def test_update_array_roulette_vacancy_picker_01(self):
array = np.array([[0, 0, 0], [0, 1, 1], [1, 2, 2]])
utility = get_utility_for_array(create_flat_utility(0.5), array, True)
states_for_picked = [
(0.1, np.array([[0, 0, 2], [0, 1, 1], [1, 0, 2]])),
(0.2, np.array([[2, 0, 0], [0, 1, 1], [1, 0, 2]])),
(1.2, np.array([[2, 0, 0], [0, 1, 1], [1, 0, 2]])),
(2.3, np.array([[0, 2, 0], [0, 1, 1], [1, 0, 2]])),
(3.4, np.array([[0, 0, 0], [2, 1, 1], [1, 0, 2]])),
(3.5, np.array([[0, 0, 0], [2, 1, 1], [1, 0, 2]])),
]
result = SimulationResult()
for picked_value, expected_state in states_for_picked:
array = np.array([[0, 0, 0], [0, 1, 1], [1, 2, 2]])
vacancy_picker = _create_roulette_picker(0.1, utility, False,
lambda *a: picked_value)
agent_picker = _create_roulette_picker(0.0, utility, True,
lambda *a: 0.0)
update_array(array, utility, result, agent_picker, vacancy_picker,
True, ['entropy_average'], True)
with self.subTest(v=picked_value, out=array, exp=expected_state):
self.assertTrue(np.array_equal(array, expected_state))
def test_update_array_multiple_utilities(self):
array = np.array([[0, 0, 0], [0, 1, 1], [1, 2, 2]])
utilities = [create_flat_utility(0.25), create_flat_utility(0.75)]
utility = get_utility_for_array(utilities, array, True)
result = SimulationResult() #dummy result
output = update_array(array, utility, result, _first_picker,
_first_picker, False, ['entropy_average'])
expected_output = np.array([[1, 0, 0], [0, 1, 0], [1, 2, 2]])
self.assertTrue(np.array_equal(array, expected_output))
def test_update_array_no_better_vacancies_first_picker(self):
no_better_vacancies_array = np.array([[0, 2, 0, 0], [1, 1, 1, 1],
[1, 1, 1, 1], [0, 2, 0, 2]])
result = SimulationResult()
expected_output = True
utility = get_utility_for_array(create_flat_utility(0.5),
no_better_vacancies_array)
output = update_array(no_better_vacancies_array, utility, result,
_first_picker, _first_picker, False,
['entropy_average'])
self.assertEqual(output, expected_output)
def test_update_array_all_agents_satisfied_should_end_simulation(self):
all_satisfied_array = np.array([[0, 0, 0, 0], [1, 1, 1, 1],
[1, 1, 1, 1], [0, 0, 0, 0]])
result = SimulationResult()
expected_output = True
utility = get_utility_for_array(create_flat_utility(0.5),
all_satisfied_array)
output = update_array(all_satisfied_array, utility, result,
_first_picker, _first_picker, False,
['entropy_average'])
self.assertEqual(output, expected_output)