from learners.genetic_learner import GeneticLearner, Task
from tools.common_run import get_coordinates, model_dict, read_net, add_common_options
if __name__ == "__main__":
usage = "map_picture.png net_params.dat"
parser = optparse.OptionParser(usage=usage)
parser.add_option('--pop', type='int', dest='population', help="size of population", default=50)
parser.add_option('--mb', type='float', dest='mutation', help="mutation probability", default=0.2)
parser.add_option('--cb', type='float', dest='crossover', help="crossover probability", default=0.5)
parser.add_option('-p', action='store_true', dest='print_to_stdout', default=True,
help='print the fitness and number of generation to stdout, runs on --save-interval')
parser = add_common_options(parser,["-i","-g","-m","-n","-s"])
opts, args = parser.parse_args()
map_filename = args[0]
start, finish = get_coordinates(map_filename)
ModelClass = model_dict[opts.model]
ModelClass.inner_layer_nods = opts.inner_nods
Task.ModelClass = ModelClass
t = Task(start, finish, map_path=map_filename, number_of_sim_steps=opts.sim_steps)
l = GeneticLearner(t, generations=opts.generations,
print_to_stdout=opts.print_to_stdout, mutation_probability=opts.mutation,
crossover_probability=opts.crossover, population=opts.population)
if opts.initial_inds is not None:
initial_net = read_net(opts.initial_inds, ModelClass)
l.add_initial_individual(initial_net)
l.savep = args[1]
l.main()
import optparse
from visualizer import Visualize
from tools.common_run import get_coordinates, model_dict, read_net, add_common_options, read_net_from_web
if __name__ == "__main__":
usage = "\t map_picture.png net_params.dat\n or if tou want to download trained net from web: \n \t map_picture.png" \
" http://adress.of.file \n where address of file must return non html plaintext with net in csv file separated " \
"by comma, and the adress must start with \"http://\""
parser = optparse.OptionParser(usage=usage)
parser = add_common_options(parser,["-n","-m"])
parser.add_option('-s', type='string', dest='save_path', help="path to save the plot after simulation", default=None)
parser.add_option('-f', type='string', dest='save_fitness_path', help="path to save the fitness data after simulation", default=None)
opts, args = parser.parse_args()
map_filename = args[0]
start, finish = get_coordinates(map_filename)
net_filename = args[1]
ModelClass = model_dict[opts.model]
ModelClass.inner_layer_nods = opts.inner_nods
net = None
if net_filename[:7] == "http://":
net = read_net_from_web(net_filename, ModelClass)
else:
net = read_net(net_filename, ModelClass)
model = ModelClass(start, finish, map_path=map_filename, neural_net=net)
Visualize.ModelClass = ModelClass
v = Visualize(model, save_path=opts.save_path, save_fitness_path=opts.save_fitness_path)
v.run()