Package org.apache.mahout.matrix

Examples of org.apache.mahout.matrix.DenseVector

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  public void test() throws Exception {
    StringWriter strWriter = new StringWriter();
    VectorWriter writer = new JWriterVectorWriter(strWriter);
    List<Vector> vectors = new ArrayList<Vector>();
    vectors.add(new DenseVector(new double[]{0.3, 1.5, 4.5}));
    vectors.add(new DenseVector(new double[]{1.3, 1.5, 3.5}));
    writer.write(vectors);
    writer.close();
    StringBuffer buffer = strWriter.getBuffer();
    assertNotNull(buffer);
    assertTrue(buffer.length() > 0);
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.assign(mm.getStdDev().times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.getStdDev() * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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  public void testMeasure() {

    DistanceMeasure distanceMeasure = new CosineDistanceMeasure();

    Vector[] vectors = {
        new DenseVector(new double[]{1, 0, 0, 0, 0, 0}),
        new DenseVector(new double[]{1, 1, 1, 0, 0, 0}),
        new DenseVector(new double[]{1, 1, 1, 1, 1, 1})
    };

    double[][] distanceMatrix = new double[3][3];

    for (int a = 0; a < 3; a++) {
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  public void testMeasureWeighted() {

    WeightedDistanceMeasure distanceMeasure = distanceMeasureFactory();

    Vector[] vectors = {
        new DenseVector(new double[]{9, 9, 1}),
        new DenseVector(new double[]{1, 9, 9}),
        new DenseVector(new double[]{9, 1, 9}),
    };
    distanceMeasure.setWeights(new DenseVector(new double[]{1, 1000, 1}));

    double[][] distanceMatrix = new double[3][3];

    for (int a = 0; a < 3; a++) {
      for (int b = 0; b < 3; b++) {
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   * Generate random document vector
   * @param numWords int number of words in the vocabulary
   * @param numWords E[count] for each word
   */
  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
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      }

      assertEquals("Number of map results", k + 1, collector2.getData().size());
      // now verify that all points are accounted for
      int count = 0;
      Vector total = new DenseVector(2);
      for (String key : collector2.getKeys()) {
        List<KMeansInfo> values = collector2.getValue(key);
        assertEquals("too many values", 1, values.size());
        //String value = values.get(0).toString();
        KMeansInfo info = values.get(0);

        count += info.getPoints();
        total = total.plus(info.getPointTotal());
      }
      assertEquals("total points", 9, count);
      assertEquals("point total[0]", 27, (int) total.get(0));
      assertEquals("point total[1]", 27, (int) total.get(1));
    }
  }
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          String[] data = SPACE_PATTERN.split(split); // first is index, second is
          int idx = Integer.parseInt(data[0]);
          result.setQuick(idx, model.getValue(data[1], idx));
        }
      } else {
        result = new DenseVector(model.getLabelSize());
        String[] splits = COMMA_PATTERN.split(line);
        for (int i = 0; i < splits.length; i++) {
          result.setQuick(i, model.getValue(splits[i], i));
        }
      }
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   */
  private void generateSamples(int num, double mx, double my, double sd) {
    System.out.println("Generating " + num + " samples m=[" + mx + ", " + my
        + "] sd=" + sd);
    for (int i = 0; i < num; i++) {
      sampleData.add(new DenseVector(new double[]{
          UncommonDistributions.rNorm(mx, sd),
          UncommonDistributions.rNorm(my, sd)}));
    }
  }
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;
    Vector dv = new DenseVector(2);
    for (Canopy canopy : canopies) {
      if (canopy.getNumPoints() > sampleData.size() * 0.05) {
        dv.assign(t1);
        g2.setColor(colors[0]);
        plotEllipse(g2, canopy.getCenter(), dv);
        dv.assign(t2);
        plotEllipse(g2, canopy.getCenter(), dv);
      }
    }
  }
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Related Classes of org.apache.mahout.matrix.DenseVector

  • org.apache.hadoop.io.Text
  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.dirichlet.DirichletClusterer
  • org.apache.mahout.clustering.dirichlet.DirichletMapper
  • org.apache.mahout.clustering.dirichlet.DirichletState
  • org.apache.mahout.clustering.dirichlet.Display2dASNDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayASNDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayASNOutputState
  • org.apache.mahout.clustering.dirichlet.DisplayDirichlet
  • org.apache.mahout.clustering.dirichlet.DisplayNDirichlet
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