public void TestManhattan()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
IMLTrain bprop = new ManhattanPropagation(network, trainingData, 0.01);
NetworkUtil.TestTraining(bprop, 0.01);
}
public void TestLMA()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
IMLTrain rprop = new LevenbergMarquardtTraining(network, trainingData);
NetworkUtil.TestTraining(rprop, 0.03);
}
public void TestRPROP()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
IMLTrain rprop = new ResilientPropagation(network, trainingData);
NetworkUtil.TestTraining(rprop, 0.03);
}
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);
}
public void TestRPROPFolded()
{
IMLDataSet trainingData = XOR.CreateNoisyXORDataSet(10);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
var folded = new FoldedDataSet(trainingData);
IMLTrain train = new ResilientPropagation(network, folded);
var trainFolded = new CrossValidationKFold(train, 4);
EncogUtility.TrainToError(trainFolded, 0.2);
XOR.VerifyXOR((IMLRegression)trainFolded.Method, 0.2);
}