Java

//ConvexHull.java
 
package convexHull1;
 
import java.awt.Canvas;
import java.awt.Graphics;
import java.awt.event.MouseEvent;
import java.awt.event.MouseListener;
import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
 
import javax.swing.JFrame;
 
public class ConvexHull extends Canvas implements MouseListener{
    private static int countA;
    private static int countB;
    private static ArrayList<Point> A = new ArrayList<Point>();
    private static ArrayList<Point> B = new ArrayList<Point>();
    private static Graphics gDC;
    public ConvexHull() {
        // TODO Auto-generated constructor stub
        countA = 0;
        countB = 0;
        addMouseListener((MouseListener) this);
    }
 
    public static void main(String[] args) {
        // TODO Auto-generated method stub
        JFrame jf = new JFrame("Convex Hull");
        ConvexHull convexHull = new ConvexHull();
        jf.setSize(800, 600);
        jf.setVisible(true);
        jf.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        jf.add(convexHull);
    }
 
    public void paint(Graphics gDC)
    {
        PrintWriter out;
        String str = new String();
        try {
            out = new PrintWriter(new BufferedWriter(new FileWriter("hulloutput.txt",true)));
        str = "";
        for(int i = 0;i < countA;i++)
        {
            gDC.fillOval(A.get(i).getX()-5, A.get(i).getY()-5, 10, 10);
            str += A.get(i).toString() + " ";
        }
        out.println("All points");
        out.println(str);
        out.println("Points on the hull");
        str = "";
        for(int i = 0;i < countB;i++)
        {
            gDC.drawLine(B.get(i).getX(),B.get(i).getY(), B.get((i+1)%countB).getX(),B.get((i+1)%countB).getY());
            System.out.println(B.get(i).getX() + " "+ B.get(i).getY());
            str += B.get(i).toString() + " ";
        }
        out.println(str);
        System.out.println();
        out.close();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
     
    @Override
    public void mouseClicked(MouseEvent e) {
        // TODO Auto-generated method stub
         
    }
 
    @Override
    public void mouseEntered(MouseEvent e) {
        // TODO Auto-generated method stub
         
    }
 
    @Override
    public void mouseExited(MouseEvent e) {
        // TODO Auto-generated method stub
         
    }
 
    @Override
    public void mousePressed(MouseEvent e) {
        // TODO Auto-generated method stub
        int x = e.getX();
        int y = e.getY();
        Point point = new Point(x,y);
        A.add(point);
        countA++;
        while(!B.isEmpty())
            B.remove(B.get(0));
        //Jarvis.Solve(A, B);
        //Graham.Solve(A, B);
        //QuickHull.Solve(A,B);
        countB = B.size();
        repaint();
    }
 
    @Override
    public void mouseReleased(MouseEvent e) {
        // TODO Auto-generated method stub
         
    }
 
}

I am working in PSO for feature selection. I use KNN algorithm with 10 cross validation for the evaluation. before I use the 10cv the algorithm is quite cheap meaning no high computational cost has been faced, but after turning to 10cv the code is running too slow, sometimes for days. may I know if there is any problem in performing the 10cv. I use the following code to perform 10 cv:

dataset data = FileHandler.loadDataset(new File(dataSetFileName+".csv"), noFeatures, ","); 
                //crossvalidationmat
                  int[][] crossvalidationmat= { 
                    {1,2,3,4,5,6,7,8,9}, 
                    {0,2,3,4,5,6,7,8,9}, 
                    {0,1,3,4,5,6,7,8,9},
                    {0,1,2,4,5,6,7,8,9}, 
                    {0,1,2,3,5,6,7,8,9},
                    {0,1,2,3,4,6,7,8,9},
                    {0,1,2,3,4,5,7,8,9},
                    {0,1,2,3,4,5,6,8,9},
                    {0,1,2,3,4,5,6,7,9},
                    {0,1,2,3,4,5,6,7,8},
                    };
        Dataset[] folds = data.folds((10), new Random(1));
        Dataset training = new DefaultDataset();    //training, testing
        Dataset testing = new DefaultDataset();   
        int[] tr =new int[9];
        int[] te = new int[1]; 
for (int di = 0; di < crossvalidationmat.length; di++) { // start crossvalidation
       System.out.println(crossvalidationmat[di].length);
for (int xj = 0; xj < crossvalidationmat[di].length; xj++) { 
         tr[xj]=crossvalidationmat[di][xj];
         System.out.print(tr[xj]);
    }
    te[0]=di;
    System.out.println("\nTraing  te[0]=di here:  "+te[0]);
        for (int i = 0; i < tr.length; i++) {
            training.addAll(folds[tr[i]]);
        }
        for (int i = 0; i < te.length; i++) {
            testing.addAll(folds[te[i]]);
        }
 Dataset[] foldsTrain = training.folds(numFolds, new Random(1));
//other code
}