Package org.apache.mahout.common.distance

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

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public final class TestClusterClassifier extends MahoutTestCase {
 
  private static ClusterClassifier newDMClassifier() {
    List<Cluster> models = new ArrayList<Cluster>();
    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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    return new ClusterClassifier(models);
  }
 
  private static ClusterClassifier newClusterClassifier() {
    List<Cluster> models = new ArrayList<Cluster>();
    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 = new ArrayList<Cluster>();
    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 = new ArrayList<Cluster>();
    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
  public void testMSCanopyClassification() {
    List<Cluster> models = new ArrayList<Cluster>();
    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);
    try {
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    job.setMapOutputValueClass(VectorWritable.class);
    Path input = getTestTempFilePath("random-input");
    Path output = getTestTempDirPath("random-output");
    ClusteringTestUtils.writePointsToFile(points, input, fs, conf);
   
    RandomSeedGenerator.buildRandom(conf, input, output, 4, new ManhattanDistanceMeasure());

    int clusterCount = 0;
    Collection<Integer> set = new HashSet<Integer>();
    for (AbstractCluster value :
         new SequenceFileValueIterable<AbstractCluster>(new Path(output, "part-randomSeed"), true, conf)) {
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  @Test
  public void testRunKMeansIterationConvergesInOneRunWithGivenDistanceThreshold() {
    double[][] rawPoints = { {0,0}, {0,0.25}, {0,0.75}, {0, 1}};
    List<Vector> points = getPoints(rawPoints);

    ManhattanDistanceMeasure distanceMeasure = new ManhattanDistanceMeasure();
    List<Cluster> clusters = Arrays.asList(
        new Cluster(points.get(0), 0, distanceMeasure),
        new Cluster(points.get(3), 3, distanceMeasure));

    // To converge in a single run, the given distance threshold should be greater than or equal to 0.125,
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    ClusteringTestUtils.writePointsToFile(points, new Path(pointsPath, "file1"), fs, conf);
    ClusteringTestUtils.writePointsToFile(points, new Path(pointsPath, "file2"), fs, conf);

    Path outputPath = getTestTempDirPath("output");
    // now run the Canopy job
    CanopyDriver.run(conf, pointsPath, outputPath, new ManhattanDistanceMeasure(), 3.1, 2.1, false, false);

    // now run the KMeans job
    KMeansDriver.run(pointsPath,
                     new Path(outputPath, "clusters-0"),
                     outputPath,
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  public DistanceMeasureClusterDistribution() {
  }

  public DistanceMeasureClusterDistribution(VectorWritable modelPrototype) {
    super(modelPrototype);
    this.measure = new ManhattanDistanceMeasure();
  }
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    ClusteringTestUtils.writePointsToFile(points, true, new Path(pointsPath, "file1"), fs, conf);
    ClusteringTestUtils.writePointsToFile(points, true, new Path(pointsPath, "file2"), fs, conf);
   
    Path outputPath = getTestTempDirPath("output");
    // now run the Canopy job
    CanopyDriver.run(conf, pointsPath, outputPath, new ManhattanDistanceMeasure(), 3.1, 2.1, false, 0.0, false);
   
    DummyOutputCollector<Text, ClusterWritable> collector1 =
        new DummyOutputCollector<Text, ClusterWritable>();

    FileStatus[] outParts = FileSystem.get(conf).globStatus(
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Related Classes of org.apache.mahout.common.distance.ManhattanDistanceMeasure

  • mia.clustering.ch09.NewsFuzzyKMeansClustering
  • org.apache.mahout.benchmark.VectorBenchmarks
  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.canopy.TestCanopyCreation
  • org.apache.mahout.clustering.classify.ClusterClassificationDriverTest
  • org.apache.mahout.clustering.dirichlet.models.DistanceMeasureClusterDistribution
  • org.apache.mahout.clustering.display.DisplayCanopy
  • org.apache.mahout.clustering.display.DisplayFuzzyKMeans
  • org.apache.mahout.clustering.display.DisplayKMeans
  • org.apache.mahout.clustering.display.DisplaySpectralKMeans
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