Package org.apache.mahout.clustering.dirichlet.models

Examples of org.apache.mahout.clustering.dirichlet.models.SampledNormalDistribution

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    generateSamples(40, 1, 1, 3);
    generateSamples(30, 1, 0, 0.1);
    generateSamples(30, 0, 1, 0.1);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new SampledNormalDistribution(new VectorWritable(
            new DenseVector(2))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 2);
    assertNotNull(result);
  }
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    generateSamples(400, 1, 1, 3);
    generateSamples(300, 1, 0, 0.1);
    generateSamples(300, 0, 1, 0.1);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new SampledNormalDistribution(new VectorWritable(
            new DenseVector(2))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 20);
    assertNotNull(result);
  }
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    generateSamples(4000, 1, 1, 3);
    generateSamples(3000, 1, 0, 0.1);
    generateSamples(3000, 0, 1, 0.1);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new SampledNormalDistribution(new VectorWritable(
            new DenseVector(2))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 200);
    assertNotNull(result);
  }
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    generateSamples(40, 1, 1, 3, 3);
    generateSamples(30, 1, 0, 0.1, 3);
    generateSamples(30, 0, 1, 0.1, 3);

    DirichletClusterer<VectorWritable> dc = new DirichletClusterer<VectorWritable>(
        sampleData, new SampledNormalDistribution(new VectorWritable(
            new DenseVector(3))), 1.0, 10, 1, 0);
    List<Model<VectorWritable>[]> result = dc.cluster(30);
    printResults(result, 2);
    assertNotNull(result);
  }
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    generateResults();
    new DisplaySNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new SampledNormalDistribution(new VectorWritable(new DenseVector(2))));
  }
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  public void testReducer() throws Exception {
    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<VectorWritable> state = new DirichletState<VectorWritable>(new SampledNormalDistribution(
        new VectorWritable(new DenseVector(2))), 20, 1);
    DirichletMapper mapper = new DirichletMapper();
    mapper.configure(state);
   
    DummyOutputCollector<Text,VectorWritable> mapCollector = new DummyOutputCollector<Text,VectorWritable>();
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  public void testMRIterations() throws Exception {
    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<VectorWritable> state = new DirichletState<VectorWritable>(new SampledNormalDistribution(
        new VectorWritable(new DenseVector(2))), 20, 1.0);
   
    List<Model<VectorWritable>[]> models = new ArrayList<Model<VectorWritable>[]>();
   
    for (int iteration = 0; iteration < 10; iteration++) {
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  @Test
  public void testDirichlet() throws Exception {
    Path output = getTestTempDirPath("output");
    NamedVector prototype = (NamedVector) sampleData.get(0).get();
    AbstractVectorModelDistribution modelDistribution = new SampledNormalDistribution(new VectorWritable(prototype));
    Configuration conf = new Configuration();
    DirichletDriver.run(conf, getTestTempDirPath("testdata"), output, modelDistribution, 15, 10, 1.0, true, true, 0, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf, output, 10), new Path(output, "clusteredPoints"));
    clusterDumper.printClusters(termDictionary);
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    generateSamples(40, 1, 1, 3);
    generateSamples(30, 1, 0, 0.1);
    generateSamples(30, 0, 1, 0.1);

    DirichletClusterer dc = new DirichletClusterer(sampleData,
                                                   new SampledNormalDistribution(new VectorWritable(new DenseVector(2))),
                                                   1.0,
                                                   10,
                                                   1,
                                                   0);
    List<Cluster[]> result = dc.cluster(30);
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    generateSamples(40, 1, 1, 3, 3);
    generateSamples(30, 1, 0, 0.1, 3);
    generateSamples(30, 0, 1, 0.1, 3);

    DirichletClusterer dc = new DirichletClusterer(sampleData,
                                                   new SampledNormalDistribution(new VectorWritable(new DenseVector(3))),
                                                   1.0,
                                                   10,
                                                   1,
                                                   0);
    List<Cluster[]> result = dc.cluster(30);
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Related Classes of org.apache.mahout.clustering.dirichlet.models.SampledNormalDistribution

  • org.apache.mahout.clustering.dirichlet.DisplaySNDirichlet
  • org.apache.mahout.clustering.dirichlet.TestDirichletClustering
  • org.apache.mahout.clustering.dirichlet.TestMapReduce
  • org.apache.mahout.clustering.TestClusterDumper
  • org.apache.mahout.math.Vector
  • org.apache.mahout.matrix.DenseVector
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