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Perform 1.4 and explicit API migrations, refactor blocking chain, make tests work

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This commit is contained in:
Iaroslav Postovalov 2020-09-27 15:16:12 +07:00
parent e35a364aa8
commit 53ebec2e01
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26 changed files with 149 additions and 296 deletions
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9
kmath-coroutines/src/commonMain/kotlin/kscience/kmath/chains/BlockingIntChain.kt
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@ -3,10 +3,7 @@ package kscience.kmath.chains
/**
* Performance optimized chain for integer values
*/
public abstract class BlockingIntChain : Chain<Int> {
public abstract fun nextInt(): Int
override suspend fun next(): Int = nextInt()
public fun nextBlock(size: Int): IntArray = IntArray(size) { nextInt() }
public interface BlockingIntChain : Chain<Int> {
public override suspend fun next(): Int
public suspend fun nextBlock(size: Int): IntArray = IntArray(size) { next() }
}

9
kmath-coroutines/src/commonMain/kotlin/kscience/kmath/chains/BlockingRealChain.kt
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@ -3,10 +3,7 @@ package kscience.kmath.chains
/**
* Performance optimized chain for real values
*/
public abstract class BlockingRealChain : Chain<Double> {
public abstract fun nextDouble(): Double
override suspend fun next(): Double = nextDouble()
public fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
public interface BlockingRealChain : Chain<Double> {
public override suspend fun next(): Double
public suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
}

8
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/BlockingIntChain.kt
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@ -1,8 +0,0 @@
package scientifik.kmath.chains
/**
* Performance optimized chain for integer values
*/
abstract class BlockingIntChain : Chain<Int> {
suspend fun nextBlock(size: Int): IntArray = IntArray(size) { next() }
}

8
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/BlockingRealChain.kt
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@ -1,8 +0,0 @@
package scientifik.kmath.chains
/**
* Performance optimized chain for real values
*/
abstract class BlockingRealChain : Chain<Double> {
suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
}

3
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/Distribution.kt
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@ -1,5 +1,6 @@
package kscience.kmath.prob
import kotlinx.coroutines.flow.first
import kscience.kmath.chains.Chain
import kscience.kmath.chains.collect
import kscience.kmath.structures.Buffer
@ -70,7 +71,7 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
}
}
suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator) = sample(generator).first()
public suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator): T = sample(generator).first()
/**
* Generate a bunch of samples from real distributions

2
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/FactorizedDistribution.kt
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@ -14,7 +14,7 @@ public interface NamedDistribution<T> : Distribution<Map<String, T>>
public class FactorizedDistribution<T>(public val distributions: Collection<NamedDistribution<T>>) :
NamedDistribution<T> {
override fun probability(arg: Map<String, T>): Double =
distributions.fold(1.0) { acc, distr -> acc * distr.probability(arg) }
distributions.fold(1.0) { acc, dist -> acc * dist.probability(arg) }
override fun sample(generator: RandomGenerator): Chain<Map<String, T>> {
val chains = distributions.map { it.sample(generator) }

7
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/RandomChain.kt
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@ -1,17 +1,22 @@
package kscience.kmath.prob
import kscience.kmath.chains.BlockingIntChain
import kscience.kmath.chains.BlockingRealChain
import kscience.kmath.chains.Chain
/**
* A possibly stateful chain producing random values.
*
* @property generator the underlying [RandomGenerator] instance.
*/
public class RandomChain<out R>(
public val generator: RandomGenerator,
private val gen: suspend RandomGenerator.() -> R
) : Chain<R> {
override suspend fun next(): R = generator.gen()
override fun fork(): Chain<R> = RandomChain(generator.fork(), gen)
}
public fun <R> RandomGenerator.chain(gen: suspend RandomGenerator.() -> R): RandomChain<R> = RandomChain(this, gen)
public fun Chain<Double>.blocking(): BlockingRealChain = object : Chain<Double> by this, BlockingRealChain {}
public fun Chain<Int>.blocking(): BlockingIntChain = object : Chain<Int> by this, BlockingIntChain {}

18
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/AhrensDieterExponentialSampler.kt
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@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.ln
import kotlin.math.pow
@ -13,8 +13,8 @@ import kotlin.math.pow
* 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
*/
class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sampler<Double> {
override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
public class AhrensDieterExponentialSampler private constructor(public val mean: Double) : Sampler<Double> {
public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
// Step 1:
var a = 0.0
var u = nextDouble()
@ -47,7 +47,7 @@ class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sam
override fun toString(): String = "Ahrens-Dieter Exponential deviate"
companion object {
public companion object {
private val EXPONENTIAL_SA_QI by lazy { DoubleArray(16) }
init {
@ -64,7 +64,7 @@ class AhrensDieterExponentialSampler private constructor(val mean: Double) : Sam
}
}
fun of(mean: Double): AhrensDieterExponentialSampler {
public fun of(mean: Double): AhrensDieterExponentialSampler {
require(mean > 0) { "mean is not strictly positive: $mean" }
return AhrensDieterExponentialSampler(mean)
}

31
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt
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@ -1,10 +1,10 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import scientifik.kmath.prob.next
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kscience.kmath.prob.next
import kotlin.math.*
/**
@ -17,15 +17,12 @@ import kotlin.math.*
* 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
*/
class AhrensDieterMarsagliaTsangGammaSampler private constructor(
public class AhrensDieterMarsagliaTsangGammaSampler private constructor(
alpha: Double,
theta: Double
) : Sampler<Double> {
private val delegate: BaseGammaSampler =
if (alpha < 1) AhrensDieterGammaSampler(alpha, theta) else MarsagliaTsangGammaSampler(
alpha,
theta
)
if (alpha < 1) AhrensDieterGammaSampler(alpha, theta) else MarsagliaTsangGammaSampler(alpha, theta)
private abstract class BaseGammaSampler internal constructor(
protected val alpha: Double,
@ -39,7 +36,7 @@ class AhrensDieterMarsagliaTsangGammaSampler private constructor(
override fun toString(): String = "Ahrens-Dieter-Marsaglia-Tsang Gamma deviate"
}
private class AhrensDieterGammaSampler internal constructor(alpha: Double, theta: Double) :
private class AhrensDieterGammaSampler(alpha: Double, theta: Double) :
BaseGammaSampler(alpha, theta) {
private val oneOverAlpha: Double = 1.0 / alpha
private val bGSOptim: Double = 1.0 + alpha / E
@ -75,7 +72,7 @@ class AhrensDieterMarsagliaTsangGammaSampler private constructor(
}
}
private class MarsagliaTsangGammaSampler internal constructor(alpha: Double, theta: Double) :
private class MarsagliaTsangGammaSampler(alpha: Double, theta: Double) :
BaseGammaSampler(alpha, theta) {
private val dOptim: Double
private val cOptim: Double
@ -110,11 +107,11 @@ class AhrensDieterMarsagliaTsangGammaSampler private constructor(
}
}
override fun sample(generator: RandomGenerator): Chain<Double> = delegate.sample(generator)
override fun toString(): String = delegate.toString()
public override fun sample(generator: RandomGenerator): Chain<Double> = delegate.sample(generator)
public override fun toString(): String = delegate.toString()
companion object {
fun of(
public companion object {
public fun of(
alpha: Double,
theta: Double
): Sampler<Double> = AhrensDieterMarsagliaTsangGammaSampler(alpha, theta)

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/AliasMethodDiscreteSampler.kt
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@ -1,10 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kotlin.jvm.JvmOverloads
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.ceil
import kotlin.math.max
import kotlin.math.min
@ -36,7 +35,7 @@ import kotlin.math.min
* 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
*/
open class AliasMethodDiscreteSampler private constructor(
public open class AliasMethodDiscreteSampler private constructor(
// Deliberate direct storage of input arrays
protected val probability: LongArray,
protected val alias: IntArray
@ -102,17 +101,16 @@ open class AliasMethodDiscreteSampler private constructor(
if (generator.nextLong() ushr 11 < probability[j]) j else alias[j]
}
override fun toString(): String = "Alias method"
public override fun toString(): String = "Alias method"
companion object {
public companion object {
private const val DEFAULT_ALPHA = 0
private const val ZERO = 0.0
private const val ONE_AS_NUMERATOR = 1L shl 53
private const val CONVERT_TO_NUMERATOR: Double = ONE_AS_NUMERATOR.toDouble()
private const val MAX_SMALL_POWER_2_SIZE = 1 shl 11
@JvmOverloads
fun of(
public fun of(
probabilities: DoubleArray,
alpha: Int = DEFAULT_ALPHA
): Sampler<Int> {

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/BoxMullerNormalizedGaussianSampler.kt
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@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.*
/**
@ -13,10 +13,10 @@ import kotlin.math.*
* 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
*/
class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
public class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
private var nextGaussian: Double = Double.NaN
override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
val random: Double
if (nextGaussian.isNaN()) {
@ -40,9 +40,9 @@ class BoxMullerNormalizedGaussianSampler private constructor() : NormalizedGauss
random
}
override fun toString(): String = "Box-Muller normalized Gaussian deviate"
public override fun toString(): String = "Box-Muller normalized Gaussian deviate"
companion object {
fun of(): BoxMullerNormalizedGaussianSampler = BoxMullerNormalizedGaussianSampler()
public companion object {
public fun of(): BoxMullerNormalizedGaussianSampler = BoxMullerNormalizedGaussianSampler()
}
}

22
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/GaussianSampler.kt
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@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.chains.map
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import kscience.kmath.chains.Chain
import kscience.kmath.chains.map
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
/**
* Sampling from a Gaussian distribution with given mean and standard deviation.
@ -11,24 +11,24 @@ import scientifik.kmath.prob.Sampler
* 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
*/
class GaussianSampler private constructor(
public class GaussianSampler private constructor(
private val mean: Double,
private val standardDeviation: Double,
private val normalized: NormalizedGaussianSampler
) : Sampler<Double> {
override fun sample(generator: RandomGenerator): Chain<Double> = normalized
public override fun sample(generator: RandomGenerator): Chain<Double> = normalized
.sample(generator)
.map { standardDeviation * it + mean }
override fun toString(): String = "Gaussian deviate [$normalized]"
companion object {
fun of(
public companion object {
public fun of(
mean: Double,
standardDeviation: Double,
normalized: NormalizedGaussianSampler
normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler.of()
): GaussianSampler {
require(standardDeviation > 0) { "standard deviation is not strictly positive: $standardDeviation" }
require(standardDeviation > 0.0) { "standard deviation is not strictly positive: $standardDeviation" }
return GaussianSampler(
mean,

2
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/InternalGamma.kt
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@ -1,4 +1,4 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import kotlin.math.PI
import kotlin.math.ln

2
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/InternalUtils.kt
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@ -1,4 +1,4 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import kotlin.math.ln
import kotlin.math.min

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/KempSmallMeanPoissonSampler.kt
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@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.exp
/**
@ -18,11 +18,11 @@ import kotlin.math.exp
* 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
*/
class KempSmallMeanPoissonSampler private constructor(
public class KempSmallMeanPoissonSampler private constructor(
private val p0: Double,
private val mean: Double
) : Sampler<Int> {
override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
// Note on the algorithm:
// - X is the unknown sample deviate (the output of the algorithm)
// - x is the current value from the distribution
@ -48,10 +48,10 @@ class KempSmallMeanPoissonSampler private constructor(
x
}
override fun toString(): String = "Kemp Small Mean Poisson deviate"
public override fun toString(): String = "Kemp Small Mean Poisson deviate"
companion object {
fun of(mean: Double): KempSmallMeanPoissonSampler {
public companion object {
public fun of(mean: Double): KempSmallMeanPoissonSampler {
require(mean > 0) { "Mean is not strictly positive: $mean" }
val p0 = exp(-mean)
// Probability must be positive. As mean increases then p(0) decreases.

22
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/LargeMeanPoissonSampler.kt
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@ -1,11 +1,11 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.chains.ConstantChain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import scientifik.kmath.prob.next
import kscience.kmath.chains.Chain
import kscience.kmath.chains.ConstantChain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kscience.kmath.prob.next
import kotlin.math.*
/**
@ -19,7 +19,7 @@ import kotlin.math.*
* 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
*/
class LargeMeanPoissonSampler private constructor(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 gaussian: Sampler<Double> = ZigguratNormalizedGaussianSampler.of()
private val factorialLog: InternalUtils.FactorialLog = NO_CACHE_FACTORIAL_LOG
@ -41,7 +41,7 @@ class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<In
else // Not used.
KempSmallMeanPoissonSampler.of(mean - lambda)
override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
// This will never be null. It may be a no-op delegate that returns zero.
val y2 = smallMeanPoissonSampler.next(generator)
var x: Double
@ -114,7 +114,7 @@ class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<In
override fun toString(): String = "Large Mean Poisson deviate"
companion object {
public companion object {
private const val MAX_MEAN: Double = 0.5 * Int.MAX_VALUE
private val NO_CACHE_FACTORIAL_LOG: InternalUtils.FactorialLog = InternalUtils.FactorialLog.create()
@ -122,7 +122,7 @@ class LargeMeanPoissonSampler private constructor(val mean: Double) : Sampler<In
override fun sample(generator: RandomGenerator): Chain<Int> = ConstantChain(0)
}
fun of(mean: Double): LargeMeanPoissonSampler {
public fun of(mean: Double): LargeMeanPoissonSampler {
require(mean >= 1) { "mean is not >= 1: $mean" }
// The algorithm is not valid if Math.floor(mean) is not an integer.
require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/MarsagliaNormalizedGaussianSampler.kt
View File

@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.ln
import kotlin.math.sqrt
@ -15,10 +15,10 @@ import kotlin.math.sqrt
* 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
*/
class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
public class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGaussianSampler, Sampler<Double> {
private var nextGaussian = Double.NaN
override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain {
if (nextGaussian.isNaN()) {
val alpha: Double
var x: Double
@ -53,9 +53,9 @@ class MarsagliaNormalizedGaussianSampler private constructor() : NormalizedGauss
}
}
override fun toString(): String = "Box-Muller (with rejection) normalized Gaussian deviate"
public override fun toString(): String = "Box-Muller (with rejection) normalized Gaussian deviate"
companion object {
fun of(): MarsagliaNormalizedGaussianSampler = MarsagliaNormalizedGaussianSampler()
public companion object {
public fun of(): MarsagliaNormalizedGaussianSampler = MarsagliaNormalizedGaussianSampler()
}
}

6
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/NormalizedGaussianSampler.kt
View File

@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.prob.Sampler
import kscience.kmath.prob.Sampler
/**
* Marker interface for a sampler that generates values from an N(0,1)
* [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution).
*/
interface NormalizedGaussianSampler : Sampler<Double>
public interface NormalizedGaussianSampler : Sampler<Double>

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/PoissonSampler.kt
View File

@ -1,8 +1,8 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
/**
* Sampler for the Poisson distribution.
@ -16,17 +16,15 @@ import scientifik.kmath.prob.Sampler
* 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
*/
class PoissonSampler private constructor(
mean: Double
) : Sampler<Int> {
public class PoissonSampler private constructor(mean: Double) : Sampler<Int> {
private val poissonSamplerDelegate: Sampler<Int> = of(mean)
override fun sample(generator: RandomGenerator): Chain<Int> = poissonSamplerDelegate.sample(generator)
override fun toString(): String = poissonSamplerDelegate.toString()
public override fun sample(generator: RandomGenerator): Chain<Int> = poissonSamplerDelegate.sample(generator)
public override fun toString(): String = poissonSamplerDelegate.toString()
companion object {
public companion object {
private const val PIVOT = 40.0
fun of(mean: Double) =// Each sampler should check the input arguments.
public fun of(mean: Double): Sampler<Int> =// Each sampler should check the input arguments.
if (mean < PIVOT) SmallMeanPoissonSampler.of(mean) else LargeMeanPoissonSampler.of(mean)
}
}

20
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/SmallMeanPoissonSampler.kt
View File

@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.ceil
import kotlin.math.exp
@ -19,7 +19,7 @@ import kotlin.math.exp
*
* See https://commons.apache.org/proper/commons-rng/commons-rng-sampling/apidocs/org/apache/commons/rng/sampling/distribution/SmallMeanPoissonSampler.html
*/
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 limit: Int = (if (p0 > 0)
@ -27,7 +27,7 @@ class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
else
throw IllegalArgumentException("No p(x=0) probability for mean: $mean")).toInt()
override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
public override fun sample(generator: RandomGenerator): Chain<Int> = generator.chain {
var n = 0
var r = 1.0
@ -39,10 +39,10 @@ class SmallMeanPoissonSampler private constructor(mean: Double) : Sampler<Int> {
n
}
override fun toString(): String = "Small Mean Poisson deviate"
public override fun toString(): String = "Small Mean Poisson deviate"
companion object {
fun of(mean: Double): SmallMeanPoissonSampler {
public companion object {
public fun of(mean: Double): SmallMeanPoissonSampler {
require(mean > 0) { "mean is not strictly positive: $mean" }
return SmallMeanPoissonSampler(mean)
}

21
kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/samplers/ZigguratNormalizedGaussianSampler.kt
View File

@ -1,9 +1,9 @@
package scientifik.kmath.prob.samplers
package kscience.kmath.prob.samplers
import scientifik.kmath.chains.Chain
import scientifik.kmath.prob.RandomGenerator
import scientifik.kmath.prob.Sampler
import scientifik.kmath.prob.chain
import kscience.kmath.chains.Chain
import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.Sampler
import kscience.kmath.prob.chain
import kotlin.math.*
/**
@ -14,7 +14,7 @@ import kotlin.math.*
* 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
*/
class ZigguratNormalizedGaussianSampler private constructor() :
public class ZigguratNormalizedGaussianSampler private constructor() :
NormalizedGaussianSampler, Sampler<Double> {
private fun sampleOne(generator: RandomGenerator): Double {
@ -23,9 +23,8 @@ class ZigguratNormalizedGaussianSampler private constructor() :
return if (abs(j) < K[i]) j * W[i] else fix(generator, j, i)
}
override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain { sampleOne(this) }
override fun toString(): String = "Ziggurat normalized Gaussian deviate"
public override fun sample(generator: RandomGenerator): Chain<Double> = generator.chain { sampleOne(this) }
public override fun toString(): String = "Ziggurat normalized Gaussian deviate"
private fun fix(
generator: RandomGenerator,
@ -59,7 +58,7 @@ class ZigguratNormalizedGaussianSampler private constructor() :
}
}
companion object {
public companion object {
private const val R: Double = 3.442619855899
private const val ONE_OVER_R: Double = 1 / R
private const val V: Double = 9.91256303526217e-3
@ -94,7 +93,7 @@ class ZigguratNormalizedGaussianSampler private constructor() :
}
}
fun of(): ZigguratNormalizedGaussianSampler = ZigguratNormalizedGaussianSampler()
public fun of(): ZigguratNormalizedGaussianSampler = ZigguratNormalizedGaussianSampler()
private fun gauss(x: Double): Double = exp(-0.5 * x * x)
}
}

46
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/FactorizedDistribution.kt
View File

@ -1,46 +0,0 @@
package scientifik.kmath.prob
import scientifik.kmath.chains.Chain
import scientifik.kmath.chains.SimpleChain
/**
* A multivariate distribution which takes a map of parameters
*/
interface NamedDistribution<T> : Distribution<Map<String, T>>
/**
* A multivariate distribution that has independent distributions for separate axis
*/
class FactorizedDistribution<T>(val distributions: Collection<NamedDistribution<T>>) : NamedDistribution<T> {
override fun probability(arg: Map<String, T>): Double {
return distributions.fold(1.0) { acc, distr -> acc * distr.probability(arg) }
}
override fun sample(generator: RandomGenerator): Chain<Map<String, T>> {
val chains = distributions.map { it.sample(generator) }
return SimpleChain<Map<String, T>> {
chains.fold(emptyMap()) { acc, chain -> acc + chain.next() }
}
}
}
class NamedDistributionWrapper<T : Any>(val name: String, val distribution: Distribution<T>) : NamedDistribution<T> {
override fun probability(arg: Map<String, T>): Double = distribution.probability(
arg[name] ?: error("Argument with name $name not found in input parameters")
)
override fun sample(generator: RandomGenerator): Chain<Map<String, T>> {
val chain = distribution.sample(generator)
return SimpleChain {
mapOf(name to chain.next())
}
}
}
class DistributionBuilder<T: Any>{
private val distributions = ArrayList<NamedDistribution<T>>()
infix fun String.to(distribution: Distribution<T>){
distributions.add(NamedDistributionWrapper(this,distribution))
}
}

30
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomChain.kt
View File

@ -1,30 +0,0 @@
package scientifik.kmath.prob
import scientifik.kmath.chains.BlockingIntChain
import scientifik.kmath.chains.BlockingRealChain
import scientifik.kmath.chains.Chain
/**
* A possibly stateful chain producing random values.
*/
class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspend RandomGenerator.() -> R) : Chain<R> {
override suspend fun next(): R = generator.gen()
override fun fork(): Chain<R> = RandomChain(generator.fork(), gen)
}
fun <R> RandomGenerator.chain(gen: suspend RandomGenerator.() -> R): RandomChain<R> = RandomChain(this, gen)
fun RandomChain<Double>.blocking(): BlockingRealChain = let {
object : BlockingRealChain() {
override suspend fun next(): Double = it.next()
override fun fork(): Chain<Double> = it.fork()
}
}
fun RandomChain<Int>.blocking(): BlockingIntChain = let {
object : BlockingIntChain() {
override suspend fun next(): Int = it.next()
override fun fork(): Chain<Int> = it.fork()
}
}

47
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomGenerator.kt
View File

@ -1,47 +0,0 @@
package scientifik.kmath.prob
import kotlin.random.Random
/**
* A basic generator
*/
interface RandomGenerator {
fun nextBoolean(): Boolean
fun nextDouble(): Double
fun nextInt(): Int
fun nextInt(until: Int): Int
fun nextLong(): Long
fun nextLong(until: Long): Long
fun fillBytes(array: ByteArray, fromIndex: Int = 0, toIndex: Int = array.size)
fun nextBytes(size: Int): ByteArray = ByteArray(size).also { fillBytes(it) }
/**
* Create a new generator which is independent from current generator (operations on new generator do not affect this one
* and vise versa). The statistical properties of new generator should be the same as for this one.
* For pseudo-random generator, the fork is keeping the same sequence of numbers for given call order for each run.
*
* The thread safety of this operation is not guaranteed since it could affect the state of the generator.
*/
fun fork(): RandomGenerator
companion object {
val default by lazy { DefaultGenerator() }
fun default(seed: Long) = DefaultGenerator(Random(seed))
}
}
inline class DefaultGenerator(private val random: Random = Random) : RandomGenerator {
override fun nextBoolean(): Boolean = random.nextBoolean()
override fun nextDouble(): Double = random.nextDouble()
override fun nextInt(): Int = random.nextInt()
override fun nextInt(until: Int): Int = random.nextInt(until)
override fun nextLong(): Long = random.nextLong()
override fun nextLong(until: Long): Long = random.nextLong(until)
override fun fillBytes(array: ByteArray, fromIndex: Int, toIndex: Int) {
random.nextBytes(array, fromIndex, toIndex)
}
override fun nextBytes(size: Int): ByteArray = random.nextBytes(size)
override fun fork(): RandomGenerator = RandomGenerator.default(random.nextLong())
}

21
kmath-prob/src/jvmMain/kotlin/scientifik/kmath/prob/RandomSourceGenerator.kt
View File

@ -1,21 +1,22 @@
package scientifik.kmath.prob
import kscience.kmath.prob.RandomGenerator
import org.apache.commons.rng.simple.RandomSource
class RandomSourceGenerator(private val source: RandomSource, seed: Long?) :
public class RandomSourceGenerator(private val source: RandomSource, seed: Long?) :
RandomGenerator {
private val random = seed?.let {
RandomSource.create(source, seed)
} ?: RandomSource.create(source)
override fun nextBoolean(): Boolean = random.nextBoolean()
override fun nextDouble(): Double = random.nextDouble()
override fun nextInt(): Int = random.nextInt()
override fun nextInt(until: Int): Int = random.nextInt(until)
override fun nextLong(): Long = random.nextLong()
override fun nextLong(until: Long): Long = random.nextLong(until)
public override fun nextBoolean(): Boolean = random.nextBoolean()
public override fun nextDouble(): Double = random.nextDouble()
public override fun nextInt(): Int = random.nextInt()
public override fun nextInt(until: Int): Int = random.nextInt(until)
public override fun nextLong(): Long = random.nextLong()
public override fun nextLong(until: Long): Long = random.nextLong(until)
override fun fillBytes(array: ByteArray, fromIndex: Int, toIndex: Int) {
public override fun fillBytes(array: ByteArray, fromIndex: Int, toIndex: Int) {
require(toIndex > fromIndex)
random.nextBytes(array, fromIndex, toIndex - fromIndex)
}
@ -23,8 +24,8 @@ class RandomSourceGenerator(private val source: RandomSource, seed: Long?) :
override fun fork(): RandomGenerator = RandomSourceGenerator(source, nextLong())
}
fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator =
public fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator =
RandomSourceGenerator(source, seed)
fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator =
public fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator =
fromSource(RandomSource.MT, seed)

11
kmath-prob/src/jvmTest/kotlin/kscience/kmath/prob/CommonsDistributionsTest.kt
View File

@ -3,25 +3,24 @@ package kscience.kmath.prob
import kotlinx.coroutines.flow.take
import kotlinx.coroutines.flow.toList
import kotlinx.coroutines.runBlocking
import kscience.kmath.prob.samplers.GaussianSampler
import org.junit.jupiter.api.Assertions
import org.junit.jupiter.api.Test
internal class CommonsDistributionsTest {
@Test
fun testNormalDistributionSuspend() {
val distribution = Distribution.normal(7.0, 2.0)
val distribution = GaussianSampler.of(7.0, 2.0)
val generator = RandomGenerator.default(1)
val sample = runBlocking {
distribution.sample(generator).take(1000).toList()
}
val sample = runBlocking { distribution.sample(generator).take(1000).toList() }
Assertions.assertEquals(7.0, sample.average(), 0.1)
}
@Test
fun testNormalDistributionBlocking() {
val distribution = Distribution.normal(7.0, 2.0)
val distribution = GaussianSampler.of(7.0, 2.0)
val generator = RandomGenerator.default(1)
val sample = distribution.sample(generator).nextBlock(1000)
val sample = runBlocking { distribution.sample(generator).blocking().nextBlock(1000) }
Assertions.assertEquals(7.0, sample.average(), 0.1)
}
}