almost_equal(energy_psi, -energy_lp)
# test loss
h_unaries = crf.latent(x, y_unaries, w)
assert_equal(crf.loss(h, h_unaries), 2)
# continuous inference and psi:
h_continuous, energy_lp = crf.inference(x, w, return_energy=True,
relaxed=True)
energy_psi = np.dot(w, crf.psi(x, h))
assert_almost_equal(energy_psi, -energy_lp)
# test continuous loss
assert_equal(crf.loss(h, h_continuous), 0)
#test loss-augmented inference energy
h_hat, energy_lp = crf.loss_augmented_inference(x, h, w,
return_energy=True)
assert_equal(-energy_lp, np.dot(w, crf.psi(x, h_hat)) + crf.loss(h_hat, y))
