def tsplib(content, f=1, r=None, learning_plot=False):
idx = content.index('DIMENSION:') + 1
n = int(content[idx])
idx = content.index('EDGE_WEIGHT_FORMAT:') + 1
if content[idx] != 'FULL_MATRIX':
return [], 0
idx = content.index('EDGE_WEIGHT_SECTION') + 1
inf = int(content[idx])
data = []
for i in range(n):
if len(content) > idx + n:
data.append(list(map(int, content[idx:idx + n])))
else:
return [], 0
idx += n
for j in range(len(data)):
print(data[j])
print('\n')
_atsp = atsp.SA(data,
initial_fitness=f,
infinity=inf,
regularization_bound=r,
learning_plot=learning_plot)
return _atsp.solve()
def atsp_array(array):
_atsp = atsp.SA(list(map(int, array)),
regularization_bound=(0.3, 3),
silent_mode=True)
output.put(_atsp.solve())
import atsp, sys
if len(sys.argv) == 2:
with open(sys.argv[1], 'r') as fp:
content = fp.read().split()
atsp = atsp.SA(list(map(int, content)),
regularization_bound=(0.3, 3),
learning_plot=True)
res = atsp.solve()
if res[1]:
print('OPTIMIZED COST:', res[1])
print(' '.join(map(str, res[0])))
else:
print('NO SOLUTION')