def test_psi_continuous():
# 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
crf = DirectionalGridCRF(n_states=3, inference_method='lp')
w = np.hstack([np.ones(3), -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,))
# first unary, then horizontal, then vertical
unary_psi = crf.get_unary_weights(psi_y)
pw_psi_horz, pw_psi_vert = crf.get_pairwise_weights(psi_y)
# test unary
xx, yy = np.indices(y.shape)
assert_array_almost_equal(unary_psi,
np.bincount(y.ravel(), x[xx, yy, y].ravel()))
def test_psi_discrete():
X, Y = toy.generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
crf = DirectionalGridCRF(n_states=3, inference_method='lp')
psi_y = crf.psi(x, y)
assert_equal(psi_y.shape, (crf.size_psi,))
# first unary, then horizontal, then vertical
unary_psi = crf.get_unary_weights(psi_y)
pw_psi_horz, pw_psi_vert = crf.get_pairwise_weights(psi_y)
xx, yy = np.indices(y.shape)
assert_array_almost_equal(unary_psi,
np.bincount(y.ravel(), x[xx, yy, y].ravel()))
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[1, 0] = 10
vert_psi[2, 1] = 10
assert_array_equal(pw_psi_horz, vert_psi)