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 = 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));
    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
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    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
 
  private static ClusterClassifier newKlusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2), 1, measure));
    models.add(new org.apache.mahout.clustering.kmeans.Kluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }
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    return new ClusterClassifier(models, new KMeansClusteringPolicy());
  }

  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, new FuzzyKMeansClusteringPolicy());
  }
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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, new CanopyClusteringPolicy());
    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, new MeanShiftClusteringPolicy());
    classifier.classify(new DenseVector(2));
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    plotSampleData((Graphics2D) g);
    plotClusters((Graphics2D) g);
  }
 
  public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
   
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, output);
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    initialize();
    this.setTitle("k-Means Clusters (>" + (int) (significance * 100) + "% of population)");
  }
 
  public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);
    HadoopUtil.delete(conf, output);
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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 = Sets.newHashSet();
    for (ClusterWritable clusterWritable :
         new SequenceFileValueIterable<ClusterWritable>(new Path(output, "part-randomSeed"), true, conf)) {
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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, 0.0, 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, 0.0, true);
  }
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    DistanceBenchmark distanceBenchmark = new DistanceBenchmark(mark);
    distanceBenchmark.benchmark(new CosineDistanceMeasure());
    distanceBenchmark.benchmark(new SquaredEuclideanDistanceMeasure());
    distanceBenchmark.benchmark(new EuclideanDistanceMeasure());
    distanceBenchmark.benchmark(new ManhattanDistanceMeasure());
    distanceBenchmark.benchmark(new TanimotoDistanceMeasure());
    distanceBenchmark.benchmark(new ChebyshevDistanceMeasure());
    distanceBenchmark.benchmark(new MinkowskiDistanceMeasure());

    if (mark.numClusters > 0) {
      ClosestCentroidBenchmark centroidBenchmark = new ClosestCentroidBenchmark(mark);
      centroidBenchmark.benchmark(new CosineDistanceMeasure());
      centroidBenchmark.benchmark(new SquaredEuclideanDistanceMeasure());
      centroidBenchmark.benchmark(new EuclideanDistanceMeasure());
      centroidBenchmark.benchmark(new ManhattanDistanceMeasure());
      centroidBenchmark.benchmark(new TanimotoDistanceMeasure());
      centroidBenchmark.benchmark(new ChebyshevDistanceMeasure());
      centroidBenchmark.benchmark(new MinkowskiDistanceMeasure());
    }
  }
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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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