Package org.apache.mahout.math

Examples of org.apache.mahout.math.VectorWritable

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    generateResults();
    new Display2dASNDirichlet();
  }
 
  private static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(
        new DenseVector(2))));
  }
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    generateResults();
    new DisplaySNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new SampledNormalDistribution(new VectorWritable(new DenseVector(2))));
  }
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                                                                 matrixPath,
                                                                 IntWritable.class,
                                                                 VectorWritable.class);
    IntWritable docId = new IntWritable();
    Text inputKey = new Text();
    VectorWritable v = new VectorWritable();

    int i = 0;
    for(FileStatus status : fs.listStatus(inputPath)) {
      SequenceFile.Reader reader = new SequenceFile.Reader(fs, status.getPath(), conf);
      while(reader.next(inputKey, v)) {
        docId.set(i);
        indexWriter.append(docId, inputKey);
        matrixWriter.append(docId, v);
        i++;
      }
      reader.close();
    }
   
    int numCols = v.get().size();
    matrixWriter.close();
    indexWriter.close();
    log.info("Wrote out matrix with {} rows and {} columns to " + matrixPath, i, numCols);
    return 0;
  }
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    generateResults();
    new DisplayNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution(new VectorWritable(new DenseVector(2))));
  }
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  private static void generateSamples(int num, double mx, double my, double sd) {
    double[] params = {mx, my, sd, sd};
    sampleParams.add(new DenseVector(params));
    log.info("Generating {} samples m=[{}, {}] sd={}", new Object[] {num, mx, my, sd});
    for (int i = 0; i < num; i++) {
      sampleData.add(new VectorWritable(new DenseVector(new double[] {UncommonDistributions.rNorm(mx, sd),
                                                                      UncommonDistributions.rNorm(my, sd)})));
    }
  }
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    double[] params = {mx, my, sdx, sdy};
    sampleParams.add(new DenseVector(params));
    log.info("Generating {} samples m=[{}, {}] sd=[{}, {}]", new Object[] {num, mx, my, sdx, sdy});
    for (int i = 0; i < num; i++) {
      sampleData
          .add(new VectorWritable(new DenseVector(new double[] {UncommonDistributions.rNorm(mx, sdx),
                                                                UncommonDistributions.rNorm(my, sdy)})));
    }
  }
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    ClusteringTestUtils.writePointsToFile(new Iterable<VectorWritable>() {
      @Override
      public Iterator<VectorWritable> iterator() {
        final Iterator<MatrixSlice> it = m.iterator();
        final VectorWritable v = new VectorWritable();
        return new Iterator<VectorWritable>() {
          @Override
          public boolean hasNext() { return it.hasNext(); }
          @Override
          public VectorWritable next() {
            MatrixSlice slice = it.next();
            v.set(slice.vector());
            return v;
          }
          @Override
          public void remove() { it.remove(); }
        };
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  private void addSample(double[] values) {
    Vector v = new DenseVector(2);
    for (int j = 0; j < values.length; j++) {
      v.setQuick(j, values[j]);
    }
    sampleData.add(new VectorWritable(v));
  }
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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>();
    for (VectorWritable v : sampleData) {
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    generateSamples(100, 0, 0, 1);
    generateSamples(100, 2, 0, 1);
    generateSamples(100, 0, 2, 1);
    generateSamples(100, 2, 2, 1);
    DirichletState<VectorWritable> state = new DirichletState<VectorWritable>(new SampledNormalDistribution(
        new VectorWritable(new DenseVector(2))), 20, 1);
    DirichletMapper mapper = new DirichletMapper();
    mapper.configure(state);
   
    DummyOutputCollector<Text,VectorWritable> mapCollector = new DummyOutputCollector<Text,VectorWritable>();
    for (VectorWritable v : sampleData) {
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