def test_obs1(self):
model = RecursiveLogitModelEstimation(
network_struct,
observations_record=[input_obs[0]],
initial_beta=-0.4,
mu=1,
optimiser=optimiser)
ll = model.get_log_likelihood()[0]
start_link_ind, fin_link_ind = model._compute_obs_path_indices(
ak.from_iter(input_obs[0]))
assert tuple(start_link_ind) == (0, 1, 2)
assert tuple(fin_link_ind) == (1, 2, 1)
assert (pytest.approx(
model.get_short_term_utility()[start_link_ind,
fin_link_ind].sum()) == -4.4)
assert pytest.approx(ll, abs=1e-6) == 2.537993985
def test_obs2(self):
obs = [input_obs[1]]
model = RecursiveLogitModelEstimation(network_struct,
observations_record=obs,
initial_beta=-0.4,
mu=1,
optimiser=optimiser)
ll = model.get_log_likelihood()[0]
start_link_ind, fin_link_ind = model._compute_obs_path_indices(
ak.from_iter(obs[0]))
assert tuple(start_link_ind) == (0, 3, 0)
assert tuple(fin_link_ind) == (3, 0, 1)
assert (pytest.approx(
model.get_short_term_utility()[start_link_ind,
fin_link_ind].sum()) == -5.2)
print(model.get_short_term_utility()[start_link_ind,
fin_link_ind].sum())
assert pytest.approx(ll) == 3.337993985