/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <returns></returns>
public static bool CheckProbabilityModel(SVMModel model)
{
IntPtr ptr_model = SVMModel.Allocate(model);
bool success = libsvm.svm_check_probability_model(ptr_model);
SVMModel.Free(ptr_model);
return(success);
}
/// <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);
double result = Predict(ptr_model, x);
SVMModel.Free(ptr_model);
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <param name="x"></param>
/// <param name="estimations"></param>
/// <returns></returns>
public static double PredictProbability(SVMModel model, SVMNode[] x, out double[] estimations)
{
IntPtr ptr_model = SVMModel.Allocate(model);
double result = PredictProbability(ptr_model, x, out estimations);
SVMModel.Free(ptr_model);
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <param name="x"></param>
/// <param name="values"></param>
/// <returns></returns>
public static double PredictValues(SVMModel model, SVMNode[] x, out double[] values)
{
IntPtr ptr_model = SVMModel.Allocate(model);
double result = PredictValues(ptr_model, x, out values);
SVMModel.Free(ptr_model);
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <returns></returns>
public static bool CheckProbabilityModel(SVMModel model)
{
if (model == null)
{
throw new ArgumentNullException("model");
}
IntPtr ptr_model = SVMModel.Allocate(model);
bool success = libsvm.svm_check_probability_model(ptr_model);
SVMModel.Free(ptr_model);
return(success);
}
/// <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="model"></param>
/// <param name="filename"></param>
/// <returns></returns>
public static bool SaveModel(SVMModel model, string filename)
{
if (String.IsNullOrWhiteSpace(filename) || model == null)
{
return(false);
}
IntPtr ptr_model = SVMModel.Allocate(model);
IntPtr ptr_filename = Marshal.StringToHGlobalAnsi(filename);
bool success = libsvm.svm_save_model(ptr_filename, ptr_model) == 0;
Marshal.FreeHGlobal(ptr_filename);
ptr_filename = IntPtr.Zero;
return(success);
}
/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <param name="x"></param>
/// <param name="estimations"></param>
/// <returns></returns>
public static double PredictProbability(SVMModel model, SVMNode[] x, out double[] estimations)
{
if (model == null)
{
throw new ArgumentNullException("model");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
IntPtr ptr_model = SVMModel.Allocate(model);
double result = PredictProbability(ptr_model, x, out estimations);
SVMModel.Free(ptr_model);
return(result);
}
/// <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)
{
if (model == null)
{
throw new ArgumentNullException("model");
}
if (x == null)
{
throw new ArgumentNullException("x");
}
IntPtr ptr_model = SVMModel.Allocate(model);
double result = Predict(ptr_model, x);
SVMModel.Free(ptr_model);
return(result);
}
/// <summary>
///
/// </summary>
/// <param name="model"></param>
/// <param name="x"></param>
/// <param name="values"></param>
/// <returns></returns>
public static double PredictValues(SVMModel model, SVMNode[] x, out double[] values)
{
int size = (int)(model.ClassCount * (model.ClassCount - 1) * 0.5);
IntPtr ptr_values = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * size);
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_values(ptr_model, ptr_nodes, ptr_values);
values = new double[size];
Marshal.Copy(ptr_values, values, 0, values.Length);
SVMModel.Free(ptr_model);
SVMNode.Free(ptr_nodes);
Marshal.FreeHGlobal(ptr_values);
ptr_values = IntPtr.Zero;
return(result);
}