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

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 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.

@since 2.0

    return new DefaultSupplierRng<Solver>() {
      @Override
      public Solver get(long seed) {
        return SolverValidator.wrap(new MultiVehicleSolverAdapter(
            ArraysSolverValidator.wrap(new RandomMVArraysSolver(
                new MersenneTwister(seed))), NonSI.MINUTE));
      }
    };
  }

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   * are treated correctly by the conversion script.
   */
  @Test
  public void testToInventoriesArrayWithDuplicatePositions() {


    final Simulator sim = new Simulator(new MersenneTwister(123),
        Measure.valueOf(1000L, SI.MILLI(SI.SECOND)));


    sim.register(new DefaultPDPModel());
    sim.register(new PDPRoadModel(new PlaneRoadModel(new Point(0, 0),
        new Point(10, 10), 50), false));

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    }
    checkState(dataSetDir.exists() || dataSetDir.mkdir());


    final DateTimeFormatter formatter = ISODateTimeFormat
        .dateHourMinuteSecondMillis();
    final RandomGenerator rng = new MersenneTwister(123);


    // 480 minutes or 8 hours
    final long dayLength = 480L;
    // 0.1 vehicle per km2
    final double vehicleDensity = .1d;

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   * @param models An optional list of models which can be added, with this
   *          option custom models for specific solutions can be added.
   */
  public DynamicPDPTWProblem(final DynamicPDPTWScenario scen, long randomSeed,
      Model<?>... models) {
    simulator = new Simulator(new MersenneTwister(randomSeed), Measure.valueOf(
        scen.getTickSize(), scen.getTimeUnit()));
    simulator.register(scen.createRoadModel());
    simulator.register(scen.createPDPModel());
    for (final Model<?> m : models) {
      simulator.register(m);

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      // get the reconstruction row that matches this image
      Matrix reconstructed_row_image = MatrixUtils.viewRowAsMatrix(reconstruct_all, j);
      
      // now generate a new image based on the reconstruction probabilities
      Matrix draw2 = MatrixUtils.genBinomialDistribution( reconstructed_row_image, 1, new MersenneTwister(123) ).times(255);
    
      if (toDisk) {
        
//        System.out.println("Label: " + input.get(j).getSecond().viewRow(0).maxValueIndex() );
  //      MatrixUtils.debug_print( input.get(j).getSecond() );

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  }
  
  @Test
  public void testMnist() throws Exception {
    MnistDataSetIterator fetcher = new MnistDataSetIterator(100,200);
    MersenneTwister rand = new MersenneTwister(123);


    double learningRate = 0.001;
    
    int[] batchSteps = { 250, 200, 150, 100, 50, 25, 5 };

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    int numIns = recordBatch.getFirst().numCols();
    int numLabels = recordBatch.getSecond().numCols();


    int n_layers = hiddenLayerSizes.length;
    RandomGenerator rng = new MersenneTwister(123);
    
    
    DeepBeliefNetwork dbn = new DeepBeliefNetwork( numIns, hiddenLayerSizes, numLabels, n_layers, rng ); //, Matrix input, Matrix labels);
        
    dbn.useRegularization = false;

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    DataSet first = fetcher.next();
    int numIns = first.getFirst().numCols();
    int numLabels = first.getSecond().numCols();


    int n_layers = hiddenLayerSizes.length;
    RandomGenerator rng = new MersenneTwister(123);
    
    
    DeepBeliefNetwork dbn = new DeepBeliefNetwork( numIns, hiddenLayerSizes, numLabels, n_layers, rng ); //, Matrix input, Matrix labels);
        
    dbn.useRegularization = false;

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  @Test
  public void testMatrixBinomialGeneration() {


    RandomGenerator g = new MersenneTwister(123);
    
    Matrix m0 = new DenseMatrix(1, 3);
    m0.setQuick(0, 0, 0.5);
    m0.setQuick(0, 1, 0.5);
    m0.setQuick(0, 2, 0.1);

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  }  
  
  @Test
  public void testUniform() {
    
    RandomGenerator g = new MersenneTwister(123);
    
    Matrix u = MatrixUtils.uniform(g, 3, 4);
    
    assertEquals(3, u.numRows());
    assertEquals(4, u.numCols());

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