def test_energy():
# make sure that energy as computed by ssvm is the same as by lp
np.random.seed(0)
for inference_method in get_installed(["lp", "ad3"]):
found_fractional = False
crf = DirectionalGridCRF(n_states=3, inference_method=inference_method)
while not found_fractional:
x = np.random.normal(size=(7, 8, 3))
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
res, energy = crf.inference(x, w, relaxed=True, return_energy=True)
found_fractional = np.any(np.max(res[0], axis=-1) != 1)
psi = crf.psi(x, res)
energy_svm = np.dot(psi, w)
assert_almost_equal(energy, -energy_svm)
if not found_fractional:
# exact discrete labels, test non-relaxed version
res, energy = crf.inference(x, w, relaxed=False,
return_energy=True)
psi = crf.psi(x, res)
energy_svm = np.dot(psi, w)
assert_almost_equal(energy, -energy_svm)
def test_energy():
# make sure that energy as computed by ssvm is the same as by lp
np.random.seed(0)
for inference_method in get_installed(["lp", "ad3"]):
found_fractional = False
crf = DirectionalGridCRF(n_states=3,
n_features=3,
inference_method=inference_method)
while not found_fractional:
x = np.random.normal(size=(7, 8, 3))
unary_params = np.random.normal(size=(3, 3))
pw1 = np.random.normal(size=(3, 3))
pw2 = np.random.normal(size=(3, 3))
w = np.hstack([unary_params.ravel(), pw1.ravel(), pw2.ravel()])
res, energy = crf.inference(x, w, relaxed=True, return_energy=True)
found_fractional = np.any(np.max(res[0], axis=-1) != 1)
psi = crf.psi(x, res)
energy_svm = np.dot(psi, w)
assert_almost_equal(energy, -energy_svm)
if not found_fractional:
# exact discrete labels, test non-relaxed version
res, energy = crf.inference(x,
w,
relaxed=False,
return_energy=True)
psi = crf.psi(x, res)
energy_svm = np.dot(psi, w)
assert_almost_equal(energy, -energy_svm)
def test_psi_continuous():
# FIXME
# first make perfect prediction, including pairwise part
X, Y = toy.generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# create crf, assemble weight, make prediction
for inference_method in get_installed(["lp", "ad3"]):
crf = DirectionalGridCRF(n_states=3, inference_method=inference_method)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=True)
# compute psi for prediction
psi_y = crf.psi(x, y_pred)
assert_equal(psi_y.shape, (crf.size_psi,))
def test_psi_continuous():
# FIXME
# first make perfect prediction, including pairwise part
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# create crf, assemble weight, make prediction
for inference_method in get_installed(["lp", "ad3"]):
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=True)
# compute psi for prediction
psi_y = crf.psi(x, y_pred)
assert_equal(psi_y.shape, (crf.size_psi, ))
def test_psi_discrete():
X, Y = toy.generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
crf = DirectionalGridCRF(n_states=3, inference_method=inference_method)
psi_y = crf.psi(x, y)
assert_equal(psi_y.shape, (crf.size_psi,))
# first horizontal, then vertical
# we trust the unaries ;)
pw_psi_horz, pw_psi_vert = psi_y[crf.n_states *
crf.n_features:].reshape(
2, crf.n_states, crf.n_states)
xx, yy = np.indices(y.shape)
assert_array_equal(pw_psi_vert, np.diag([9 * 4, 9 * 4, 9 * 4]))
vert_psi = np.diag([10 * 3, 10 * 3, 10 * 3])
vert_psi[0, 1] = 10
vert_psi[1, 2] = 10
assert_array_equal(pw_psi_horz, vert_psi)
def test_psi_discrete():
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
psi_y = crf.psi(x, y)
assert_equal(psi_y.shape, (crf.size_psi, ))
# first horizontal, then vertical
# we trust the unaries ;)
pw_psi_horz, pw_psi_vert = psi_y[crf.n_states *
crf.n_features:].reshape(
2, crf.n_states, crf.n_states)
xx, yy = np.indices(y.shape)
assert_array_equal(pw_psi_vert, np.diag([9 * 4, 9 * 4, 9 * 4]))
vert_psi = np.diag([10 * 3, 10 * 3, 10 * 3])
vert_psi[0, 1] = 10
vert_psi[1, 2] = 10
assert_array_equal(pw_psi_horz, vert_psi)
