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Fix post-merge issues

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This commit is contained in:
Iaroslav Postovalov 2021-03-31 02:24:21 +07:00
parent f26cad6d18
commit 9574099f9b
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30 changed files with 234 additions and 262 deletions
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1
examples/build.gradle.kts
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@ -106,6 +106,7 @@ kotlin.sourceSets.all {
with(languageSettings) {
useExperimentalAnnotation("kotlin.contracts.ExperimentalContracts")
useExperimentalAnnotation("kotlin.ExperimentalUnsignedTypes")
useExperimentalAnnotation("space.kscience.kmath.misc.UnstableKMathAPI")
}
}

34
examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt
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@ -1,20 +1,22 @@
package kscience.kmath.commons.fit
package space.kscience.kmath.commons.fit
import kotlinx.html.br
import kotlinx.html.h3
import kscience.kmath.commons.optimization.chiSquared
import kscience.kmath.commons.optimization.minimize
import kscience.kmath.expressions.symbol
import kscience.kmath.real.RealVector
import kscience.kmath.real.map
import kscience.kmath.real.step
import kscience.kmath.stat.*
import kscience.kmath.stat.distributions.NormalDistribution
import kscience.kmath.structures.asIterable
import kscience.kmath.structures.toList
import kscience.plotly.*
import kscience.plotly.models.ScatterMode
import kscience.plotly.models.TraceValues
import space.kscience.kmath.commons.optimization.chiSquared
import space.kscience.kmath.commons.optimization.minimize
import space.kscience.kmath.misc.symbol
import space.kscience.kmath.optimization.FunctionOptimization
import space.kscience.kmath.optimization.OptimizationResult
import space.kscience.kmath.real.DoubleVector
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.distributions.NormalDistribution
import space.kscience.kmath.structures.asIterable
import space.kscience.kmath.structures.toList
import kotlin.math.pow
import kotlin.math.sqrt
@ -27,7 +29,7 @@ private val c by symbol
/**
* Shortcut to use buffers in plotly
*/
operator fun TraceValues.invoke(vector: RealVector) {
operator fun TraceValues.invoke(vector: DoubleVector) {
numbers = vector.asIterable()
}
@ -58,12 +60,12 @@ suspend fun main() {
val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
// compute differentiable chi^2 sum for given model ax^2 + bx + c
val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
//bind variables to autodiff context
val a = bind(a)
val b = bind(b)
//Include default value for c if it is not provided as a parameter
val c = bindOrNull(c) ?: one
val c = bindSymbolOrNull(c) ?: one
a * x1.pow(2) + b * x1 + c
}
@ -90,10 +92,10 @@ suspend fun main() {
}
}
br()
h3{
h3 {
+"Fit result: $result"
}
h3{
h3 {
+"Chi2/dof = ${result.value / (x.size - 3)}"
}
}

4
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat
package space.kscience.kmath.stat
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.async
import kotlinx.coroutines.runBlocking
import kscience.kmath.stat.samplers.GaussianSampler
import space.kscience.kmath.stat.samplers.GaussianSampler
import org.apache.commons.rng.simple.RandomSource
import java.time.Duration
import java.time.Instant

8
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat
package space.kscience.kmath.stat
import kotlinx.coroutines.runBlocking
import kscience.kmath.chains.Chain
import kscience.kmath.chains.collectWithState
import kscience.kmath.stat.distributions.NormalDistribution
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.collectWithState
import space.kscience.kmath.stat.distributions.NormalDistribution
/**
* The state of distribution averager.

27
kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt
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@ -1,13 +1,13 @@
package kscience.kmath.commons.optimization
package space.kscience.kmath.commons.optimization
import kotlinx.coroutines.runBlocking
import kscience.kmath.commons.expressions.DerivativeStructureExpression
import kscience.kmath.expressions.symbol
import kscience.kmath.stat.Fitting
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.distributions.NormalDistribution
import org.junit.jupiter.api.Test
import space.kscience.kmath.commons.expressions.DerivativeStructureExpression
import space.kscience.kmath.misc.symbol
import space.kscience.kmath.optimization.FunctionOptimization
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.distributions.NormalDistribution
import kotlin.math.pow
import kotlin.test.Test
internal class OptimizeTest {
val x by symbol
@ -34,6 +34,7 @@ internal class OptimizeTest {
simplexSteps(x to 2.0, y to 0.5)
//this sets simplex optimizer
}
println(result.point)
println(result.value)
}
@ -43,15 +44,20 @@ internal class OptimizeTest {
val a by symbol
val b by symbol
val c by symbol
val sigma = 1.0
val generator = NormalDistribution(0.0, sigma)
val chain = generator.sample(RandomGenerator.default(112667))
val x = (1..100).map(Int::toDouble)
val y = x.map { it.pow(2) + it + 1.0 + chain.next() }
val y = x.map {
it.pow(2) + it + 1 + chain.next()
}
val yErr = List(x.size) { sigma }
val chi2 = Fitting.chiSquared(x, y, yErr) { x1 ->
val cWithDefault = bindOrNull(c) ?: one
val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
val cWithDefault = bindSymbolOrNull(c) ?: one
bind(a) * x1.pow(2) + bind(b) * x1 + cWithDefault
}
@ -59,5 +65,4 @@ internal class OptimizeTest {
println(result)
println("Chi2/dof = ${result.value / (x.size - 3)}")
}
}

155
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
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@ -1,4 +1,4 @@
package kscience.kmath.structures
package space.kscience.kmath.structures
import kotlin.reflect.KClass
@ -17,11 +17,13 @@ public typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
public typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>
/**
* A generic immutable random-access structure for both primitives and objects.
* A generic read-only random-access structure for both primitives and objects.
*
* [Buffer] is in general identity-free. [Buffer.contentEquals] should be used for content equality checks.
*
* @param T the type of elements contained in the buffer.
*/
public interface Buffer<T> {
public interface Buffer<out T> {
/**
* The size of this buffer.
*/
@ -37,49 +39,45 @@ public interface Buffer<T> {
*/
public operator fun iterator(): Iterator<T>
/**
* Checks content equality with another buffer.
*/
public fun contentEquals(other: Buffer<*>): Boolean =
asSequence().mapIndexed { index, value -> value == other[index] }.all { it }
public companion object {
/**
* Creates a [RealBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
* Check the element-by-element match of content of two buffers.
*/
public inline fun real(size: Int, initializer: (Int) -> Double): RealBuffer =
RealBuffer(size) { initializer(it) }
public fun <T: Any> contentEquals(first: Buffer<T>, second: Buffer<T>): Boolean{
if (first.size != second.size) return false
for (i in first.indices) {
if (first[i] != second[i]) return false
}
return true
}
/**
* Creates a [ListBuffer] of given type [T] with given [size]. Each element is calculated by calling the
* specified [initializer] function.
*/
public inline fun <T> boxing(size: Int, initializer: (Int) -> T): Buffer<T> =
ListBuffer(List(size, initializer))
// TODO add resolution based on Annotation or companion resolution
List(size, initializer).asBuffer()
/**
* Creates a [Buffer] of given [type]. If the type is primitive, specialized buffers are used ([IntBuffer],
* [RealBuffer], etc.), [ListBuffer] is returned otherwise.
* [DoubleBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<T>, size: Int, initializer: (Int) -> T): Buffer<T> =
when (type) {
Double::class -> real(size) { initializer(it) as Double } as Buffer<T>
Short::class -> ShortBuffer(size) { initializer(it) as Short } as Buffer<T>
Int::class -> IntBuffer(size) { initializer(it) as Int } as Buffer<T>
Long::class -> LongBuffer(size) { initializer(it) as Long } as Buffer<T>
Float::class -> FloatBuffer(size) { initializer(it) as Float } as Buffer<T>
Double::class -> MutableBuffer.double(size) { initializer(it) as Double } as Buffer<T>
Short::class -> MutableBuffer.short(size) { initializer(it) as Short } as Buffer<T>
Int::class -> MutableBuffer.int(size) { initializer(it) as Int } as Buffer<T>
Long::class -> MutableBuffer.long(size) { initializer(it) as Long } as Buffer<T>
Float::class -> MutableBuffer.float(size) { initializer(it) as Float } as Buffer<T>
else -> boxing(size, initializer)
}
/**
* Creates a [Buffer] of given type [T]. If the type is primitive, specialized buffers are used ([IntBuffer],
* [RealBuffer], etc.), [ListBuffer] is returned otherwise.
* [DoubleBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@ -89,21 +87,6 @@ public interface Buffer<T> {
}
}
/**
* Creates a sequence that returns all elements from this [Buffer].
*/
public fun <T> Buffer<T>.asSequence(): Sequence<T> = Sequence(::iterator)
/**
* Creates an iterable that returns all elements from this [Buffer].
*/
public fun <T> Buffer<T>.asIterable(): Iterable<T> = Iterable(::iterator)
/**
* Converts this [Buffer] to a new [List]
*/
public fun <T> Buffer<T>.toList(): List<T> = asSequence().toList()
/**
* Returns an [IntRange] of the valid indices for this [Buffer].
*/
@ -126,6 +109,43 @@ public interface MutableBuffer<T> : Buffer<T> {
public fun copy(): MutableBuffer<T>
public companion object {
/**
* Creates a [DoubleBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun double(size: Int, initializer: (Int) -> Double): DoubleBuffer =
DoubleBuffer(size, initializer)
/**
* Creates a [ShortBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun short(size: Int, initializer: (Int) -> Short): ShortBuffer =
ShortBuffer(size, initializer)
/**
* Creates a [IntBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun int(size: Int, initializer: (Int) -> Int): IntBuffer =
IntBuffer(size, initializer)
/**
* Creates a [LongBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun long(size: Int, initializer: (Int) -> Long): LongBuffer =
LongBuffer(size, initializer)
/**
* Creates a [FloatBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun float(size: Int, initializer: (Int) -> Float): FloatBuffer =
FloatBuffer(size, initializer)
/**
* Create a boxing mutable buffer of given type
*/
@ -134,37 +154,30 @@ public interface MutableBuffer<T> : Buffer<T> {
/**
* Creates a [MutableBuffer] of given [type]. If the type is primitive, specialized buffers are used
* ([IntBuffer], [RealBuffer], etc.), [ListBuffer] is returned otherwise.
* ([IntBuffer], [DoubleBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
when (type) {
Double::class -> RealBuffer(size) { initializer(it) as Double } as MutableBuffer<T>
Short::class -> ShortBuffer(size) { initializer(it) as Short } as MutableBuffer<T>
Int::class -> IntBuffer(size) { initializer(it) as Int } as MutableBuffer<T>
Float::class -> FloatBuffer(size) { initializer(it) as Float } as MutableBuffer<T>
Long::class -> LongBuffer(size) { initializer(it) as Long } as MutableBuffer<T>
Double::class -> double(size) { initializer(it) as Double } as MutableBuffer<T>
Short::class -> short(size) { initializer(it) as Short } as MutableBuffer<T>
Int::class -> int(size) { initializer(it) as Int } as MutableBuffer<T>
Float::class -> float(size) { initializer(it) as Float } as MutableBuffer<T>
Long::class -> long(size) { initializer(it) as Long } as MutableBuffer<T>
else -> boxing(size, initializer)
}
/**
* Creates a [MutableBuffer] of given type [T]. If the type is primitive, specialized buffers are used
* ([IntBuffer], [RealBuffer], etc.), [ListBuffer] is returned otherwise.
* ([IntBuffer], [DoubleBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
auto(T::class, size, initializer)
/**
* Creates a [RealBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun real(size: Int, initializer: (Int) -> Double): RealBuffer =
RealBuffer(size) { initializer(it) }
}
}
@ -187,15 +200,6 @@ public inline class ListBuffer<T>(public val list: List<T>) : Buffer<T> {
*/
public fun <T> List<T>.asBuffer(): ListBuffer<T> = ListBuffer(this)
/**
* Creates a new [ListBuffer] with the specified [size], where each element is calculated by calling the specified
* [init] function.
*
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an array element given its index.
*/
public inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> = List(size, init).asBuffer()
/**
* [MutableBuffer] implementation over [MutableList].
*
@ -216,16 +220,20 @@ public inline class MutableListBuffer<T>(public val list: MutableList<T>) : Muta
override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
}
/**
* Returns an [ListBuffer] that wraps the original list.
*/
public fun <T> MutableList<T>.asMutableBuffer(): MutableListBuffer<T> = MutableListBuffer(this)
/**
* [MutableBuffer] implementation over [Array].
*
* @param T the type of elements contained in the buffer.
* @property array The underlying array.
*/
public class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
public class ArrayBuffer<T>(internal val array: Array<T>) : MutableBuffer<T> {
// Can't inline because array is invariant
override val size: Int
get() = array.size
override val size: Int get() = array.size
override operator fun get(index: Int): T = array[index]
@ -237,6 +245,7 @@ public class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
override fun copy(): MutableBuffer<T> = ArrayBuffer(array.copyOf())
}
/**
* Returns an [ArrayBuffer] that wraps the original array.
*/
@ -269,27 +278,9 @@ public class VirtualBuffer<T>(override val size: Int, private val generator: (In
}
override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
override fun contentEquals(other: Buffer<*>): Boolean {
return if (other is VirtualBuffer) {
this.size == other.size && this.generator == other.generator
} else {
super.contentEquals(other)
}
}
}
/**
* Convert this buffer to read-only buffer.
*/
public fun <T> Buffer<T>.asReadOnly(): Buffer<T> = if (this is MutableBuffer) ReadOnlyBuffer(this) else this
/**
* Typealias for buffer transformations.
*/
public typealias BufferTransform<T, R> = (Buffer<T>) -> Buffer<R>
/**
* Typealias for buffer transformations with suspend function.
*/
public typealias SuspendBufferTransform<T, R> = suspend (Buffer<T>) -> Buffer<R>

9
kmath-coroutines/src/commonMain/kotlin/kscience/kmath/chains/BlockingRealChain.kt
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@ -1,9 +0,0 @@
package kscience.kmath.chains
/**
* Performance optimized chain for real values
*/
public interface BlockingRealChain : Chain<Double> {
public override suspend fun next(): Double
public suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
}

13
kmath-coroutines/src/commonMain/kotlin/space/kscience/kmath/chains/BlockingDoubleChain.kt
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@ -1,12 +1,13 @@
package space.kscience.kmath.chains
/**
* Performance optimized chain for real values
* Chunked, specialized chain for real values.
*/
public abstract class BlockingDoubleChain : Chain<Double> {
public abstract fun nextDouble(): Double
public interface BlockingDoubleChain : Chain<Double> {
public override suspend fun next(): Double
override suspend fun next(): Double = nextDouble()
public open fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { nextDouble() }
/**
* Returns an [DoubleArray] chunk of [size] values of [next].
*/
public suspend fun nextBlock(size: Int): DoubleArray = DoubleArray(size) { next() }
}

2
kmath-dimensions/src/commonTest/kotlin/kscience/dimensions/DMatrixContextTest.kt
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@ -1,4 +1,4 @@
package kscience.dimensions
package space.kscience.dimensions
import space.kscience.kmath.dimensions.D2
import space.kscience.kmath.dimensions.D3

34
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/SamplerAlgebra.kt
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@ -1,34 +0,0 @@
package kscience.kmath.stat
import kscience.kmath.chains.Chain
import kscience.kmath.chains.ConstantChain
import kscience.kmath.chains.map
import kscience.kmath.chains.zip
import kscience.kmath.operations.Space
import kscience.kmath.operations.invoke
/**
* Implements [Sampler] by sampling only certain [value].
*
* @property value the value to sample.
*/
public class ConstantSampler<T : Any>(public val value: T) : Sampler<T> {
public override fun sample(generator: RandomGenerator): Chain<T> = ConstantChain(value)
}
/**
* A space of samplers. Allows to perform simple operations on distributions.
*
* @property space the space to provide addition and scalar multiplication for [T].
*/
public class SamplerSpace<T : Any>(public val space: Space<T>) : Space<Sampler<T>> {
public override val zero: Sampler<T> = ConstantSampler(space.zero)
public override fun add(a: Sampler<T>, b: Sampler<T>): Sampler<T> = Sampler { generator ->
a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space { aValue + bValue } }
}
public override fun multiply(a: Sampler<T>, k: Number): Sampler<T> = Sampler { generator ->
a.sample(generator).map { space { it * k.toDouble() } }
}
}

19
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Distribution.kt
View File

@ -1,11 +1,12 @@
package kscience.kmath.stat
package space.kscience.kmath.stat
import kotlinx.coroutines.flow.first
import kscience.kmath.chains.Chain
import kscience.kmath.chains.collect
import kscience.kmath.structures.Buffer
import kscience.kmath.structures.BufferFactory
import kscience.kmath.structures.IntBuffer
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.collect
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.MutableBuffer
import kotlin.jvm.JvmName
/**
@ -22,7 +23,7 @@ public fun interface Sampler<T : Any> {
}
/**
* A distribution of typed objects
* A distribution of typed objects.
*/
public interface Distribution<T : Any> : Sampler<T> {
/**
@ -60,7 +61,7 @@ public fun <T : Comparable<T>> UnivariateDistribution<T>.integral(from: T, to: T
public fun <T : Any> Sampler<T>.sampleBuffer(
generator: RandomGenerator,
size: Int,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): Chain<Buffer<T>> {
require(size > 1)
//creating temporary storage once
@ -86,7 +87,7 @@ public suspend fun <T : Any> Sampler<T>.next(generator: RandomGenerator): T = sa
*/
@JvmName("sampleRealBuffer")
public fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Double>> =
sampleBuffer(generator, size, Buffer.Companion::real)
sampleBuffer(generator, size, MutableBuffer.Companion::double)
/**
* Generates [size] integer samples and chunks them into some buffers.

10
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/RandomChain.kt
View File

@ -1,8 +1,8 @@
package kscience.kmath.stat
package space.kscience.kmath.stat
import kscience.kmath.chains.BlockingIntChain
import kscience.kmath.chains.BlockingRealChain
import kscience.kmath.chains.Chain
import space.kscience.kmath.chains.BlockingDoubleChain
import space.kscience.kmath.chains.BlockingIntChain
import space.kscience.kmath.chains.Chain
/**
* A possibly stateful chain producing random values.
@ -18,5 +18,5 @@ public class RandomChain<out R>(
}
public fun <R> RandomGenerator.chain(gen: suspend RandomGenerator.() -> R): RandomChain<R> = RandomChain(this, gen)
public fun Chain<Double>.blocking(): BlockingRealChain = object : Chain<Double> by this, BlockingRealChain {}
public fun Chain<Double>.blocking(): BlockingDoubleChain = object : Chain<Double> by this, BlockingDoubleChain {}
public fun Chain<Int>.blocking(): BlockingIntChain = object : Chain<Int> by this, BlockingIntChain {}

28
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/SamplerAlgebra.kt
View File

@ -8,16 +8,28 @@ import space.kscience.kmath.operations.Group
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.operations.invoke
public class BasicSampler<T : Any>(public val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
public override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
}
/**
* Implements [Sampler] by sampling only certain [value].
*
* @property value the value to sample.
*/
public class ConstantSampler<T : Any>(public val value: T) : Sampler<T> {
public override fun sample(generator: RandomGenerator): Chain<T> = ConstantChain(value)
}
/**
* A space for samplers. Allows to perform simple operations on distributions
* Implements [Sampler] by delegating sampling to value of [chainBuilder].
*
* @property chainBuilder the provider of [Chain].
*/
public class BasicSampler<T : Any>(public val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
public override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
}
/**
* A space of samplers. Allows to perform simple operations on distributions.
*
* @property algebra the space to provide addition and scalar multiplication for [T].
*/
public class SamplerSpace<T : Any, S>(public val algebra: S) : Group<Sampler<T>>,
ScaleOperations<Sampler<T>> where S : Group<T>, S : ScaleOperations<T> {
@ -29,8 +41,10 @@ public class SamplerSpace<T : Any, S>(public val algebra: S) : Group<Sampler<T>>
}
public override fun scale(a: Sampler<T>, value: Double): Sampler<T> = BasicSampler { generator ->
a.sample(generator).map { algebra { it * value } }
a.sample(generator).map { a ->
algebra { a * value }
}
}
override fun Sampler<T>.unaryMinus(): Sampler<T> = scale(this, -1.0)
public override fun Sampler<T>.unaryMinus(): Sampler<T> = scale(this, -1.0)
}

16
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/distributions/NormalDistribution.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/distributions/NormalDistribution.kt
View File

@ -1,12 +1,12 @@
package kscience.kmath.stat.distributions
package space.kscience.kmath.stat.distributions
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.UnivariateDistribution
import kscience.kmath.stat.internal.InternalErf
import kscience.kmath.stat.samplers.GaussianSampler
import kscience.kmath.stat.samplers.NormalizedGaussianSampler
import kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.UnivariateDistribution
import space.kscience.kmath.stat.internal.InternalErf
import space.kscience.kmath.stat.samplers.GaussianSampler
import space.kscience.kmath.stat.samplers.NormalizedGaussianSampler
import space.kscience.kmath.stat.samplers.ZigguratNormalizedGaussianSampler
import kotlin.math.*
/**

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/internal/InternalErf.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalErf.kt
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@ -1,4 +1,4 @@
package kscience.kmath.stat.internal
package space.kscience.kmath.stat.internal
import kotlin.math.abs

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/internal/InternalGamma.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalGamma.kt
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@ -1,4 +1,4 @@
package kscience.kmath.stat.internal
package space.kscience.kmath.stat.internal
import kotlin.math.*

2
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/internal/InternalUtils.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/internal/InternalUtils.kt
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@ -1,4 +1,4 @@
package kscience.kmath.stat.internal
package space.kscience.kmath.stat.internal
import kotlin.math.ln
import kotlin.math.min

12
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterExponentialSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AhrensDieterExponentialSampler.kt
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@ -1,10 +1,10 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import kscience.kmath.stat.internal.InternalUtils
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import space.kscience.kmath.stat.internal.InternalUtils
import kotlin.math.ln
import kotlin.math.pow

12
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AhrensDieterMarsagliaTsangGammaSampler.kt
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@ -1,10 +1,10 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import kscience.kmath.stat.next
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import space.kscience.kmath.stat.next
import kotlin.math.*
/**

12
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/AliasMethodDiscreteSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/AliasMethodDiscreteSampler.kt
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@ -1,10 +1,10 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import kscience.kmath.stat.internal.InternalUtils
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import space.kscience.kmath.stat.internal.InternalUtils
import kotlin.math.ceil
import kotlin.math.max
import kotlin.math.min

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/BoxMullerNormalizedGaussianSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/BoxMullerNormalizedGaussianSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import kotlin.math.*
/**

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/GaussianSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/GaussianSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.chains.map
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.map
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
/**
* Sampling from a Gaussian distribution with given mean and standard deviation.

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/KempSmallMeanPoissonSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/KempSmallMeanPoissonSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import kotlin.math.exp
/**

16
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/LargeMeanPoissonSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/LargeMeanPoissonSampler.kt
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@ -1,12 +1,12 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.chains.ConstantChain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import kscience.kmath.stat.internal.InternalUtils
import kscience.kmath.stat.next
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.ConstantChain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import space.kscience.kmath.stat.internal.InternalUtils
import space.kscience.kmath.stat.next
import kotlin.math.*
/**

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/MarsagliaNormalizedGaussianSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/MarsagliaNormalizedGaussianSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import kotlin.math.ln
import kotlin.math.sqrt

4
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/NormalizedGaussianSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/NormalizedGaussianSampler.kt
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@ -1,6 +1,6 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.Sampler
/**
* Marker interface for a sampler that generates values from an N(0,1)

8
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/PoissonSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/PoissonSampler.kt
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@ -1,8 +1,8 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
/**
* Sampler for the Poisson distribution.

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/SmallMeanPoissonSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/SmallMeanPoissonSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import kotlin.math.ceil
import kotlin.math.exp

10
kmath-stat/src/commonMain/kotlin/kscience/kmath/stat/samplers/ZigguratNormalizedGaussianSampler.kt → kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/samplers/ZigguratNormalizedGaussianSampler.kt
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@ -1,9 +1,9 @@
package kscience.kmath.stat.samplers
package space.kscience.kmath.stat.samplers
import kscience.kmath.chains.Chain
import kscience.kmath.stat.RandomGenerator
import kscience.kmath.stat.Sampler
import kscience.kmath.stat.chain
import space.kscience.kmath.chains.Chain
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.Sampler
import space.kscience.kmath.stat.chain
import kotlin.math.*
/**

2
kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/CommonsDistributionsTest.kt
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@ -3,9 +3,9 @@ package space.kscience.kmath.stat
import kotlinx.coroutines.flow.take
import kotlinx.coroutines.flow.toList
import kotlinx.coroutines.runBlocking
import kscience.kmath.stat.samplers.GaussianSampler
import org.junit.jupiter.api.Assertions
import org.junit.jupiter.api.Test
import space.kscience.kmath.stat.samplers.GaussianSampler
internal class CommonsDistributionsTest {
@Test