无法实例化类型集群，Mahout 中的 KMean 集群示例

Posted 2023-03-12

【中文标题】无法实例化类型集群，Mahout 中的 KMean 集群示例

【英文标题】：Cannot Instantiate The type Cluster, KMean clustering Example in Mahout

【发布时间】：2013-06-01 23:49:53

【问题描述】：

您好，我试图在 Mahout 中运行 KmeanClustering 示例，但遇到示例代码中的错误。我在下面的代码片段中遇到错误

Cluster cluster = new Cluster(vec, i, new EuclideanDistanceMeasure());

报错

无法实例化类型集群

（这是一个接口，我的理解）。我想在我的示例数据集上运行 kmeans，任何人都可以指导我吗。

我在我的 EClipse IDE 中包含了以下罐子

mahout-math-0.7-cdh4.3.0.jar

hadoop-common-2.0.0-cdh4.2.1.jar

hadoop-hdfs-2.0.0-cdh4.2.1.jar

hadoop-mapreduce-client-core-2.0.0-cdh4.2.1.jar

mahout-core-0.7-cdh4.3.0.jar

检查我是否缺少任何必要的 jar，我将在 Hadoop CDH4.2.1 上运行它

这里附上我的整个代码，取自Github

package tryout;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.classify.WeightedVectorWritable;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;

public class SimpleKMeansClustering 
    public static final double[][] points =  1, 1, 2, 1, 1, 2, 
                                              2, 2, 3, 3, 8, 8,
                                              9, 8, 8, 9, 9, 9;    


    public static void writePointsToFile(List<Vector> points,
            String fileName,FileSystem fs,Configuration conf) throws IOException     
        Path path = new Path(fileName);    
        SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,path, LongWritable.class, VectorWritable.class);    

        long recNum = 0;    
        VectorWritable vec = new VectorWritable();    
        for (Vector point : points)       
         vec.set(point);      
          writer.append(new LongWritable(recNum++), vec);    
            writer.close();  
        

    public static List<Vector> getPoints(double[][] raw)     
        List<Vector> points = new ArrayList<Vector>();    
        for (int i = 0; i < raw.length; i++)       
            double[] fr = raw[i];      
            Vector vec = new RandomAccessSparseVector(fr.length);      
            vec.assign(fr);      
            points.add(vec);    
            
        return points;  
        
    public static void main(String args[]) throws Exception         
        int k = 2;        
        List<Vector> vectors = getPoints(points);        
        File testData = new File("testdata");    
        if (!testData.exists())       
            testData.mkdir();    
            
        testData = new File("testdata/points");    
        if (!testData.exists())       
            testData.mkdir();    
                
        Configuration conf = new Configuration();    
        FileSystem fs = FileSystem.get(conf);    
        writePointsToFile(vectors, "testdata/points/file1", fs, conf);        
        Path path = new Path("testdata/clusters/part-00000");    
        SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,path, Text.class, Cluster.class);
        for (int i = 0; i < k; i++)       
            Vector vec = vectors.get(i);      
            Cluster cluster = new Cluster(vec, i, new EuclideanDistanceMeasure());      
            writer.append(new Text(cluster.getIdentifier()), cluster);    
            
        writer.close();        


        KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"),      
                new Path("output"), new EuclideanDistanceMeasure(), 0.001, 10,
                true, false);        
        SequenceFile.Reader reader = new SequenceFile.Reader(fs,new Path("output/" + Cluster.CLUSTERED_POINTS_DIR+ "/part-m-00000"), conf);        
        IntWritable key = new IntWritable();   
        WeightedVectorWritable value = new WeightedVectorWritable();    
        while (reader.next(key, value))       
            System.out.println(value.toString() + " belongs to cluster " + key.toString());    
            
        reader.close();  
    

如果我有自己的数据集，也请指导我。

【问题讨论】：

如果我没记错的话你需要Kluster这个类。

谢谢Thomas 我做了修改，现在我可以用正确的Class文件和jar文件编译代码了。

【参考方案1】：

我也一直在尝试让 Mahout in Action 书中的这个例子发挥作用。我最终成功了。这是我所做的：

SequenceFile.Writer writer= new SequenceFile.Writer(fs, conf, path, Text.class, Kluster.class);
for (int i = 0; i < k; i++) 
Vector vec = vectors.get(i);
Kluster cluster = new Kluster(vec, i, new EuclideanDistanceMeasure());
writer.append(new Text(Kluster.getIdentifier()), cluster);

我不敢相信书中的代码是不正确的。我还设法在不使用 Maven 的情况下让它工作。我在这里更全面地描述了这一点，但基本上我是用用户库做到的：Using mahout in eclipse WITHOUT USING MAVEN

更新：好的，书的内容没有错，但是老了。此页面包含本书中更新代码的链接

http://alexott.blogspot.co.uk/2012/07/getting-started-with-examples-from.html