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

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

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

  @SuppressWarnings("unchecked")
  public void testNormalModelDistributionSerialization() {
    NormalModelDistribution dist = new NormalModelDistribution();
    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, NormalModel[].class);
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    getResults();
    new DisplayASNOutputState();
  }

  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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    getResults();
    new DisplayOutputState();
  }

  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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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 NormalModelDistribution(), 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 NormalModelDistribution(), 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 NormalModelDistribution(), 1.0, 10, 1, 0);
    List<Model<Vector>[]> result = dc.cluster(30);
    printResults(result, 200);
    assertNotNull(result);
  }
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    canopies = populateCanopies(new ManhattanDistanceMeasure(), points, t1, t2);
    new DisplayCanopy();
  }

  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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  /** Test the basic Mapper */
  public void testMapper() throws Exception {
    generateSamples(10, 0, 0, 1);
    DirichletState<Vector> state = new DirichletState<Vector>(
        new NormalModelDistribution(), 5, 1, 0, 0);
    DirichletMapper mapper = new DirichletMapper();
    mapper.configure(state);

    DummyOutputCollector<Text, Vector> collector = new DummyOutputCollector<Text, Vector>();
    for (Vector v : sampleData) {
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    assertEquals("models", model.toString(), model2.toString());
  }

  @SuppressWarnings("unchecked")
  public void testNormalModelDistributionSerialization() {
    NormalModelDistribution dist = new NormalModelDistribution();
    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, NormalModel[].class);
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      System.out.println(canopy.toString());
    new DisplayMeanShift();
  }

  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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Related Classes of org.apache.mahout.clustering.dirichlet.models.NormalModelDistribution

  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.dirichlet.DisplayASNOutputState
  • org.apache.mahout.clustering.dirichlet.DisplayNDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayOutputState
  • org.apache.mahout.clustering.dirichlet.TestDirichletClustering
  • org.apache.mahout.clustering.dirichlet.TestMapReduce
  • org.apache.mahout.clustering.meanshift.DisplayMeanShift
  • org.apache.mahout.math.Vector
  • org.apache.mahout.matrix.DenseVector
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