public override void PostProcessing()
{
NNClassificationQualityEvaluator evaluator = this._problem.SolutionQualityEvaluator as NNClassificationQualityEvaluator;
//NeuralNetwork network = evaluator.NeuralNetwork;
NeuralNetwork network = evaluator.CreateNeuralNetwork(this._bestAnt.Solution);
this.NetworkBeforePostProcessing = network;
//this.PerformLocalSearch(this._bestAnt);
NeuralNetworks.LearningMethods.BackPropagation BP = new NeuralNetworks.LearningMethods.BackPropagation(0.01, 1000, 0.9, true);
this.FinalNetwork = evaluator.CreateNeuralNetwork(this._bestAnt.Solution, BP);
}
public void PostProcessing2()
{
NNClassificationQualityEvaluator evaluator = this._problem.SolutionQualityEvaluator as NNClassificationQualityEvaluator;
//NeuralNetwork network = evaluator.NeuralNetwork;
NeuralNetwork network = evaluator.CreateNeuralNetwork(this._bestAnt.Solution);
this.NetworkBeforePostProcessing = network;
//this.PerformLocalSearch(this._bestAnt);
int maxIterations = 1000;
int colonySize = 1;
int convergence = 100;
int archive = 25;
double q = 0.25;
double segma = 0.85;
int problemSize = network.Size;
NNClassificationQualityEvaluator2 evaluator2 = new NNClassificationQualityEvaluator2(-5, 5, evaluator.Measure);
evaluator2.LearningSet = _trainingSet;
evaluator2.ValidationSet = _trainingSet;
evaluator2.NeuralNetwork = network;
Problem <double> problem = new Problem <double>(null, null, evaluator2, null);
ACO_RNN acornn = new ACO_RNN(maxIterations, colonySize, convergence, problem, problemSize, archive, q, segma);
acornn.OnPostColonyIteration += OnPostColonyIteration;
acornn.TrainNetwork(network, _trainingSet);
this.FinalNetwork = network;
}
