def run(self):
logging.basicConfig(
format=_red +
"[%(levelname)s][%(asctime)s][%(name)s] %(message)s" + _normal,
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.WARN)
genome = self.create_genome()
population = NEAT.Population(genome, self.neat_parameters, True, 1.0,
0)
previous_best_genomes = None
current_best_genomes = None
difference = 0
self.LOGGER.warn("Population size: {}".format(population.NumGenomes()))
self.LOGGER.warn(
"{:>14s} {:>14s} {:>14s} {:>14s} {:>14s} {:>14s}".format(
"n_species", "stagnation", "mpc", "search_mode",
"compat_thresh", "improvement"))
# run for 100 generations
for generation in range(200):
tournament = Tournament(population, self.table_size,
self.money_vector_size, self.buy_in,
self.min_denomination,
self.tournament_rounds)
tournament.play()
if current_best_genomes:
previous_best_genomes = current_best_genomes
current_best_genomes = self.get_best_n_genomes(
population, self.table_size // 2)
if previous_best_genomes:
playoff = PlayOff(current_best_genomes, previous_best_genomes,
self.table_size, self.money_vector_size,
self.buy_in, self.min_denomination,
self.tournament_rounds)
difference += playoff.play()
for index in range(population.NumGenomes()):
genome = population.AccessGenomeByIndex(index)
genome.SetFitness(genome.GetFitness() + difference)
self.LOGGER.warn(
"{:14d} {:14d} {:14.2f} {:>14s} {:14.3f} {:14.2f}".format(
len(population.Species), population.GetStagnation(),
population.GetCurrentMPC(),
population.GetSearchMode().name,
population.Parameters.CompatTreshold, difference))
population.Epoch()
self.LOGGER.warn("Training complete")
return self.get_best_n_genomes(population, self.table_size)
