def test_compute_pareto_front_smart_randomized(self):
import random
for i in range(10):
labels = [LabelTimeWithBoardingsCount(random.randint(0, 1000), random.randint(0, 1000), random.randint(0, 10), 0)
for _ in range(1000)]
pareto_optimal_labels_old = compute_pareto_front_naive(labels)
pareto_optimal_labels_smart = compute_pareto_front(labels)
self.assertEqual(len(pareto_optimal_labels_old), len(pareto_optimal_labels_smart))
def get_final_optimal_labels(self):
non_walk_valid_labels = []
for label in self._labels:
if label.duration() < self._walk_to_target_duration:
non_walk_valid_labels.append(label)
return [
label.get_copy()
for label in compute_pareto_front_naive(non_walk_valid_labels)
]
def test_compute_pareto_front_smart(self):
labels = []
for n in [1, 2, 10]: #, 500]:
for dep_time in range(0, n):
for n_veh_legs in range(2):
for arr_time in range(dep_time, dep_time + 10):
label = LabelTimeWithBoardingsCount(dep_time, arr_time - n_veh_legs, n_veh_legs, False)
labels.append(label)
import random
random.shuffle(labels)
labels_copy = copy.deepcopy(labels)
pareto_optimal_labels = compute_pareto_front_naive(labels)
self.assertEqual(len(pareto_optimal_labels), n * 2)
pareto_optimal_labels = compute_pareto_front(labels_copy)
self.assertEqual(len(pareto_optimal_labels), n * 2)