List of nextGaussian() Examples

Examples of nextGaussian()

com.heatonresearch.aifh.randomize.GenerateRandom.nextGaussian()
@return The next normally distributed random number.
com.heatonresearch.aifh.randomize.MersenneTwisterGenerateRandom.nextGaussian()
com.orientechnologies.common.util.MersenneTwisterFast.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
org.apache.commons.math.random.JDKRandomGenerator.nextGaussian()
org.apache.commons.math.random.RandomData.nextGaussian()
tl.nist.gov/div898/handbook/eda/section3/eda3661.htm"> Normal Distribution

Preconditions:
- sigma > 0 (otherwise an IllegalArgumentException is thrown.)
@param mu Mean of the distribution @param sigma Standard deviation of the distribution @return random value from Gaussian distribution with mean = mu,standard deviation = sigma
org.apache.commons.math.random.RandomDataImpl.nextGaussian()
Generate a random value from a Normal (a.k.a. Gaussian) distribution with the given mean, mu and the given standard deviation, sigma. @param mu the mean of the distribution @param sigma the standard deviation of the distribution @return the random Normal value @throws NotStrictlyPositiveException if {@code sigma <= 0}.
org.apache.commons.math.random.RandomGenerator.nextGaussian()
Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence. @return the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence
org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()
Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence. @return the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence
org.apache.commons.math3.random.RandomData.nextGaussian()
tl.nist.gov/div898/handbook/eda/section3/eda3661.htm"> Normal Distribution
@param mu the mean of the distribution @param sigma the standard deviation of the distribution @return a random value following the specified Gaussian distribution @throws NotStrictlyPositiveException if {@code sigma <= 0}.
org.apache.commons.math3.random.RandomDataImpl.nextGaussian()
{@inheritDoc}
org.apache.commons.math3.random.RandomGenerator.nextGaussian()
Returns the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence. @return the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence
org.apache.mahout.common.RandomWrapper.nextGaussian()

Examples of java.util.Random.nextGaussian()

            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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Examples of java.util.Random.nextGaussian()


            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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Examples of java.util.Random.nextGaussian()


        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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Examples of java.util.Random.nextGaussian()

        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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Examples of java.util.Random.nextGaussian()


        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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Examples of java.util.Random.nextGaussian()

        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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Examples of java.util.Random.nextGaussian()


        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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Examples of java.util.Random.nextGaussian()

        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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Examples of java.util.Random.nextGaussian()


        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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Examples of java.util.Random.nextGaussian()

        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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