forked from kscience/kmath
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Author | SHA1 | Date | |
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9efc6da74a | |||
9c239ebfbd | |||
2892ea11e2 | |||
c267372c70 |
@ -8,7 +8,7 @@ fun main(args: Array<String>) {
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val dim = 1000
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val n = 1000
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// automatically build context most suited for given type.
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// automatically build coroutineContext most suited for given type.
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val autoField = NDField.auto(RealField, dim, dim)
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// specialized nd-field for Double. It works as generic Double field as well
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val specializedField = NDField.real(dim, dim)
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@ -111,7 +111,7 @@ fun DiffExpression.derivative(vararg orders: Pair<String, Int>) = derivative(map
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fun DiffExpression.derivative(name: String) = derivative(name to 1)
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/**
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* A context for [DiffExpression] (not to be confused with [DerivativeStructure])
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* A coroutineContext for [DiffExpression] (not to be confused with [DerivativeStructure])
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*/
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object DiffExpressionContext : ExpressionContext<Double>, Field<DiffExpression> {
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override fun variable(name: String, default: Double?) =
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@ -4,7 +4,9 @@ import org.apache.commons.math3.random.JDKRandomGenerator
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import scientifik.kmath.prob.RandomGenerator
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import org.apache.commons.math3.random.RandomGenerator as CMRandom
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inline class CMRandomGeneratorWrapper(val generator: CMRandom) : RandomGenerator {
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class CMRandomGeneratorWrapper(seed: Long?, val builder: (Long?) -> CMRandom) : RandomGenerator {
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val generator = builder(seed)
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override fun nextDouble(): Double = generator.nextDouble()
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override fun nextInt(): Int = generator.nextInt()
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@ -13,20 +15,12 @@ inline class CMRandomGeneratorWrapper(val generator: CMRandom) : RandomGenerator
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override fun nextBlock(size: Int): ByteArray = ByteArray(size).apply { generator.nextBytes(this) }
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override fun fork(): RandomGenerator {
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TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
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}
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override fun fork(): RandomGenerator = CMRandomGeneratorWrapper(nextLong(), builder)
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}
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fun CMRandom.asKmathGenerator(): RandomGenerator = CMRandomGeneratorWrapper(this)
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fun RandomGenerator.asCMGenerator(): CMRandom =
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(this as? CMRandomGeneratorWrapper)?.generator ?: TODO("Implement reverse CM wrapper")
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val RandomGenerator.Companion.default: RandomGenerator by lazy { JDKRandomGenerator().asKmathGenerator() }
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fun RandomGenerator.Companion.jdk(seed: Int? = null): RandomGenerator = if (seed == null) {
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JDKRandomGenerator()
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} else {
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JDKRandomGenerator(seed)
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}.asKmathGenerator()
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fun RandomGenerator.Companion.jdk(seed: Long? = null): RandomGenerator =
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CMRandomGeneratorWrapper(seed) { JDKRandomGenerator() }
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@ -8,19 +8,19 @@ kotlin.sourceSets {
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dependencies {
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api(project(":kmath-core"))
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api("org.jetbrains.kotlinx:kotlinx-coroutines-core-common:${Versions.coroutinesVersion}")
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compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
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api("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
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}
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}
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jvmMain {
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dependencies {
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api("org.jetbrains.kotlinx:kotlinx-coroutines-core:${Versions.coroutinesVersion}")
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compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
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api("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
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}
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}
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jsMain {
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dependencies {
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api("org.jetbrains.kotlinx:kotlinx-coroutines-core-js:${Versions.coroutinesVersion}")
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compileOnly("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
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api("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
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}
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}
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}
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@ -7,19 +7,19 @@ kotlin.sourceSets {
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commonMain {
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dependencies {
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api(project(":kmath-coroutines"))
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compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
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//compileOnly("org.jetbrains.kotlinx:atomicfu-common:${Versions.atomicfuVersion}")
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}
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}
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jvmMain {
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dependencies {
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// https://mvnrepository.com/artifact/org.apache.commons/commons-rng-simple
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//api("org.apache.commons:commons-rng-sampling:1.2")
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compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
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// compileOnly("org.jetbrains.kotlinx:atomicfu:${Versions.atomicfuVersion}")
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}
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}
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jsMain {
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dependencies {
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compileOnly("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
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// compileOnly("org.jetbrains.kotlinx:atomicfu-js:${Versions.atomicfuVersion}")
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}
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}
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@ -5,7 +5,6 @@ import scientifik.kmath.chains.Chain
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/**
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* A possibly stateful chain producing random values.
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* TODO make random chain properly fork generator
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*/
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class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspend RandomGenerator.() -> R) : Chain<R> {
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private val atomicValue = atomic<R?>(null)
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@ -13,4 +12,9 @@ class RandomChain<out R>(val generator: RandomGenerator, private val gen: suspen
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override suspend fun next(): R = generator.gen().also { atomicValue.lazySet(it) }
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override fun fork(): Chain<R> = RandomChain(generator.fork(), gen)
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}
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}
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/**
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* Create a chain of doubles from generator after forking it so the chain is not affected by operations on generator
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*/
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fun RandomGenerator.doubles(): Chain<Double> = RandomChain(fork()) { nextDouble() }
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@ -1,5 +1,7 @@
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package scientifik.kmath.prob
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import kotlin.random.Random
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/**
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* A basic generator
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*/
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@ -10,9 +12,30 @@ interface RandomGenerator {
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fun nextBlock(size: Int): ByteArray
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/**
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* Fork the current state of generator
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* Create a new generator which is independent from current generator (operations on new generator do not affect this one
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* and vise versa). The statistical properties of new generator should be the same as for this one.
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* For pseudo-random generator, the fork is keeping the same sequence of numbers for given call order for each run.
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*
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* The thread safety of this operation is not guaranteed since it could affect the state of the generator.
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*/
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fun fork(): RandomGenerator
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companion object
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companion object {
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val default by lazy { DefaultGenerator(Random.nextLong()) }
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}
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}
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class DefaultGenerator(seed: Long?) : RandomGenerator {
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private val random = seed?.let { Random(it) } ?: Random
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override fun nextDouble(): Double = random.nextDouble()
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override fun nextInt(): Int = random.nextInt()
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override fun nextLong(): Long = random.nextLong()
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override fun nextBlock(size: Int): ByteArray = random.nextBytes(size)
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override fun fork(): RandomGenerator = DefaultGenerator(nextLong())
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}
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@ -0,0 +1,44 @@
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package scientifik.kmath.prob
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import kotlinx.coroutines.*
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import kotlin.coroutines.CoroutineContext
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import kotlin.coroutines.EmptyCoroutineContext
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import kotlin.coroutines.coroutineContext
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/**
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* A scope for a monte-carlo simulation or multi-coroutine random number generation
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*/
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class MCScope(override val coroutineContext: CoroutineContext, val random: RandomGenerator): CoroutineScope
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/**
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* Launches a supervised Monte-Carlo scope
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*/
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suspend fun <T> mc(generator: RandomGenerator, block: suspend MCScope.() -> T): T =
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MCScope(coroutineContext, generator).block()
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suspend fun <T> mc(seed: Long = -1, block: suspend MCScope.() -> T): T =
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mc(SplitRandomWrapper(seed), block)
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inline fun MCScope.launch(
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context: CoroutineContext = EmptyCoroutineContext,
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start: CoroutineStart = CoroutineStart.DEFAULT,
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crossinline block: suspend MCScope.() -> Unit
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): Job {
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val newRandom = synchronized(this){random.fork()}
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return CoroutineScope(coroutineContext).launch(context, start) {
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MCScope(coroutineContext, newRandom).block()
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}
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}
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inline fun <T> MCScope.async(
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context: CoroutineContext = EmptyCoroutineContext,
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start: CoroutineStart = CoroutineStart.DEFAULT,
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crossinline block: suspend MCScope.() -> T
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): Deferred<T> {
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val newRandom = synchronized(this){random.fork()}
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return CoroutineScope(coroutineContext).async(context, start) {
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MCScope(coroutineContext, newRandom).block()
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}
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}
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@ -0,0 +1,25 @@
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package scientifik.kmath.prob
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import java.util.*
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class SplitRandomWrapper(random: SplittableRandom) : RandomGenerator {
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var random = random
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private set
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constructor(seed: Long) : this(SplittableRandom(seed))
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override fun nextDouble(): Double = random.nextDouble()
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override fun nextInt(): Int = random.nextInt()
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override fun nextLong(): Long = random.nextLong()
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override fun nextBlock(size: Int): ByteArray = ByteArray(size).also { random.nextBytes(it) }
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override fun fork(): RandomGenerator {
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synchronized(this) {
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return SplitRandomWrapper(random.split()).also { this.random = random.split() }
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}
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}
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}
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@ -0,0 +1,85 @@
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package scientifik.kmath.prob
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import kotlinx.coroutines.*
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import java.util.*
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import kotlin.collections.HashSet
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import kotlin.test.Test
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import kotlin.test.assertEquals
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data class RandomResult(val branch: String, val order: Int, val value: Int)
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typealias ATest = suspend CoroutineScope.() -> Set<RandomResult>
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class MCScopeTest {
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val simpleTest: ATest = {
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mc(1111) {
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val res = Collections.synchronizedSet(HashSet<RandomResult>())
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launch {
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//println(random)
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repeat(10) {
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delay(10)
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res.add(RandomResult("first", it, random.nextInt()))
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}
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launch {
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"empty fork"
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}
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}
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launch {
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//println(random)
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repeat(10) {
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delay(10)
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res.add(RandomResult("second", it, random.nextInt()))
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}
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}
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res
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}
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}
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val testWithJoin: ATest = {
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mc(1111) {
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val res = Collections.synchronizedSet(HashSet<RandomResult>())
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val job = launch {
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repeat(10) {
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delay(10)
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res.add(RandomResult("first", it, random.nextInt()))
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}
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}
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launch {
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repeat(10) {
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delay(10)
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if (it == 4) job.join()
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res.add(RandomResult("second", it, random.nextInt()))
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}
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}
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res
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}
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}
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fun compareResult(test: ATest) {
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val res1 = runBlocking(Dispatchers.Default) { test() }
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val res2 = runBlocking(newSingleThreadContext("test")) { test() }
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assertEquals(
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res1.find { it.branch == "first" && it.order == 7 }?.value,
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res2.find { it.branch == "first" && it.order == 7 }?.value
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)
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assertEquals(res1, res2)
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}
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@Test
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fun testParallel() {
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compareResult(simpleTest)
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}
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@Test
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fun testConditionalJoin() {
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compareResult(testWithJoin)
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}
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}
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