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

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

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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 AsymmetricSampledNormalDistribution(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 AsymmetricSampledNormalDistribution(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 AsymmetricSampledNormalDistribution(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 AsymmetricSampledNormalDistribution(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 DisplayASNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(new DenseVector(2))));
  }
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    generateResults();
    new Display2dASNDirichlet();
  }
 
  private static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(
        new DenseVector(2))));
  }
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  public static void main(String[] args) throws Exception {
    VectorWritable modelPrototype = new VectorWritable(new DenseVector(2));
    //ModelDistribution<VectorWritable> modelDist = new NormalModelDistribution(modelPrototype);
    // ModelDistribution<VectorWritable> modelDist = new SampledNormalDistribution(modelPrototype);
    ModelDistribution<VectorWritable> modelDist = new AsymmetricSampledNormalDistribution(modelPrototype);
    //ModelDistribution<VectorWritable> modelDist = new GaussianClusterDistribution(modelPrototype);

    RandomUtils.useTestSeed();
    generateSamples();
    int numIterations = 40;
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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 AsymmetricSampledNormalDistribution(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 AsymmetricSampledNormalDistribution(new VectorWritable(new DenseVector(3))),
                                                   1.0,
                                                   10,
                                                   1,
                                                   0);
    List<Cluster[]> result = dc.cluster(30);
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    generateResults();
    new DisplayASNDirichlet();
  }

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

  • org.apache.mahout.clustering.dirichlet.Display2dASNDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayASNDirichlet
  • org.apache.mahout.clustering.dirichlet.TestDirichletClustering
  • org.apache.mahout.clustering.dirichlet.TestMapReduce
  • org.apache.mahout.clustering.display.DisplayDirichlet
  • org.apache.mahout.math.Vector
  • org.apache.mahout.matrix.DenseVector
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