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)