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from greedrl.feature import *
from cvrp import make_problem as make_cvrp_problem
from torch.utils.data import Dataset, IterableDataset, DataLoader
def make_problem(batch_count, batch_size, task_count):
features = [continuous_feature('task_demand_x'),
continuous_feature('distance_matrix')]
problem_list = make_cvrp_problem(batch_count, batch_size, task_count)
for problem in problem_list:
problem.features = features
return problem_list
class Dataset(IterableDataset):
def __init__(self, batch_count, batch_size, task_count):
self._batch_size = batch_size
self._task_count = task_count
self._batch_count = batch_count
self._index = 0
def __iter__(self):
self._index = 0
return self
def __next__(self):
if self._batch_count is not None \
and self._index >= self._batch_count:
raise StopIteration()
p = make_problem(1, self._batch_size, self._task_count)[0]
self._index += 1
return p
def write_vrplib(filename, name, size, demand, capacity, location):
with open(filename, 'w') as f:
f.write('\n'.join([
"{} : {}".format(k, v)
for k, v in (
('NAME', name),
('TYPE', 'CVRP'),
('COMMENT', 'NONE'),
('DIMENSION', size + 1),
('EDGE_WEIGHT_TYPE', 'EUC_2D'),
('CAPACITY', capacity)
)
]))
f.write('\n')
f.write('NODE_COORD_SECTION\n')
f.write('\n'.join(['{}\t{}\t{}'.format(i + 1, x, y) for i, (x, y) in enumerate(location)]))
f.write('\n')
f.write('DEMAND_SECTION\n')
f.write('\n'.join(['{}\t{}'.format(i + 1, d) for i, d in enumerate([0] + demand)]))
f.write('\n')
f.write('DEPOT_SECTION\n')
f.write('1\n')
f.write('-1\n')
f.write('EOF\n')
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