MapReduce Kmeans聚类算法

最近在网上查看用MapReduce实现的Kmeans算法，例子是不错，http://blog.csdn.net/jshayzf/article/details/22739063

但注释太少了，而且参数太多，如果新手学习的话不太好理解。所以自己按照个人的理解写了一个简单的例子并添加了详细的注释。

大致的步骤是：

1，Map每读取一条数据就与中心做对比，求出该条记录对应的中心，然后以中心的ID为Key，该条数据为value将数据输出。

2，利用reduce的归并功能将相同的Key归并到一起，集中与该Key对应的数据，再求出这些数据的平均值，输出平均值。

3，对比reduce求出的平均值与原来的中心，如果不相同，这将清空原中心的数据文件，将reduce的结果写到中心文件中。（中心的值存在一个HDFS的文件中）

删掉reduce的输出目录以便下次输出。

继续运行任务。

4，对比reduce求出的平均值与原来的中心，如果相同。则删掉reduce的输出目录，运行一个没有reduce的任务将中心ID与值对应输出。

 package MyKmeans;

 import java.io.IOException;
 import java.util.ArrayList;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.Text;

 import java.util.Arrays;
 import java.util.Iterator;

 import org.apache.hadoop.io.IntWritable;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.mapreduce.Job;
 import org.apache.hadoop.mapreduce.Mapper;
 import org.apache.hadoop.mapreduce.Reducer;
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

 public class MapReduce {

     public static class Map extends Mapper<LongWritable, Text, IntWritable, Text>{

         //中心集合
         ArrayList<ArrayList<Double>> centers = null;
         //用k个中心
         int k = 0;

         //读取中心
         protected void setup(Context context) throws IOException,
                 InterruptedException {
             centers = Utils.getCentersFromHDFS(context.getConfiguration().get("centersPath"),false);
             k = centers.size();
         }

         /**
          * 1.每次读取一条要分类的条记录与中心做对比，归类到对应的中心
          * 2.以中心ID为key，中心包含的记录为value输出(例如： 1 0.2 。  1为聚类中心的ID，0.2为靠近聚类中心的某个值)
          */
         protected void map(LongWritable key, Text value, Context context)
                 throws IOException, InterruptedException {
             //读取一行数据
             ArrayList<Double> fileds = Utils.textToArray(value);
             int sizeOfFileds = fileds.size();

             double minDistance = 99999999;
             int centerIndex = 0;

             //依次取出k个中心点与当前读取的记录做计算
             for(int i=0;i<k;i++){
                 double currentDistance = 0;
                 for(int j=0;j<sizeOfFileds;j++){
                     double centerPoint = Math.abs(centers.get(i).get(j));
                     double filed = Math.abs(fileds.get(j));
                     currentDistance += Math.pow((centerPoint - filed) / (centerPoint + filed), 2);
                 }
                 //循环找出距离该记录最接近的中心点的ID
                 if(currentDistance<minDistance){
                     minDistance = currentDistance;
                     centerIndex = i;
                 }
             }
             //以中心点为Key 将记录原样输出
             context.write(new IntWritable(centerIndex+1), value);
         }

     }

     //利用reduce的归并功能以中心为Key将记录归并到一起
     public static class Reduce extends Reducer<IntWritable, Text, Text, Text>{

         /**
          * 1.Key为聚类中心的ID value为该中心的记录集合
          * 2.计数所有记录元素的平均值，求出新的中心
          */
         protected void reduce(IntWritable key, Iterable<Text> value,Context context)
                 throws IOException, InterruptedException {
             ArrayList<ArrayList<Double>> filedsList = new ArrayList<ArrayList<Double>>();

             //依次读取记录集，每行为一个ArrayList<Double>
             for(Iterator<Text> it =value.iterator();it.hasNext();){
                 ArrayList<Double> tempList = Utils.textToArray(it.next());
                 filedsList.add(tempList);
             }

             //计算新的中心
             //每行的元素个数
             int filedSize = filedsList.get(0).size();
             double[] avg = new double[filedSize];
             for(int i=0;i<filedSize;i++){
                 //求没列的平均值
                 double sum = 0;
                 int size = filedsList.size();
                 for(int j=0;j<size;j++){
                     sum += filedsList.get(j).get(i);
                 }
                 avg[i] = sum / size;
             }
             context.write(new Text("") , new Text(Arrays.toString(avg).replace("[", "").replace("]", "")));
         }

     }

     @SuppressWarnings("deprecation")
     public static void run(String centerPath,String dataPath,String newCenterPath,boolean runReduce) throws IOException, ClassNotFoundException, InterruptedException{

         Configuration conf = new Configuration();
         conf.set("centersPath", centerPath);

         Job job = new Job(conf, "mykmeans");
         job.setJarByClass(MapReduce.class);

         job.setMapperClass(Map.class);

         job.setMapOutputKeyClass(IntWritable.class);
         job.setMapOutputValueClass(Text.class);

         if(runReduce){
             //最后依次输出不许要reduce
             job.setReducerClass(Reduce.class);
             job.setOutputKeyClass(Text.class);
             job.setOutputValueClass(Text.class);
         }

         FileInputFormat.addInputPath(job, new Path(dataPath));

         FileOutputFormat.setOutputPath(job, new Path(newCenterPath));

         System.out.println(job.waitForCompletion(true));
     }

     public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException {
         String centerPath = "hdfs://localhost:9000/input/centers.txt";
         String dataPath = "hdfs://localhost:9000/input/wine.txt";
         String newCenterPath = "hdfs://localhost:9000/out/kmean";

         int count = 0;

         while(true){
             run(centerPath,dataPath,newCenterPath,true);
             System.out.println(" 第 " + ++count + " 次计算 ");
             if(Utils.compareCenters(centerPath,newCenterPath )){
                 run(centerPath,dataPath,newCenterPath,false);
                 break;
             }
         }
     }

 }

 package MyKmeans;

 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.List;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.FSDataInputStream;
 import org.apache.hadoop.fs.FSDataOutputStream;
 import org.apache.hadoop.fs.FileStatus;
 import org.apache.hadoop.fs.FileSystem;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.IOUtils;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.util.LineReader;

 public class Utils {

     //读取中心文件的数据
     public static ArrayList<ArrayList<Double>> getCentersFromHDFS(String centersPath,boolean isDirectory) throws IOException{

         ArrayList<ArrayList<Double>> result = new ArrayList<ArrayList<Double>>();

         Path path = new Path(centersPath);

         Configuration conf = new Configuration();

         FileSystem fileSystem = path.getFileSystem(conf);

         if(isDirectory){
             FileStatus[] listFile = fileSystem.listStatus(path);
             for (int i = 0; i < listFile.length; i++) {
                 result.addAll(getCentersFromHDFS(listFile[i].getPath().toString(),false));
             }
             return result;
         }

         FSDataInputStream fsis = fileSystem.open(path);
         LineReader lineReader = new LineReader(fsis, conf);

         Text line = new Text();

         while(lineReader.readLine(line) > 0){
             ArrayList<Double> tempList = textToArray(line);
             result.add(tempList);
         }
         lineReader.close();
         return result;
     }

     //删掉文件
     public static void deletePath(String pathStr) throws IOException{
         Configuration conf = new Configuration();
         Path path = new Path(pathStr);
         FileSystem hdfs = path.getFileSystem(conf);
         hdfs.delete(path ,true);
     }

     public static ArrayList<Double> textToArray(Text text){
         ArrayList<Double> list = new ArrayList<Double>();
         String[] fileds = text.toString().split(",");
         for(int i=0;i<fileds.length;i++){
             list.add(Double.parseDouble(fileds[i]));
         }
         return list;
     }

     public static boolean compareCenters(String centerPath,String newPath) throws IOException{

         List<ArrayList<Double>> oldCenters = Utils.getCentersFromHDFS(centerPath,false);
         List<ArrayList<Double>> newCenters = Utils.getCentersFromHDFS(newPath,true);

         int size = oldCenters.size();
         int fildSize = oldCenters.get(0).size();
         double distance = 0;
         for(int i=0;i<size;i++){
             for(int j=0;j<fildSize;j++){
                 double t1 = Math.abs(oldCenters.get(i).get(j));
                 double t2 = Math.abs(newCenters.get(i).get(j));
                 distance += Math.pow((t1 - t2) / (t1 + t2), 2);
             }
         }

         if(distance == 0.0){
             //删掉新的中心文件以便最后依次归类输出
             Utils.deletePath(newPath);
             return true;
         }else{
             //先清空中心文件，将新的中心文件复制到中心文件中，再删掉中心文件

             Configuration conf = new Configuration();
             Path outPath = new Path(centerPath);
             FileSystem fileSystem = outPath.getFileSystem(conf);

             FSDataOutputStream overWrite = fileSystem.create(outPath,true);
             overWrite.writeChars("");
             overWrite.close();

             Path inPath = new Path(newPath);
             FileStatus[] listFiles = fileSystem.listStatus(inPath);
             for (int i = 0; i < listFiles.length; i++) {
                 FSDataOutputStream out = fileSystem.create(outPath);
                 FSDataInputStream in = fileSystem.open(listFiles[i].getPath());
                 IOUtils.copyBytes(in, out, 4096, true);
             }
             //删掉新的中心文件以便第二次任务运行输出
             Utils.deletePath(newPath);
         }

         return false;
     }
 }

数据集   http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

运行结果可以与http://blog.csdn.net/jshayzf/article/details/22739063的结果做对比（前提是初始的中心相同）