/// <summary>
/// Create a PSO trainer.
/// </summary>
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
ParamsHolder holder = new ParamsHolder(args);
int particles = holder.GetInt(
MLTrainFactory.PropertyParticles, false, 20);
ICalculateScore score = new TrainingSetScore(training);
IRandomizer randomizer = new NguyenWidrowRandomizer();
IMLTrain train = new NeuralPSO((BasicNetwork)method, randomizer, score, particles);
return train;
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
BasicNetwork network = EncogUtility.SimpleFeedForward(2, 2, 0, 1, false);
ICalculateScore score = new TrainingSetScore(trainingSet);
IRandomizer randomizer = new NguyenWidrowRandomizer();
IMLTrain train = new NeuralPSO(network, randomizer, score, 20);
EncogUtility.TrainToError(train, 0.01);
network = (BasicNetwork)train.Method;
// test the neural network
Console.WriteLine("Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
EncogFramework.Instance.Shutdown();
}
public void Execute(IExampleInterface app)
{
var rangeRandom = new RangeRandomizer(-1, 1);
var nwrRandom = new NguyenWidrowRandomizer(-1, 1);
var fanRandom = new FanInRandomizer();
var gaussianRandom = new GaussianRandomizer(0, 1);
var training = new BasicMLDataSet(XOR_INPUT,
XOR_IDEAL);
BasicNetwork network = EncogUtility.SimpleFeedForward(2, 10, 0, 1, true);
Console.WriteLine("Range random: "
+ EvaluateRandomizer(rangeRandom, network, training));
Console.WriteLine("Nguyen-Widrow: "
+ EvaluateRandomizer(nwrRandom, network, training));
Console.WriteLine("Fan-In: "
+ EvaluateRandomizer(fanRandom, network, training));
Console.WriteLine("Gaussian: "
+ EvaluateRandomizer(gaussianRandom, network, training));
}
