/// <summary>
///
/// </summary>
/// <param name="ptr_model"></param>
/// <param name="x"></param>
/// <param name="estimations"></param>
/// <returns></returns>
public static double PredictProbability(IntPtr ptr_model, SVMNode[] x, out double[] estimations)
{
if (ptr_model == IntPtr.Zero)
{
throw new ArgumentNullException("ptr_model");
}
bool isProbabilityModel = libsvm.svm_check_probability_model(ptr_model);
if (!isProbabilityModel)
{
SVMModel.Free(ptr_model);
estimations = null;
return(-1);
}
int classCount = libsvm.svm_get_nr_class(ptr_model);
IntPtr ptr_estimations = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * classCount);
List <SVMNode> nodes = x.Select(a => a.Clone()).ToList();
nodes.Add(new SVMNode(-1, 0));
IntPtr ptr_nodes = SVMNode.Allocate(nodes.ToArray());
double result = libsvm.svm_predict_probability(ptr_model, ptr_nodes, ptr_estimations);
estimations = new double[classCount];
Marshal.Copy(ptr_estimations, estimations, 0, estimations.Length);
SVMNode.Free(ptr_nodes);
Marshal.FreeHGlobal(ptr_estimations);
ptr_estimations = IntPtr.Zero;
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="ptr_model"></param>
/// <param name="x"></param>
/// <param name="values"></param>
/// <returns></returns>
public static double PredictValues(IntPtr ptr_model, SVMNode[] x, out double[] values)
{
if (ptr_model == IntPtr.Zero)
{
throw new ArgumentNullException("ptr_model");
}
int classCount = libsvm.svm_get_nr_class(ptr_model);
int size = (int)(classCount * (classCount - 1) * 0.5);
IntPtr ptr_values = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * size);
List <SVMNode> nodes = x.Select(a => a.Clone()).ToList();
nodes.Add(new SVMNode(-1, 0));
IntPtr ptr_nodes = SVMNode.Allocate(nodes.ToArray());
double result = libsvm.svm_predict_values(ptr_model, ptr_nodes, ptr_values);
values = new double[size];
Marshal.Copy(ptr_values, values, 0, values.Length);
SVMNode.Free(ptr_nodes);
Marshal.FreeHGlobal(ptr_values);
ptr_values = IntPtr.Zero;
return(result);
}
public static void Free(svm_model x)
{
Marshal.FreeHGlobal(x.rho);
x.rho = IntPtr.Zero;
Marshal.FreeHGlobal(x.probA);
x.probA = IntPtr.Zero;
Marshal.FreeHGlobal(x.probB);
x.probB = IntPtr.Zero;
Marshal.FreeHGlobal(x.sv_indices);
x.sv_indices = IntPtr.Zero;
Marshal.FreeHGlobal(x.label);
x.label = IntPtr.Zero;
Marshal.FreeHGlobal(x.nSV);
x.nSV = IntPtr.Zero;
SVMParameter.Free(x.param);
IntPtr i_ptr_sv = x.SV;
for (int i = 0; i < x.l; i++)
{
IntPtr ptr_nodes = (IntPtr)Marshal.PtrToStructure(i_ptr_sv, typeof(IntPtr));
SVMNode.Free(ptr_nodes);
i_ptr_sv = IntPtr.Add(i_ptr_sv, Marshal.SizeOf(typeof(IntPtr)));
}
Marshal.FreeHGlobal(x.SV);
x.SV = IntPtr.Zero;
IntPtr i_ptr_svcoef = x.sv_coef;
for (int i = 0; i < x.nr_class - 1; i++)
{
IntPtr temp = (IntPtr)Marshal.PtrToStructure(i_ptr_svcoef, typeof(IntPtr));
Marshal.FreeHGlobal(temp);
temp = IntPtr.Zero;
i_ptr_svcoef = IntPtr.Add(i_ptr_svcoef, Marshal.SizeOf(typeof(IntPtr)));
}
Marshal.FreeHGlobal(x.sv_coef);
x.sv_coef = IntPtr.Zero;
}
/// <summary>
/// This function does classification or regression on a test vector x given a model.
/// </summary>
/// <param name="model">SVM model.</param>
/// <param name="x">Test vector.</param>
/// <returns>For a classification model, the predicted class for x is returned.
/// For a regression model, the function value of x calculated using the model is returned.
/// For an one-class model, +1 or -1 is returned.</returns>
public static double Predict(SVMModel model, SVMNode[] x)
{
IntPtr ptr_model = SVMModel.Allocate(model);
List <SVMNode> nodes = x.Select(a => a.Clone()).ToList();
nodes.Add(new SVMNode(-1, 0));
IntPtr ptr_nodes = SVMNode.Allocate(nodes.ToArray());
double result = libsvm.svm_predict(ptr_model, ptr_nodes);
SVMModel.Free(ptr_model);
SVMNode.Free(ptr_nodes);
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="ptr_model"></param>
/// <param name="x"></param>
/// <returns></returns>
public static double Predict(IntPtr ptr_model, SVMNode[] x)
{
if (ptr_model == IntPtr.Zero)
{
throw new ArgumentNullException("ptr_model");
}
List <SVMNode> nodes = x.Select(a => a.Clone()).ToList();
nodes.Add(new SVMNode(-1, 0));
IntPtr ptr_nodes = SVMNode.Allocate(nodes.ToArray());
double result = libsvm.svm_predict(ptr_model, ptr_nodes);
SVMNode.Free(ptr_nodes);
return(result);
}
public static void Free(svm_problem x)
{
Marshal.FreeHGlobal(x.y);
x.y = IntPtr.Zero;
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.Free(ptr_nodes);
i_ptr_x = IntPtr.Add(i_ptr_x, Marshal.SizeOf(typeof(IntPtr)));
}
Marshal.FreeHGlobal(x.x);
x.x = IntPtr.Zero;
}
