def __init__(self, matrix: np.ndarray, **kwargs):
(self.length, self.tour), self.matrix = greedy(matrix), matrix
collect = kwargs.get('collect', False)
self.collector = None if not collect else Collector(['length', 'gain'], {'lkh_search': len(self.tour)})
self.data = TabuSet()
self.data.append(self.length, self.tour)
def opts(opt, size, number, info, neighbours, k, mul, sb, ns):
if info:
logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO)
average = 0.
ft_start = time()
for _ in range(number):
tsp = generator(size)
matrix = adjacency_matrix(tsp)
length, tour = greedy(matrix)
t_start = time()
searcher = opts_type[opt](length, tour, matrix, neighbours=neighbours, k=k, mul=mul, subgradient=sb, non_seq=ns)
searcher.optimize()
average += (time() - t_start)
click.echo(f'all: {time() - ft_start} s')
click.echo(f'average: {average / number} s')
def test_greedy():
tsp = generator(size)
matrix = adjacency_matrix(tsp)
length, tour = greedy(matrix)
assert round(get_length(tour, matrix),
2) == round(length, 2), 'generated wrong tour'
def generate_metric_tsp():
""" Генерируем данные для метрической задачи коммивояжера """
tsp = generator(size)
matrix = adjacency_matrix(tsp)
length, tour = greedy(matrix)
return length, tour, matrix
def __init__(self, opt: str, matrix: np.ndarray, **kwargs):
length, tour = greedy(matrix)
self.matrix = matrix
self.opt = opts_type[opt](length, tour, matrix, **kwargs)
self.length, self.tour = self.opt.length, self.opt.tour
self.proc = kwargs.get('proc', 4)