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

      for(int i = 0 ; i < nbPosTest; i++)
      {
        double[] t = new double[dimension];
        for(int x = 0 ; x < dimension; x++)
        {
          t[x] = posstart + ran.nextGaussian();
        }


        test.add(new TrainingSample<double[]>(t, 1));
      }
      //5. generate negative test samples

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      for(int i = 0 ; i < nbNegTest; i++)
      {
        double[] t = new double[dimension];
        for(int x = 0 ; x < dimension; x++)
        {
          t[x] = negstart + ran.nextGaussian();
        }


        test.add(new TrainingSample<double[]>(t, -1));
      }

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        Random rand = new Random();
        if (seed != null) rand.setSeed(seed);
        for (int t = 0; t < n_trial; ++t) {
            double[] prob = initProbability();
            double alpha = this.alpha + rand.nextGaussian() * ALPHA_WIDTH;


            for (int i = 0;; ++i) {
                int r = rand.nextInt(ngrams.size());
                updateLangProb(prob, ngrams.get(r), alpha);
                if (i % 5 == 0) {

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        final double p = 4.1;
        final HarmonicOscillator f = new HarmonicOscillator(a, w, p);


        final WeightedObservedPoints points = new WeightedObservedPoints();
        for (double x = 0.0; x < 10.0; x += 0.1) {
            points.add(1, x, f.value(x) + 0.01 * randomizer.nextGaussian());
        }


        final HarmonicCurveFitter fitter = HarmonicCurveFitter.create();
        final double[] fitted = fitter.fit(points.toList());
        Assert.assertEquals(a, fitted[0], 7.6e-4);

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    public void testTinyVariationsData() {
        final Random randomizer = new Random(64925784252L);


        final WeightedObservedPoints points = new WeightedObservedPoints();
        for (double x = 0.0; x < 10.0; x += 0.1) {
            points.add(1, x, 1e-7 * randomizer.nextGaussian());
        }


        final HarmonicCurveFitter fitter = HarmonicCurveFitter.create();
        fitter.fit(points.toList());

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        final double p = 4.1;
        final HarmonicOscillator f = new HarmonicOscillator(a, w, p);


        final WeightedObservedPoints points = new WeightedObservedPoints();
        for (double x = 0.0; x < 10.0; x += 0.1) {
            points.add(1, x, f.value(x) + 0.01 * randomizer.nextGaussian());
        }


        final HarmonicCurveFitter fitter = HarmonicCurveFitter.create()
            .withStartPoint(new double[] { 0.15, 3.6, 4.5 });
        final double[] fitted = fitter.fit(points.toList());

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        final int size = 100;
        final double[] xTab = new double[size];
        final 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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            PolynomialFunction p = buildRandomPolynomial(degree, randomizer);


            PolynomialFitter fitter = new PolynomialFitter(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());
            }


            final double[] init = new double[degree + 1];
            PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

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            PolynomialFitter fitter = new PolynomialFitter(new LevenbergMarquardtOptimizer());
            for (int i = 0; i < 40000; ++i) {
                double x = -1.0 + i / 20000.0;
                fitter.addObservedPoint(1.0, x,
                                        p.value(x) + 0.1 * randomizer.nextGaussian());
            }


            final double[] init = new double[degree + 1];
            PolynomialFunction fitted = new PolynomialFunction(fitter.fit(init));

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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();
        Assert.assertEquals(a, fitted[0], 7.6e-4);
        Assert.assertEquals(w, fitted[1], 2.7e-3);

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