def resetParameters(): ne_genetics.resetState((2 * VISION + 1)**2 - 1, OUTPUTS)
def resetParameters(): ne_genetics.resetState((2*VISION + 1)**2 - 1, OUTPUTS)
DEFAULT_POP_SIZE = 30 COMPATABILITY_THRESHOLD = 2.0 COMPATABILITY_THRESHOLD_MODIFIER = 0.4 TARGET_SPECIES = 5 #Mutation parameters DYNAMIC_MUTATION_RATES = False MUTATOR_MULTIPLIER = 0.1 MUTATOR_SPREAD = 0.05 #I/O parameters LOGGING = False FPS = 3 SAVE_PREFIX = "ne_mazepop" ne_genetics.resetState((2 * VISION + 1)**2 - 1, OUTPUTS) rand = random.Random() class Player: def __init__(self, parents=None): if (not parents): self.net = ne_genetics.Network() else: self.net = ne_genetics.Network((parents[0].net, parents[1].net)) self.species = -1 self.fitness = 0 self.reset() def respond(self, inputs):
DEFAULT_POP_SIZE = 30 COMPATABILITY_THRESHOLD = 2.0 COMPATABILITY_THRESHOLD_MODIFIER = 0.4 TARGET_SPECIES = 5 #Mutation parameters DYNAMIC_MUTATION_RATES = False MUTATOR_MULTIPLIER = 0.1 MUTATOR_SPREAD = 0.05 #I/O parameters LOGGING = False FPS = 3 SAVE_PREFIX = "ne_mazepop" ne_genetics.resetState((2*VISION + 1)**2 - 1, OUTPUTS) rand = random.Random() class Player: def __init__(self, parents = None): if (not parents): self.net = ne_genetics.Network() else: self.net = ne_genetics.Network((parents[0].net, parents[1].net)) self.species = -1 self.fitness = 0 self.reset() def respond(self, inputs): out = self.net.timestep(inputs)