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

Class org.apache.commons.math3.random.JDKRandomGenerator

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

org.apache.commons.math3.random.JDKRandomGenerator.nextDouble()
Returns the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence. @return the next pseudorandom, uniformly distributeddouble value between 0.0 and 1.0 from this random number generator's sequence

        final ISAACRandom rand = new ISAACRandom(10L);// Seed can be changed
        final SimpleRegression whole = new SimpleRegression(includeIntercept);// regression of the whole set
        final SimpleRegression parts = new SimpleRegression(includeIntercept);// regression with parts.


        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);

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        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);
                whole.addData(x, y);
            }
            parts.append(sub);

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        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);
                whole.addData(x, y);
            }
            parts.append(sub);
            Assert.assertTrue(equals(parts, whole, 1E-6));

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        final ISAACRandom rand = new ISAACRandom(10L);// Seed can be changed
        final SimpleRegression whole = new SimpleRegression(includeIntercept);// regression of the whole set
        final SimpleRegression parts = new SimpleRegression(includeIntercept);// regression with parts.


        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);

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        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);
                whole.addData(x, y);
            }
            parts.append(sub);

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        for (int s = 0; s < sets; s++) {// loop through each subset of data.
            final double coef = rand.nextDouble();
            final SimpleRegression sub = new SimpleRegression(includeIntercept);// sub regression
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                final double x = rand.nextDouble();
                final double y = x * coef + rand.nextDouble();// some noise
                sub.addData(x, y);
                whole.addData(x, y);
            }
            parts.append(sub);
            Assert.assertTrue(equals(parts, whole, 1E-6));

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        ISAACRandom rand = new ISAACRandom(10L);// Seed can be changed
        for (int s = 0; s < num_sets; s++) {// loop through sets of samlpes
            StorelessBivariateCovariance covs = new StorelessBivariateCovariance();
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                double x = rand.nextDouble();
                double y = rand.nextDouble();
                covs.increment(x, y);// add sample to the subset 
                cov.increment(x, y);// add sample to the superset
            }
           chk.append(covs);

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        ISAACRandom rand = new ISAACRandom(10L);// Seed can be changed
        for (int s = 0; s < num_sets; s++) {// loop through sets of samlpes
            StorelessBivariateCovariance covs = new StorelessBivariateCovariance();
            for (int i = 0; i < 5; i++) { // loop through individual samlpes.
                double x = rand.nextDouble();
                double y = rand.nextDouble();
                covs.increment(x, y);// add sample to the subset 
                cov.increment(x, y);// add sample to the superset
            }
           chk.append(covs);
        }

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        // Assume model has 16 observations (will use Longley data).  Start by generating
        // non-constant variances for the 16 error terms.
        final int nObs = 16;
        double[] sigma = new double[nObs];
        for (int i = 0; i < nObs; i++) {
            sigma[i] = 10 * rg.nextDouble();
        }
        
        // Now generate 1000 error vectors to use to estimate the covariance matrix
        // Columns are draws on N(0, sigma[col])
        final int numSeeds = 1000;

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        // Assume model has 16 observations (will use Longley data).  Start by generating
        // non-constant variances for the 16 error terms.
        final int nObs = 16;
        double[] sigma = new double[nObs];
        for (int i = 0; i < nObs; i++) {
            sigma[i] = 10 * rg.nextDouble();
        }
        
        // Now generate 1000 error vectors to use to estimate the covariance matrix
        // Columns are draws on N(0, sigma[col])
        final int numSeeds = 1000;

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