Package org.apache.mahout.clustering.evaluation

Examples of org.apache.mahout.clustering.evaluation.ClusterEvaluator.interClusterDensity()


  public void testCluster1() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.5, measure);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

  @Test
  public void testCluster2() throws IOException {
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  public void testCluster2() throws IOException {
    ClusteringTestUtils.writePointsToFile(referenceData, new Path(testdata, "file1"), fs, conf);
    DistanceMeasure measure = new EuclideanDistanceMeasure();
    initData(1, 0.75, measure);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

  @Test
  public void testEmptyCluster() throws IOException {
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    Canopy cluster = new Canopy(new DenseVector(new double[] { 10, 10 }), 19, measure);
    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    representativePoints.put(cluster.getId(), points);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

  @Test
  public void testSingleValueCluster() throws IOException {
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    clusters.add(cluster);
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    points.add(new VectorWritable(cluster.getCenter().plus(new DenseVector(new double[] { 1, 1 }))));
    representativePoints.put(cluster.getId(), points);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

  /**
   * Representative points extraction will duplicate the cluster center if the cluster has no
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    points.add(new VectorWritable(cluster.getCenter()));
    points.add(new VectorWritable(cluster.getCenter()));
    points.add(new VectorWritable(cluster.getCenter()));
    representativePoints.put(cluster.getId(), points);
    ClusterEvaluator evaluator = new ClusterEvaluator(representativePoints, clusters, measure);
    assertEquals("inter cluster density", 0.33333333333333315, evaluator.interClusterDensity(), EPSILON);
    assertEquals("intra cluster density", 0.3656854249492381, evaluator.intraClusterDensity(), EPSILON);
  }

  @Test
  public void testCanopy() throws Exception {
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    Path clustersIn = new Path(output, "clusters-0");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());
    System.out.println("Inter-cluster density = " + evaluator.interClusterDensity());

    printRepPoints(numIterations);
  }

  @Test
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    Path clustersIn = new Path(output, "clusters-2");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());
    System.out.println("Inter-cluster density = " + evaluator.interClusterDensity());
    printRepPoints(numIterations);
  }

  @Test
  public void testFuzzyKmeans() throws Exception {
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    Path clustersIn = new Path(output, "clusters-4");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());
    System.out.println("Inter-cluster density = " + evaluator.interClusterDensity());
    printRepPoints(numIterations);
  }

  @Test
  public void testMeanShift() throws Exception {
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    Path clustersIn = new Path(output, "clusters-10");
    RepresentativePointsDriver.run(conf, clustersIn, new Path(output, "clusteredPoints"), output, measure, numIterations, true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());
    System.out.println("Inter-cluster density = " + evaluator.interClusterDensity());
    printRepPoints(numIterations);
  }

  @Test
  public void testDirichlet() throws Exception {
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                                   numIterations,
                                   true);
    ClusterEvaluator evaluator = new ClusterEvaluator(conf, clustersIn);
    // now print out the Results
    System.out.println("Intra-cluster density = " + evaluator.intraClusterDensity());
    System.out.println("Inter-cluster density = " + evaluator.interClusterDensity());
    printRepPoints(numIterations);
  }

}
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