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commit 756c2512e788975384e1f6ea76744424f0dfd911
Author: Alex Herbert <aherb...@apache.org>
AuthorDate: Sun Jul 21 11:50:31 2019 +0100
RNG-110: Provide factory constructors for released samplers.
These samplers must maintain an instance constructor for binary
compatibility.
---
.../ContinuousSamplersPerformance.java | 24 ++++++------
.../distribution/DiscreteSamplersPerformance.java | 8 ++--
.../distribution/GeometricSamplersPerformance.java | 2 +-
.../PoissonSamplerCachePerformance.java | 18 +++++----
.../distribution/PoissonSamplersPerformance.java | 4 +-
.../commons/rng/examples/jpms/lib/DiceGame.java | 2 +-
.../ProbabilityDensityApproximationCommand.java | 38 +++++++++----------
.../UniformSamplingVisualCheckCommand.java | 6 +--
.../commons/rng/sampling/UnitSphereSampler.java | 4 +-
.../AhrensDieterExponentialSampler.java | 13 +++++++
.../AhrensDieterMarsagliaTsangGammaSampler.java | 29 +++++++++++---
.../distribution/BoxMullerLogNormalSampler.java | 2 +-
.../BoxMullerNormalizedGaussianSampler.java | 13 +++++++
.../sampling/distribution/ChengBetaSampler.java | 15 ++++++++
.../distribution/ContinuousUniformSampler.java | 16 +++++++-
.../distribution/DiscreteUniformSampler.java | 40 ++++++++++++++------
.../rng/sampling/distribution/GaussianSampler.java | 20 ++++++++++
.../sampling/distribution/GeometricSampler.java | 2 +-
.../InverseTransformContinuousSampler.java | 17 +++++++++
.../InverseTransformDiscreteSampler.java | 17 +++++++++
.../InverseTransformParetoSampler.java | 15 ++++++++
.../distribution/LargeMeanPoissonSampler.java | 21 +++++++++--
.../sampling/distribution/LogNormalSampler.java | 20 ++++++++++
.../MarsagliaNormalizedGaussianSampler.java | 13 +++++++
.../rng/sampling/distribution/PoissonSampler.java | 25 ++++++++++--
.../sampling/distribution/PoissonSamplerCache.java | 6 +--
.../RejectionInversionZipfSampler.java | 16 ++++++++
.../distribution/SmallMeanPoissonSampler.java | 15 +++++++-
.../ZigguratNormalizedGaussianSampler.java | 13 +++++++
.../AhrensDieterExponentialSamplerTest.java | 8 ++--
...AhrensDieterMarsagliaTsangGammaSamplerTest.java | 25 ++++++++----
.../BoxMullerNormalisedGaussianSamplerTest.java | 4 +-
.../distribution/ChengBetaSamplerTest.java | 12 ++----
.../distribution/ContinuousSamplersList.java | 44 +++++++++++-----------
.../distribution/ContinuousUniformSamplerTest.java | 6 +--
.../distribution/DiscreteSamplersList.java | 22 +++++------
.../distribution/DiscreteUniformSamplerTest.java | 19 ++++++++--
.../sampling/distribution/GaussianSamplerTest.java | 20 +++++-----
.../InverseTransformContinuousSamplerTest.java | 4 +-
.../InverseTransformDiscreteSamplerTest.java | 4 +-
.../InverseTransformParetoSamplerTest.java | 12 ++----
.../distribution/LargeMeanPoissonSamplerTest.java | 22 +++++------
.../distribution/LogNormalSamplerTest.java | 26 ++++++-------
.../MarsagliaNormalisedGaussianSamplerTest.java | 4 +-
.../distribution/PoissonSamplerCacheTest.java | 2 +-
.../sampling/distribution/PoissonSamplerTest.java | 15 +++++++-
.../RejectionInversionZipfSamplerTest.java | 12 ++----
.../distribution/SmallMeanPoissonSamplerTest.java | 12 +++---
.../ZigguratNormalizedGaussianSamplerTest.java | 6 +--
.../rng/simple/ThreadLocalRandomSource.java | 4 +-
50 files changed, 496 insertions(+), 221 deletions(-)
diff --git
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/ContinuousSamplersPerformance.java
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/ContinuousSamplersPerformance.java
index cb311cb..fea6cfa 100644
---
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/ContinuousSamplersPerformance.java
+++
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/ContinuousSamplersPerformance.java
@@ -97,31 +97,31 @@ public class ContinuousSamplersPerformance {
super.setup();
final UniformRandomProvider rng = getGenerator();
if ("BoxMullerNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new BoxMullerNormalizedGaussianSampler(rng);
+ sampler = BoxMullerNormalizedGaussianSampler.of(rng);
} else if
("MarsagliaNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new MarsagliaNormalizedGaussianSampler(rng);
+ sampler = MarsagliaNormalizedGaussianSampler.of(rng);
} else if
("ZigguratNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new ZigguratNormalizedGaussianSampler(rng);
+ sampler = ZigguratNormalizedGaussianSampler.of(rng);
} else if ("AhrensDieterExponentialSampler".equals(samplerType)) {
- sampler = new AhrensDieterExponentialSampler(rng, 4.56);
+ sampler = AhrensDieterExponentialSampler.of(rng, 4.56);
} else if ("AhrensDieterGammaSampler".equals(samplerType)) {
// This tests the Ahrens-Dieter algorithm since alpha < 1
- sampler = new AhrensDieterMarsagliaTsangGammaSampler(rng,
0.76, 9.8);
+ sampler = AhrensDieterMarsagliaTsangGammaSampler.of(rng, 0.76,
9.8);
} else if ("MarsagliaTsangGammaSampler".equals(samplerType)) {
// This tests the Marsaglia-Tsang algorithm since alpha > 1
- sampler = new AhrensDieterMarsagliaTsangGammaSampler(rng,
12.34, 9.8);
+ sampler = AhrensDieterMarsagliaTsangGammaSampler.of(rng,
12.34, 9.8);
} else if
("LogNormalBoxMullerNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new LogNormalSampler(new
BoxMullerNormalizedGaussianSampler(rng), 12.3, 4.6);
+ sampler =
LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(rng), 12.3, 4.6);
} else if
("LogNormalMarsagliaNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new LogNormalSampler(new
MarsagliaNormalizedGaussianSampler(rng), 12.3, 4.6);
+ sampler =
LogNormalSampler.of(MarsagliaNormalizedGaussianSampler.of(rng), 12.3, 4.6);
} else if
("LogNormalZigguratNormalizedGaussianSampler".equals(samplerType)) {
- sampler = new LogNormalSampler(new
ZigguratNormalizedGaussianSampler(rng), 12.3, 4.6);
+ sampler =
LogNormalSampler.of(ZigguratNormalizedGaussianSampler.of(rng), 12.3, 4.6);
} else if ("ChengBetaSampler".equals(samplerType)) {
- sampler = new ChengBetaSampler(rng, 0.45, 6.7);
+ sampler = ChengBetaSampler.of(rng, 0.45, 6.7);
} else if ("ContinuousUniformSampler".equals(samplerType)) {
- sampler = new ContinuousUniformSampler(rng, 123.4, 5678.9);
+ sampler = ContinuousUniformSampler.of(rng, 123.4, 5678.9);
} else if ("InverseTransformParetoSampler".equals(samplerType)) {
- sampler = new InverseTransformParetoSampler(rng, 23.45,
0.1234);
+ sampler = InverseTransformParetoSampler.of(rng, 23.45, 0.1234);
}
}
}
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 03bae18..ef41587 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
@@ -110,14 +110,14 @@ public class DiscreteSamplersPerformance {
super.setup();
final UniformRandomProvider rng = getGenerator();
if ("DiscreteUniformSampler".equals(samplerType)) {
- sampler = new DiscreteUniformSampler(rng, -98, 76);
+ sampler = DiscreteUniformSampler.of(rng, -98, 76);
} else if ("RejectionInversionZipfSampler".equals(samplerType)) {
- sampler = new RejectionInversionZipfSampler(rng, 43, 2.1);
+ sampler = RejectionInversionZipfSampler.of(rng, 43, 2.1);
} else if ("SmallMeanPoissonSampler".equals(samplerType)) {
- sampler = new SmallMeanPoissonSampler(rng, 8.9);
+ sampler = SmallMeanPoissonSampler.of(rng, 8.9);
} else if ("LargeMeanPoissonSampler".equals(samplerType)) {
// Note: Use with a fractional part to the mean includes a
small mean sample
- sampler = new LargeMeanPoissonSampler(rng, 41.7);
+ sampler = LargeMeanPoissonSampler.of(rng, 41.7);
} else if ("GeometricSampler".equals(samplerType)) {
sampler = GeometricSampler.of(rng, 0.21);
} else if
("MarsagliaTsangWangDiscreteSampler".equals(samplerType)) {
diff --git
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/GeometricSamplersPerformance.java
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/GeometricSamplersPerformance.java
index 7fc3e57..954a3bd 100644
---
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/GeometricSamplersPerformance.java
+++
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/GeometricSamplersPerformance.java
@@ -99,7 +99,7 @@ public class GeometricSamplersPerformance {
} else {
final DiscreteInverseCumulativeProbabilityFunction
geometricFunction =
new
GeometricDiscreteInverseCumulativeProbabilityFunction(probabilityOfSuccess);
- sampler = new InverseTransformDiscreteSampler(rng,
geometricFunction);
+ sampler = InverseTransformDiscreteSampler.of(rng,
geometricFunction);
}
}
}
diff --git
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplerCachePerformance.java
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplerCachePerformance.java
index 7311cde..efe0113 100644
---
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplerCachePerformance.java
+++
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplerCachePerformance.java
@@ -45,7 +45,7 @@ import org.openjdk.jmh.infra.Blackhole;
* cache.
*
* <p>The benchmark is designed for a worse case scenario of Poisson means
that are uniformly spread
- * over a range and non-integer. A single sample is required per mean, E.g.
+ * over a range and non-integer. A single sample is required per mean, E.g.</p>
*
* <pre>
* int min = 40;
@@ -55,7 +55,7 @@ import org.openjdk.jmh.infra.Blackhole;
*
* // Compare ...
* for (int i = 0; i < 1000; i++) {
- * new PoissonSampler(rng, min + rng.nextDouble() * range).sample();
+ * PoissonSampler.of(rng, min + rng.nextDouble() * range).sample();
* }
*
* // To ...
@@ -67,7 +67,7 @@ import org.openjdk.jmh.infra.Blackhole;
*
* <p>The alternative scenario where the means are integer is not considered
as this could be easily
* handled by creating an array to hold the PoissonSamplers for each mean.
This does not require any
- * specialised caching of state and is simple enough to perform for single
threaded applications:
+ * specialised caching of state and is simple enough to perform for single
threaded applications:</p>
*
* <pre>
* public class SimpleUnsafePoissonSamplerCache {
@@ -77,12 +77,12 @@ import org.openjdk.jmh.infra.Blackhole;
*
* public PoissonSampler createPoissonSampler(UniformRandomProvider rng, int
mean) {
* if (mean < min || mean > max) {
- * return new PoissonSampler(rng, mean);
+ * return PoissonSampler.of(rng, mean);
* }
* int index = mean - min;
* PoissonSampler sample = samplers[index];
* if (sampler == null) {
- * sampler = new PoissonSampler(rng, mean);
+ * sampler = PoissonSampler.of(rng, mean);
* samplers[index] = sampler;
* }
* return sampler;
@@ -90,8 +90,10 @@ import org.openjdk.jmh.infra.Blackhole;
* }
* </pre>
*
- * Note that in this example the UniformRandomProvider is also cached and so
this is only
- * applicable to a single threaded application.
+ * <p>Note that in this example the UniformRandomProvider is also cached and
so this is only
+ * applicable to a single threaded application. Thread safety could be ensured
using the
+ * {@link org.apache.commons.rng.sampling.SharedStateSampler
SharedStateSampler} functionality
+ * of the cached sampler.</p>
*
* <p>Re-written to use the PoissonSamplerCache would provide a new
PoissonSampler per call in a
* thread-safe manner:
@@ -308,7 +310,7 @@ public class PoissonSamplerCachePerformance {
final PoissonSamplerFactory factory = new PoissonSamplerFactory() {
@Override
public DiscreteSampler createPoissonSampler(double mean) {
- return new PoissonSampler(r, mean);
+ return PoissonSampler.of(r, mean);
}
};
runSample(factory, range, bh);
diff --git
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplersPerformance.java
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplersPerformance.java
index 2ef6f8b..4fab0a2 100644
---
a/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplersPerformance.java
+++
b/commons-rng-examples/examples-jmh/src/main/java/org/apache/commons/rng/examples/jmh/distribution/PoissonSamplersPerformance.java
@@ -178,7 +178,7 @@ public class PoissonSamplersPerformance {
factory = new DiscreteSamplerFactory() {
@Override
public DiscreteSampler create() {
- return new SmallMeanPoissonSampler(generator, mean);
+ return SmallMeanPoissonSampler.of(generator, mean);
}
};
} else if ("KempSmallMeanPoissonSampler".equals(samplerType)) {
@@ -221,7 +221,7 @@ public class PoissonSamplersPerformance {
@Override
public DiscreteSampler create() {
// Note this is not valid when mean < 1
- return new LargeMeanPoissonSampler(generator, mean);
+ return LargeMeanPoissonSampler.of(generator, mean);
}
};
} else if ("TinyMeanPoissonSampler".equals(samplerType)) {
diff --git
a/commons-rng-examples/examples-jpms/jpms-lib/src/main/java/org/apache/commons/rng/examples/jpms/lib/DiceGame.java
b/commons-rng-examples/examples-jpms/jpms-lib/src/main/java/org/apache/commons/rng/examples/jpms/lib/DiceGame.java
index c12d4f0..db91f72 100644
---
a/commons-rng-examples/examples-jpms/jpms-lib/src/main/java/org/apache/commons/rng/examples/jpms/lib/DiceGame.java
+++
b/commons-rng-examples/examples-jpms/jpms-lib/src/main/java/org/apache/commons/rng/examples/jpms/lib/DiceGame.java
@@ -50,7 +50,7 @@ public class DiceGame {
this.rng = rng;
this.rounds = rounds;
this.players = players;
- sampler = new GaussianSampler(new
ZigguratNormalizedGaussianSampler(rng), mu, sigma);
+ sampler =
GaussianSampler.of(ZigguratNormalizedGaussianSampler.of(rng), mu, sigma);
}
/**
diff --git
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
index 5dd0453..8a80405 100644
---
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
+++
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/ProbabilityDensityApproximationCommand.java
@@ -187,18 +187,18 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double gaussMin = -9;
final double gaussMax = 11;
if (samplers.contains(Sampler.ZigguratGaussianSampler)) {
- createDensity(new GaussianSampler(new
ZigguratNormalizedGaussianSampler(rng),
- gaussMean, gaussSigma),
+
createDensity(GaussianSampler.of(ZigguratNormalizedGaussianSampler.of(rng),
+ gaussMean, gaussSigma),
gaussMin, gaussMax, "gauss.ziggurat.txt");
}
if (samplers.contains(Sampler.MarsagliaGaussianSampler)) {
- createDensity(new GaussianSampler(new
MarsagliaNormalizedGaussianSampler(rng),
- gaussMean, gaussSigma),
+
createDensity(GaussianSampler.of(MarsagliaNormalizedGaussianSampler.of(rng),
+ gaussMean, gaussSigma),
gaussMin, gaussMax, "gauss.marsaglia.txt");
}
if (samplers.contains(Sampler.BoxMullerGaussianSampler)) {
- createDensity(new GaussianSampler(new
BoxMullerNormalizedGaussianSampler(rng),
- gaussMean, gaussSigma),
+
createDensity(GaussianSampler.of(BoxMullerNormalizedGaussianSampler.of(rng),
+ gaussMean, gaussSigma),
gaussMin, gaussMax, "gauss.boxmuller.txt");
}
@@ -207,13 +207,13 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
if (samplers.contains(Sampler.ChengBetaSamplerCase1)) {
final double alphaBeta = 4.3;
final double betaBeta = 2.1;
- createDensity(new ChengBetaSampler(rng, alphaBeta, betaBeta),
+ createDensity(ChengBetaSampler.of(rng, alphaBeta, betaBeta),
betaMin, betaMax, "beta.case1.txt");
}
if (samplers.contains(Sampler.ChengBetaSamplerCase2)) {
final double alphaBetaAlt = 0.5678;
final double betaBetaAlt = 0.1234;
- createDensity(new ChengBetaSampler(rng, alphaBetaAlt, betaBetaAlt),
+ createDensity(ChengBetaSampler.of(rng, alphaBetaAlt, betaBetaAlt),
betaMin, betaMax, "beta.case2.txt");
}
@@ -221,7 +221,7 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double meanExp = 3.45;
final double expMin = 0;
final double expMax = 60;
- createDensity(new AhrensDieterExponentialSampler(rng, meanExp),
+ createDensity(AhrensDieterExponentialSampler.of(rng, meanExp),
expMin, expMax, "exp.txt");
}
@@ -230,13 +230,13 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double thetaGamma = 3.456;
if
(samplers.contains(Sampler.AhrensDieterMarsagliaTsangGammaSamplerCase1)) {
final double alphaGammaSmallerThanOne = 0.1234;
- createDensity(new AhrensDieterMarsagliaTsangGammaSampler(rng,
alphaGammaSmallerThanOne, thetaGamma),
+ createDensity(AhrensDieterMarsagliaTsangGammaSampler.of(rng,
alphaGammaSmallerThanOne, thetaGamma),
gammaMin, gammaMax1, "gamma.case1.txt");
}
if
(samplers.contains(Sampler.AhrensDieterMarsagliaTsangGammaSamplerCase2)) {
final double alphaGammaLargerThanOne = 2.345;
final double gammaMax2 = 70;
- createDensity(new AhrensDieterMarsagliaTsangGammaSampler(rng,
alphaGammaLargerThanOne, thetaGamma),
+ createDensity(AhrensDieterMarsagliaTsangGammaSampler.of(rng,
alphaGammaLargerThanOne, thetaGamma),
gammaMin, gammaMax2, "gamma.case2.txt");
}
@@ -245,14 +245,14 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double paretoMin = 23;
final double paretoMax = 400;
if (samplers.contains(Sampler.InverseTransformParetoSampler)) {
- createDensity(new InverseTransformParetoSampler(rng, scalePareto,
shapePareto),
+ createDensity(InverseTransformParetoSampler.of(rng, scalePareto,
shapePareto),
paretoMin, paretoMax, "pareto.txt");
}
final double loUniform = -9.876;
final double hiUniform = 5.432;
if (samplers.contains(Sampler.ContinuousUniformSampler)) {
- createDensity(new ContinuousUniformSampler(rng, loUniform,
hiUniform),
+ createDensity(ContinuousUniformSampler.of(rng, loUniform,
hiUniform),
loUniform, hiUniform, "uniform.txt");
}
@@ -261,18 +261,18 @@ class ProbabilityDensityApproximationCommand implements
Callable<Void> {
final double logNormalMin = 5;
final double logNormalMax = 25;
if (samplers.contains(Sampler.LogNormalZigguratGaussianSampler)) {
- createDensity(new LogNormalSampler(new
ZigguratNormalizedGaussianSampler(rng),
- scaleLogNormal, shapeLogNormal),
+
createDensity(LogNormalSampler.of(ZigguratNormalizedGaussianSampler.of(rng),
+ scaleLogNormal, shapeLogNormal),
logNormalMin, logNormalMax,
"lognormal.ziggurat.txt");
}
if (samplers.contains(Sampler.LogNormalMarsagliaGaussianSampler)) {
- createDensity(new LogNormalSampler(new
MarsagliaNormalizedGaussianSampler(rng),
- scaleLogNormal, shapeLogNormal),
+
createDensity(LogNormalSampler.of(MarsagliaNormalizedGaussianSampler.of(rng),
+ scaleLogNormal, shapeLogNormal),
logNormalMin, logNormalMax,
"lognormal.marsaglia.txt");
}
if (samplers.contains(Sampler.LogNormalBoxMullerGaussianSampler)) {
- createDensity(new LogNormalSampler(new
BoxMullerNormalizedGaussianSampler(rng),
- scaleLogNormal, shapeLogNormal),
+
createDensity(LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(rng),
+ scaleLogNormal, shapeLogNormal),
logNormalMin, logNormalMax,
"lognormal.boxmuller.txt");
}
diff --git
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/UniformSamplingVisualCheckCommand.java
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/UniformSamplingVisualCheckCommand.java
index c22b5e4..13a170d 100644
---
a/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/UniformSamplingVisualCheckCommand.java
+++
b/commons-rng-examples/examples-sampling/src/main/java/org/apache/commons/rng/examples/sampling/UniformSamplingVisualCheckCommand.java
@@ -64,9 +64,9 @@ class UniformSamplingVisualCheckCommand implements
Callable<Void> {
private final UniformRandomProvider rng =
RandomSource.create(RandomSource.XOR_SHIFT_1024_S_PHI);
/** Samplers. */
private final ContinuousSampler[] samplers = new ContinuousSampler[] {
- new ZigguratNormalizedGaussianSampler(rng),
- new MarsagliaNormalizedGaussianSampler(rng),
- new BoxMullerNormalizedGaussianSampler(rng),
+ ZigguratNormalizedGaussianSampler.of(rng),
+ MarsagliaNormalizedGaussianSampler.of(rng),
+ BoxMullerNormalizedGaussianSampler.of(rng),
};
// Allow System.out
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/UnitSphereSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/UnitSphereSampler.java
index 67cecd1..10c1702 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/UnitSphereSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/UnitSphereSampler.java
@@ -53,7 +53,7 @@ public class UnitSphereSampler implements
SharedStateSampler<UnitSphereSampler>
}
this.dimension = dimension;
- sampler = new ZigguratNormalizedGaussianSampler(rng);
+ sampler = ZigguratNormalizedGaussianSampler.of(rng);
}
/**
@@ -63,7 +63,7 @@ public class UnitSphereSampler implements
SharedStateSampler<UnitSphereSampler>
private UnitSphereSampler(UniformRandomProvider rng,
UnitSphereSampler source) {
// The Gaussian sampler has no shared state so create a new instance
- sampler = new ZigguratNormalizedGaussianSampler(rng);
+ sampler = ZigguratNormalizedGaussianSampler.of(rng);
dimension = source.dimension;
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.java
index 0340a45..e8ad681 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.java
@@ -141,4 +141,17 @@ public class AhrensDieterExponentialSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new AhrensDieterExponentialSampler(rng, this);
}
+
+ /**
+ * Create a new Exponential distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param mean Mean of the distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code mean <= 0}
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+ double mean) {
+ return new AhrensDieterExponentialSampler(rng, mean);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.java
index 24aa406..db52483 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.java
@@ -219,7 +219,7 @@ public class AhrensDieterMarsagliaTsangGammaSampler
double alpha,
double theta) {
super(rng, alpha, theta);
- gaussian = new ZigguratNormalizedGaussianSampler(rng);
+ gaussian = ZigguratNormalizedGaussianSampler.of(rng);
dOptim = alpha - ONE_THIRD;
cOptim = ONE_THIRD / Math.sqrt(dOptim);
}
@@ -231,7 +231,7 @@ public class AhrensDieterMarsagliaTsangGammaSampler
MarsagliaTsangGammaSampler(UniformRandomProvider rng,
MarsagliaTsangGammaSampler source) {
super(rng, source);
- gaussian = new ZigguratNormalizedGaussianSampler(rng);
+ gaussian = ZigguratNormalizedGaussianSampler.of(rng);
dOptim = source.dOptim;
cOptim = source.cOptim;
}
@@ -268,6 +268,9 @@ public class AhrensDieterMarsagliaTsangGammaSampler
}
/**
+ * This instance delegates sampling. Use the factory method
+ * {@link #of(UniformRandomProvider, double, double)} to create an optimal
sampler.
+ *
* @param rng Generator of uniformly distributed random numbers.
* @param alpha Alpha parameter of the distribution (this is a shape
parameter).
* @param theta Theta parameter of the distribution (this is a scale
parameter).
@@ -277,9 +280,7 @@ public class AhrensDieterMarsagliaTsangGammaSampler
double alpha,
double theta) {
super(null);
- delegate = alpha < 1 ?
- new AhrensDieterGammaSampler(rng, alpha, theta) :
- new MarsagliaTsangGammaSampler(rng, alpha, theta);
+ delegate = of(rng, alpha, theta);
}
/** {@inheritDoc} */
@@ -300,4 +301,22 @@ public class AhrensDieterMarsagliaTsangGammaSampler
// Direct return of the optimised sampler
return delegate.withUniformRandomProvider(rng);
}
+
+ /**
+ * Creates a new Gamma distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param alpha Alpha parameter of the distribution (this is a shape
parameter).
+ * @param theta Theta parameter of the distribution (this is a scale
parameter).
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code alpha <= 0} or {@code theta
<= 0}
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+ double alpha,
+ double theta) {
+ // Each sampler should check the input arguments.
+ return alpha < 1 ?
+ new AhrensDieterGammaSampler(rng, alpha, theta) :
+ new MarsagliaTsangGammaSampler(rng, alpha, theta);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerLogNormalSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerLogNormalSampler.java
index de8fc29..d4177d1 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerLogNormalSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerLogNormalSampler.java
@@ -46,7 +46,7 @@ public class BoxMullerLogNormalSampler
double scale,
double shape) {
super(null);
- sampler = new LogNormalSampler(new
BoxMullerNormalizedGaussianSampler(rng),
+ sampler =
LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(rng),
scale, shape);
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.java
index d707670..39422cf 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.java
@@ -81,4 +81,17 @@ public class BoxMullerNormalizedGaussianSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new BoxMullerNormalizedGaussianSampler(rng);
}
+
+ /**
+ * Create a new normalised Gaussian sampler.
+ *
+ * @param <S> Sampler type.
+ * @param rng Generator of uniformly distributed random numbers.
+ * @return the sampler
+ */
+ @SuppressWarnings("unchecked")
+ public static <S extends NormalizedGaussianSampler &
SharedStateContinuousSampler> S
+ of(UniformRandomProvider rng) {
+ return (S) new BoxMullerNormalizedGaussianSampler(rng);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ChengBetaSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ChengBetaSampler.java
index 6fe83e7..1a01e1f 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ChengBetaSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ChengBetaSampler.java
@@ -207,4 +207,19 @@ public class ChengBetaSampler
double b) {
return Math.abs(a - b) <= Double.MIN_VALUE;
}
+
+ /**
+ * Creates a new Beta distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param alpha Distribution first shape parameter.
+ * @param beta Distribution second shape parameter.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code alpha <= 0} or {@code beta
<= 0}
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+ double alpha,
+ double beta) {
+ return new ChengBetaSampler(rng, alpha, beta);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSampler.java
index 43a7ec9..4aea1ab 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSampler.java
@@ -64,7 +64,21 @@ public class ContinuousUniformSampler
/** {@inheritDoc} */
@Override
- public ContinuousUniformSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new ContinuousUniformSampler(rng, lo, hi);
+ }
+
+ /**
+ * Creates a new continuous uniform distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param lo Lower bound.
+ * @param hi Higher bound.
+ * @return the sampler
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+ double lo,
+ double hi) {
return new ContinuousUniformSampler(rng, lo, hi);
}
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSampler.java
index b9f6fad..2814799 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSampler.java
@@ -139,6 +139,9 @@ public class DiscreteUniformSampler
}
/**
+ * This instance delegates sampling. Use the factory method
+ * {@link #of(UniformRandomProvider, int, int)} to create an optimal
sampler.
+ *
* @param rng Generator of uniformly distributed random numbers.
* @param lower Lower bound (inclusive) of the distribution.
* @param upper Upper bound (inclusive) of the distribution.
@@ -148,17 +151,7 @@ public class DiscreteUniformSampler
int lower,
int upper) {
super(null);
- if (lower > upper) {
- throw new IllegalArgumentException(lower + " > " + upper);
- }
- // Choose the algorithm depending on the range
- final int range = (upper - lower) + 1;
- delegate = range <= 0 ?
- // The range is too wide to fit in a positive int (larger
- // than 2^31); use a simple rejection method.
- new LargeRangeDiscreteUniformSampler(rng, lower, upper) :
- // Use a sample from the range added to the lower bound.
- new SmallRangeDiscreteUniformSampler(rng, lower, range);
+ delegate = of(rng, lower, upper);
}
/** {@inheritDoc} */
@@ -179,4 +172,29 @@ public class DiscreteUniformSampler
// Direct return of the optimised sampler
return delegate.withUniformRandomProvider(rng);
}
+
+ /**
+ * Creates a new discrete uniform distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param lower Lower bound (inclusive) of the distribution.
+ * @param upper Upper bound (inclusive) of the distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code lower > upper}.
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+ int lower,
+ int upper) {
+ if (lower > upper) {
+ throw new IllegalArgumentException(lower + " > " + upper);
+ }
+ // Choose the algorithm depending on the range
+ final int range = (upper - lower) + 1;
+ return range <= 0 ?
+ // The range is too wide to fit in a positive int (larger
+ // than 2^31); use a simple rejection method.
+ new LargeRangeDiscreteUniformSampler(rng, lower, upper) :
+ // Use a sample from the range added to the lower bound.
+ new SmallRangeDiscreteUniformSampler(rng, lower, range);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GaussianSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GaussianSampler.java
index 91c29e7..6b70b7c 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GaussianSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GaussianSampler.java
@@ -88,4 +88,24 @@ public class GaussianSampler implements
SharedStateContinuousSampler {
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new GaussianSampler(rng, this);
}
+
+ /**
+ * Create a new normalised Gaussian sampler.
+ *
+ * <p>Note: The shared-state functionality is available if the {@link
NormalizedGaussianSampler}
+ * is a {@link SharedStateSampler}. Otherwise a run-time exception will be
thrown when the
+ * sampler is used to share state.</p>
+ *
+ * @param normalized Generator of N(0,1) Gaussian distributed random
numbers.
+ * @param mean Mean of the Gaussian distribution.
+ * @param standardDeviation Standard deviation of the Gaussian
distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code standardDeviation <= 0}
+ * @see #withUniformRandomProvider(UniformRandomProvider)
+ */
+ public static SharedStateContinuousSampler of(NormalizedGaussianSampler
normalized,
+ double mean,
+ double standardDeviation) {
+ return new GaussianSampler(normalized, mean, standardDeviation);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
index 7cbca6b..a3f3339 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GeometricSampler.java
@@ -100,7 +100,7 @@ public final class GeometricSampler {
// is noted in the class Javadoc that the use of a small p leads
to truncation so
// no checks are made for this case.
final double exponentialMean = 1.0 /
(-Math.log1p(-probabilityOfSuccess));
- exponentialSampler = new AhrensDieterExponentialSampler(rng,
exponentialMean);
+ exponentialSampler = AhrensDieterExponentialSampler.of(rng,
exponentialMean);
}
/**
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
index 4401d9e..c0761d9 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSampler.java
@@ -96,4 +96,21 @@ public class InverseTransformContinuousSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new InverseTransformContinuousSampler(rng, function);
}
+
+ /**
+ * Create a new inverse-transform continuous sampler.
+ *
+ * <p>To use the sampler to
+ * {@link org.apache.commons.rng.sampling.SharedStateSampler share state}
the function must be
+ * suitable for concurrent use.</p>
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param function Inverse cumulative probability function.
+ * @return the sampler
+ * @see #withUniformRandomProvider(UniformRandomProvider)
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+
ContinuousInverseCumulativeProbabilityFunction function) {
+ return new InverseTransformContinuousSampler(rng, function);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
index 6067a8c..da7ee63 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSampler.java
@@ -96,4 +96,21 @@ public class InverseTransformDiscreteSampler
public SharedStateDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new InverseTransformDiscreteSampler(rng, function);
}
+
+ /**
+ * Create a new inverse-transform discrete sampler.
+ *
+ * <p>To use the sampler to
+ * {@link org.apache.commons.rng.sampling.SharedStateSampler share state}
the function must be
+ * suitable for concurrent use.</p>
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param function Inverse cumulative probability function.
+ * @return the sampler
+ * @see #withUniformRandomProvider(UniformRandomProvider)
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+
DiscreteInverseCumulativeProbabilityFunction function) {
+ return new InverseTransformDiscreteSampler(rng, function);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.java
index a7b6d7c..ff84cc6 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSampler.java
@@ -85,4 +85,19 @@ public class InverseTransformParetoSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new InverseTransformParetoSampler(rng, this);
}
+
+ /**
+ * Creates a new Pareto distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param scale Scale of the distribution.
+ * @param shape Shape of the distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code scale <= 0} or {@code shape
<= 0}
+ */
+ public static SharedStateContinuousSampler of(UniformRandomProvider rng,
+ double scale,
+ double shape) {
+ return new InverseTransformParetoSampler(rng, scale, shape);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java
index bc71e5b..72b3da5 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.java
@@ -132,8 +132,8 @@ public class LargeMeanPoissonSampler
}
this.rng = rng;
- gaussian = new ZigguratNormalizedGaussianSampler(rng);
- exponential = new AhrensDieterExponentialSampler(rng, 1);
+ gaussian = ZigguratNormalizedGaussianSampler.of(rng);
+ exponential = AhrensDieterExponentialSampler.of(rng, 1);
// Plain constructor uses the uncached function.
factorialLog = NO_CACHE_FACTORIAL_LOG;
@@ -177,8 +177,8 @@ public class LargeMeanPoissonSampler
}
this.rng = rng;
- gaussian = new ZigguratNormalizedGaussianSampler(rng);
- exponential = new AhrensDieterExponentialSampler(rng, 1);
+ gaussian = ZigguratNormalizedGaussianSampler.of(rng);
+ exponential = AhrensDieterExponentialSampler.of(rng, 1);
// Plain constructor uses the uncached function.
factorialLog = NO_CACHE_FACTORIAL_LOG;
@@ -307,6 +307,19 @@ public class LargeMeanPoissonSampler
}
/**
+ * Creates a new Poisson distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param mean Mean.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code mean < 1} or {@code mean >
0.5 *}
+ * {@link Integer#MAX_VALUE}.
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+ double mean) {
+ return new LargeMeanPoissonSampler(rng, mean);
+ }
+ /**
* Gets the initialisation state of the sampler.
*
* <p>The state is computed using an integer {@code lambda} value of
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LogNormalSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LogNormalSampler.java
index 9bf9568..9fbfd90 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LogNormalSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/LogNormalSampler.java
@@ -89,4 +89,24 @@ public class LogNormalSampler implements
SharedStateContinuousSampler {
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new LogNormalSampler(rng, this);
}
+
+ /**
+ * Create a new log-normal distribution sampler.
+ *
+ * <p>Note: The shared-state functionality is available if the {@link
NormalizedGaussianSampler}
+ * is a {@link SharedStateSampler}. Otherwise a run-time exception will be
thrown when the
+ * sampler is used to share state.</p>
+ *
+ * @param gaussian N(0,1) generator.
+ * @param scale Scale of the log-normal distribution.
+ * @param shape Shape of the log-normal distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code scale < 0} or {@code shape
<= 0}.
+ * @see #withUniformRandomProvider(UniformRandomProvider)
+ */
+ public static SharedStateContinuousSampler of(NormalizedGaussianSampler
gaussian,
+ double scale,
+ double shape) {
+ return new LogNormalSampler(gaussian, scale, shape);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.java
index 7068052..516f67c 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.java
@@ -90,4 +90,17 @@ public class MarsagliaNormalizedGaussianSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new MarsagliaNormalizedGaussianSampler(rng);
}
+
+ /**
+ * Create a new normalised Gaussian sampler.
+ *
+ * @param <S> Sampler type.
+ * @param rng Generator of uniformly distributed random numbers.
+ * @return the sampler
+ */
+ @SuppressWarnings("unchecked")
+ public static <S extends NormalizedGaussianSampler &
SharedStateContinuousSampler> S
+ of(UniformRandomProvider rng) {
+ return (S) new MarsagliaNormalizedGaussianSampler(rng);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSampler.java
index 6834db9..7fe581c 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSampler.java
@@ -63,6 +63,9 @@ public class PoissonSampler
private final SharedStateDiscreteSampler poissonSamplerDelegate;
/**
+ * This instance delegates sampling. Use the factory method
+ * {@link #of(UniformRandomProvider, double)} to create an optimal sampler.
+ *
* @param rng Generator of uniformly distributed random numbers.
* @param mean Mean.
* @throws IllegalArgumentException if {@code mean <= 0} or
@@ -73,10 +76,7 @@ public class PoissonSampler
super(null);
// Delegate all work to specialised samplers.
- // These should check the input arguments.
- poissonSamplerDelegate = mean < PIVOT ?
- new SmallMeanPoissonSampler(rng, mean) :
- new LargeMeanPoissonSampler(rng, mean);
+ poissonSamplerDelegate = of(rng, mean);
}
/** {@inheritDoc} */
@@ -97,4 +97,21 @@ public class PoissonSampler
// Direct return of the optimised sampler
return poissonSamplerDelegate.withUniformRandomProvider(rng);
}
+
+ /**
+ * Creates a new Poisson distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param mean Mean.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code mean <= 0} or {@code mean >}
+ * {@link Integer#MAX_VALUE}.
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+ double mean) {
+ // Each sampler should check the input arguments.
+ return mean < PIVOT ?
+ SmallMeanPoissonSampler.of(rng, mean) :
+ LargeMeanPoissonSampler.of(rng, mean);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
index 748b2ba..184f8d5 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCache.java
@@ -157,20 +157,20 @@ public class PoissonSamplerCache {
// Ensure the same functionality as the PoissonSampler by
// using a SmallMeanPoissonSampler under the switch point.
if (mean < PoissonSampler.PIVOT) {
- return new SmallMeanPoissonSampler(rng, mean);
+ return SmallMeanPoissonSampler.of(rng, mean);
}
if (mean > maxN) {
// Outside the range of the cache.
// This avoids extra parameter checks and handles the case when
// the cache is empty or if Math.floor(mean) is not an integer.
- return new LargeMeanPoissonSampler(rng, mean);
+ return LargeMeanPoissonSampler.of(rng, mean);
}
// Convert the mean into an integer.
final int n = (int) Math.floor(mean);
if (n < minN) {
// Outside the lower range of the cache.
- return new LargeMeanPoissonSampler(rng, mean);
+ return LargeMeanPoissonSampler.of(rng, mean);
}
// Look in the cache for a state that can be reused.
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.java
index fb8cb9d..7459c7b 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSampler.java
@@ -199,6 +199,22 @@ public class RejectionInversionZipfSampler
}
/**
+ * Creates a new Zipf distribution sampler.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param numberOfElements Number of elements.
+ * @param exponent Exponent.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code numberOfElements <= 0} or
+ * {@code exponent <= 0}.
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+ int numberOfElements,
+ double exponent) {
+ return new RejectionInversionZipfSampler(rng, numberOfElements,
exponent);
+ }
+
+ /**
* {@code H(x)} is defined as
* <ul>
* <li>{@code (x^(1 - exponent) - 1) / (1 - exponent)}, if {@code
exponent != 1}</li>
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.java
index 331b6b0..d444003 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.java
@@ -56,7 +56,7 @@ public class SmallMeanPoissonSampler
/**
* @param rng Generator of uniformly distributed random numbers.
* @param mean Mean.
- * @throws IllegalArgumentException if {@code mean <= 0} or {@code
Math.exp(-mean)} is not positive.
+ * @throws IllegalArgumentException if {@code mean <= 0} or {@code
Math.exp(-mean) == 0}
*/
public SmallMeanPoissonSampler(UniformRandomProvider rng,
double mean) {
@@ -113,4 +113,17 @@ public class SmallMeanPoissonSampler
public SharedStateDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new SmallMeanPoissonSampler(rng, this);
}
+
+ /**
+ * Creates a new sampler for the Poisson distribution.
+ *
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param mean Mean of the distribution.
+ * @return the sampler
+ * @throws IllegalArgumentException if {@code mean <= 0} or {@code
Math.exp(-mean) == 0}.
+ */
+ public static SharedStateDiscreteSampler of(UniformRandomProvider rng,
+ double mean) {
+ return new SmallMeanPoissonSampler(rng, mean);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.java
index 6b8588a..96c3626 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.java
@@ -166,4 +166,17 @@ public class ZigguratNormalizedGaussianSampler
public SharedStateContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
return new ZigguratNormalizedGaussianSampler(rng);
}
+
+ /**
+ * Create a new normalised Gaussian sampler.
+ *
+ * @param <S> Sampler type.
+ * @param rng Generator of uniformly distributed random numbers.
+ * @return the sampler
+ */
+ @SuppressWarnings("unchecked")
+ public static <S extends NormalizedGaussianSampler &
SharedStateContinuousSampler> S
+ of(UniformRandomProvider rng) {
+ return (S) new ZigguratNormalizedGaussianSampler(rng);
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSamplerTest.java
index 6fe5139..e509d50 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSamplerTest.java
@@ -34,9 +34,7 @@ public class AhrensDieterExponentialSamplerTest {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double mean = 0;
- @SuppressWarnings("unused")
- final AhrensDieterExponentialSampler sampler =
- new AhrensDieterExponentialSampler(rng, mean);
+ AhrensDieterExponentialSampler.of(rng, mean);
}
/**
@@ -47,8 +45,8 @@ public class AhrensDieterExponentialSamplerTest {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double mean = 1.23;
- final AhrensDieterExponentialSampler sampler1 =
- new AhrensDieterExponentialSampler(rng1, mean);
+ final SharedStateContinuousSampler sampler1 =
+ AhrensDieterExponentialSampler.of(rng1, mean);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSamplerTest.java
index 27a4198..f1290d0 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSamplerTest.java
@@ -20,6 +20,7 @@ import org.apache.commons.rng.RestorableUniformRandomProvider;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.RandomAssert;
import org.apache.commons.rng.simple.RandomSource;
+import org.junit.Assert;
import org.junit.Test;
/**
@@ -32,12 +33,10 @@ public class AhrensDieterMarsagliaTsangGammaSamplerTest {
@Test(expected = IllegalArgumentException.class)
public void testConstructorThrowsWithZeroAlpha() {
final RestorableUniformRandomProvider rng =
- RandomSource.create(RandomSource.SPLIT_MIX_64);
+ RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double alpha = 0;
final double theta = 1;
- @SuppressWarnings("unused")
- final AhrensDieterMarsagliaTsangGammaSampler sampler =
- new AhrensDieterMarsagliaTsangGammaSampler(rng, alpha, theta);
+ AhrensDieterMarsagliaTsangGammaSampler.of(rng, alpha, theta);
}
/**
@@ -46,12 +45,10 @@ public class AhrensDieterMarsagliaTsangGammaSamplerTest {
@Test(expected = IllegalArgumentException.class)
public void testConstructorThrowsWithZeroTheta() {
final RestorableUniformRandomProvider rng =
- RandomSource.create(RandomSource.SPLIT_MIX_64);
+ RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double alpha = 1;
final double theta = 0;
- @SuppressWarnings("unused")
- final AhrensDieterMarsagliaTsangGammaSampler sampler =
- new AhrensDieterMarsagliaTsangGammaSampler(rng, alpha, theta);
+ AhrensDieterMarsagliaTsangGammaSampler.of(rng, alpha, theta);
}
/**
@@ -79,9 +76,21 @@ public class AhrensDieterMarsagliaTsangGammaSamplerTest {
private static void testSharedStateSampler(double alpha, double theta) {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ // Use instance constructor not factory constructor to exercise 1.X
public API
final AhrensDieterMarsagliaTsangGammaSampler sampler1 =
new AhrensDieterMarsagliaTsangGammaSampler(rng1, alpha, theta);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
+
+ /**
+ * Test the toString method. This is added to ensure coverage as the
factory constructor
+ * used in other tests does not create an instance of the wrapper class.
+ */
+ @Test
+ public void testToString() {
+ final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ Assert.assertTrue(new AhrensDieterMarsagliaTsangGammaSampler(rng, 1.0,
2.0).toString()
+ .toLowerCase().contains("gamma"));
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalisedGaussianSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalisedGaussianSamplerTest.java
index 6e21e62..e928bec 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalisedGaussianSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalisedGaussianSamplerTest.java
@@ -32,8 +32,8 @@ public class BoxMullerNormalisedGaussianSamplerTest {
public void testSharedStateSampler() {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final BoxMullerNormalizedGaussianSampler sampler1 =
- new BoxMullerNormalizedGaussianSampler(rng1);
+ final SharedStateContinuousSampler sampler1 =
+ BoxMullerNormalizedGaussianSampler.of(rng1);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ChengBetaSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ChengBetaSamplerTest.java
index cb630aa..8325244 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ChengBetaSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ChengBetaSamplerTest.java
@@ -35,9 +35,7 @@ public class ChengBetaSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double alpha = 0;
final double beta = 1;
- @SuppressWarnings("unused")
- final ChengBetaSampler sampler =
- new ChengBetaSampler(rng, alpha, beta);
+ ChengBetaSampler.of(rng, alpha, beta);
}
/**
@@ -49,9 +47,7 @@ public class ChengBetaSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double alpha = 1;
final double beta = 0;
- @SuppressWarnings("unused")
- final ChengBetaSampler sampler =
- new ChengBetaSampler(rng, alpha, beta);
+ ChengBetaSampler.of(rng, alpha, beta);
}
/**
@@ -63,8 +59,8 @@ public class ChengBetaSamplerTest {
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double alpha = 1.23;
final double beta = 4.56;
- final ChengBetaSampler sampler1 =
- new ChengBetaSampler(rng1, alpha, beta);
+ final SharedStateContinuousSampler sampler1 =
+ ChengBetaSampler.of(rng1, alpha, beta);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousSamplersList.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousSamplersList.java
index 2cd8325..f4ff9ae 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousSamplersList.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousSamplersList.java
@@ -52,16 +52,16 @@ public final class ContinuousSamplersList {
new
BoxMullerGaussianSampler(RandomSource.create(RandomSource.MT), meanNormal,
sigmaNormal));
// Gaussian ("Box-Muller").
add(LIST, new
org.apache.commons.math3.distribution.NormalDistribution(unusedRng, meanNormal,
sigmaNormal),
- new GaussianSampler(new
BoxMullerNormalizedGaussianSampler(RandomSource.create(RandomSource.MT)),
- meanNormal, sigmaNormal));
+
GaussianSampler.of(BoxMullerNormalizedGaussianSampler.of(RandomSource.create(RandomSource.MT)),
+ meanNormal, sigmaNormal));
// Gaussian ("Marsaglia").
add(LIST, new
org.apache.commons.math3.distribution.NormalDistribution(unusedRng, meanNormal,
sigmaNormal),
- new GaussianSampler(new
MarsagliaNormalizedGaussianSampler(RandomSource.create(RandomSource.MT)),
- meanNormal, sigmaNormal));
+
GaussianSampler.of(MarsagliaNormalizedGaussianSampler.of(RandomSource.create(RandomSource.MT)),
+ meanNormal, sigmaNormal));
// Gaussian ("Ziggurat").
add(LIST, new
org.apache.commons.math3.distribution.NormalDistribution(unusedRng, meanNormal,
sigmaNormal),
- new GaussianSampler(new
ZigguratNormalizedGaussianSampler(RandomSource.create(RandomSource.MT)),
- meanNormal, sigmaNormal));
+
GaussianSampler.of(ZigguratNormalizedGaussianSampler.of(RandomSource.create(RandomSource.MT)),
+ meanNormal, sigmaNormal));
// Beta ("inverse method").
final double alphaBeta = 4.3;
@@ -70,16 +70,16 @@ public final class ContinuousSamplersList {
RandomSource.create(RandomSource.ISAAC));
// Beta ("Cheng").
add(LIST, new
org.apache.commons.math3.distribution.BetaDistribution(unusedRng, alphaBeta,
betaBeta),
- new
ChengBetaSampler(RandomSource.create(RandomSource.MWC_256), alphaBeta,
betaBeta));
+ ChengBetaSampler.of(RandomSource.create(RandomSource.MWC_256),
alphaBeta, betaBeta));
add(LIST, new
org.apache.commons.math3.distribution.BetaDistribution(unusedRng, betaBeta,
alphaBeta),
- new
ChengBetaSampler(RandomSource.create(RandomSource.WELL_19937_A), betaBeta,
alphaBeta));
+
ChengBetaSampler.of(RandomSource.create(RandomSource.WELL_19937_A), betaBeta,
alphaBeta));
// Beta ("Cheng", alternate algorithm).
final double alphaBetaAlt = 0.5678;
final double betaBetaAlt = 0.1234;
add(LIST, new
org.apache.commons.math3.distribution.BetaDistribution(unusedRng, alphaBetaAlt,
betaBetaAlt),
- new
ChengBetaSampler(RandomSource.create(RandomSource.WELL_512_A), alphaBetaAlt,
betaBetaAlt));
+
ChengBetaSampler.of(RandomSource.create(RandomSource.WELL_512_A), alphaBetaAlt,
betaBetaAlt));
add(LIST, new
org.apache.commons.math3.distribution.BetaDistribution(unusedRng, betaBetaAlt,
alphaBetaAlt),
- new
ChengBetaSampler(RandomSource.create(RandomSource.WELL_19937_C), betaBetaAlt,
alphaBetaAlt));
+
ChengBetaSampler.of(RandomSource.create(RandomSource.WELL_19937_C),
betaBetaAlt, alphaBetaAlt));
// Cauchy ("inverse method").
final double medianCauchy = 0.123;
@@ -98,7 +98,7 @@ public final class ContinuousSamplersList {
RandomSource.create(RandomSource.WELL_44497_A));
// Exponential.
add(LIST, new
org.apache.commons.math3.distribution.ExponentialDistribution(unusedRng,
meanExp),
- new
AhrensDieterExponentialSampler(RandomSource.create(RandomSource.MT), meanExp));
+
AhrensDieterExponentialSampler.of(RandomSource.create(RandomSource.MT),
meanExp));
// F ("inverse method").
final int numDofF = 4;
@@ -114,11 +114,11 @@ public final class ContinuousSamplersList {
RandomSource.create(RandomSource.SPLIT_MIX_64));
// Gamma (alpha < 1).
add(LIST, new
org.apache.commons.math3.distribution.GammaDistribution(unusedRng,
alphaGammaSmallerThanOne, thetaGamma),
- new
AhrensDieterMarsagliaTsangGammaSampler(RandomSource.create(RandomSource.XOR_SHIFT_1024_S),
+
AhrensDieterMarsagliaTsangGammaSampler.of(RandomSource.create(RandomSource.XOR_SHIFT_1024_S),
alphaGammaSmallerThanOne, thetaGamma));
// Gamma (alpha > 1).
add(LIST, new
org.apache.commons.math3.distribution.GammaDistribution(unusedRng,
alphaGammaLargerThanOne, thetaGamma),
- new
AhrensDieterMarsagliaTsangGammaSampler(RandomSource.create(RandomSource.WELL_44497_B),
+
AhrensDieterMarsagliaTsangGammaSampler.of(RandomSource.create(RandomSource.WELL_44497_B),
alphaGammaLargerThanOne, thetaGamma));
// Gumbel ("inverse method").
@@ -149,16 +149,16 @@ public final class ContinuousSamplersList {
new
BoxMullerLogNormalSampler(RandomSource.create(RandomSource.XOR_SHIFT_1024_S),
scaleLogNormal, shapeLogNormal));
// Log-normal ("Box-Muller").
add(LIST, new
org.apache.commons.math3.distribution.LogNormalDistribution(unusedRng,
scaleLogNormal, shapeLogNormal),
- new LogNormalSampler(new
BoxMullerNormalizedGaussianSampler(RandomSource.create(RandomSource.XOR_SHIFT_1024_S)),
- scaleLogNormal, shapeLogNormal));
+
LogNormalSampler.of(BoxMullerNormalizedGaussianSampler.of(RandomSource.create(RandomSource.XOR_SHIFT_1024_S)),
+ scaleLogNormal, shapeLogNormal));
// Log-normal ("Marsaglia").
add(LIST, new
org.apache.commons.math3.distribution.LogNormalDistribution(unusedRng,
scaleLogNormal, shapeLogNormal),
- new LogNormalSampler(new
MarsagliaNormalizedGaussianSampler(RandomSource.create(RandomSource.MT_64)),
- scaleLogNormal, shapeLogNormal));
+
LogNormalSampler.of(MarsagliaNormalizedGaussianSampler.of(RandomSource.create(RandomSource.MT_64)),
+ scaleLogNormal, shapeLogNormal));
// Log-normal ("Ziggurat").
add(LIST, new
org.apache.commons.math3.distribution.LogNormalDistribution(unusedRng,
scaleLogNormal, shapeLogNormal),
- new LogNormalSampler(new
ZigguratNormalizedGaussianSampler(RandomSource.create(RandomSource.MWC_256)),
- scaleLogNormal, shapeLogNormal));
+
LogNormalSampler.of(ZigguratNormalizedGaussianSampler.of(RandomSource.create(RandomSource.MWC_256)),
+ scaleLogNormal, shapeLogNormal));
// Logistic ("inverse method").
final double muLogistic = -123.456;
@@ -180,7 +180,7 @@ public final class ContinuousSamplersList {
RandomSource.create(RandomSource.TWO_CMRES_SELECT, null, 9,
11));
// Pareto.
add(LIST, new
org.apache.commons.math3.distribution.ParetoDistribution(unusedRng,
scalePareto, shapePareto),
- new
InverseTransformParetoSampler(RandomSource.create(RandomSource.XOR_SHIFT_1024_S),
scalePareto, shapePareto));
+
InverseTransformParetoSampler.of(RandomSource.create(RandomSource.XOR_SHIFT_1024_S),
scalePareto, shapePareto));
// T ("inverse method").
final double dofT = 0.76543;
@@ -201,7 +201,7 @@ public final class ContinuousSamplersList {
RandomSource.create(RandomSource.TWO_CMRES));
// Uniform.
add(LIST, new
org.apache.commons.math3.distribution.UniformRealDistribution(unusedRng,
loUniform, hiUniform),
- new
ContinuousUniformSampler(RandomSource.create(RandomSource.MT_64), loUniform,
hiUniform));
+
ContinuousUniformSampler.of(RandomSource.create(RandomSource.MT_64), loUniform,
hiUniform));
// Weibull ("inverse method").
final double alphaWeibull = 678.9;
@@ -231,7 +231,7 @@ public final class ContinuousSamplersList {
final
org.apache.commons.math3.distribution.RealDistribution dist,
UniformRandomProvider rng) {
final ContinuousSampler inverseMethodSampler =
- new InverseTransformContinuousSampler(rng,
+ InverseTransformContinuousSampler.of(rng,
new ContinuousInverseCumulativeProbabilityFunction() {
@Override
public double inverseCumulativeProbability(double p) {
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSamplerTest.java
index 36b9eba..5e04f8b 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ContinuousUniformSamplerTest.java
@@ -40,7 +40,7 @@ public class ContinuousUniformSamplerTest {
private static void testSampleInRange(UniformRandomProvider rng,
double low, double high) {
- ContinuousUniformSampler sampler = new ContinuousUniformSampler(rng,
low, high);
+ final SharedStateContinuousSampler sampler =
ContinuousUniformSampler.of(rng, low, high);
final double min = Math.min(low, high);
final double max = Math.max(low, high);
for (int i = 0; i < 10; i++) {
@@ -58,8 +58,8 @@ public class ContinuousUniformSamplerTest {
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double low = 1.23;
final double high = 4.56;
- final ContinuousUniformSampler sampler1 =
- new ContinuousUniformSampler(rng1, low, high);
+ final SharedStateContinuousSampler sampler1 =
+ ContinuousUniformSampler.of(rng1, low, high);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
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 73c7cb7..b2be61e 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
@@ -94,14 +94,14 @@ public final class DiscreteSamplersList {
// Uniform.
add(LIST, new
org.apache.commons.math3.distribution.UniformIntegerDistribution(unusedRng,
loUniform, hiUniform),
MathArrays.sequence(8, -3, 1),
- new
DiscreteUniformSampler(RandomSource.create(RandomSource.MT_64), loUniform,
hiUniform));
+
DiscreteUniformSampler.of(RandomSource.create(RandomSource.MT_64), loUniform,
hiUniform));
// Uniform (large range).
final int halfMax = Integer.MAX_VALUE / 2;
final int hiLargeUniform = halfMax + 10;
final int loLargeUniform = -hiLargeUniform;
add(LIST, new
org.apache.commons.math3.distribution.UniformIntegerDistribution(unusedRng,
loLargeUniform, hiLargeUniform),
MathArrays.sequence(20, -halfMax, halfMax / 10),
- new
DiscreteUniformSampler(RandomSource.create(RandomSource.WELL_1024_A),
loLargeUniform, hiLargeUniform));
+
DiscreteUniformSampler.of(RandomSource.create(RandomSource.WELL_1024_A),
loLargeUniform, hiLargeUniform));
// Zipf ("inverse method").
final int numElementsZipf = 5;
@@ -112,12 +112,12 @@ public final class DiscreteSamplersList {
// Zipf.
add(LIST, new
org.apache.commons.math3.distribution.ZipfDistribution(unusedRng,
numElementsZipf, exponentZipf),
MathArrays.sequence(5, 1, 1),
- new
RejectionInversionZipfSampler(RandomSource.create(RandomSource.WELL_19937_C),
numElementsZipf, exponentZipf));
+
RejectionInversionZipfSampler.of(RandomSource.create(RandomSource.WELL_19937_C),
numElementsZipf, exponentZipf));
// Zipf (exponent close to 1).
final double exponentCloseToOneZipf = 1 - 1e-10;
add(LIST, new
org.apache.commons.math3.distribution.ZipfDistribution(unusedRng,
numElementsZipf, exponentCloseToOneZipf),
MathArrays.sequence(5, 1, 1),
- new
RejectionInversionZipfSampler(RandomSource.create(RandomSource.WELL_19937_C),
numElementsZipf, exponentCloseToOneZipf));
+
RejectionInversionZipfSampler.of(RandomSource.create(RandomSource.WELL_19937_C),
numElementsZipf, exponentCloseToOneZipf));
// Poisson ("inverse method").
final double epsilonPoisson =
org.apache.commons.math3.distribution.PoissonDistribution.DEFAULT_EPSILON;
@@ -129,11 +129,11 @@ public final class DiscreteSamplersList {
// Poisson.
add(LIST, new
org.apache.commons.math3.distribution.PoissonDistribution(unusedRng,
meanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(10, 0, 1),
- new PoissonSampler(RandomSource.create(RandomSource.KISS),
meanPoisson));
+ PoissonSampler.of(RandomSource.create(RandomSource.KISS),
meanPoisson));
// 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.XO_SHI_RO_256_PLUS),
meanPoisson));
+
SmallMeanPoissonSampler.of(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),
KempSmallMeanPoissonSampler.of(RandomSource.create(RandomSource.XO_SHI_RO_128_PLUS),
meanPoisson));
@@ -144,11 +144,11 @@ public final class DiscreteSamplersList {
final double largeMeanPoisson = 67.89;
add(LIST, new
org.apache.commons.math3.distribution.PoissonDistribution(unusedRng,
largeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(50, (int) (largeMeanPoisson - 25), 1),
- new
PoissonSampler(RandomSource.create(RandomSource.SPLIT_MIX_64),
largeMeanPoisson));
+
PoissonSampler.of(RandomSource.create(RandomSource.SPLIT_MIX_64),
largeMeanPoisson));
// Dedicated large mean poisson sampler
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));
+
LargeMeanPoissonSampler.of(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),
MarsagliaTsangWangDiscreteSampler.Poisson.of(RandomSource.create(RandomSource.XO_RO_SHI_RO_128_PLUS),
largeMeanPoisson));
@@ -156,11 +156,11 @@ public final class DiscreteSamplersList {
final double veryLargeMeanPoisson = 543.21;
add(LIST, new
org.apache.commons.math3.distribution.PoissonDistribution(unusedRng,
veryLargeMeanPoisson, epsilonPoisson, maxIterationsPoisson),
MathArrays.sequence(100, (int) (veryLargeMeanPoisson - 50), 1),
- new
PoissonSampler(RandomSource.create(RandomSource.SPLIT_MIX_64),
veryLargeMeanPoisson));
+
PoissonSampler.of(RandomSource.create(RandomSource.SPLIT_MIX_64),
veryLargeMeanPoisson));
// Dedicated large mean poisson sampler
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));
+
LargeMeanPoissonSampler.of(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),
MarsagliaTsangWangDiscreteSampler.Poisson.of(RandomSource.create(RandomSource.XO_RO_SHI_RO_64_SS),
veryLargeMeanPoisson));
@@ -198,7 +198,7 @@ public final class DiscreteSamplersList {
int[] points,
UniformRandomProvider rng) {
final DiscreteSampler inverseMethodSampler =
- new InverseTransformDiscreteSampler(rng,
+ InverseTransformDiscreteSampler.of(rng,
new DiscreteInverseCumulativeProbabilityFunction() {
@Override
public int inverseCumulativeProbability(double p) {
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
index 9b76e7a..0fd662a 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
@@ -19,6 +19,7 @@ package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.RandomAssert;
import org.apache.commons.rng.simple.RandomSource;
+import org.junit.Assert;
import org.junit.Test;
/**
@@ -32,9 +33,8 @@ public class DiscreteUniformSamplerTest {
public void testConstructorThrowsWithLowerAboveUpper() {
final int upper = 55;
final int lower = upper + 1;
- final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- @SuppressWarnings("unused")
- DiscreteUniformSampler sampler = new DiscreteUniformSampler(rng,
lower, upper);
+ final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ DiscreteUniformSampler.of(rng, lower, upper);
}
/**
@@ -62,9 +62,20 @@ public class DiscreteUniformSamplerTest {
private static void testSharedStateSampler(int lower, int upper) {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final DiscreteUniformSampler sampler1 =
+ // Use instance constructor not factory constructor to exercise 1.X
public API
+ final SharedStateDiscreteSampler sampler1 =
new DiscreteUniformSampler(rng1, lower, upper);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
+
+ /**
+ * Test the toString method. This is added to ensure coverage as the
factory constructor
+ * used in other tests does not create an instance of the wrapper class.
+ */
+ @Test
+ public void testToString() {
+ final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ Assert.assertTrue(new DiscreteUniformSampler(rng, 1,
2).toString().toLowerCase().contains("uniform"));
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GaussianSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GaussianSamplerTest.java
index 28ca3c0..cbf1606 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GaussianSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GaussianSamplerTest.java
@@ -34,12 +34,10 @@ public class GaussianSamplerTest {
public void testConstructorThrowsWithZeroStandardDeviation() {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- final NormalizedGaussianSampler gauss = new
ZigguratNormalizedGaussianSampler(rng);
+ final NormalizedGaussianSampler gauss =
ZigguratNormalizedGaussianSampler.of(rng);
final double mean = 1;
final double standardDeviation = 0;
- @SuppressWarnings("unused")
- final GaussianSampler sampler =
- new GaussianSampler(gauss, mean, standardDeviation);
+ GaussianSampler.of(gauss, mean, standardDeviation);
}
/**
@@ -49,11 +47,11 @@ public class GaussianSamplerTest {
public void testSharedStateSampler() {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final NormalizedGaussianSampler gauss = new
ZigguratNormalizedGaussianSampler(rng1);
+ final NormalizedGaussianSampler gauss =
ZigguratNormalizedGaussianSampler.of(rng1);
final double mean = 1.23;
final double standardDeviation = 4.56;
- final GaussianSampler sampler1 =
- new GaussianSampler(gauss, mean, standardDeviation);
+ final SharedStateContinuousSampler sampler1 =
+ GaussianSampler.of(gauss, mean, standardDeviation);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
@@ -73,8 +71,8 @@ public class GaussianSamplerTest {
};
final double mean = 1.23;
final double standardDeviation = 4.56;
- final GaussianSampler sampler1 =
- new GaussianSampler(gauss, mean, standardDeviation);
+ final SharedStateContinuousSampler sampler1 =
+ GaussianSampler.of(gauss, mean, standardDeviation);
sampler1.withUniformRandomProvider(rng2);
}
@@ -88,8 +86,8 @@ public class GaussianSamplerTest {
final NormalizedGaussianSampler gauss = new
BadSharedStateNormalizedGaussianSampler();
final double mean = 1.23;
final double standardDeviation = 4.56;
- final GaussianSampler sampler1 =
- new GaussianSampler(gauss, mean, standardDeviation);
+ final SharedStateContinuousSampler sampler1 =
+ GaussianSampler.of(gauss, mean, standardDeviation);
sampler1.withUniformRandomProvider(rng2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSamplerTest.java
index 99938cf..ad4b033 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSamplerTest.java
@@ -39,8 +39,8 @@ public class InverseTransformContinuousSamplerTest {
};
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final InverseTransformContinuousSampler sampler1 =
- new InverseTransformContinuousSampler(rng1, function);
+ final SharedStateContinuousSampler sampler1 =
+ InverseTransformContinuousSampler.of(rng1, function);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSamplerTest.java
index 778ffe5..1ed4475 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSamplerTest.java
@@ -39,8 +39,8 @@ public class InverseTransformDiscreteSamplerTest {
};
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final InverseTransformDiscreteSampler sampler1 =
- new InverseTransformDiscreteSampler(rng1, function);
+ final SharedStateDiscreteSampler sampler1 =
+ InverseTransformDiscreteSampler.of(rng1, function);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSamplerTest.java
index 3837d1d..22c3303 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformParetoSamplerTest.java
@@ -35,9 +35,7 @@ public class InverseTransformParetoSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double scale = 0;
final double shape = 1;
- @SuppressWarnings("unused")
- final InverseTransformParetoSampler sampler =
- new InverseTransformParetoSampler(rng, scale, shape);
+ InverseTransformParetoSampler.of(rng, scale, shape);
}
/**
@@ -49,9 +47,7 @@ public class InverseTransformParetoSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double scale = 1;
final double shape = 0;
- @SuppressWarnings("unused")
- final InverseTransformParetoSampler sampler =
- new InverseTransformParetoSampler(rng, scale, shape);
+ InverseTransformParetoSampler.of(rng, scale, shape);
}
/**
@@ -63,8 +59,8 @@ public class InverseTransformParetoSamplerTest {
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double scale = 1.23;
final double shape = 4.56;
- final InverseTransformParetoSampler sampler1 =
- new InverseTransformParetoSampler(rng1, scale, shape);
+ final SharedStateContinuousSampler sampler1 =
+ InverseTransformParetoSampler.of(rng1, scale, shape);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSamplerTest.java
index 9a8c674..034a8f1 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSamplerTest.java
@@ -41,8 +41,7 @@ public class LargeMeanPoissonSamplerTest {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double mean = Integer.MAX_VALUE / 2 + 1;
- @SuppressWarnings("unused")
- LargeMeanPoissonSampler sampler = new LargeMeanPoissonSampler(rng,
mean);
+ LargeMeanPoissonSampler.of(rng, mean);
}
/**
@@ -53,8 +52,7 @@ public class LargeMeanPoissonSamplerTest {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double mean = Math.nextAfter(1, -1);
- @SuppressWarnings("unused")
- LargeMeanPoissonSampler sampler = new LargeMeanPoissonSampler(rng,
mean);
+ LargeMeanPoissonSampler.of(rng, mean);
}
/**
@@ -131,17 +129,15 @@ public class LargeMeanPoissonSamplerTest {
final RestorableUniformRandomProvider rng1,
final RestorableUniformRandomProvider rng2,
double mean) {
- final DiscreteSampler s1 = new LargeMeanPoissonSampler(rng1, mean);
+ final LargeMeanPoissonSampler s1 = new LargeMeanPoissonSampler(rng1,
mean);
final int n = (int) Math.floor(mean);
final double lambdaFractional = mean - n;
- final LargeMeanPoissonSamplerState state1 =
((LargeMeanPoissonSampler)s1).getState();
- final DiscreteSampler s2 = new LargeMeanPoissonSampler(rng2, state1,
lambdaFractional);
- final LargeMeanPoissonSamplerState state2 =
((LargeMeanPoissonSampler)s2).getState();
+ final LargeMeanPoissonSamplerState state1 = s1.getState();
+ final LargeMeanPoissonSampler s2 = new LargeMeanPoissonSampler(rng2,
state1, lambdaFractional);
+ final LargeMeanPoissonSamplerState state2 = s2.getState();
Assert.assertEquals("State lambdas are not equal", state1.getLambda(),
state2.getLambda());
Assert.assertNotSame("States are the same object", state1, state2);
- for (int j = 0; j < 10; j++) {
- Assert.assertEquals("Not the same sample", s1.sample(),
s2.sample());
- }
+ RandomAssert.assertProduceSameSequence(s1, s2);
}
/**
@@ -169,8 +165,8 @@ public class LargeMeanPoissonSamplerTest {
private static void testSharedStateSampler(double mean) {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final LargeMeanPoissonSampler sampler1 =
- new LargeMeanPoissonSampler(rng1, mean);
+ final SharedStateDiscreteSampler sampler1 =
+ LargeMeanPoissonSampler.of(rng1, mean);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LogNormalSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LogNormalSamplerTest.java
index 3da6447..4bc093d 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LogNormalSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/LogNormalSamplerTest.java
@@ -34,12 +34,10 @@ public class LogNormalSamplerTest {
public void testConstructorThrowsWithNegativeScale() {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- final NormalizedGaussianSampler gauss = new
ZigguratNormalizedGaussianSampler(rng);
+ final NormalizedGaussianSampler gauss =
ZigguratNormalizedGaussianSampler.of(rng);
final double scale = -1e-6;
final double shape = 1;
- @SuppressWarnings("unused")
- final LogNormalSampler sampler =
- new LogNormalSampler(gauss, scale, shape);
+ LogNormalSampler.of(gauss, scale, shape);
}
/**
@@ -49,12 +47,10 @@ public class LogNormalSamplerTest {
public void testConstructorThrowsWithZeroShape() {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- final NormalizedGaussianSampler gauss = new
ZigguratNormalizedGaussianSampler(rng);
+ final NormalizedGaussianSampler gauss =
ZigguratNormalizedGaussianSampler.of(rng);
final double scale = 1;
final double shape = 0;
- @SuppressWarnings("unused")
- final LogNormalSampler sampler =
- new LogNormalSampler(gauss, scale, shape);
+ LogNormalSampler.of(gauss, scale, shape);
}
/**
@@ -64,11 +60,11 @@ public class LogNormalSamplerTest {
public void testSharedStateSampler() {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final NormalizedGaussianSampler gauss = new
ZigguratNormalizedGaussianSampler(rng1);
+ final NormalizedGaussianSampler gauss =
ZigguratNormalizedGaussianSampler.of(rng1);
final double scale = 1.23;
final double shape = 4.56;
- final LogNormalSampler sampler1 =
- new LogNormalSampler(gauss, scale, shape);
+ final SharedStateContinuousSampler sampler1 =
+ LogNormalSampler.of(gauss, scale, shape);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
@@ -88,8 +84,8 @@ public class LogNormalSamplerTest {
};
final double scale = 1.23;
final double shape = 4.56;
- final LogNormalSampler sampler1 =
- new LogNormalSampler(gauss, scale, shape);
+ final SharedStateContinuousSampler sampler1 =
+ LogNormalSampler.of(gauss, scale, shape);
sampler1.withUniformRandomProvider(rng2);
}
@@ -103,8 +99,8 @@ public class LogNormalSamplerTest {
final NormalizedGaussianSampler gauss = new
BadSharedStateNormalizedGaussianSampler();
final double scale = 1.23;
final double shape = 4.56;
- final LogNormalSampler sampler1 =
- new LogNormalSampler(gauss, scale, shape);
+ final SharedStateContinuousSampler sampler1 =
+ LogNormalSampler.of(gauss, scale, shape);
sampler1.withUniformRandomProvider(rng2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalisedGaussianSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalisedGaussianSamplerTest.java
index 1155ce4..ae58bd0 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalisedGaussianSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalisedGaussianSamplerTest.java
@@ -32,8 +32,8 @@ public class MarsagliaNormalisedGaussianSamplerTest {
public void testSharedStateSampler() {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final MarsagliaNormalizedGaussianSampler sampler1 =
- new MarsagliaNormalizedGaussianSampler(rng1);
+ final SharedStateContinuousSampler sampler1 =
+ MarsagliaNormalizedGaussianSampler.of(rng1);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCacheTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCacheTest.java
index 2fe8a00..54c6fbf 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCacheTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerCacheTest.java
@@ -384,7 +384,7 @@ public class PoissonSamplerCacheTest {
final RestorableUniformRandomProvider rng2,
PoissonSamplerCache cache,
double mean) {
- final DiscreteSampler s1 = new PoissonSampler(rng1, mean);
+ final DiscreteSampler s1 = PoissonSampler.of(rng1, mean);
final DiscreteSampler s2 = cache.createPoissonSampler(rng2, mean);
for (int j = 0; j < 10; j++) {
Assert.assertEquals(s1.sample(), s2.sample());
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerTest.java
index 362397d..4f433f3 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerTest.java
@@ -19,8 +19,10 @@ package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.RandomAssert;
import org.apache.commons.rng.simple.RandomSource;
+import org.junit.Assert;
import org.junit.Test;
+
/**
* This test checks the {@link PoissonSampler} can be created
* from a saved state.
@@ -50,9 +52,20 @@ public class PoissonSamplerTest {
private static void testSharedStateSampler(double mean) {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final PoissonSampler sampler1 =
+ // Use instance constructor not factory constructor to exercise 1.X
public API
+ final SharedStateDiscreteSampler sampler1 =
new PoissonSampler(rng1, mean);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
+
+ /**
+ * Test the toString method. This is added to ensure coverage as the
factory constructor
+ * used in other tests does not create an instance of the wrapper class.
+ */
+ @Test
+ public void testToString() {
+ final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ Assert.assertTrue(new PoissonSampler(rng,
1.23).toString().toLowerCase().contains("poisson"));
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSamplerTest.java
index 15621da..b75f70c 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/RejectionInversionZipfSamplerTest.java
@@ -35,9 +35,7 @@ public class RejectionInversionZipfSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final int numberOfElements = 0;
final double exponent = 1;
- @SuppressWarnings("unused")
- final RejectionInversionZipfSampler sampler =
- new RejectionInversionZipfSampler(rng, numberOfElements, exponent);
+ RejectionInversionZipfSampler.of(rng, numberOfElements, exponent);
}
/**
@@ -49,9 +47,7 @@ public class RejectionInversionZipfSamplerTest {
RandomSource.create(RandomSource.SPLIT_MIX_64);
final int numberOfElements = 1;
final double exponent = 0;
- @SuppressWarnings("unused")
- final RejectionInversionZipfSampler sampler =
- new RejectionInversionZipfSampler(rng, numberOfElements, exponent);
+ RejectionInversionZipfSampler.of(rng, numberOfElements, exponent);
}
/**
@@ -63,8 +59,8 @@ public class RejectionInversionZipfSamplerTest {
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final int numberOfElements = 7;
final double exponent = 1.23;
- final RejectionInversionZipfSampler sampler1 =
- new RejectionInversionZipfSampler(rng1, numberOfElements,
exponent);
+ final SharedStateDiscreteSampler sampler1 =
+ RejectionInversionZipfSampler.of(rng1, numberOfElements, exponent);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSamplerTest.java
index f21bf95..4b0f677 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSamplerTest.java
@@ -37,8 +37,7 @@ public class SmallMeanPoissonSamplerTest {
// Note: p0 = Math.exp(-mean) => mean = -Math.log(p0).
// Add to the limit on the mean to cause p0 to be zero.
final double mean = -Math.log(p0) + 1;
- @SuppressWarnings("unused")
- SmallMeanPoissonSampler sampler = new SmallMeanPoissonSampler(rng,
mean);
+ SmallMeanPoissonSampler.of(rng, mean);
}
/**
@@ -49,8 +48,7 @@ public class SmallMeanPoissonSamplerTest {
final UniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
final double mean = 0;
- @SuppressWarnings("unused")
- SmallMeanPoissonSampler sampler = new SmallMeanPoissonSampler(rng,
mean);
+ SmallMeanPoissonSampler.of(rng, mean);
}
/**
@@ -73,7 +71,7 @@ public class SmallMeanPoissonSamplerTest {
// CHECKSTYLE: resume all
};
for (double mean : new double[] {0.5, 1, 1.5, 2.2}) {
- final SmallMeanPoissonSampler sampler = new
SmallMeanPoissonSampler(rng, mean);
+ final SharedStateDiscreteSampler sampler =
SmallMeanPoissonSampler.of(rng, mean);
final int expected = (int) Math.ceil(1000 * mean);
Assert.assertEquals(expected, sampler.sample());
}
@@ -87,8 +85,8 @@ public class SmallMeanPoissonSamplerTest {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final double mean = 1.23;
- final SmallMeanPoissonSampler sampler1 =
- new SmallMeanPoissonSampler(rng1, mean);
+ final SharedStateDiscreteSampler sampler1 =
+ SmallMeanPoissonSampler.of(rng1, mean);
final SharedStateDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSamplerTest.java
index 1772de4..a223ebe 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSamplerTest.java
@@ -45,7 +45,7 @@ public class ZigguratNormalizedGaussianSamplerTest {
};
// Infinite loop (in v1.1).
- new ZigguratNormalizedGaussianSampler(bad).sample();
+ ZigguratNormalizedGaussianSampler.of(bad).sample();
}
/**
@@ -55,8 +55,8 @@ public class ZigguratNormalizedGaussianSamplerTest {
public void testSharedStateSampler() {
final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
- final ZigguratNormalizedGaussianSampler sampler1 =
- new ZigguratNormalizedGaussianSampler(rng1);
+ final SharedStateContinuousSampler sampler1 =
+ ZigguratNormalizedGaussianSampler.of(rng1);
final SharedStateContinuousSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
diff --git
a/commons-rng-simple/src/main/java/org/apache/commons/rng/simple/ThreadLocalRandomSource.java
b/commons-rng-simple/src/main/java/org/apache/commons/rng/simple/ThreadLocalRandomSource.java
index ee6054c..8a2c7dd 100644
---
a/commons-rng-simple/src/main/java/org/apache/commons/rng/simple/ThreadLocalRandomSource.java
+++
b/commons-rng-simple/src/main/java/org/apache/commons/rng/simple/ThreadLocalRandomSource.java
@@ -37,7 +37,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* // One-time Poisson sample
* double mean = 12.3;
- * int counts = new PoissonSampler(rng, mean).sample();
+ * int counts = PoissonSampler.of(rng, mean).sample();
* </code></pre>
*
* <p>Note if the {@link RandomSource} requires additional arguments then it
is not
@@ -58,7 +58,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* // One-time Poisson sample using a thread-safe random number generator
* double mean = 12.3;
- * int counts = new PoissonSampler(rng.get(), mean).sample();
+ * int counts = PoissonSampler.of(rng.get(), mean).sample();
* </code></pre>
*
* @since 1.3
