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

Class org.apache.commons.math3.random.RandomGenerator

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

org.apache.commons.math3.random.RandomGenerator.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

    
    for ( int r = 0; r < res.numRows(); r++ ) {
      
      for ( int c = 0; c < res.numCols(); c++ ) {
        
        res.set(r,  c, rnd.nextDouble());
        //System.out.println( "next: " + rnd.nextDouble() );
        
      }
      
    }

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    for ( int r = 0; r < res.numRows(); r++ ) {
      
      for ( int c = 0; c < res.numCols(); c++ ) {
        
        res.set(r,  c, rnd.nextDouble());
        //System.out.println( "next: " + rnd.nextDouble() );
        
      }
      
    }

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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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        // we compare accurate versus naive dot product implementations
        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(553267312521321234l);


        for (int i = 0; i < 10000; ++i) {
            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();

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        // on regular vectors (i.e. not extreme cases like in the previous test)
        Well1024a random = new Well1024a(553267312521321234l);


        for (int i = 0; i < 10000; ++i) {
            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();
            final double sInline = MathArrays.linearCombination(ux, vx,

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        Well1024a random = new Well1024a(553267312521321234l);


        for (int i = 0; i < 10000; ++i) {
            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();
            final double sInline = MathArrays.linearCombination(ux, vx,
                                                                uy, vy,

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        for (int i = 0; i < 10000; ++i) {
            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();
            final double sInline = MathArrays.linearCombination(ux, vx,
                                                                uy, vy,
                                                                uz, vz);

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        for (int i = 0; i < 10000; ++i) {
            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();
            final double sInline = MathArrays.linearCombination(ux, vx,
                                                                uy, vy,
                                                                uz, vz);
            final double sArray = MathArrays.linearCombination(new double[] {ux, uy, uz},

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            final double ux = 1e17 * random.nextDouble();
            final double uy = 1e17 * random.nextDouble();
            final double uz = 1e17 * random.nextDouble();
            final double vx = 1e17 * random.nextDouble();
            final double vy = 1e17 * random.nextDouble();
            final double vz = 1e17 * random.nextDouble();
            final double sInline = MathArrays.linearCombination(ux, vx,
                                                                uy, vy,
                                                                uz, vz);
            final double sArray = MathArrays.linearCombination(new double[] {ux, uy, uz},
                                                               new double[] {vx, vy, vz});

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        RandomGenerator random = new Well1024a(0x35ddecfc78131e1dl);
        final UnitSphereRandomVectorGenerator sr = new UnitSphereRandomVectorGenerator(3, random);
        for (int k = 0; k < 50; ++k) {


            // define the reference sphere we want to compute
            double d = 25 * random.nextDouble();
            double refRadius = 10 * random.nextDouble();
            Vector3D refCenter = new Vector3D(d, new Vector3D(sr.nextVector()));
            // set up a large sample inside the reference sphere
            int nbPoints = random.nextInt(1000);
            List<Vector3D> points = new ArrayList<Vector3D>();

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