/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements.  See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You 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 chen.test.kmeans;import java.util.List;import java.util.Map;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.util.ToolRunner;import org.apache.mahout.clustering.Cluster;import org.apache.mahout.clustering.canopy.CanopyDriver;import org.apache.mahout.clustering.conversion.InputDriver;import org.apache.mahout.clustering.kmeans.KMeansDriver;import org.apache.mahout.clustering.kmeans.RandomSeedGenerator;import org.apache.mahout.common.AbstractJob;import org.apache.mahout.common.ClassUtils;import org.apache.mahout.common.HadoopUtil;import org.apache.mahout.common.commandline.DefaultOptionCreator;import org.apache.mahout.common.distance.DistanceMeasure;import org.apache.mahout.common.distance.EuclideanDistanceMeasure;import org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure;import org.apache.mahout.utils.clustering.ClusterDumper;import org.slf4j.Logger;import org.slf4j.LoggerFactory;public final class TwoJob extends AbstractJob {    private static final Logger log = LoggerFactory.getLogger(TwoJob.class);    private static final String DIRECTORY_CONTAINING_CONVERTED_INPUT = "data";    private TwoJob() {  }    public static void main(String[] args) throws Exception {    if (args.length > 0) {      log.info("Running with only user-supplied arguments");      ToolRunner.run(new Configuration(), new TwoJob(), args);    } else {      log.info("Running with default arguments");      Path output = new Path("output");      Configuration conf = new Configuration();      HadoopUtil.delete(conf, output);      run(conf, new Path("testdata"), output, new EuclideanDistanceMeasure(), 2, 0.5, 10);    }  }    @Override  public int run(String[] args) throws Exception {    addInputOption();    addOutputOption();    addOption(DefaultOptionCreator.distanceMeasureOption().create());    addOption(DefaultOptionCreator.numClustersOption().create());    addOption(DefaultOptionCreator.t1Option().create());    addOption(DefaultOptionCreator.t2Option().create());    addOption(DefaultOptionCreator.convergenceOption().create());    addOption(DefaultOptionCreator.maxIterationsOption().create());    addOption(DefaultOptionCreator.overwriteOption().create());        Map<String,List<String>> argMap = parseArguments(args);    if (argMap == null) {      return -1;    }        Path input = getInputPath();    Path output = getOutputPath();    String measureClass = getOption(DefaultOptionCreator.DISTANCE_MEASURE_OPTION);    if (measureClass == null) {      measureClass = SquaredEuclideanDistanceMeasure.class.getName();    }    double convergenceDelta = Double.parseDouble(getOption(DefaultOptionCreator.CONVERGENCE_DELTA_OPTION));    int maxIterations = Integer.parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION));    if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {      HadoopUtil.delete(getConf(), output);    }    DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class);    if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {      int k = Integer.parseInt(getOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION));      run(getConf(), input, output, measure, k, convergenceDelta, maxIterations);    } else {      double t1 = Double.parseDouble(getOption(DefaultOptionCreator.T1_OPTION));      double t2 = Double.parseDouble(getOption(DefaultOptionCreator.T2_OPTION));      run(getConf(), input, output, measure, t1, t2, convergenceDelta, maxIterations);    }    return 0;  }    /**   * Run the kmeans clustering job on an input dataset using the given the number of clusters k and iteration   * parameters. All output data will be written to the output directory, which will be initially deleted if it exists.   * The clustered points will reside in the path <output>/clustered-points. By default, the job expects a file   * containing equal length space delimited data that resides in a directory named "testdata", and writes output to a   * directory named "output".   *    * @param conf   *          the Configuration to use   * @param input   *          the String denoting the input directory path   * @param output   *          the String denoting the output directory path   * @param measure   *          the DistanceMeasure to use   * @param k   *          the number of clusters in Kmeans   * @param convergenceDelta   *          the double convergence criteria for iterations   * @param maxIterations   *          the int maximum number of iterations   */  public static void run(Configuration conf, Path input, Path output, DistanceMeasure measure, int k,      double convergenceDelta, int maxIterations) throws Exception {    Path directoryContainingConvertedInput = new Path(output, DIRECTORY_CONTAINING_CONVERTED_INPUT);    log.info("Preparing Input");    InputDriver.runJob(input, directoryContainingConvertedInput, "org.apache.mahout.math.RandomAccessSparseVector");    log.info("Running random seed to get initial clusters");    Path clusters = new Path(output, "random-seeds");    clusters = RandomSeedGenerator.buildRandom(conf, directoryContainingConvertedInput, clusters, k, measure);    log.info("Running KMeans with k = {}", k);    KMeansDriver.run(conf, directoryContainingConvertedInput, clusters, output, convergenceDelta,        maxIterations, true, 0.0, false);    // run ClusterDumper    Path outGlob = new Path(output, "clusters-*-final");    Path clusteredPoints = new Path(output,"clusteredPoints");    log.info("Dumping out clusters from clusters: {} and clusteredPoints: {}", outGlob, clusteredPoints);    ClusterDumper clusterDumper = new ClusterDumper(outGlob, clusteredPoints);        //print the result    clusterDumper.printClusters(null);          }    /**   * Run the kmeans clustering job on an input dataset using the given distance measure, t1, t2 and iteration   * parameters. All output data will be written to the output directory, which will be initially deleted if it exists.   * The clustered points will reside in the path <output>/clustered-points. By default, the job expects the a file   * containing synthetic_control.data as obtained from   * http://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series resides in a directory named "testdata",   * and writes output to a directory named "output".   *    * @param conf   *          the Configuration to use   * @param input   *          the String denoting the input directory path   * @param output   *          the String denoting the output directory path   * @param measure   *          the DistanceMeasure to use   * @param t1   *          the canopy T1 threshold   * @param t2   *          the canopy T2 threshold   * @param convergenceDelta   *          the double convergence criteria for iterations   * @param maxIterations   *          the int maximum number of iterations   */  public static void run(Configuration conf, Path input, Path output, DistanceMeasure measure, double t1, double t2,      double convergenceDelta, int maxIterations) throws Exception {    Path directoryContainingConvertedInput = new Path(output, DIRECTORY_CONTAINING_CONVERTED_INPUT);    log.info("Preparing Input");    InputDriver.runJob(input, directoryContainingConvertedInput, "org.apache.mahout.math.RandomAccessSparseVector");    log.info("Running Canopy to get initial clusters");    Path canopyOutput = new Path(output, "canopies");    CanopyDriver.run(new Configuration(), directoryContainingConvertedInput, canopyOutput, measure, t1, t2, false, 0.0,        false);    log.info("Running KMeans");    KMeansDriver.run(conf, directoryContainingConvertedInput, new Path(canopyOutput, Cluster.INITIAL_CLUSTERS_DIR        + "-final"), output, convergenceDelta, maxIterations, true, 0.0, false);    // run ClusterDumper    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output,        "clusteredPoints"));    clusterDumper.printClusters(null);  }}

<project name="mahout_test" default="jar">   <property name="root.dir" value="." />   <property name="src.dir" value="${root.dir}/src" />   <property name="lib.dir" value="${root.dir}/lib" />   <property name="build.dir" value="${root.dir}/build" />      <target name="clean" depends="">      <echo>root = ${root.dir}</echo>      <delete dir="${build.dir}" />            <mkdir dir="${build.dir}" />   </target>      <target name="build" depends="clean">      <javac fork="true" debug="true" srcdir="${src.dir}" destdir="${build.dir}">         <classpath>            <fileset dir="${lib.dir}" includes="*.jar" />         </classpath>      </javac>         </target>      <target name="jar" depends="build">         <mkdir dir="${build.dir}/lib" />       <!--         <copy file="${lib.dir}/mahout-core-0.9.jar" todir="${build.dir}/lib" />         <copy file="${lib.dir}/mahout-integration-0.9.jar" todir="${build.dir}/lib" />      <copy file="${lib.dir}/hadoop-core-1.2.1.jar" todir="${build.dir}/lib" />       -->              <copy file="${lib.dir}/mahout-examples-0.9-job.jar" todir="${build.dir}/lib" />       <!--       <copy file="${lib.dir}/mahout-integration-0.9.jar" todir="${build.dir}/lib" />       -->         <jar destfile="${root.dir}/mahout_test.jar" basedir="${build.dir}" >         <manifest>            <!--             <attribute name="Main-Class" value="http://www.mamicode.com/chen/test/Job" />             -->         </manifest>         </jar>   </target>   </project>

ant -f build.xml

<copy file="${lib.dir}/mahout-examples-0.9-job.jar" todir="${build.dir}/lib" />

bin/hadoop jar /mnt/hgfs/mnt/chenfool/mahout_test.jar  chen.test.kmeans.TwoJob