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

    generateResults();
    new Display2dASNDirichlet();
  }


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

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

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

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


  @SuppressWarnings("unchecked")
  public void testAsymmetricSampledNormalDistributionSerialization() {
    AsymmetricSampledNormalDistribution dist = new AsymmetricSampledNormalDistribution();
    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,

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

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

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


  @SuppressWarnings("unchecked")
  public void testAsymmetricSampledNormalDistributionSerialization() {
    AsymmetricSampledNormalDistribution dist = new AsymmetricSampledNormalDistribution();
    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,

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