def test_multinomial_grid_binary():
# test handling on unaries for multinomial grid CRFs
# on binary datasets
for ds in toy_datasets.binary:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
crf = MultinomialGridCRF()
w_unaries_only = np.zeros(5)
w_unaries_only[:2] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((2, 2), dtype=np.int32)
un = np.ascontiguousarray(-1000 * x).astype(np.int32)
unaries = binary_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))
def test_multinomial_grid_unaries():
# test handling on unaries for multinomial grid CRFs
# on multinomial datasets
for ds in toy_datasets.multinomial:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
n_labels = len(np.unique(Y))
crf = MultinomialGridCRF(n_states=n_labels)
w_unaries_only = np.zeros(crf.size_psi)
w_unaries_only[:n_labels] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((n_labels, n_labels), dtype=np.int32)
un = np.ascontiguousarray(-1000 * x).astype(np.int32)
unaries = alpha_expansion_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))
def test_multinomial_grid_binary():
# test handling on unaries for multinomial grid CRFs
# on binary datasets
for ds in toy_datasets.binary:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
crf = MultinomialGridCRF()
w_unaries_only = np.zeros(5)
w_unaries_only[:2] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((2, 2), dtype=np.int32)
un = np.ascontiguousarray(
-1000 * x).astype(np.int32)
unaries = binary_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))
def test_multinomial_grid_unaries():
# test handling on unaries for multinomial grid CRFs
# on multinomial datasets
for ds in toy_datasets.multinomial:
X, Y = ds(n_samples=1)
x, y = X[0], Y[0]
n_labels = len(np.unique(Y))
crf = MultinomialGridCRF(n_states=n_labels)
w_unaries_only = np.zeros(crf.size_psi)
w_unaries_only[:n_labels] = 1.
# test that inference with unaries only is the
# same as argmax
inf_unaries = crf.inference(x, w_unaries_only)
pw_z = np.zeros((n_labels, n_labels), dtype=np.int32)
un = np.ascontiguousarray(
-1000 * x).astype(np.int32)
unaries = alpha_expansion_grid(un, pw_z)
assert_array_equal(inf_unaries, unaries)
assert_array_equal(inf_unaries, np.argmax(x, axis=2))