def test_LDL_first_missing(ft_mvar, theta_mvar_diffuse, Yt_mvar, Xt_mvar):
"""
Test when first measurement is missing
"""
kf = Filter(ft_mvar, Yt_mvar, Xt_mvar, for_smoother=True)
kf.init_attr(theta_mvar_diffuse)
Y_t, H_t, D_t, R_t, L_t, L_inv = kf._LDL(1)
assert kf.n_t[1] == 2
R_t_move = np.array([[4, 3, 2], [3, 6, 1], [2, 1, 3]])
L_t_expected, R_t_expected, _ = linalg.ldl(R_t_move)
L_inv_expected, _ = linalg.lapack.dtrtri(L_t_expected, lower=True)
np.testing.assert_array_equal(L_t, L_t_expected)
np.testing.assert_array_equal(R_t, R_t_expected)
Y_t_expected = linalg.pinv(L_t_expected).dot(
np.array([2.2, 3, 0]).reshape(-1, 1))
np.testing.assert_array_almost_equal(Y_t, Y_t_expected)
H_t_expected = L_inv_expected.dot(np.array([2, 2.4, 1]).reshape(-1, 1))
np.testing.assert_array_almost_equal(H_t, H_t_expected)
expected_partitioned_index = np.array([1, 2, 0])
np.testing.assert_array_equal(kf.partitioned_index[1],
expected_partitioned_index)
def test_LDL(ft_mvar, theta_mvar_diffuse, Yt_mvar, Xt_mvar):
"""
Test normal run
"""
kf = Filter(ft_mvar, Yt_mvar, Xt_mvar, for_smoother=True)
kf.init_attr(theta_mvar_diffuse)
Y_t, H_t, D_t, R_t, L_t, L_inv = kf._LDL(0)
assert kf.n_t[0] == 3
R_t_move = np.array([[3, 2, 1], [2, 4, 3], [1, 3, 6]])
L_t_expected, R_t_expected, _ = linalg.ldl(R_t_move)
L_inv_expected, _ = linalg.lapack.dtrtri(L_t_expected, lower=True)
np.testing.assert_array_equal(L_t, L_t_expected)
np.testing.assert_array_equal(R_t, R_t_expected)
Y_t_expected = linalg.pinv(L_t_expected).dot(
np.array([1, 2, 2.1]).reshape(-1, 1))
np.testing.assert_array_almost_equal(Y_t, Y_t_expected)
H_t_expected = L_inv_expected.dot(np.array([1, 2, 2.4]).reshape(-1, 1))
np.testing.assert_array_almost_equal(H_t, H_t_expected)
expected_partitioned_index = np.array([0, 1, 2])
np.testing.assert_array_equal(kf.partitioned_index[0],
expected_partitioned_index)
