Dev #280
@ -1,2 +0,0 @@
|
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plugins { id("scientifik.mpp") }
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kotlin.sourceSets { commonMain.get().dependencies { api(project(":kmath-coroutines")) } }
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@ -1,7 +0,0 @@
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package scientifik.commons.rng.sampling
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import scientifik.commons.rng.UniformRandomProvider
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interface SharedStateSampler<R> {
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fun withUniformRandomProvider(rng: UniformRandomProvider): R
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}
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@ -1,5 +0,0 @@
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package scientifik.commons.rng.sampling.distribution
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interface ContinuousSampler {
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fun sample(): Double
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}
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@ -1,5 +0,0 @@
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package scientifik.commons.rng.sampling.distribution
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interface DiscreteSampler {
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fun sample(): Int
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}
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@ -1,3 +0,0 @@
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package scientifik.commons.rng.sampling.distribution
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interface NormalizedGaussianSampler : ContinuousSampler
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@ -1,7 +0,0 @@
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package scientifik.commons.rng.sampling.distribution
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import scientifik.commons.rng.sampling.SharedStateSampler
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interface SharedStateContinuousSampler : ContinuousSampler,
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SharedStateSampler<SharedStateContinuousSampler?> {
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}
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@ -3,17 +3,10 @@ plugins {
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}
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kotlin.sourceSets {
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commonMain {
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dependencies {
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api(project(":kmath-coroutines"))
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api(project(":kmath-commons-rng-part"))
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}
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}
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commonMain.get().dependencies { api(project(":kmath-coroutines")) }
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jvmMain {
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dependencies {
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jvmMain.get().dependencies {
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api("org.apache.commons:commons-rng-sampling:1.3")
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api("org.apache.commons:commons-rng-simple:1.3")
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}
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}
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}
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@ -1,4 +1,4 @@
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package scientifik.commons.rng
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package scientifik.kmath.commons.rng
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interface UniformRandomProvider {
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fun nextBytes(bytes: ByteArray)
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@ -0,0 +1,7 @@
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package scientifik.kmath.commons.rng.sampling
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import scientifik.kmath.commons.rng.UniformRandomProvider
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interface SharedStateSampler<R> {
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fun withUniformRandomProvider(rng: UniformRandomProvider): R
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}
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@ -1,6 +1,6 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
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import kotlin.math.ln
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import kotlin.math.pow
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@ -76,7 +76,11 @@ class AhrensDieterExponentialSampler : SamplerBase,
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fun of(
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rng: UniformRandomProvider,
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mean: Double
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): SharedStateContinuousSampler = AhrensDieterExponentialSampler(rng, mean)
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): SharedStateContinuousSampler =
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AhrensDieterExponentialSampler(
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rng,
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mean
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)
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init {
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/**
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@ -87,7 +91,9 @@ class AhrensDieterExponentialSampler : SamplerBase,
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var qi = 0.0
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EXPONENTIAL_SA_QI.indices.forEach { i ->
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qi += ln2.pow(i + 1.0) / InternalUtils.factorial(i + 1)
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qi += ln2.pow(i + 1.0) / InternalUtils.factorial(
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i + 1
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)
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EXPONENTIAL_SA_QI[i] = qi
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}
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}
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@ -1,6 +1,6 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
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import kotlin.math.*
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class BoxMullerNormalizedGaussianSampler(
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@ -0,0 +1,5 @@
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package scientifik.kmath.commons.rng.sampling.distribution
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interface ContinuousSampler {
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fun sample(): Double
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}
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@ -0,0 +1,5 @@
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package scientifik.kmath.commons.rng.sampling.distribution
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interface DiscreteSampler {
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fun sample(): Int
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}
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@ -1,8 +1,9 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
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class GaussianSampler : SharedStateContinuousSampler {
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class GaussianSampler :
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SharedStateContinuousSampler {
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private val mean: Double
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private val standardDeviation: Double
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private val normalized: NormalizedGaussianSampler
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@ -24,7 +25,11 @@ class GaussianSampler : SharedStateContinuousSampler {
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) {
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mean = source.mean
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standardDeviation = source.standardDeviation
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normalized = InternalUtils.newNormalizedGaussianSampler(source.normalized, rng)
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normalized =
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InternalUtils.newNormalizedGaussianSampler(
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source.normalized,
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rng
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)
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}
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override fun sample(): Double = standardDeviation * normalized.sample() + mean
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@ -40,6 +45,11 @@ class GaussianSampler : SharedStateContinuousSampler {
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normalized: NormalizedGaussianSampler,
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mean: Double,
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standardDeviation: Double
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): SharedStateContinuousSampler = GaussianSampler(normalized, mean, standardDeviation)
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): SharedStateContinuousSampler =
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GaussianSampler(
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normalized,
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mean,
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standardDeviation
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)
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}
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}
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@ -1,4 +1,4 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import kotlin.math.PI
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import kotlin.math.ln
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@ -1,7 +1,7 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.commons.rng.sampling.SharedStateSampler
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import scientifik.kmath.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.sampling.SharedStateSampler
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import kotlin.math.ln
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import kotlin.math.min
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@ -63,30 +63,45 @@ internal object InternalUtils {
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if (cache != null && cache.size > BEGIN_LOG_FACTORIALS) {
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// Copy available values.
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endCopy = min(cache.size, numValues)
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cache.copyInto(logFactorials, BEGIN_LOG_FACTORIALS, BEGIN_LOG_FACTORIALS, endCopy)
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cache.copyInto(logFactorials,
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BEGIN_LOG_FACTORIALS,
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BEGIN_LOG_FACTORIALS, endCopy)
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}
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// All values to be computed
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else
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endCopy = BEGIN_LOG_FACTORIALS
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endCopy =
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BEGIN_LOG_FACTORIALS
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// Compute remaining values.
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(endCopy until numValues).forEach { i ->
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if (i < FACTORIALS.size) logFactorials[i] = ln(FACTORIALS[i].toDouble()) else logFactorials[i] =
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if (i < FACTORIALS.size) logFactorials[i] = ln(
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FACTORIALS[i].toDouble()) else logFactorials[i] =
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logFactorials[i - 1] + ln(i.toDouble())
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}
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}
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fun withCache(cacheSize: Int): FactorialLog = FactorialLog(cacheSize, logFactorials)
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fun withCache(cacheSize: Int): FactorialLog =
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FactorialLog(
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cacheSize,
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logFactorials
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)
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fun value(n: Int): Double {
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if (n < logFactorials.size)
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return logFactorials[n]
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return if (n < FACTORIALS.size) ln(FACTORIALS[n].toDouble()) else InternalGamma.logGamma(n + 1.0)
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return if (n < FACTORIALS.size) ln(
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FACTORIALS[n].toDouble()) else InternalGamma.logGamma(
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n + 1.0
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)
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}
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companion object {
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fun create(): FactorialLog = FactorialLog(0, null)
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fun create(): FactorialLog =
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FactorialLog(
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0,
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null
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)
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}
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}
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}
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@ -1,6 +1,6 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
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import kotlin.math.exp
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class KempSmallMeanPoissonSampler private constructor(
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@ -48,7 +48,11 @@ class KempSmallMeanPoissonSampler private constructor(
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val p0: Double = exp(-mean)
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// Probability must be positive. As mean increases then p(0) decreases.
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if (p0 > 0) return KempSmallMeanPoissonSampler(rng, p0, mean)
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if (p0 > 0) return KempSmallMeanPoissonSampler(
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rng,
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p0,
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mean
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)
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throw IllegalArgumentException("No probability for mean: $mean")
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}
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}
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@ -1,9 +1,10 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
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import kotlin.math.*
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class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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class LargeMeanPoissonSampler :
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SharedStateDiscreteSampler {
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private val rng: UniformRandomProvider
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private val exponential: SharedStateContinuousSampler
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private val gaussian: SharedStateContinuousSampler
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@ -27,8 +28,13 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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// The algorithm is not valid if Math.floor(mean) is not an integer.
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require(mean <= MAX_MEAN) { "mean $mean > $MAX_MEAN" }
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this.rng = rng
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gaussian = ZigguratNormalizedGaussianSampler(rng)
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exponential = AhrensDieterExponentialSampler.of(rng, 1.0)
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gaussian =
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ZigguratNormalizedGaussianSampler(rng)
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exponential =
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AhrensDieterExponentialSampler.of(
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rng,
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1.0
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)
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// Plain constructor uses the uncached function.
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factorialLog = NO_CACHE_FACTORIAL_LOG!!
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// Cache values used in the algorithm
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@ -49,7 +55,10 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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val lambdaFractional = mean - lambda
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smallMeanPoissonSampler =
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if (lambdaFractional < Double.MIN_VALUE) NO_SMALL_MEAN_POISSON_SAMPLER else // Not used.
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KempSmallMeanPoissonSampler.of(rng, lambdaFractional)
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KempSmallMeanPoissonSampler.of(
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rng,
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lambdaFractional
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)
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}
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internal constructor(
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@ -59,8 +68,13 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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) {
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require(!(lambdaFractional < 0 || lambdaFractional >= 1)) { "lambdaFractional must be in the range 0 (inclusive) to 1 (exclusive): $lambdaFractional" }
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this.rng = rng
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gaussian = ZigguratNormalizedGaussianSampler(rng)
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exponential = AhrensDieterExponentialSampler.of(rng, 1.0)
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gaussian =
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ZigguratNormalizedGaussianSampler(rng)
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exponential =
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AhrensDieterExponentialSampler.of(
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rng,
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1.0
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)
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// Plain constructor uses the uncached function.
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factorialLog = NO_CACHE_FACTORIAL_LOG!!
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// Use the state to initialise the algorithm
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@ -79,7 +93,10 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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if (lambdaFractional < Double.MIN_VALUE)
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NO_SMALL_MEAN_POISSON_SAMPLER
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else // Not used.
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KempSmallMeanPoissonSampler.of(rng, lambdaFractional)
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KempSmallMeanPoissonSampler.of(
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rng,
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lambdaFractional
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)
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}
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/**
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@ -222,7 +239,8 @@ class LargeMeanPoissonSampler : SharedStateDiscreteSampler {
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fun of(
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rng: UniformRandomProvider,
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mean: Double
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): SharedStateDiscreteSampler = LargeMeanPoissonSampler(rng, mean)
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): SharedStateDiscreteSampler =
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LargeMeanPoissonSampler(rng, mean)
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init {
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// Create without a cache.
|
@ -1,6 +1,6 @@
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package scientifik.commons.rng.sampling.distribution
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package scientifik.kmath.commons.rng.sampling.distribution
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
|
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import kotlin.math.ln
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import kotlin.math.sqrt
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|
@ -0,0 +1,4 @@
|
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package scientifik.kmath.commons.rng.sampling.distribution
|
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|
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interface NormalizedGaussianSampler :
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ContinuousSampler
|
@ -1,11 +1,12 @@
|
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package scientifik.commons.rng.sampling.distribution
|
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package scientifik.kmath.commons.rng.sampling.distribution
|
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import scientifik.commons.rng.UniformRandomProvider
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import scientifik.kmath.commons.rng.UniformRandomProvider
|
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|
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class PoissonSampler(
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rng: UniformRandomProvider,
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mean: Double
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) : SamplerBase(null), SharedStateDiscreteSampler {
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) : SamplerBase(null),
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SharedStateDiscreteSampler {
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private val poissonSamplerDelegate: SharedStateDiscreteSampler
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|
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override fun sample(): Int = poissonSamplerDelegate.sample()
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@ -22,11 +23,18 @@ class PoissonSampler(
|
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rng: UniformRandomProvider,
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mean: Double
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): SharedStateDiscreteSampler =// Each sampler should check the input arguments.
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if (mean < PIVOT) SmallMeanPoissonSampler.of(rng, mean) else LargeMeanPoissonSampler.of(rng, mean)
|
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if (mean < PIVOT) SmallMeanPoissonSampler.of(
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rng,
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mean
|
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) else LargeMeanPoissonSampler.of(
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rng,
|
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mean
|
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)
|
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}
|
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|
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init {
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// Delegate all work to specialised samplers.
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poissonSamplerDelegate = of(rng, mean)
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poissonSamplerDelegate =
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of(rng, mean)
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}
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}
|
@ -1,6 +1,6 @@
|
||||
package scientifik.commons.rng.sampling.distribution
|
||||
package scientifik.kmath.commons.rng.sampling.distribution
|
||||
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
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import scientifik.kmath.commons.rng.UniformRandomProvider
|
||||
|
||||
@Deprecated("Since version 1.1. Class intended for internal use only.")
|
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open class SamplerBase protected constructor(private val rng: UniformRandomProvider?) {
|
@ -0,0 +1,7 @@
|
||||
package scientifik.kmath.commons.rng.sampling.distribution
|
||||
|
||||
import scientifik.kmath.commons.rng.sampling.SharedStateSampler
|
||||
|
||||
interface SharedStateContinuousSampler : ContinuousSampler,
|
||||
SharedStateSampler<SharedStateContinuousSampler?> {
|
||||
}
|
@ -1,6 +1,6 @@
|
||||
package scientifik.commons.rng.sampling.distribution
|
||||
package scientifik.kmath.commons.rng.sampling.distribution
|
||||
|
||||
import scientifik.commons.rng.sampling.SharedStateSampler
|
||||
import scientifik.kmath.commons.rng.sampling.SharedStateSampler
|
||||
|
||||
interface SharedStateDiscreteSampler : DiscreteSampler,
|
||||
SharedStateSampler<SharedStateDiscreteSampler?> { // Composite interface
|
@ -1,10 +1,11 @@
|
||||
package scientifik.commons.rng.sampling.distribution
|
||||
package scientifik.kmath.commons.rng.sampling.distribution
|
||||
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
||||
import scientifik.kmath.commons.rng.UniformRandomProvider
|
||||
import kotlin.math.ceil
|
||||
import kotlin.math.exp
|
||||
|
||||
class SmallMeanPoissonSampler : SharedStateDiscreteSampler {
|
||||
class SmallMeanPoissonSampler :
|
||||
SharedStateDiscreteSampler {
|
||||
private val p0: Double
|
||||
private val limit: Int
|
||||
private val rng: UniformRandomProvider
|
||||
@ -53,6 +54,7 @@ class SmallMeanPoissonSampler : SharedStateDiscreteSampler {
|
||||
fun of(
|
||||
rng: UniformRandomProvider,
|
||||
mean: Double
|
||||
): SharedStateDiscreteSampler = SmallMeanPoissonSampler(rng, mean)
|
||||
): SharedStateDiscreteSampler =
|
||||
SmallMeanPoissonSampler(rng, mean)
|
||||
}
|
||||
}
|
@ -1,6 +1,6 @@
|
||||
package scientifik.commons.rng.sampling.distribution
|
||||
package scientifik.kmath.commons.rng.sampling.distribution
|
||||
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
||||
import scientifik.kmath.commons.rng.UniformRandomProvider
|
||||
import kotlin.math.*
|
||||
|
||||
class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider) :
|
||||
@ -26,9 +26,13 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
|
||||
|
||||
init {
|
||||
// Filling the tables.
|
||||
var d = R
|
||||
var d =
|
||||
R
|
||||
var t = d
|
||||
var fd = gauss(d)
|
||||
var fd =
|
||||
gauss(
|
||||
d
|
||||
)
|
||||
val q = V / fd
|
||||
K[0] = (d / q * MAX).toLong()
|
||||
K[1] = 0
|
||||
@ -39,7 +43,10 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
|
||||
|
||||
(LAST - 1 downTo 1).forEach { i ->
|
||||
d = sqrt(-2 * ln(V / d + fd))
|
||||
fd = gauss(d)
|
||||
fd =
|
||||
gauss(
|
||||
d
|
||||
)
|
||||
K[i + 1] = (d / t * MAX).toLong()
|
||||
t = d
|
||||
F[i] = fd
|
||||
@ -80,7 +87,10 @@ class ZigguratNormalizedGaussianSampler(private val rng: UniformRandomProvider)
|
||||
// else
|
||||
// Try again.
|
||||
// This branch is called about 0.012362 times per sample.
|
||||
if (F[iz] + rng.nextDouble() * (F[iz - 1] - F[iz]) < gauss(x)) x else sample()
|
||||
if (F[iz] + rng.nextDouble() * (F[iz - 1] - F[iz]) < gauss(
|
||||
x
|
||||
)
|
||||
) x else sample()
|
||||
}
|
||||
}
|
||||
|
@ -1,53 +0,0 @@
|
||||
package scientifik.kmath.prob
|
||||
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
||||
import scientifik.commons.rng.sampling.distribution.*
|
||||
import scientifik.kmath.chains.BlockingIntChain
|
||||
import scientifik.kmath.chains.BlockingRealChain
|
||||
import scientifik.kmath.chains.Chain
|
||||
|
||||
abstract class ContinuousSamplerDistribution : Distribution<Double> {
|
||||
|
||||
private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() {
|
||||
private val sampler = buildCMSampler(generator)
|
||||
|
||||
override fun nextDouble(): Double = sampler.sample()
|
||||
|
||||
override fun fork(): Chain<Double> = ContinuousSamplerChain(generator.fork())
|
||||
}
|
||||
|
||||
protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler
|
||||
|
||||
override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator)
|
||||
}
|
||||
|
||||
abstract class DiscreteSamplerDistribution : Distribution<Int> {
|
||||
|
||||
private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() {
|
||||
private val sampler = buildSampler(generator)
|
||||
|
||||
override fun nextInt(): Int = sampler.sample()
|
||||
|
||||
override fun fork(): Chain<Int> = ContinuousSamplerChain(generator.fork())
|
||||
}
|
||||
|
||||
protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler
|
||||
|
||||
override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator)
|
||||
}
|
||||
|
||||
enum class NormalSamplerMethod {
|
||||
BoxMuller,
|
||||
Marsaglia,
|
||||
Ziggurat
|
||||
}
|
||||
|
||||
fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomProvider): NormalizedGaussianSampler =
|
||||
when (method) {
|
||||
NormalSamplerMethod.BoxMuller -> BoxMullerNormalizedGaussianSampler(
|
||||
provider
|
||||
)
|
||||
NormalSamplerMethod.Marsaglia -> MarsagliaNormalizedGaussianSampler(provider)
|
||||
NormalSamplerMethod.Ziggurat -> ZigguratNormalizedGaussianSampler(provider)
|
||||
}
|
||||
|
@ -1,9 +1,10 @@
|
||||
package scientifik.kmath.prob
|
||||
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
||||
import scientifik.kmath.commons.rng.UniformRandomProvider
|
||||
|
||||
|
||||
inline class RandomGeneratorProvider(val generator: RandomGenerator) : UniformRandomProvider {
|
||||
inline class RandomGeneratorProvider(val generator: RandomGenerator) :
|
||||
UniformRandomProvider {
|
||||
override fun nextBoolean(): Boolean = generator.nextBoolean()
|
||||
|
||||
override fun nextFloat(): Float = generator.nextDouble().toFloat()
|
||||
|
@ -1,9 +1,9 @@
|
||||
package scientifik.kmath.prob
|
||||
|
||||
import scientifik.commons.rng.sampling.distribution.ContinuousSampler
|
||||
import scientifik.commons.rng.sampling.distribution.DiscreteSampler
|
||||
import scientifik.commons.rng.sampling.distribution.GaussianSampler
|
||||
import scientifik.commons.rng.sampling.distribution.PoissonSampler
|
||||
import scientifik.kmath.commons.rng.sampling.distribution.ContinuousSampler
|
||||
import scientifik.kmath.commons.rng.sampling.distribution.DiscreteSampler
|
||||
import scientifik.kmath.commons.rng.sampling.distribution.GaussianSampler
|
||||
import scientifik.kmath.commons.rng.sampling.distribution.PoissonSampler
|
||||
import kotlin.math.PI
|
||||
import kotlin.math.exp
|
||||
import kotlin.math.pow
|
||||
@ -33,7 +33,11 @@ fun Distribution.Companion.normal(
|
||||
override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
|
||||
val provider = generator.asUniformRandomProvider()
|
||||
val normalizedSampler = normalSampler(method, provider)
|
||||
return GaussianSampler(normalizedSampler, mean, sigma)
|
||||
return GaussianSampler(
|
||||
normalizedSampler,
|
||||
mean,
|
||||
sigma
|
||||
)
|
||||
}
|
||||
|
||||
override fun probability(arg: Double): Double {
|
@ -1,7 +1,7 @@
|
||||
package scientifik.kmath.prob
|
||||
|
||||
import org.apache.commons.rng.simple.RandomSource
|
||||
import scientifik.commons.rng.UniformRandomProvider
|
||||
import scientifik.kmath.commons.rng.UniformRandomProvider
|
||||
|
||||
class RandomSourceGenerator(val source: RandomSource, seed: Long?) :
|
||||
RandomGenerator {
|
||||
|
@ -29,13 +29,13 @@ pluginManagement {
|
||||
}
|
||||
|
||||
rootProject.name = "kmath"
|
||||
|
||||
include(
|
||||
":kmath-memory",
|
||||
":kmath-core",
|
||||
":kmath-functions",
|
||||
// ":kmath-io",
|
||||
":kmath-coroutines",
|
||||
"kmath-commons-rng-part",
|
||||
":kmath-histograms",
|
||||
":kmath-commons",
|
||||
":kmath-viktor",
|
||||
|
Loading…
Reference in New Issue
Block a user