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

            private Random rand = new Random();
            
            @Override
            public void actuate(AbstractEnvironment2D<AbstractAgent2D<?>> env, AbstractAgent2D<?> agent) {
                this.left += rand.nextGaussian() / 10;
                this.right += rand.nextGaussian() / 10;
                
                this.left *= 0.998;
                this.right *= 0.998;
                
                if (rand.nextDouble() < 0.15) {

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            runnable.addCollidingAgent(agent, pos, rand.nextDouble() * 360, new Vector2D(agentSize, agentSize));
        }


        startSize = runnable.getAgents().size();
        while (runnable.getAgents().size() < params.getParValueInt("NumberOfInjured") + startSize) {
            Vector2D pos = new Vector2D(rand.nextGaussian(), rand.nextGaussian());
            AbstractDesasterAgent agent = new InjuredAgent(runnable.getAgents().size(), runnable, params, rand);
            agent.implantBrain(new RescueBrain(agent));
            runnable.addCollidingAgent(agent, pos, rand.nextDouble() * 360, new Vector2D(agentSize, agentSize));
        }

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            runnable.addCollidingAgent(agent, pos, rand.nextDouble() * 360, new Vector2D(agentSize, agentSize));
        }


        startSize = runnable.getAgents().size();
        while (runnable.getAgents().size() < params.getParValueInt("NumberOfInjured") + startSize) {
            Vector2D pos = new Vector2D(rand.nextGaussian(), rand.nextGaussian());
            AbstractDesasterAgent agent = new InjuredAgent(runnable.getAgents().size(), runnable, params, rand);
            agent.implantBrain(new RescueBrain(agent));
            runnable.addCollidingAgent(agent, pos, rand.nextDouble() * 360, new Vector2D(agentSize, agentSize));
        }

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    @Test
    public void testGaussian() {
        final Random random = new Random(317780561); // See class javadoc.
        final Statistics statistics = new Statistics(null);
        for (int i=0; i<10000; i++) {
            statistics.accept(random.nextGaussian());
        }
        assertEquals(0, statistics.countNaN());
        assertEquals(10000, statistics.count());
        assertEquals(0, statistics.mean(), 0.01);
        assertEquals(1, statistics.rms (), 0.01);

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            for (int j=0; j<1000; j++) {
                final double value;
                if (random.nextInt(800) == 0) {
                    value = Double.NaN;
                } else {
                    value = random.nextGaussian() + 10*random.nextDouble();
                }
                global.accept(value);
                block.accept(value);
            }
            byBlock.combine(block);

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        double nz = Math.round(avgNZero * (rnd.nextGaussian() + 1));
        if (nz < 0) {
          nz = 0;
        }
        for (int j = 1; j < nz; j++) {
          vector.set(rnd.nextInt(n), rnd.nextGaussian() * 25 + 3);
        }
        iw.set(i);
        vw.set(vector);
        w.append(iw, vw);
      }

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      VectorWritable vw = new VectorWritable();
      int avgNZero = 40;
      int n = 4500000;
      for (int i = 1; i < n; i++) {
        Vector vector = new RandomAccessSparseVector(n);
        double nz = Math.round(avgNZero * (rnd.nextGaussian() + 1));
        if (nz < 0) {
          nz = 0;
        }
        for (int j = 1; j < nz; j++) {
          vector.set(rnd.nextInt(n), rnd.nextGaussian() * 25 + 3);

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    Random r = RandomUtils.getRandom();
    for (int i = 0; i < nonNullRows; i++) {
      Vector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);

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    // TODO rejigger tests so that it doesn't expect this particular seed
    Random r = new Random(1234L);
    for(int row = 0; row < numRows; row++) {
      Vector v = new DenseVector(numCols);
      for(int col = 0; col < numCols; col++) {
        double val = r.nextGaussian();
        v.set(col, val);
      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }

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    Random gen = RandomUtils.getRandom();
    for (int row = 0; row < test.rowSize(); row++) {
      int j = gen.nextInt(test.columnSize());
      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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