Examples of AsymmetricSampledNormalDistribution

org.apache.mahout.clustering.dirichlet.models.AsymmetricSampledNormalDistribution
An implementation of the ModelDistribution interface suitable for testing the DirichletCluster algorithm. Uses a Normal Distribution to sample the prior model values. Model values have a vector standard deviation, allowing assymetrical regions to be covered by a model.

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

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

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

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

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

    generateResults();
    new DisplayASNDirichlet();
  }
  
  static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(new DenseVector(2))));
  }

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

    generateResults();
    new Display2dASNDirichlet();
  }
  
  private static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(
        new DenseVector(2))));
  }

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


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

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

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

    generateResults();
    new DisplayASNDirichlet();
  }


  static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution());
  }

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