def modify(self, population):
"""
Apply the backpropagation mutation in someone in population
@author Thiago F Pappacena
@param object population The population object
@return void
"""
if random.random() <= self.probability:
self.stopMaxIter.setIterations( random.randrange(1, self.maxIterations) )
chromo = random.choice(population.getChromossomes())
genes = chromo.getGenes()
weights = tuple(x.getValue() for x in genes)
# rebuild the net and train
net = Network.fromPlanifiedWeights(self.netArch, weights)
self.trainer.setNetwork(net)
self.trainer.train(self.patterns)
self.netArch = net.getDimension()
# copy values to genes
for gene, weight in zip(genes, net.planify()):
gene.setValue(weight)
chromo.setFitness(None)
def evaluate(self, chromossome):
"""
Evaluate a chromossome and check it's fitness to patterns
@author Thiago F Pappacena
@param object chromossome The chromossome to check
@return float The fitness of the chromossome
"""
weights = tuple(i.getValue() for i in chromossome.getGenes())
net = Network.fromPlanifiedWeights(self.netArch, weights)
error = 0.0
for p in self.patterns:
net.clearActivations()
error += sum( (i[1] - i[0])**2 for i in zip(net.getOutput(p.getInput()), p.getOutput()) ) / 2.0
return -error
