startup(file, train, dialog.getMaxError().getValue() / 100.0);
}
}
private void performRPROP(ProjectEGFile file, MLDataSet trainingData) {
InputResilient dialog = new InputResilient();
if (dialog.process()) {
final double initialUpdate = dialog.getInitialUpdate().getValue();
final double maxStep = dialog.getMaxStep().getValue();
int kFold = dialog.getKfold().getValue();
if( kFold>0 ) {
trainingData = this.wrapTrainingData(trainingData);
}
MLTrain train = new ResilientPropagation(
(ContainsFlat) file.getObject(), trainingData,
initialUpdate, maxStep);
switch( dialog.getRpropType().getSelectedIndex() )
{
case 0:
((ResilientPropagation)train).setRPROPType(RPROPType.RPROPp);
break;
case 1:
((ResilientPropagation)train).setRPROPType(RPROPType.RPROPm);
break;
case 2:
((ResilientPropagation)train).setRPROPType(RPROPType.iRPROPp);
break;
case 3:
((ResilientPropagation)train).setRPROPType(RPROPType.iRPROPm);
break;
}
if( kFold>0 ) {
train = this.wrapTrainer(trainingData,train,kFold);
}
startup(file, train, dialog.getMaxError().getValue() / 100.0);
}
}