def test_get_filtered_state_not_fitted(ft_ll_mvar_diffuse,
Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
t = 6
with pytest.raises(ValueError) as error:
result = kf.get_filtered_state(t)
expected_result = 'Kalman filter is not fitted yet'
result = str(error.value)
assert expected_result == result
def test_get_filtered_state_max(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
t = 6
with pytest.raises(ValueError) as error:
result = kf.get_filtered_state(t)
expected_result = 'Maximum t allowed is 4'
result = str(error.value)
assert expected_result == result
def test_get_filtered_state_diffuse(ft_ll_mvar_diffuse,
Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
t = 1
with pytest.raises(ValueError) as error:
result = kf.get_filtered_state(t)
expected_result = 'Diffuse state at time 1'
result = str(error.value)
assert expected_result == result
def test_get_filtered_state_T(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
t = kf.T
result = kf.get_filtered_state(t)
expected_result = {
'P_star_t': kf.P_T1,
'P_inf_t': np.zeros([2, 2]),
'xi_t': kf.xi_T1,
'q': 0
}
for i in ['P_star_t', 'P_inf_t', 'xi_t']:
np.testing.assert_array_equal(result[i], expected_result[i])
assert result['q'] == expected_result['q']
def test_get_filtered_state_t(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
"""
get values at t < self.T
"""
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse)
t = kf.T - 1
result = kf.get_filtered_state(t)
expected_result = {
'P_star_t': kf.P_star_t[t][0],
'P_inf_t': kf.P_inf_t[t][0],
'xi_t': kf.xi_t[t][0],
'q': 0
}
for i in ['P_star_t', 'P_inf_t', 'xi_t']:
np.testing.assert_array_equal(result[i], expected_result[i])
assert result['q'] == expected_result['q']
def test_over_ride(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing,
theta_ll_mvar_diffuse):
"""
Force a diffuse kalman filter to become regular filter
"""
init_val = {
'P_star_t': np.zeros([2, 2]),
'xi_t': 100 * np.ones([2, 1]),
'q': 0
}
kf = Filter(ft_ll_mvar_diffuse, Yt_mvar_diffuse_missing, for_smoother=True)
kf.fit(theta_ll_mvar_diffuse, init_state=init_val)
t = 0
result = kf.get_filtered_state(t)
expected_result = {
'P_star_t': np.zeros([2, 2]),
'P_inf_t': np.zeros([2, 2]),
'xi_t': 100 * np.ones([2, 1]),
'q': 0
}
for i in ['P_star_t', 'P_inf_t', 'xi_t']:
np.testing.assert_array_equal(result[i], expected_result[i])
assert result['q'] == expected_result['q']
