Hello World聚类代码(一)

使用Mahout 聚类 如下

package org.test.mymahout.chapter07;

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.clustering.WeightedVectorWritable;

import org.apache.mahout.clustering.kmeans.Cluster;

import org.apache.mahout.clustering.kmeans.KMeansDriver;

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;

/**

*

* <p>Title:SimpleKMeansClustering </p>

* <p>Description:hellworld 聚类 </p>、

* @date 2016年3月4日上午10:54:08

*/

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();

}

//生成向量

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();

//WeightedPropertyVectorWritable value = new WeightedPropertyVectorWritable();

WeightedVectorWritable value = new WeightedVectorWritable();

while (reader.next(key, value)) {

System.out.println(value.toString() + " belongs to cluster "

+ key.toString());

}

reader.close();

}



}