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 randomizer = new Random(64925784252l);


        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1, x, 1e-7 * randomizer.nextGaussian());
        }


        fitter.fit();
        // This test serves to cover the part of the code of "guessAOmega"
        // when the algorithm using integrals fails.

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        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1, x,
                                    f.value(x) + 0.01 * randomizer.nextGaussian());
        }


        final double[] fitted = fitter.fit(new double[] { 0.15, 3.6, 4.5 });
        Assert.assertEquals(a, fitted[0], 1.2e-3);
        Assert.assertEquals(w, fitted[1], 3.3e-3);

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        int size = 100;
        double[] xTab = new double[size];
        double[] yTab = new double[size];
        for (int i = 0; i < size; ++i) {
            xTab[i] = 0.1 * i;
            yTab[i] = f.value(xTab[i]) + 0.01 * randomizer.nextGaussian();
        }


        // shake it
        for (int i = 0; i < size; ++i) {
            int i1 = randomizer.nextInt(size);

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        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer());
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1.0, x,
                                   f.value(x) + 0.01 * randomizer.nextGaussian());
        }


        HarmonicFunction fitted = fitter.fit();
        assertEquals(f.getAmplitude(), fitted.getAmplitude(), 7.6e-4);
        assertEquals(f.getPulsation(), fitted.getPulsation(), 2.7e-3);

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        HarmonicFitter fitter =
            new HarmonicFitter(new LevenbergMarquardtOptimizer(), new double[] { 0.15, 3.6, 4.5 });
        for (double x = 0.0; x < 10.0; x += 0.1) {
            fitter.addObservedPoint(1.0, x,
                                   f.value(x) + 0.01 * randomizer.nextGaussian());
        }


        HarmonicFunction fitted = fitter.fit();
        assertEquals(f.getAmplitude(), fitted.getAmplitude(), 1.2e-3);
        assertEquals(f.getPulsation(), fitted.getPulsation(), 3.3e-3);

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        int size = 100;
        double[] xTab = new double[size];
        double[] yTab = new double[size];
        for (int i = 0; i < size; ++i) {
            xTab[i] = 0.1 * i;
            yTab[i] = f.value(xTab[i]) + 0.01 * randomizer.nextGaussian();
        }


        // shake it
        for (int i = 0; i < size; ++i) {
            int i1 = randomizer.nextInt(size);

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            PolynomialFitter fitter =
                new PolynomialFitter(degree, new LevenbergMarquardtOptimizer());
            for (double x = -1.0; x < 1.0; x += 0.01) {
                fitter.addObservedPoint(1.0, x,
                                        p.value(x) + 0.1 * randomizer.nextGaussian());
            }


            PolynomialFunction fitted = fitter.fit();


            for (double x = -1.0; x < 1.0; x += 0.01) {

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            long startTime = System.currentTimeMillis();
            int totalEntriesReturned = 0;
            
            for (int id = 0; id < queryCount; id++) {
              float x = (float) random.nextGaussian() * canvasSize;
              float y = (float) random.nextGaussian() * canvasSize;
              
              List<Integer> l = ld.nearest(new Point(x, y), Float.POSITIVE_INFINITY);
              totalEntriesReturned += l.size();

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            long startTime = System.currentTimeMillis();
            int totalEntriesReturned = 0;
            
            for (int id = 0; id < queryCount; id++) {
              float x = (float) random.nextGaussian() * canvasSize;
              float y = (float) random.nextGaussian() * canvasSize;
              
              List<Integer> l = ld.nearest(new Point(x, y), Float.POSITIVE_INFINITY);
              totalEntriesReturned += l.size();
              
              StringBuffer tempBuffer = new StringBuffer("  " + id + " " +

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            long startTime = System.currentTimeMillis();
            int totalEntriesReturned = 0;
            
            for (int id = 0; id < queryCount; id++) {
              float x = (float) random.nextGaussian() * canvasSize;
              float y = (float) random.nextGaussian() * canvasSize;
              
              List<Integer> l = ld.nearestN(new Point(x, y), n, Float.POSITIVE_INFINITY);
              
              totalEntriesReturned += l.size();

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