public object Clone()
{
SVMNode clone = new SVMNode();
clone.copy(this);
return(clone);
}
public void Copy(SVMModel rhs)
{
_param = rhs._param;
_numberClass = rhs._numberClass;
_totalNumberSV = rhs._totalNumberSV; // total #SV
_SV = new SVMNode[rhs._SV.Length][]; // SVs (SV[l])
for (int i = 0; i < rhs._SV.Length; ++i)
{
_SV[i] = new SVMNode[rhs._SV[i].Length];
for (int j = 0; j < rhs._SV[i].Length; ++j)
{
_SV[i][j] = (SVMNode)rhs._SV[i][j].Clone();
}
}
_coefSV = new double[rhs._coefSV.Length][]; // coefficients for SVs in decision functions (sv_coef[k-1][l])
for (int i = 0; i < rhs._coefSV.Length; ++i)
{
_coefSV[i] = (double[])rhs._coefSV[i].Clone();
}
_rho = rhs._rho == null ? null : (double[]) rhs._rho.Clone(); // constants in decision functions (rho[k*(k-1)/2])
ProbA = rhs.ProbA == null ? null : (double[]) rhs.ProbA.Clone(); // pariwise probability information
ProbB = rhs.ProbB == null ? null : (double[]) rhs.ProbB.Clone();
_indicesSV = rhs._indicesSV == null ? null : (int[]) rhs._indicesSV.Clone(); // sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set
// for classification only
Label = rhs.Label == null ? null : (int[]) rhs.Label.Clone(); // label of each class (label[k])
_numberSV4EachClass = rhs._numberSV4EachClass == null ? null : (int[]) rhs._numberSV4EachClass.Clone(); // number of SVs for each class (nSV[k])
}
public void copy(SVMNode rhs)
{
index = rhs.index;
value = rhs.value;
}
