def generate_genome(self):
genome = list()
for x in range(self.nstates):
state = dict()
for key in itertools.product(tiles.all(), repeat=4):
action = random.choice(self.choices)
newstate = random.randint(0, self.nstates - 1)
state[key] = (action, newstate)
genome.append(state)
return genome
def initialize_evolver(self):
# get all tiles
keys = list(itertools.product(tiles.all(), repeat=4))
# create sample with all possible genome keys
sample = GMap(keys, Peater.choices, nstates=8)
sample.evaluator.set = GMapEvaluator
# initialize evolver
ga = GSimulationGA(sample)
ga.setPopulationSize(o.ga.general.population)
ga.setGenerations(o.ga.general.generations)
ga.setCrossoverRate(o.ga.crossover.rate)
ga.setMutationRate(o.ga.mutation.rate)
ga.setElitismReplacement(o.ga.crossover.elites)
ga.setElitism(o.ga.crossover.elites > 0)
ga.selector.set(Selectors.GTournamentSelector)
ga.minimax = Consts.minimaxType[o.ga.evaluator.minimax]
self.ga = ga
def get_neighbors(self, world):
neighbors = list()
if self.heading:
neighbors.append(self.neighbor_forward(world))
neighbors.append(self.neighbor_right(world))
neighbors.append(self.neighbor_backward(world))
neighbors.append(self.neighbor_left(world))
else:
neighbors.append(self.neighbor_north(world))
neighbors.append(self.neighbor_east(world))
neighbors.append(self.neighbor_south(world))
neighbors.append(self.neighbor_west(world))
for idx, n in enumerate(neighbors):
for cls in tiles.all():
if cls.char == n.char:
neighbors[idx] = cls
break
return tuple(neighbors)
