Initial work on distributions
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f706122266
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2e76073712
@ -18,6 +18,7 @@ package scientifik.kmath.chains
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import kotlinx.atomicfu.atomic
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import kotlinx.coroutines.FlowPreview
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import kotlinx.coroutines.flow.Flow
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/**
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@ -28,7 +29,7 @@ interface Chain<out R> {
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/**
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* Last cached value of the chain. Returns null if [next] was not called
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*/
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val value: R?
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fun peek(): R?
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/**
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* Generate next value, changing state if needed
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@ -46,13 +47,12 @@ interface Chain<out R> {
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* Chain as a coroutine flow. The flow emit affects chain state and vice versa
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*/
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@FlowPreview
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val <R> Chain<R>.flow
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val <R> Chain<R>.flow: Flow<R>
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get() = kotlinx.coroutines.flow.flow { while (true) emit(next()) }
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fun <T> Iterator<T>.asChain(): Chain<T> = SimpleChain { next() }
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fun <T> Sequence<T>.asChain(): Chain<T> = iterator().asChain()
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/**
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* Map the chain result using suspended transformation. Initial chain result can no longer be safely consumed
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* since mapped chain consumes tokens. Accepts regular transformation function
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@ -60,7 +60,7 @@ fun <T> Sequence<T>.asChain(): Chain<T> = iterator().asChain()
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fun <T, R> Chain<T>.map(func: (T) -> R): Chain<R> {
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val parent = this;
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return object : Chain<R> {
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override val value: R? get() = parent.value?.let(func)
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override fun peek(): R? = parent.peek()?.let(func)
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override suspend fun next(): R {
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return func(parent.next())
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@ -77,7 +77,7 @@ fun <T, R> Chain<T>.map(func: (T) -> R): Chain<R> {
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*/
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class SimpleChain<out R>(private val gen: suspend () -> R) : Chain<R> {
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private val atomicValue = atomic<R?>(null)
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override val value: R? get() = atomicValue.value
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override fun peek(): R? = atomicValue.value
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override suspend fun next(): R = gen().also { atomicValue.lazySet(it) }
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@ -95,16 +95,16 @@ class MarkovChain<out R : Any>(private val seed: () -> R, private val gen: suspe
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constructor(seed: R, gen: suspend (R) -> R) : this({ seed }, gen)
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private val atomicValue = atomic<R?>(null)
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override val value: R get() = atomicValue.value ?: seed()
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override fun peek(): R = atomicValue.value ?: seed()
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override suspend fun next(): R {
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val newValue = gen(value)
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val newValue = gen(peek())
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atomicValue.lazySet(newValue)
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return value
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return peek()
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}
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override fun fork(): Chain<R> {
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return MarkovChain(value, gen)
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return MarkovChain(peek(), gen)
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}
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}
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@ -121,12 +121,12 @@ class StatefulChain<S, out R>(
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constructor(state: S, seed: R, gen: suspend S.(R) -> R) : this(state, { seed }, gen)
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private val atomicValue = atomic<R?>(null)
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override val value: R get() = atomicValue.value ?: seed(state)
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override fun peek(): R = atomicValue.value ?: seed(state)
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override suspend fun next(): R {
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val newValue = gen(state, value)
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val newValue = gen(state, peek())
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atomicValue.lazySet(newValue)
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return value
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return peek()
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}
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override fun fork(): Chain<R> {
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@ -137,10 +137,10 @@ class StatefulChain<S, out R>(
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/**
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* A chain that repeats the same value
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*/
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class ConstantChain<out T>(override val value: T) : Chain<T> {
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override suspend fun next(): T {
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return value
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}
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class ConstantChain<out T>(val value: T) : Chain<T> {
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override fun peek(): T? = value
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override suspend fun next(): T = value
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override fun fork(): Chain<T> {
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return this
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@ -1,4 +1,4 @@
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package scientifik.kmath
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package scientifik.kmath.coroutines
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import kotlinx.coroutines.*
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import kotlinx.coroutines.channels.produce
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@ -42,13 +42,14 @@ fun <T, R> Flow<T>.async(
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}
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@FlowPreview
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fun <T, R> AsyncFlow<T>.map(action: (T) -> R) = AsyncFlow(deferredFlow.map { input ->
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//TODO add function composition
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LazyDeferred(input.dispatcher) {
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input.start(this)
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action(input.await())
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}
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})
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fun <T, R> AsyncFlow<T>.map(action: (T) -> R) =
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AsyncFlow(deferredFlow.map { input ->
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//TODO add function composition
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LazyDeferred(input.dispatcher) {
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input.start(this)
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action(input.await())
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}
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})
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@ExperimentalCoroutinesApi
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@FlowPreview
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@ -22,7 +22,7 @@ fun <T> Flow<Buffer<out T>>.spread(): Flow<T> = flatMapConcat { it.asFlow() }
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* Collect incoming flow into fixed size chunks
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*/
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@FlowPreview
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fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>) = flow {
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fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>): Flow<Buffer<T>> = flow {
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require(bufferSize > 0) { "Resulting chunk size must be more than zero" }
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val list = ArrayList<T>(bufferSize)
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var counter = 0
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@ -46,7 +46,7 @@ fun <T> Flow<T>.chunked(bufferSize: Int, bufferFactory: BufferFactory<T>) = flow
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* Specialized flow chunker for real buffer
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*/
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@FlowPreview
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fun Flow<Double>.chunked(bufferSize: Int) = flow {
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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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@ -27,7 +27,7 @@ fun <R> Chain<R>.asSequence(): Sequence<R> = object : Sequence<R> {
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fun <T, R> Chain<T>.map(func: suspend (T) -> R): Chain<R> {
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val parent = this;
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return object : Chain<R> {
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override val value: R? get() = runBlocking { parent.value?.let { func(it) } }
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override fun peek(): R? = runBlocking { parent.peek()?.let { func(it) } }
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override suspend fun next(): R {
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return func(parent.next())
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@ -1,7 +1,7 @@
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package scientifik.kmath.structures
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import kotlinx.coroutines.*
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import scientifik.kmath.Math
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import scientifik.kmath.coroutines.Math
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class LazyNDStructure<T>(
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val scope: CoroutineScope,
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@ -4,9 +4,9 @@ import kotlinx.coroutines.*
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import kotlinx.coroutines.flow.asFlow
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import kotlinx.coroutines.flow.collect
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import org.junit.Test
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import scientifik.kmath.async
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import scientifik.kmath.collect
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import scientifik.kmath.map
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import scientifik.kmath.coroutines.async
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import scientifik.kmath.coroutines.collect
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import scientifik.kmath.coroutines.map
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import java.util.concurrent.Executors
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@ -6,13 +6,14 @@ plugins {
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kotlin.sourceSets {
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commonMain {
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dependencies {
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api(project(":kmath-core"))
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api(project(":kmath-coroutines"))
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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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}
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}
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@ -0,0 +1,28 @@
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package scientifik.kmath.prob
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import scientifik.kmath.chains.Chain
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/**
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* A distribution of typed objects
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*/
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interface Distribution<T : Any> {
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/**
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* A probability value for given argument [arg].
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* For continuous distributions returns PDF
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*/
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fun probability(arg: T): Double
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/**
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* Create a chain of samples from this distribution.
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* The chain is not guaranteed to be stateless.
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*/
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fun sample(generator: RandomGenerator): Chain<T>
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//TODO add sample bunch generator
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}
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interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
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/**
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* Cumulative distribution for ordered parameter
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*/
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fun cumulative(arg: T): Double
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}
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@ -0,0 +1,11 @@
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package scientifik.kmath.prob
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/**
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* A basic generator
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*/
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interface RandomGenerator {
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fun nextDouble(): Double
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fun nextInt(): Int
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fun nextLong(): Long
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fun nextBlock(size: Int): ByteArray
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}
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@ -0,0 +1,53 @@
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package scientifik.kmath.prob
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import org.apache.commons.rng.sampling.distribution.*
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import scientifik.kmath.chains.Chain
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import scientifik.kmath.chains.SimpleChain
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import kotlin.math.PI
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import kotlin.math.exp
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import kotlin.math.sqrt
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class NormalDistribution(val mean: Double, val sigma: Double) : UnivariateDistribution<Double> {
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enum class Sampler {
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BoxMuller,
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Marsaglia,
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Ziggurat
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}
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override fun probability(arg: Double): Double {
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val d = (arg - mean) / sigma
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return 1.0 / sqrt(2.0 * PI * sigma) * exp(-d * d / 2)
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}
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override fun cumulative(arg: Double): Double {
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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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fun sample(generator: RandomGenerator, sampler: Sampler): Chain<Double> {
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val normalized = when (sampler) {
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Sampler.BoxMuller -> BoxMullerNormalizedGaussianSampler(generator.asProvider())
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Sampler.Marsaglia -> MarsagliaNormalizedGaussianSampler(generator.asProvider())
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Sampler.Ziggurat -> ZigguratNormalizedGaussianSampler(generator.asProvider())
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}
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val gauss = GaussianSampler(normalized, mean, sigma)
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//TODO add generator to chain state to allow stateful forks
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return SimpleChain { gauss.sample() }
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}
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override fun sample(generator: RandomGenerator): Chain<Double> = sample(generator, Sampler.BoxMuller)
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}
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class PoissonDistribution(val mean: Double): UnivariateDistribution<Int>{
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override fun probability(arg: Int): Double {
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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 cumulative(arg: Int): Double {
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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 sample(generator: RandomGenerator): Chain<Int> {
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val sampler = PoissonSampler(generator.asProvider(), mean)
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return SimpleChain{sampler.sample()}
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}
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}
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@ -0,0 +1,18 @@
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package scientifik.kmath.prob
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import org.apache.commons.rng.UniformRandomProvider
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inline class CommonsRandomProviderWrapper(val provider: UniformRandomProvider) : RandomGenerator {
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override fun nextDouble(): Double = provider.nextDouble()
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override fun nextInt(): Int = provider.nextInt()
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override fun nextLong(): Long = provider.nextLong()
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override fun nextBlock(size: Int): ByteArray = ByteArray(size).also { provider.nextBytes(it) }
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}
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fun UniformRandomProvider.asGenerator(): RandomGenerator = CommonsRandomProviderWrapper(this)
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fun RandomGenerator.asProvider(): UniformRandomProvider =
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(this as? CommonsRandomProviderWrapper)?.provider ?: TODO("implement reverse wrapper")
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