Examples of org.apache.mahout.math.DenseVector

org.apache.mahout.math.DenseVector
Implements vector as an array of doubles

   * @param dv
   *          a Vector of ellipse dimensions
   */
  public static void plotEllipse(Graphics2D g2, Vector v, Vector dv) {
    double[] flip = {1, -1};
    Vector v2 = v.times(new DenseVector(flip));
    v2 = v2.minus(dv.divide(2));
    int h = size / 2;
    double x = v2.get(0) + h;
    double y = v2.get(1) + h;
    g2.draw(new Ellipse2D.Double(x * ds, y * ds, dv.get(0) * ds, dv.get(1) * ds));

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   * @param sd
   *          double standard deviation of the samples
   */
  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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   * @param sdy
   *          double y-value standard deviation of the samples
   */
  private static void generate2dSamples(int num, double mx, double my, double sdx, double sdy) {
    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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      addSample(new double[] {UncommonDistributions.rNorm(mx, sdx), UncommonDistributions.rNorm(my, sdy)});
    }
  }
  
  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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    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, 1.0);
    
    List<Model<VectorWritable>[]> models = new ArrayList<Model<VectorWritable>[]>();
    
    for (int iteration = 0; iteration < 10; iteration++) {
      DirichletMapper mapper = new DirichletMapper();

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  // =================== New Tests of Writable Implementations ====================
  
  public void testNormalModelWritableSerialization() throws Exception {
    double[] m = {1.1, 2.2, 3.3};
    Model<?> model = new NormalModel(new DenseVector(m), 3.3);
    DataOutputBuffer out = new DataOutputBuffer();
    model.write(out);
    Model<?> model2 = new NormalModel();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());

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    assertEquals("models", model.toString(), model2.toString());
  }
  
  public void testSampledNormalModelWritableSerialization() throws Exception {
    double[] m = {1.1, 2.2, 3.3};
    Model<?> model = new SampledNormalModel(new DenseVector(m), 3.3);
    DataOutputBuffer out = new DataOutputBuffer();
    model.write(out);
    Model<?> model2 = new SampledNormalModel();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());

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  }
  
  public void testAsymmetricSampledNormalModelWritableSerialization() throws Exception {
    double[] m = {1.1, 2.2, 3.3};
    double[] s = {3.3, 4.4, 5.5};
    Model<?> model = new AsymmetricSampledNormalModel(new DenseVector(m), new DenseVector(s));
    DataOutputBuffer out = new DataOutputBuffer();
    model.write(out);
    Model<?> model2 = new AsymmetricSampledNormalModel();
    DataInputBuffer in = new DataInputBuffer();
    in.reset(out.getData(), out.getLength());

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