/// <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;
}
/// <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>
///
/// </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>
/// 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);
}