/// <summary>
/// Create a NEAT population.
/// </summary>
/// <param name="architecture">The architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The population.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
if (input <= 0)
{
throw new EncogError("Must have at least one input for NEAT.");
}
if (output <= 0)
{
throw new EncogError("Must have at least one output for NEAT.");
}
IDictionary <String, String> args = ArchitectureParse.ParseParams(architecture);
ParamsHolder holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLMethodFactory.PropertyPopulationSize, false, 1000);
int cycles = holder.GetInt(
MLMethodFactory.PropertyCycles, false, NEATPopulation.DefaultCycles);
IActivationFunction af = this.factory.Create(
holder.GetString(MLMethodFactory.PropertyAF, false, MLActivationFactory.AF_SSIGMOID));
NEATPopulation pop = new NEATPopulation(input, output, populationSize);
pop.Reset();
pop.ActivationCycles = cycles;
pop.NEATActivationFunction = af;
return(pop);
}
static void Main(string[] args)
{
// this form of ANN uses genetic algorithm to produce
// hidden layer of neurons
// A NEAT network starts with only an
// input layer and output layer. The rest is evolved as the training progresses.
// Connections inside of a NEAT neural network can be feedforward, recurrent,
// or self - connected.All of these connection types will be tried by NEAT as it
// attempts to evolve a neural network capable of the given task.
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
NEATPopulation pop = new NEATPopulation(2, 1, 1000);
pop.Reset();
pop.InitialConnectionDensity = 1.0; // not required, but speeds processing.
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
TrainEA train = NEATUtil.ConstructNEATTrainer(pop, score);
EncogUtility.TrainToError(train, 0.01);
NEATNetwork network = (NEATNetwork)train.CODEC.Decode(train.BestGenome);
// TODO no persistance? no means to peek structure?
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
public void ResetTraining()
{
Substrate substrate = SubstrateFactory.factorSandwichSubstrate(11, 11);
BoxesScore score = new BoxesScore(11);
pop = new NEATPopulation(substrate, 500);
pop.ActivationCycles = 4;
pop.Reset();
train = NEATUtil.ConstructNEATTrainer(pop, score);
OriginalNEATSpeciation speciation = new OriginalNEATSpeciation();
train.Speciation = new OriginalNEATSpeciation();
}
/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var pop = new NEATPopulation(2, 1, 1000);
pop.Reset();
pop.InitialConnectionDensity = 1.0; // not required, but speeds processing.
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
var train = NEATUtil.ConstructNEATTrainer(pop, score);
EncogUtility.TrainToError(train, 0.01);
var network = (NEATNetwork)train.CODEC.Decode(train.BestGenome);
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
