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Posted to commits@commons.apache.org by ah...@apache.org on 2019/05/30 19:47:10 UTC
[commons-rng] 03/07: RNG-101: Added MarsagliaTsangWang samplers to
the JMH benchmark.
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 5d681da5775127c43f642ec1dec0ac3a5d960b33
Author: aherbert <ah...@apache.org>
AuthorDate: Fri May 10 15:02:43 2019 +0100
RNG-101: Added MarsagliaTsangWang samplers to the JMH benchmark.
---
.../jmh/distribution/DiscreteSamplersPerformance.java | 12 ++++++++++++
1 file changed, 12 insertions(+)
diff --git a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/DiscreteSamplersPerformance.java b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/DiscreteSamplersPerformance.java
index eef191e..81dc616 100644
--- a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/DiscreteSamplersPerformance.java
+++ b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/DiscreteSamplersPerformance.java
@@ -23,6 +23,9 @@ import org.apache.commons.rng.sampling.distribution.DiscreteSampler;
import org.apache.commons.rng.sampling.distribution.DiscreteUniformSampler;
import org.apache.commons.rng.sampling.distribution.GeometricSampler;
import org.apache.commons.rng.sampling.distribution.LargeMeanPoissonSampler;
+import org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangBinomialSampler;
+import org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangDiscreteSampler;
+import org.apache.commons.rng.sampling.distribution.MarsagliaTsangWangSmallMeanPoissonSampler;
import org.apache.commons.rng.sampling.distribution.RejectionInversionZipfSampler;
import org.apache.commons.rng.sampling.distribution.SmallMeanPoissonSampler;
@@ -66,6 +69,9 @@ public class DiscreteSamplersPerformance {
"SmallMeanPoissonSampler",
"LargeMeanPoissonSampler",
"GeometricSampler",
+ "MarsagliaTsangWangDiscreteSampler",
+ "MarsagliaTsangWangSmallMeanPoissonSampler",
+ "MarsagliaTsangWangBinomialSampler",
})
private String samplerType;
@@ -96,6 +102,12 @@ public class DiscreteSamplersPerformance {
sampler = new LargeMeanPoissonSampler(rng, 41.7);
} else if ("GeometricSampler".equals(samplerType)) {
sampler = new GeometricSampler(rng, 0.21);
+ } else if ("MarsagliaTsangWangDiscreteSampler".equals(samplerType)) {
+ sampler = new MarsagliaTsangWangDiscreteSampler(rng, new double[] {0.1, 0.2, 0.3, 0.4});
+ } else if ("MarsagliaTsangWangSmallMeanPoissonSampler".equals(samplerType)) {
+ sampler = new MarsagliaTsangWangSmallMeanPoissonSampler(rng, 8.9);
+ } else if ("MarsagliaTsangWangBinomialSampler".equals(samplerType)) {
+ sampler = new MarsagliaTsangWangBinomialSampler(rng, 20, 0.33);
}
}
}