You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@commons.apache.org by ah...@apache.org on 2019/05/30 19:47:09 UTC
[commons-rng] 02/07: RNG-101: Added MarsagliaTsangWang Poisson
Sampler to the test list.
This is an automated email from the ASF dual-hosted git repository.
aherbert pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-rng.git
commit abff8ee3587c9dcfeb64f976a1c414e4a18ab68e
Author: aherbert <ah...@apache.org>
AuthorDate: Fri May 10 14:51:52 2019 +0100
RNG-101: Added MarsagliaTsangWang Poisson Sampler to the test list.
---
.../rng/sampling/distribution/DiscreteSamplersList.java | 15 ++++++++++++---
1 file changed, 12 insertions(+), 3 deletions(-)
diff --git a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteSamplersList.java b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteSamplersList.java
index 6158a2d..1cb06c4 100644
--- a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteSamplersList.java
+++ b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteSamplersList.java
@@ -133,10 +133,13 @@ public class DiscreteSamplersList {
// Dedicated small mean poisson sampler
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, meanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(10, 0, 1),
- new SmallMeanPoissonSampler(RandomSource.create(RandomSource.KISS), meanPoisson));
+ new SmallMeanPoissonSampler(RandomSource.create(RandomSource.XO_SHI_RO_256_PLUS), meanPoisson));
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, meanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(10, 0, 1),
- new KempSmallMeanPoissonSampler(RandomSource.create(RandomSource.MT), meanPoisson));
+ new KempSmallMeanPoissonSampler(RandomSource.create(RandomSource.XO_SHI_RO_128_PLUS), meanPoisson));
+ add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, meanPoisson, epsilonPoisson, maxIterationsPoisson),
+ MathArrays.sequence(10, 0, 1),
+ new MarsagliaTsangWangSmallMeanPoissonSampler(RandomSource.create(RandomSource.MT), meanPoisson));
// Poisson (40 < mean < 80).
final double largeMeanPoisson = 67.89;
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, largeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
@@ -146,6 +149,9 @@ public class DiscreteSamplersList {
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, largeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(50, (int) (largeMeanPoisson - 25), 1),
new LargeMeanPoissonSampler(RandomSource.create(RandomSource.SPLIT_MIX_64), largeMeanPoisson));
+ add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, largeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
+ MathArrays.sequence(50, (int) (largeMeanPoisson - 25), 1),
+ new MarsagliaTsangWangSmallMeanPoissonSampler(RandomSource.create(RandomSource.XO_RO_SHI_RO_128_PLUS), largeMeanPoisson));
// Poisson (mean >> 40).
final double veryLargeMeanPoisson = 543.21;
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, veryLargeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
@@ -155,9 +161,12 @@ public class DiscreteSamplersList {
add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, veryLargeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(100, (int) (veryLargeMeanPoisson - 50), 1),
new LargeMeanPoissonSampler(RandomSource.create(RandomSource.SPLIT_MIX_64), veryLargeMeanPoisson));
+ add(LIST, new org.apache.commons.math3.distribution.PoissonDistribution(unusedRng, veryLargeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
+ MathArrays.sequence(100, (int) (veryLargeMeanPoisson - 50), 1),
+ new MarsagliaTsangWangSmallMeanPoissonSampler(RandomSource.create(RandomSource.XO_RO_SHI_RO_64_SS), veryLargeMeanPoisson));
// Any discrete distribution
- double[] discreteProbabilities = new double[] { 0.1, 0.2, 0.3, 0.4 };
+ double[] discreteProbabilities = new double[] { 0.1, 0.2, 0.3, 0.4, 0.5 };
add(LIST, discreteProbabilities,
new MarsagliaTsangWangDiscreteSampler(RandomSource.create(RandomSource.XO_SHI_RO_512_PLUS), discreteProbabilities));
} catch (Exception e) {