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Merge branch 'dev' into feature/polynomials

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This commit is contained in:
Gleb Minaev 2022-07-16 17:23:39 +03:00
commit db19df4329
34 changed files with 2204 additions and 174 deletions
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2
.space.kts
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@ -1,3 +1,3 @@
job("Build") {
gradlew("openjdk:11", "build")
}
}

2
CHANGELOG.md
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@ -2,6 +2,8 @@
## [Unreleased]
### Added
- Autodiff for generic algebra elements in core!
- Algebra now has an obligatory `bufferFactory` (#477).
### Changed
- Kotlin 1.7

2
README.md
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@ -44,7 +44,7 @@ module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
with `@UnstableKmathAPI` or other stability warning annotations.
with `@UnstableKMathAPI` or other stability warning annotations.
* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
versions, but not in patch versions. API is protected
with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.

3
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -16,7 +16,6 @@ import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.multik.multikAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensorflow.produceWithTF
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -84,7 +83,7 @@ internal class DotBenchmark {
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}

10
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -20,7 +20,6 @@ import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.one
import space.kscience.kmath.tensors.core.tensorAlgebra
@ -28,12 +27,6 @@ import space.kscience.kmath.viktor.viktorAlgebra
@State(Scope.Benchmark)
internal class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd(blackhole: Blackhole) = with(autoField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
@ -95,9 +88,8 @@ internal class NDFieldBenchmark {
private const val dim = 1000
private const val n = 100
private val shape = intArrayOf(dim, dim)
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val specializedField = DoubleField.ndAlgebra
private val genericField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing)
private val genericField = BufferedFieldOpsND(DoubleField)
private val nd4jField = DoubleField.nd4j
private val multikField = DoubleField.multikAlgebra
private val viktorField = DoubleField.viktorAlgebra

21
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
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@ -10,25 +10,19 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.*
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
internal class ViktorBenchmark {
@Benchmark
fun automaticFieldAddition(blackhole: Blackhole) {
with(autoField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
}
@Benchmark
fun realFieldAddition(blackhole: Blackhole) {
with(realField) {
fun doubleFieldAddition(blackhole: Blackhole) {
with(doubleField) {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
blackhole.consume(res)
@ -58,8 +52,7 @@ internal class ViktorBenchmark {
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val doubleField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

7
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
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@ -10,19 +10,17 @@ import kotlinx.benchmark.Blackhole
import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import org.jetbrains.bio.viktor.F64Array
import space.kscience.kmath.nd.BufferedFieldOpsND
import space.kscience.kmath.nd.Shape
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.one
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.viktor.ViktorFieldND
@State(Scope.Benchmark)
internal class ViktorLogBenchmark {
@Benchmark
fun realFieldLog(blackhole: Blackhole) {
with(realField) {
with(doubleField) {
val fortyTwo = structureND(shape) { 42.0 }
var res = one(shape)
repeat(n) { res = ln(fortyTwo) }
@ -54,8 +52,7 @@ internal class ViktorLogBenchmark {
private val shape = Shape(dim, dim)
// automatically build context most suited for given type.
private val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
private val realField = DoubleField.ndAlgebra
private val doubleField = DoubleField.ndAlgebra
private val viktorField = ViktorFieldND(dim, dim)
}
}

2
docs/templates/README-TEMPLATE.md vendored
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@ -44,7 +44,7 @@ module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
with `@UnstableKmathAPI` or other stability warning annotations.
with `@UnstableKMathAPI` or other stability warning annotations.
* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
versions, but not in patch versions. API is protected
with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.

5
examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt
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@ -7,7 +7,6 @@ package space.kscience.kmath.operations
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.algebra
import space.kscience.kmath.complex.bufferAlgebra
import space.kscience.kmath.complex.ndAlgebra
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.StructureND
@ -18,7 +17,7 @@ fun main() = Complex.algebra {
println(complex * 8 - 5 * i)
//flat buffer
val buffer = with(bufferAlgebra){
val buffer = with(bufferAlgebra) {
buffer(8) { Complex(it, -it) }.map { Complex(it.im, it.re) }
}
println(buffer)
@ -30,7 +29,7 @@ fun main() = Complex.algebra {
println(element)
// 1d element operation
val result: StructureND<Complex> = ndAlgebra{
val result: StructureND<Complex> = ndAlgebra {
val a = structureND(8) { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)

21
examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
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@ -32,12 +32,10 @@ fun main() {
val shape = Shape(dim, dim)
// automatically build context most suited for given type.
val autoField = BufferedFieldOpsND(DoubleField, Buffer.Companion::auto)
// specialized nd-field for Double. It works as generic Double field as well.
val realField = DoubleField.ndAlgebra
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = BufferedFieldOpsND(DoubleField, Buffer.Companion::boxing)
val doubleField = DoubleField.ndAlgebra
//A generic field. It should be used for objects, not primitives.
val genericField = BufferedFieldOpsND(DoubleField)
// Nd4j specialized field.
val nd4jField = DoubleField.nd4j
//viktor field
@ -46,14 +44,14 @@ fun main() {
val parallelField = DoubleField.ndStreaming(dim, dim)
measureAndPrint("Boxing addition") {
boxingField {
genericField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Specialized addition") {
realField {
doubleField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
@ -80,15 +78,8 @@ fun main() {
}
}
measureAndPrint("Automatic field addition") {
autoField {
var res: StructureND<Double> = one(shape)
repeat(n) { res += 1.0 }
}
}
measureAndPrint("Lazy addition") {
val res = realField.one(shape).mapAsync(GlobalScope) {
val res = doubleField.one(shape).mapAsync(GlobalScope) {
var c = 0.0
repeat(n) {
c += 1.0

2
gradle.properties
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@ -12,4 +12,4 @@ org.gradle.configureondemand=true
org.gradle.parallel=true
org.gradle.jvmargs=-Xmx4096m
toolsVersion=0.11.7-kotlin-1.7.0
toolsVersion=0.11.8-kotlin-1.7.10

8
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt
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@ -10,10 +10,7 @@ import space.kscience.kmath.memory.MemorySpec
import space.kscience.kmath.memory.MemoryWriter
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MemoryBuffer
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableMemoryBuffer
import space.kscience.kmath.structures.*
import kotlin.math.*
/**
@ -54,6 +51,9 @@ public object ComplexField :
Norm<Complex, Complex>,
NumbersAddOps<Complex>,
ScaleOperations<Complex> {
override val bufferFactory: MutableBufferFactory<Complex> = MutableBufferFactory { size, init ->
MutableMemoryBuffer.create(Complex, size, init)
}
override val zero: Complex = 0.0.toComplex()
override val one: Complex = 1.0.toComplex()

5
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt
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@ -56,11 +56,6 @@ public sealed class ComplexFieldOpsND : BufferedFieldOpsND<Complex, ComplexField
public companion object : ComplexFieldOpsND()
}
@UnstableKMathAPI
public val ComplexField.bufferAlgebra: BufferFieldOps<Complex, ComplexField>
get() = bufferAlgebra(Buffer.Companion::complex)
@OptIn(UnstableKMathAPI::class)
public class ComplexFieldND(override val shape: Shape) :
ComplexFieldOpsND(), FieldND<Complex, ComplexField>,

456
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DSAlgebra.kt Normal file
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@ -0,0 +1,456 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.expressions
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MutableBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import space.kscience.kmath.structures.asBuffer
import kotlin.math.max
import kotlin.math.min
/**
* Class representing both the value and the differentials of a function.
*
* This class is the workhorse of the differentiation package.
*
* This class is an implementation of the extension to Rall's numbers described in Dan Kalman's paper
* [Doubly Recursive Multivariate Automatic Differentiation](http://www1.american.edu/cas/mathstat/People/kalman/pdffiles/mmgautodiff.pdf),
* Mathematics Magazine, vol. 75, no. 3, June 2002. Rall's numbers are an extension to the real numbers used
* throughout mathematical expressions; they hold the derivative together with the value of a function. Dan Kalman's
* derivative structures hold all partial derivatives up to any specified order, with respect to any number of free
* parameters. Rall's numbers therefore can be seen as derivative structures for order one derivative and one free
* parameter, and real numbers can be seen as derivative structures with zero order derivative and no free parameters.
*
* Derived from
* [Commons Math's `DerivativeStructure`](https://github.com/apache/commons-math/blob/924f6c357465b39beb50e3c916d5eb6662194175/commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/analysis/differentiation/DerivativeStructure.java).
*/
@UnstableKMathAPI
public interface DS<T, A : Ring<T>> {
public val derivativeAlgebra: DSAlgebra<T, A>
public val data: Buffer<T>
}
/**
* Get a partial derivative.
*
* @param orders derivation orders with respect to each variable (if all orders are 0, the value is returned).
* @return partial derivative.
* @see value
*/
@UnstableKMathAPI
private fun <T, A : Ring<T>> DS<T, A>.getPartialDerivative(vararg orders: Int): T =
data[derivativeAlgebra.compiler.getPartialDerivativeIndex(*orders)]
/**
* Provide a partial derivative with given symbols. On symbol could me mentioned multiple times
*/
@UnstableKMathAPI
public fun <T, A : Ring<T>> DS<T, A>.derivative(symbols: List<Symbol>): T {
require(symbols.size <= derivativeAlgebra.order) { "The order of derivative ${symbols.size} exceeds computed order ${derivativeAlgebra.order}" }
val ordersCount: Map<String, Int> = symbols.map { it.identity }.groupBy { it }.mapValues { it.value.size }
return getPartialDerivative(*symbols.map { ordersCount[it] ?: 0 }.toIntArray())
}
/**
* Provide a partial derivative with given symbols. On symbol could me mentioned multiple times
*/
@UnstableKMathAPI
public fun <T, A : Ring<T>> DS<T, A>.derivative(vararg symbols: Symbol): T {
require(symbols.size <= derivativeAlgebra.order) { "The order of derivative ${symbols.size} exceeds computed order ${derivativeAlgebra.order}" }
val ordersCount: Map<String, Int> = symbols.map { it.identity }.groupBy { it }.mapValues { it.value.size }
return getPartialDerivative(*symbols.map { ordersCount[it] ?: 0 }.toIntArray())
}
/**
* The value part of the derivative structure.
*
* @see getPartialDerivative
*/
@UnstableKMathAPI
public val <T, A : Ring<T>> DS<T, A>.value: T get() = data[0]
@UnstableKMathAPI
public abstract class DSAlgebra<T, A : Ring<T>>(
public val algebra: A,
public val order: Int,
bindings: Map<Symbol, T>,
public val valueBufferFactory: MutableBufferFactory<T> = algebra.bufferFactory,
) : ExpressionAlgebra<T, DS<T, A>>, SymbolIndexer {
/**
* Get the compiler for number of free parameters and order.
*
* @return cached rules set.
*/
@PublishedApi
internal val compiler: DSCompiler<T, A> by lazy {
val numberOfVariables = bindings.size
// get the cached compilers
val cache: Array<Array<DSCompiler<T, A>?>>? = null
// we need to create more compilers
val maxParameters: Int = max(numberOfVariables, cache?.size ?: 0)
val maxOrder: Int = max(order, if (cache == null) 0 else cache[0].size)
val newCache: Array<Array<DSCompiler<T, A>?>> = Array(maxParameters + 1) { arrayOfNulls(maxOrder + 1) }
if (cache != null) {
// preserve the already created compilers
for (i in cache.indices) {
cache[i].copyInto(newCache[i], endIndex = cache[i].size)
}
}
// create the array in increasing diagonal order
for (diag in 0..numberOfVariables + order) {
for (o in max(0, diag - numberOfVariables)..min(order, diag)) {
val p: Int = diag - o
if (newCache[p][o] == null) {
val valueCompiler: DSCompiler<T, A>? = if (p == 0) null else newCache[p - 1][o]!!
val derivativeCompiler: DSCompiler<T, A>? = if (o == 0) null else newCache[p][o - 1]!!
newCache[p][o] = DSCompiler(
algebra,
valueBufferFactory,
p,
o,
valueCompiler,
derivativeCompiler,
)
}
}
}
return@lazy newCache[numberOfVariables][order]!!
}
private val variables: Map<Symbol, DSSymbol> by lazy {
bindings.entries.mapIndexed { index, (key, value) ->
key to DSSymbol(
index,
key,
value,
)
}.toMap()
}
override val symbols: List<Symbol> = bindings.map { it.key }
private fun bufferForVariable(index: Int, value: T): Buffer<T> {
val buffer = valueBufferFactory(compiler.size) { algebra.zero }
buffer[0] = value
if (compiler.order > 0) {
// the derivative of the variable with respect to itself is 1.
val indexOfDerivative = compiler.getPartialDerivativeIndex(*IntArray(symbols.size).apply {
set(index, 1)
})
buffer[indexOfDerivative] = algebra.one
}
return buffer
}
@UnstableKMathAPI
private inner class DSImpl(
override val data: Buffer<T>,
) : DS<T, A> {
override val derivativeAlgebra: DSAlgebra<T, A> get() = this@DSAlgebra
}
protected fun DS(data: Buffer<T>): DS<T, A> = DSImpl(data)
/**
* Build an instance representing a variable.
*
* Instances built using this constructor are considered to be the free variables with respect to which
* differentials are computed. As such, their differential with respect to themselves is +1.
*/
public fun variable(
index: Int,
value: T,
): DS<T, A> {
require(index < compiler.freeParameters) { "number is too large: $index >= ${compiler.freeParameters}" }
return DS(bufferForVariable(index, value))
}
/**
* Build an instance from all its derivatives.
*
* @param derivatives derivatives sorted according to [DSCompiler.getPartialDerivativeIndex].
*/
public fun ofDerivatives(
vararg derivatives: T,
): DS<T, A> {
require(derivatives.size == compiler.size) { "dimension mismatch: ${derivatives.size} and ${compiler.size}" }
val data = derivatives.asBuffer()
return DS(data)
}
/**
* A class implementing both [DS] and [Symbol].
*/
@UnstableKMathAPI
public inner class DSSymbol internal constructor(
index: Int,
symbol: Symbol,
value: T,
) : Symbol by symbol, DS<T, A> {
override val derivativeAlgebra: DSAlgebra<T, A> get() = this@DSAlgebra
override val data: Buffer<T> = bufferForVariable(index, value)
}
public override fun const(value: T): DS<T, A> {
val buffer = valueBufferFactory(compiler.size) { algebra.zero }
buffer[0] = value
return DS(buffer)
}
override fun bindSymbolOrNull(value: String): DSSymbol? = variables[StringSymbol(value)]
override fun bindSymbol(value: String): DSSymbol =
bindSymbolOrNull(value) ?: error("Symbol '$value' is not supported in $this")
public fun bindSymbolOrNull(symbol: Symbol): DSSymbol? = variables[symbol.identity]
public fun bindSymbol(symbol: Symbol): DSSymbol =
bindSymbolOrNull(symbol.identity) ?: error("Symbol '${symbol}' is not supported in $this")
public fun DS<T, A>.derivative(symbols: List<Symbol>): T {
require(symbols.size <= order) { "The order of derivative ${symbols.size} exceeds computed order $order" }
val ordersCount = symbols.groupBy { it }.mapValues { it.value.size }
return getPartialDerivative(*variables.keys.map { ordersCount[it] ?: 0 }.toIntArray())
}
public fun DS<T, A>.derivative(vararg symbols: Symbol): T = derivative(symbols.toList())
}
/**
* A ring over [DS].
*
* @property order The derivation order.
* @param bindings The map of bindings values. All bindings are considered free parameters.
*/
@UnstableKMathAPI
public open class DSRing<T, A>(
algebra: A,
order: Int,
bindings: Map<Symbol, T>,
valueBufferFactory: MutableBufferFactory<T>,
) : DSAlgebra<T, A>(algebra, order, bindings, valueBufferFactory),
Ring<DS<T, A>>, ScaleOperations<DS<T, A>>,
NumericAlgebra<DS<T, A>>,
NumbersAddOps<DS<T, A>> where A : Ring<T>, A : NumericAlgebra<T>, A : ScaleOperations<T> {
override fun bindSymbolOrNull(value: String): DSSymbol? =
super<DSAlgebra>.bindSymbolOrNull(value)
override fun DS<T, A>.unaryMinus(): DS<T, A> = mapData { -it }
/**
* Create a copy of given [Buffer] and modify it according to [block]
*/
protected inline fun DS<T, A>.transformDataBuffer(block: A.(MutableBuffer<T>) -> Unit): DS<T, A> {
require(derivativeAlgebra == this@DSRing) { "All derivative operations should be done in the same algebra" }
val newData = valueBufferFactory(compiler.size) { data[it] }
algebra.block(newData)
return DS(newData)
}
protected fun DS<T, A>.mapData(block: A.(T) -> T): DS<T, A> {
require(derivativeAlgebra == this@DSRing) { "All derivative operations should be done in the same algebra" }
val newData: Buffer<T> = data.map(valueBufferFactory) {
algebra.block(it)
}
return DS(newData)
}
protected fun DS<T, A>.mapDataIndexed(block: (Int, T) -> T): DS<T, A> {
require(derivativeAlgebra == this@DSRing) { "All derivative operations should be done in the same algebra" }
val newData: Buffer<T> = data.mapIndexed(valueBufferFactory, block)
return DS(newData)
}
override val zero: DS<T, A> by lazy {
const(algebra.zero)
}
override val one: DS<T, A> by lazy {
const(algebra.one)
}
override fun number(value: Number): DS<T, A> = const(algebra.number(value))
override fun add(left: DS<T, A>, right: DS<T, A>): DS<T, A> = left.transformDataBuffer { result ->
require(right.derivativeAlgebra == this@DSRing) { "All derivative operations should be done in the same algebra" }
compiler.add(left.data, 0, right.data, 0, result, 0)
}
override fun scale(a: DS<T, A>, value: Double): DS<T, A> = a.mapData {
it.times(value)
}
override fun multiply(
left: DS<T, A>,
right: DS<T, A>,
): DS<T, A> = left.transformDataBuffer { result ->
compiler.multiply(left.data, 0, right.data, 0, result, 0)
}
//
// override fun DS<T, A>.minus(arg: DS): DS<T, A> = transformDataBuffer { result ->
// subtract(data, 0, arg.data, 0, result, 0)
// }
override operator fun DS<T, A>.plus(other: Number): DS<T, A> = transformDataBuffer {
it[0] += number(other)
}
//
// override operator fun DS<T, A>.minus(other: Number): DS<T, A> =
// this + (-other.toDouble())
override operator fun Number.plus(other: DS<T, A>): DS<T, A> = other + this
override operator fun Number.minus(other: DS<T, A>): DS<T, A> = other - this
}
@UnstableKMathAPI
public class DerivativeStructureRingExpression<T, A>(
public val algebra: A,
public val elementBufferFactory: MutableBufferFactory<T> = algebra.bufferFactory,
public val function: DSRing<T, A>.() -> DS<T, A>,
) : DifferentiableExpression<T> where A : Ring<T>, A : ScaleOperations<T>, A : NumericAlgebra<T> {
override operator fun invoke(arguments: Map<Symbol, T>): T =
DSRing(algebra, 0, arguments, elementBufferFactory).function().value
override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression { arguments ->
with(
DSRing(
algebra,
symbols.size,
arguments,
elementBufferFactory
)
) { function().derivative(symbols) }
}
}
/**
* A field over commons-math [DerivativeStructure].
*
* @property order The derivation order.
* @param bindings The map of bindings values. All bindings are considered free parameters.
*/
@UnstableKMathAPI
public class DSField<T, A : ExtendedField<T>>(
algebra: A,
order: Int,
bindings: Map<Symbol, T>,
valueBufferFactory: MutableBufferFactory<T>,
) : DSRing<T, A>(algebra, order, bindings, valueBufferFactory), ExtendedField<DS<T, A>> {
override fun number(value: Number): DS<T, A> = const(algebra.number(value))
override fun divide(left: DS<T, A>, right: DS<T, A>): DS<T, A> = left.transformDataBuffer { result ->
compiler.divide(left.data, 0, right.data, 0, result, 0)
}
override fun sin(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.sin(arg.data, 0, result, 0)
}
override fun cos(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.cos(arg.data, 0, result, 0)
}
override fun tan(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.tan(arg.data, 0, result, 0)
}
override fun asin(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.asin(arg.data, 0, result, 0)
}
override fun acos(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.acos(arg.data, 0, result, 0)
}
override fun atan(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.atan(arg.data, 0, result, 0)
}
override fun sinh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.sinh(arg.data, 0, result, 0)
}
override fun cosh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.cosh(arg.data, 0, result, 0)
}
override fun tanh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.tanh(arg.data, 0, result, 0)
}
override fun asinh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.asinh(arg.data, 0, result, 0)
}
override fun acosh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.acosh(arg.data, 0, result, 0)
}
override fun atanh(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.atanh(arg.data, 0, result, 0)
}
override fun power(arg: DS<T, A>, pow: Number): DS<T, A> = when (pow) {
is Int -> arg.transformDataBuffer { result ->
compiler.pow(arg.data, 0, pow, result, 0)
}
else -> arg.transformDataBuffer { result ->
compiler.pow(arg.data, 0, pow.toDouble(), result, 0)
}
}
override fun sqrt(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.sqrt(arg.data, 0, result, 0)
}
public fun power(arg: DS<T, A>, pow: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.pow(arg.data, 0, pow.data, 0, result, 0)
}
override fun exp(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.exp(arg.data, 0, result, 0)
}
override fun ln(arg: DS<T, A>): DS<T, A> = arg.transformDataBuffer { result ->
compiler.ln(arg.data, 0, result, 0)
}
}
@UnstableKMathAPI
public class DSFieldExpression<T, A : ExtendedField<T>>(
public val algebra: A,
private val valueBufferFactory: MutableBufferFactory<T> = algebra.bufferFactory,
public val function: DSField<T, A>.() -> DS<T, A>,
) : DifferentiableExpression<T> {
override operator fun invoke(arguments: Map<Symbol, T>): T =
DSField(algebra, 0, arguments, valueBufferFactory).function().value
override fun derivativeOrNull(symbols: List<Symbol>): Expression<T> = Expression { arguments ->
DSField(
algebra,
symbols.size,
arguments,
valueBufferFactory,
).run { function().derivative(symbols) }
}
}

1487
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DSCompiler.kt Normal file
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File diff suppressed because it is too large Load Diff

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt
View File

@ -11,13 +11,12 @@ import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.VirtualBuffer
import space.kscience.kmath.structures.indices
public class BufferedLinearSpace<T, out A : Ring<T>>(
private val bufferAlgebra: BufferAlgebra<T, A>
private val bufferAlgebra: BufferAlgebra<T, A>,
) : LinearSpace<T, A> {
override val elementAlgebra: A get() = bufferAlgebra.elementAlgebra
@ -91,5 +90,5 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
}
public fun <T, A : Ring<T>> A.linearSpace(bufferFactory: BufferFactory<T>): BufferedLinearSpace<T, A> =
BufferedLinearSpace(BufferRingOps(this, bufferFactory))
public val <T, A : Ring<T>> A.linearSpace: BufferedLinearSpace<T, A>
get() = BufferedLinearSpace(BufferRingOps(this))

16
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
View File

@ -11,12 +11,9 @@ import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureFeature
import space.kscience.kmath.nd.as1D
import space.kscience.kmath.operations.BufferRingOps
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.reflect.KClass
/**
@ -187,18 +184,9 @@ public interface LinearSpace<T, out A : Ring<T>> {
* A structured matrix with custom buffer
*/
public fun <T : Any, A : Ring<T>> buffered(
algebra: A,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
): LinearSpace<T, A> = BufferedLinearSpace(BufferRingOps(algebra, bufferFactory))
algebra: A
): LinearSpace<T, A> = BufferedLinearSpace(BufferRingOps(algebra))
@Deprecated("use DoubleField.linearSpace")
public val double: LinearSpace<Double, DoubleField> = buffered(DoubleField, ::DoubleBuffer)
/**
* Automatic buffered matrix, unboxed if it is possible
*/
public inline fun <reified T : Any, A : Ring<T>> auto(ring: A): LinearSpace<T, A> =
buffered(ring, Buffer.Companion::auto)
}
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt
View File

@ -27,5 +27,5 @@ public annotation class UnstableKMathAPI
RequiresOptIn.Level.WARNING,
)
public annotation class PerformancePitfall(
val message: String = "Potential performance problem"
val message: String = "Potential performance problem",
)

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt
View File

@ -10,7 +10,6 @@ package space.kscience.kmath.nd
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.BufferFactory
public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
public val indexerBuilder: (IntArray) -> ShapeIndexer
@ -60,7 +59,7 @@ public inline fun <T, A : Algebra<T>> BufferAlgebraND<T, A>.mapInline(
return BufferND(
indexes,
bufferAlgebra.run {
bufferFactory(buffer.size) { elementAlgebra.transform(buffer[it]) }
elementBufferFactory(buffer.size) { elementAlgebra.transform(buffer[it]) }
}
)
}
@ -74,7 +73,7 @@ internal inline fun <T, A : Algebra<T>> BufferAlgebraND<T, A>.mapIndexedInline(
return BufferND(
indexes,
bufferAlgebra.run {
bufferFactory(buffer.size) { elementAlgebra.transform(indexes.index(it), buffer[it]) }
elementBufferFactory(buffer.size) { elementAlgebra.transform(indexes.index(it), buffer[it]) }
}
)
}
@ -91,7 +90,7 @@ internal inline fun <T, A : Algebra<T>> BufferAlgebraND<T, A>.zipInline(
return BufferND(
indexes,
bufferAlgebra.run {
bufferFactory(lbuffer.size) { elementAlgebra.block(lbuffer[it], rbuffer[it]) }
elementBufferFactory(lbuffer.size) { elementAlgebra.block(lbuffer[it], rbuffer[it]) }
}
)
}
@ -116,9 +115,8 @@ public open class BufferedFieldOpsND<T, out A : Field<T>>(
public constructor(
elementAlgebra: A,
bufferFactory: BufferFactory<T>,
indexerBuilder: (IntArray) -> ShapeIndexer = BufferAlgebraND.defaultIndexerBuilder,
) : this(BufferFieldOps(elementAlgebra, bufferFactory), indexerBuilder)
) : this(BufferFieldOps(elementAlgebra), indexerBuilder)
@OptIn(PerformancePitfall::class)
override fun scale(a: StructureND<T>, value: Double): StructureND<T> = a.map { it * value }

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt
View File

@ -69,7 +69,7 @@ public class MutableBufferND<T>(
* Transform structure to a new structure using provided [MutableBufferFactory] and optimizing if argument is [MutableBufferND]
*/
public inline fun <T, reified R : Any> MutableStructureND<T>.mapToMutableBuffer(
factory: MutableBufferFactory<R> = MutableBuffer.Companion::auto,
factory: MutableBufferFactory<R> = MutableBufferFactory(MutableBuffer.Companion::auto),
crossinline transform: (T) -> R,
): MutableBufferND<R> {
return if (this is MutableBufferND<T>)

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt
View File

@ -120,7 +120,7 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
*/
public fun <T> buffered(
strides: Strides,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
initializer: (IntArray) -> T,
): BufferND<T> = BufferND(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
@ -140,7 +140,7 @@ public interface StructureND<out T> : Featured<StructureFeature>, WithShape {
public fun <T> buffered(
shape: IntArray,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
initializer: (IntArray) -> T,
): BufferND<T> = buffered(DefaultStrides(shape), bufferFactory, initializer)

13
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt
View File

@ -8,12 +8,7 @@ package space.kscience.kmath.operations
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.Ring.Companion.optimizedPower
/**
* Stub for DSL the [Algebra] is.
*/
@DslMarker
public annotation class KMathContext
import space.kscience.kmath.structures.MutableBufferFactory
/**
* Represents an algebraic structure.
@ -21,6 +16,12 @@ public annotation class KMathContext
* @param T the type of element of this structure.
*/
public interface Algebra<T> {
/**
* Provide a factory for buffers, associated with this [Algebra]
*/
public val bufferFactory: MutableBufferFactory<T> get() = MutableBufferFactory.boxing()
/**
* Wraps a raw string to [T] object. This method is designed for three purposes:
*

13
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt
View File

@ -10,7 +10,6 @@ import space.kscience.kmath.nd.BufferedRingOpsND
import space.kscience.kmath.operations.BigInt.Companion.BASE
import space.kscience.kmath.operations.BigInt.Companion.BASE_SIZE
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MutableBuffer
import kotlin.math.log2
import kotlin.math.max
import kotlin.math.min
@ -528,19 +527,11 @@ public fun String.parseBigInteger(): BigInt? {
public val BigInt.algebra: BigIntField get() = BigIntField
@Deprecated("Use BigInt::buffer")
public inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
boxing(size, initializer)
public inline fun BigInt.Companion.buffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
Buffer.boxing(size, initializer)
@Deprecated("Use BigInt::mutableBuffer")
public inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> =
boxing(size, initializer)
public inline fun BigInt.mutableBuffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
public inline fun BigInt.Companion.mutableBuffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
Buffer.boxing(size, initializer)
public val BigIntField.nd: BufferedRingOpsND<BigInt, BigIntField>
get() = BufferedRingOpsND(BufferRingOps(BigIntField, BigInt::buffer))
get() = BufferedRingOpsND(BufferRingOps(BigIntField))

47
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BufferAlgebra.kt
View File

@ -7,8 +7,6 @@ package space.kscience.kmath.operations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.ShortBuffer
public interface WithSize {
public val size: Int
@ -19,11 +17,11 @@ public interface WithSize {
*/
public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
public val elementAlgebra: A
public val bufferFactory: BufferFactory<T>
public val elementBufferFactory: BufferFactory<T> get() = elementAlgebra.bufferFactory
public fun buffer(size: Int, vararg elements: T): Buffer<T> {
require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
return bufferFactory(size) { elements[it] }
return elementBufferFactory(size) { elements[it] }
}
//TODO move to multi-receiver inline extension
@ -36,13 +34,13 @@ public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
override fun unaryOperationFunction(operation: String): (arg: Buffer<T>) -> Buffer<T> {
val operationFunction = elementAlgebra.unaryOperationFunction(operation)
return { arg -> bufferFactory(arg.size) { operationFunction(arg[it]) } }
return { arg -> elementBufferFactory(arg.size) { operationFunction(arg[it]) } }
}
override fun binaryOperationFunction(operation: String): (left: Buffer<T>, right: Buffer<T>) -> Buffer<T> {
val operationFunction = elementAlgebra.binaryOperationFunction(operation)
return { left, right ->
bufferFactory(left.size) { operationFunction(left[it], right[it]) }
elementBufferFactory(left.size) { operationFunction(left[it], right[it]) }
}
}
}
@ -53,7 +51,7 @@ public interface BufferAlgebra<T, out A : Algebra<T>> : Algebra<Buffer<T>> {
private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
buffer: Buffer<T>,
crossinline block: A.(T) -> T,
): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(buffer[it]) }
): Buffer<T> = elementBufferFactory(buffer.size) { elementAlgebra.block(buffer[it]) }
/**
* Inline map
@ -61,7 +59,7 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapInline(
private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.mapIndexedInline(
buffer: Buffer<T>,
crossinline block: A.(index: Int, arg: T) -> T,
): Buffer<T> = bufferFactory(buffer.size) { elementAlgebra.block(it, buffer[it]) }
): Buffer<T> = elementBufferFactory(buffer.size) { elementAlgebra.block(it, buffer[it]) }
/**
* Inline zip
@ -72,15 +70,15 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.zipInline(
crossinline block: A.(l: T, r: T) -> T,
): Buffer<T> {
require(l.size == r.size) { "Incompatible buffer sizes. left: ${l.size}, right: ${r.size}" }
return bufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) }
return elementBufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) }
}
public fun <T> BufferAlgebra<T, *>.buffer(size: Int, initializer: (Int) -> T): Buffer<T> {
return bufferFactory(size, initializer)
return elementBufferFactory(size, initializer)
}
public fun <T, A> A.buffer(initializer: (Int) -> T): Buffer<T> where A : BufferAlgebra<T, *>, A : WithSize {
return bufferFactory(size, initializer)
return elementBufferFactory(size, initializer)
}
public fun <T, A : TrigonometricOperations<T>> BufferAlgebra<T, A>.sin(arg: Buffer<T>): Buffer<T> =
@ -131,7 +129,6 @@ public fun <T, A : PowerOperations<T>> BufferAlgebra<T, A>.pow(arg: Buffer<T>, p
public open class BufferRingOps<T, A : Ring<T>>(
override val elementAlgebra: A,
override val bufferFactory: BufferFactory<T>,
) : BufferAlgebra<T, A>, RingOps<Buffer<T>> {
override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
@ -146,15 +143,13 @@ public open class BufferRingOps<T, A : Ring<T>>(
}
public val ShortRing.bufferAlgebra: BufferRingOps<Short, ShortRing>
get() = BufferRingOps(ShortRing, ::ShortBuffer)
get() = BufferRingOps(ShortRing)
public open class BufferFieldOps<T, A : Field<T>>(
elementAlgebra: A,
bufferFactory: BufferFactory<T>,
) : BufferRingOps<T, A>(elementAlgebra, bufferFactory), BufferAlgebra<T, A>, FieldOps<Buffer<T>>,
ScaleOperations<Buffer<T>> {
) : BufferRingOps<T, A>(elementAlgebra), BufferAlgebra<T, A>, FieldOps<Buffer<T>>, ScaleOperations<Buffer<T>> {
// override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
// override fun add(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l + r }
// override fun multiply(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l * r }
override fun divide(left: Buffer<T>, right: Buffer<T>): Buffer<T> = zipInline(left, right) { l, r -> l / r }
@ -167,30 +162,26 @@ public open class BufferFieldOps<T, A : Field<T>>(
public class BufferField<T, A : Field<T>>(
elementAlgebra: A,
bufferFactory: BufferFactory<T>,
override val size: Int,
) : BufferFieldOps<T, A>(elementAlgebra, bufferFactory), Field<Buffer<T>>, WithSize {
) : BufferFieldOps<T, A>(elementAlgebra), Field<Buffer<T>>, WithSize {
override val zero: Buffer<T> = bufferFactory(size) { elementAlgebra.zero }
override val one: Buffer<T> = bufferFactory(size) { elementAlgebra.one }
override val zero: Buffer<T> = elementAlgebra.bufferFactory(size) { elementAlgebra.zero }
override val one: Buffer<T> = elementAlgebra.bufferFactory(size) { elementAlgebra.one }
}
/**
* Generate full buffer field from given buffer operations
*/
public fun <T, A : Field<T>> BufferFieldOps<T, A>.withSize(size: Int): BufferField<T, A> =
BufferField(elementAlgebra, bufferFactory, size)
BufferField(elementAlgebra, size)
//Double buffer specialization
public fun BufferField<Double, *>.buffer(vararg elements: Number): Buffer<Double> {
require(elements.size == size) { "Expected $size elements but found ${elements.size}" }
return bufferFactory(size) { elements[it].toDouble() }
return elementBufferFactory(size) { elements[it].toDouble() }
}
public fun <T, A : Field<T>> A.bufferAlgebra(bufferFactory: BufferFactory<T>): BufferFieldOps<T, A> =
BufferFieldOps(this, bufferFactory)
public val DoubleField.bufferAlgebra: BufferFieldOps<Double, DoubleField>
get() = BufferFieldOps(DoubleField, ::DoubleBuffer)
public val <T, A : Field<T>> A.bufferAlgebra: BufferFieldOps<T, A>
get() = BufferFieldOps(this)

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/DoubleBufferOps.kt
View File

@ -6,12 +6,10 @@
package space.kscience.kmath.operations
import space.kscience.kmath.linear.Point
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.MutableBufferFactory
import space.kscience.kmath.structures.asBuffer
import kotlin.math.*
/**
@ -21,7 +19,7 @@ public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, Exte
Norm<Buffer<Double>, Double> {
override val elementAlgebra: DoubleField get() = DoubleField
override val bufferFactory: BufferFactory<Double> get() = ::DoubleBuffer
override val elementBufferFactory: MutableBufferFactory<Double> get() = elementAlgebra.bufferFactory
override fun Buffer<Double>.map(block: DoubleField.(Double) -> Double): DoubleBuffer =
mapInline { DoubleField.block(it) }

26
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/bufferOperation.kt
View File

@ -61,31 +61,39 @@ public inline fun <reified T> Buffer<T>.toTypedArray(): Array<T> = Array(size, :
/**
* Create a new buffer from this one with the given mapping function and using [Buffer.Companion.auto] buffer factory.
*/
public inline fun <T : Any, reified R : Any> Buffer<T>.map(block: (T) -> R): Buffer<R> =
public inline fun <T, reified R : Any> Buffer<T>.map(block: (T) -> R): Buffer<R> =
Buffer.auto(size) { block(get(it)) }
/**
* Create a new buffer from this one with the given mapping function.
* Provided [bufferFactory] is used to construct the new buffer.
*/
public inline fun <T : Any, R : Any> Buffer<T>.map(
public inline fun <T, R> Buffer<T>.map(
bufferFactory: BufferFactory<R>,
crossinline block: (T) -> R,
): Buffer<R> = bufferFactory(size) { block(get(it)) }
/**
* Create a new buffer from this one with the given indexed mapping function.
* Provided [BufferFactory] is used to construct the new buffer.
* Create a new buffer from this one with the given mapping (indexed) function.
* Provided [bufferFactory] is used to construct the new buffer.
*/
public inline fun <T : Any, reified R : Any> Buffer<T>.mapIndexed(
bufferFactory: BufferFactory<R> = Buffer.Companion::auto,
public inline fun <T, R> Buffer<T>.mapIndexed(
bufferFactory: BufferFactory<R>,
crossinline block: (index: Int, value: T) -> R,
): Buffer<R> = bufferFactory(size) { block(it, get(it)) }
/**
* Create a new buffer from this one with the given indexed mapping function.
* Provided [BufferFactory] is used to construct the new buffer.
*/
public inline fun <T, reified R : Any> Buffer<T>.mapIndexed(
crossinline block: (index: Int, value: T) -> R,
): Buffer<R> = Buffer.auto(size) { block(it, get(it)) }
/**
* Fold given buffer according to [operation]
*/
public inline fun <T : Any, R> Buffer<T>.fold(initial: R, operation: (acc: R, T) -> R): R {
public inline fun <T, R> Buffer<T>.fold(initial: R, operation: (acc: R, T) -> R): R {
var accumulator = initial
for (index in this.indices) accumulator = operation(accumulator, get(index))
return accumulator
@ -104,9 +112,9 @@ public inline fun <T : Any, R> Buffer<T>.foldIndexed(initial: R, operation: (ind
* Zip two buffers using given [transform].
*/
@UnstableKMathAPI
public inline fun <T1 : Any, T2 : Any, reified R : Any> Buffer<T1>.zip(
public inline fun <T1, T2 : Any, reified R : Any> Buffer<T1>.zip(
other: Buffer<T2>,
bufferFactory: BufferFactory<R> = Buffer.Companion::auto,
bufferFactory: BufferFactory<R> = BufferFactory.auto(),
crossinline transform: (T1, T2) -> R,
): Buffer<R> {
require(size == other.size) { "Buffer size mismatch in zip: expected $size but found ${other.size}" }

33
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/numbers.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.operations
import space.kscience.kmath.structures.*
import kotlin.math.pow as kpow
/**
@ -65,6 +66,8 @@ public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, PowerOperatio
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object DoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
override val bufferFactory: MutableBufferFactory<Double> = MutableBufferFactory(::DoubleBuffer)
override inline val zero: Double get() = 0.0
override inline val one: Double get() = 1.0
@ -123,6 +126,8 @@ public val Double.Companion.algebra: DoubleField get() = DoubleField
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
override val bufferFactory: MutableBufferFactory<Float> = MutableBufferFactory(::FloatBuffer)
override inline val zero: Float get() = 0.0f
override inline val one: Float get() = 1.0f
@ -177,11 +182,10 @@ public val Float.Companion.algebra: FloatField get() = FloatField
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object IntRing : Ring<Int>, Norm<Int, Int>, NumericAlgebra<Int> {
override inline val zero: Int
get() = 0
override val bufferFactory: MutableBufferFactory<Int> = MutableBufferFactory(::IntBuffer)
override inline val one: Int
get() = 1
override inline val zero: Int get() = 0
override inline val one: Int get() = 1
override fun number(value: Number): Int = value.toInt()
override inline fun add(left: Int, right: Int): Int = left + right
@ -201,11 +205,10 @@ public val Int.Companion.algebra: IntRing get() = IntRing
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object ShortRing : Ring<Short>, Norm<Short, Short>, NumericAlgebra<Short> {
override inline val zero: Short
get() = 0
override val bufferFactory: MutableBufferFactory<Short> = MutableBufferFactory(::ShortBuffer)
override inline val one: Short
get() = 1
override inline val zero: Short get() = 0
override inline val one: Short get() = 1
override fun number(value: Number): Short = value.toShort()
override inline fun add(left: Short, right: Short): Short = (left + right).toShort()
@ -225,11 +228,10 @@ public val Short.Companion.algebra: ShortRing get() = ShortRing
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object ByteRing : Ring<Byte>, Norm<Byte, Byte>, NumericAlgebra<Byte> {
override inline val zero: Byte
get() = 0
override val bufferFactory: MutableBufferFactory<Byte> = MutableBufferFactory(::ByteBuffer)
override inline val one: Byte
get() = 1
override inline val zero: Byte get() = 0
override inline val one: Byte get() = 1
override fun number(value: Number): Byte = value.toByte()
override inline fun add(left: Byte, right: Byte): Byte = (left + right).toByte()
@ -249,11 +251,10 @@ public val Byte.Companion.algebra: ByteRing get() = ByteRing
*/
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public object LongRing : Ring<Long>, Norm<Long, Long>, NumericAlgebra<Long> {
override inline val zero: Long
get() = 0L
override val bufferFactory: MutableBufferFactory<Long> = MutableBufferFactory(::LongBuffer)
override inline val one: Long
get() = 1L
override inline val zero: Long get() = 0L
override inline val one: Long get() = 1L
override fun number(value: Number): Long = value.toLong()
override inline fun add(left: Long, right: Long): Long = left + right

24
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt
View File

@ -14,14 +14,34 @@ import kotlin.reflect.KClass
*
* @param T the type of buffer.
*/
public typealias BufferFactory<T> = (Int, (Int) -> T) -> Buffer<T>
public fun interface BufferFactory<T> {
public operator fun invoke(size: Int, builder: (Int) -> T): Buffer<T>
public companion object{
public inline fun <reified T : Any> auto(): BufferFactory<T> =
BufferFactory(Buffer.Companion::auto)
public fun <T> boxing(): BufferFactory<T> =
BufferFactory(Buffer.Companion::boxing)
}
}
/**
* Function that produces [MutableBuffer] from its size and function that supplies values.
*
* @param T the type of buffer.
*/
public typealias MutableBufferFactory<T> = (Int, (Int) -> T) -> MutableBuffer<T>
public fun interface MutableBufferFactory<T> : BufferFactory<T> {
override fun invoke(size: Int, builder: (Int) -> T): MutableBuffer<T>
public companion object {
public inline fun <reified T : Any> auto(): MutableBufferFactory<T> =
MutableBufferFactory(MutableBuffer.Companion::auto)
public fun <T> boxing(): MutableBufferFactory<T> =
MutableBufferFactory(MutableBuffer.Companion::boxing)
}
}
/**
* A generic read-only random-access structure for both primitives and objects.

57
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/ByteBuffer.kt Normal file
View File

@ -0,0 +1,57 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.structures
import kotlin.jvm.JvmInline
/**
* Specialized [MutableBuffer] implementation over [ByteArray].
*
* @property array the underlying array.
*/
@JvmInline
public value class ByteBuffer(public val array: ByteArray) : MutableBuffer<Byte> {
override val size: Int get() = array.size
override operator fun get(index: Int): Byte = array[index]
override operator fun set(index: Int, value: Byte) {
array[index] = value
}
override operator fun iterator(): ByteIterator = array.iterator()
override fun copy(): MutableBuffer<Byte> = ByteBuffer(array.copyOf())
}
/**
* Creates a new [ByteBuffer] 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 a buffer element given its index.
*/
public inline fun ByteBuffer(size: Int, init: (Int) -> Byte): ByteBuffer = ByteBuffer(ByteArray(size) { init(it) })
/**
* Returns a new [ByteBuffer] of given elements.
*/
public fun ByteBuffer(vararg bytes: Byte): ByteBuffer = ByteBuffer(bytes)
/**
* Returns a new [ByteArray] containing all the elements of this [Buffer].
*/
public fun Buffer<Byte>.toByteArray(): ByteArray = when (this) {
is ByteBuffer -> array.copyOf()
else -> ByteArray(size, ::get)
}
/**
* Returns [ByteBuffer] over this array.
*
* @receiver the array.
* @return the new buffer.
*/
public fun ByteArray.asBuffer(): ByteBuffer = ByteBuffer(this)

59
kmath-core/src/commonTest/kotlin/space/kscience/kmath/expressions/DSTest.kt Normal file
View File

@ -0,0 +1,59 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
@file:OptIn(UnstableKMathAPI::class)
package space.kscience.kmath.expressions
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertFails
internal inline fun diff(
order: Int,
vararg parameters: Pair<Symbol, Double>,
block: DSField<Double, DoubleField>.() -> Unit,
) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
DSField(DoubleField, order, mapOf(*parameters), ::DoubleBuffer).block()
}
internal class DSTest {
private val x by symbol
private val y by symbol
@Test
fun dsAlgebraTest() {
diff(2, x to 1.0, y to 1.0) {
val x = bindSymbol(x)//by binding()
val y = bindSymbol("y")
val z = x * (-sin(x * y) + y) + 2.0
println(z.derivative(x))
println(z.derivative(y, x))
assertEquals(z.derivative(x, y), z.derivative(y, x))
// check improper order cause failure
assertFails { z.derivative(x, x, y) }
}
}
@Test
fun dsExpressionTest() {
val f = DSFieldExpression(DoubleField, ::DoubleBuffer) {
val x by binding
val y by binding
x.pow(2) + 2 * x * y + y.pow(2) + 1
}
assertEquals(10.0, f(x to 1.0, y to 2.0))
assertEquals(6.0, f.derivative(x)(x to 1.0, y to 2.0))
assertEquals(2.0, f.derivative(x, x)(x to 1.234, y to -2.0))
assertEquals(2.0, f.derivative(x, y)(x to 1.0, y to 2.0))
}
}

14
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt
View File

@ -21,14 +21,14 @@ public typealias HyperSquareBin<V> = DomainBin<Double, HyperSquareDomain, V>
/**
* Multivariate histogram space for hyper-square real-field bins.
* @param bufferFactory is an optional parameter used to optimize buffer production.
* @param valueBufferFactory is an optional parameter used to optimize buffer production.
*/
public class UniformHistogramGroupND<V : Any, A : Field<V>>(
override val valueAlgebraND: FieldOpsND<V, A>,
private val lower: Buffer<Double>,
private val upper: Buffer<Double>,
private val binNums: IntArray = IntArray(lower.size) { 20 },
private val bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
private val valueBufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
) : HistogramGroupND<Double, HyperSquareDomain, V> {
init {
@ -94,7 +94,7 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
}
}
hBuilder.apply(builder)
val values: BufferND<V> = ndCounter.mapToBuffer(bufferFactory) { it.value }
val values: BufferND<V> = ndCounter.mapToBuffer(valueBufferFactory) { it.value }
return HistogramND(this, values)
}
@ -114,12 +114,12 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
valueAlgebraND: FieldOpsND<V, A>,
vararg ranges: ClosedFloatingPointRange<Double>,
bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
bufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
valueAlgebraND,
ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer(),
bufferFactory = bufferFactory
valueBufferFactory = bufferFactory
)
public fun Histogram.Companion.uniformDoubleNDFromRanges(
@ -140,7 +140,7 @@ public fun Histogram.Companion.uniformDoubleNDFromRanges(
public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
valueAlgebraND: FieldOpsND<V, A>,
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
bufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
valueAlgebraND,
ListBuffer(
@ -154,7 +154,7 @@ public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
.map(ClosedFloatingPointRange<Double>::endInclusive)
),
ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
bufferFactory = bufferFactory
valueBufferFactory = bufferFactory
)
public fun Histogram.Companion.uniformDoubleNDFromRanges(

7
kmath-multik/src/main/kotlin/space/kscience/kmath/multik/MultikDoubleAlgebra.kt
View File

@ -6,6 +6,7 @@
package space.kscience.kmath.multik
import org.jetbrains.kotlinx.multik.ndarray.data.DataType
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExponentialOperations
@ -22,10 +23,13 @@ public object MultikDoubleAlgebra : MultikDivisionTensorAlgebra<Double, DoubleFi
override fun tan(arg: StructureND<Double>): MultikTensor<Double> = sin(arg) / cos(arg)
@PerformancePitfall
override fun asin(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asin(it) }
@PerformancePitfall
override fun acos(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acos(it) }
@PerformancePitfall
override fun atan(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atan(it) }
override fun exp(arg: StructureND<Double>): MultikTensor<Double> = multikMath.mathEx.exp(arg.asMultik().array).wrap()
@ -42,10 +46,13 @@ public object MultikDoubleAlgebra : MultikDivisionTensorAlgebra<Double, DoubleFi
return (expPlus - expMinus) / (expPlus + expMinus)
}
@PerformancePitfall
override fun asinh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { asinh(it) }
@PerformancePitfall
override fun acosh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { acosh(it) }
@PerformancePitfall
override fun atanh(arg: StructureND<Double>): MultikTensor<Double> = arg.map { atanh(it) }
}

3
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/internal/InternalUtils.kt
View File

@ -48,7 +48,8 @@ internal object InternalUtils {
cache.copyInto(
logFactorials,
BEGIN_LOG_FACTORIALS,
BEGIN_LOG_FACTORIALS, endCopy
BEGIN_LOG_FACTORIALS,
endCopy,
)
} else
// All values to be computed

2
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt
View File

@ -35,7 +35,7 @@ public fun interface Sampler<out T : Any> {
public fun <T : Any> Sampler<T>.sampleBuffer(
generator: RandomGenerator,
size: Int,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
bufferFactory: BufferFactory<T> = BufferFactory.boxing(),
): Chain<Buffer<T>> {
require(size > 1)
//creating temporary storage once