public class KMeansCluster {
private int k;//簇的个数
private int num = 100000;//迭代次数
private List<double> datas;//原始样本集
private String address;//样本集路径
private List<point> data = new ArrayList<point>();
private AbstractDistance distance = new AbstractDistance() {
@Override
public double getDis(Point p1, Point p2) {
//欧几里德距离
return Math.sqrt(Math.pow(p1.getX() - p2.getX(), 2) + Math.pow(p1.getY() - p2.getY(), 2));
}
};
public KMeansCluster(int k, int num, String address) {
this.k = k;
this.num = num;
this.address = address;
}
public KMeansCluster(int k, String address) {
this.k = k;
this.address = address;
}
public KMeansCluster(int k, List<double> datas) {
this.k = k;
this.datas = datas;
}
public KMeansCluster(int k, int num, List<double> datas) {
this.k = k;
this.num = num;
this.datas = datas;
}
private void check() {
if (k == 0)
throw new IllegalArgumentException("k must be the number > 0");
if (address == null && datas == null)
throw new IllegalArgumentException("program can't get real data");
}
/**
* 初始化数据
*
* @throws java.io.FileNotFoundException
*/
public void init() throws FileNotFoundException {
check();
//读取文件，init data
//处理原始数据
for (int i = 0, j = datas.size(); i < j; i++)
data.add(new Point(i, datas.get(i), 0));
}
/**
* 第一次随机选取中心点
*
* @return
*/
public Set<point> chooseCenter() {
Set<point> center = new HashSet<point>();
Random ran = new Random();
int roll = 0;
while (center.size() < k) {
roll = ran.nextInt(data.size());
center.add(data.get(roll));
}
return center;
}
/**
* @param center
* @return
*/
public List<cluster> prepare(Set<point> center) {
List<cluster> cluster = new ArrayList<cluster>();
Iterator<point> it = center.iterator();
int id = 0;
while (it.hasNext()) {
Point p = it.next();
if (p.isBeyond()) {
Cluster c = new Cluster(id++, p);
c.addPoint(p);
cluster.add(c);
} else
cluster.add(new Cluster(id++, p));
}
return cluster;
}
/**
* 第一次运算，中心点为样本值
*
* @param center
* @param cluster
* @return
*/
public List<cluster> clustering(Set<point> center, List<cluster> cluster) {
Point[] p = center.toArray(new Point[0]);
TreeSet<distence> distence = new TreeSet<distence>();//存放距离信息
Point source;
Point dest;
boolean flag = false;
for (int i = 0, n = data.size(); i < n; i++) {
distence.clear();
for (int j = 0; j < center.size(); j++) {
if (center.contains(data.get(i)))
break;
flag = true;
// 计算距离
source = data.get(i);
dest = p[j];
distence.add(new Distence(source, dest, distance));
}
if (flag == true) {
Distence min = distence.first();
for (int m = 0, k = cluster.size(); m < k; m++) {
if (cluster.get(m).getCenter().equals(min.getDest()))
cluster.get(m).addPoint(min.getSource());
}
}
flag = false;
}
return cluster;
}
/**
* 迭代运算，中心点为簇内样本均值
*
* @param cluster
* @return
*/
public List<cluster> cluster(List<cluster> cluster) {
// double error;
Set<point> lastCenter = new HashSet<point>();
for (int m = 0; m < num; m++) {
// error = 0;
Set<point> center = new HashSet<point>();
// 重新计算聚类中心
for (int j = 0; j < k; j++) {
List<point> ps = cluster.get(j).getMembers();
int size = ps.size();
if (size < 3) {
center.add(cluster.get(j).getCenter());
continue;
}
// 计算距离
double x = 0.0, y = 0.0;
for (int k1 = 0; k1 < size; k1++) {
x += ps.get(k1).getX();
y += ps.get(k1).getY();
}
//得到新的中心点
Point nc = new Point(-1, x / size, y / size, false);
center.add(nc);
}
if (lastCenter.containsAll(center))//中心点不在变化，退出迭代
break;
lastCenter = center;
// 迭代运算
cluster = clustering(center, prepare(center));
// for (int nz = 0; nz < k; nz++) {
// error += cluster.get(nz).getError();//计算误差
// }
}
return cluster;
}
/**
* 输出聚类信息到控制台
*
* @param cs
*/
public void out2console(List<cluster> cs) {
for (int i = 0; i < cs.size(); i++) {
System.out.println("No." + (i + 1) + " cluster:");
Cluster c = cs.get(i);
List<point> p = c.getMembers();
for (int j = 0; j < p.size(); j++) {
System.out.println("\t" + p.get(j).getX() + " ");
}
System.out.println();
}
}
}
