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);
}
/// <summary>
/// Create a trainer, use cross validation if enabled.
/// </summary>
/// <param name="method">The method to use.</param>
/// <param name="trainingSet">The training set to use.</param>
/// <returns>The trainer.</returns>
private IMLTrain CreateTrainer(IMLMethod method,
IMLDataSet trainingSet)
{
var factory = new MLTrainFactory();
String type = Prop.GetPropertyString(
ScriptProperties.MlTrainType);
String args = Prop.GetPropertyString(
ScriptProperties.MlTrainArguments);
EncogLogging.Log(EncogLogging.LevelDebug, "training type:" + type);
EncogLogging.Log(EncogLogging.LevelDebug, "training args:" + args);
IMLTrain train = factory.Create(method, trainingSet, type, args);
if (_kfold > 0)
{
train = new CrossValidationKFold(train, _kfold);
}
return train;
}
