/// <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);
double startTemp = holder.GetDouble(
MLTrainFactory.PropertyTemperatureStart, false, 10);
double stopTemp = holder.GetDouble(
MLTrainFactory.PropertyTemperatureStop, false, 2);
int cycles = holder.GetInt(MLTrainFactory.Cycles, false, 100);
IMLTrain train = new NeuralSimulatedAnnealing(
(BasicNetwork) method, score, startTemp, stopTemp, cycles);
return train;
}
/// <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>
/// 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);
IMLTrain train = new MLMethodGeneticAlgorithm( () => {
IMLMethod result = (IMLMethod) ObjectCloner.DeepCopy(method);
((IMLResettable)result).Reset();
return result;
}, score, populationSize);
return train;
}
public void TestAnneal()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
ICalculateScore score = new TrainingSetScore(trainingData);
NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
NetworkUtil.TestTraining(anneal, 0.01);
}
/// <summary>
/// Create an NEAT GA 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)
{
ICalculateScore score = new TrainingSetScore(training);
TrainEA train = NEATUtil.ConstructNEATTrainer((NEATPopulation)method, score);
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);
}
public void TestAnneal()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
ICalculateScore score = new TrainingSetScore(trainingData);
NeuralSimulatedAnnealing anneal = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
NetworkUtil.TestTraining(anneal, 0.01);
}
public void TestGenetic()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingData);
MLMethodGeneticAlgorithm genetic = new MLMethodGeneticAlgorithm(() =>
{
return(NetworkUtil.CreateXORNetworkUntrained());
}, score, 500);
NetworkUtil.TestTraining(genetic, 0.00001);
}
private NEATPopulation Generate()
{
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
NEATTraining train = new NEATTraining(
score, 2, 1, 10);
return (NEATPopulation)train.Population;
}
/// <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>
/// Create an EPL GA 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)
{
PrgPopulation pop = (PrgPopulation)method;
ICalculateScore score = new TrainingSetScore(training);
TrainEA train = new TrainEA(pop, score);
train.Rules.AddRewriteRule(new RewriteConstants());
train.Rules.AddRewriteRule(new RewriteAlgebraic());
train.CODEC = new PrgCODEC();
train.AddOperation(0.8, new SubtreeCrossover());
train.AddOperation(0.1, new SubtreeMutation(pop.Context, 4));
train.AddOperation(0.1, new ConstMutation(pop.Context, 0.5, 1.0));
train.AddScoreAdjuster(new ComplexityAdjustedScore());
train.Speciation = new PrgSpeciation();
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);
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);
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
public static double TrainNetwork(String what,
FreeformNetwork network, IMLDataSet trainingSet) {
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new FreeformBackPropagation(network, trainingSet,0.00001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
EncogUtility.TrainToError(trainMain, 0.01);
return trainMain.Error;
}
/// <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 IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
double num2;
int num3;
if (!(method is BasicNetwork))
{
throw new TrainingError("Invalid method type, requires BasicNetwork");
}
ICalculateScore calculateScore = new TrainingSetScore(training);
ParamsHolder holder = new ParamsHolder(ArchitectureParse.ParseParams(argsStr));
double startTemp = holder.GetDouble("startTemp", false, 10.0);
if (((((uint) num3) & 0) != 0) || ((((uint) num2) - ((uint) num2)) < 0))
{
IMLTrain train;
return train;
}
num2 = holder.GetDouble("stopTemp", false, 2.0);
return new NeuralSimulatedAnnealing((BasicNetwork) method, calculateScore, startTemp, num2, holder.GetInt("cycles", false, 100));
}
/// <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);
var pop = new NEATPopulation(2, 1, 1000);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
var step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
var train = new NEATTraining(score, pop);
EncogUtility.TrainToError(train, 0.01);
var network = (NEATNetwork) train.Method;
network.ClearContext();
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
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;
}
private double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, trainingSet, 0.00001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop())
{
trainMain.Iteration();
app.WriteLine("Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
return trainMain.Error;
}
public void TestGenetic()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingData);
MLMethodGeneticAlgorithm genetic = new MLMethodGeneticAlgorithm(() =>
{
return NetworkUtil.CreateXORNetworkUntrained();
}, score, 500);
NetworkUtil.TestTraining(genetic, 0.00001);
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
Backpropagation trainMain = new Backpropagation(network, minis,0.0001,0.6);
//set the number of threads below.
trainMain.ThreadCount = 0;
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
// IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
//prune strategy not in GIT!...Removing it.
//PruneStrategy strategypruning = new PruneStrategy(0.91d, 0.001d, 10, network,minis, 0, 20);
//trainMain.AddStrategy(strategypruning);
EncogUtility.TrainConsole(trainMain,network,minis, 15.2);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
Console.WriteLine(@"Total elapsed time in seconds:" + TimeSpan.FromMilliseconds(sw.ElapsedMilliseconds).Seconds);
return trainMain.Error;
}
public void TestParticleSwarmOptimization()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
ICalculateScore score = new TrainingSetScore(trainingData);
ParticleSwarmOptimizationAlgorithm pso = new ParticleSwarmOptimizationAlgorithm(network, new RangeRandomizer(-1,1),score);
NetworkUtil.TestTraining(pso, 0.00001);
}
public void Train(BasicNetwork network, IMLDataSet training)
{
IMLTrain trainMain = new LevenbergMarquardtTraining(network, training);
// train the neural network
var stop = new StopTrainingStrategy();
ICalculateScore score = new TrainingSetScore(trainMain.Training);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop() && trainMain.IterationNumber < 1500)
{
trainMain.Iteration();
Console.WriteLine("Training " + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
}
private void trainNetworkBackprop()
{
// IMLTrain train = new Backpropagation(this.network, this.input,this.ideal, 0.000001, 0.1);
IMLDataSet aset = new BasicMLDataSet(input, ideal);
int epoch = 1;
// train the neural network
ICalculateScore score = new TrainingSetScore(aset);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, aset, 0.001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
var pop = new NEATPopulation(INPUT_SIZE, OUTPUT_SIZE, 1000);
// train the neural network
var step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
var train = new NEATTraining(score, pop);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
trainMain.AddStrategy(new HybridStrategy(train));
network.ClearContext();
while (!stop.ShouldStop())
{
trainMain.Iteration();
train.Iteration();
Console.WriteLine(@"Training " + @"Epoch #" + epoch + @" Error:" + trainMain.Error+ @" Genetic iteration:"+trainAlt.IterationNumber+ @"neat iteration:"+train.IterationNumber );
epoch++;
}
}
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);
}
private double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet, string Method)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain;
if (Method.Equals("Leven"))
{
Console.WriteLine("Using LevenbergMarquardtTraining");
trainMain = new LevenbergMarquardtTraining(network, trainingSet);
}
else
trainMain = new Backpropagation(network, trainingSet);
var stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop())
{
trainMain.Iteration();
app.WriteLine("Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
return trainMain.Error;
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
return trainMain.Error;
}
private void Validate(NEATPopulation pop)
{
Assert.AreEqual(0.3, pop.OldAgePenalty);
Assert.AreEqual(50, pop.OldAgeThreshold);
Assert.AreEqual(10, pop.PopulationSize);
Assert.AreEqual(0.2, pop.SurvivalRate);
Assert.AreEqual(10, pop.YoungBonusAgeThreshold);
Assert.AreEqual(0.3, pop.YoungScoreBonus);
// see if the population can actually be used to train
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
NEATTraining train = new NEATTraining(score, pop);
train.Iteration();
}
/// <summary>
/// Builds and trains a neat network.
/// </summary>
/// <param name="aset">The IMLDataset.</param>
/// <param name="inputcounts">The inputcounts.</param>
/// <param name="outputcounts">The outputcounts.</param>
/// <param name="populationsize">The populationsize.</param>
/// <param name="ToErrorTraining">To error rate you want to train too.</param>
/// <returns>a trained netnetwork.</returns>
public static NEATNetwork BuildTrainNeatNetwork(IMLDataSet aset, int inputcounts, int outputcounts, int populationsize, double ToErrorTraining)
{
NEATPopulation pop = new NEATPopulation(inputcounts, outputcounts, populationsize);
ICalculateScore score = new TrainingSetScore(aset);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
NEATTraining train = new NEATTraining(score, pop);
EncogUtility.TrainToError(train, ToErrorTraining);
NEATNetwork network = (NEATNetwork)train.Method;
return network;
}
private void Validate(NEATPopulation pop)
{
Assert.AreEqual(10, pop.PopulationSize);
Assert.AreEqual(0.2, pop.SurvivalRate);
// see if the population can actually be used to train
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
IEvolutionaryAlgorithm train = NEATUtil.ConstructNEATTrainer(pop, score);
train.Iteration();
}
private NEATPopulation Generate()
{
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
IEvolutionaryAlgorithm train = NEATUtil.ConstructNEATTrainer(
score, 2, 1, 10);
return (NEATPopulation)train.Population;
}
