k_means.Kmeans.java Source code

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Here is the source code for k_means.Kmeans.java

Source

/*
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package k_means;

import java.awt.Color;
import java.util.ArrayList;
import javafx.scene.chart.NumberAxis;
import org.jfree.chart.ChartFactory;
import org.jfree.chart.ChartFrame;
import org.jfree.chart.ChartPanel;
import org.jfree.chart.JFreeChart;
import org.jfree.chart.axis.NumberTickUnit;
import org.jfree.chart.plot.PlotOrientation;
import org.jfree.chart.plot.XYPlot;
import org.jfree.chart.renderer.xy.XYItemRenderer;
import org.jfree.data.xy.XYDataset;

/**
 *
 * @author asimadnan
 */
public class Kmeans {

    int k; // number of cluster

    private static ArrayList<Data> dataSet = new ArrayList<Data>();
    private static ArrayList<Centroid> centroids = new ArrayList<Centroid>();
    // private static ArrayList<Centroid> centroidTemp = new ArrayList<Centroid>();

    Kmeans() {

    }

    public void setNumClusters(int num) {
        this.k = num;
    }

    public void addData(Data D) {

        dataSet.add(D);
    }

    public void addCentroid(Centroid C) {

        centroids.add(C);

    }

    public void calDist() {

        double distance = 0;
        double minimum = 0;
        int cluster = 0;
        double bignum = Math.pow(10, 10);

        for (int i = 0; i < dataSet.size(); i++) {
            minimum = bignum;
            for (int j = 0; j < k; j++) {
                distance = Distance.EDistance(dataSet.get(i), centroids.get(j));
                if (distance < minimum) {
                    minimum = distance;
                    cluster = j;
                }
                dataSet.get(i).setCluster(cluster);
            }
        }

    }

    public void ChooseNewCenter() {
        for (int i = 0; i < k; i++) {
            double totalX = 0;
            double totalY = 0;
            int totalInCluster = 0;

            for (int j = 0; j < dataSet.size(); j++) {

                if (dataSet.get(j).getCluster() == i) {

                    totalX += dataSet.get(j).getX();
                    totalY += dataSet.get(j).getY();
                    totalInCluster++;

                }
            }
            if (totalInCluster > 0) {

                centroids.get(i).setX(totalX / totalInCluster);
                centroids.get(i).setY(totalY / totalInCluster);
            }
        }

    }

    public void Display() {

        for (int i = 0; i < dataSet.size(); i++) {
            for (int j = 0; j < centroids.size(); j++) {
                if (dataSet.get(i).getCluster() == j) {
                    System.out.println(
                            "Cluster :" + i + " X :" + centroids.get(j).getX() + " Y :" + centroids.get(j).getX());
                }
                System.out.println();
            }
        }

    }

    public void DisplayData() {
        for (int i = 0; i < dataSet.size(); i++) {
            System.out.println("X:" + dataSet.get(i).getX() + " Y:" + dataSet.get(i).getY() + " CLUSTER :"
                    + dataSet.get(i).getCluster());
        }

        XYDataset d = Plot.createDataset(dataSet, k);

        JFreeChart chart = ChartFactory.createScatterPlot("DataPlot", "X", "Y", d, PlotOrientation.VERTICAL, true,
                true, false);
        // create and display a frame...
        ChartFrame frame = new ChartFrame("First", chart);
        frame.pack();
        frame.setVisible(true);

    }

    public void DisplayCentroids() {
        for (int i = 0; i < centroids.size(); i++) {
            System.out.println("XCC:" + centroids.get(i).getX() + " Y:" + centroids.get(i).getY());
        }
    }

    /*     public void KmeansCluster(){
            
        final double bigNumber = Math.pow(10, 10);    // some big number that's sure to be larger than our data range.
        double minimum = bigNumber;                   // The minimum value to beat. 
        double distance = 0.0;                        // The current minimum value.
        int sampleNumber = 0;
        int cluster = 0;
        boolean isStillMoving = true;
        Data newData = null;
        //Distance D =  new Distance();
            
        for(int i=0;i<dataSet.size();i++){
                
            minimum = bigNumber;
            for(int j=0; i< k;j++){
                distance = Distance.EDistance(dataSet.get(i), centroids.get(j));
                if (distance < minimum){
                    minimum = distance;
                    cluster = j;
                }
                dataSet.get(i).setCluster(cluster);
            }
        }
            
        for(int l=0;l<k;l++){
            int totalX =0;
            int totalY =0;
            int totalInCluster =0;
            for(int m=0;m<dataSet.size();m++){
                if(dataSet.get(m).getCluster() == k){
                        
                    totalX += dataSet.get(m).getX();
                    totalY += dataSet.get(m).getY();
                    totalInCluster++;
                        
                }
            }
             if (totalInCluster > 0 ){
                    centroids.get(l).setX(totalX/totalInCluster);
                    centroids.get(l).setY(totalY/totalInCluster);
                }
        }
            
    } */
}