/// <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;
}
public void Process(String methodName, String methodArchitecture, String trainerName, String trainerArgs,
int outputNeurons)
{
// first, create the machine learning method
var methodFactory = new MLMethodFactory();
IMLMethod method = methodFactory.Create(methodName, methodArchitecture, 2, outputNeurons);
// second, create the data set
IMLDataSet dataSet = new BasicMLDataSet(XORInput, XORIdeal);
// third, create the trainer
var trainFactory = new MLTrainFactory();
IMLTrain train = trainFactory.Create(method, dataSet, trainerName, trainerArgs);
// reset if improve is less than 1% over 5 cycles
if (method is IMLResettable && !(train is ManhattanPropagation))
{
train.AddStrategy(new RequiredImprovementStrategy(500));
}
// fourth, train and evaluate.
EncogUtility.TrainToError(train, 0.01);
method = train.Method;
EncogUtility.Evaluate((IMLRegression) method, dataSet);
// finally, write out what we did
Console.WriteLine(@"Machine Learning Type: " + methodName);
Console.WriteLine(@"Machine Learning Architecture: " + methodArchitecture);
Console.WriteLine(@"Training Method: " + trainerName);
Console.WriteLine(@"Training Args: " + trainerArgs);
}
/// <summary>
/// Perform the training option.
/// </summary>
public void Train()
{
// first, create the machine learning method
var methodFactory = new MLMethodFactory();
IMLMethod method = methodFactory.Create(Config.MethodType, Config.MethodArchitecture, Config.InputWindow, 1);
// second, create the data set
string filename = FileUtil.CombinePath(new FileInfo(_path), Config.FilenameTrain).ToString();
IMLDataSet dataSet = EncogUtility.LoadEGB2Memory(new FileInfo(filename));
// third, create the trainer
var trainFactory = new MLTrainFactory();
IMLTrain train = trainFactory.Create(method, dataSet, Config.TrainType, Config.TrainParams);
// reset if improve is less than 1% over 5 cycles
if (method is IMLResettable && !(train is ManhattanPropagation))
{
train.AddStrategy(new RequiredImprovementStrategy(500));
}
// fourth, train and evaluate.
EncogUtility.TrainToError(train, Config.TargetError);
method = train.Method;
EncogDirectoryPersistence.SaveObject(FileUtil.CombinePath(new FileInfo(_path), Config.MethodName), method);
// finally, write out what we did
Console.WriteLine(@"Machine Learning Type: " + Config.MethodType);
Console.WriteLine(@"Machine Learning Architecture: " + Config.MethodArchitecture);
Console.WriteLine(@"Training Method: " + Config.TrainType);
Console.WriteLine(@"Training Args: " + Config.TrainParams);
}
/// <summary>
/// Create a trainer.
/// </summary>
/// <param name="method">The method to train.</param>
/// <param name="dataset">The dataset.</param>
/// <returns>The trainer.</returns>
private IMLTrain CreateTrainer(IMLMethod method, IMLDataSet dataset)
{
if (_trainingType == null)
{
throw new EncogError(
"Please call selectTraining first to choose how to train.");
}
var trainFactory = new MLTrainFactory();
IMLTrain train = trainFactory.Create(method, dataset, _trainingType,
_trainingArgs);
return train;
}
private IMLTrain x8d3a841b4ad80901(IMLMethod x1306445c04667cc7, IMLDataSet x1c9e132f434262d8)
{
string propertyString;
string str2;
MLTrainFactory factory = new MLTrainFactory();
if (0 == 0)
{
propertyString = base.Prop.GetPropertyString("ML:TRAIN_type");
do
{
str2 = base.Prop.GetPropertyString("ML:TRAIN_arguments");
EncogLogging.Log(0, "training type:" + propertyString);
EncogLogging.Log(0, "training args:" + str2);
}
while (0 != 0);
}
IMLTrain train = factory.Create(x1306445c04667cc7, x1c9e132f434262d8, propertyString, str2);
while (this._x33d31451731666c6 > 0)
{
return new CrossValidationKFold(train, this._x33d31451731666c6);
}
return train;
}
private void TrainCommand()
{
String methodFile = _cmd.Args[0];
String trainingFile = _cmd.Args[1];
String type = _cmd.PromptString("type", "rprop");
String args = _cmd.PromptString("args", "");
double maxError = _cmd.PromptDouble("maxError", 0.01);
var dataSet = new BufferedMLDataSet(trainingFile);
var method = (IMLMethod) EncogDirectoryPersistence.LoadObject(new FileInfo(methodFile));
var factory = new MLTrainFactory();
IMLTrain train = factory.Create(method, dataSet, type, args);
_sw.Start();
EncogUtility.TrainToError(train, maxError);
Console.WriteLine(@"Saving machine learning method");
EncogDirectoryPersistence.SaveObject(new FileInfo(methodFile), method);
}
