mahout学习之二——mahout0.9kmeans聚类实例

最近学习《Mahout实战》，但是书中的代码是实用mahout0.5版本，很多地方在mahout0.9版本中已经改头换面了，经调试，阅读mahout0.9api，

运行结果如图：

修改代码如下：

package cn.kelaile.hadooptest;


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.clustering.Cluster;
import org.apache.mahout.clustering.classify.WeightedPropertyVectorWritable;
import org.apache.mahout.clustering.kmeans.KMeansDriver;
import org.apache.mahout.clustering.kmeans.Kluster;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;

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

public class TestKMeansClusteringBy9 {

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("clustering/testdata");
if (!testData.exists()) {
testData.mkdir();
}
testData = new File("clustering/testdata/points");
if (!testData.exists()) {
testData.mkdir();
}

Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
writePointsToFile(vectors, "clustering/testdata/points/file1", fs, conf);

Path path = new Path("clustering/testdata/clusters/part-00000");
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(cluster.getIdentifier()), cluster);
}
writer.close();

KMeansDriver.run(conf,
new Path("clustering/testdata/points"),
new Path("clustering/testdata/clusters"),
new Path("clustering/output"),
0.001,
10,
true,
0,
true);
SequenceFile.Reader reader = new SequenceFile.Reader(fs,
new Path("clustering/output/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-0"), conf);

IntWritable key = new IntWritable();
WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();
while (reader.next(key, value)) {
System.out.println(value.toString() + " belongs to cluster " + key.toString());
}
reader.close();
}

}