public static SVMProblem Convert(svm_problem x)
{
double[] y_array = new double[x.l];
Marshal.Copy(x.y, y_array, 0, y_array.Length);
List <SVMNode[]> x_array = new List <SVMNode[]>();
IntPtr i_ptr_x = x.x;
for (int i = 0; i < x.l; i++)
{
IntPtr ptr_nodes = (IntPtr)Marshal.PtrToStructure(i_ptr_x, typeof(IntPtr));
SVMNode[] nodes = SVMNode.Convert(ptr_nodes);
x_array.Add(nodes);
i_ptr_x = IntPtr.Add(i_ptr_x, Marshal.SizeOf(typeof(IntPtr)));
}
SVMProblem y = new SVMProblem();
for (int i = 0; i < x.l; i++)
{
y.Add(x_array[i], y_array[i]);
}
return(y);
}
public OneClassClassifier(List<SVMNode[]> trainingData)
{
SVMProblem problem = new SVMProblem();
for (int i = 0; i < trainingData.Count; i++)
{
problem.Add(trainingData[i], 1);
}
_trainingData = problem;
}
public SVMProblem Clone()
{
SVMProblem y = new SVMProblem();
for (int i = 0; i < Length; i++)
{
SVMNode[] nodes = X[i].Select(x => x.Clone()).ToArray();
y.Add(nodes, Y[i]);
}
return y;
}
public SVMProblem Clone()
{
SVMProblem y = new SVMProblem();
for (int i = 0; i < Length; i++)
{
SVMNode[] nodes = X[i].Select(x => x.Clone()).ToArray();
y.Add(nodes, Y[i]);
}
return(y);
}
public static SVMProblem Convert(svm_problem x)
{
double[] y_array = new double[x.l];
Marshal.Copy(x.y, y_array, 0, y_array.Length);
List<SVMNode[]> x_array = new List<SVMNode[]>();
IntPtr i_ptr_x = x.x;
for (int i = 0; i < x.l; i++)
{
IntPtr ptr_nodes = (IntPtr)Marshal.PtrToStructure(i_ptr_x, typeof(IntPtr));
SVMNode[] nodes = SVMNode.Convert(ptr_nodes);
x_array.Add(nodes);
i_ptr_x = IntPtr.Add(i_ptr_x, Marshal.SizeOf(typeof(IntPtr)));
}
SVMProblem y = new SVMProblem();
for (int i = 0; i < x.l; i++)
{
y.Add(x_array[i], y_array[i]);
}
return y;
}
