package kmeans;import java.io.*;/** * The kmeans function perfotms a K-means clustering of 18 modes into two * clusters. * @author liyun * @version v1.1 */public class Kmeans{    /**     *it means ture     */    private static int SUCCESS=1;    private static int TRUE=1;    private static int FALSE=0;    private static int MAXVECTDIM=20;    private static int MAXPATTERN=20;    private static int MAXCLUSTER=10;    /**     * The input Matrix     * @see     */    private  double    pattern[][] = new double[MAXPATTERN][MAXVECTDIM] ;    /**The Cluster arrays     */    private  ACluster   Cluster[] = new ACluster[MAXCLUSTER];    /**number of patterns     */    private  int       numPatterns;    /** Number of dimensions in vector     */    private  int       sizeVector;    /** Number of clusters     */    private  int       numClusters;        /**     * Creates a new instance of System     */    public Kmeans() {    }  /** initial the clusters   */      public void initClusters(){        System.out.println("Initial cluster centers:");        for (int i=0; i<numClusters; i++) {            Cluster[i]=new ACluster();            for (int j=0; j<sizeVector; j++) {                Cluster[i].center[j]=pattern[i][j];            } /* endfor */        } /* endfor */        for (int i=0; i<numClusters; i++) {            System.out.println("ClusterCenter["+i+"]="+Cluster[i].center[0]+"  "+Cluster[i].center[1]);        }    }    /**     * Run the Kmeans     */    public void runKmeans(){        int converged=FALSE;        int pass =1;        while (converged==FALSE) {            System.out.println("PASS= "+(pass++));            distributeSamples();            converged=calcNewClustCenters();            showCluster();        } /* endwhile */    }        private void distributeSamples(){        int i,pat,clustid,memberIndex;        //Clear membership list for all current clusters        for (i=0; i<numClusters;i++){            Cluster[i].numMembers=0;        }        for (pat=0; pat<numPatterns; pat++) {            /*Find cluster center to which the pattern is closest*/            clustid= findClosestCluster(pat);            System.out.println("pattern "+pat+" belong to  " +"cluster["+clustid +"]");            /*post this pattern to the cluster*/            memberIndex=Cluster[clustid].numMembers;            Cluster[clustid].member[memberIndex]=pat;            Cluster[clustid].numMembers++;        } /* endfor */    }        private int findClosestCluster(int pat){        int i, clustID;        double minDist, d;        minDist =9.9e+99;        clustID=-1;        for (i=0; i<numClusters; i++) {            d=eucNorm(pat,i);            System.out.println("pattern "+pat+" to the cluster [" +i+"]'s distance is "+d+" !");            if (d<minDist) {                minDist=d;                clustID=i;            } /* endif */        } /* endfor */        if (clustID<0) {            System.out.println("Aaargh");            System.exit(0);        } /* endif */        return clustID;    }    /**  Calc Euclidean norm of vector difference between pattern vector, p, and cluster  center, c.     */    private double eucNorm(int p, int c){        double dist=0,x=0;        int i;        System.out.println("Now calculate pattern "+p+" to"+" the cluster["+c+"['s distance:");        for (i=0; i<sizeVector ;i++){            x += (Cluster[c].center[i]-pattern[p][i])*(Cluster[c].center[i]-pattern[p][i]);        } /* endfor */        return dist=Math.sqrt(x);    }        private int loadPatterns(String fname) throws java.io.IOException {        File InFilePtr=new File(fname);        FileInputStream f1=new FileInputStream(InFilePtr);        DataInputStream fd1=new DataInputStream(f1);        int i,j;        if(fd1!=null){            numPatterns=fd1.readInt();            sizeVector=fd1.readInt();            numClusters=fd1.readInt();            for(i=0;i<numPatterns;i++){                for(j=0;j<numClusters;j++){                    double x=fd1.readDouble();                    pattern[i][j]=x;                }            }        }        fd1.close();        f1.close();        for (i=0;i<18;i++){            System.out.println("Pattern:"+pattern[i][0] +"  " +  pattern[i][1]);        }        return SUCCESS;    }    /** calculate the new clustCenters     * @see     */    public int calcNewClustCenters(){        int k=0;        int vectID;        int ConvFlag=TRUE;        double tmp[]=new double[MAXVECTDIM];        for(int j=0;j<MAXVECTDIM;j++){            tmp[j]=0.0;        }        for(int i=0;i<numClusters;i++){            for(int j=0;j<Cluster[i].numMembers;j++){                vectID=Cluster[i].member[j];                for (k=0;k<sizeVector;k++){                    tmp[k]+=pattern[vectID][k];                }            }            for(k=0;k<sizeVector;k++){                tmp[k]=tmp[k]/(Cluster[i].numMembers);                                if(tmp[k]!=Cluster[i].center[k])                    ConvFlag=FALSE;                Cluster[i].center[k]=tmp[k];            }        }        return ConvFlag;    }        /**     * show clusters     */        public void showCluster(){        int cl;        for(cl=0;cl<numClusters;cl++){            System.out.println("CLUSTER["+cl+"]: "+Cluster[cl].center[0]+"   "+Cluster[cl].center[1]);            for(int i=0;i<Cluster[cl].numMembers;i++)                System.out.println("the Patterns in the Cluster["+cl+"] "+" are: "+pattern[Cluster[cl].member[i]][0]+                "  "+pattern[Cluster[cl].member[i]][1] );        }    }        /**     * save clusters     * @param     * @since     */        public void saveCluster(String fname){}        /**     * main methoes     * @param     * @     */        public static void main(String[ ] args)throws java.io.IOException{        Kmeans kmean=new Kmeans();        if(kmean.loadPatterns("D:/javasources/kmeans/Data.txt")!=SUCCESS){            System.out.println("UNABLE TO READ PATTERN_FILE:");            System.exit(0);        }        kmean.initClusters();        kmean.runKmeans();    }}