/*
/// <summary>
/// Copy all values from another solution
/// </summary>
/// <param name="sourceSolution">The source to copy from</param>
public void Load(SVM sourceSolution)
{
dimension = sourceSolution.dimension;
alphaList = new float[dimension];
for (int i = 0; i < dimension; i++)
{
alphaList[i] = sourceSolution.alphaList[i];
}
b = sourceSolution.b;
}
*/
/// <summary>
/// Copy all values from another solution
/// </summary>
/// <param name="FileName">File containing alpha's data</param>
public void Load(string FileName)
{
DataSet d = new DataSet();
d.ReadXml(FileName);
DataTable t = d.Tables["Solution"];
dimension = t.Rows.Count;
//Configuration
DataTable TblCfg = d.Tables["Config"];
float valC, valTol; int valKernel, valMaxP;
valC = (float)((double)TblCfg.Rows[0]["dblValues"]);
valKernel = (int)((double)TblCfg.Rows[1]["dblValues"]);
valTol = (float)((double)TblCfg.Rows[2]["dblValues"]);
valMaxP = (int)((double)TblCfg.Rows[3]["dblValues"]);
this.b = (float)((double)TblCfg.Rows[4]["dblValues"]);
float Lambda = (float)((double)TblCfg.Rows[5]["dblValues"]);
int xDim = (int)((double)TblCfg.Rows[6]["dblValues"]);
//Reads classifications
DataTable TblClassif = d.Tables["Classifications"];
alphaList = new List<float>();
TrainingSet = new TrainingSet();
for (int i = 0; i < dimension; i++)
{
TrainingSet.addTrainingUnit(new TrainingUnit(new float[xDim], -1));
}
for (int i = 0; i < dimension; i++)
{
alphaList.Add((float)((double)t.Rows[i]["dblValues"]));
TrainingSet.trainingArray[i].y = (float)((double)TblClassif.Rows[i]["dblValues"]) > 0 ? 1 : -1;
}
//Reads training set
//Creates datatables for training examples
DataTable Tbl = d.Tables["Examples"];
for (int i = 0; i < dimension; i ++)
{
for (int j = 0; j < xDim; j++)
{
TrainingSet.trainingArray[i].xVector[j] = (float)((double)Tbl.Rows[j + i*xDim]["dblValues"]);
}
}
this.ProblemCfg = new ProblemConfig(Lambda, valC, valTol, valMaxP, (ProblemConfig.KernelType)valKernel);
if (OpenTKWrapper.CLCalc.CLAcceleration == OpenTKWrapper.CLCalc.CLAccelerationType.UsingCL)
{
this.WriteToDevice();
}
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
public MultiClassSVM(TrainingSet TSet)
{
ProblemConfig cfg = new ProblemConfig(2.529822E-8f * (float)Math.Sqrt(TSet.getN), 127.922182f, 1e-3f, 1, ProblemConfig.KernelType.RBF);
initMultiSVM(TSet, cfg, true);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
/// <param name="PreCalibrate">Precalibrate RBF parameter lambda? This will ignore the given value</param>
public MultiClassSVM(TrainingSet TSet, ProblemConfig SVMCfg, bool PreCalibrate)
{
initMultiSVM(TSet, SVMCfg, PreCalibrate);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
/// <param name="PreCalibrate">Precalibrate RBF parameter lambda? This will ignore the given value</param>
private void initMultiSVM(TrainingSet TSet, ProblemConfig SVMCfg, bool PreCalibrate)
{
//Determines how many different classifications are there
Classifications = new List<float>();
foreach (TrainingUnit tu in TSet.trainingArray)
{
if (Classifications.IndexOf(tu.y) < 0 && tu.y != -1.0f) Classifications.Add(tu.y);
}
//For each different possible classification, create a different SVM
SVMs = new List<SVM>();
foreach (float c in Classifications)
{
SVM svm = new SVM();
svm.TrainingSet = new TrainingSet();
svm.ProblemCfg = SVMCfg.Clone();
SVMs.Add(svm);
foreach (TrainingUnit tu in TSet.trainingArray)
{
TrainingUnit newTu = tu.Clone();
newTu.y = tu.y == c ? 1 : -1;
svm.TrainingSet.addTrainingUnit(newTu);
}
//Train svm
if (PreCalibrate) svm.PreCalibrateCfg(0.8f / (float)Math.Sqrt(svm.TrainingSet.getN), 0.3f / (float)Math.Sqrt(svm.TrainingSet.getN));
svm.Train();
svm.RemoveNonSupportVectors();
}
}
