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Reimplement random-forking chain

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This commit is contained in:
Alexander Nozik 2024-08-14 19:20:05 +03:00
parent 2becee7f59
commit 91513a1629
9 changed files with 40 additions and 46 deletions
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2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/matrixAttributes.kt
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@ -124,7 +124,7 @@ public val <T> MatrixScope<T>.QR: QRDecompositionAttribute<T>
public interface CholeskyDecomposition<T> {
/**
* The triangular matrix in this decomposition. It may have either [UpperTriangular] or [LowerTriangular].
* The lower triangular matrix in this decomposition. It should have [LowerTriangular].
*/
public val l: Matrix<T>
}

5
kmath-ejml/build.gradle.kts
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@ -5,10 +5,7 @@ plugins {
val ejmlVerision = "0.43.1"
dependencies {
api("org.ejml:ejml-ddense:$ejmlVerision")
api("org.ejml:ejml-fdense:$ejmlVerision")
api("org.ejml:ejml-dsparse:$ejmlVerision")
api("org.ejml:ejml-fsparse:$ejmlVerision")
api("org.ejml:ejml-all:$ejmlVerision")
api(projects.kmathCore)
}

2
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Circle2D.kt
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@ -27,7 +27,7 @@ public data class Float64Circle2D(
override val radius: Float64,
) : Circle2D<Double>
public fun Circle2D(center: Vector2D<Float64>, radius: Double): Circle2D<Double> = Float64Circle2D(
public fun Circle2D(center: Vector2D<Float64>, radius: Double): Float64Circle2D = Float64Circle2D(
center as? Float64Vector2D ?: Float64Vector2D(center.x, center.y),
radius
)

11
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/FactorizedDistribution.kt
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@ -17,8 +17,9 @@ public interface NamedDistribution<T> : Distribution<Map<String, T>>
/**
* A multivariate distribution that has independent distributions for separate axis.
*/
public class FactorizedDistribution<T>(public val distributions: Collection<NamedDistribution<T>>) :
NamedDistribution<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, dist -> acc * dist.probability(arg) }
@ -28,8 +29,10 @@ public class FactorizedDistribution<T>(public val distributions: Collection<Name
}
}
public class NamedDistributionWrapper<T : Any>(public val name: String, public val distribution: Distribution<T>) :
NamedDistribution<T> {
public class NamedDistributionWrapper<T : Any>(
public val name: String,
public 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")
)

15
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt
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@ -9,11 +9,24 @@ import space.kscience.kmath.chains.BlockingDoubleChain
import space.kscience.kmath.operations.Float64Field.pow
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.samplers.GaussianSampler
import space.kscience.kmath.samplers.InternalErf
import space.kscience.kmath.samplers.InternalGamma
import space.kscience.kmath.samplers.NormalizedGaussianSampler
import space.kscience.kmath.samplers.ZigguratNormalizedGaussianSampler
import kotlin.math.*
/**
* 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 {
fun erfc(x: Double): Double {
if (abs(x) > 40) return if (x > 0) 0.0 else 2.0
val ret = InternalGamma.regularizedGammaQ(0.5, x * x, 10000)
return if (x < 0) 2 - ret else ret
}
}
/**
* Implements [Distribution1D] for the normal (gaussian) distribution.
*/

20
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/InternalErf.kt
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@ -1,20 +0,0 @@
/*
* Copyright 2018-2024 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.samplers
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 {
fun erfc(x: Double): Double {
if (abs(x) > 40) return if (x > 0) 0.0 else 2.0
val ret = InternalGamma.regularizedGammaQ(0.5, x * x, 10000)
return if (x < 0) 2 - ret else ret
}
}

6
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/MetropolisHastingsSampler.kt
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@ -21,7 +21,7 @@ import space.kscience.kmath.structures.Float64
*/
public class MetropolisHastingsSampler<T>(
public val algebra: Group<T>,
public val startPoint: T,
public val startPoint: suspend (RandomGenerator) ->T,
public val stepSampler: Sampler<T>,
public val targetPdf: suspend (T) -> Float64,
) : Sampler<T> {
@ -30,7 +30,7 @@ public class MetropolisHastingsSampler<T>(
override fun sample(generator: RandomGenerator): Chain<T> = StatefulChain<Chain<T>, T>(
state = stepSampler.sample(generator),
seed = { startPoint },
seed = { startPoint(generator) },
forkState = Chain<T>::fork
) { previousPoint: T ->
val proposalPoint = with(algebra) { previousPoint + next() }
@ -59,7 +59,7 @@ public class MetropolisHastingsSampler<T>(
targetPdf: suspend (Float64) -> Float64,
): MetropolisHastingsSampler<Double> = MetropolisHastingsSampler(
algebra = Float64.algebra,
startPoint = startPoint,
startPoint = {startPoint},
stepSampler = stepSampler,
targetPdf = targetPdf
)

22
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/samplers/RandomForkingEvent.kt
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@ -7,13 +7,8 @@
package space.kscience.kmath.samplers
import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.Deferred
import kotlinx.coroutines.ExperimentalCoroutinesApi
import kotlinx.coroutines.async
import kotlinx.coroutines.*
import kotlinx.coroutines.channels.Channel
import kotlinx.coroutines.isActive
import kotlinx.coroutines.launch
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.operations.Group
@ -28,6 +23,7 @@ public data class RandomForkingSample<T>(
val value: Deferred<T>,
val generation: Int,
val energy: Float64,
val generator: RandomGenerator,
val stepChain: Chain<T>
)
@ -38,11 +34,11 @@ public data class RandomForkingSample<T>(
public class RandomForkingSampler<T : Any>(
private val scope: CoroutineScope,
private val initialValue: suspend (RandomGenerator) -> T,
private val makeStep: suspend RandomGenerator.(T) -> List<T>
private val makeStep: suspend (T) -> List<T>
) : Sampler<T?> {
override fun sample(generator: RandomGenerator): Chain<T?> =
buildChain(scope, initial = { initialValue(generator) }) { generator.makeStep(it) }
buildChain(scope, initial = { initialValue(generator) }) { makeStep(it) }
public companion object {
private suspend fun <T> Channel<T>.receiveEvents(
@ -95,13 +91,14 @@ public class RandomForkingSampler<T : Any>(
stepScaleRule: suspend (Float64) -> Float64 = { 1.0 },
targetPdf: suspend (T) -> Float64,
): RandomForkingSampler<RandomForkingSample<T>> where A : Group<T>, A : ScaleOperations<T> =
RandomForkingSampler<RandomForkingSample<T>>(
RandomForkingSampler(
scope = scope,
initialValue = { generator ->
RandomForkingSample<T>(
value = scope.async { startPoint(generator) },
generation = 0,
energy = initialEnergy,
generator = generator,
stepChain = stepSampler.sample(generator)
)
}
@ -111,14 +108,14 @@ public class RandomForkingSampler<T : Any>(
RandomForkingSample<T>(
value = scope.async<T> {
val proposalPoint = with(algebra) {
value + previousSample.stepChain.next() * stepScaleRule(previousSample.energy)
value + previousSample.stepChain.next() * stepScaleRule(energy)
}
val ratio = targetPdf(proposalPoint) / targetPdf(value)
if (ratio >= 1.0) {
proposalPoint
} else {
val acceptanceProbability = nextDouble()
val acceptanceProbability = previousSample.generator.nextDouble()
if (acceptanceProbability <= ratio) {
proposalPoint
} else {
@ -127,7 +124,8 @@ public class RandomForkingSampler<T : Any>(
}
},
generation = previousSample.generation + 1,
energy = 0.0,
energy = energy,
generator = previousSample.generator,
stepChain = previousSample.stepChain
)
}

3
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/samplers/TestMetropolisHastingsSampler.kt
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@ -85,4 +85,7 @@ class TestMetropolisHastingsSampler {
assertEquals(setup.mean * sqrt(PI / 2), Float64Field.mean(sampledValues), 1e-2)
}
}
}