def test_pykalman_update(self):
n_meas = np.random.randint(5) + 1
n_state = n_meas + np.random.randint(5)
mu_state_pred = rand_vec(n_state)
var_state_pred = rand_mat(n_state)
x_meas = rand_vec(n_meas)
mu_meas = rand_vec(n_meas)
wgt_meas = rand_mat(n_meas, n_state, pd=False)
var_meas = rand_mat(n_meas)
_, mu_state_filt, var_state_filt = (pks._filter_correct(
wgt_meas, var_meas, mu_meas, mu_state_pred, var_state_pred,
x_meas))
ktv = KalmanTV(n_meas, n_state)
mu_state_filt2 = np.empty(n_state)
var_state_filt2 = np.empty((n_state, n_state), order='F')
ktv.update(mu_state_filt2, var_state_filt2, mu_state_pred,
var_state_pred, x_meas, mu_meas, wgt_meas, var_meas)
self.assertAlmostEqual(rel_err(mu_state_filt, mu_state_filt2), 0.0)
self.assertAlmostEqual(rel_err(var_state_filt, var_state_filt2), 0.0)
def test_update(self):
n_meas = np.random.randint(5) + 1
n_state = n_meas + np.random.randint(5)
mu_state_pred = rand_vec(n_state)
var_state_pred = rand_mat(n_state)
x_meas = rand_vec(n_meas)
mu_meas = rand_vec(n_meas)
wgt_meas = rand_mat(n_meas, n_state, pd=False)
var_meas = rand_mat(n_meas)
# pure python
KFS = KTV_py(n_meas, n_state)
mu_state_filt, var_state_filt = KFS.update(mu_state_pred,
var_state_pred, x_meas,
mu_meas, wgt_meas, var_meas)
# cython
ktv = KalmanTV(n_meas, n_state)
mu_state_filt2 = np.empty(n_state)
var_state_filt2 = np.empty((n_state, n_state), order='F')
ktv.update(mu_state_filt2, var_state_filt2, mu_state_pred,
var_state_pred, x_meas, mu_meas, wgt_meas, var_meas)
self.assertAlmostEqual(rel_err(mu_state_filt, mu_state_filt2), 0.0)
self.assertAlmostEqual(rel_err(var_state_filt, var_state_filt2), 0.0)