Package org.apache.mahout.common.distance

Examples of org.apache.mahout.common.distance.DistanceMeasure

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      HadoopUtil.overwriteOutput(output);
    }
    boolean emitMostLikely = Boolean.parseBoolean(getOption(DefaultOptionCreator.EMIT_MOST_LIKELY_OPTION));
    double threshold = Double.parseDouble(getOption(DefaultOptionCreator.THRESHOLD_OPTION));
    ClassLoader ccl = Thread.currentThread().getContextClassLoader();
    DistanceMeasure measure = ccl.loadClass(measureClass).asSubclass(DistanceMeasure.class).newInstance();

    if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {
      clusters = RandomSeedGenerator.buildRandom(input, clusters, Integer.parseInt(parseArguments(args)
          .get(DefaultOptionCreator.NUM_CLUSTERS_OPTION)), measure);
    }
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    Constructor<? extends Vector> v = vcl.getConstructor(int.class);
    modelDistribution.setModelPrototype(new VectorWritable(v.newInstance(prototypeSize)));

    if (modelDistribution instanceof DistanceMeasureClusterDistribution) {
      Class<? extends DistanceMeasure> measureCl = ccl.loadClass(distanceMeasure).asSubclass(DistanceMeasure.class);
      DistanceMeasure measure = measureCl.newInstance();
      ((DistanceMeasureClusterDistribution) modelDistribution).setMeasure(measure);
    }
    return modelDistribution;
  }
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    return null;
  }

  @Test
  public void testCanopy() throws Exception { // now run the Job
    DistanceMeasure measure = new EuclideanDistanceMeasure();

    Path output = getTestTempDirPath("output");
    CanopyDriver.run(new Configuration(), getTestTempDirPath("testdata"), output, measure, 8, 4, true, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-0"), new Path(output, "clusteredPoints"));
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testKmeans() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, getTestTempDirPath("testdata"), output, measure, 8, 4, false, false);
    // now run the KMeans job
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testFuzzyKmeans() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // now run the Canopy job to prime kMeans canopies
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    CanopyDriver.run(conf, getTestTempDirPath("testdata"), output, measure, 8, 4, false, false);
    // now run the Fuzzy KMeans job
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testMeanShift() throws Exception {
    DistanceMeasure measure = new CosineDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Configuration conf = new Configuration();
    new MeanShiftCanopyDriver().run(conf, getTestTempDirPath("testdata"), output, measure, 0.5, 0.01, 0.05, 10, false, true, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf, output, 10), new Path(output, "clusteredPoints"));
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testKmeansSVD() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    int desiredRank = 15;
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration conf = new Configuration();
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testKmeansDSVD() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    int desiredRank = 13;
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration config = new Configuration();
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    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testKmeansDSVD2() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    Path output = getTestTempDirPath("output");
    Path tmp = getTestTempDirPath("tmp");
    int desiredRank = 13;
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration config = new Configuration();
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  }

  public static void main(String[] args) throws Exception {
    t1 = 1.5;
    t2 = 0.5;
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    significance = 0.02;

    Path samples = new Path("samples");
    Path output = new Path("output");
    HadoopUtil.overwriteOutput(samples);
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Related Classes of org.apache.mahout.common.distance.DistanceMeasure

  • mia.clustering.ch10.InterClusterDistances
  • org.apache.mahout.clustering.canopy.CanopyConfigKeys
  • org.apache.mahout.clustering.canopy.CanopyDriver
  • org.apache.mahout.clustering.cdbw.TestCDbwEvaluator
  • org.apache.mahout.clustering.classify.ClusterClassificationDriver
  • org.apache.mahout.clustering.classify.ClusterClassificationMapper
  • org.apache.mahout.clustering.ClusteringUtils
  • org.apache.mahout.clustering.dirichlet.DirichletDriver
  • org.apache.mahout.clustering.dirichlet.models.DistributionDescription
  • org.apache.mahout.clustering.display.DisplayFuzzyKMeans
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