/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
BasicNetwork network = CreateNetwork();
IMLTrain train;
if (app.Args.Length > 0 && String.Compare(app.Args[0], "anneal", true) == 0)
{
train = new NeuralSimulatedAnnealing(
network, new PilotScore(), 10, 2, 100);
}
else
{
train = new NeuralGeneticAlgorithm(
network, new FanInRandomizer(),
new PilotScore(), 500, 0.1, 0.25);
}
int epoch = 1;
for (int i = 0; i < 50; i++)
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Score:" + train.Error);
epoch++;
}
Console.WriteLine(@"\nHow the winning network landed:");
network = (BasicNetwork) train.Method;
var pilot = new NeuralPilot(network, true);
Console.WriteLine(pilot.ScorePilot());
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Create an annealing 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)
{
if (!(method is BasicNetwork))
{
throw new TrainingError(
"Invalid method type, requires BasicNetwork");
}
ICalculateScore score = new TrainingSetScore(training);
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLTrainFactory.PropertyPopulationSize, false, 5000);
double mutation = holder.GetDouble(
MLTrainFactory.PropertyMutation, false, 0.1d);
double mate = holder.GetDouble(MLTrainFactory.PropertyMate,
false, 0.25d);
IMLTrain train = new NeuralGeneticAlgorithm((BasicNetwork) method,
new RangeRandomizer(-1, 1), score, populationSize, mutation,
mate);
return train;
}
/// <summary>
/// Construct a neural network genome.
/// </summary>
/// <param name="nga">The neural genetic algorithm.</param>
/// <param name="network">The network.</param>
public NeuralGenome(NeuralGeneticAlgorithm nga, BasicNetwork network)
: base(nga.Helper)
{
this.Organism = network;
this.networkChromosome = new Chromosome();
// create an array of "double genes"
int size = network.Structure.CalculateSize();
for (int i = 0; i < size; i++)
{
IGene gene = new DoubleGene();
this.networkChromosome.Genes.Add(gene);
}
this.Chromosomes.Add(this.networkChromosome);
Encode();
}
/// <summary>
/// Construct a neural network genome.
/// </summary>
/// <param name="nga">The neural genetic algorithm.</param>
/// <param name="network">The network.</param>
public NeuralGenome(NeuralGeneticAlgorithm nga, BasicNetwork network)
: base(nga.Helper)
{
this.Organism = network;
this.networkChromosome = new Chromosome();
// create an array of "double genes"
int size = network.Structure.CalculateSize();
for (int i = 0; i < size; i++)
{
IGene gene = new DoubleGene();
this.networkChromosome.Genes.Add(gene);
}
this.Chromosomes.Add(this.networkChromosome);
Encode();
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
int num;
double num3;
IMLTrain train;
if (!(method is BasicNetwork))
{
throw new TrainingError("Invalid method type, requires BasicNetwork");
}
ICalculateScore calculateScore = new TrainingSetScore(training);
do
{
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
num = holder.GetInt("population", false, 0x1388);
double mutationPercent = holder.GetDouble("mutate", false, 0.1);
num3 = holder.GetDouble("mate", false, 0.25);
train = new NeuralGeneticAlgorithm((BasicNetwork) method, new RangeRandomizer(-1.0, 1.0), calculateScore, num, mutationPercent, num3);
}
while ((((uint) num) - ((uint) num3)) < 0);
return train;
}
public void TestGenetic()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
ICalculateScore score = new TrainingSetScore(trainingData);
NeuralGeneticAlgorithm genetic = new NeuralGeneticAlgorithm(network, new RangeRandomizer(-1, 1), score, 500, 0.1, 0.25);
NetworkUtil.TestTraining(genetic, 0.00001);
}