def start_tt_server():
print('Press 1 + <Enter> to run as a OCL GA Server for remote clients.')
print('Press 2 + <Enter> to run Taiwan Travel OCL GA L-O-C-A-L-L-Y.')
best = None
city_info = None
statistics = None
def callback_from_client(info):
nonlocal statistics, best, city_info
if 'best' in info:
cities = info['best'].dna
best = list(range(len(cities)))
city_info = [(float(c['x']), float(c['y'])) for c in cities]
if 'statistics' in info:
statistics = info['statistics']
pass
tt_info = get_taiwan_travel_info()
while True:
user_input = get_input()
if user_input == '1':
ui = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ui')
start_ocl_ga_server(tt_info, 12345, { 'message' : callback_from_client }, ui)
break
elif user_input == '2':
start_ocl_ga_local(tt_info)
break
elif user_input == 'exit':
break
if best and city_info:
utils.plot_tsp_result(city_info, best)
if statistics:
utils.plot_ga_result(statistics)
def start_ocl_ga_local(info):
info['saved_filename'] = info['saved_filename'] % (0, 0)
info['generation_callback'] = show_generation_info
prob_mutation = info['prob_mutation']
prob_crossover = info['prob_crossover']
ga_target = OpenCLGA(info)
ga_target.prepare()
try:
print('Press run + <Enter> to run')
print('Press pause + <Enter> to pause')
print('Press restore + <Enter> to restore')
print('Press save + <Enter> to save')
print('Press stop + <Enter> to stop')
print('Press get_st + <Enter> to get statistics information')
print('Press get_best + <Enter> to get best information')
print('Press ctrl + c to exit')
while True:
user_input = get_input()
if user_input == 'pause':
ga_target.pause()
elif user_input == 'run':
ga_target.run(prob_mutation, prob_crossover)
elif user_input == 'stop':
ga_target.stop()
elif user_input == 'get_st':
st = ga_target.get_statistics()
utils.plot_ga_result(st)
elif user_input == 'get_best':
best_chromosome, best_fitness, best_info = ga_target.get_the_best(
)
cities, city_info, city_infoX, city_infoY = read_all_cities(
'TW319_368Addresses-no-far-islands.json')
utils.plot_tsp_result(city_info, best_chromosome)
elif user_input == 'exit':
break
elif user_input == 'save':
ga_target.save()
elif user_input == 'restore':
ga_target.restore()
except KeyboardInterrupt:
traceback.print_exc()
def start_tt_server():
print('Press 1 + <Enter> to run as a OCL GA Server for remote clients.')
print('Press 2 + <Enter> to run Taiwan Travel OCL GA L-O-C-A-L-L-Y.')
best = None
city_info = None
statistics = None
def callback_from_client(info):
nonlocal statistics, best, city_info
if 'best' in info:
cities, city_info, city_infoX, city_infoY = read_all_cities(
'TW319_368Addresses-no-far-islands.json')
best = info['best']
if 'statistics' in info:
statistics = info['statistics']
pass
tt_info = get_taiwan_travel_info()
while True:
user_input = get_input()
if user_input == '1':
ui = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'ui')
start_ocl_ga_server(tt_info, 12345,
{'message': callback_from_client}, ui)
break
elif user_input == '2':
start_ocl_ga_local(tt_info)
break
elif user_input == 'exit':
break
if best and city_info:
utils.plot_tsp_result(city_info, best)
if statistics:
utils.plot_ga_result(statistics)
def run(num_chromosomes, generations):
num_cities = 20
random.seed()
city_ids = list(range(0, num_cities))
city_info = {
city_id: (random.random() * 100, random.random() * 100)
for city_id in city_ids
}
sample = ShufflerChromosome([SimpleGene(v, city_ids) for v in city_ids])
tsp_path = os.path.dirname(os.path.abspath(__file__))
tsp_kernels = os.path.join(tsp_path, 'kernel')
f = open(os.path.join(tsp_kernels, 'simple_tsp.cl'), 'r')
fstr = ''.join(f.readlines())
f.close()
pointX = [str(city_info[v][0]) for v in city_info]
pointY = [str(city_info[v][1]) for v in city_info]
import threading
evt = threading.Event()
evt.clear()
def state_changed(state):
if 'stopped' == state:
evt.set()
tsp_ga_cl = OpenCLGA(
{
'sample_chromosome':
sample,
'termination': {
'type': 'count',
'count': generations
},
'population':
num_chromosomes,
'fitness_kernel_str':
fstr,
'fitness_func':
'simple_tsp_fitness',
'fitness_args': [{
't': 'float',
'v': pointX,
'n': 'x'
}, {
't': 'float',
'v': pointY,
'n': 'y'
}],
'opt_for_max':
'min',
'debug':
True,
'generation_callback':
show_generation_info
},
action_callbacks={'state': state_changed})
tsp_ga_cl.prepare()
prob_mutate = 0.1
prob_cross = 0.8
tsp_ga_cl.run(prob_mutate, prob_cross)
evt.wait()
utils.plot_ga_result(tsp_ga_cl.get_statistics())
print('run took', tsp_ga_cl.elapsed_time, 'seconds')
best_chromosome, best_fitness, best_info = tsp_ga_cl.get_the_best()
print('Best Fitness: %f' % (best_fitness))
print('Shortest Path: ' + ' => '.join(str(g) for g in best_chromosome))
utils.plot_tsp_result(city_info, best_chromosome)
for generation in range(self.__iterations):
self.__execute_single_generation(generation)
print('best fitness #{}: {}'.format(generation,
self.__best_fitness))
return (self.__best_result, self.__best_fitness)
if __name__ == '__main__':
random.seed(1)
city_info = {
city_id: (random.random() * 100, random.random() * 100)
for city_id in range(30)
}
print('cities:')
print(city_info)
ant = PythonAntTSP({
'iterations': 20,
'ants': 100,
'alpha': 1,
'beta': 9,
'evaporation': 0.9,
'q': 10000,
'nodes': city_info
})
result = ant.run()
print('Length: {}'.format(result[1]))
print('Shortest Path: ' + ' => '.join(str(g) for g in result[0]))
utils.plot_tsp_result(city_info, result[0])