public virtual void SetTuneMinimizer(ILineSearcher minimizer)
{
this.tuneMinimizer = minimizer;
}
public virtual void HeldOutSetC(GeneralDataset <L, F> train, double percentHeldOut, IScorer <L> scorer, ILineSearcher minimizer)
{
Pair <GeneralDataset <L, F>, GeneralDataset <L, F> > data = train.Split(percentHeldOut);
HeldOutSetC(data.First(), data.Second(), scorer, minimizer);
}
/// <summary>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C.
/// </summary>
/// <remarks>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C. The scorer is how you determine what to
/// optimize for (F-score, accuracy, etc). The C is then saved, so that
/// if you train a classifier after calling this method, that C will be used.
/// </remarks>
public virtual void HeldOutSetC(GeneralDataset <L, F> trainSet, GeneralDataset <L, F> devSet, IScorer <L> scorer, ILineSearcher minimizer)
{
useAlphaFile = true;
bool oldUseSigmoid = useSigmoid;
useSigmoid = false;
IDoubleUnaryOperator negativeScorer = null;
C = minimizer.Minimize(negativeScorer);
useAlphaFile = false;
useSigmoid = oldUseSigmoid;
}
/// <summary>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C.
/// </summary>
/// <remarks>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C. The scorer is how you determine what to
/// optimize for (F-score, accuracy, etc). The C is then saved, so that
/// if you train a classifier after calling this method, that C will be used.
/// </remarks>
public virtual void CrossValidateSetC(GeneralDataset <L, F> dataset, int numFolds, IScorer <L> scorer, ILineSearcher minimizer)
{
System.Console.Out.WriteLine("in Cross Validate");
useAlphaFile = true;
bool oldUseSigmoid = useSigmoid;
useSigmoid = false;
CrossValidator <L, F> crossValidator = new CrossValidator <L, F>(dataset, numFolds);
IToDoubleFunction <Triple <GeneralDataset <L, F>, GeneralDataset <L, F>, CrossValidator.SavedState> > score = null;
//train(trainSet,true,true);
IDoubleUnaryOperator negativeScorer = null;
C = minimizer.Minimize(negativeScorer);
useAlphaFile = false;
useSigmoid = oldUseSigmoid;
}
