Package org.apache.mahout.common.distance

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

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    if (hasOption(DefaultOptionCreator.OVERWRITE_OPTION)) {
      HadoopUtil.delete(getConf(), output);
    }
    boolean emitMostLikely = Boolean.parseBoolean(getOption(DefaultOptionCreator.EMIT_MOST_LIKELY_OPTION));
    double threshold = Double.parseDouble(getOption(DefaultOptionCreator.THRESHOLD_OPTION));
    DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class);

    if (hasOption(DefaultOptionCreator.NUM_CLUSTERS_OPTION)) {
      clusters = RandomSeedGenerator.buildRandom(getConf(),
                                                 input,
                                                 clusters,
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    int maxIterations = Integer
        .parseInt(getOption(DefaultOptionCreator.MAX_ITERATIONS_OPTION));
    boolean inputIsCanopies = hasOption(INPUT_IS_CANOPIES_OPTION);
    boolean runSequential = getOption(DefaultOptionCreator.METHOD_OPTION)
        .equalsIgnoreCase(DefaultOptionCreator.SEQUENTIAL_METHOD);
    DistanceMeasure measure = ClassUtils.instantiateAs(measureClass, DistanceMeasure.class);
    IKernelProfile kernelProfile = ClassUtils.instantiateAs(kernelProfileClass, IKernelProfile.class);
    run(getConf(), input, output, measure, kernelProfile, t1, t2,
        convergenceDelta, maxIterations, inputIsCanopies, runClustering,
        runSequential);
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      Class<? extends Vector> vcl = ccl.loadClass(modelPrototype).asSubclass(Vector.class);
      Constructor<? extends Vector> v = vcl.getConstructor(int.class);
      modelDistribution.setModelPrototype(new VectorWritable(v.newInstance(prototypeSize)));

      if (modelDistribution instanceof DistanceMeasureClusterDistribution) {
        DistanceMeasure measure = ClassUtils.instantiateAs(distanceMeasure, DistanceMeasure.class);
        ((DistanceMeasureClusterDistribution) modelDistribution).setMeasure(measure);
      }
    } catch (ClassNotFoundException cnfe) {
      throw new IllegalStateException(cnfe);
    } catch (NoSuchMethodException nsme) {
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public final class TestClusterClassifier extends MahoutTestCase {
 
  private static ClusterClassifier newDMClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(1), 0,
        measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2), 1, measure));
    models.add(new DistanceMeasureCluster(new DenseVector(2).assign(-1), 2,
        measure));
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  }
 
  private void topLevelClustering(Path pointsPath, Configuration conf) throws IOException,
                                                                      InterruptedException,
                                                                      ClassNotFoundException {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    CanopyDriver.run(conf, pointsPath, outputPathForCanopy, measure, 4.0, 3.0, true, true);
    Path clustersIn = new Path(outputPathForCanopy, new Path(Cluster.CLUSTERS_DIR + '0'
                                                                   + Cluster.FINAL_ITERATION_SUFFIX));
    KMeansDriver.run(conf, pointsPath, clustersIn, outputPathForKMeans, measure, 1, 1, true, true);
  }
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    return new ClusterClassifier(models);
  }
 
  private static ClusterClassifier newClusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
        2).assign(1), 0, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
        2), 1, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Cluster(new DenseVector(
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    return new ClusterClassifier(models);
  }
 
  private static ClusterClassifier newSoftClusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new SoftCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new SoftCluster(new DenseVector(2), 1, measure));
    models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models);
  }
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  }
 
  @Test
  public void testCanopyClassification() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new Canopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new Canopy(new DenseVector(2), 1, measure));
    models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
    ClusterClassifier classifier = new ClusterClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
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  }
 
  @Test(expected = UnsupportedOperationException.class)
  public void testMSCanopyClassification() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
    ClusterClassifier classifier = new ClusterClassifier(models);
    classifier.classify(new DenseVector(2));
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  /** Story: Test the reference implementation */
  @Test
  public void testReferenceImplementation() throws Exception {
    List<Vector> points = getPoints(REFERENCE);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    // try all possible values of k
    for (int k = 0; k < points.size(); k++) {
      System.out.println("Test k=" + (k + 1) + ':');
      // pick k initial cluster centers at random
      List<Cluster> clusters = Lists.newArrayList();
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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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