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

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

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

org.apache.mahout.clustering.dirichlet.models.SampledNormalDistribution
An implementation of the ModelDistribution interface suitable for testing the DirichletCluster algorithm. Uses a Normal Distribution to sample the prior model values.

    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<Vector> state = new DirichletState<Vector>(
        new SampledNormalDistribution(), 20, 1.0, 1, 0);


    List<Model<Vector>[]> models = new ArrayList<Model<Vector>[]>();


    for (int iteration = 0; iteration < 10; iteration++) {
      DirichletMapper mapper = new DirichletMapper();

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    assertEquals("models", model.toString(), model2.toString());
  }


  @SuppressWarnings("unchecked")
  public void testSampledNormalDistributionSerialization() {
    SampledNormalDistribution dist = new SampledNormalDistribution();
    Model[] models = dist.sampleFromPrior(20);
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(Vector.class, new JsonVectorAdapter());
    Gson gson = builder.create();
    String jsonString = gson.toJson(models);
    Model[] models2 = gson.fromJson(jsonString, SampledNormalModel[].class);

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  public void testStateSerialization() {
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(DirichletState.class,
        new JsonDirichletStateAdapter());
    Gson gson = builder.create();
    DirichletState state = new DirichletState(new SampledNormalDistribution(),
        20, 1, 1, 0);
    String format = gson.toJson(state);
    System.out.println(format);
    DirichletState state2 = gson.fromJson(format, DirichletState.class);
    assertNotNull("State2 null", state2);

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


    DirichletClusterer<Vector> dc = new DirichletClusterer<Vector>(sampleData,
        new SampledNormalDistribution(), 1.0, 10, 1, 0);
    List<Model<Vector>[]> 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<Vector> dc = new DirichletClusterer<Vector>(sampleData,
        new SampledNormalDistribution(), 1.0, 10, 1, 0);
    List<Model<Vector>[]> 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<Vector> dc = new DirichletClusterer<Vector>(sampleData,
        new SampledNormalDistribution(), 1.0, 10, 1, 0);
    List<Model<Vector>[]> result = dc.cluster(30);
    printResults(result, 200);
    assertNotNull(result);
  }

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    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<Vector> state = new DirichletState<Vector>(
        new SampledNormalDistribution(), 20, 1, 1, 0);
    DirichletMapper mapper = new DirichletMapper();
    mapper.configure(state);


    DummyOutputCollector<Text, Vector> mapCollector = new DummyOutputCollector<Text, Vector>();
    for (Vector v : sampleData) {

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    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<Vector> state = new DirichletState<Vector>(
        new SampledNormalDistribution(), 20, 1.0, 1, 0);


    List<Model<Vector>[]> models = new ArrayList<Model<Vector>[]>();


    for (int iteration = 0; iteration < 10; iteration++) {
      DirichletMapper mapper = new DirichletMapper();

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    assertEquals("models", model.toString(), model2.toString());
  }


  @SuppressWarnings("unchecked")
  public void testSampledNormalDistributionSerialization() {
    SampledNormalDistribution dist = new SampledNormalDistribution();
    Model[] models = dist.sampleFromPrior(20);
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(Vector.class, new JsonVectorAdapter());
    Gson gson = builder.create();
    String jsonString = gson.toJson(models);
    Model[] models2 = gson.fromJson(jsonString, SampledNormalModel[].class);

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  public void testStateSerialization() {
    GsonBuilder builder = new GsonBuilder();
    builder.registerTypeAdapter(DirichletState.class,
        new JsonDirichletStateAdapter());
    Gson gson = builder.create();
    DirichletState state = new DirichletState(new SampledNormalDistribution(),
        20, 1, 1, 0);
    String format = gson.toJson(state);
    System.out.println(format);
    DirichletState state2 = gson.fromJson(format, DirichletState.class);
    assertNotNull("State2 null", state2);

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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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