cluster.java

用java实现的一种聚类方法

/*
 * Cluster.java
 *
 * Created on 2007年5月29日, 上午12:35
 *
 * To change this template, choose Tools | Template Manager
 * and open the template in the editor.
 */

package cindy;

import java.util.*;

/**
 *
 * @author njchenyi
 */
public class Cluster {
    int index =0;
    Vector v;
    Sample sam;
    /** Creates a new instance of Cluster */
    public Cluster() {
        v = new Vector();//样本堆栈
        sam = new Sample();
    }
    public Vector getSample(){
        return v;
    }
    
    public void addSample(Sample s){
        v.add(s); //添加样本
    }
    
    public void removeSample(Sample s){
        v.removeElement(s);//删除样本
    }
    
    //判断是否在聚类中
    public boolean isInCluster(Sample s){
        for(int i =0; i<v.size();i++){
            if(v.elementAt(i).equals(s))
                return true;
        }
        return false;
    }
    
    //获取样本个数
    public int getSampleNo(){
        return v.size();
    }
    
    //获取重心点
    public double[] getWeightPoint(){
        //int i = v.size();
        sam = ((Sample)v.elementAt(0));
        int NO = sam.getParmNo();
        double[] temp = new double[NO];
        for(int i=0; i<v.size();i++){
            for(int j=0; j<NO;j++){
                sam=(Sample)v.elementAt(i);
                temp[j]=temp[j]+(sam.getParam())[j];
            }
        }
        for(int j=0; j<NO;j++){
            temp[j] = temp[j]/v.size();
        }
        return temp;
    }
    
    //获取距离平方和
    public double getDistanceSum(Sample s){
        double distanceSum=0.0;
        double distance = 0.0;
        int NO = s.getParmNo();
        for(int i =0; i<NO; i++){
            distance = Math.pow((s.getParam()[i]-getWeightPoint()[i]),2);
            distanceSum = distance + distanceSum;
        }
        return distanceSum;
    }
    
    public double getClusterDistanceSum(){
        double clusterDistanceSum=0.0;
        for(int i=0; i<v.size();i++){
            clusterDistanceSum=clusterDistanceSum+getDistanceSum((Sample)v.elementAt(i));
        }
        return clusterDistanceSum;
    }
    
}