def main():
bf_limit = 11
sizes = range(4, 19)
t = PrettyTable(['Cities', 'Brute Force (s)', 'Held-Karp (s)', 'BF/HK'])
bf_elapsed_prev = 0
for size in sizes:
P = read_tsp_instance('test.tsp', size)
D = create_distance_matrix(P)
bf_elapsed = 0
if size < bf_limit:
start_time = time()
brute_force(D)
bf_elapsed = (time() - start_time)
bf_elapsed_str = format(bf_elapsed, '.5f')
else:
bf_elapsed = bf_elapsed_prev * size
bf_elapsed_str = colored('{0:,.5f}'.format(bf_elapsed), 'yellow')
bf_elapsed_prev = bf_elapsed
start_time = time()
held_karp(D)
hc_elapsed = (time() - start_time)
t.add_row([
size, bf_elapsed_str, '{0:,.5f}'.format(hc_elapsed),
'{0:,.5f}'.format(bf_elapsed / hc_elapsed)
])
print(t)
def main():
global edges, edges2, edges3, edges4
if args.distances is None:
generate_random_graph()
else:
parse_distances()
bf_load_graph(graph)
print("Using BruteForce algorithm")
edges = brute_force()
edges[0] = edges[0][:-1]
hm_load_graph(graph)
print("Using HeuristicMax algorithm")
edges2 = heuristic("MAX")
edges2[0] = edges2[0][:-1]
hm_load_graph(graph)
print("Using HeuristicMin algorithm")
edges3 = heuristic("MIN")
edges3[0] = edges3[0][:-1]
hm_load_graph(graph)
print("Using HeuristicRandom algorithm")
edges4 = heuristic("RANDOM")
edges4[0] = edges4[0][:-1]
url = "http://0.0.0.0:8000"
webbrowser.open_new_tab(url)
app.run(port=8000, host="0.0.0.0")
def main():
global edges, edges2, edges3, edges4
if args.distances is None:
generate_random_graph()
else:
parse_distances()
bf_load_graph(graph)
print("Using BruteForce algorithm")
edges = brute_force()
edges[0] = edges[0][:-1]
hm_load_graph(graph)
print("Using HeuristicMax algorithm")
edges2 = heuristic("MAX")
edges2[0] = edges2[0][:-1]
hm_load_graph(graph)
print("Using HeuristicMin algorithm")
edges3 = heuristic("MIN")
edges3[0] = edges3[0][:-1]
hm_load_graph(graph)
print("Using HeuristicRandom algorithm")
edges4 = heuristic("RANDOM")
edges4[0] = edges4[0][:-1]
url = "http://0.0.0.0:8000"
webbrowser.open_new_tab(url)
app.run(port=8000, host="0.0.0.0")
def run_brute_force(filename):
boards = read_boards(filename)
stats = []
success = 0
max_states = -1
min_states = sys.maxint
total_states = 0
for board in boards:
states = []
r = brute_force(board, states)
if r == True:
cur_states = len(states)
max_states = max(max_states, cur_states)
min_states = min(min_states, cur_states)
total_states += cur_states
stats.append(cur_states)
success += 1
print "success " + str(success)
result = {}
result['max'] = max_states
result['min'] = min_states
result['total'] = total_states
result['average'] = float(total_states) / 20
result['median'] = numpy.median(stats)
return result
def main():
size = 14
P = read_tsp_instance('test.tsp', size)
D = create_distance_matrix(P)
D = D[:size + 1]
for i in range(len(D)):
D[i] = D[i][:size + 1]
if 1:
hc_path = held_karp(D)
test_path(hc_path, D, P)
else:
bf_path = brute_force(D)
test_path(bf_path, D, P)
def POST(self):
signal = web.input().action
if signal == 'load_easy':
print "easy"
board = json.dumps(get_easy_board())
return board
if signal == 'b_easy':
board = get_easy_board()
result = []
brute_force(board, result)
return json.dumps(result)
if signal == 's_easy':
board = get_easy_board()
result = []
simulated_annealing(board, 3, result)
return json.dumps(result)
if signal == 'load_medium':
board = json.dumps(get_medium_board())
return board
if signal == 'b_medium':
board = get_medium_board()
result = []
brute_force(board, result)
return json.dumps(result)
if signal == 's_medium':
board = get_medium_board()
result = []
simulated_annealing(board, 3, result)
return json.dumps(result)
if signal == 'load_hard':
board = json.dumps(get_hard_board())
return board
if signal == 'b_hard':
board = get_hard_board()
result = []
brute_force(board, result)
return json.dumps(result)
if signal == 's_hard':
board = get_hard_board()
result = []
simulated_annealing(board, 3, result)
return json.dumps(result)
return "..."
# Create Routing Model
routing = pywrapcp.RoutingModel(len(coords), 1, 0)
# Define weight of each edge
distance_callback = create_distance_callback(algorithm_ID, coords)
routing.SetArcCostEvaluatorOfAllVehicles(distance_callback)
add_distance_dimension(routing, distance_callback)
# Setting first solution heuristic (cheapest addition).
search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters()
search_parameters.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
if assignment:
solution, overallDistance, points = get_solution(
coords, routing, assignment)
t = timeit.default_timer() - start
print_solution_on_console(solution, overallDistance, t)
save_to_output_file(input_file, points, overallDistance, t)
if __name__ == '__main__':
input_file, algorithm_ID = parse_input()
with open(str(input_file)) as f:
locations = [tuple(map(float, i.split(','))) for i in f]
if algorithm_ID != BRUTE_FORCE:
find_routing(locations)
else:
brute_force(locations, input_file)
