/**

 * 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="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