class NeuralNetworkOptimizationProblem:
#/**
#* Make a neural network optimization
#* @param examples the examples
#* @param network the neural network
#* @param measure the error measure
#*/
def __init__(self, examples, network, measure):
self.eval = NeuralNetworkEvaluationFunction(network, examples, measure)
self.crossover = UniformCrossOver()
self.neighbor = ContinuousAddOneNeighbor()
self.mutate = ContinuousAddOneMutation()
self.dist = NeuralNetworkWeightDistribution(len(network.getLinks()))
#/**
#* @see opt.OptimizationProblem#value(opt.OptimizationData)
#*/
def value(self, d):
return self.eval.value(d)
#/**
#* @see opt.OptimizationProblem#random()
#*/
def random(self):
return self.dist.sample(None)
#/**
#* @see opt.OptimizationProblem#neighbor(opt.Instance)
#*/
def neighbor(self, d):
return self.neighbor.neighbor(d)
#/**
#* @see opt.GeneticAlgorithmProblem#mate(opt.Instance, opt.Instance)
#*/
def mate(self, da, db):
return self.crossover.mate(da, db)
#/**
#* @see opt.GeneticAlgorithmProblem#mutate(opt.Instance)
#*/
def mutate(self, d):
self.mutate.mutate(d)
def __init__(self, examples, network, measure):
self.eval = NeuralNetworkEvaluationFunction(network, examples, measure)
self.crossover = UniformCrossOver()
self.neighbor = ContinuousAddOneNeighbor()
self.mutate = ContinuousAddOneMutation()
self.dist = NeuralNetworkWeightDistribution(len(network.getLinks()))