Optimized blocking chains for primitive numbers generation.
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@ -0,0 +1,71 @@
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package scientifik.kmath.commons.prob
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import kotlinx.coroutines.Dispatchers
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import kotlinx.coroutines.async
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import kotlinx.coroutines.runBlocking
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import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler
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import org.apache.commons.rng.simple.RandomSource
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import scientifik.kmath.chains.BlockingRealChain
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import scientifik.kmath.prob.*
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import java.time.Duration
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import java.time.Instant
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private suspend fun runChain(): Duration {
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val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
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val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
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val chain = normal.sample(generator) as BlockingRealChain
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val startTime = Instant.now()
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var sum = 0.0
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repeat(10000001) { counter ->
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sum += chain.nextDouble()
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if (counter % 100000 == 0) {
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val duration = Duration.between(startTime, Instant.now())
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val meanValue = sum / counter
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println("Chain sampler completed $counter elements in $duration: $meanValue")
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}
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}
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return Duration.between(startTime, Instant.now())
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}
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private fun runDirect(): Duration {
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val provider = RandomSource.create(RandomSource.MT, 123L)
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val sampler = ZigguratNormalizedGaussianSampler(provider)
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val startTime = Instant.now()
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var sum = 0.0
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repeat(10000001) { counter ->
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sum += sampler.sample()
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if (counter % 100000 == 0) {
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val duration = Duration.between(startTime, Instant.now())
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val meanValue = sum / counter
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println("Direct sampler completed $counter elements in $duration: $meanValue")
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}
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}
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return Duration.between(startTime, Instant.now())
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}
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/**
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* Comparing chain sampling performance with direct sampling performance
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*/
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fun main() {
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runBlocking(Dispatchers.Default) {
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val chainJob = async {
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runChain()
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}
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val directJob = async {
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runDirect()
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}
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println("Chain: ${chainJob.await()}")
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println("Direct: ${directJob.await()}")
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}
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}
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@ -0,0 +1,12 @@
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package scientifik.kmath.chains
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/**
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* Performance optimized chain for integer values
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*/
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abstract class BlockingIntChain : Chain<Int> {
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abstract fun nextInt(): Int
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override suspend fun next(): Int = nextInt()
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fun nextBlock(size: Int): IntArray = IntArray(size) { nextInt() }
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}
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@ -0,0 +1,12 @@
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package scientifik.kmath.chains
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/**
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* Performance optimized chain for real values
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*/
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abstract class BlockingRealChain : Chain<Double> {
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abstract fun nextDouble(): Double
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override suspend fun next(): Double = nextDouble()
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fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
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}
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@ -48,7 +48,6 @@ interface Chain<out R>: Flow<R> {
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}
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companion object
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}
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@ -2,9 +2,11 @@ package scientifik.kmath.streaming
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import kotlinx.coroutines.FlowPreview
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import kotlinx.coroutines.flow.*
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import scientifik.kmath.chains.BlockingRealChain
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import scientifik.kmath.structures.Buffer
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import scientifik.kmath.structures.BufferFactory
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import scientifik.kmath.structures.DoubleBuffer
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import scientifik.kmath.structures.asBuffer
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/**
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* Create a [Flow] from buffer
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@ -45,20 +47,28 @@ fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>): Flow<
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*/
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fun Flow<Double>.chunked(bufferSize: Int): Flow<DoubleBuffer> = flow {
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require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
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val array = DoubleArray(bufferSize)
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var counter = 0
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this@chunked.collect { element ->
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array[counter] = element
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counter++
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if (counter == bufferSize) {
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val buffer = DoubleBuffer(array)
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emit(buffer)
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counter = 0
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if (this@chunked is BlockingRealChain) {
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//performance optimization for blocking primitive chain
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while (true) {
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emit(nextBlock(bufferSize).asBuffer())
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}
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} else {
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val array = DoubleArray(bufferSize)
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var counter = 0
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this@chunked.collect { element ->
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array[counter] = element
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counter++
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if (counter == bufferSize) {
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val buffer = DoubleBuffer(array)
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emit(buffer)
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counter = 0
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}
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}
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if (counter > 0) {
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emit(DoubleBuffer(counter) { array[it] })
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}
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}
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if (counter > 0) {
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emit(DoubleBuffer(counter) { array[it] })
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}
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}
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@ -5,7 +5,7 @@ import org.apache.commons.rng.simple.RandomSource
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class RandomSourceGenerator(val source: RandomSource, seed: Long?) : RandomGenerator {
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internal val random: UniformRandomProvider = seed?.let {
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RandomSource.create(source, seed, null)
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RandomSource.create(source, seed)
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} ?: RandomSource.create(source)
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override fun nextBoolean(): Boolean = random.nextBoolean()
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@ -59,3 +59,9 @@ fun RandomGenerator.asUniformRandomProvider(): UniformRandomProvider = if (this
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} else {
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RandomGeneratorProvider(this)
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}
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fun RandomGenerator.Companion.fromSource(source: RandomSource, seed: Long? = null): RandomSourceGenerator =
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RandomSourceGenerator(source, seed)
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fun RandomGenerator.Companion.mersenneTwister(seed: Long? = null): RandomSourceGenerator =
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fromSource(RandomSource.MT, seed)
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@ -2,6 +2,8 @@ package scientifik.kmath.prob
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import org.apache.commons.rng.UniformRandomProvider
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import org.apache.commons.rng.sampling.distribution.*
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import scientifik.kmath.chains.BlockingIntChain
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import scientifik.kmath.chains.BlockingRealChain
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import scientifik.kmath.chains.Chain
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import java.util.*
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import kotlin.math.PI
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@ -11,32 +13,32 @@ import kotlin.math.sqrt
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abstract class ContinuousSamplerDistribution : Distribution<Double> {
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private inner class ContinuousSamplerChain(val generator: RandomGenerator) : Chain<Double> {
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private val sampler = buildSampler(generator)
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private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingRealChain() {
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private val sampler = buildCMSampler(generator)
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override suspend fun next(): Double = sampler.sample()
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override fun nextDouble(): Double = sampler.sample()
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override fun fork(): Chain<Double> = ContinuousSamplerChain(generator.fork())
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}
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protected abstract fun buildSampler(generator: RandomGenerator): ContinuousSampler
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protected abstract fun buildCMSampler(generator: RandomGenerator): ContinuousSampler
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override fun sample(generator: RandomGenerator): Chain<Double> = ContinuousSamplerChain(generator)
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override fun sample(generator: RandomGenerator): BlockingRealChain = ContinuousSamplerChain(generator)
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}
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abstract class DiscreteSamplerDistribution : Distribution<Int> {
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private inner class ContinuousSamplerChain(val generator: RandomGenerator) : Chain<Int> {
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private inner class ContinuousSamplerChain(val generator: RandomGenerator) : BlockingIntChain() {
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private val sampler = buildSampler(generator)
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override suspend fun next(): Int = sampler.sample()
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override fun nextInt(): Int = sampler.sample()
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override fun fork(): Chain<Int> = ContinuousSamplerChain(generator.fork())
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}
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protected abstract fun buildSampler(generator: RandomGenerator): DiscreteSampler
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override fun sample(generator: RandomGenerator): Chain<Int> = ContinuousSamplerChain(generator)
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override fun sample(generator: RandomGenerator): BlockingIntChain = ContinuousSamplerChain(generator)
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}
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enum class NormalSamplerMethod {
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@ -55,7 +57,7 @@ private fun normalSampler(method: NormalSamplerMethod, provider: UniformRandomPr
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fun Distribution.Companion.normal(
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method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat
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): Distribution<Double> = object : ContinuousSamplerDistribution() {
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override fun buildSampler(generator: RandomGenerator): ContinuousSampler {
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override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
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val provider: UniformRandomProvider = generator.asUniformRandomProvider()
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return normalSampler(method, provider)
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}
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@ -69,11 +71,11 @@ fun Distribution.Companion.normal(
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mean: Double,
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sigma: Double,
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method: NormalSamplerMethod = NormalSamplerMethod.Ziggurat
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): Distribution<Double> = object : ContinuousSamplerDistribution() {
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): ContinuousSamplerDistribution = object : ContinuousSamplerDistribution() {
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private val sigma2 = sigma.pow(2)
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private val norm = sigma * sqrt(PI * 2)
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override fun buildSampler(generator: RandomGenerator): ContinuousSampler {
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override fun buildCMSampler(generator: RandomGenerator): ContinuousSampler {
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val provider: UniformRandomProvider = generator.asUniformRandomProvider()
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val normalizedSampler = normalSampler(method, provider)
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return GaussianSampler(normalizedSampler, mean, sigma)
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@ -86,7 +88,7 @@ fun Distribution.Companion.normal(
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fun Distribution.Companion.poisson(
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lambda: Double
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): Distribution<Int> = object : DiscreteSamplerDistribution() {
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): DiscreteSamplerDistribution = object : DiscreteSamplerDistribution() {
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override fun buildSampler(generator: RandomGenerator): DiscreteSampler {
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return PoissonSampler.of(generator.asUniformRandomProvider(), lambda)
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class CommonsDistributionsTest {
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@Test
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fun testNormalDistribution(){
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val distribution = Distribution.normal(7.0,2.0)
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fun testNormalDistributionSuspend() {
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val distribution = Distribution.normal(7.0, 2.0)
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val generator = RandomGenerator.default(1)
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val sample = runBlocking {
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distribution.sample(generator).take(1000).toList()
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}
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Assertions.assertEquals(7.0, sample.average(), 0.1)
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}
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@Test
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fun testNormalDistributionBlocking() {
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val distribution = Distribution.normal(7.0, 2.0)
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val generator = RandomGenerator.default(1)
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val sample = distribution.sample(generator).nextBlock(1000)
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Assertions.assertEquals(7.0, sample.average(), 0.1)
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}
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}
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