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 org.apache.commons.math.random.JDKRandomGenerator.nextGaussian()

        // Columns are draws on N(0, sigma[col])
        final int numSeeds = 1000;
        RealMatrix errorSeeds = MatrixUtils.createRealMatrix(numSeeds, nObs);
        for (int i = 0; i < numSeeds; i++) {
            for (int j = 0; j < nObs; j++) {
                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();

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Examples of org.apache.commons.math.random.RandomData.nextGaussian()

        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = randomData.nextGaussian(mu, sigma);
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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Examples of org.apache.commons.math.random.RandomDataImpl.nextGaussian()

        // Fill values array with random data from N(mu, sigma)
        // and fill valuesList with values from values array with
        // values[i] repeated weights[i] times, each i
        List<Double> valuesList = new ArrayList<Double>();
        for (int i = 0; i < len; i++) {
            double value = randomData.nextGaussian(mu, sigma);
            values[i] = value;
            for (int j = 0; j < intWeights[i]; j++) {
                valuesList.add(new Double(value));
            }
        }

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Examples of org.apache.commons.math.random.RandomGenerator.nextGaussian()

        // Columns are draws on N(0, sigma[col])
        final int numSeeds = 1000;
        RealMatrix errorSeeds = MatrixUtils.createRealMatrix(numSeeds, nObs);
        for (int i = 0; i < numSeeds; i++) {
            for (int j = 0; j < nObs; j++) {
                errorSeeds.setEntry(i, j, rg.nextGaussian() * sigma[j]);
            }
        }
        
        // Get covariance matrix for columns
        RealMatrix cov = (new Covariance(errorSeeds)).getCovarianceMatrix();

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());


            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);


            filter.correct(z);

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict(u);


            // Simulate the process
            RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian());


            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()


            // x = A * x + B * u + pNoise
            x = A.operate(x).add(B.operate(u)).add(pNoise);


            // Simulate the measurement
            double mNoise = measurementNoise * rand.nextGaussian();


            // z = H * x + m_noise
            RealVector z = H.operate(x).mapAdd(mNoise);


            filter.correct(z);

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()

        // iterate 60 steps
        for (int i = 0; i < 60; i++) {
            filter.predict();


            // Simulate the process
            pNoise.setEntry(0, processNoise * rand.nextGaussian());


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement

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Examples of org.apache.commons.math3.random.JDKRandomGenerator.nextGaussian()


            // x = A * x + p_noise
            x = A.operate(x).add(pNoise);


            // Simulate the measurement
            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());


            // z = H * x + m_noise
            RealVector z = H.operate(x).add(mNoise);


            filter.correct(z);

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