hadoop - 无法在 Mahout 中实例化类型 Cluster、KMean 聚类示例

Question

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

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

报错

Cannot instantiate the Type Cluster

(这是一个接口(interface)，我的理解)。我想在我的样本数据集上运行 kmeans，任何人都可以指导我吗？

我在我的 EClipse IDE 中包含了以下 Jar

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

这里附上我的全部代码，摘自 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();  
    }
}

还指导我，如果我有自己的数据集，该如何处理。

Answer 1

我也一直在尝试使 Mahout in Action 一书中的这个示例起作用。我最终做到了。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错误的。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的:Using mahout in eclipse WITHOUT USING MAVEN

更新:好的，书的内容没有错，只是旧了。此页面包含指向本书中更新代码的链接

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