/// <summary>
/// Perform either a train or a cross validation. If the folds property is
/// greater than 1 then cross validation will be done. Cross validation does
/// not produce a usable model, but it does set the error.
/// If you are cross validating try C and Gamma values until you have a good
/// error rate. Then use those values to train, producing the final model.
/// </summary>
///
public override sealed void Iteration()
{
_network.Params.C = _c;
_network.Params.gamma = _gamma;
EncogLogging.Log(EncogLogging.LevelInfo, "Training with parameters C = " + _c + ", gamma = " + _gamma);
if (_fold > 1)
{
// cross validate
var target = new double[_problem.l];
svm.svm_cross_validation(_problem, _network.Params,
_fold, target);
_network.Model = null;
Error = Evaluate(_network.Params, _problem, target);
}
else
{
// train
_network.Model = svm.svm_train(_problem,
_network.Params);
Error = _network.CalculateError(Training);
}
_trainingDone = true;
}
