Package org.apache.commons.math.random

Examples of org.apache.commons.math.random.Well44497b

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        this.maxMinFreeIndicesCount = maxMinFreeIndicesCount;
        this.indicesCount = indicesCount;
        this.maxIndicesPerTensor = maxIndicesPerTensor;
        this.maxTensorCount = maxTensorCount;
        this.maxScalarsCount = maxScalarsCount;
        random = new Well44497b();
        reset();
    }
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    private final Context context;

    public NameManager(Context context, Long seed) {
        this.context = context;
        if (seed == null) {
            random = new Well44497b();
            random.setSeed(this.seed = random.nextLong());
        } else
            random = new Well44497b(this.seed = seed.longValue());
    }
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    private final Context context;

    public NameManager(Context context, Long seed) {
        this.context = context;
        if (seed == null) {
            random = new Well44497b();
            random.setSeed(this.seed = random.nextLong());
        } else
            random = new Well44497b(this.seed = seed.longValue());
    }
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//    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(0.5,3);//seems good
    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(power,powerCoefficient);
    //define partition prior
    FragmentPartitionPrior fragPrior = new FragmentPartitionPrior(fragmentPenalty);
    //define tuning distribution
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    common.TuningDistribution tuningDistribution = new PoissonTuningDistribution(rng,poisson);
    //initial jumping weights
    double[] initJumpWeight = new double[n];
    java.util.Arrays.fill(initJumpWeight, 10);
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public class Test {
  public static void main(String[] args) throws MathException {
    int[][] grid = new int[5][5];
    double[][] data = new double[5][5];
    RandomGenerator rg = new Well44497b(20111013);
    RandomDataImpl rng = new RandomDataImpl(rg);
    initializeGrid2(grid);
    initializeData(data, rng);
    printGrid(grid);
//    System.out.println(rng.nextInt(0, 4));
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import org.apache.commons.math.random.Well44497b;

public class RunMCMC {
  public static void main(String[] args){
    //-------------- random number generator -----------------
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    //--------------- initial data points ----------------
   
    //-----initial currentPartition and proposedPartition---------
   
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    SizePenalty sizePenalty = new LinearSizePenalty(0);
    //define partition prior
//    PartitionPrior partitionPrior = new DistPartitionPrior(locs,3,fragmentPenalty,sizePenalty);
    PartitionPrior fragPrior = new FragmentPartitionPrior(fragmentPenalty);
    //define tuning distribution
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    TuningDistribution tuningDistribution = new PoissonTuningDistribution(rng,1);
    //initial jumping weights
    double[] initJumpWeight = new double[n];
    java.util.Arrays.fill(initJumpWeight, 10);
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//    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(0.5,3);//seems good
    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(power,powerCoefficient);
    //define partition prior
    FragmentPartitionPrior fragPrior = new FragmentPartitionPrior(fragmentPenalty);
    //define tuning distribution
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    common.TuningDistribution tuningDistribution = new PoissonTuningDistribution(rng,poisson);
    //initial jumping weights
    double[] initJumpWeight = new double[n];
    java.util.Arrays.fill(initJumpWeight, 10);
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//    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(0.5,3);//seems good
    FragmentPenalty fragmentPenalty = new PowerFragmentPenalty(power,powerCoefficient);
    //define partition prior
    FragmentPartitionPrior fragPrior = new FragmentPartitionPrior(fragmentPenalty);
    //define tuning distribution
    RandomGenerator rg = new Well44497b();
    RandomDataImpl rng = new RandomDataImpl(rg);
    common.TuningDistribution tuningDistribution = new PoissonTuningDistribution(rng,poisson);
    //initial jumping weights
    double[] initJumpWeight = new double[n];
    java.util.Arrays.fill(initJumpWeight, 10);
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import dclong.stat.Combination;
import dclong.util.Timer;

public class TestSPTsRep {
  public static void main(String[] args) throws IOException{
    RandomGenerator rg = new Well44497b(31);
    RandomDataImpl rng = new RandomDataImpl(rg);
    int sizeOfEachDataSet = 26;
    int sizeOfFirstGroup = 5;
    int numberOfTests = 12625;
    Combination comb = new Combination(sizeOfEachDataSet,sizeOfFirstGroup);
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Related Classes of org.apache.commons.math.random.Well44497b

  • cc.redberry.core.context.NameManager
  • cc.redberry.core.tensor.random.RandomProduct
  • complex1.RunMCMC
  • normal1.RunSimpleMCMC
  • normal2.RunMCMC
  • normal2.RunSimpleMCMC
  • orientation2.Test
  • orientation3.RunMCMC
  • spt.TestSPTsImpl
  • spt.TestSPTsRep
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