def test_check_imperfect_solution(self):
n = 4
cols = 10
rows = 5
dg = DataGenerator()
some_instance_np_array = np.array(
[[1, 10, 1], [2, 10, 2], [1, 10, 3], [5, 10, 4], [1, 10, 1]])
solution_checker = SolutionChecker(n, cols, rows)
self.assertEqual(
solution_checker.get_reward(some_instance_np_array),
(10 * 5) / (cols * rows)
)
def test_check_imperfect_solution_count_files_2(self):
n = 4
cols = 10
rows = 5
dg = DataGenerator()
some_instance_np_array = np.array(
[[1, 10, 1], [6, 10, 2], [2, 10, 3], [1, 10, 4], [1, 10, 1]])
solution_checker = SolutionChecker(n, cols, rows)
self.assertEqual(
solution_checker.get_reward(some_instance_np_array, count_tiles=True),
1 / n
)
def test_check_perfect_solution(self):
n = 20
w = 40
h = 40
dg = DataGenerator()
some_instance_visual = dg.gen_instance_visual(n, w, h)
perfect_bin_configuration = sorted(some_instance_visual, key=lambda x: (x[2][0], x[2][1]))
some_instance_np_array = dg._transform_instance_visual_to_np_array(some_instance_visual)
solution_checker = SolutionChecker(n, h, w)
self.assertEqual(
solution_checker.get_reward(np.array(perfect_bin_configuration)),
0
)
def test_check_imperfect_solution_count_tiles(self):
n = 4
cols = 10
rows = 5
dg = DataGenerator()
some_instance_visual = dg.gen_instance_visual(n, cols, rows)
# NOTE: first bin always repeated
some_instance_np_array = np.array(
[[1, 10, 1], [2, 10, 2], [1, 10, 3], [5, 10, 4], [1, 10, 1]])
solution_checker = SolutionChecker(n, cols, rows)
self.assertEqual(
solution_checker.get_reward(some_instance_np_array, count_tiles=True),
1 / n
)
freeze_first_batch=config.freeze_first_batch
)
feed = {actor.input_: input_batch} # Get feed dict
tour, reward = sess.run([actor.tour, actor.reward], feed_dict=feed) # sample tours
j = np.argmin(reward) # find best solution
best_permutation = tour[j][:-1]
predictions_length.append(reward[j])
solution_checker = SolutionChecker(actor.n, actor.w, actor.h)
# TODO: find how this is called in numpy (sort by index)
bins = []
for el in best_permutation:
bins.append(input_batch[0][el])
solution_checker.get_reward(bins)
grid = solution_checker.grid
print('reward',reward[j])
solution_checker.visualize_grid()
#dataset.visualize_2D_trip(input_batch[0][best_permutation])
#dataset.visualize_sampling(tour)
# dataset.visualize_2D_trip(opt_tour)
predictions_length = np.asarray(predictions_length) # average tour length
predictions_length_w2opt = np.asarray(predictions_length_w2opt)
print("Testing COMPLETED ! Mean length1:",np.mean(predictions_length), "Mean length2:",np.mean(predictions_length_w2opt))
n1, bins1, patches1 = plt.hist(predictions_length, 50, facecolor='b', alpha=0.75) # Histogram
n2, bins2, patches2 = plt.hist(predictions_length_w2opt, 50, facecolor='g', alpha=0.75) # Histogram
