Examples of cern.jet.random.engine.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

                               + num.format(chiSq(ids.count(id), expected)) + "\t"
                               + prct.format((ids.count(id) - expected) / expected));
            chiSq += chiSq(ids.count(id), expected);
        }
        System.out.println("X^2 = " + chiSq);
        ChiSquare dist = new ChiSquare(df, new MersenneTwister());
        // p-value is ~= prob of seeing this distribution from fair router
        double pValue = 1.0 - dist.cdf(chiSq);
        System.out.println("p-value = " + pValue);
        assertTrue("Non-uniform load distribution detected.", pValue >= 0.05);
    }

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    public void intervalsComplete() {
        runSimulation();
    }


    protected void runSimulation() {
        u = new MersenneTwister();
        super.runSimulation();
    }

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    }


    private static final boolean USE_RATIO = true;
    private void runSimulation() {
        long start = System.currentTimeMillis();
        RandomEngine random = new MersenneTwister();


        int sampleCount = Settings.getInt("ev.simulationSize", BOOTSTRAP_SIZE);
        if (USE_RATIO && indivDates == null) {
            double factor = Math.exp(0.75 * Math.log(randomObjects.size()));
            sampleCount = (int) (sampleCount / factor);

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        rational = Math.sqrt((s*s*(1 + (s*s/2))) / n);
        base = logmean + s*s/2;
    }


    private double[] generateBootstrapSamples() {
        RandomEngine u = new MersenneTwister();
        Normal normal = new Normal(0, 1, u);
        ChiSquare chisquare = new ChiSquare(numSamples-1, u);
        int bootstrapSize = Settings.getInt("logCI.bootstrapSize", 2000);
        double[] samples = new double[bootstrapSize];
        for (int i = bootstrapSize;   i-- > 0; )

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    }
    // LOGGING ->


    this.seed = seed;
    generators = new HashMap<String, RandomEngine>();
    generators.put(null, new MersenneTwister(seed));
    
    distributions = new HashMap<String, AbstractDistribution>();


    if (logger.isDebugEnabled()) {
      distributions.put(UNIFORM_DEFAULT, new UUniformDistributionController(generators.get(null)));

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        + " and invalidate all distributions and general generator!");
    // LOGGING ->


    this.seed = seed;
    invalidateDistributions();
    generators.put(null, new MersenneTwister(seed));
    if (logger.isDebugEnabled()) {
      distributions.put(UNIFORM_DEFAULT, new UUniformDistributionController(generators.get(null)));
    } else {
      distributions.put(UNIFORM_DEFAULT, new Uniform(generators.get(null)));
    }

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    if(!(argForward > 0 && argForward < 1) || !(argBackward > 0 && argBackward <= 1))
      throw new IllegalArgumentException("invalid scopes for backwards or forwards");
    visited = new HashSet<DeterministicVertex>();
    machine = new DirectedSparseGraph();
    vertices = new ArrayList<DeterministicVertex>();
    generator  = new MersenneTwister(seed);
    boolGenerator = new Random(seed);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      DeterministicVertex v=new DeterministicVertex(new VertexID(VertexID.VertKind.NEUTRAL,0));
      annotateVertex(v);

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    if(!(argForward > 0 && argForward < 1) || !(argBackward > 0 && argBackward <= 1))
      throw new IllegalArgumentException("invalid scopes for backwards or forwards");
    visited = new HashSet<CmpVertex>();
    machine = new LearnerGraphND(Configuration.getDefaultConfiguration().copy());
    vertices = new ArrayList<CmpVertex>();
    generator  = new MersenneTwister(seed);
    this.alphabet = alphabetArg;
    boolGenerator = new Random(seed);
    CmpVertex v= AbstractLearnerGraph.generateNewCmpVertex(new VertexID(VertexID.VertKind.NEUTRAL,0), Configuration.getDefaultConfiguration());  //new ();
    annotateVertex(v);
    vertices.add(v);// permits v to be chosen as a target, creating self-loops

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    if(!(argForward > 0 && argForward < 1) || !(argBackward > 0 && argBackward <= 1))
      throw new IllegalArgumentException("invalid scopes for backwards or forwards");
    visited = new HashSet<CmpVertex>();
    machine = new LearnerGraphND(Configuration.getDefaultConfiguration());
    vertices = new ArrayList<CmpVertex>();
    generator  = new MersenneTwister(seed);
    this.alphabet = alphabet;
    boolGenerator = new Random(seed);
    CmpVertex v= AbstractLearnerGraph.generateNewCmpVertex(new VertexID(VertexID.VertKind.NEUTRAL,0), Configuration.getDefaultConfiguration());  //new ();
    annotateVertex(v);
    vertices.add(v);// permits v to be chosen as a target, creating self-loops

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    if(!(argForward > 0 && argForward < 1) || !(argBackward > 0 && argBackward <= 1))
      throw new IllegalArgumentException("invalid scopes for backwards or forwards");
    visited = new HashSet<DeterministicVertex>();
    machine = new DirectedSparseGraph();
    vertices = new ArrayList<DeterministicVertex>();
    generator  = new MersenneTwister(seed);
    boolGenerator = new Random(seed);
    DeterministicVertex v=new DeterministicVertex(new VertexID(VertexID.VertKind.NEUTRAL,0));
    annotateVertex(v);
    machine.addVertex(v);
    vertices.add(v);// permits v to be chosen as a target, creating self-loops

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