Examples of MersenneTwister

cern.jet.random.engine.MersenneTwister
fsu.edu/~geo/diehard.html">diehard test of G. Marsaglia and the load test of P. Hellekalek and S. Wegenkittl.
Performance: Its speed is comparable to other modern generators (in particular, as fast as java.util.Random.nextFloat()). 2.5 million calls to raw() per second (Pentium Pro 200 Mhz, JDK 1.2, NT). Be aware, however, that there is a non-negligible amount of overhead required to initialize the data structures used by a MersenneTwister. Code like
```
 double sum = 0.0; for (int i=0; i<100000; ++i) { RandomElement twister = new MersenneTwister(new java.util.Date()); sum += twister.raw(); } 
```
will be wildly inefficient. Consider using
```
 double sum = 0.0; RandomElement twister = new MersenneTwister(new java.util.Date()); for (int i=0; i<100000; ++i) { sum += twister.raw(); } 
```
instead. This allows the cost of constructing the MersenneTwister object to be borne only once, rather than once for each iteration in the loop.
Implementation: After M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator", ACM Transactions on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3--30.
More info on Masumoto's homepage.
More info on Pseudo-random number generators is on the Web.
Yet some more info.
The correctness of this implementation has been verified against the published output sequence mt19937-2.out of the C-implementation mt19937-2.c. (Call test(1000) to print the sequence).
Note that this implementation is not synchronized.
Details: MersenneTwister is designed with consideration of the flaws of various existing generators in mind. It is an improved version of TT800, a very successful generator. MersenneTwister is based on linear recurrences modulo 2. Such generators are very fast, have extremely long periods, and appear quite robust. MersenneTwister produces 32-bit numbers, and every k-dimensional vector of such numbers appears the same number of times as k successive values over the period length, for each k <= 623 (except for the zero vector, which appears one time less). If one looks at only the first n <= 16 bits of each number, then the property holds for even larger k, as shown in the following table (taken from the publication cited above):

n

1

2

3

4

5

6

7

8

9

10

11

12 .. 16

17 .. 32

k

19937

9968

6240

4984

3738

3115

2493

2492

1869

1869

1248

1246

623

MersenneTwister generates random numbers in batches of 624 numbers at a time, so the caching and pipelining of modern systems is exploited. The generator is implemented to generate the output by using the fastest arithmetic operations only: 32-bit additions and bit operations (no division, no multiplication, no mod). These operations generate sequences of 32 random bits (int's). long's are formed by concatenating two 32 bit int's. float's are formed by dividing the interval [0.0,1.0] into 2³² sub intervals, then randomly choosing one subinterval. double's are formed by dividing the interval [0.0,1.0] into 2⁶⁴ sub intervals, then randomly choosing one subinterval.
@author wolfgang.hoschek@cern.ch @version 1.0, 09/24/99 @see java.util.Random
de.yaams.extensions.basemap.tiled.util.MersenneTwister
ath.keio.ac.jp/matumoto/emt.html
email: matumoto@math.keio.ac.jp
@author Makoto Matsumoto (matumoto@math.keio.ac.jp) @author Takuji Nishimura
freenet.support.math.MersenneTwister
This is a synchronized wrapper around {@link org.spaceroots.mantissa.random.MersenneTwister}which also adds additional {@code setSeed()} methods. @author infinity0
jmt.engine.random.engine.MersenneTwister
ath.keio.ac.jp/matumoto/emt.html
email: matumoto@math.keio.ac.jp

@author (translated in java by) Federico Granata

org.apache.commons.math.random.MersenneTwister

ath.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html"> http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html.

This generator is described in a paper by Makoto Matsumoto and Takuji Nishimura in 1998: Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator, ACM Transactions on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3--30

This class is mainly a Java port of the 2002-01-26 version of the generator written in C by Makoto Matsumoto and Takuji Nishimura. Here is their original copyright:

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

@version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 2.0

org.apache.commons.math3.random.MersenneTwister

ath.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html"> http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html.

This class is mainly a Java port of the 2002-01-26 version of the generator written in C by Makoto Matsumoto and Takuji Nishimura. Here is their original copyright:

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission.

@since 2.0

org.apache.mahout.math.jet.random.engine.MersenneTwister
@deprecated until unit tests are in place. Until this time, this class/interface is unsupported.
org.jamesii.core.math.random.generators.mersennetwister.MersenneTwister
This class implements the Mersenne Twister pseudo-random number generator algorithm MT19937 as described by Matsumoto and Nishimura.
The Mersenne Twister is a very high-quality PRNG specifically suited for stochastic sampling and simulation. It has very high-order of dimensional equidistribution and passes even the Diehard tests. The period of this algorithm is 2¹⁹⁹³⁷ − 1, so repetition is pretty far away. That said it is also pretty fast in average, except for the first number to be generated and if the internal pool runs out. It is not recommend for cryptographic purposes! @author Johannes Rössel
tiled.util.MersenneTwister
ath.keio.ac.jp/matumoto/emt.html
email: matumoto@math.keio.ac.jp
@author Makoto Matsumoto (matumoto@math.keio.ac.jp) @author Takuji Nishimura

Examples of org.apache.commons.math.random.MersenneTwister

    case SUPER_DUPER:
      throw new EvalException(rng.RNG_kind + " not implemented yet");


    case MERSENNE_TWISTER:
      if (rng.mersenneTwisterAlg == null) {
        rng.mersenneTwisterAlg = new MersenneTwister(seed);
      } else {
        rng.mersenneTwisterAlg.setSeed(seed);
      }
      return;

View Full Code Here

Examples of org.apache.commons.math.random.MersenneTwister

    case MERSENNE_TWISTER:
      if (mersenneTwisterAlg == null) {
        if (this.randomseed == 0) {
          Randomize(RNG_kind);
        }
        mersenneTwisterAlg = new MersenneTwister((long) this.randomseed);
      }
      return (mersenneTwisterAlg.nextDouble());


    case KNUTH_TAOCP:
    case KNUTH_TAOCP2:

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister


    @Before
    public void setUp() {
        // by default, do not include collinear points
        generator = createConvexHullGenerator(false);
        random = new MersenneTwister(10);
    }

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister

            int maxEvaluations,
            PointValuePair expected) {
        int dim = startPoint.length;
        // test diagonalOnly = 0 - slow but normally fewer feval#
        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
                                                  0, new MersenneTwister(), false, null);
        final double[] lB = boundaries == null ? null : boundaries[0];
        final double[] uB = boundaries == null ? null : boundaries[1];
        PointValuePair result = boundaries == null ?
            optim.optimize(maxEvaluations, func, goal,
                           new InitialGuess(startPoint),

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister

    private RandomGenerator generator;


    @Before
    public void setUp() {
        field = new DfpField(40);
        generator = new MersenneTwister(6176597458463500194l);
    }

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister


    @Test
    public void testMath864() {
        final CMAESOptimizer optimizer
            = new CMAESOptimizer(30000, 0, true, 10,
                                 0, new MersenneTwister(), false, null);
        final MultivariateFunction fitnessFunction = new MultivariateFunction() {
                public double value(double[] parameters) {
                    final double target = 1;
                    final double error = target - parameters[0];
                    return error * error;

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Examples of org.apache.commons.math3.random.MersenneTwister

     */
    @Test
    public void testFitAccuracyDependsOnBoundary() {
        final CMAESOptimizer optimizer
            = new CMAESOptimizer(30000, 0, true, 10,
                                 0, new MersenneTwister(), false, null);
        final MultivariateFunction fitnessFunction = new MultivariateFunction() {
                public double value(double[] parameters) {
                    final double target = 11.1;
                    final double error = target - parameters[0];
                    return error * error;

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister

                        int maxEvaluations,
                        PointValuePair expected) {
        int dim = startPoint.length;
        // test diagonalOnly = 0 - slow but normally fewer feval#
        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
                                                  0, new MersenneTwister(), false, null);
        PointValuePair result = boundaries == null ?
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           new InitialGuess(startPoint),

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister

    private RandomGenerator generator;


    @Before
    public void setUp() {
        field = new DfpField(40);
        generator = new MersenneTwister(6176597458463500194l);
    }

View Full Code Here

Examples of org.apache.commons.math3.random.MersenneTwister

            int maxEvaluations,
            PointValuePair expected) {
        int dim = startPoint.length;
        // test diagonalOnly = 0 - slow but normally fewer feval#
        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
                                                  0, new MersenneTwister(), false, null);
        final double[] lB = boundaries == null ? null : boundaries[0];
        final double[] uB = boundaries == null ? null : boundaries[1];
        PointValuePair result = boundaries == null ?
            optim.optimize(maxEvaluations, func, goal,
                           new InitialGuess(startPoint),

View Full Code Here

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n	1	2	3	4	5	6	7	8	9	10	11	12 .. 16	17 .. 32
k	19937	9968	6240	4984	3738	3115	2493	2492	1869	1869	1248	1246	623