Package org.apache.mahout.matrix

Examples of org.apache.mahout.matrix.DenseVector$Element

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  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.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, dv);
      }
    }
  }
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    rmr("testdata");
    raw = new Vector[100];
    for (int i = 0; i < 10; i++)
      for (int j = 0; j < 10; j++) {
        int ix = i * 10 + j;
        Vector v = new DenseVector(3);
        v.setQuick(0, i);
        v.setQuick(1, j);
        if (i == j)
          v.setQuick(2, 9);
        else if (i + j == 9)
          v.setQuick(2, 4.5);
        raw[ix] = v;
      }
  }
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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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  public void testMeasure() {

    DistanceMeasure distanceMeasure = distanceMeasureFactory();

    Vector[] vectors = {
        new DenseVector(new double[]{1, 1, 1, 1, 1, 1}),
        new DenseVector(new double[]{2, 2, 2, 2, 2, 2}),
        new DenseVector(new double[]{6, 6, 6, 6, 6, 6})
    };

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

    for (int a = 0; a < 3; a++) {
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    }
  }

  public void testRmultinom1() {
    double[] b = { 0.4, 0.6 };
    Vector v = new DenseVector(b);
    Vector t = v.like();
    for (int i = 1; i <= 100; i++) {
      Vector multinom = UncommonDistributions.rMultinom(100, v);
      t = t.plus(multinom);
    }
    System.out.println("sum(rMultinom(" + 100 + ", [0.4, 0.6]))/100="
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  }

  public void testRmultinom2() {
    double[] b = { 0.1, 0.2, 0.7 };
    Vector v = new DenseVector(b);
    Vector t = v.like();
    for (int i = 1; i <= 100; i++) {
      Vector multinom = UncommonDistributions.rMultinom(100, v);
      t = t.plus(multinom);
    }
    System.out.println("sum(rMultinom(" + 100 + ", [ 0.1, 0.2, 0.7 ]))/100="
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  }

  public void testRmultinom() {
    double[] b = { 0.1, 0.2, 0.8 };
    Vector v = new DenseVector(b);
    for (int i = 1; i <= 100; i++)
      System.out.println("rMultinom(" + 100 + ", [0.1, 0.2, 0.8])="
          + UncommonDistributions.rMultinom(100, v).asFormatString());
  }
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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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            collector2, null);

      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<Text> values = collector2.getValue(key);
        assertEquals("too many values", 1, values.size());
        String value = values.get(0).toString();

        String[] pointInfo = value.split("\t");
        count += Integer.parseInt(pointInfo[0]);
        total = total.plus(AbstractVector.decodeVector(pointInfo[1]));
      }
      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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  @Override
  public Model<Vector>[] sampleFromPrior(int howMany) {
    Model<Vector>[] result = new NormalModel[howMany];
    for (int i = 0; i < howMany; i++)
      result[i] = new NormalModel(new DenseVector(2), 1);
    return result;
  }
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