/// <summary>
/// Trains an %SVM with optimal parameters.
/// </summary>
/// <param name="data">the training data that can be constructed using
/// TrainData::create or TrainData::loadFromCSV.</param>
/// <param name="kFold">Cross-validation parameter. The training set is divided into kFold subsets.
/// One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
/// executed kFold times.</param>
/// <param name="cGrid">grid for C</param>
/// <param name="gammaGrid">grid for gamma</param>
/// <param name="pGrid">grid for p</param>
/// <param name="nuGrid">grid for nu</param>
/// <param name="coeffGrid">grid for coeff</param>
/// <param name="degreeGrid">grid for degree</param>
/// <param name="balanced">If true and the problem is 2-class classification then the method creates
/// more balanced cross-validation subsets that is proportions between classes in subsets are close
/// to such proportion in the whole train dataset.</param>
/// <returns></returns>
public bool TrainAuto(TrainData data, int kFold = 10,
ParamGrid?cGrid = null,
ParamGrid?gammaGrid = null,
ParamGrid?pGrid = null,
ParamGrid?nuGrid = null,
ParamGrid?coeffGrid = null,
ParamGrid?degreeGrid = null,
bool balanced = false)
{
throw new NotImplementedException();
/*
* var cGridValue = cGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.C));
* var gammaGridValue = gammaGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Gamma));
* var pGridValue = pGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.P));
* var nuGridValue = nuGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Nu));
* var coeffGridValue = coeffGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Coef));
* var degreeGridValue = degreeGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Degree));*/
}
/// <summary>
/// Trains the statistical model
/// </summary>
/// <param name="trainData">training data that can be loaded from file using TrainData::loadFromCSV
/// or created with TrainData::create.</param>
/// <param name="flags"> optional flags, depending on the model. Some of the models can be updated with the
/// new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).</param>
/// <returns></returns>
public virtual bool Train(TrainData trainData, int flags = 0)
{
throw new NotImplementedException();
}
/// <summary>
/// Computes error on the training or test dataset
/// </summary>
/// <param name="data">the training data</param>
/// <param name="test">if true, the error is computed over the test subset of the data,
/// otherwise it's computed over the training subset of the data. Please note that if you
/// loaded a completely different dataset to evaluate already trained classifier, you will
/// probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio
/// and specify test=false, so that the error is computed for the whole new set. Yes, this
/// sounds a bit confusing.</param>
/// <param name="resp">the optional output responses.</param>
/// <returns></returns>
public virtual float CalcError(TrainData data, bool test, OutputArray resp)
{
throw new NotImplementedException();
}
/// <summary>
/// Trains the statistical model
/// </summary>
/// <param name="trainData">training data that can be loaded from file using TrainData::loadFromCSV
/// or created with TrainData::create.</param>
/// <param name="flags"> optional flags, depending on the model. Some of the models can be updated with the
/// new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).</param>
/// <returns></returns>
public virtual bool Train(TrainData trainData, int flags = 0)
{
throw new NotImplementedException();
}
/// <summary>
/// Computes error on the training or test dataset
/// </summary>
/// <param name="data">the training data</param>
/// <param name="test">if true, the error is computed over the test subset of the data,
/// otherwise it's computed over the training subset of the data. Please note that if you
/// loaded a completely different dataset to evaluate already trained classifier, you will
/// probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio
/// and specify test=false, so that the error is computed for the whole new set. Yes, this
/// sounds a bit confusing.</param>
/// <param name="resp">the optional output responses.</param>
/// <returns></returns>
public virtual float CalcError(TrainData data, bool test, OutputArray resp)
{
throw new NotImplementedException();
}
/// <summary>
/// Trains an %SVM with optimal parameters.
/// </summary>
/// <param name="data">the training data that can be constructed using
/// TrainData::create or TrainData::loadFromCSV.</param>
/// <param name="kFold">Cross-validation parameter. The training set is divided into kFold subsets.
/// One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
/// executed kFold times.</param>
/// <param name="cGrid">grid for C</param>
/// <param name="gammaGrid">grid for gamma</param>
/// <param name="pGrid">grid for p</param>
/// <param name="nuGrid">grid for nu</param>
/// <param name="coeffGrid">grid for coeff</param>
/// <param name="degreeGrid">grid for degree</param>
/// <param name="balanced">If true and the problem is 2-class classification then the method creates
/// more balanced cross-validation subsets that is proportions between classes in subsets are close
/// to such proportion in the whole train dataset.</param>
/// <returns></returns>
public bool TrainAuto(TrainData data, int kFold = 10,
ParamGrid? cGrid = null,
ParamGrid? gammaGrid = null,
ParamGrid? pGrid = null,
ParamGrid? nuGrid = null,
ParamGrid? coeffGrid = null,
ParamGrid? degreeGrid = null,
bool balanced = false)
{
throw new NotImplementedException();
/*
var cGridValue = cGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.C));
var gammaGridValue = gammaGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Gamma));
var pGridValue = pGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.P));
var nuGridValue = nuGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Nu));
var coeffGridValue = coeffGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Coef));
var degreeGridValue = degreeGrid.GetValueOrDefault(GetDefaultGrid(ParamTypes.Degree));*/
}