def test_pykalman_predict(self):
n_meas = np.random.randint(5)
n_state = n_meas + np.random.randint(5)
mu_state_past = rand_vec(n_state)
var_state_past = rand_mat(n_state)
mu_state = rand_vec(n_state)
wgt_state = rand_mat(n_state, pd=False)
var_state = rand_mat(n_state)
mu_state_pred, var_state_pred = (pks._filter_predict(
wgt_state, var_state, mu_state, mu_state_past, var_state_past))
ktv = KalmanTV(n_meas, n_state)
mu_state_pred2 = np.empty(n_state)
var_state_pred2 = np.empty((n_state, n_state), order='F')
ktv.predict(mu_state_pred2, var_state_pred2, mu_state_past,
var_state_past, mu_state, wgt_state, var_state)
self.assertAlmostEqual(rel_err(mu_state_pred, mu_state_pred2), 0.0)
self.assertAlmostEqual(rel_err(var_state_pred, var_state_pred2), 0.0)
def test_predict(self):
n_meas = np.random.randint(5)
n_state = n_meas + np.random.randint(5)
mu_state_past = rand_vec(n_state)
var_state_past = rand_mat(n_state)
mu_state = rand_vec(n_state)
wgt_state = rand_mat(n_state, pd=False)
var_state = rand_mat(n_state)
# pure python
KFS = KTV_py(n_meas, n_state)
mu_state_pred, var_state_pred = KFS.predict(mu_state_past,
var_state_past, mu_state,
wgt_state, var_state)
# cython
ktv = KalmanTV(n_meas, n_state)
mu_state_pred2 = np.empty(n_state)
var_state_pred2 = np.empty((n_state, n_state), order='F')
ktv.predict(mu_state_pred2, var_state_pred2, mu_state_past,
var_state_past, mu_state, wgt_state, var_state)
self.assertAlmostEqual(rel_err(mu_state_pred, mu_state_pred2), 0.0)
self.assertAlmostEqual(rel_err(var_state_pred, var_state_pred2), 0.0)