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from pyrobot.brain.ga import *
from pyrobot.brain.conx import *
class NNGA(GA):
def __init__(self, cnt):
n = Network()
n.add( Layer('input', 2) )
n.add( Layer('hidden', 3) )
n.add( Layer('output', 1) )
n.connect('input', 'hidden')
n.connect('hidden','output')
n.setInputs([[0.0, 0.0],
[0.0, 1.0],
[1.0, 0.0],
[1.0, 1.0]])
n.setOutputs([[0.0],
[1.0],
[1.0],
[0.0]])
n.setVerbosity(0)
n.setTolerance(.4)
n.setLearning(0)
g = n.arrayify()
self.network = n
GA.__init__(self,
Population(cnt, Gene, size=len(g), verbose=1,
min=-10, max=10, elitePercent = .1),
mutationRate = 0.5, crossoverRate = 0.25,
maxGeneration = 400, verbose = 1)
def fitnessFunction(self, genePos):
self.network.unArrayify(self.pop.individuals[genePos].genotype)
error, correct, count, pcorrect = self.network.sweep()
return 4 - error
def isDone(self):
self.network.unArrayify(self.pop.bestMember.genotype)
error, correct, count, pcorrect = self.network.sweep()
print "Correct:", correct
return correct == 4
ga = NNGA(20)
ga.evolve()
ga.network.unArrayify(ga.pop.bestMember.genotype)
ga.network.setInteractive(1)
ga.network.sweep() |
