Examples of java.util.Random.nextGaussian()

java.util.Random.nextGaussian()
een -1.0 and 1.0 v2=2 * nextDouble() - 1; // between -1.0 and 1.0 s=v1 * v1 + v2 * v2; } while (s >= 1 || s == 0); double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s); nextNextGaussian = v2 * multiplier; haveNextNextGaussian = true; return v1 * multiplier; } }} This uses the polar method of G. E. P. Box, M. E. Muller, and G. Marsaglia, as described by Donald E. Knuth in The Art of Computer Programming, Volume 3: Seminumerical Algorithms, section 3.4.1, subsection C, algorithm P. Note that it generates two independent values at the cost of only one call to {@code StrictMath.log}and one call to {@code StrictMath.sqrt}. @return the next pseudorandom, Gaussian ("normally") distributed{@code double} value with mean {@code 0.0} andstandard deviation {@code 1.0} from this random numbergenerator's sequence


    Random gen = RandomUtils.getRandom();
    for (int col = 0; col < test.columnSize(); col++) {
      int j = gen.nextInt(test.columnSize());
      double old = test.get(col, j);
      double v = gen.nextGaussian();
      test.viewColumn(col).set(j, v);
      assertEquals(v, test.get(j, col), 0);
      assertEquals(v, test.viewColumn(col).get(j), 0);
      test.set(j, col, old);
      assertEquals(old, test.get(j, col), 0);

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      double[] x0 = new double[2];
      double[] x1 = new double[2];


      for (int i = 0; i < x0.length; i++)
      {
        x0[i] = r.nextGaussian();
      }
      for (int i = 0; i < x1.length; i++)
      {
        x1[i] = r.nextGaussian();
      }

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      {
        x0[i] = r.nextGaussian();
      }
      for (int i = 0; i < x1.length; i++)
      {
        x1[i] = r.nextGaussian();
      }
      p[k] = getPValOfMeanDifference(x0, x1);
    }


    int cnt = 0;

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    Histogram2D h = new Histogram2D(0.1);
    Histogram2D s = new Histogram2D(0.1);


    for (int i = 0; i < 100000; i++)
    {
      double x = r.nextGaussian();
      double y = r.nextGaussian();
      h.count(x, y);
      if (x + y + r.nextGaussian() > 0) s.count(x, y);


    }

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    Histogram2D s = new Histogram2D(0.1);


    for (int i = 0; i < 100000; i++)
    {
      double x = r.nextGaussian();
      double y = r.nextGaussian();
      h.count(x, y);
      if (x + y + r.nextGaussian() > 0) s.count(x, y);


    }

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    for (int i = 0; i < 100000; i++)
    {
      double x = r.nextGaussian();
      double y = r.nextGaussian();
      h.count(x, y);
      if (x + y + r.nextGaussian() > 0) s.count(x, y);


    }


    Histogram2DPlot p = new Histogram2DPlot(h, s);
    p.setVisible(true);

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        Random random = new Random();


        for (int i = 0; i <= 1000; i++) {
            this.addPoint(0, 5.0 * Math.cos((Math.PI * i) / 500.0));
            this.addPoint(1, (10.0 * random.nextDouble()) - 5.0);
            this.addPoint(2, 2.0 * random.nextGaussian());
        }


        this.repaint();
    }

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    Random gen = RandomUtils.getRandom();
    for (int row = 0; row < c[ROW]; row++) {
      int j = gen.nextInt(c[COL]);
      double old = test.get(row, j);
      double v = gen.nextGaussian();
      test.viewRow(row).set(j, v);
      assertEquals(v, test.get(row, j), 0);
      assertEquals(v, test.viewRow(row).get(j), 0);
      test.set(row, j, old);
      assertEquals(old, test.get(row, j), 0);

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    Random gen = RandomUtils.getRandom();
    for (int col = 0; col < c[COL]; col++) {
      int j = gen.nextInt(c[COL]);
      double old = test.get(col, j);
      double v = gen.nextGaussian();
      test.viewColumn(col).set(j, v);
      assertEquals(v, test.get(j, col), 0);
      assertEquals(v, test.viewColumn(col).get(j), 0);
      test.set(j, col, old);
      assertEquals(old, test.get(j, col), 0);

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      BitSet featureSpace = new BitSet(cardinality);
      int[] indexes = new int[sparsity];
      double[] values = new double[sparsity];
      int j = 0;
      while (j < sparsity) {
        double value = r.nextGaussian();
        int index = r.nextInt(cardinality);
        if (!featureSpace.get(index)) {
          featureSpace.set(index);
          indexes[j] = index;
          values[j++] = value;

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