/// <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);
}
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>
/// Fit the model using cross validation.
/// </summary>
/// <param name="k">The number of folds total.</param>
/// <param name="foldNum">The current fold.</param>
/// <param name="fold">The current fold.</param>
private void FitFold(int k, int foldNum, DataFold fold)
{
IMLMethod method = CreateMethod();
IMLTrain train = CreateTrainer(method, fold.Training);
if (train.ImplementationType == TrainingImplementationType.Iterative)
{
var earlyStop = new SimpleEarlyStoppingStrategy(
fold.Validation);
train.AddStrategy(earlyStop);
var line = new StringBuilder();
while (!train.TrainingDone)
{
train.Iteration();
line.Length = 0;
line.Append("Fold #");
line.Append(foldNum);
line.Append("/");
line.Append(k);
line.Append(": Iteration #");
line.Append(train.IterationNumber);
line.Append(", Training Error: ");
line.Append(Format.FormatDouble(train.Error, 8));
line.Append(", Validation Error: ");
line.Append(Format.FormatDouble(earlyStop.ValidationError,
8));
Report.Report(k, foldNum, line.ToString());
}
fold.Score = earlyStop.ValidationError;
fold.Method = method;
}
else if (train.ImplementationType == TrainingImplementationType.OnePass)
{
train.Iteration();
double validationError = CalculateError(method,
fold.Validation);
Report.Report(k, k,
"Trained, Training Error: " + train.Error
+ ", Validatoin Error: " + validationError);
fold.Score = validationError;
fold.Method = method;
}
else
{
throw new EncogError("Unsupported training type for EncogModel: "
+ train.ImplementationType);
}
}