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Add reference to Commons Math implementation of InternalErf, fix markdown issues, rename prob package in examples to stat

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Iaroslav Postovalov 2020-11-29 22:02:06 +07:00
parent 12758d589e
commit 5a3fccb455
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15 changed files with 21 additions and 16 deletions
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5
examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt → examples/src/main/kotlin/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,11 +1,8 @@
package kscience.kmath.commons.prob package kscience.kmath.stat
import kotlinx.coroutines.Dispatchers import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.async import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking import kotlinx.coroutines.runBlocking
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.blocking
import kscience.kmath.stat.fromSource
import kscience.kmath.stat.samplers.GaussianSampler import kscience.kmath.stat.samplers.GaussianSampler
import org.apache.commons.rng.simple.RandomSource import org.apache.commons.rng.simple.RandomSource
import java.time.Duration import java.time.Duration

2
examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionDemo.kt → examples/src/main/kotlin/kscience/kmath/stat/DistributionDemo.kt
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@ -1,4 +1,4 @@
package kscience.kmath.commons.prob package kscience.kmath.stat
import kotlinx.coroutines.runBlocking import kotlinx.coroutines.runBlocking
import kscience.kmath.chains.Chain import kscience.kmath.chains.Chain

3
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/distributions/NormalDistribution.kt
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@ -9,6 +9,9 @@ import kscience.kmath.stat.samplers.NormalizedGaussianSampler
import kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler import kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler
import kotlin.math.* import kotlin.math.*
/**
* Implements [UnivariateDistribution] for the normal (gaussian) distribution.
*/
public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> { public inline class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
public constructor( public constructor(
mean: Double, mean: Double,

4
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/internal/InternalErf.kt
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@ -2,6 +2,10 @@ package kscience.kmath.stat.internal
import kotlin.math.abs import kotlin.math.abs
/**
* Based on Commons Math implementation.
* See [https://commons.apache.org/proper/commons-math/javadocs/api-3.3/org/apache/commons/math3/special/Erf.html].
*/
internal object InternalErf { internal object InternalErf {
fun erfc(x: Double): Double { fun erfc(x: Double): Double {
if (abs(x) > 40) return if (x > 0) 0.0 else 2.0 if (abs(x) > 40) return if (x > 0) 0.0 else 2.0

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterExponentialSampler.kt
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@ -12,7 +12,7 @@ import kotlin.math.pow
* Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html). * Sampling from an [exponential distribution](http://mathworld.wolfram.com/ExponentialDistribution.html).
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterExponentialSampler.html].
*/ */
public class AhrensDieterExponentialSampler private constructor(public val mean: Double) : Sampler<Double> { public class AhrensDieterExponentialSampler private constructor(public val mean: Double) : Sampler<Double> {
public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain { public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {

3
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt
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@ -15,7 +15,8 @@ import kotlin.math.*
* Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000. * Marsaglia and Tsang, A Simple Method for Generating Gamma Variables. ACM Transactions on Mathematical Software, Volume 26 Issue 3, September, 2000.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html *
* See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AhrensDieterMarsagliaTsangGammaSampler.html].
*/ */
public class AhrensDieterMarsagliaTsangGammaSampler private constructor( public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
alpha: Double, alpha: Double,

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AliasMethodDiscreteSampler.kt
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@ -34,7 +34,7 @@ import kotlin.math.min
* that exploit the power of 2. * that exploit the power of 2.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/AliasMethodDiscreteSampler.html].
*/ */
public open class AliasMethodDiscreteSampler private constructor( public open class AliasMethodDiscreteSampler private constructor(
// Deliberate direct storage of input arrays // Deliberate direct storage of input arrays

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/BoxMullerNormalizedGaussianSampler.kt
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@ -11,7 +11,7 @@ import kotlin.math.*
* distribution. * distribution.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/BoxMullerNormalizedGaussianSampler.html].
*/ */
public class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> { public class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
private var nextGaussian: Double = Double.NaN private var nextGaussian: Double = Double.NaN

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/GaussianSampler.kt
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@ -9,7 +9,7 @@ import kscience.kmath.stat.Sampler
* Sampling from a Gaussian distribution with given mean and standard deviation. * Sampling from a Gaussian distribution with given mean and standard deviation.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/GaussianSampler.html].
* *
* @property mean the mean of the distribution. * @property mean the mean of the distribution.
* @property standardDeviation the variance of the distribution. * @property standardDeviation the variance of the distribution.

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/KempSmallMeanPoissonSampler.kt
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@ -16,7 +16,7 @@ import kotlin.math.exp
* Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double. * Sampling uses 1 call to UniformRandomProvider.nextDouble(). This method provides an alternative to the SmallMeanPoissonSampler for slow generators of double.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/KempSmallMeanPoissonSampler.html].
*/ */
public class KempSmallMeanPoissonSampler private constructor( public class KempSmallMeanPoissonSampler private constructor(
private val p0: Double, private val p0: Double,

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/LargeMeanPoissonSampler.kt
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@ -18,7 +18,7 @@ import kotlin.math.*
* This sampler is suitable for mean >= 40. * This sampler is suitable for mean >= 40.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/LargeMeanPoissonSampler.html].
*/ */
public class LargeMeanPoissonSampler private constructor(public val mean: Double) : Sampler<Int> { public class LargeMeanPoissonSampler private constructor(public val mean: Double) : Sampler<Int> {
private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0) private val exponential: Sampler<Double> = AhrensDieterExponentialSampler.of(1.0)

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/MarsagliaNormalizedGaussianSampler.kt
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@ -13,7 +13,7 @@ import kotlin.math.sqrt
* [BoxMullerNormalizedGaussianSampler]. * [BoxMullerNormalizedGaussianSampler].
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/MarsagliaNormalizedGaussianSampler.html]
*/ */
public class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> { public class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
private var nextGaussian = Double.NaN private var nextGaussian = Double.NaN

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/PoissonSampler.kt
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@ -14,7 +14,7 @@ import kscience.kmath.stat.Sampler
* Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207. * Devroye, Luc. (1981). The Computer Generation of Poisson Random Variables Computing vol. 26 pp. 197-207.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/PoissonSampler.html].
*/ */
public class PoissonSampler private constructor(mean: Double) : Sampler<Int> { public class PoissonSampler private constructor(mean: Double) : Sampler<Int> {
private val poissonSamplerDelegate: Sampler<Int> = of(mean) private val poissonSamplerDelegate: Sampler<Int> = of(mean)

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/SmallMeanPoissonSampler.kt
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@ -17,7 +17,7 @@ import kotlin.math.exp
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* *
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html].
*/ */
public class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> { public class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
private val p0: Double = exp(-mean) private val p0: Double = exp(-mean)

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/ZigguratNormalizedGaussianSampler.kt
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@ -12,7 +12,7 @@ import kotlin.math.*
* implementation has been adapted from the C code provided therein. * implementation has been adapted from the C code provided therein.
* *
* Based on Commons RNG implementation. * Based on Commons RNG implementation.
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html * See [https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/ZigguratNormalizedGaussianSampler.html].
*/ */
public class ZigguratNormalizedGaussianSampler private constructor() : public class ZigguratNormalizedGaussianSampler private constructor() :
NormalizedGaussianSampler, Sampler<Double> { NormalizedGaussianSampler, Sampler<Double> {