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

            final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(degree);
 
            final WeightedObservedPoints obs = new WeightedObservedPoints();
            for (int i = 0; i < 40000; ++i) {
                final double x = -1.0 + i / 20000.0;
                obs.add(1.0, x, p.value(x) + 0.1 * randomizer.nextGaussian());
            }


            final PolynomialFunction fitted = new PolynomialFunction(fitter.fit(obs.toList()));
            for (double x = -1.0; x < 1.0; x += 0.01) {
                final double error = FastMath.abs(p.value(x) - fitted.value(x)) /

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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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    Random r = new Random(1234L);
    for (int i = 0; i < nonNullRows; i++) {
      SequentialAccessSparseVector 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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      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) == false) {
          featureSpace.set(index);
          indexes[j] = index;
          values[j++] = value;

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      for (int j = 0; j < 10; j++) {
        input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
      }
      JSONEvent e = new JSONEvent();
      e.setHeaders(input);
      e.setBody(String.valueOf(rand.nextGaussian()).getBytes(encoding));
      events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    StringEntity input = new StringEntity(json);

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        input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
      }
      input.put("MsgNum", String.valueOf(i));
      JSONEvent e = new JSONEvent();
      e.setHeaders(input);
      e.setBody(String.valueOf(rand.nextGaussian()).getBytes("UTF-8"));
      events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    HttpsURLConnection httpsURLConnection = null;

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            input.put(String.valueOf(i) + String.valueOf(j), String.valueOf(i));
        }
        input.put("MsgNum", String.valueOf(i));
        JSONEvent e = new JSONEvent();
        e.setHeaders(input);
        e.setBody(String.valueOf(rand.nextGaussian()).getBytes("UTF-8"));
        events.add(e);
    }
    Gson gson = new Gson();
    String json = gson.toJson(events, listType);
    HttpURLConnection httpURLConnection = null;

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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();
        final double[] fitted = 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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