网站的建设特色,网站建设服务器都有哪些,品牌网站建设美丽,建筑设计怎么学K-中心点聚类算法
#xff08;1#xff09;任意选择k个对象作为初始的簇中心点 #xff08;2#xff09;指派每个剩余对象给离他最近的中心点所表示的簇 #xff08;3#xff09;选择一个未被选择的中心点直到所有的中心点都被选择过 #xff08;4#xff09;选择一个…K-中心点聚类算法
1任意选择k个对象作为初始的簇中心点 2指派每个剩余对象给离他最近的中心点所表示的簇 3选择一个未被选择的中心点直到所有的中心点都被选择过 4选择一个未被选择过的非中心点对象计算用代替的总代价并记录在S中 直到所有非中心点都被选择过。 5如果在S中的所有非中心点代替所有中心点后的计算出总代价有小于0的存在然后找出S中的用非中心点替代中心点后代价最小的一个并用该非中心点替代对应的中心点形成一个新的k个中心点的集合 6重复步骤2-5直到没有再发生簇的重新分配即所有的S都大于0.
代码
public class Cluster {private int id;// 标识private Point center;// 中心private ListPoint members new ArrayListPoint();// 成员public Cluster(int id, Point center) {this.id id;this.center center;}public Cluster(int id, Point center, ListPoint members) {this.id id;this.center center;this.members members;}public void addPoint(Point newPoint) {if (!members.contains(newPoint)){members.add(newPoint);}else{System.out.println(样本数据点 {newPoint.toString()} 已经存在);}}public float getdis() {float cur0;for (Point point : members) {curpoint.getDist()*point.getDist();}return cur;}public int getId() {return id;}public Point getCenter() {return center;}public void setCenter(Point center) {this.center center;}public ListPoint getMembers() {return members;}Overridepublic String toString() {String toString -----------Clusterthis.getId()---------\n;toStringMid_Point: center Points_num: members.size();for (Point point : members) {toString\npoint.toString();}return toString\n;}
} public class datahandler {public static Listfloat[] readTxt(String fileName){Listfloat[] listnew ArrayList();try {File filename new File(fileName); // 读取input.txt文件InputStreamReader reader new InputStreamReader(new FileInputStream(filename)); // 建立一个输入流对象readerBufferedReader br new BufferedReader(reader);String line ;line br.readLine();while (true) {line br.readLine();if(linenull) break;String[] templine.split(,);float[] cnew float[temp.length];for(int i0;itemp.length;i){c[i]Float.parseFloat(temp[i]);}list.add(c);}} catch (Exception e) {e.printStackTrace();}return list;}public static void writeTxt(String content){try { // 防止文件建立或读取失败用catch捕捉错误并打印也可以throw/* 读入TXT文件 */File writename new File(src/k/output.txt); // 相对路径如果没有则要建立一个新的output。txt文件writename.createNewFile(); // 创建新文件BufferedWriter out new BufferedWriter(new FileWriter(writename));out.write(content); // \r\n即为换行out.flush(); // 把缓存区内容压入文件out.close(); // 最后记得关闭文件} catch (Exception e) {e.printStackTrace();}}public static void main(String[] args) {
/* Listfloat[] ret readTxt(src/k/t2.txt);long sSystem.currentTimeMillis();KMeansRun kRun new KMeansRun(5, ret);SetCluster clusterSet kRun.run();System.out.println(K-means聚类算法运行时间:(System.currentTimeMillis()-s)ms);System.out.println(单次迭代运行次数 kRun.getIterTimes());StringBuilder stringBuildernew StringBuilder();for (Cluster cluster : clusterSet) {System.out.println(Mid_Point: cluster.getCenter() clusterId: cluster.getId() Points_num: cluster.getMembers().size());stringBuilder.append(cluster).append(\n);}writeTxt(stringBuilder.toString());*/Listfloat[] ret readTxt(src/k/t2.txt);XYSeries series new XYSeries(xySeries);for (int x 1; x 20; x) {KMeansRun kRun new KMeansRun(x, ret);SetCluster clusterSet kRun.run();float y 0;for (Cluster cluster : clusterSet){ycluster.getdis();}series.add(x, y);}XYSeriesCollection dataset new XYSeriesCollection();dataset.addSeries(series);JFreeChart chart ChartFactory.createXYLineChart(sum of the squared errors, // chart titleK, // x axis labelSSE, // y axis labeldataset, // dataPlotOrientation.VERTICAL,false, // include legendfalse, // tooltipsfalse // urls);ChartFrame frame new ChartFrame(my picture, chart);frame.pack();frame.setVisible(true);frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);}
}
public class DistanceCompute {/*** 求欧式距离*/public double getEuclideanDis(Point p1, Point p2) {double count_dis 0;float[] p1_local_array p1.getlocalArray();float[] p2_local_array p2.getlocalArray();if (p1_local_array.length ! p2_local_array.length) {throw new IllegalArgumentException(length of array must be equal!);}for (int i 0; i p1_local_array.length; i) {count_dis Math.pow(p1_local_array[i] - p2_local_array[i], 2);}return Math.sqrt(count_dis);}
} import java.util.*;public class KMeansRun {private int kNum; //簇的个数private int iterNum 200; //迭代次数private int iterMaxTimes 100000; //单次迭代最大运行次数private int iterRunTimes 0; //单次迭代实际运行次数private float disDiff (float) 0.01; //单次迭代终止条件两次运行中类中心的距离差private Listfloat[] original_data null; //用于存放原始数据集private static ListPoint pointList null; //用于存放原始数据集所构建的点集private DistanceCompute disC new DistanceCompute();private int len 0; //用于记录每个数据点的维度public KMeansRun(int k, Listfloat[] original_data) {this.kNum k;this.original_data original_data;this.len original_data.get(0).length;//检查规范check();//初始化点集。init();}/*** 检查规范*/private void check() {if (kNum 0){throw new IllegalArgumentException(k must be the number 0);}if (original_data null){throw new IllegalArgumentException(program cant get real data);}}/*** 初始化数据集把数组转化为Point类型。*/private void init() {pointList new ArrayListPoint();for (int i 0, j original_data.size(); i j; i){pointList.add(new Point(i, original_data.get(i)));}}/*** 随机选取中心点构建成中心类。*/private SetCluster chooseCenterCluster() {SetCluster clusterSet new HashSetCluster();Random random new Random();for (int id 0; id kNum; ) {Point point pointList.get(random.nextInt(pointList.size()));// 用于标记是否已经选择过该数据。boolean flag true;for (Cluster cluster : clusterSet) {if (cluster.getCenter().equals(point)) {flag false;}}// 如果随机选取的点没有被选中过则生成一个clusterif (flag) {Cluster cluster new Cluster(id, point);clusterSet.add(cluster);id;}}return clusterSet;}/*** 为每个点分配一个类*/public void cluster(SetCluster clusterSet){// 计算每个点到K个中心的距离并且为每个点标记类别号for (Point point : pointList) {float min_dis Integer.MAX_VALUE;for (Cluster cluster : clusterSet) {float tmp_dis (float) Math.min(disC.getEuclideanDis(point, cluster.getCenter()), min_dis);if (tmp_dis ! min_dis) {min_dis tmp_dis;point.setClusterId(cluster.getId());point.setDist(min_dis);}}}// 新清除原来所有的类中成员。把所有的点分别加入每个类别for (Cluster cluster : clusterSet) {cluster.getMembers().clear();for (Point point : pointList) {if (point.getClusterid()cluster.getId()) {cluster.addPoint(point);}}}}/*** 计算每个类的中心位置*/public boolean calculateCenter(SetCluster clusterSet) {boolean ifNeedIter false;for (Cluster cluster : clusterSet) {ListPoint point_list cluster.getMembers();float[] sumAll new float[len];// 所有点对应各个维度进行求和for (int i 0; i len; i) {for (int j 0; j point_list.size(); j) {sumAll[i] point_list.get(j).getlocalArray()[i];}}// 计算平均值for (int i 0; i sumAll.length; i) {sumAll[i] (float) sumAll[i]/point_list.size();}// 计算两个新、旧中心的距离如果任意一个类中心移动的距离大于dis_diff则继续迭代。if(disC.getEuclideanDis(cluster.getCenter(), new Point(sumAll)) disDiff){ifNeedIter true;}// 设置新的类中心位置cluster.setCenter(new Point(sumAll));}return ifNeedIter;}/*** 运行 k-means*/public SetCluster run() {SetCluster clusterSet chooseCenterCluster();boolean ifNeedIter true;while (ifNeedIter) {cluster(clusterSet);ifNeedIter calculateCenter(clusterSet);iterRunTimes ;}return clusterSet;}/*** 返回实际运行次数*/public int getIterTimes() {return iterRunTimes;}} public class Point {private float[] localArray;private int id;private int clusterId; // 标识属于哪个类中心。private float dist; // 标识和所属类中心的距离。public Point(int id, float[] localArray) {this.id id;this.localArray localArray;}public Point(float[] localArray) {this.id -1; //表示不属于任意一个类this.localArray localArray;}public float[] getlocalArray() {return localArray;}public int getId() {return id;}public void setClusterId(int clusterId) {this.clusterId clusterId;}public int getClusterid() {return clusterId;}public float getDist() {return dist;}public void setDist(float dist) {this.dist dist;}Overridepublic String toString() {String result Point_id id [;for (int i 0; i localArray.length; i) {result localArray[i] ;}return result.trim()] clusterId: clusterId;}Overridepublic boolean equals(Object obj) {if (obj null || getClass() ! obj.getClass())return false;Point point (Point) obj;if (point.localArray.length ! localArray.length)return false;for (int i 0; i localArray.length; i) {if (Float.compare(point.localArray[i], localArray[i]) ! 0) {return false;}}return true;}Overridepublic int hashCode() {float x localArray[0];float y localArray[localArray.length - 1];long temp x ! 0.0d ? Double.doubleToLongBits(x) : 0L;int result (int) (temp ^ (temp 32));temp y ! 0.0d ? Double.doubleToLongBits(y) : 0L;result 31 * result (int) (temp ^ (temp 32));return result;}
}
