最近在网上查看用MapReduce实现的Kmeans算法，例子是不错，http://blog.****.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.****.net/jshayzf/article/details/22739063的结果做对比（前提是初始的中心相同）