我已经下载了k-means（in hadoopmapreduce）开源。但是，它有编译错误。
---------------------源----------------------------
/*
* Copyright 2012
* Parallel and Distributed Systems Group (PVS)
* Institute of Computer Science (IFI)
* Heidelberg University
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package examples;
import algorithms.kmeans.Cluster;
import algorithms.kmeans.Clusters;
import algorithms.kmeans.SamplesCache;
import org.apache.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.DenseVectorWritable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.StringTokenizer;
public class KMeansHadoop {
private final static Logger LOG = LoggerFactory.getLogger(KMeansHadoop.class);
public static class KMeansMapper extends
MRMapper<LongWritable, Text, IntWritable, Clusters, Clusters> {
private SamplesCache cache = new SamplesCache(500);
private int cacheSize = 10000;
private Clusters clusters = null;
private int k = 0;
private int nextCentroidToInit = 0;
/**
* Configures the mapper by reading two configuration options:
* - "numClusters": the k in k-Means
* - "numAuxClusters": the number of in-memory auxiliary clusters representing the input data
*
* @param context the mapper context, used to access the configuration
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void setup(Context context) throws IOException, InterruptedException {
super.setup(context);
Configuration conf = context.getConfiguration();
this.k = conf.getInt("numCluster", 5);
this.clusters = new Clusters(k);
this.cacheSize = conf.getInt("numAuxCluster", 500);
this.cache = new SamplesCache(cacheSize);
}
/**
* Maps the input lines to initial centroids and, as a side-effect, stores auxiliary clusters representing the
* input data in memory
*
* @param key the key provided by the input format, not used here
* @param value one line of the input; input format: one data point per line, vector components delimited by spaces
* @param context the mapper context used to send initial centroids to the reducer
* @throws IOException
* @throws InterruptedException
*/
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// Input format: one data point per line, components delimited by spaces
final List<Double> doubleValues = new ArrayList<Double>();
final StringTokenizer tk = new StringTokenizer(value.toString());
while(tk.hasMoreElements()) {
final String token = tk.nextToken();
doubleValues.add(Double.parseDouble(token));
}
double[] dv = new double[doubleValues.size()];
for(int i=0; i<doubleValues.size(); i++) {
dv[i] = doubleValues.get(i);
}
DenseVector dvec = new DenseVector(dv);
DenseVectorWritable sample = new DenseVectorWritable(dvec);
// add sample to local auxiliary clusters
this.cache.addSample(sample);
// first k points are chosen as initial centroids
if (nextCentroidToInit < k) {
this.clusters.set(nextCentroidToInit, new Cluster(sample, sample));
this.nextCentroidToInit += 1;
} else if (nextCentroidToInit == k) {
// send initial centroids to reducer
context.write(new IntWritable(0), this.clusters);
this.nextCentroidToInit += 1;
}
}
/**
* Remaps the input data when a new set of preliminary clusters is received from the reducer by recalculating
* the assignment of the local input data, as represented by the auxiliary clusters, to the preliminary clusters
* and sends the updated centroids to the reducer.
* @param cs the preliminary clusters computed by the reducer
* @param context the mapper context used to send the locally recomputed centroids to the reducer
* @throws IOException
* @throws InterruptedException
*/
public void remap(List<Clusters> cs, Context context) throws IOException, InterruptedException {
LOG.info("Remapping preliminary clusters");
// set the preliminary clusters as new clusters
this.clusters = cs.get(0).clone();
this.clusters.reset();
// reassign the local input data, represented by the auxiliary clusters, to the clusters, thereby readjusting
// the clusters centroids
this.cache.reAssignAll(clusters);
// send the locally updated clusters to the reducer
context.write(new IntWritable(0), this.clusters);
}
}
public static class KMeansReducer extends
MRReducer<IntWritable, Clusters, IntWritable, Clusters, Clusters> {
private double lastError = Double.MAX_VALUE;
private float epsilon = Float.MAX_VALUE;
/**
* Configures the mapper by reading the configuration option "epsilon": The minimum change of the MSE needed to
* trigger a new iteration.
*
* @param context the reducer context, used to access the configuration
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void setup(Context context) throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
epsilon = conf.getFloat("epsilon", 100f);
}
/**
* Reduces a list of clusters locally computed by the mappers into a preliminary global set of clusters, which
* is then restreamed to the mappers, or, iff the MSE of the global set of clusters has not changed by more than
* epsilon since the last reduce invocation ends the iteration by emiting the final set of clusters.
*
* @param key the key set by the mapper, not used here
* @param values the list of locally computed clusters computed by the mappers
* @param context the reducer context, used to restream preliminary clusters to the mappers and emit the final
* clusters
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void reduce(IntWritable key, Iterable<Clusters> values,
MRReduceContext<IntWritable, Clusters, IntWritable, Clusters, Clusters> context) throws IOException, InterruptedException {
// Merge the list of clusters into one set of clusters
Clusters results = null;
for(Clusters clusters : values) {
if( results == null ) {
results = clusters;
} else {
results.merge(clusters);
}
}
Double error = results.getMSE();
LOG.info("Last error " + lastError + ", current error " + error);
if (lastError < Double.MAX_VALUE &&
error <= lastError + epsilon &&
error >= lastError - epsilon) {
// MSE has changed by less than epsilon: Emit final result
context.write(new IntWritable(0), results);
LOG.info("Final result written.");
} else {
// MSE has changed by more than epsilon: Send recomputed preliminary clusters to mappers to start a new
// iteration
this.lastError = error;
results.computeNewCentroids();
context.restream(results);
LOG.info("Preliminary result restreamed.");
}
}
}
/**
* Executes the streaming Hadoop MapReduce program
* @param args first arg is input path, second arg is output path
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.setBoolean("mrstreamer.hadoop.streaming", true);
// has to be 1 to ensure the algorithm producing valid results
conf.setInt(JobContext.NUM_REDUCES, 1);
conf.setInt(JobContext.NUM_MAPS, 4);
conf.set("numCluster", "5");
conf.set("numAuxCluster", "500");
Job job = new MRSJob(conf, "kmeanshadoop");
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Clusters.class);
job.setMapperClass(KMeansMapper.class);
job.setReducerClass(KMeansReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}
---------------------------错误------------------------------
Exception in thread "main" java.lang.Error: Unresolved compilation problems:
NUM_REDUCES cannot be resolved or is not a field
NUM_MAPS cannot be resolved or is not a field
在例子中。KMeansHadoop.main（KMeansHadoop.java：222）
可能，你使用的Hadoop版本与此代码的作者不同。它应该被以下行覆盖：
import org.apache.hadoop.mapred.JobContext;
使用这些设置，请更新到 Hadoop版本 2.2.0（或更高版本）。
否则，您可以在旧 API 上使用以下命令代替这两个命令：
conf.setNumReduceTasks(1);
conf.setNumMapTasks(4); //but this is only a suggestion to hadoop