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commit 153b11633b1c4fa9bbfec6c5ac79635b91600400
Author: Alex Herbert <aherb...@apache.org>
AuthorDate: Sat Jul 6 20:20:02 2019 +0100
RNG-102: Implement the SharedStateSampler interface.
---
.../commons/rng/sampling/CollectionSampler.java | 18 +++-
.../commons/rng/sampling/CombinationSampler.java | 27 +++++-
.../DiscreteProbabilityCollectionSampler.java | 20 +++-
.../commons/rng/sampling/PermutationSampler.java | 26 +++++-
.../commons/rng/sampling/UnitSphereSampler.java | 19 +++-
.../AhrensDieterExponentialSampler.java | 20 +++-
.../AhrensDieterMarsagliaTsangGammaSampler.java | 70 +++++++++++++-
.../distribution/AliasMethodDiscreteSampler.java | 20 +++-
.../BoxMullerNormalizedGaussianSampler.java | 9 +-
.../sampling/distribution/ChengBetaSampler.java | 21 ++++-
.../distribution/ContinuousUniformSampler.java | 9 +-
.../distribution/DiscreteUniformSampler.java | 33 ++++++-
.../rng/sampling/distribution/GaussianSampler.java | 35 ++++++-
.../sampling/distribution/GeometricSampler.java | 46 +++++++++-
.../distribution/GuideTableDiscreteSampler.java | 20 +++-
.../InverseTransformContinuousSampler.java | 14 ++-
.../InverseTransformDiscreteSampler.java | 14 ++-
.../InverseTransformParetoSampler.java | 21 ++++-
.../distribution/KempSmallMeanPoissonSampler.java | 20 +++-
.../distribution/LargeMeanPoissonSampler.java | 45 ++++++++-
.../sampling/distribution/LogNormalSampler.java | 35 ++++++-
.../MarsagliaNormalizedGaussianSampler.java | 9 +-
.../MarsagliaTsangWangDiscreteSampler.java | 101 +++++++++++++++++++--
.../rng/sampling/distribution/PoissonSampler.java | 26 +++++-
.../RejectionInversionZipfSampler.java | 24 ++++-
.../distribution/SmallMeanPoissonSampler.java | 20 +++-
.../ZigguratNormalizedGaussianSampler.java | 9 +-
.../rng/sampling/CollectionSamplerTest.java | 30 +++++-
.../rng/sampling/CombinationSamplerTest.java | 27 ++++++
.../DiscreteProbabilityCollectionSamplerTest.java | 28 ++++++
.../rng/sampling/PermutationSamplerTest.java | 27 ++++++
.../apache/commons/rng/sampling/RandomAssert.java | 3 +-
.../rng/sampling/UnitSphereSamplerTest.java | 26 ++++++
.../AhrensDieterExponentialSamplerTest.java | 16 ++++
...AhrensDieterMarsagliaTsangGammaSamplerTest.java | 33 +++++++
.../AliasMethodDiscreteSamplerTest.java | 32 +++++++
...=> BoxMullerNormalisedGaussianSamplerTest.java} | 22 +++--
.../distribution/ChengBetaSamplerTest.java | 17 ++++
.../distribution/ContinuousUniformSamplerTest.java | 16 ++++
.../distribution/DiscreteUniformSamplerTest.java | 32 +++++++
.../sampling/distribution/GaussianSamplerTest.java | 74 +++++++++++++++
.../distribution/GeometricSamplerTest.java | 32 +++++++
.../GuideTableDiscreteSamplerTest.java | 15 +++
.../InverseTransformContinuousSamplerTest.java | 47 ++++++++++
.../InverseTransformDiscreteSamplerTest.java | 47 ++++++++++
.../InverseTransformParetoSamplerTest.java | 25 ++++-
.../KempSmallMeanPoissonSamplerTest.java | 16 ++++
.../distribution/LargeMeanPoissonSamplerTest.java | 33 +++++++
.../distribution/LogNormalSamplerTest.java | 74 +++++++++++++++
...=> MarsagliaNormalisedGaussianSamplerTest.java} | 22 +++--
.../MarsagliaTsangWangDiscreteSamplerTest.java | 89 ++++++++++++++++--
.../sampling/distribution/PoissonSamplerTest.java | 58 ++++++++++++
.../RejectionInversionZipfSamplerTest.java | 17 ++++
.../distribution/SmallMeanPoissonSamplerTest.java | 15 +++
.../ZigguratNormalizedGaussianSamplerTest.java | 15 +++
55 files changed, 1539 insertions(+), 80 deletions(-)
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CollectionSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CollectionSampler.java
index 54f9ee9..12c8b93 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CollectionSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CollectionSampler.java
@@ -32,7 +32,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* @since 1.0
*/
-public class CollectionSampler<T> {
+public class CollectionSampler<T> implements
SharedStateSampler<CollectionSampler<T>> {
/** Collection to be sampled from. */
private final List<T> items;
/** RNG. */
@@ -57,6 +57,16 @@ public class CollectionSampler<T> {
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private CollectionSampler(UniformRandomProvider rng,
+ CollectionSampler<T> source) {
+ this.rng = rng;
+ items = source.items;
+ }
+
+ /**
* Picks one of the items from the
* {@link #CollectionSampler(UniformRandomProvider,Collection)
* collection passed to the constructor}.
@@ -66,4 +76,10 @@ public class CollectionSampler<T> {
public T sample() {
return items.get(rng.nextInt(items.size()));
}
+
+ /** {@inheritDoc} */
+ @Override
+ public CollectionSampler<T>
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new CollectionSampler<T>(rng, this);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CombinationSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CombinationSampler.java
index eeae0d5..848f737 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CombinationSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/CombinationSampler.java
@@ -39,7 +39,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* @see PermutationSampler
*/
-public class CombinationSampler {
+public class CombinationSampler implements
SharedStateSampler<CombinationSampler> {
/** Domain of the combination. */
private final int[] domain;
/** The number of steps of a full shuffle to perform. */
@@ -90,6 +90,25 @@ public class CombinationSampler {
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private CombinationSampler(UniformRandomProvider rng,
+ CombinationSampler source) {
+ // Do not clone the domain. This ensures:
+ // 1. Thread safety as the domain may be shuffled during the clone
+ // and a shuffle swap step can result in duplicates and missing
elements
+ // in the array.
+ // 2. If the argument RNG is an exact match for the RNG in the source
+ // then the output sequence will differ unless the domain is
currently
+ // in natural order.
+ domain = PermutationSampler.natural(source.domain.length);
+ steps = source.steps;
+ upper = source.upper;
+ this.rng = rng;
+ }
+
+ /**
* Return a combination of {@code k} whose entries are selected randomly,
* without repetition, from the integers 0, 1, ..., {@code n}-1
(inclusive).
*
@@ -101,4 +120,10 @@ public class CombinationSampler {
public int[] sample() {
return SubsetSamplerUtils.partialSample(domain, steps, rng, upper);
}
+
+ /** {@inheritDoc} */
+ @Override
+ public CombinationSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new CombinationSampler(rng, this);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSampler.java
index 06f108a..4bc50b4 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSampler.java
@@ -38,7 +38,8 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* @since 1.1
*/
-public class DiscreteProbabilityCollectionSampler<T> {
+public class DiscreteProbabilityCollectionSampler<T>
+ implements SharedStateSampler<DiscreteProbabilityCollectionSampler<T>> {
/** Collection to be sampled from. */
private final List<T> items;
/** RNG. */
@@ -125,6 +126,17 @@ public class DiscreteProbabilityCollectionSampler<T> {
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private DiscreteProbabilityCollectionSampler(UniformRandomProvider rng,
+
DiscreteProbabilityCollectionSampler<T> source) {
+ this.rng = rng;
+ this.items = source.items;
+ this.cumulativeProbabilities = source.cumulativeProbabilities;
+ }
+
+ /**
* Picks one of the items from the collection passed to the constructor.
*
* @return a random sample.
@@ -148,6 +160,12 @@ public class DiscreteProbabilityCollectionSampler<T> {
return items.get(items.size() - 1);
}
+ /** {@inheritDoc} */
+ @Override
+ public DiscreteProbabilityCollectionSampler<T>
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new DiscreteProbabilityCollectionSampler<T>(rng, this);
+ }
+
/**
* @param collection Collection to be sampled.
* @param probabilities Probability associated to each item of the
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/PermutationSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/PermutationSampler.java
index 7cb72cc..37aa7bc 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/PermutationSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/PermutationSampler.java
@@ -27,7 +27,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* <p>This class also contains utilities for shuffling an {@code int[]} array
in-place.</p>
*/
-public class PermutationSampler {
+public class PermutationSampler implements
SharedStateSampler<PermutationSampler> {
/** Domain of the permutation. */
private final int[] domain;
/** Size of the permutation. */
@@ -60,6 +60,24 @@ public class PermutationSampler {
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private PermutationSampler(UniformRandomProvider rng,
+ PermutationSampler source) {
+ // Do not clone the domain. This ensures:
+ // 1. Thread safety as the domain may be shuffled during the clone
+ // and an incomplete shuffle swap step can result in duplicates and
+ // missing elements in the array.
+ // 2. If the argument RNG is an exact match for the RNG in the source
+ // then the output sequence will differ unless the domain is
currently
+ // in natural order.
+ domain = PermutationSampler.natural(source.domain.length);
+ size = source.size;
+ this.rng = rng;
+ }
+
+ /**
* @return a random permutation.
*
* @see #PermutationSampler(UniformRandomProvider,int,int)
@@ -68,6 +86,12 @@ public class PermutationSampler {
return SubsetSamplerUtils.partialSample(domain, size, rng, true);
}
+ /** {@inheritDoc} */
+ @Override
+ public PermutationSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new PermutationSampler(rng, this);
+ }
+
/**
* Shuffles the entries of the given array.
*
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 661702b..67cecd1 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
@@ -34,7 +34,7 @@ import
org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSa
*
* @since 1.1
*/
-public class UnitSphereSampler {
+public class UnitSphereSampler implements
SharedStateSampler<UnitSphereSampler> {
/** Sampler used for generating the individual components of the vectors.
*/
private final NormalizedGaussianSampler sampler;
/** Space dimension. */
@@ -57,6 +57,17 @@ public class UnitSphereSampler {
}
/**
+ * @param rng Generator for the individual components of the vectors.
+ * @param source Source to copy.
+ */
+ private UnitSphereSampler(UniformRandomProvider rng,
+ UnitSphereSampler source) {
+ // The Gaussian sampler has no shared state so create a new instance
+ sampler = new ZigguratNormalizedGaussianSampler(rng);
+ dimension = source.dimension;
+ }
+
+ /**
* @return a random normalized Cartesian vector.
*/
public double[] nextVector() {
@@ -87,4 +98,10 @@ public class UnitSphereSampler {
return v;
}
+
+ /** {@inheritDoc} */
+ @Override
+ public UnitSphereSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new UnitSphereSampler(rng, this);
+ }
}
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 3f8a2e7..2d93c28 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampling from an <a
href="http://mathworld.wolfram.com/ExponentialDistribution.html";>exponential
distribution</a>.
@@ -27,7 +28,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class AhrensDieterExponentialSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler,
SharedStateSampler<AhrensDieterExponentialSampler> {
/**
* Table containing the constants
* \( q_i = sum_{j=1}^i (\ln 2)^j / j! = \ln 2 + (\ln 2)^2 / 2 + ... +
(\ln 2)^i / i! \)
@@ -78,6 +79,17 @@ public class AhrensDieterExponentialSampler
this.mean = mean;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private AhrensDieterExponentialSampler(UniformRandomProvider rng,
+ AhrensDieterExponentialSampler
source) {
+ super(null);
+ this.rng = rng;
+ this.mean = source.mean;
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -124,4 +136,10 @@ public class AhrensDieterExponentialSampler
public String toString() {
return "Ahrens-Dieter Exponential deviate [" + rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public AhrensDieterExponentialSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new AhrensDieterExponentialSampler(rng, this);
+ }
}
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 219c848..08901ff 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampling from the <a
href="http://mathworld.wolfram.com/GammaDistribution.html";>Gamma
distribution</a>.
@@ -52,7 +53,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class AhrensDieterMarsagliaTsangGammaSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler,
SharedStateSampler<AhrensDieterMarsagliaTsangGammaSampler> {
/** The appropriate gamma sampler for the parameters. */
private final ContinuousSampler delegate;
@@ -60,7 +61,7 @@ public class AhrensDieterMarsagliaTsangGammaSampler
* Base class for a sampler from the Gamma distribution.
*/
private abstract static class BaseGammaSampler
- implements ContinuousSampler {
+ implements ContinuousSampler, SharedStateSampler<ContinuousSampler> {
/** Underlying source of randomness. */
protected final UniformRandomProvider rng;
@@ -89,6 +90,17 @@ public class AhrensDieterMarsagliaTsangGammaSampler
this.theta = theta;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ BaseGammaSampler(UniformRandomProvider rng,
+ BaseGammaSampler source) {
+ this.rng = rng;
+ this.alpha = source.alpha;
+ this.theta = source.theta;
+ }
+
/** {@inheritDoc} */
@Override
public String toString() {
@@ -127,6 +139,17 @@ public class AhrensDieterMarsagliaTsangGammaSampler
bGSOptim = 1 + alpha / Math.E;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ AhrensDieterGammaSampler(UniformRandomProvider rng,
+ AhrensDieterGammaSampler source) {
+ super(rng, source);
+ oneOverAlpha = source.oneOverAlpha;
+ bGSOptim = source.bGSOptim;
+ }
+
@Override
public double sample() {
// [1]: p. 228, Algorithm GS.
@@ -158,6 +181,11 @@ public class AhrensDieterMarsagliaTsangGammaSampler
// Reject and continue.
}
}
+
+ @Override
+ public ContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new AhrensDieterGammaSampler(rng, this);
+ }
}
/**
@@ -189,14 +217,26 @@ public class AhrensDieterMarsagliaTsangGammaSampler
* @throws IllegalArgumentException if {@code alpha <= 0} or {@code
theta <= 0}
*/
MarsagliaTsangGammaSampler(UniformRandomProvider rng,
- double alpha,
- double theta) {
+ double alpha,
+ double theta) {
super(rng, alpha, theta);
gaussian = new ZigguratNormalizedGaussianSampler(rng);
dOptim = alpha - ONE_THIRD;
cOptim = ONE_THIRD / Math.sqrt(dOptim);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ MarsagliaTsangGammaSampler(UniformRandomProvider rng,
+ MarsagliaTsangGammaSampler source) {
+ super(rng, source);
+ gaussian = new ZigguratNormalizedGaussianSampler(rng);
+ dOptim = source.dOptim;
+ cOptim = source.cOptim;
+ }
+
@Override
public double sample() {
while (true) {
@@ -221,6 +261,11 @@ public class AhrensDieterMarsagliaTsangGammaSampler
}
}
}
+
+ @Override
+ public ContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaTsangGammaSampler(rng, this);
+ }
}
/**
@@ -238,6 +283,17 @@ public class AhrensDieterMarsagliaTsangGammaSampler
new MarsagliaTsangGammaSampler(rng, alpha, theta);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ @SuppressWarnings("unchecked")
+ private AhrensDieterMarsagliaTsangGammaSampler(UniformRandomProvider rng,
+
AhrensDieterMarsagliaTsangGammaSampler source) {
+ super(null);
+ delegate =
((SharedStateSampler<ContinuousSampler>)(source.delegate)).withUniformRandomProvider(rng);
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -249,4 +305,10 @@ public class AhrensDieterMarsagliaTsangGammaSampler
public String toString() {
return delegate.toString();
}
+
+ /** {@inheritDoc} */
+ @Override
+ public AhrensDieterMarsagliaTsangGammaSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new AhrensDieterMarsagliaTsangGammaSampler(rng, this);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.java
index ac93c9b..c5018e0 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.java
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
import java.util.Arrays;
@@ -69,7 +70,8 @@ import java.util.Arrays;
* @see <a href="https://ieeexplore.ieee.org/document/92917";>Vose (1991) IEEE
Transactions
* on Software Engineering 17, 972-975.</a>
*/
-public class AliasMethodDiscreteSampler implements DiscreteSampler {
+public class AliasMethodDiscreteSampler
+ implements DiscreteSampler, SharedStateSampler<AliasMethodDiscreteSampler>
{
/**
* The default alpha factor for zero-padding an input probability table.
The default
* value will pad the probabilities by to the next power-of-2.
@@ -182,8 +184,8 @@ public class AliasMethodDiscreteSampler implements
DiscreteSampler {
* @param alias Alias table.
*/
SmallTableAliasMethodDiscreteSampler(final UniformRandomProvider rng,
- final long[] probability,
- final int[] alias) {
+ final long[] probability,
+ final int[] alias) {
super(rng, probability, alias);
// Assume the table size is a power of 2 and create the mask
mask = alias.length - 1;
@@ -208,6 +210,12 @@ public class AliasMethodDiscreteSampler implements
DiscreteSampler {
// Choose between the two. Use a 53-bit long for the probability.
return (longBits >>> 11) < probability[j] ? j : alias[j];
}
+
+ /** {@inheritDoc} */
+ @Override
+ public SmallTableAliasMethodDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new SmallTableAliasMethodDiscreteSampler(rng, probability,
alias);
+ }
}
/**
@@ -270,6 +278,12 @@ public class AliasMethodDiscreteSampler implements
DiscreteSampler {
return "Alias method [" + rng.toString() + "]";
}
+ /** {@inheritDoc} */
+ @Override
+ public AliasMethodDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new AliasMethodDiscreteSampler(rng, probability, alias);
+ }
+
/**
* Creates a sampler.
*
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 616e768..5910060 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* <a href="https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform";>
@@ -28,7 +29,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* @since 1.1
*/
public class BoxMullerNormalizedGaussianSampler
- implements NormalizedGaussianSampler {
+ implements NormalizedGaussianSampler,
SharedStateSampler<BoxMullerNormalizedGaussianSampler> {
/** Next gaussian. */
private double nextGaussian = Double.NaN;
/** Underlying source of randomness. */
@@ -75,4 +76,10 @@ public class BoxMullerNormalizedGaussianSampler
public String toString() {
return "Box-Muller normalized Gaussian deviate [" + rng.toString() +
"]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public BoxMullerNormalizedGaussianSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return 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 5a64bf4..682ae9c 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Utility class implementing Cheng's algorithms for beta distribution
sampling.
@@ -35,7 +36,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class ChengBetaSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler, SharedStateSampler<ChengBetaSampler> {
/** 1/2. */
private static final double ONE_HALF = 1d / 2;
/** 1/4. */
@@ -71,6 +72,18 @@ public class ChengBetaSampler
betaShape = beta;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private ChengBetaSampler(UniformRandomProvider rng,
+ ChengBetaSampler source) {
+ super(null);
+ this.rng = rng;
+ alphaShape = source.alphaShape;
+ betaShape = source.betaShape;
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -90,6 +103,12 @@ public class ChengBetaSampler
return "Cheng Beta deviate [" + rng.toString() + "]";
}
+ /** {@inheritDoc} */
+ @Override
+ public ChengBetaSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new ChengBetaSampler(rng, this);
+ }
+
/**
* Computes one sample using Cheng's BB algorithm, when \( \alpha \) and
* \( \beta \) are both larger than 1.
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 e7d749e..2a3a9ea 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampling from a uniform distribution.
@@ -27,7 +28,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class ContinuousUniformSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler, SharedStateSampler<ContinuousUniformSampler>
{
/** Lower bound. */
private final double lo;
/** Higher bound. */
@@ -61,4 +62,10 @@ public class ContinuousUniformSampler
public String toString() {
return "Uniform deviate [" + rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public ContinuousUniformSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ 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 2b5ea52..1809c4c 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
@@ -18,6 +18,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Discrete uniform distribution sampler.
@@ -30,7 +31,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class DiscreteUniformSampler
extends SamplerBase
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<DiscreteUniformSampler> {
/** The appropriate uniform sampler for the parameters. */
private final DiscreteSampler delegate;
@@ -39,7 +40,7 @@ public class DiscreteUniformSampler
* Base class for a sampler from a discrete uniform distribution.
*/
private abstract static class AbstractDiscreteUniformSampler
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<DiscreteSampler> {
/** Underlying source of randomness. */
protected final UniformRandomProvider rng;
@@ -56,7 +57,6 @@ public class DiscreteUniformSampler
this.lower = lower;
}
- /** {@inheritDoc} */
@Override
public String toString() {
return "Uniform deviate [" + rng.toString() + "]";
@@ -89,6 +89,11 @@ public class DiscreteUniformSampler
public int sample() {
return lower + rng.nextInt(range);
}
+
+ @Override
+ public DiscreteSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new SmallRangeDiscreteUniformSampler(rng, lower, range);
+ }
}
/**
@@ -127,6 +132,11 @@ public class DiscreteUniformSampler
}
}
}
+
+ @Override
+ public DiscreteSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new LargeRangeDiscreteUniformSampler(rng, lower, upper);
+ }
}
/**
@@ -152,6 +162,17 @@ public class DiscreteUniformSampler
new SmallRangeDiscreteUniformSampler(rng, lower, range);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ @SuppressWarnings("unchecked")
+ private DiscreteUniformSampler(UniformRandomProvider rng,
+ DiscreteUniformSampler source) {
+ super(null);
+ delegate =
((SharedStateSampler<DiscreteSampler>)(source.delegate)).withUniformRandomProvider(rng);
+ }
+
/** {@inheritDoc} */
@Override
public int sample() {
@@ -163,4 +184,10 @@ public class DiscreteUniformSampler
public String toString() {
return delegate.toString();
}
+
+ /** {@inheritDoc} */
+ @Override
+ public DiscreteUniformSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new DiscreteUniformSampler(rng, this);
+ }
}
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 9c99742..e8cb095 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
@@ -16,13 +16,16 @@
*/
package org.apache.commons.rng.sampling.distribution;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
+
/**
* Sampling from a Gaussian distribution with given mean and
* standard deviation.
*
* @since 1.1
*/
-public class GaussianSampler implements ContinuousSampler {
+public class GaussianSampler implements ContinuousSampler,
SharedStateSampler<GaussianSampler> {
/** Mean. */
private final double mean;
/** standardDeviation. */
@@ -48,6 +51,21 @@ public class GaussianSampler implements ContinuousSampler {
this.standardDeviation = standardDeviation;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private GaussianSampler(UniformRandomProvider rng,
+ GaussianSampler source) {
+ if (!(source.normalized instanceof SharedStateSampler<?>)) {
+ throw new UnsupportedOperationException("The underlying sampler is
not a SharedStateSampler");
+ }
+ this.mean = source.mean;
+ this.standardDeviation = source.standardDeviation;
+ this.normalized = (NormalizedGaussianSampler)
+
((SharedStateSampler<?>)source.normalized).withUniformRandomProvider(rng);
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -59,4 +77,19 @@ public class GaussianSampler implements ContinuousSampler {
public String toString() {
return "Gaussian deviate [" + normalized.toString() + "]";
}
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>Note: This function is available if the underlying {@link
NormalizedGaussianSampler}
+ * is a {@link SharedStateSampler}. Otherwise a run-time exception is
thrown.</p>
+ *
+ * @throws UnsupportedOperationException if the underlying sampler is not
a {@link SharedStateSampler}.
+ * @throws ClassCastException if the underlying {@link SharedStateSampler}
does not return a
+ * {@link NormalizedGaussianSampler}.
+ */
+ @Override
+ public GaussianSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new GaussianSampler(rng, this);
+ }
}
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 f4683ac..f1e3470 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampling from a <a
href="https://en.wikipedia.org/wiki/Geometric_distribution";>geometric
@@ -45,14 +46,15 @@ import org.apache.commons.rng.UniformRandomProvider;
*
* @since 1.3
*/
-public class GeometricSampler implements DiscreteSampler {
+public class GeometricSampler implements DiscreteSampler,
SharedStateSampler<GeometricSampler> {
/** The appropriate geometric sampler for the parameters. */
private final DiscreteSampler delegate;
/**
* Sample from the geometric distribution when the probability of success
is 1.
*/
- private static class GeometricP1Sampler implements DiscreteSampler {
+ private static class GeometricP1Sampler
+ implements DiscreteSampler, SharedStateSampler<DiscreteSampler> {
/** The single instance. */
static final GeometricP1Sampler INSTANCE = new GeometricP1Sampler();
@@ -62,17 +64,23 @@ public class GeometricSampler implements DiscreteSampler {
return 0;
}
- /** {@inheritDoc} */
@Override
public String toString() {
return "Geometric(p=1) deviate";
}
+
+ @Override
+ public DiscreteSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ // No requirement for a new instance
+ return this;
+ }
}
/**
* Sample from the geometric distribution by using a related exponential
distribution.
*/
- private static class GeometricExponentialSampler implements
DiscreteSampler {
+ private static class GeometricExponentialSampler
+ implements DiscreteSampler, SharedStateSampler<DiscreteSampler> {
/** Underlying source of randomness. Used only for the {@link
#toString()} method. */
private final UniformRandomProvider rng;
/** The related exponential sampler for the geometric distribution. */
@@ -99,17 +107,30 @@ public class GeometricSampler implements DiscreteSampler {
exponentialSampler = new AhrensDieterExponentialSampler(rng,
exponentialMean);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers
+ * @param source Source to copy.
+ */
+ GeometricExponentialSampler(UniformRandomProvider rng,
GeometricExponentialSampler source) {
+ this.rng = rng;
+ exponentialSampler =
source.exponentialSampler.withUniformRandomProvider(rng);
+ }
+
@Override
public int sample() {
// Return the floor of the exponential sample
return (int) Math.floor(exponentialSampler.sample());
}
- /** {@inheritDoc} */
@Override
public String toString() {
return "Geometric deviate [" + rng.toString() + "]";
}
+
+ @Override
+ public DiscreteSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new GeometricExponentialSampler(rng, this);
+ }
}
/**
@@ -134,6 +155,15 @@ public class GeometricSampler implements DiscreteSampler {
}
/**
+ * @param rng Generator of uniformly distributed random numbers
+ * @param source Source to copy.
+ */
+ @SuppressWarnings("unchecked")
+ private GeometricSampler(UniformRandomProvider rng, GeometricSampler
source) {
+ delegate =
((SharedStateSampler<DiscreteSampler>)(source.delegate)).withUniformRandomProvider(rng);
+ }
+
+ /**
* Create a sample from a geometric distribution.
*
* <p>The sample will take the values in the set {@code [0, 1, 2, ...]},
equivalent to the
@@ -149,4 +179,10 @@ public class GeometricSampler implements DiscreteSampler {
public String toString() {
return delegate.toString();
}
+
+ /** {@inheritDoc} */
+ @Override
+ public GeometricSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new GeometricSampler(rng, this);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSampler.java
index beb7a5f..cb02cbb 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSampler.java
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Compute a sample from a discrete probability distribution. The cumulative
probability
@@ -40,7 +41,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* @since 1.3
*/
public class GuideTableDiscreteSampler
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<GuideTableDiscreteSampler> {
/** The default value for {@code alpha}. */
private static final double DEFAULT_ALPHA = 1.0;
/** Underlying source of randomness. */
@@ -142,6 +143,17 @@ public class GuideTableDiscreteSampler
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private GuideTableDiscreteSampler(UniformRandomProvider rng,
+ GuideTableDiscreteSampler source) {
+ this.rng = rng;
+ cumulativeProbabilities = source.cumulativeProbabilities;
+ guideTable = source.guideTable;
+ }
+
+ /**
* Validate the parameters.
*
* @param probabilities The probabilities.
@@ -198,4 +210,10 @@ public class GuideTableDiscreteSampler
public String toString() {
return "Guide table deviate [" + rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public GuideTableDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new GuideTableDiscreteSampler(rng, this);
+ }
}
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 5ba32fd..f60e1d0 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Distribution sampler that uses the
@@ -57,7 +58,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class InverseTransformContinuousSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler,
SharedStateSampler<InverseTransformContinuousSampler> {
/** Inverse cumulative probability function. */
private final ContinuousInverseCumulativeProbabilityFunction function;
/** Underlying source of randomness. */
@@ -85,4 +86,15 @@ public class InverseTransformContinuousSampler
public String toString() {
return function.toString() + " (inverse method) [" + rng.toString() +
"]";
}
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>Note: The new sampler will share the inverse cumulative probability
function. This
+ * must be suitable for concurrent use to ensure thread safety.</p>
+ */
+ @Override
+ public InverseTransformContinuousSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ 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 276fb8e..e10e2c4 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Distribution sampler that uses the
@@ -57,7 +58,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class InverseTransformDiscreteSampler
extends SamplerBase
- implements DiscreteSampler {
+ implements DiscreteSampler,
SharedStateSampler<InverseTransformDiscreteSampler> {
/** Inverse cumulative probability function. */
private final DiscreteInverseCumulativeProbabilityFunction function;
/** Underlying source of randomness. */
@@ -85,4 +86,15 @@ public class InverseTransformDiscreteSampler
public String toString() {
return function.toString() + " (inverse method) [" + rng.toString() +
"]";
}
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>Note: The new sampler will share the inverse cumulative probability
function. This
+ * must be suitable for concurrent use to ensure thread safety.</p>
+ */
+ @Override
+ public InverseTransformDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ 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 69f7a11..4068b49 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampling from a <a
href="https://en.wikipedia.org/wiki/Pareto_distribution";>Pareto
distribution</a>.
@@ -27,7 +28,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class InverseTransformParetoSampler
extends SamplerBase
- implements ContinuousSampler {
+ implements ContinuousSampler,
SharedStateSampler<InverseTransformParetoSampler> {
/** Scale. */
private final double scale;
/** 1 / Shape. */
@@ -56,6 +57,18 @@ public class InverseTransformParetoSampler
this.oneOverShape = 1 / shape;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private InverseTransformParetoSampler(UniformRandomProvider rng,
+ InverseTransformParetoSampler
source) {
+ super(null);
+ this.rng = rng;
+ scale = source.scale;
+ oneOverShape = source.oneOverShape;
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -67,4 +80,10 @@ public class InverseTransformParetoSampler
public String toString() {
return "[Inverse method for Pareto distribution " + rng.toString() +
"]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public InverseTransformParetoSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new InverseTransformParetoSampler(rng, this);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.java
index b0692b0..b3203d3 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.java
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampler for the <a
href="http://mathworld.wolfram.com/PoissonDistribution.html";>Poisson
@@ -46,7 +47,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* 249-253</a>
*/
public class KempSmallMeanPoissonSampler
- implements DiscreteSampler {
+ implements DiscreteSampler,
SharedStateSampler<KempSmallMeanPoissonSampler> {
/** Underlying source of randomness. */
private final UniformRandomProvider rng;
/**
@@ -81,6 +82,17 @@ public class KempSmallMeanPoissonSampler
}
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private KempSmallMeanPoissonSampler(UniformRandomProvider rng,
+ KempSmallMeanPoissonSampler source) {
+ this.rng = rng;
+ p0 = source.p0;
+ mean = source.mean;
+ }
+
/** {@inheritDoc} */
@Override
public int sample() {
@@ -114,4 +126,10 @@ public class KempSmallMeanPoissonSampler
public String toString() {
return "Kemp Small Mean Poisson deviate [" + rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public KempSmallMeanPoissonSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new KempSmallMeanPoissonSampler(rng, this);
+ }
}
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 3421db9..ce8d9ae 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
import org.apache.commons.rng.sampling.distribution.InternalUtils.FactorialLog;
/**
@@ -44,13 +45,13 @@ import
org.apache.commons.rng.sampling.distribution.InternalUtils.FactorialLog;
* @since 1.1
*/
public class LargeMeanPoissonSampler
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<LargeMeanPoissonSampler> {
/** Upper bound to avoid truncation. */
private static final double MAX_MEAN = 0.5 * Integer.MAX_VALUE;
/** Class to compute {@code log(n!)}. This has no cached values. */
private static final InternalUtils.FactorialLog NO_CACHE_FACTORIAL_LOG;
/** Used when there is no requirement for a small mean Poisson sampler. */
- private static final DiscreteSampler NO_SMALL_MEAN_POISSON_SAMPLER = null;
+ private static final KempSmallMeanPoissonSampler
NO_SMALL_MEAN_POISSON_SAMPLER = null;
static {
// Create without a cache.
@@ -60,7 +61,7 @@ public class LargeMeanPoissonSampler
/** Underlying source of randomness. */
private final UniformRandomProvider rng;
/** Exponential. */
- private final ContinuousSampler exponential;
+ private final AhrensDieterExponentialSampler exponential;
/** Gaussian. */
private final ContinuousSampler gaussian;
/** Local class to compute {@code log(n!)}. This may have cached values. */
@@ -100,7 +101,7 @@ public class LargeMeanPoissonSampler
private final double c1;
/** The internal Poisson sampler for the lambda fraction. */
- private final DiscreteSampler smallMeanPoissonSampler;
+ private final KempSmallMeanPoissonSampler smallMeanPoissonSampler;
/**
* @param rng Generator of uniformly distributed random numbers.
@@ -186,6 +187,36 @@ public class LargeMeanPoissonSampler
new KempSmallMeanPoissonSampler(rng, lambdaFractional);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private LargeMeanPoissonSampler(UniformRandomProvider rng,
+ LargeMeanPoissonSampler source) {
+ this.rng = rng;
+
+ // The Gaussian sampler has no shared state
+ gaussian = new ZigguratNormalizedGaussianSampler(rng);
+ exponential = source.exponential.withUniformRandomProvider(rng);
+ // Reuse the cache
+ factorialLog = source.factorialLog;
+
+ lambda = source.lambda;
+ logLambda = source.logLambda;
+ logLambdaFactorial = source.logLambdaFactorial;
+ delta = source.delta;
+ halfDelta = source.halfDelta;
+ twolpd = source.twolpd;
+ p1 = source.p1;
+ p2 = source.p2;
+ c1 = source.c1;
+
+ // Share the state of the small sampler
+ smallMeanPoissonSampler = source.smallMeanPoissonSampler == null ?
+ NO_SMALL_MEAN_POISSON_SAMPLER : // Not used.
+ source.smallMeanPoissonSampler.withUniformRandomProvider(rng);
+ }
+
/** {@inheritDoc} */
@Override
public int sample() {
@@ -262,6 +293,12 @@ public class LargeMeanPoissonSampler
return "Large Mean Poisson deviate [" + rng.toString() + "]";
}
+ /** {@inheritDoc} */
+ @Override
+ public LargeMeanPoissonSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new LargeMeanPoissonSampler(rng, this);
+ }
+
/**
* Gets the initialisation state of the sampler.
*
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 258f2f5..3879cbc 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
@@ -16,12 +16,15 @@
*/
package org.apache.commons.rng.sampling.distribution;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
+
/**
* Sampling from a log-normal distribution.
*
* @since 1.1
*/
-public class LogNormalSampler implements ContinuousSampler {
+public class LogNormalSampler implements ContinuousSampler,
SharedStateSampler<LogNormalSampler> {
/** Scale. */
private final double scale;
/** Shape. */
@@ -49,6 +52,21 @@ public class LogNormalSampler implements ContinuousSampler {
this.gaussian = gaussian;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private LogNormalSampler(UniformRandomProvider rng,
+ LogNormalSampler source) {
+ if (!(source.gaussian instanceof SharedStateSampler<?>)) {
+ throw new UnsupportedOperationException("The underlying sampler is
not a SharedStateSampler");
+ }
+ this.scale = source.scale;
+ this.shape = source.shape;
+ this.gaussian = (NormalizedGaussianSampler)
+
((SharedStateSampler<?>)source.gaussian).withUniformRandomProvider(rng);
+ }
+
/** {@inheritDoc} */
@Override
public double sample() {
@@ -60,4 +78,19 @@ public class LogNormalSampler implements ContinuousSampler {
public String toString() {
return "Log-normal deviate [" + gaussian.toString() + "]";
}
+
+ /**
+ * {@inheritDoc}
+ *
+ * <p>Note: This function is available if the underlying {@link
NormalizedGaussianSampler}
+ * is a {@link SharedStateSampler}. Otherwise a run-time exception is
thrown.</p>
+ *
+ * @throws UnsupportedOperationException if the underlying sampler is not
a {@link SharedStateSampler}.
+ * @throws ClassCastException if the underlying {@link SharedStateSampler}
does not return a
+ * {@link NormalizedGaussianSampler}.
+ */
+ @Override
+ public LogNormalSampler withUniformRandomProvider(UniformRandomProvider
rng) {
+ return new LogNormalSampler(rng, this);
+ }
}
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 af50b55..421288a 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* <a href="https://en.wikipedia.org/wiki/Marsaglia_polar_method";>
@@ -30,7 +31,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* @since 1.1
*/
public class MarsagliaNormalizedGaussianSampler
- implements NormalizedGaussianSampler {
+ implements NormalizedGaussianSampler,
SharedStateSampler<MarsagliaNormalizedGaussianSampler> {
/** Next gaussian. */
private double nextGaussian = Double.NaN;
/** Underlying source of randomness. */
@@ -84,4 +85,10 @@ public class MarsagliaNormalizedGaussianSampler
public String toString() {
return "Box-Muller (with rejection) normalized Gaussian deviate [" +
rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public MarsagliaNormalizedGaussianSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaNormalizedGaussianSampler(rng);
+ }
}
diff --git
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSampler.java
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSampler.java
index 55b82d5..c7528f4 100644
---
a/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSampler.java
+++
b/commons-rng-sampling/src/main/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSampler.java
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampler for a discrete distribution using an optimised look-up table.
@@ -49,7 +50,8 @@ import org.apache.commons.rng.UniformRandomProvider;
* @see <a href="http://dx.doi.org/10.18637/jss.v011.i03";>Margsglia, et al
(2004) JSS Vol.
* 11, Issue 3</a>
*/
-public abstract class MarsagliaTsangWangDiscreteSampler implements
DiscreteSampler {
+public abstract class MarsagliaTsangWangDiscreteSampler
+ implements DiscreteSampler,
SharedStateSampler<MarsagliaTsangWangDiscreteSampler> {
/** The value 2<sup>8</sup> as an {@code int}. */
private static final int INT_8 = 1 << 8;
/** The value 2<sup>16</sup> as an {@code int}. */
@@ -193,6 +195,24 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private
MarsagliaTsangWangBase64Int8DiscreteSampler(UniformRandomProvider rng,
+ MarsagliaTsangWangBase64Int8DiscreteSampler source) {
+ super(rng, source);
+ t1 = source.t1;
+ t2 = source.t2;
+ t3 = source.t3;
+ t4 = source.t4;
+ table1 = source.table1;
+ table2 = source.table2;
+ table3 = source.table3;
+ table4 = source.table4;
+ table5 = source.table5;
+ }
+
+ /**
* Fill the table with the value.
*
* @param table Table.
@@ -206,7 +226,6 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
}
- /** {@inheritDoc} */
@Override
public int sample() {
final int j = rng.nextInt() >>> 2;
@@ -227,6 +246,11 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
// difference. So the tables *must* be constructed correctly.
return table5[j - t4] & MASK;
}
+
+ @Override
+ public MarsagliaTsangWangDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaTsangWangBase64Int8DiscreteSampler(rng, this);
+ }
}
/**
@@ -311,6 +335,24 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private
MarsagliaTsangWangBase64Int16DiscreteSampler(UniformRandomProvider rng,
+ MarsagliaTsangWangBase64Int16DiscreteSampler source) {
+ super(rng, source);
+ t1 = source.t1;
+ t2 = source.t2;
+ t3 = source.t3;
+ t4 = source.t4;
+ table1 = source.table1;
+ table2 = source.table2;
+ table3 = source.table3;
+ table4 = source.table4;
+ table5 = source.table5;
+ }
+
+ /**
* Fill the table with the value.
*
* @param table Table.
@@ -324,7 +366,6 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
}
- /** {@inheritDoc} */
@Override
public int sample() {
final int j = rng.nextInt() >>> 2;
@@ -345,6 +386,11 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
// difference. So the tables *must* be constructed correctly.
return table5[j - t4] & MASK;
}
+
+ @Override
+ public MarsagliaTsangWangDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaTsangWangBase64Int16DiscreteSampler(rng, this);
+ }
}
/**
@@ -353,7 +399,6 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
*/
private static class MarsagliaTsangWangBase64Int32DiscreteSampler
extends MarsagliaTsangWangDiscreteSampler {
-
/** Limit for look-up table 1. */
private final int t1;
/** Limit for look-up table 2. */
@@ -426,6 +471,24 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private
MarsagliaTsangWangBase64Int32DiscreteSampler(UniformRandomProvider rng,
+ MarsagliaTsangWangBase64Int32DiscreteSampler source) {
+ super(rng, source);
+ t1 = source.t1;
+ t2 = source.t2;
+ t3 = source.t3;
+ t4 = source.t4;
+ table1 = source.table1;
+ table2 = source.table2;
+ table3 = source.table3;
+ table4 = source.table4;
+ table5 = source.table5;
+ }
+
+ /**
* Fill the table with the value.
*
* @param table Table.
@@ -439,7 +502,6 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
}
}
- /** {@inheritDoc} */
@Override
public int sample() {
final int j = rng.nextInt() >>> 2;
@@ -460,6 +522,11 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
// difference. So the tables *must* be constructed correctly.
return table5[j - t4];
}
+
+ @Override
+ public MarsagliaTsangWangDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaTsangWangBase64Int32DiscreteSampler(rng, this);
+ }
}
/**
@@ -484,11 +551,16 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
return result;
}
- /** {@inheritDoc} */
@Override
public String toString() {
return BINOMIAL_NAME + " deviate";
}
+
+ @Override
+ public MarsagliaTsangWangDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ // No shared state
+ return this;
+ }
}
/**
@@ -522,11 +594,16 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
return trials - sampler.sample();
}
- /** {@inheritDoc} */
@Override
public String toString() {
return sampler.toString();
}
+
+ @Override
+ public MarsagliaTsangWangDiscreteSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new MarsagliaTsangWangInversionBinomialSampler(this.trials,
+ this.sampler.withUniformRandomProvider(rng));
+ }
}
/**
@@ -539,6 +616,16 @@ public abstract class MarsagliaTsangWangDiscreteSampler
implements DiscreteSampl
this.distributionName = distributionName;
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ MarsagliaTsangWangDiscreteSampler(UniformRandomProvider rng,
+ MarsagliaTsangWangDiscreteSampler
source) {
+ this.rng = rng;
+ this.distributionName = source.distributionName;
+ }
+
/** {@inheritDoc} */
@Override
public String toString() {
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 56472ad..804a9e3 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampler for the <a
href="http://mathworld.wolfram.com/PoissonDistribution.html";>Poisson
distribution</a>.
@@ -51,7 +52,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class PoissonSampler
extends SamplerBase
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<PoissonSampler> {
/**
* Value for switching sampling algorithm.
@@ -79,6 +80,23 @@ public class PoissonSampler
new LargeMeanPoissonSampler(rng, mean);
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private PoissonSampler(UniformRandomProvider rng,
+ PoissonSampler source) {
+ super(null);
+
+ if (source.poissonSamplerDelegate instanceof SmallMeanPoissonSampler) {
+ poissonSamplerDelegate =
+
((SmallMeanPoissonSampler)source.poissonSamplerDelegate).withUniformRandomProvider(rng);
+ } else {
+ poissonSamplerDelegate =
+
((LargeMeanPoissonSampler)source.poissonSamplerDelegate).withUniformRandomProvider(rng);
+ }
+ }
+
/** {@inheritDoc} */
@Override
public int sample() {
@@ -90,4 +108,10 @@ public class PoissonSampler
public String toString() {
return poissonSamplerDelegate.toString();
}
+
+ /** {@inheritDoc} */
+ @Override
+ public PoissonSampler withUniformRandomProvider(UniformRandomProvider rng)
{
+ return new PoissonSampler(rng, this);
+ }
}
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 3a477e3..112f3fe 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
@@ -18,6 +18,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Implementation of the <a
href="https://en.wikipedia.org/wiki/Zipf's_law">Zipf distribution</a>.
@@ -28,7 +29,7 @@ import org.apache.commons.rng.UniformRandomProvider;
*/
public class RejectionInversionZipfSampler
extends SamplerBase
- implements DiscreteSampler {
+ implements DiscreteSampler,
SharedStateSampler<RejectionInversionZipfSampler> {
/** Threshold below which Taylor series will be used. */
private static final double TAYLOR_THRESHOLD = 1e-8;
/** 1/2. */
@@ -77,6 +78,21 @@ public class RejectionInversionZipfSampler
}
/**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private RejectionInversionZipfSampler(UniformRandomProvider rng,
+ RejectionInversionZipfSampler
source) {
+ super(null);
+ this.rng = rng;
+ this.numberOfElements = source.numberOfElements;
+ this.exponent = source.exponent;
+ this.hIntegralX1 = source.hIntegralX1;
+ this.hIntegralNumberOfElements = source.hIntegralNumberOfElements;
+ this.s = source.s;
+ }
+
+ /**
* Rejection inversion sampling method for a discrete, bounded Zipf
* distribution that is based on the method described in
* <blockquote>
@@ -177,6 +193,12 @@ public class RejectionInversionZipfSampler
return "Rejection inversion Zipf deviate [" + rng.toString() + "]";
}
+ /** {@inheritDoc} */
+ @Override
+ public RejectionInversionZipfSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new RejectionInversionZipfSampler(rng, this);
+ }
+
/**
* {@code H(x)} is defined as
* <ul>
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 22b7864..e6a9722 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
@@ -17,6 +17,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* Sampler for the <a
href="http://mathworld.wolfram.com/PoissonDistribution.html";>Poisson
distribution</a>.
@@ -42,7 +43,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* @since 1.1
*/
public class SmallMeanPoissonSampler
- implements DiscreteSampler {
+ implements DiscreteSampler, SharedStateSampler<SmallMeanPoissonSampler> {
/**
* Pre-compute {@code Math.exp(-mean)}.
* Note: This is the probability of the Poisson sample {@code P(n=0)}.
@@ -74,6 +75,17 @@ public class SmallMeanPoissonSampler
}
}
+ /**
+ * @param rng Generator of uniformly distributed random numbers.
+ * @param source Source to copy.
+ */
+ private SmallMeanPoissonSampler(UniformRandomProvider rng,
+ SmallMeanPoissonSampler source) {
+ this.rng = rng;
+ p0 = source.p0;
+ limit = source.limit;
+ }
+
/** {@inheritDoc} */
@Override
public int sample() {
@@ -96,4 +108,10 @@ public class SmallMeanPoissonSampler
public String toString() {
return "Small Mean Poisson deviate [" + rng.toString() + "]";
}
+
+ /** {@inheritDoc} */
+ @Override
+ public SmallMeanPoissonSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new SmallMeanPoissonSampler(rng, this);
+ }
}
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 bb9b52a..a1ba383 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
@@ -18,6 +18,7 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.SharedStateSampler;
/**
* <a href="https://en.wikipedia.org/wiki/Ziggurat_algorithm";>
@@ -38,7 +39,7 @@ import org.apache.commons.rng.UniformRandomProvider;
* @since 1.1
*/
public class ZigguratNormalizedGaussianSampler
- implements NormalizedGaussianSampler {
+ implements NormalizedGaussianSampler,
SharedStateSampler<ZigguratNormalizedGaussianSampler> {
/** Start of tail. */
private static final double R = 3.442619855899;
/** Inverse of R. */
@@ -160,4 +161,10 @@ public class ZigguratNormalizedGaussianSampler
private static double gauss(double x) {
return Math.exp(-0.5 * x * x);
}
+
+ /** {@inheritDoc} */
+ @Override
+ public ZigguratNormalizedGaussianSampler
withUniformRandomProvider(UniformRandomProvider rng) {
+ return new ZigguratNormalizedGaussianSampler(rng);
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CollectionSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CollectionSamplerTest.java
index 3ea70c1..9d83631 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CollectionSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CollectionSamplerTest.java
@@ -17,10 +17,12 @@
package org.apache.commons.rng.sampling;
import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
import org.junit.Assert;
import org.junit.Test;
-
+import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.simple.RandomSource;
/**
@@ -53,4 +55,30 @@ public class CollectionSamplerTest {
new CollectionSampler<String>(RandomSource.create(RandomSource.MT),
new ArrayList<String>());
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final List<String> list = Arrays.asList("Apache", "Commons", "RNG");
+ final CollectionSampler<String> sampler1 =
+ new CollectionSampler<String>(rng1, list);
+ final CollectionSampler<String> sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(
+ new RandomAssert.Sampler<String>() {
+ @Override
+ public String sample() {
+ return sampler1.sample();
+ }
+ },
+ new RandomAssert.Sampler<String>() {
+ @Override
+ public String sample() {
+ return sampler2.sample();
+ }
+ });
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CombinationSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CombinationSamplerTest.java
index 3472a89..928ff1b 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CombinationSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/CombinationSamplerTest.java
@@ -113,6 +113,33 @@ public class CombinationSamplerTest {
new CombinationSampler(rng, n, k);
}
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final int n = 17;
+ final int k = 3;
+ final CombinationSampler sampler1 =
+ new CombinationSampler(rng1, n, k);
+ final CombinationSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(
+ new RandomAssert.Sampler<int[]>() {
+ @Override
+ public int[] sample() {
+ return sampler1.sample();
+ }
+ },
+ new RandomAssert.Sampler<int[]>() {
+ @Override
+ public int[] sample() {
+ return sampler2.sample();
+ }
+ });
+ }
+
//// Support methods.
/**
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSamplerTest.java
index cae03cf..78a4391 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/DiscreteProbabilityCollectionSamplerTest.java
@@ -152,4 +152,32 @@ public class DiscreteProbabilityCollectionSamplerTest {
// Test the two samples are different items
Assert.assertNotSame("Item1 and 2 should be different", item1, item2);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final List<Double> items = Arrays.asList(new Double[] {1d, 2d, 3d,
4d});
+ final DiscreteProbabilityCollectionSampler<Double> sampler1 =
+ new DiscreteProbabilityCollectionSampler<Double>(rng1,
+ items,
+ new double[]
{0.1, 0.2, 0.3, 04});
+ final DiscreteProbabilityCollectionSampler<Double> sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(
+ new RandomAssert.Sampler<Double>() {
+ @Override
+ public Double sample() {
+ return sampler1.sample();
+ }
+ },
+ new RandomAssert.Sampler<Double>() {
+ @Override
+ public Double sample() {
+ return sampler2.sample();
+ }
+ });
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/PermutationSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/PermutationSamplerTest.java
index 21c9c42..9b88fce 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/PermutationSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/PermutationSamplerTest.java
@@ -186,6 +186,33 @@ public class PermutationSamplerTest {
Assert.assertTrue(ok);
}
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final int n = 17;
+ final int k = 13;
+ final PermutationSampler sampler1 =
+ new PermutationSampler(rng1, n, k);
+ final PermutationSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(
+ new RandomAssert.Sampler<int[]>() {
+ @Override
+ public int[] sample() {
+ return sampler1.sample();
+ }
+ },
+ new RandomAssert.Sampler<int[]>() {
+ @Override
+ public int[] sample() {
+ return sampler2.sample();
+ }
+ });
+ }
+
//// Support methods.
private void runSampleChiSquareTest(int n,
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/RandomAssert.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/RandomAssert.java
index 113471e..5860501 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/RandomAssert.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/RandomAssert.java
@@ -97,7 +97,8 @@ public final class RandomAssert {
final T value1 = sampler1.sample();
final T value2 = sampler2.sample();
if (isArray(value1) && isArray(value2)) {
- Assert.assertArrayEquals((Object[]) value1, (Object[]) value2);
+ // JUnit assertArrayEquals will handle nested primitive arrays
+ Assert.assertArrayEquals(new Object[] {value1}, new Object[]
{value2});
} else {
Assert.assertEquals(value1, value2);
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/UnitSphereSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/UnitSphereSamplerTest.java
index 311f87e..3c9df0b 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/UnitSphereSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/UnitSphereSamplerTest.java
@@ -115,6 +115,32 @@ public class UnitSphereSamplerTest {
}
/**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final int n = 3;
+ final UnitSphereSampler sampler1 =
+ new UnitSphereSampler(n, rng1);
+ final UnitSphereSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(
+ new RandomAssert.Sampler<double[]>() {
+ @Override
+ public double[] sample() {
+ return sampler1.nextVector();
+ }
+ },
+ new RandomAssert.Sampler<double[]>() {
+ @Override
+ public double[] sample() {
+ return sampler2.nextVector();
+ }
+ });
+ }
+
+ /**
* @return the length (L2-norm) of given vector.
*/
private static double length(double[] vector) {
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 1dceaeb..739a178 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
@@ -17,6 +17,8 @@
package org.apache.commons.rng.sampling.distribution;
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.Test;
@@ -36,4 +38,18 @@ public class AhrensDieterExponentialSamplerTest {
final AhrensDieterExponentialSampler sampler =
new AhrensDieterExponentialSampler(rng, mean);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ 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 AhrensDieterExponentialSampler 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 eee689b..11ba878 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
@@ -17,6 +17,8 @@
package org.apache.commons.rng.sampling.distribution;
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.Test;
@@ -51,4 +53,35 @@ public class AhrensDieterMarsagliaTsangGammaSamplerTest {
final AhrensDieterMarsagliaTsangGammaSampler sampler =
new AhrensDieterMarsagliaTsangGammaSampler(rng, alpha, theta);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWithAlphaBelowOne() {
+ testSharedStateSampler(0.5, 3.456);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWithAlphaAboveOne() {
+ testSharedStateSampler(3.5, 3.456);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param alpha Alpha.
+ * @param theta Theta.
+ */
+ 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);
+ final AhrensDieterMarsagliaTsangGammaSampler sampler1 =
+ new AhrensDieterMarsagliaTsangGammaSampler(rng1, alpha, theta);
+ final AhrensDieterMarsagliaTsangGammaSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSamplerTest.java
index 19f5451..e162f19 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSamplerTest.java
@@ -20,6 +20,7 @@ import
org.apache.commons.math3.distribution.BinomialDistribution;
import org.apache.commons.math3.distribution.PoissonDistribution;
import org.apache.commons.math3.stat.inference.ChiSquareTest;
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;
@@ -248,4 +249,35 @@ public class AliasMethodDiscreteSamplerTest {
// Pass if we cannot reject null hypothesis that the distributions are
the same.
Assert.assertFalse(chiSquareTest.chiSquareTest(expected, observed,
0.001));
}
+
+ /**
+ * Test the SharedStateSampler implementation for the specialised
power-of-2 table size.
+ */
+ @Test
+ public void testSharedStateSamplerWithPowerOf2TableSize() {
+ testSharedStateSampler(new double[] {0.1, 0.2, 0.3, 0.4});
+ }
+
+ /**
+ * Test the SharedStateSampler implementation for the generic non
power-of-2 table size.
+ */
+ @Test
+ public void testSharedStateSamplerWithNonPowerOf2TableSize() {
+ testSharedStateSampler(new double[] {0.1, 0.2, 0.3});
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param probabilities The probabilities
+ */
+ private static void testSharedStateSampler(double[] probabilities) {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ // Use negative alpha to disable padding
+ final AliasMethodDiscreteSampler sampler1 =
+ AliasMethodDiscreteSampler.create(rng1, probabilities, -1);
+ final AliasMethodDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
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/BoxMullerNormalisedGaussianSamplerTest.java
similarity index 57%
copy from
commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
copy to
commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/BoxMullerNormalisedGaussianSamplerTest.java
index a0ab018..191cbc5 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/BoxMullerNormalisedGaussianSamplerTest.java
@@ -17,22 +17,24 @@
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.Test;
/**
- * Test for the {@link DiscreteUniformSampler}. The tests hit edge cases for
the sampler.
+ * Test for the {@link BoxMullerNormalizedGaussianSampler}.
*/
-public class DiscreteUniformSamplerTest {
+public class BoxMullerNormalisedGaussianSamplerTest {
/**
- * Test the constructor with a bad range.
+ * Test the SharedStateSampler implementation.
*/
- @Test(expected = IllegalArgumentException.class)
- 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);
+ @Test
+ 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 BoxMullerNormalizedGaussianSampler 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 ea1f6ef..24325b0 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
@@ -17,6 +17,8 @@
package org.apache.commons.rng.sampling.distribution;
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.Test;
@@ -51,4 +53,19 @@ public class ChengBetaSamplerTest {
final ChengBetaSampler sampler =
new ChengBetaSampler(rng, alpha, beta);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ 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 ChengBetaSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
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 ba69c3b..06bf53f 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
@@ -17,6 +17,7 @@
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;
@@ -47,4 +48,19 @@ public class ContinuousUniformSamplerTest {
Assert.assertTrue("Value not in range", value >= min && value <=
max);
}
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ 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 ContinuousUniformSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
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 a0ab018..3a0a34c 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
@@ -17,6 +17,7 @@
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.Test;
@@ -35,4 +36,35 @@ public class DiscreteUniformSamplerTest {
@SuppressWarnings("unused")
DiscreteUniformSampler sampler = new DiscreteUniformSampler(rng,
lower, upper);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWithSmallRange() {
+ testSharedStateSampler(5, 67);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWithLargeRange() {
+ testSharedStateSampler(-99999999, Integer.MAX_VALUE);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param lower Lower.
+ * @param upper Upper.
+ */
+ 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 =
+ new DiscreteUniformSampler(rng1, lower, upper);
+ final DiscreteUniformSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
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 0cc0a61..63bbf88 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
@@ -17,6 +17,9 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.RestorableUniformRandomProvider;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.RandomAssert;
+import org.apache.commons.rng.sampling.SharedStateSampler;
import org.apache.commons.rng.simple.RandomSource;
import org.junit.Test;
@@ -38,4 +41,75 @@ public class GaussianSamplerTest {
final GaussianSampler sampler =
new GaussianSampler(gauss, mean, standardDeviation);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ 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 double mean = 1.23;
+ final double standardDeviation = 4.56;
+ final GaussianSampler sampler1 =
+ new GaussianSampler(gauss, mean, standardDeviation);
+ final GaussianSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation throws if the underlying
sampler is
+ * not a SharedStateSampler.
+ */
+ @Test(expected = UnsupportedOperationException.class)
+ public void
testSharedStateSamplerThrowsIfUnderlyingSamplerDoesNotShareState() {
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final NormalizedGaussianSampler gauss = new
NormalizedGaussianSampler() {
+ @Override
+ public double sample() {
+ return 0;
+ }
+ };
+ final double mean = 1.23;
+ final double standardDeviation = 4.56;
+ final GaussianSampler sampler1 =
+ new GaussianSampler(gauss, mean, standardDeviation);
+ sampler1.withUniformRandomProvider(rng2);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation throws if the underlying
sampler is
+ * a SharedStateSampler that returns an incorrect type.
+ */
+ @Test(expected = ClassCastException.class)
+ public void
testSharedStateSamplerThrowsIfUnderlyingSamplerReturnsWrongSharedState() {
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final NormalizedGaussianSampler gauss = new
BadSharedStateNormalizedGaussianSampler();
+ final double mean = 1.23;
+ final double standardDeviation = 4.56;
+ final GaussianSampler sampler1 =
+ new GaussianSampler(gauss, mean, standardDeviation);
+ sampler1.withUniformRandomProvider(rng2);
+ }
+
+ /**
+ * Test class to return an incorrect sampler from the SharedStateSampler
method.
+ *
+ * <p>Note that due to type erasure the type returned by the
SharedStateSampler is not
+ * available at run-time and the GaussianSampler has to assume it is the
correct type.</p>
+ */
+ private static class BadSharedStateNormalizedGaussianSampler
+ implements NormalizedGaussianSampler, SharedStateSampler<Integer> {
+ @Override
+ public double sample() {
+ return 0;
+ }
+
+ @Override
+ public Integer withUniformRandomProvider(UniformRandomProvider rng) {
+ // Something that is not a NormalizedGaussianSampler
+ return Integer.valueOf(44);
+ }
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GeometricSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GeometricSamplerTest.java
index b3bab01..66eaee4 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GeometricSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GeometricSamplerTest.java
@@ -17,6 +17,7 @@
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;
@@ -111,4 +112,35 @@ public class GeometricSamplerTest {
@SuppressWarnings("unused")
final GeometricSampler sampler = new GeometricSampler(unusedRng,
probabilityOfSuccess);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ testSharedStateSampler(0.5);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with the edge case when the
probability of
+ * success is {@code 1.0}.
+ */
+ @Test
+ public void testSharedStateSamplerWithProbabilityOfSuccessOne() {
+ testSharedStateSampler(1.0);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param probabilityOfSuccess Probability of success.
+ */
+ private static void testSharedStateSampler(double probabilityOfSuccess) {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final GeometricSampler sampler1 =
+ new GeometricSampler(rng1, probabilityOfSuccess);
+ final GeometricSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSamplerTest.java
index f312c93..ec89352 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/GuideTableDiscreteSamplerTest.java
@@ -20,6 +20,7 @@ import
org.apache.commons.math3.distribution.BinomialDistribution;
import org.apache.commons.math3.distribution.PoissonDistribution;
import org.apache.commons.math3.stat.inference.ChiSquareTest;
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;
@@ -234,4 +235,18 @@ public class GuideTableDiscreteSamplerTest {
// Pass if we cannot reject null hypothesis that the distributions are
the same.
Assert.assertFalse(chiSquareTest.chiSquareTest(expected, observed,
0.001));
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final double[] probabilities = {0.1, 0, 0.2, 0.3, 0.1, 0.3, 0};
+ final GuideTableDiscreteSampler sampler1 =
+ new GuideTableDiscreteSampler(rng1, probabilities);
+ final GuideTableDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
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
new file mode 100644
index 0000000..6ddbb07
--- /dev/null
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformContinuousSamplerTest.java
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+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.Test;
+
+/**
+ * Test for the {@link InverseTransformContinuousSampler}.
+ */
+public class InverseTransformContinuousSamplerTest {
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ ContinuousInverseCumulativeProbabilityFunction function =
+ new ContinuousInverseCumulativeProbabilityFunction() {
+ @Override
+ public double inverseCumulativeProbability(double p) {
+ return 456.99 * p;
+ }
+ };
+ 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 InverseTransformContinuousSampler 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
new file mode 100644
index 0000000..b9d3f89
--- /dev/null
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/InverseTransformDiscreteSamplerTest.java
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+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.Test;
+
+/**
+ * Test for the {@link InverseTransformDiscreteSampler}.
+ */
+public class InverseTransformDiscreteSamplerTest {
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ DiscreteInverseCumulativeProbabilityFunction function =
+ new DiscreteInverseCumulativeProbabilityFunction() {
+ @Override
+ public int inverseCumulativeProbability(double p) {
+ return (int) Math.round(789 * p);
+ }
+ };
+ 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 InverseTransformDiscreteSampler 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 0119ed2..b4314c7 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
@@ -17,6 +17,8 @@
package org.apache.commons.rng.sampling.distribution;
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.Test;
@@ -31,11 +33,11 @@ public class InverseTransformParetoSamplerTest {
public void testConstructorThrowsWithZeroScale() {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- final double shape = 1;
final double scale = 0;
+ final double shape = 1;
@SuppressWarnings("unused")
final InverseTransformParetoSampler sampler =
- new InverseTransformParetoSampler(rng, shape, scale);
+ new InverseTransformParetoSampler(rng, scale, shape);
}
/**
@@ -45,10 +47,25 @@ public class InverseTransformParetoSamplerTest {
public void testConstructorThrowsWithZeroShape() {
final RestorableUniformRandomProvider rng =
RandomSource.create(RandomSource.SPLIT_MIX_64);
- final double shape = 0;
final double scale = 1;
+ final double shape = 0;
@SuppressWarnings("unused")
final InverseTransformParetoSampler sampler =
- new InverseTransformParetoSampler(rng, shape, scale);
+ new InverseTransformParetoSampler(rng, scale, shape);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ 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 InverseTransformParetoSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSamplerTest.java
index 013c1c0..04b8e7e 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSamplerTest.java
@@ -18,6 +18,8 @@ package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.math3.distribution.PoissonDistribution;
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;
@@ -145,6 +147,20 @@ public class KempSmallMeanPoissonSamplerTest {
}
/**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ 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 KempSmallMeanPoissonSampler sampler1 =
+ new KempSmallMeanPoissonSampler(rng1, mean);
+ final KempSmallMeanPoissonSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+
+ /**
* Test a sample from the Poisson distribution at the given cumulative
probability.
*
* @param rng the fixed random generator backing the sampler
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 63c1f55..89022d1 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
@@ -18,6 +18,8 @@ package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.RandomProviderState;
import org.apache.commons.rng.RestorableUniformRandomProvider;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.RandomAssert;
import
org.apache.commons.rng.sampling.distribution.LargeMeanPoissonSampler.LargeMeanPoissonSamplerState;
import org.apache.commons.rng.simple.RandomSource;
import org.junit.Assert;
@@ -141,4 +143,35 @@ public class LargeMeanPoissonSamplerTest {
Assert.assertEquals("Not the same sample", s1.sample(),
s2.sample());
}
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWithFractionalMean() {
+ testSharedStateSampler(34.5);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with the edge case when
there is no
+ * small mean sampler (i.e. no fraction part to the mean).
+ */
+ @Test
+ public void testSharedStateSamplerWithIntegerMean() {
+ testSharedStateSampler(34.0);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param mean Mean.
+ */
+ 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 LargeMeanPoissonSampler 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 104e552..e708fc4 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
@@ -17,6 +17,9 @@
package org.apache.commons.rng.sampling.distribution;
import org.apache.commons.rng.RestorableUniformRandomProvider;
+import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.RandomAssert;
+import org.apache.commons.rng.sampling.SharedStateSampler;
import org.apache.commons.rng.simple.RandomSource;
import org.junit.Test;
@@ -53,4 +56,75 @@ public class LogNormalSamplerTest {
final LogNormalSampler sampler =
new LogNormalSampler(gauss, scale, shape);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ 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 double scale = 1.23;
+ final double shape = 4.56;
+ final LogNormalSampler sampler1 =
+ new LogNormalSampler(gauss, scale, shape);
+ final LogNormalSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation throws if the underlying
sampler is
+ * not a SharedStateSampler.
+ */
+ @Test(expected = UnsupportedOperationException.class)
+ public void
testSharedStateSamplerThrowsIfUnderlyingSamplerDoesNotShareState() {
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final NormalizedGaussianSampler gauss = new
NormalizedGaussianSampler() {
+ @Override
+ public double sample() {
+ return 0;
+ }
+ };
+ final double scale = 1.23;
+ final double shape = 4.56;
+ final LogNormalSampler sampler1 =
+ new LogNormalSampler(gauss, scale, shape);
+ sampler1.withUniformRandomProvider(rng2);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation throws if the underlying
sampler is
+ * a SharedStateSampler that returns an incorrect type.
+ */
+ @Test(expected = ClassCastException.class)
+ public void
testSharedStateSamplerThrowsIfUnderlyingSamplerReturnsWrongSharedState() {
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final NormalizedGaussianSampler gauss = new
BadSharedStateNormalizedGaussianSampler();
+ final double scale = 1.23;
+ final double shape = 4.56;
+ final LogNormalSampler sampler1 =
+ new LogNormalSampler(gauss, scale, shape);
+ sampler1.withUniformRandomProvider(rng2);
+ }
+
+ /**
+ * Test class to return an incorrect sampler from the SharedStateSampler
method.
+ *
+ * <p>Note that due to type erasure the type returned by the
SharedStateSampler is not
+ * available at run-time and the LogNormalSampler has to assume it is the
correct type.</p>
+ */
+ private static class BadSharedStateNormalizedGaussianSampler
+ implements NormalizedGaussianSampler, SharedStateSampler<Integer> {
+ @Override
+ public double sample() {
+ return 0;
+ }
+
+ @Override
+ public Integer withUniformRandomProvider(UniformRandomProvider rng) {
+ // Something that is not a NormalizedGaussianSampler
+ return Integer.valueOf(44);
+ }
+ }
}
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/MarsagliaNormalisedGaussianSamplerTest.java
similarity index 57%
copy from
commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/DiscreteUniformSamplerTest.java
copy to
commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaNormalisedGaussianSamplerTest.java
index a0ab018..89b7339 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/MarsagliaNormalisedGaussianSamplerTest.java
@@ -17,22 +17,24 @@
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.Test;
/**
- * Test for the {@link DiscreteUniformSampler}. The tests hit edge cases for
the sampler.
+ * Test for the {@link MarsagliaNormalizedGaussianSampler}.
*/
-public class DiscreteUniformSamplerTest {
+public class MarsagliaNormalisedGaussianSamplerTest {
/**
- * Test the constructor with a bad range.
+ * Test the SharedStateSampler implementation.
*/
- @Test(expected = IllegalArgumentException.class)
- 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);
+ @Test
+ 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 MarsagliaNormalizedGaussianSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
}
}
diff --git
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSamplerTest.java
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSamplerTest.java
index 5ec43df..37164f7 100644
---
a/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSamplerTest.java
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/MarsagliaTsangWangDiscreteSamplerTest.java
@@ -20,6 +20,7 @@ import org.apache.commons.math3.stat.inference.ChiSquareTest;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.core.source32.IntProvider;
import org.apache.commons.rng.core.source64.SplitMix64;
+import org.apache.commons.rng.sampling.RandomAssert;
import org.apache.commons.rng.simple.RandomSource;
import org.junit.Assert;
import org.junit.Test;
@@ -162,8 +163,8 @@ public class MarsagliaTsangWangDiscreteSamplerTest {
/**
* Creates the probabilities using zero padding below the values.
*
- * @param offset the offset
- * @param prob the probability values
+ * @param offset Offset for first given probability (i.e. the zero padding
size).
+ * @param prob Probability values.
* @return the zero-padded probabilities
*/
private static double[] createProbabilities(int offset, int[] prob) {
@@ -241,7 +242,7 @@ public class MarsagliaTsangWangDiscreteSamplerTest {
* tests the limits described in the class Javadoc is correct.
*
* @param k Base is 2^k.
- * @param expectedLimitMB the expected limit in MB
+ * @param expectedLimitMB Expected limit in MB.
*/
private static void checkStorageRequirements(int k, double
expectedLimitMB) {
// Worst case scenario is a uniform distribution of 2^k samples each
with the highest
@@ -283,8 +284,8 @@ public class MarsagliaTsangWangDiscreteSamplerTest {
/**
* Gets the k<sup>th</sup> base 64 digit of {@code m}.
*
- * @param m the value m.
- * @param k the digit.
+ * @param m Value m.
+ * @param k Digit.
* @return the base 64 digit
*/
private static int getBase64Digit(int m, int k) {
@@ -489,8 +490,8 @@ public class MarsagliaTsangWangDiscreteSamplerTest {
/**
* Gets the p(0) value for the Binomial distribution.
*
- * @param trials the trials
- * @param probabilityOfSuccess the probability of success
+ * @param trials Number of trials.
+ * @param probabilityOfSuccess Probability of success.
* @return the p(0) value
*/
private static double getBinomialP0(int trials, double
probabilityOfSuccess) {
@@ -575,6 +576,80 @@ public class MarsagliaTsangWangDiscreteSamplerTest {
}
/**
+ * Test the SharedStateSampler implementation with the 8-bit storage
implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWith8bitStorage() {
+ testSharedStateSampler(0, new int[] {1, 2, 3, 4, 5});
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with the 16-bit storage
implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWith16bitStorage() {
+ testSharedStateSampler(1 << 8, new int[] {1, 2, 3, 4, 5});
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with the 32-bit storage
implementation.
+ */
+ @Test
+ public void testSharedStateSamplerWith32bitStorage() {
+ testSharedStateSampler(1 << 16, new int[] {1, 2, 3, 4, 5});
+ }
+
+ /**
+ * Test the SharedStateSampler implementation using zero padded
probabilities to force
+ * different storage implementations.
+ *
+ * @param offset Offset for first given probability (i.e. the zero padding
size).
+ * @param prob Probability values.
+ */
+ private static void testSharedStateSampler(int offset, int[] prob) {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ double[] probabilities = createProbabilities(offset, prob);
+ final MarsagliaTsangWangDiscreteSampler sampler1 =
+
MarsagliaTsangWangDiscreteSampler.createDiscreteDistribution(rng1,
probabilities);
+ final MarsagliaTsangWangDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with a Binomial distribution
with a fixed result.
+ */
+ @Test
+ public void testSharedStateSamplerWithFixedBinomialDistribution() {
+ testSharedStateSampler(10, 1.0);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with a Binomial distribution
that requires
+ * inversion (probability of success > 0.5).
+ */
+ @Test
+ public void testSharedStateSamplerWithInvertedBinomialDistribution() {
+ testSharedStateSampler(10, 0.999);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation using a binomial
distribution to exercise
+ * special implementations.
+ *
+ * @param trials Number of trials.
+ * @param probabilityOfSuccess Probability of success.
+ */
+ private static void testSharedStateSampler(int trials, double
probabilityOfSuccess) {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final UniformRandomProvider rng2 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ final MarsagliaTsangWangDiscreteSampler sampler1 =
+
MarsagliaTsangWangDiscreteSampler.createBinomialDistribution(rng1, trials,
probabilityOfSuccess);
+ final MarsagliaTsangWangDiscreteSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+
+ /**
* Return a fixed sequence of {@code int} output.
*/
private static class FixedSequenceIntProvider extends IntProvider {
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
new file mode 100644
index 0000000..9899b61
--- /dev/null
+++
b/commons-rng-sampling/src/test/java/org/apache/commons/rng/sampling/distribution/PoissonSamplerTest.java
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+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.Test;
+
+/**
+ * This test checks the {@link PoissonSampler} can be created
+ * from a saved state.
+ */
+public class PoissonSamplerTest {
+ /**
+ * Test the SharedStateSampler implementation with a mean below 40.
+ */
+ @Test
+ public void testSharedStateSamplerWithSmallMean() {
+ testSharedStateSampler(34.5);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation with a mean above 40.
+ */
+ @Test
+ public void testSharedStateSamplerWithLargeMean() {
+ testSharedStateSampler(67.8);
+ }
+
+ /**
+ * Test the SharedStateSampler implementation.
+ *
+ * @param mean Mean.
+ */
+ 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 =
+ new PoissonSampler(rng1, mean);
+ final PoissonSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
+}
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 d022e3e..d7e3654 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
@@ -17,6 +17,8 @@
package org.apache.commons.rng.sampling.distribution;
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.Test;
@@ -51,4 +53,19 @@ public class RejectionInversionZipfSamplerTest {
final RejectionInversionZipfSampler sampler =
new RejectionInversionZipfSampler(rng, numberOfElements, exponent);
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ final UniformRandomProvider rng1 =
RandomSource.create(RandomSource.SPLIT_MIX_64, 0L);
+ 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 RejectionInversionZipfSampler 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 5b22785..8d34fac 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
@@ -17,6 +17,7 @@
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;
@@ -77,4 +78,18 @@ public class SmallMeanPoissonSamplerTest {
Assert.assertEquals(expected, sampler.sample());
}
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ public void testSharedStateSampler() {
+ 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 SmallMeanPoissonSampler 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 e9a3657..d4a2e16 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
@@ -18,6 +18,8 @@ package org.apache.commons.rng.sampling.distribution;
import org.junit.Test;
import org.apache.commons.rng.UniformRandomProvider;
+import org.apache.commons.rng.sampling.RandomAssert;
+import org.apache.commons.rng.simple.RandomSource;
/**
* Test for {@link ZigguratNormalizedGaussianSampler}.
@@ -45,4 +47,17 @@ public class ZigguratNormalizedGaussianSamplerTest {
// Infinite loop (in v1.1).
new ZigguratNormalizedGaussianSampler(bad).sample();
}
+
+ /**
+ * Test the SharedStateSampler implementation.
+ */
+ @Test
+ 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 ZigguratNormalizedGaussianSampler sampler2 =
sampler1.withUniformRandomProvider(rng2);
+ RandomAssert.assertProduceSameSequence(sampler1, sampler2);
+ }
}
