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

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

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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 NormalModelDistribution(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 NormalModelDistribution(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 NormalModelDistribution(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 NormalModelDistribution(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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    getResults();
    new DisplayASNOutputState();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution(new VectorWritable(new DenseVector(2))));
  }
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    CanopyClusterer.updateCentroids(canopies);
    new DisplayCanopy();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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    }
    new DisplayMeanShift();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution());
  }
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    getResults();
    new DisplayOutputState();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution(new VectorWritable(new DenseVector(2))));
  }
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    generateResults();
    new DisplayNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution(new VectorWritable(new DenseVector(2))));
  }
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  }
 
  /** Test the basic Mapper */
  public void testMapper() throws Exception {
    generateSamples(10, 0, 0, 1);
    DirichletState<VectorWritable> state = new DirichletState<VectorWritable>(new NormalModelDistribution(
        new VectorWritable(new DenseVector(2))), 5, 1);
    DirichletMapper mapper = new DirichletMapper();
    mapper.configure(state);
   
    DummyOutputCollector<Text,VectorWritable> collector = new DummyOutputCollector<Text,VectorWritable>();
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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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