public void face_training(SVMProblem f_training)
{
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towp.txt");
SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\0921_towpt.txt");
// f_training.Save(@"C:\Users\temp\Desktop\1005f.txt");
// trainingSet.Insert(index, f_training.X[0], 2);
trainingSet.Add(f_training.X[0], 1);
trainingSet.Save(@"C:\Users\temp\Desktop\flag.txt");
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
// Console.WriteLine();
// SVMNode node = new SVMNode();
// node.Index = Convert.ToInt32(o);
// node.Value = Convert.ToDouble(f_training.X);
// nodes.Add(node);
// trainingSet.Add(nodes.ToArray(), 1);
// int number = randon.Next(0, trainingSet.X.Count);
// int trainingsample = Convert.ToInt32(trainingSet.X.Count * 2 / 3);
// int testingsample = Convert.ToInt32(trainingSet.X.Count / 3);
trainingSet = trainingSet.Normalize(SVMNormType.L2);
testSet = testSet.Normalize(SVMNormType.L2);
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.NU_SVC;
parameter.Kernel = SVMKernelType.SIGMOID;
parameter.C = 1;
parameter.Gamma = 1;
parameter.Probability = true;
int nFold = 10;
MainWindow main = new MainWindow();
double[] crossValidationResults; // output labels
trainingSet.CrossValidation(parameter, nFold, out crossValidationResults);
double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
SVMModel model = SVM.Train(trainingSet, parameter);
// SVMModel model = trainingSet.Train(parameter);
SVM.SaveModel(model, @"C:\Users\temp\Desktop\1005.txt");
double[] testResults = testSet.Predict(model);
// Console.WriteLine("");
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
// Console.WriteLine("testAccuracy:" + testAccuracy);
// Console.WriteLine(Convert.ToString(trainingSet.X.Count));
main.Training_result.Content = "testAccuracy:" + testAccuracy + "\nCross validation accuracy: " + crossValidationAccuracy + "\nCount " + trainingSet.X.Count;
main.Training_result.FontSize = 14;
main.Training_result.FontStyle = FontStyles.Normal;
main.Training_result.Foreground = Brushes.Red;
main.Training_result.Background = Brushes.Black;
// Console.WriteLine(trainingSet1.Length);
// trainingSet.Save(@"C:\Users\temp\Desktop\1005.txt");
index++;
}
