@Test public void testComprehensiveOnMixture() { RandomEngine r = new MersenneTwister64(0); Normal[] dists = new Normal[]{ new Normal(100, 50, r),
public Long getNextSeed() { return seedGenerator.nextLong(); }
/** * Returns a 64 bit uniformly distributed random number in the open unit interval <code>(0.0,1.0)</code> (excluding 0.0 and 1.0). */ public double raw() { return nextDouble(); } }
/** * @param seed * the seed to set */ public void setSeed(Long seed) { this.seed = seed; this.seedGenerator = new MersenneTwister64(seed.intValue()); }
public Long getNextSeed() { return seedGenerator.nextLong(); }
/** * Returns a 64 bit uniformly distributed random number in the open unit interval <code>(0.0,1.0)</code> (excluding 0.0 and 1.0). */ public double raw() { return nextDouble(); } }
/** * @param seed * the seed to set */ public void setSeed(Long seed) { this.seed = seed; this.rndEngine = new MersenneTwister64(seed.intValue()); }
/** * @param seed * the seed to set */ public void setSeed(Long seed) { this.seed = seed; this.rndEngine = new MersenneTwister64(seed.intValue()); }
/** * @param seed * the seed to set */ public void setSeed(Long seed) { this.seed = seed; this.seedGenerator = new MersenneTwister64(seed.intValue()); }
/** * Create a new Gaussian stream with the given number of classes. The map * contains a mapping of attribute-names to double-pairs each of which * describes the mean/variance of a Gaussian distribution. * * @param numberOfClasses * The number of classes. * @param attributeDistributions * The parameterization of the attribute distributions. */ public ColtGaussianStream() { super((SourceURL) null); seed = System.currentTimeMillis(); generators = new LinkedHashMap<String, ColtGaussian>(); types = new LinkedHashMap<String, Class<?>>(); seedGenerator = new MersenneTwister64(); }
/** * Create a new Gaussian stream with the given number of classes. The map * contains a mapping of attribute-names to double-pairs each of which * describes the mean/variance of a Gaussian distribution. * * @param numberOfClasses * The number of classes. * @param attributeDistributions * The parameterization of the attribute distributions. */ public ColtGaussianStream() { super((SourceURL) null); seed = System.currentTimeMillis(); generators = new LinkedHashMap<String, ColtGaussian>(); types = new LinkedHashMap<String, Class<?>>(); seedGenerator = new MersenneTwister64(); }
public ColtGaussian(Double mean, Double variance, Long seed) { rndEngine = new MersenneTwister64(seed.intValue()); this.rnd = new Normal(mean, variance, rndEngine); this.mean = mean; this.variance = variance; }
public ColtGaussian(Double mean, Double variance, Long seed) { rndEngine = new MersenneTwister64(seed.intValue()); this.rnd = new Normal(mean, variance, rndEngine); this.mean = mean; this.variance = variance; }
gen = new MersenneTwister64(); System.out.println("\n MersenneTwister64:"); timer.reset().start();
gen = new MersenneTwister64(); System.out.println("\n MersenneTwister64:"); timer.reset().start();