def test_city_15():
problem = TSP()
problem.from_array([[],
[29],
[82, 55],
[46, 46, 68],
[68, 42, 46, 82],
[52, 43, 55, 15, 74],
[72, 43, 23, 72, 23, 61],
[42, 23, 43, 31, 52, 23, 42],
[51, 23, 41, 62, 21, 55, 23, 33],
[55, 31, 29, 42, 46, 31, 31, 15, 29],
[29, 41, 79, 21, 82, 33, 77, 37, 62, 51],
[74, 51, 21, 51, 58, 37, 37, 33, 46, 21, 65],
[23, 11, 64, 51, 46, 51, 51, 33, 29, 41, 42, 61],
[72, 52, 31, 43, 65, 29, 46, 31, 51, 23, 59, 11, 62],
[46, 21, 51, 64, 23, 59, 33, 37, 11, 37, 61, 55, 23, 59]])
path, length = solve_dp(problem)
ans = [13, 2, 15, 9, 5, 7, 3, 12, 14, 10, 8, 6, 4, 11, 1]
ans.reverse()
assert list(map(lambda node: node.id, path)) == ans
assert length == 291
def tsp():
# setup
tsp = TSP()
tsp.from_array([
[],
[3.0],
[4.0, 4.0],
[2.0, 6.0, 5.0],
[7.0, 3.0, 8.0, 6.0]
])
return tsp
def test_burma_14():
problem = TSP()
problem.from_file('problems/burma14.tsp', 'geo')
assert len(problem.nodes) == 14
path, length = solve_dp(problem)
assert list(map(lambda node: node.id, path)) == [
1, 2, 14, 3, 4, 5, 6, 12, 7, 13, 8, 11, 9, 10]
assert int(length) == 3346
def test_city_5():
problem = TSP()
problem.from_array([
[],
[3.0],
[4.0, 4.0],
[2.0, 6.0, 5.0],
[7.0, 3.0, 8.0, 6.0]
])
path, length = solve_dp(problem)
assert list(map(lambda node: node.id, path)) == [1, 3, 2, 5, 4]
assert length == 19.0
def test_tsp_solve():
# Prepare the square symmetric distance matrix for 3 nodes:
# Distance from A to B is 1.0
# B to C is 3.0
# A to C is 2.0
problem = TSP()
problem.from_array([
[],
[1.0],
[3.0, 2.0],
[5.0, 4.0, 1.0]
])
path, length = solve_exhaustive(problem)
assert list(map(lambda node: node.id, path)) == [1, 2, 3, 4]
assert length == 9.0
def test_read_file():
problem = TSP()
problem.from_file('problems/bier127.tsp')
assert len(problem.nodes) == 127
assert len(problem.distance_matrix.matrix) == 127
print('distance_path: {}'.format(distance))
for _ in range(optimize_count):
distance = optimize(nodes, path, distance_matrix)
print('optimized_distance: {}'.format(distance))
for node in nodes:
# reset nodes
# TODO: use connected list just as local variable (only used in greedy module)
node.connected = []
node.degree = 0
return path
if __name__ == '__main__':
from tsp_solver import TSP
tsp = TSP()
filename = 'rl11849'
tsp.from_file('problems/{}.tsp'.format(filename))
path = solve_greedy(tsp)
# print(list(map(lambda node: node.id, path)))
# Save answer
with open('sol_{}_greedy.csv'.format(filename), 'w') as f:
f.writelines(list(map(lambda node: str(node.id) + '\n', path)))
args = parser.parse_args()
verbose = args.verbose
file_path = args.filepath
population_size = args.population
fitness_evaluations = args.evaluations
logging.basicConfig(filename='run{}.log'.format(int(time.time())),
filemode='w',
level=logging.INFO)
from tsp_solver import TSP
import inspect
problem = TSP()
mode = "geo" if args.geo else "euc2d"
problem.from_file(file_path, mode=mode)
logging.info('file: {}'.format(file_path))
logging.info('population size: {}'.format(population_size))
logging.info('fitness_evaluations: {}'.format(fitness_evaluations))
logging.info(inspect.getsource(GA._crossover))
logging.info(inspect.getsource(GA._select))
logging.info(inspect.getsource(GA._mutate))
def solve_GA(problem, generations):
Graph.set_graph(problem.nodes, problem.distance_matrix)
tours = []
seed = Tour(path=solve_greedy(problem))
def test_greedy_large(tsp):
tsp = TSP()
tsp.from_file('problems/fl1400.tsp')
path = solve_greedy(tsp)
assert sorted(path) == sorted(tsp.nodes)