static void Main(string[] args)
{
SVMProblem problem = SVMProblemHelper.Load(@"Datasets\wine.txt");
problem = SVMProblemHelper.Normalize(problem, SVMNormType.L2); // Optional
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 1;
parameter.Gamma = 1;
// Do 10-fold cross validation
double[] target;
SVM.CrossValidation(problem, parameter, 10, out target);
double crossValidationAccuracy = SVMHelper.EvaluateClassificationProblem(problem, target);
// Train the model
SVMModel model = SVM.Train(problem, parameter);
double correct = 0;
for (int i = 0; i < problem.Length; i++)
{
double y = SVM.Predict(model, problem.X[i]);
if (y == problem.Y[i])
{
correct++;
}
}
double trainingAccuracy = correct / (double)problem.Length;
Console.WriteLine("\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTraining accuracy: " + trainingAccuracy);
Console.ReadLine();
}
public static SVMProblem Normalize(this SVMProblem problem, SVMNormType type)
{
return(SVMProblemHelper.Normalize(problem, type));
}