/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <returns></returns>
public static string CheckParameter(SVMProblem problem, SVMParameter parameter)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_output = libsvm.svm_check_parameter(ptr_problem, ptr_parameter);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
string output = Marshal.PtrToStringAnsi(ptr_output);
Marshal.FreeHGlobal(ptr_output);
ptr_output = IntPtr.Zero;
return(output);
}
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);
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <param name="nFolds"></param>
/// <param name="target"></param>
public static void CrossValidation(SVMProblem problem, SVMParameter parameter, int nFolds, out double[] target)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
if (nFolds < 2)
{
throw new ArgumentOutOfRangeException("nFolds");
}
IntPtr ptr_target = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * problem.Length);
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
libsvm.svm_cross_validation(ptr_problem, ptr_parameter, nFolds, ptr_target);
target = new double[problem.Length];
Marshal.Copy(ptr_target, target, 0, target.Length);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
Marshal.FreeHGlobal(ptr_target);
ptr_target = IntPtr.Zero;
}
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(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
return(null);
}
svm_problem x = (svm_problem)Marshal.PtrToStructure(ptr, typeof(svm_problem));
return(SVMProblem.Convert(x));
}
/// <summary>
/// This function constructs and returns an SVM model according to the given training data and parameters.
/// </summary>
/// <param name="problem">Training data.</param>
/// <param name="parameter">Parameter set.</param>
/// <returns>SVM model according to the given training data and parameters.</returns>
public static SVMModel Train(SVMProblem problem, SVMParameter parameter)
{
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_model = libsvm.svm_train(ptr_problem, ptr_parameter);
SVMModel model = SVMModel.Convert(ptr_model);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
libsvm.svm_free_model_content(ptr_model);
return model;
}
/// <summary>
/// This function constructs and returns an SVM model according to the given training data and parameters.
/// </summary>
/// <param name="problem">Training data.</param>
/// <param name="parameter">Parameter set.</param>
/// <returns>SVM model according to the given training data and parameters.</returns>
public static SVMModel Train(SVMProblem problem, SVMParameter parameter)
{
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_model = libsvm.svm_train(ptr_problem, ptr_parameter);
SVMModel model = SVMModel.Convert(ptr_model);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
libsvm.svm_free_model_content(ptr_model);
return(model);
}
public static void Free(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
return;
}
svm_problem x = (svm_problem)Marshal.PtrToStructure(ptr, typeof(svm_problem));
SVMProblem.Free(x);
Marshal.DestroyStructure(ptr, typeof(svm_problem));
Marshal.FreeHGlobal(ptr);
ptr = IntPtr.Zero;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <param name="nFolds"></param>
/// <param name="target"></param>
public static void CrossValidation(SVMProblem problem, SVMParameter parameter, int nFolds, out double[] target)
{
IntPtr ptr_target = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * problem.Length);
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
libsvm.svm_cross_validation(ptr_problem, ptr_parameter, nFolds, ptr_target);
target = new double[problem.Length];
Marshal.Copy(ptr_target, target, 0, target.Length);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
Marshal.FreeHGlobal(ptr_target);
ptr_target = IntPtr.Zero;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <returns></returns>
public static string CheckParameter(SVMProblem problem, SVMParameter parameter)
{
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_output = libsvm.svm_check_parameter(ptr_problem, ptr_parameter);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
string output = Marshal.PtrToStringAnsi(ptr_output);
Marshal.FreeHGlobal(ptr_output);
ptr_output = IntPtr.Zero;
return output;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <returns></returns>
public static string CheckParameter(SVMProblem problem, SVMParameter parameter)
{
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_output = libsvm.svm_check_parameter(ptr_problem, ptr_parameter);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
string output = Marshal.PtrToStringAnsi(ptr_output);
Marshal.FreeHGlobal(ptr_output);
ptr_output = IntPtr.Zero;
return(output);
}
public static IntPtr Allocate(SVMProblem x)
{
if (x == null || x.X == null || x.Y == null || x.Length < 1)
{
return(IntPtr.Zero);
}
svm_problem y = new svm_problem();
y.l = x.Length;
// Allocate problem.y
y.y = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.Y.Count);
Marshal.Copy(x.Y.ToArray(), 0, y.y, x.Y.Count);
// Allocate problem.x
y.x = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(IntPtr)) * x.X.Count);
IntPtr i_ptr_x = y.x;
for (int i = 0; i < x.X.Count; i++)
{
// Prepare each node array
// 1) All nodes containing zero value is removed
// 2) A node which index is -1 is added to the end
List <SVMNode> temp = x.X[i].Where(a => a.Value != 0).ToList();
temp.Add(new SVMNode(-1, 0));
SVMNode[] nodes = temp.ToArray();
// Allocate node array
IntPtr ptr_nodes = SVMNode.Allocate(nodes);
Marshal.StructureToPtr(ptr_nodes, i_ptr_x, true);
i_ptr_x = IntPtr.Add(i_ptr_x, Marshal.SizeOf(typeof(IntPtr)));
}
// Allocate the problem
int size = Marshal.SizeOf(y);
IntPtr ptr = Marshal.AllocHGlobal(size);
Marshal.StructureToPtr(y, ptr, true);
return(ptr);
}
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;
}
/// <summary>
/// This function constructs and returns an SVM model according to the given training data and parameters.
/// </summary>
/// <param name="problem">Training data.</param>
/// <param name="parameter">Parameter set.</param>
/// <returns>SVM model according to the given training data and parameters.</returns>
public static SVMModel Train(SVMProblem problem, SVMParameter parameter)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_model = libsvm.svm_train(ptr_problem, ptr_parameter);
SVMModel model = SVMModel.Convert(ptr_model);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
libsvm.svm_free_model_content(ptr_model);
return(model);
}
/// <summary>
/// This function constructs and returns an SVM model according to the given training data and parameters.
/// </summary>
/// <param name="problem">Training data.</param>
/// <param name="parameter">Parameter set.</param>
/// <returns>SVM model according to the given training data and parameters.</returns>
public static SVMModel Train(SVMProblem problem, SVMParameter parameter)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_model = libsvm.svm_train(ptr_problem, ptr_parameter);
SVMModel model = SVMModel.Convert(ptr_model);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
libsvm.svm_free_model_content(ptr_model);
return model;
}
/// <summary>
/// Constructor
/// </summary>
/// <param name="trainingData">The training data which the svm will learn the "normal" data from</param>
public OneClassClassifier(List<List<double>> trainingData)
{
_trainingData = trainingData.CreateCompleteProblemOneClass(); ;
}
public Clasification()
{
problem = SVMProblemHelper.Load(@"C:\Training.txt");
testProblem = SVMProblemHelper.Load(@"C:\Training.txt");
}
public static IntPtr Allocate(SVMProblem x)
{
if (x == null || x.X == null || x.Y == null || x.Length < 1)
{
return IntPtr.Zero;
}
svm_problem y = new svm_problem();
y.l = x.Length;
// Allocate problem.y
y.y = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.Y.Count);
Marshal.Copy(x.Y.ToArray(), 0, y.y, x.Y.Count);
// Allocate problem.x
y.x = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(IntPtr)) * x.X.Count);
IntPtr i_ptr_x = y.x;
for (int i = 0; i < x.X.Count; i++)
{
// Prepare each node array
// 1) All nodes containing zero value is removed
// 2) A node which index is -1 is added to the end
List<SVMNode> temp = x.X[i].Where(a => a.Value != 0).ToList();
temp.Add(new SVMNode(-1, 0));
SVMNode[] nodes = temp.ToArray();
// Allocate node array
IntPtr ptr_nodes = SVMNode.Allocate(nodes);
Marshal.StructureToPtr(ptr_nodes, i_ptr_x, true);
i_ptr_x = IntPtr.Add(i_ptr_x, Marshal.SizeOf(typeof(IntPtr)));
}
// Allocate the problem
int size = Marshal.SizeOf(y);
IntPtr ptr = Marshal.AllocHGlobal(size);
Marshal.StructureToPtr(y, ptr, true);
return ptr;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <param name="nFolds"></param>
/// <param name="target"></param>
public static void CrossValidation(SVMProblem problem, SVMParameter parameter, int nFolds, out double[] target)
{
IntPtr ptr_target = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * problem.Length);
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
libsvm.svm_cross_validation(ptr_problem, ptr_parameter, nFolds, ptr_target);
target = new double[problem.Length];
Marshal.Copy(ptr_target, target, 0, target.Length);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
Marshal.FreeHGlobal(ptr_target);
ptr_target = IntPtr.Zero;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <returns></returns>
public static string CheckParameter(SVMProblem problem, SVMParameter parameter)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
IntPtr ptr_output = libsvm.svm_check_parameter(ptr_problem, ptr_parameter);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
string output = Marshal.PtrToStringAnsi(ptr_output);
Marshal.FreeHGlobal(ptr_output);
ptr_output = IntPtr.Zero;
return output;
}
/// <summary>
///
/// </summary>
/// <param name="problem"></param>
/// <param name="parameter"></param>
/// <param name="nFolds"></param>
/// <param name="target"></param>
public static void CrossValidation(SVMProblem problem, SVMParameter parameter, int nFolds, out double[] target)
{
if (problem == null)
{
throw new ArgumentNullException("problem");
}
if (parameter == null)
{
throw new ArgumentNullException("parameter");
}
if (nFolds < 2)
{
throw new ArgumentOutOfRangeException("nFolds");
}
IntPtr ptr_target = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * problem.Length);
IntPtr ptr_problem = SVMProblem.Allocate(problem);
IntPtr ptr_parameter = SVMParameter.Allocate(parameter);
libsvm.svm_cross_validation(ptr_problem, ptr_parameter, nFolds, ptr_target);
target = new double[problem.Length];
Marshal.Copy(ptr_target, target, 0, target.Length);
SVMProblem.Free(ptr_problem);
SVMParameter.Free(ptr_parameter);
Marshal.FreeHGlobal(ptr_target);
ptr_target = IntPtr.Zero;
}
public Clasification()
{
problem = SVMProblemHelper.Load(@"C:\Training.txt");
testProblem = SVMProblemHelper.Load(@"C:\Training.txt");
System.Diagnostics.Debug.WriteLine("creo el objeto");
}
