/** * Create an object that will use a default RNG ({@link MersenneTwister}), * in order to generate the individual components. * * @param dimension Space dimension. */ public UnitSphereRandomVectorGenerator(final int dimension) { this(dimension, new MersenneTwister()); }
RandomWrapper(long seed) { random = new MersenneTwister(seed); }
@Override public MersenneTwister generator() { MersenneTwister random = new MersenneTwister(); random.setSeed(seed); return random; } }
public DefaultRandom(long seed) { this.seed = seed; this.randomGenerator = new SynchronizedRandomGenerator(new MersenneTwister(seed)); }
RandomWrapper() { random = new MersenneTwister(); random.setSeed(System.currentTimeMillis() + System.identityHashCode(random)); }
public static void main (String... args) { System.out.println("Testing histogram store with normal distribution, mean 0"); Histogram h = new Histogram("Normal"); MersenneTwister mt = new MersenneTwister(); IntStream.range(0, 1000000).map(i -> (int) Math.round(mt.nextGaussian() * 20 + 2.5)).forEach(h::add); h.displayHorizontal(); System.out.println("mean: " + h.mean()); } }
return new Iterator<List<HStoreFile>>() { private GaussianRandomGenerator gen = new GaussianRandomGenerator(new MersenneTwister(random.nextInt())); private int count = 0;
/** * Test if two clusters are significantly different in the metrics we look at for balancing. * * @param orig the utilization matrix from the original cluster * @param optimized the utilization matrix from the optimized cluster * @return The P value that the various derived resources come from the same probability distribution. The probability * that the null hypothesis is correct. */ public static double[] testDifference(double[][] orig, double[][] optimized) { int nResources = RawAndDerivedResource.values().length; if (orig.length != nResources) { throw new IllegalArgumentException("orig must have number of rows equal to RawAndDerivedResource."); } if (optimized.length != nResources) { throw new IllegalArgumentException("optimized must have number of rows equal to RawAndDerivedResource."); } if (orig[0].length != optimized[0].length) { throw new IllegalArgumentException("The number of brokers must be the same."); } double[] pValues = new double[orig.length]; //TODO: For small N we want to do statistical bootstrapping (not the same as bootstrapping data). for (int resourceIndex = 0; resourceIndex < nResources; resourceIndex++) { RandomGenerator rng = new MersenneTwister(0x5d11121018463324L); KolmogorovSmirnovTest kolmogorovSmirnovTest = new KolmogorovSmirnovTest(rng); pValues[resourceIndex] = kolmogorovSmirnovTest.kolmogorovSmirnovTest(orig[resourceIndex], optimized[resourceIndex]); } return pValues; }
@Override public Random createRandom() { switch (randomType) { case JDK: return randomSeed == null ? new Random() : new Random(randomSeed); case MERSENNE_TWISTER: return new RandomAdaptor(randomSeed == null ? new MersenneTwister() : new MersenneTwister(randomSeed)); case WELL512A: return new RandomAdaptor(randomSeed == null ? new Well512a() : new Well512a(randomSeed)); case WELL1024A: return new RandomAdaptor(randomSeed == null ? new Well1024a() : new Well1024a(randomSeed)); case WELL19937A: return new RandomAdaptor(randomSeed == null ? new Well19937a() : new Well19937a(randomSeed)); case WELL19937C: return new RandomAdaptor(randomSeed == null ? new Well19937c() : new Well19937c(randomSeed)); case WELL44497A: return new RandomAdaptor(randomSeed == null ? new Well44497a() : new Well44497a(randomSeed)); case WELL44497B: return new RandomAdaptor(randomSeed == null ? new Well44497b() : new Well44497b(randomSeed)); default: throw new IllegalStateException("The randomType (" + randomType + ") is not implemented."); } }
/** Constructor */ public MersenneTwisterGenerator(long seed) { this.seed = seed ; rnd = new MersenneTwister(seed) ; }
/** * Create an object that will use a default RNG ({@link MersenneTwister}), * in order to generate the individual components. * * @param dimension Space dimension. */ public UnitSphereRandomVectorGenerator(final int dimension) { this(dimension, new MersenneTwister()); }
/** * Create an object that will use a default RNG ({@link MersenneTwister}), * in order to generate the individual components. * * @param dimension Space dimension. */ public UnitSphereRandomVectorGenerator(final int dimension) { this(dimension, new MersenneTwister()); }
public RandomRangedToString(long min, long max, long seed) { this.theTwister = new MersenneTwister(seed); if (max<=min) { throw new RuntimeException("max must be >= min"); } this.min = min; this.max = max; this._length = max - min; }
@Override public Solver get(long seed) { return new RandomSolver(new MersenneTwister(seed)); } };
/** * Creates a random route planner using the specified random seed. * @param seed The random seed. */ public RandomRoutePlanner(long seed) { LOGGER.info("constructor {}", seed); assignedParcels = LinkedHashMultiset.create(); current = Optional.absent(); rng = new RandomAdaptor(new MersenneTwister(seed)); }
public RandomLineToString(String filename, long seed) { this.rng = new MersenneTwister(seed); this.filename = filename; this.lines = ResourceFinder.readDataFileLines(filename); itemDistribution= new UniformIntegerDistribution(rng, 0, lines.size()-2); }
public RandomLineToStringMap(String paramFile, int maxSize) { rng = new MersenneTwister(System.nanoTime()); this.sizeDistribution = new UniformIntegerDistribution(rng, 0,maxSize-1); this.lineDataMapper = new RandomLineToString(paramFile); }
public RandomLineToStringMap(String paramFile, int maxSize, long seed) { this.rng = new MersenneTwister(seed); this.sizeDistribution = new UniformIntegerDistribution(rng, 0,maxSize-1); this.lineDataMapper = new RandomLineToString(paramFile); }
public RandomFileExtractToString(String fileName, int minsize, int maxsize, long seed) { this.fileName = fileName; this.minsize = minsize; this.maxsize = maxsize; loadData(); this.rng = new MersenneTwister(seed); this.sizeDistribution = new UniformIntegerDistribution(rng, minsize, maxsize); this.positionDistribution = new UniformIntegerDistribution(rng, 1, fileDataImage.limit() - maxsize); }
@Override public void setSolverProvider(SimSolverBuilder provider) { solver = Optional.of(provider.setVehicles(new LinkedHashSet<>(asList(this))) .build(new RandomSolver(new MersenneTwister(123)))); }