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0.1.4 #137

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altavir merged 30 commits from dev into master 2020-09-14 22:49:30 +03:00
Conversation 0 Commits 30 Files Changed 105 +787 -774
82 changed files with 787 additions and 774 deletions
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5
CHANGELOG.md
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@ -20,11 +20,14 @@
- Norm support for `Complex`
### Changed
- `readAsMemory` now has `throws IOException` in JVM signature.
- Several functions taking functional types were made `inline`.
- Several functions taking functional types now have `callsInPlace` contracts.
- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor optimizations
- `power(T, Int)` extension function has preconditions and supports `Field<T>`
- Memory objects have more preconditions (overflow checking)
- `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)
- Gradle version: 6.3 -> 6.5.1
- Gradle version: 6.3 -> 6.6
- Moved probability distributions to commons-rng and to `kmath-prob`
### Fixed

1
examples/build.gradle.kts
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@ -60,5 +60,6 @@ benchmark {
tasks.withType<KotlinCompile> {
kotlinOptions {
jvmTarget = Scientifik.JVM_TARGET.toString()
freeCompilerArgs = freeCompilerArgs + "-Xopt-in=kotlin.RequiresOptIn"
}
}

24
examples/src/benchmarks/kotlin/scientifik/kmath/structures/NDFieldBenchmark.kt
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@ -4,46 +4,38 @@ import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
@State(Scope.Benchmark)
class NDFieldBenchmark {
@Benchmark
fun autoFieldAdd() {
bufferedField.run {
bufferedField {
var res: NDBuffer<Double> = one
repeat(n) {
res += one
}
repeat(n) { res += one }
}
}
@Benchmark
fun autoElementAdd() {
var res = genericField.one
repeat(n) {
res += 1.0
}
repeat(n) { res += 1.0 }
}
@Benchmark
fun specializedFieldAdd() {
specializedField.run {
specializedField {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
}
repeat(n) { res += 1.0 }
}
}
@Benchmark
fun boxingFieldAdd() {
genericField.run {
genericField {
var res: NDBuffer<Double> = one
repeat(n) {
res += one
}
repeat(n) { res += one }
}
}

15
examples/src/benchmarks/kotlin/scientifik/kmath/structures/ViktorBenchmark.kt
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@ -5,23 +5,22 @@ import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
import scientifik.kmath.viktor.ViktorNDField
@State(Scope.Benchmark)
class ViktorBenchmark {
final val dim = 1000
final val n = 100
// automatically build context most suited for given type.
final val autoField = NDField.auto(RealField, dim, dim)
final val realField = NDField.real(dim, dim)
final val viktorField = ViktorNDField(intArrayOf(dim, dim))
final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
final val realField: RealNDField = NDField.real(dim, dim)
final val viktorField: ViktorNDField = ViktorNDField(intArrayOf(dim, dim))
@Benchmark
fun automaticFieldAddition() {
autoField.run {
autoField {
var res = one
repeat(n) { res += one }
}
@ -29,7 +28,7 @@ class ViktorBenchmark {
@Benchmark
fun viktorFieldAddition() {
viktorField.run {
viktorField {
var res = one
repeat(n) { res += one }
}
@ -44,7 +43,7 @@ class ViktorBenchmark {
@Benchmark
fun realdFieldLog() {
realField.run {
realField {
val fortyTwo = produce { 42.0 }
var res = one
repeat(n) { res = ln(fortyTwo) }

5
examples/src/benchmarks/kotlin/scientifik/kmath/utils/utils.kt
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@ -1,8 +1,13 @@
package scientifik.kmath.utils
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.system.measureTimeMillis
@OptIn(ExperimentalContracts::class)
internal inline fun measureAndPrint(title: String, block: () -> Unit) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
val time = measureTimeMillis(block)
println("$title completed in $time millis")
}

24
examples/src/main/kotlin/scientifik/kmath/linear/LinearAlgebraBenchmark.kt
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@ -5,6 +5,7 @@ import scientifik.kmath.commons.linear.CMMatrixContext
import scientifik.kmath.commons.linear.inverse
import scientifik.kmath.commons.linear.toCM
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.Matrix
import kotlin.contracts.ExperimentalContracts
import kotlin.random.Random
@ -21,29 +22,18 @@ fun main() {
val n = 5000 // iterations
MatrixContext.real.run {
repeat(50) {
val res = inverse(matrix)
}
val inverseTime = measureTimeMillis {
repeat(n) {
val res = inverse(matrix)
}
}
MatrixContext.real {
repeat(50) { val res = inverse(matrix) }
val inverseTime = measureTimeMillis { repeat(n) { val res = inverse(matrix) } }
println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
}
//commons-math
val commonsTime = measureTimeMillis {
CMMatrixContext.run {
CMMatrixContext {
val cm = matrix.toCM() //avoid overhead on conversion
repeat(n) {
val res = inverse(cm)
}
repeat(n) { val res = inverse(cm) }
}
}
@ -53,7 +43,7 @@ fun main() {
//koma-ejml
val komaTime = measureTimeMillis {
KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
(KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
val km = matrix.toKoma() //avoid overhead on conversion
repeat(n) {
val res = inverse(km)

6
examples/src/main/kotlin/scientifik/kmath/linear/MultiplicationBenchmark.kt
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@ -4,6 +4,7 @@ import koma.matrix.ejml.EJMLMatrixFactory
import scientifik.kmath.commons.linear.CMMatrixContext
import scientifik.kmath.commons.linear.toCM
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.Matrix
import kotlin.random.Random
import kotlin.system.measureTimeMillis
@ -18,7 +19,7 @@ fun main() {
// //warmup
// matrix1 dot matrix2
CMMatrixContext.run {
CMMatrixContext {
val cmMatrix1 = matrix1.toCM()
val cmMatrix2 = matrix2.toCM()
@ -29,8 +30,7 @@ fun main() {
println("CM implementation time: $cmTime")
}
KomaMatrixContext(EJMLMatrixFactory(), RealField).run {
(KomaMatrixContext(EJMLMatrixFactory(), RealField)) {
val komaMatrix1 = matrix1.toKoma()
val komaMatrix2 = matrix2.toKoma()

4
examples/src/main/kotlin/scientifik/kmath/operations/ComplexDemo.kt
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@ -9,13 +9,11 @@ fun main() {
Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble())
}
val compute = NDField.complex(8).run {
val compute = (NDField.complex(8)) {
val a = produce { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)
(a pow b) + c
}
}

15
examples/src/main/kotlin/scientifik/kmath/structures/ComplexND.kt
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@ -13,9 +13,8 @@ fun main() {
val realField = NDField.real(dim, dim)
val complexField = NDField.complex(dim, dim)
val realTime = measureTimeMillis {
realField.run {
realField {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
@ -26,18 +25,15 @@ fun main() {
println("Real addition completed in $realTime millis")
val complexTime = measureTimeMillis {
complexField.run {
complexField {
var res: NDBuffer<Complex> = one
repeat(n) {
res += 1.0
}
repeat(n) { res += 1.0 }
}
}
println("Complex addition completed in $complexTime millis")
}
fun complexExample() {
//Create a context for 2-d structure with complex values
ComplexField {
@ -46,10 +42,7 @@ fun complexExample() {
val x = one * 2.5
operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)
//a structure generator specific to this context
val matrix = produce { (k, l) ->
k + l * i
}
val matrix = produce { (k, l) -> k + l * i }
//Perform sum
val sum = matrix + x + 1.0

28
examples/src/main/kotlin/scientifik/kmath/structures/NDField.kt
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@ -2,14 +2,19 @@ package scientifik.kmath.structures
import kotlinx.coroutines.GlobalScope
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.system.measureTimeMillis
@OptIn(ExperimentalContracts::class)
internal inline fun measureAndPrint(title: String, block: () -> Unit) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
val time = measureTimeMillis(block)
println("$title completed in $time millis")
}
fun main() {
val dim = 1000
val n = 1000
@ -22,27 +27,21 @@ fun main() {
val genericField = NDField.boxing(RealField, dim, dim)
measureAndPrint("Automatic field addition") {
autoField.run {
autoField {
var res: NDBuffer<Double> = one
repeat(n) {
res += number(1.0)
}
repeat(n) { res += number(1.0) }
}
}
measureAndPrint("Element addition") {
var res = genericField.one
repeat(n) {
res += 1.0
}
repeat(n) { res += 1.0 }
}
measureAndPrint("Specialized addition") {
specializedField.run {
specializedField {
var res: NDBuffer<Double> = one
repeat(n) {
res += 1.0
}
repeat(n) { res += 1.0 }
}
}
@ -60,12 +59,11 @@ fun main() {
measureAndPrint("Generic addition") {
//genericField.run(action)
genericField.run {
genericField {
var res: NDBuffer<Double> = one
repeat(n) {
res += one // con't avoid using `one` due to resolution ambiguity
res += one // couldn't avoid using `one` due to resolution ambiguity }
}
}
}
}

6
examples/src/main/kotlin/scientifik/kmath/structures/typeSafeDimensions.kt
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@ -16,6 +16,7 @@ fun DMatrixContext<Double, RealField>.simple() {
object D5 : Dimension {
@OptIn(ExperimentalUnsignedTypes::class)
override val dim: UInt = 5u
}
@ -23,13 +24,10 @@ fun DMatrixContext<Double, RealField>.custom() {
val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() }
val m2 = produce<D5, D2> { i, j -> (i - j).toDouble() }
val m3 = produce<D2, D2> { i, j -> (i - j).toDouble() }
(m1 dot m2) + m3
}
fun main() {
DMatrixContext.real.run {
fun main(): Unit = with(DMatrixContext.real) {
simple()
custom()
}
}

66
kmath-ast/src/commonMain/kotlin/scientifik/kmath/ast/MstExpression.kt
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@ -2,6 +2,9 @@ package scientifik.kmath.ast
import scientifik.kmath.expressions.*
import scientifik.kmath.operations.*
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* The expression evaluates MST on-flight. Should be much faster than functional expression, but slower than
@ -24,7 +27,7 @@ class MstExpression<T>(val algebra: Algebra<T>, val mst: MST) : Expression<T> {
error("Numeric nodes are not supported by $this")
}
override fun invoke(arguments: Map<String, T>): T = InnerAlgebra(arguments).evaluate(mst)
override operator fun invoke(arguments: Map<String, T>): T = InnerAlgebra(arguments).evaluate(mst)
}
/**
@ -38,51 +41,72 @@ inline fun <reified T : Any, A : Algebra<T>, E : Algebra<MST>> A.mst(
/**
* Builds [MstExpression] over [Space].
*/
inline fun <reified T : Any> Space<T>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> =
MstExpression(this, MstSpace.block())
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any> Space<T>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstSpace.block())
}
/**
* Builds [MstExpression] over [Ring].
*/
inline fun <reified T : Any> Ring<T>.mstInRing(block: MstRing.() -> MST): MstExpression<T> =
MstExpression(this, MstRing.block())
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any> Ring<T>.mstInRing(block: MstRing.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstRing.block())
}
/**
* Builds [MstExpression] over [Field].
*/
inline fun <reified T : Any> Field<T>.mstInField(block: MstField.() -> MST): MstExpression<T> =
MstExpression(this, MstField.block())
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any> Field<T>.mstInField(block: MstField.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstField.block())
}
/**
* Builds [MstExpression] over [ExtendedField].
*/
inline fun <reified T : Any> Field<T>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> =
MstExpression(this, MstExtendedField.block())
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any> Field<T>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return MstExpression(this, MstExtendedField.block())
}
/**
* Builds [MstExpression] over [FunctionalExpressionSpace].
*/
inline fun <reified T : Any, A : Space<T>> FunctionalExpressionSpace<T, A>.mstInSpace(
block: MstSpace.() -> MST
): MstExpression<T> = algebra.mstInSpace(block)
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any, A : Space<T>> FunctionalExpressionSpace<T, A>.mstInSpace(block: MstSpace.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInSpace(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionRing].
*/
inline fun <reified T : Any, A : Ring<T>> FunctionalExpressionRing<T, A>.mstInRing(
block: MstRing.() -> MST
): MstExpression<T> = algebra.mstInRing(block)
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any, A : Ring<T>> FunctionalExpressionRing<T, A>.mstInRing(block: MstRing.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInRing(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionField].
*/
inline fun <reified T : Any, A : Field<T>> FunctionalExpressionField<T, A>.mstInField(
block: MstField.() -> MST
): MstExpression<T> = algebra.mstInField(block)
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any, A : Field<T>> FunctionalExpressionField<T, A>.mstInField(block: MstField.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInField(block)
}
/**
* Builds [MstExpression] over [FunctionalExpressionExtendedField].
*/
inline fun <reified T : Any, A : ExtendedField<T>> FunctionalExpressionExtendedField<T, A>.mstInExtendedField(
block: MstExtendedField.() -> MST
): MstExpression<T> = algebra.mstInExtendedField(block)
@OptIn(ExperimentalContracts::class)
inline fun <reified T : Any, A : ExtendedField<T>> FunctionalExpressionExtendedField<T, A>.mstInExtendedField(block: MstExtendedField.() -> MST): MstExpression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return algebra.mstInExtendedField(block)
}

32
kmath-ast/src/jvmMain/kotlin/scientifik/kmath/asm/internal/codegenUtils.kt
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@ -7,6 +7,9 @@ import scientifik.kmath.ast.MST
import scientifik.kmath.expressions.Expression
import scientifik.kmath.operations.Algebra
import java.lang.reflect.Method
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.reflect.KClass
private val methodNameAdapters: Map<Pair<String, Int>, String> by lazy {
@ -26,8 +29,11 @@ internal val KClass<*>.asm: Type
/**
* Returns singleton array with this value if the [predicate] is true, returns empty array otherwise.
*/
internal inline fun <reified T> T.wrapToArrayIf(predicate: (T) -> Boolean): Array<T> =
if (predicate(this)) arrayOf(this) else emptyArray()
@OptIn(ExperimentalContracts::class)
internal inline fun <reified T> T.wrapToArrayIf(predicate: (T) -> Boolean): Array<T> {
contract { callsInPlace(predicate, InvocationKind.EXACTLY_ONCE) }
return if (predicate(this)) arrayOf(this) else emptyArray()
}
/**
* Creates an [InstructionAdapter] from this [MethodVisitor].
@ -37,8 +43,11 @@ private fun MethodVisitor.instructionAdapter(): InstructionAdapter = Instruction
/**
* Creates an [InstructionAdapter] from this [MethodVisitor] and applies [block] to it.
*/
internal fun MethodVisitor.instructionAdapter(block: InstructionAdapter.() -> Unit): InstructionAdapter =
instructionAdapter().apply(block)
@OptIn(ExperimentalContracts::class)
internal inline fun MethodVisitor.instructionAdapter(block: InstructionAdapter.() -> Unit): InstructionAdapter {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return instructionAdapter().apply(block)
}
/**
* Constructs a [Label], then applies it to this visitor.
@ -63,10 +72,14 @@ internal tailrec fun buildName(mst: MST, collision: Int = 0): String {
return buildName(mst, collision + 1)
}
@OptIn(ExperimentalContracts::class)
@Suppress("FunctionName")
internal inline fun ClassWriter(flags: Int, block: ClassWriter.() -> Unit): ClassWriter =
ClassWriter(flags).apply(block)
internal inline fun ClassWriter(flags: Int, block: ClassWriter.() -> Unit): ClassWriter {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return ClassWriter(flags).apply(block)
}
@OptIn(ExperimentalContracts::class)
internal inline fun ClassWriter.visitField(
access: Int,
name: String,
@ -74,7 +87,10 @@ internal inline fun ClassWriter.visitField(
signature: String?,
value: Any?,
block: FieldVisitor.() -> Unit
): FieldVisitor = visitField(access, name, descriptor, signature, value).apply(block)
): FieldVisitor {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return visitField(access, name, descriptor, signature, value).apply(block)
}
private fun <T> AsmBuilder<T>.findSpecific(context: Algebra<T>, name: String, parameterTypes: Array<MstType>): Method? =
context.javaClass.methods.find { method ->
@ -151,6 +167,7 @@ private fun <T> AsmBuilder<T>.tryInvokeSpecific(
/**
* Builds specialized algebra call with option to fallback to generic algebra operation accepting String.
*/
@OptIn(ExperimentalContracts::class)
internal inline fun <T> AsmBuilder<T>.buildAlgebraOperationCall(
context: Algebra<T>,
name: String,
@ -158,6 +175,7 @@ internal inline fun <T> AsmBuilder<T>.buildAlgebraOperationCall(
parameterTypes: Array<MstType>,
parameters: AsmBuilder<T>.() -> Unit
) {
contract { callsInPlace(parameters, InvocationKind.EXACTLY_ONCE) }
val arity = parameterTypes.size
loadAlgebra()
if (!buildExpectationStack(context, name, parameterTypes)) loadStringConstant(name)

48
kmath-commons/src/main/kotlin/scientifik/kmath/commons/expressions/DiffExpression.kt
View File

@ -5,6 +5,7 @@ import scientifik.kmath.expressions.Expression
import scientifik.kmath.expressions.ExpressionAlgebra
import scientifik.kmath.operations.ExtendedField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.invoke
import kotlin.properties.ReadOnlyProperty
import kotlin.reflect.KProperty
@ -15,7 +16,6 @@ class DerivativeStructureField(
val order: Int,
val parameters: Map<String, Double>
) : ExtendedField<DerivativeStructure> {
override val zero: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size) }
override val one: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size, 1.0) }
@ -23,17 +23,15 @@ class DerivativeStructureField(
DerivativeStructure(parameters.size, order, parameters.keys.indexOf(key), value)
}
val variable = object : ReadOnlyProperty<Any?, DerivativeStructure> {
override fun getValue(thisRef: Any?, property: KProperty<*>): DerivativeStructure {
return variables[property.name] ?: error("A variable with name ${property.name} does not exist")
}
val variable: ReadOnlyProperty<Any?, DerivativeStructure> = object : ReadOnlyProperty<Any?, DerivativeStructure> {
override fun getValue(thisRef: Any?, property: KProperty<*>): DerivativeStructure =
variables[property.name] ?: error("A variable with name ${property.name} does not exist")
}
fun variable(name: String, default: DerivativeStructure? = null): DerivativeStructure =
variables[name] ?: default ?: error("A variable with name $name does not exist")
fun Number.const() = DerivativeStructure(order, parameters.size, toDouble())
fun Number.const(): DerivativeStructure = DerivativeStructure(order, parameters.size, toDouble())
fun DerivativeStructure.deriv(parName: String, order: Int = 1): Double {
return deriv(mapOf(parName to order))
@ -83,16 +81,15 @@ class DerivativeStructureField(
override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
override operator fun Number.plus(b: DerivativeStructure) = b + this
override operator fun Number.minus(b: DerivativeStructure) = b - this
override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
}
/**
* A constructs that creates a derivative structure with required order on-demand
*/
class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStructure) : Expression<Double> {
override fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
override operator fun invoke(arguments: Map<String, Double>): Double = DerivativeStructureField(
0,
arguments
).run(function).value
@ -101,45 +98,40 @@ class DiffExpression(val function: DerivativeStructureField.() -> DerivativeStru
* Get the derivative expression with given orders
* TODO make result [DiffExpression]
*/
fun derivative(orders: Map<String, Int>): Expression<Double> {
return object : Expression<Double> {
override fun invoke(arguments: Map<String, Double>): Double =
DerivativeStructureField(orders.values.max() ?: 0, arguments)
.run {
function().deriv(orders)
}
}
fun derivative(orders: Map<String, Int>): Expression<Double> = object : Expression<Double> {
override operator fun invoke(arguments: Map<String, Double>): Double =
(DerivativeStructureField(orders.values.max() ?: 0, arguments)) { function().deriv(orders) }
}
//TODO add gradient and maybe other vector operators
}
fun DiffExpression.derivative(vararg orders: Pair<String, Int>) = derivative(mapOf(*orders))
fun DiffExpression.derivative(name: String) = derivative(name to 1)
fun DiffExpression.derivative(vararg orders: Pair<String, Int>): Expression<Double> = derivative(mapOf(*orders))
fun DiffExpression.derivative(name: String): Expression<Double> = derivative(name to 1)
/**
* A context for [DiffExpression] (not to be confused with [DerivativeStructure])
*/
object DiffExpressionAlgebra : ExpressionAlgebra<Double, DiffExpression>, Field<DiffExpression> {
override fun variable(name: String, default: Double?) =
override fun variable(name: String, default: Double?): DiffExpression =
DiffExpression { variable(name, default?.const()) }
override fun const(value: Double): DiffExpression =
DiffExpression { value.const() }
override fun add(a: DiffExpression, b: DiffExpression) =
override fun add(a: DiffExpression, b: DiffExpression): DiffExpression =
DiffExpression { a.function(this) + b.function(this) }
override val zero = DiffExpression { 0.0.const() }
override val zero: DiffExpression = DiffExpression { 0.0.const() }
override fun multiply(a: DiffExpression, k: Number) =
override fun multiply(a: DiffExpression, k: Number): DiffExpression =
DiffExpression { a.function(this) * k }
override val one = DiffExpression { 1.0.const() }
override val one: DiffExpression = DiffExpression { 1.0.const() }
override fun multiply(a: DiffExpression, b: DiffExpression) =
override fun multiply(a: DiffExpression, b: DiffExpression): DiffExpression =
DiffExpression { a.function(this) * b.function(this) }
override fun divide(a: DiffExpression, b: DiffExpression) =
override fun divide(a: DiffExpression, b: DiffExpression): DiffExpression =
DiffExpression { a.function(this) / b.function(this) }
}

33
kmath-commons/src/main/kotlin/scientifik/kmath/commons/linear/CMMatrix.kt
View File

@ -1,8 +1,6 @@
package scientifik.kmath.commons.linear
import org.apache.commons.math3.linear.*
import org.apache.commons.math3.linear.RealMatrix
import org.apache.commons.math3.linear.RealVector
import scientifik.kmath.linear.*
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.NDStructure
@ -14,12 +12,12 @@ class CMMatrix(val origin: RealMatrix, features: Set<MatrixFeature>? = null) :
override val features: Set<MatrixFeature> = features ?: sequence<MatrixFeature> {
if (origin is DiagonalMatrix) yield(DiagonalFeature)
}.toSet()
}.toHashSet()
override fun suggestFeature(vararg features: MatrixFeature) =
override fun suggestFeature(vararg features: MatrixFeature): CMMatrix =
CMMatrix(origin, this.features + features)
override fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
override fun equals(other: Any?): Boolean {
return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
@ -40,24 +38,22 @@ fun Matrix<Double>.toCM(): CMMatrix = if (this is CMMatrix) {
CMMatrix(Array2DRowRealMatrix(array))
}
fun RealMatrix.asMatrix() = CMMatrix(this)
fun RealMatrix.asMatrix(): CMMatrix = CMMatrix(this)
class CMVector(val origin: RealVector) : Point<Double> {
override val size: Int get() = origin.dimension
override fun get(index: Int): Double = origin.getEntry(index)
override operator fun get(index: Int): Double = origin.getEntry(index)
override fun iterator(): Iterator<Double> = origin.toArray().iterator()
override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
}
fun Point<Double>.toCM(): CMVector = if (this is CMVector) {
this
} else {
fun Point<Double>.toCM(): CMVector = if (this is CMVector) this else {
val array = DoubleArray(size) { this[it] }
CMVector(ArrayRealVector(array))
}
fun RealVector.toPoint() = CMVector(this)
fun RealVector.toPoint(): CMVector = CMVector(this)
object CMMatrixContext : MatrixContext<Double> {
override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): CMMatrix {
@ -65,30 +61,31 @@ object CMMatrixContext : MatrixContext<Double> {
return CMMatrix(Array2DRowRealMatrix(array))
}
override fun Matrix<Double>.dot(other: Matrix<Double>) =
override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
CMMatrix(this.toCM().origin.multiply(other.toCM().origin))
override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
CMVector(this.toCM().origin.preMultiply(vector.toCM().origin))
override fun Matrix<Double>.unaryMinus(): CMMatrix =
override operator fun Matrix<Double>.unaryMinus(): CMMatrix =
produce(rowNum, colNum) { i, j -> -get(i, j) }
override fun add(a: Matrix<Double>, b: Matrix<Double>) =
override fun add(a: Matrix<Double>, b: Matrix<Double>): CMMatrix =
CMMatrix(a.toCM().origin.multiply(b.toCM().origin))
override fun Matrix<Double>.minus(b: Matrix<Double>) =
override operator fun Matrix<Double>.minus(b: Matrix<Double>): CMMatrix =
CMMatrix(this.toCM().origin.subtract(b.toCM().origin))
override fun multiply(a: Matrix<Double>, k: Number) =
override fun multiply(a: Matrix<Double>, k: Number): CMMatrix =
CMMatrix(a.toCM().origin.scalarMultiply(k.toDouble()))
override fun Matrix<Double>.times(value: Double): Matrix<Double> =
override operator fun Matrix<Double>.times(value: Double): Matrix<Double> =
produce(rowNum, colNum) { i, j -> get(i, j) * value }
}
operator fun CMMatrix.plus(other: CMMatrix): CMMatrix =
CMMatrix(this.origin.add(other.origin))
operator fun CMMatrix.minus(other: CMMatrix): CMMatrix =
CMMatrix(this.origin.subtract(other.origin))

7
kmath-commons/src/main/kotlin/scientifik/kmath/commons/random/CMRandomGeneratorWrapper.kt
View File

@ -4,10 +4,9 @@ import scientifik.kmath.prob.RandomGenerator
class CMRandomGeneratorWrapper(val factory: (IntArray) -> RandomGenerator) :
org.apache.commons.math3.random.RandomGenerator {
private var generator = factory(intArrayOf())
private var generator: RandomGenerator = factory(intArrayOf())
override fun nextBoolean(): Boolean = generator.nextBoolean()
override fun nextFloat(): Float = generator.nextDouble().toFloat()
override fun setSeed(seed: Int) {
@ -27,12 +26,8 @@ class CMRandomGeneratorWrapper(val factory: (IntArray) -> RandomGenerator) :
}
override fun nextInt(): Int = generator.nextInt()
override fun nextInt(n: Int): Int = generator.nextInt(n)
override fun nextGaussian(): Double = TODO()
override fun nextDouble(): Double = generator.nextDouble()
override fun nextLong(): Long = generator.nextLong()
}

10
kmath-commons/src/test/kotlin/scientifik/kmath/commons/expressions/AutoDiffTest.kt
View File

@ -1,11 +1,17 @@
package scientifik.kmath.commons.expressions
import scientifik.kmath.expressions.invoke
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.test.Test
import kotlin.test.assertEquals
inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R) =
DerivativeStructureField(order, mapOf(*parameters)).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <R> diff(order: Int, vararg parameters: Pair<String, Double>, block: DerivativeStructureField.() -> R): R {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return DerivativeStructureField(order, mapOf(*parameters)).run(block)
}
class AutoDiffTest {
@Test

32
kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/Builders.kt
View File

@ -4,28 +4,42 @@ import scientifik.kmath.operations.ExtendedField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.Space
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* Creates a functional expression with this [Space].
*/
fun <T> Space<T>.spaceExpression(block: FunctionalExpressionSpace<T, Space<T>>.() -> Expression<T>): Expression<T> =
FunctionalExpressionSpace(this).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <T> Space<T>.spaceExpression(block: FunctionalExpressionSpace<T, Space<T>>.() -> Expression<T>): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionSpace(this).block()
}
/**
* Creates a functional expression with this [Ring].
*/
fun <T> Ring<T>.ringExpression(block: FunctionalExpressionRing<T, Ring<T>>.() -> Expression<T>): Expression<T> =
FunctionalExpressionRing(this).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <T> Ring<T>.ringExpression(block: FunctionalExpressionRing<T, Ring<T>>.() -> Expression<T>): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionRing(this).block()
}
/**
* Creates a functional expression with this [Field].
*/
fun <T> Field<T>.fieldExpression(block: FunctionalExpressionField<T, Field<T>>.() -> Expression<T>): Expression<T> =
FunctionalExpressionField(this).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <T> Field<T>.fieldExpression(block: FunctionalExpressionField<T, Field<T>>.() -> Expression<T>): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionField(this).block()
}
/**
* Creates a functional expression with this [ExtendedField].
*/
fun <T> ExtendedField<T>.fieldExpression(
block: FunctionalExpressionExtendedField<T, ExtendedField<T>>.() -> Expression<T>
): Expression<T> = FunctionalExpressionExtendedField(this).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <T> ExtendedField<T>.extendedFieldExpression(block: FunctionalExpressionExtendedField<T, ExtendedField<T>>.() -> Expression<T>): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionExtendedField(this).block()
}

2
kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/Expression.kt
View File

@ -22,7 +22,7 @@ interface Expression<T> {
*/
fun <T> Algebra<T>.expression(block: Algebra<T>.(arguments: Map<String, T>) -> T): Expression<T> =
object : Expression<T> {
override fun invoke(arguments: Map<String, T>): T = block(arguments)
override operator fun invoke(arguments: Map<String, T>): T = block(arguments)
}
/**

10
kmath-core/src/commonMain/kotlin/scientifik/kmath/expressions/FunctionalExpressionAlgebra.kt
View File

@ -4,7 +4,7 @@ import scientifik.kmath.operations.*
internal class FunctionalUnaryOperation<T>(val context: Algebra<T>, val name: String, private val expr: Expression<T>) :
Expression<T> {
override fun invoke(arguments: Map<String, T>): T = context.unaryOperation(name, expr.invoke(arguments))
override operator fun invoke(arguments: Map<String, T>): T = context.unaryOperation(name, expr.invoke(arguments))
}
internal class FunctionalBinaryOperation<T>(
@ -13,17 +13,17 @@ internal class FunctionalBinaryOperation<T>(
val first: Expression<T>,
val second: Expression<T>
) : Expression<T> {
override fun invoke(arguments: Map<String, T>): T =
override operator fun invoke(arguments: Map<String, T>): T =
context.binaryOperation(name, first.invoke(arguments), second.invoke(arguments))
}
internal class FunctionalVariableExpression<T>(val name: String, val default: T? = null) : Expression<T> {
override fun invoke(arguments: Map<String, T>): T =
override operator fun invoke(arguments: Map<String, T>): T =
arguments[name] ?: default ?: error("Parameter not found: $name")
}
internal class FunctionalConstantExpression<T>(val value: T) : Expression<T> {
override fun invoke(arguments: Map<String, T>): T = value
override operator fun invoke(arguments: Map<String, T>): T = value
}
internal class FunctionalConstProductExpression<T>(
@ -31,7 +31,7 @@ internal class FunctionalConstProductExpression<T>(
private val expr: Expression<T>,
val const: Number
) : Expression<T> {
override fun invoke(arguments: Map<String, T>): T = context.multiply(expr.invoke(arguments), const)
override operator fun invoke(arguments: Map<String, T>): T = context.multiply(expr.invoke(arguments), const)
}
/**

12
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/BufferMatrix.kt
View File

@ -53,16 +53,12 @@ class BufferMatrix<T : Any>(
override fun suggestFeature(vararg features: MatrixFeature): BufferMatrix<T> =
BufferMatrix(rowNum, colNum, buffer, this.features + features)
override fun get(index: IntArray): T = get(index[0], index[1])
override operator fun get(index: IntArray): T = get(index[0], index[1])
override fun get(i: Int, j: Int): T = buffer[i * colNum + j]
override operator fun get(i: Int, j: Int): T = buffer[i * colNum + j]
override fun elements(): Sequence<Pair<IntArray, T>> = sequence {
for (i in 0 until rowNum) {
for (j in 0 until colNum) {
yield(intArrayOf(i, j) to get(i, j))
}
}
for (i in 0 until rowNum) for (j in 0 until colNum) yield(intArrayOf(i, j) to get(i, j))
}
override fun equals(other: Any?): Boolean {
@ -95,7 +91,7 @@ class BufferMatrix<T : Any>(
* Optimized dot product for real matrices
*/
infix fun BufferMatrix<Double>.dot(other: BufferMatrix<Double>): BufferMatrix<Double> {
if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val array = DoubleArray(this.rowNum * other.colNum)

11
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/FeaturedMatrix.kt
View File

@ -4,6 +4,8 @@ import scientifik.kmath.operations.Ring
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.Structure2D
import scientifik.kmath.structures.asBuffer
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.math.sqrt
/**
@ -26,15 +28,18 @@ interface FeaturedMatrix<T : Any> : Matrix<T> {
companion object
}
fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> =
MatrixContext.real.produce(rows, columns, initializer)
@OptIn(ExperimentalContracts::class)
inline fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> {
contract { callsInPlace(initializer) }
return MatrixContext.real.produce(rows, columns, initializer)
}
/**
* Build a square matrix from given elements.
*/
fun <T : Any> Structure2D.Companion.square(vararg elements: T): FeaturedMatrix<T> {
val size: Int = sqrt(elements.size.toDouble()).toInt()
if (size * size != elements.size) error("The number of elements ${elements.size} is not a full square")
require(size * size == elements.size) { "The number of elements ${elements.size} is not a full square" }
val buffer = elements.asBuffer()
return BufferMatrix(size, size, buffer)
}

61
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUPDecomposition.kt
View File

@ -3,6 +3,7 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.BufferAccessor2D
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.Structure2D
@ -60,15 +61,13 @@ class LUPDecomposition<T : Any>(
* @return determinant of the matrix
*/
override val determinant: T by lazy {
with(elementContext) {
(0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] }
}
elementContext { (0 until lu.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] } }
}
}
fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.abs(value: T): T =
if (value > elementContext.zero) value else with(elementContext) { -value }
if (value > elementContext.zero) value else elementContext { -value }
/**
@ -88,43 +87,34 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
//TODO just waits for KEEP-176
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
elementContext.run {
elementContext {
val lu = create(matrix)
// Initialize permutation array and parity
for (row in 0 until m) {
pivot[row] = row
}
for (row in 0 until m) pivot[row] = row
var even = true
// Initialize permutation array and parity
for (row in 0 until m) {
pivot[row] = row
}
for (row in 0 until m) pivot[row] = row
// Loop over columns
for (col in 0 until m) {
// upper
for (row in 0 until col) {
val luRow = lu.row(row)
var sum = luRow[col]
for (i in 0 until row) {
sum -= luRow[i] * lu[i, col]
}
for (i in 0 until row) sum -= luRow[i] * lu[i, col]
luRow[col] = sum
}
// lower
var max = col // permutation row
var largest = -one
for (row in col until m) {
val luRow = lu.row(row)
var sum = luRow[col]
for (i in 0 until col) {
sum -= luRow[i] * lu[i, col]
}
for (i in 0 until col) sum -= luRow[i] * lu[i, col]
luRow[col] = sum
// maintain best permutation choice
@ -135,19 +125,19 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
}
// Singularity check
if (checkSingular(this@lup.abs(lu[max, col]))) {
error("The matrix is singular")
}
check(!checkSingular(this@lup.abs(lu[max, col]))) { "The matrix is singular" }
// Pivot if necessary
if (max != col) {
val luMax = lu.row(max)
val luCol = lu.row(col)
for (i in 0 until m) {
val tmp = luMax[i]
luMax[i] = luCol[i]
luCol[i] = tmp
}
val temp = pivot[max]
pivot[max] = pivot[col]
pivot[col] = temp
@ -156,9 +146,7 @@ fun <T : Comparable<T>, F : Field<T>> GenericMatrixContext<T, F>.lup(
// Divide the lower elements by the "winning" diagonal elt.
val luDiag = lu[col, col]
for (row in col + 1 until m) {
lu[row, col] /= luDiag
}
for (row in col + 1 until m) lu[row, col] /= luDiag
}
return LUPDecomposition(this@lup, lu.collect(), pivot, even)
@ -175,28 +163,23 @@ fun GenericMatrixContext<Double, RealField>.lup(matrix: Matrix<Double>): LUPDeco
lup(Double::class, matrix) { it < 1e-11 }
fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Matrix<T> {
if (matrix.rowNum != pivot.size) {
error("Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}")
}
require(matrix.rowNum == pivot.size) { "Matrix dimension mismatch. Expected ${pivot.size}, but got ${matrix.colNum}" }
BufferAccessor2D(type, matrix.rowNum, matrix.colNum).run {
elementContext.run {
elementContext {
// Apply permutations to b
val bp = create { _, _ -> zero }
for (row in pivot.indices) {
val bpRow = bp.row(row)
val pRow = pivot[row]
for (col in 0 until matrix.colNum) {
bpRow[col] = matrix[pRow, col]
}
for (col in 0 until matrix.colNum) bpRow[col] = matrix[pRow, col]
}
// Solve LY = b
for (col in pivot.indices) {
val bpCol = bp.row(col)
for (i in col + 1 until pivot.size) {
val bpI = bp.row(i)
val luICol = lu[i, col]
@ -210,17 +193,15 @@ fun <T : Any> LUPDecomposition<T>.solve(type: KClass<T>, matrix: Matrix<T>): Mat
for (col in pivot.size - 1 downTo 0) {
val bpCol = bp.row(col)
val luDiag = lu[col, col]
for (j in 0 until matrix.colNum) {
bpCol[j] /= luDiag
}
for (j in 0 until matrix.colNum) bpCol[j] /= luDiag
for (i in 0 until col) {
val bpI = bp.row(i)
val luICol = lu[i, col]
for (j in 0 until matrix.colNum) {
bpI[j] -= bpCol[j] * luICol
}
for (j in 0 until matrix.colNum) bpI[j] -= bpCol[j] * luICol
}
}
return context.produce(pivot.size, matrix.colNum) { i, j -> bp[i, j] }
}
}

2
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixBuilder.kt
View File

@ -7,7 +7,7 @@ import scientifik.kmath.structures.asBuffer
class MatrixBuilder(val rows: Int, val columns: Int) {
operator fun <T : Any> invoke(vararg elements: T): FeaturedMatrix<T> {
if (rows * columns != elements.size) error("The number of elements ${elements.size} is not equal $rows * $columns")
require(rows * columns == elements.size) { "The number of elements ${elements.size} is not equal $rows * $columns" }
val buffer = elements.asBuffer()
return BufferMatrix(rows, columns, buffer)
}

40
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/MatrixContext.kt
View File

@ -2,6 +2,7 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.SpaceOperations
import scientifik.kmath.operations.invoke
import scientifik.kmath.operations.sum
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.BufferFactory
@ -37,8 +38,7 @@ interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
fun <T : Any, R : Ring<T>> buffered(
ring: R,
bufferFactory: BufferFactory<T> = Buffer.Companion::boxing
): GenericMatrixContext<T, R> =
BufferMatrixContext(ring, bufferFactory)
): GenericMatrixContext<T, R> = BufferMatrixContext(ring, bufferFactory)
/**
* Automatic buffered matrix, unboxed if it is possible
@ -61,45 +61,49 @@ interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
//TODO add typed error
if (this.colNum != other.rowNum) error("Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})")
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
return produce(rowNum, other.colNum) { i, j ->
val row = rows[i]
val column = other.columns[j]
with(elementContext) {
sum(row.asSequence().zip(column.asSequence(), ::multiply))
}
elementContext { sum(row.asSequence().zip(column.asSequence(), ::multiply)) }
}
}
override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
//TODO add typed error
if (this.colNum != vector.size) error("Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})")
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
return point(rowNum) { i ->
val row = rows[i]
with(elementContext) {
sum(row.asSequence().zip(vector.asSequence(), ::multiply))
}
elementContext { sum(row.asSequence().zip(vector.asSequence(), ::multiply)) }
}
}
override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
produce(rowNum, colNum) { i, j -> elementContext.run { -get(i, j) } }
produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
if (a.rowNum != b.rowNum || a.colNum != b.colNum) error("Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]")
return produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a[i, j] + b[i, j] } }
require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
"Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
}
return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
}
override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
if (rowNum != b.rowNum || colNum != b.colNum) error("Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]")
return produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) + b[i, j] } }
require(rowNum == b.rowNum && colNum == b.colNum) {
"Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
}
return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
}
override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
produce(a.rowNum, a.colNum) { i, j -> elementContext.run { a[i, j] * k } }
produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
override fun Matrix<T>.times(value: T): Matrix<T> =
produce(rowNum, colNum) { i, j -> elementContext.run { get(i, j) * value } }
override operator fun Matrix<T>.times(value: T): Matrix<T> =
produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
}

17
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/VectorSpace.kt
View File

@ -2,6 +2,7 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.BufferFactory
@ -10,10 +11,9 @@ import scientifik.kmath.structures.BufferFactory
* Could be used on any point-like structure
*/
interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
val size: Int
val space: S
override val zero: Point<T> get() = produce { space.zero }
fun produce(initializer: (Int) -> T): Point<T>
@ -22,30 +22,25 @@ interface VectorSpace<T : Any, S : Space<T>> : Space<Point<T>> {
*/
//fun produceElement(initializer: (Int) -> T): Vector<T, S>
override val zero: Point<T> get() = produce { space.zero }
override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { space { a[it] + b[it] } }
override fun add(a: Point<T>, b: Point<T>): Point<T> = produce { with(space) { a[it] + b[it] } }
override fun multiply(a: Point<T>, k: Number): Point<T> = produce { with(space) { a[it] * k } }
override fun multiply(a: Point<T>, k: Number): Point<T> = produce { space { a[it] * k } }
//TODO add basis
companion object {
private val realSpaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
private val realSpaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
/**
* Non-boxing double vector space
*/
fun real(size: Int): BufferVectorSpace<Double, RealField> {
return realSpaceCache.getOrPut(size) {
fun real(size: Int): BufferVectorSpace<Double, RealField> = realSpaceCache.getOrPut(size) {
BufferVectorSpace(
size,
RealField,
Buffer.Companion::auto
)
}
}
/**
* A structured vector space with custom buffer

2
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/VirtualMatrix.kt
View File

@ -18,7 +18,7 @@ class VirtualMatrix<T : Any>(
override val shape: IntArray get() = intArrayOf(rowNum, colNum)
override fun get(i: Int, j: Int): T = generator(i, j)
override operator fun get(i: Int, j: Int): T = generator(i, j)
override fun suggestFeature(vararg features: MatrixFeature): VirtualMatrix<T> =
VirtualMatrix(rowNum, colNum, this.features + features, generator)

113
kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/AutoDiff.kt
View File

@ -3,8 +3,12 @@ package scientifik.kmath.misc
import scientifik.kmath.linear.Point
import scientifik.kmath.operations.ExtendedField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.invoke
import scientifik.kmath.operations.sum
import scientifik.kmath.structures.asBuffer
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/*
* Implementation of backward-mode automatic differentiation.
@ -27,15 +31,14 @@ class DerivationResult<T : Any>(
/**
* compute divergence
*/
fun div(): T = context.run { sum(deriv.values) }
fun div(): T = context { sum(deriv.values) }
/**
* Compute a gradient for variables in given order
*/
fun grad(vararg variables: Variable<T>): Point<T> = if (variables.isEmpty()) {
error("Variable order is not provided for gradient construction")
} else {
variables.map(::deriv).asBuffer()
fun grad(vararg variables: Variable<T>): Point<T> {
check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" }
return variables.map(::deriv).asBuffer()
}
}
@ -52,19 +55,28 @@ class DerivationResult<T : Any>(
* assertEquals(9.0, x.d) // dy/dx
* ```
*/
fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> =
AutoDiffContext(this).run {
@OptIn(ExperimentalContracts::class)
inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> {
contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) }
return (AutoDiffContext(this)) {
val result = body()
result.d = context.one// computing derivative w.r.t result
result.d = context.one // computing derivative w.r.t result
runBackwardPass()
DerivationResult(result.value, derivatives, this@deriv)
}
}
abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
abstract val context: F
/**
* A variable accessing inner state of derivatives.
* Use this function in inner builders to avoid creating additional derivative bindings
*/
abstract var Variable<T>.d: T
/**
* Performs update of derivative after the rest of the formula in the back-pass.
*
@ -78,12 +90,6 @@ abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
*/
abstract fun <R> derive(value: R, block: F.(R) -> Unit): R
/**
* A variable accessing inner state of derivatives.
* Use this function in inner builders to avoid creating additional derivative bindings
*/
abstract var Variable<T>.d: T
abstract fun variable(value: T): Variable<T>
inline fun variable(block: F.() -> T): Variable<T> = variable(context.block())
@ -98,46 +104,35 @@ abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
override operator fun Variable<T>.plus(b: Number): Variable<T> = b.plus(this)
override operator fun Number.minus(b: Variable<T>): Variable<T> =
derive(variable { this@minus.toDouble() * one - b.value }) { z ->
b.d -= z.d
}
derive(variable { this@minus.toDouble() * one - b.value }) { z -> b.d -= z.d }
override operator fun Variable<T>.minus(b: Number): Variable<T> =
derive(variable { this@minus.value - one * b.toDouble() }) { z ->
this@minus.d += z.d
}
derive(variable { this@minus.value - one * b.toDouble() }) { z -> this@minus.d += z.d }
}
/**
* Automatic Differentiation context class.
*/
private class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) : AutoDiffField<T, F>() {
@PublishedApi
internal class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) : AutoDiffField<T, F>() {
// this stack contains pairs of blocks and values to apply them to
private var stack = arrayOfNulls<Any?>(8)
private var sp = 0
internal val derivatives = HashMap<Variable<T>, T>()
private var stack: Array<Any?> = arrayOfNulls<Any?>(8)
private var sp: Int = 0
val derivatives: MutableMap<Variable<T>, T> = hashMapOf()
override val zero: Variable<T> get() = Variable(context.zero)
override val one: Variable<T> get() = Variable(context.one)
/**
* A variable coupled with its derivative. For internal use only
*/
private class VariableWithDeriv<T : Any>(x: T, var d: T) : Variable<T>(x)
override fun variable(value: T): Variable<T> =
VariableWithDeriv(value, context.zero)
override var Variable<T>.d: T
get() = (this as? VariableWithDeriv)?.d ?: derivatives[this] ?: context.zero
set(value) {
if (this is VariableWithDeriv) {
d = value
} else {
derivatives[this] = value
}
}
set(value) = if (this is VariableWithDeriv) d = value else derivatives[this] = value
@Suppress("UNCHECKED_CAST")
override fun <R> derive(value: R, block: F.(R) -> Unit): R {
@ -160,67 +155,49 @@ private class AutoDiffContext<T : Any, F : Field<T>>(override val context: F) :
// Basic math (+, -, *, /)
override fun add(a: Variable<T>, b: Variable<T>): Variable<T> =
derive(variable { a.value + b.value }) { z ->
override fun add(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value + b.value }) { z ->
a.d += z.d
b.d += z.d
}
override fun multiply(a: Variable<T>, b: Variable<T>): Variable<T> =
derive(variable { a.value * b.value }) { z ->
override fun multiply(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value * b.value }) { z ->
a.d += z.d * b.value
b.d += z.d * a.value
}
override fun divide(a: Variable<T>, b: Variable<T>): Variable<T> =
derive(variable { a.value / b.value }) { z ->
override fun divide(a: Variable<T>, b: Variable<T>): Variable<T> = derive(variable { a.value / b.value }) { z ->
a.d += z.d / b.value
b.d -= z.d * a.value / (b.value * b.value)
}
override fun multiply(a: Variable<T>, k: Number): Variable<T> =
derive(variable { k.toDouble() * a.value }) { z ->
override fun multiply(a: Variable<T>, k: Number): Variable<T> = derive(variable { k.toDouble() * a.value }) { z ->
a.d += z.d * k.toDouble()
}
override val zero: Variable<T> get() = Variable(context.zero)
override val one: Variable<T> get() = Variable(context.one)
}
// Extensions for differentiation of various basic mathematical functions
// x ^ 2
fun <T : Any, F : Field<T>> AutoDiffField<T, F>.sqr(x: Variable<T>): Variable<T> =
derive(variable { x.value * x.value }) { z ->
x.d += z.d * 2 * x.value
}
derive(variable { x.value * x.value }) { z -> x.d += z.d * 2 * x.value }
// x ^ 1/2
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.sqrt(x: Variable<T>): Variable<T> =
derive(variable { sqrt(x.value) }) { z ->
x.d += z.d * 0.5 / z.value
}
derive(variable { sqrt(x.value) }) { z -> x.d += z.d * 0.5 / z.value }
// x ^ y (const)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Double): Variable<T> =
derive(variable { power(x.value, y) }) { z ->
x.d += z.d * y * power(x.value, y - 1)
}
derive(variable { power(x.value, y) }) { z -> x.d += z.d * y * power(x.value, y - 1) }
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Int): Variable<T> = pow(x, y.toDouble())
// exp(x)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.exp(x: Variable<T>): Variable<T> =
derive(variable { exp(x.value) }) { z ->
x.d += z.d * z.value
}
derive(variable { exp(x.value) }) { z -> x.d += z.d * z.value }
// ln(x)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.ln(x: Variable<T>): Variable<T> = derive(
variable { ln(x.value) }
) { z ->
x.d += z.d / x.value
}
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.ln(x: Variable<T>): Variable<T> =
derive(variable { ln(x.value) }) { z -> x.d += z.d / x.value }
// x ^ y (any)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: Variable<T>): Variable<T> =
@ -228,12 +205,8 @@ fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.pow(x: Variable<T>, y: V
// sin(x)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.sin(x: Variable<T>): Variable<T> =
derive(variable { sin(x.value) }) { z ->
x.d += z.d * cos(x.value)
}
derive(variable { sin(x.value) }) { z -> x.d += z.d * cos(x.value) }
// cos(x)
fun <T : Any, F : ExtendedField<T>> AutoDiffField<T, F>.cos(x: Variable<T>): Variable<T> =
derive(variable { cos(x.value) }) { z ->
x.d -= z.d * sin(x.value)
}
derive(variable { cos(x.value) }) { z -> x.d -= z.d * sin(x.value) }

2
kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/Grids.kt
View File

@ -41,6 +41,6 @@ fun ClosedFloatingPointRange<Double>.toSequenceWithPoints(numPoints: Int): Seque
*/
@Deprecated("Replace by 'toSequenceWithPoints'")
fun ClosedFloatingPointRange<Double>.toGrid(numPoints: Int): DoubleArray {
if (numPoints < 2) error("Can't create generic grid with less than two points")
require(numPoints >= 2) { "Can't create generic grid with less than two points" }
return DoubleArray(numPoints) { i -> start + (endInclusive - start) / (numPoints - 1) * i }
}

54
kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/cumulative.kt
View File

@ -2,6 +2,8 @@ package scientifik.kmath.misc
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.invoke
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.jvm.JvmName
/**
@ -11,67 +13,69 @@ import kotlin.jvm.JvmName
* @param R the type of resulting iterable.
* @param initial lazy evaluated.
*/
fun <T, R> Iterator<T>.cumulative(initial: R, operation: (R, T) -> R): Iterator<R> = object : Iterator<R> {
@OptIn(ExperimentalContracts::class)
inline fun <T, R> Iterator<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterator<R> {
contract { callsInPlace(operation) }
return object : Iterator<R> {
var state: R = initial
override fun hasNext(): Boolean = this@cumulative.hasNext()
override fun next(): R {
state = operation(state, this@cumulative.next())
return state
}
}
}
fun <T, R> Iterable<T>.cumulative(initial: R, operation: (R, T) -> R): Iterable<R> = object : Iterable<R> {
override fun iterator(): Iterator<R> = this@cumulative.iterator().cumulative(initial, operation)
}
inline fun <T, R> Iterable<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Iterable<R> =
Iterable { this@cumulative.iterator().cumulative(initial, operation) }
fun <T, R> Sequence<T>.cumulative(initial: R, operation: (R, T) -> R): Sequence<R> = object : Sequence<R> {
override fun iterator(): Iterator<R> = this@cumulative.iterator().cumulative(initial, operation)
inline fun <T, R> Sequence<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Sequence<R> = Sequence {
this@cumulative.iterator().cumulative(initial, operation)
}
fun <T, R> List<T>.cumulative(initial: R, operation: (R, T) -> R): List<R> =
this.iterator().cumulative(initial, operation).asSequence().toList()
iterator().cumulative(initial, operation).asSequence().toList()
//Cumulative sum
/**
* Cumulative sum with custom space
*/
fun <T> Iterable<T>.cumulativeSum(space: Space<T>): Iterable<T> = space {
cumulative(zero) { element: T, sum: T -> sum + element }
}
fun <T> Iterable<T>.cumulativeSum(space: Space<T>): Iterable<T> =
space { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
fun Iterable<Double>.cumulativeSum(): Iterable<Double> = this.cumulative(0.0) { element, sum -> sum + element }
fun Iterable<Double>.cumulativeSum(): Iterable<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
fun Iterable<Int>.cumulativeSum(): Iterable<Int> = this.cumulative(0) { element, sum -> sum + element }
fun Iterable<Int>.cumulativeSum(): Iterable<Int> = cumulative(0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfLong")
fun Iterable<Long>.cumulativeSum(): Iterable<Long> = this.cumulative(0L) { element, sum -> sum + element }
fun Iterable<Long>.cumulativeSum(): Iterable<Long> = cumulative(0L) { element, sum -> sum + element }
fun <T> Sequence<T>.cumulativeSum(space: Space<T>): Sequence<T> = with(space) {
cumulative(zero) { element: T, sum: T -> sum + element }
}
fun <T> Sequence<T>.cumulativeSum(space: Space<T>): Sequence<T> =
space { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
fun Sequence<Double>.cumulativeSum(): Sequence<Double> = this.cumulative(0.0) { element, sum -> sum + element }
fun Sequence<Double>.cumulativeSum(): Sequence<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
fun Sequence<Int>.cumulativeSum(): Sequence<Int> = this.cumulative(0) { element, sum -> sum + element }
fun Sequence<Int>.cumulativeSum(): Sequence<Int> = cumulative(0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfLong")
fun Sequence<Long>.cumulativeSum(): Sequence<Long> = this.cumulative(0L) { element, sum -> sum + element }
fun Sequence<Long>.cumulativeSum(): Sequence<Long> = cumulative(0L) { element, sum -> sum + element }
fun <T> List<T>.cumulativeSum(space: Space<T>): List<T> = with(space) {
cumulative(zero) { element: T, sum: T -> sum + element }
}
fun <T> List<T>.cumulativeSum(space: Space<T>): List<T> =
space { cumulative(zero) { element: T, sum: T -> sum + element } }
@JvmName("cumulativeSumOfDouble")
fun List<Double>.cumulativeSum(): List<Double> = this.cumulative(0.0) { element, sum -> sum + element }
fun List<Double>.cumulativeSum(): List<Double> = cumulative(0.0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfInt")
fun List<Int>.cumulativeSum(): List<Int> = this.cumulative(0) { element, sum -> sum + element }
fun List<Int>.cumulativeSum(): List<Int> = cumulative(0) { element, sum -> sum + element }
@JvmName("cumulativeSumOfLong")
fun List<Long>.cumulativeSum(): List<Long> = this.cumulative(0L) { element, sum -> sum + element }
fun List<Long>.cumulativeSum(): List<Long> = cumulative(0L) { element, sum -> sum + element }

23
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/BigInt.kt
View File

@ -3,6 +3,8 @@ package scientifik.kmath.operations
import scientifik.kmath.operations.BigInt.Companion.BASE
import scientifik.kmath.operations.BigInt.Companion.BASE_SIZE
import scientifik.kmath.structures.*
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.math.log2
import kotlin.math.max
import kotlin.math.min
@ -431,8 +433,8 @@ fun ULong.toBigInt(): BigInt = BigInt(
* Create a [BigInt] with this array of magnitudes with protective copy
*/
fun UIntArray.toBigInt(sign: Byte): BigInt {
if (sign == 0.toByte() && isNotEmpty()) error("")
return BigInt(sign, this.copyOf())
require(sign != 0.toByte() || !isNotEmpty())
return BigInt(sign, copyOf())
}
val hexChToInt: MutableMap<Char, Int> = hashMapOf(
@ -485,11 +487,17 @@ fun String.parseBigInteger(): BigInt? {
return res * sign
}
inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
boxing(size, initializer)
@OptIn(ExperimentalContracts::class)
inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> {
contract { callsInPlace(initializer) }
return boxing(size, initializer)
}
inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> =
boxing(size, initializer)
@OptIn(ExperimentalContracts::class)
inline fun MutableBuffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): MutableBuffer<BigInt> {
contract { callsInPlace(initializer) }
return boxing(size, initializer)
}
fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInt, BigIntField> =
BoxingNDRing(shape, BigIntField, Buffer.Companion::bigInt)
@ -497,5 +505,4 @@ fun NDAlgebra.Companion.bigInt(vararg shape: Int): BoxingNDRing<BigInt, BigIntFi
fun NDElement.Companion.bigInt(
vararg shape: Int,
initializer: BigIntField.(IntArray) -> BigInt
): BufferedNDRingElement<BigInt, BigIntField> =
NDAlgebra.bigInt(*shape).produce(initializer)
): BufferedNDRingElement<BigInt, BigIntField> = NDAlgebra.bigInt(*shape).produce(initializer)

6
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Complex.kt
View File

@ -6,6 +6,8 @@ import scientifik.kmath.structures.MutableBuffer
import scientifik.memory.MemoryReader
import scientifik.memory.MemorySpec
import scientifik.memory.MemoryWriter
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.math.*
/**
@ -196,10 +198,14 @@ data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Compl
*/
fun Number.toComplex(): Complex = Complex(this, 0.0)
@OptIn(ExperimentalContracts::class)
inline fun Buffer.Companion.complex(size: Int, crossinline init: (Int) -> Complex): Buffer<Complex> {
contract { callsInPlace(init) }
return MemoryBuffer.create(Complex, size, init)
}
@OptIn(ExperimentalContracts::class)
inline fun MutableBuffer.Companion.complex(size: Int, crossinline init: (Int) -> Complex): Buffer<Complex> {
contract { callsInPlace(init) }
return MemoryBuffer.create(Complex, size, init)
}

9
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BoxingNDField.kt
View File

@ -8,19 +8,17 @@ class BoxingNDField<T, F : Field<T>>(
override val elementContext: F,
val bufferFactory: BufferFactory<T>
) : BufferedNDField<T, F> {
override val zero: BufferedNDFieldElement<T, F> by lazy { produce { zero } }
override val one: BufferedNDFieldElement<T, F> by lazy { produce { one } }
override val strides: Strides = DefaultStrides(shape)
fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
bufferFactory(size, initializer)
override fun check(vararg elements: NDBuffer<T>) {
if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
check(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
}
override val zero: BufferedNDFieldElement<T, F> by lazy { produce { zero } }
override val one: BufferedNDFieldElement<T, F> by lazy { produce { one } }
override fun produce(initializer: F.(IntArray) -> T): BufferedNDFieldElement<T, F> =
BufferedNDFieldElement(
this,
@ -28,6 +26,7 @@ class BoxingNDField<T, F : Field<T>>(
override fun map(arg: NDBuffer<T>, transform: F.(T) -> T): BufferedNDFieldElement<T, F> {
check(arg)
return BufferedNDFieldElement(
this,
buildBuffer(arg.strides.linearSize) { offset -> elementContext.transform(arg.buffer[offset]) })

15
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BoxingNDRing.kt
View File

@ -8,19 +8,16 @@ class BoxingNDRing<T, R : Ring<T>>(
override val elementContext: R,
val bufferFactory: BufferFactory<T>
) : BufferedNDRing<T, R> {
override val strides: Strides = DefaultStrides(shape)
fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
bufferFactory(size, initializer)
override fun check(vararg elements: NDBuffer<T>) {
if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
}
override val zero: BufferedNDRingElement<T, R> by lazy { produce { zero } }
override val one: BufferedNDRingElement<T, R> by lazy { produce { one } }
fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = bufferFactory(size, initializer)
override fun check(vararg elements: NDBuffer<T>) {
require(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
}
override fun produce(initializer: R.(IntArray) -> T): BufferedNDRingElement<T, R> =
BufferedNDRingElement(
this,

8
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferAccessor2D.kt
View File

@ -6,7 +6,6 @@ import kotlin.reflect.KClass
* A context that allows to operate on a [MutableBuffer] as on 2d array
*/
class BufferAccessor2D<T : Any>(val type: KClass<T>, val rowNum: Int, val colNum: Int) {
operator fun Buffer<T>.get(i: Int, j: Int): T = get(i + colNum * j)
operator fun MutableBuffer<T>.set(i: Int, j: Int, value: T) {
@ -26,15 +25,14 @@ class BufferAccessor2D<T : Any>(val type: KClass<T>, val rowNum: Int, val colNum
inner class Row(val buffer: MutableBuffer<T>, val rowIndex: Int) : MutableBuffer<T> {
override val size: Int get() = colNum
override fun get(index: Int): T = buffer[rowIndex, index]
override operator fun get(index: Int): T = buffer[rowIndex, index]
override fun set(index: Int, value: T) {
override operator fun set(index: Int, value: T) {
buffer[rowIndex, index] = value
}
override fun copy(): MutableBuffer<T> = MutableBuffer.auto(type, colNum) { get(it) }
override fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
override operator fun iterator(): Iterator<T> = (0 until colNum).map(::get).iterator()
}

5
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferedNDAlgebra.kt
View File

@ -5,9 +5,8 @@ import scientifik.kmath.operations.*
interface BufferedNDAlgebra<T, C> : NDAlgebra<T, C, NDBuffer<T>> {
val strides: Strides
override fun check(vararg elements: NDBuffer<T>) {
if (!elements.all { it.strides == this.strides }) error("Strides mismatch")
}
override fun check(vararg elements: NDBuffer<T>): Unit =
require(elements.all { it.strides == strides }) { ("Strides mismatch") }
/**
* Convert any [NDStructure] to buffered structure using strides from this context.

2
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/BufferedNDElement.kt
View File

@ -30,7 +30,6 @@ class BufferedNDRingElement<T, R : Ring<T>>(
override val context: BufferedNDRing<T, R>,
override val buffer: Buffer<T>
) : BufferedNDElement<T, R>(), RingElement<NDBuffer<T>, BufferedNDRingElement<T, R>, BufferedNDRing<T, R>> {
override fun unwrap(): NDBuffer<T> = this
override fun NDBuffer<T>.wrap(): BufferedNDRingElement<T, R> {
@ -43,7 +42,6 @@ class BufferedNDFieldElement<T, F : Field<T>>(
override val context: BufferedNDField<T, F>,
override val buffer: Buffer<T>
) : BufferedNDElement<T, F>(), FieldElement<NDBuffer<T>, BufferedNDFieldElement<T, F>, BufferedNDField<T, F>> {
override fun unwrap(): NDBuffer<T> = this
override fun NDBuffer<T>.wrap(): BufferedNDFieldElement<T, F> {

40
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
View File

@ -2,6 +2,8 @@ package scientifik.kmath.structures
import scientifik.kmath.operations.Complex
import scientifik.kmath.operations.complex
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.reflect.KClass
/**
@ -117,15 +119,14 @@ interface MutableBuffer<T> : Buffer<T> {
MutableListBuffer(MutableList(size, initializer))
@Suppress("UNCHECKED_CAST")
inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> {
return when (type) {
inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
when (type) {
Double::class -> RealBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as MutableBuffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as MutableBuffer<T>
else -> boxing(size, initializer)
}
}
/**
* Create most appropriate mutable buffer for given type avoiding boxing wherever possible
@ -150,9 +151,8 @@ inline class ListBuffer<T>(val list: List<T>) : Buffer<T> {
override val size: Int
get() = list.size
override fun get(index: Int): T = list[index]
override fun iterator(): Iterator<T> = list.iterator()
override operator fun get(index: Int): T = list[index]
override operator fun iterator(): Iterator<T> = list.iterator()
}
/**
@ -167,7 +167,11 @@ fun <T> List<T>.asBuffer(): ListBuffer<T> = ListBuffer(this)
* 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.
*/
inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> = List(size, init).asBuffer()
@OptIn(ExperimentalContracts::class)
inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> {
contract { callsInPlace(init) }
return List(size, init).asBuffer()
}
/**
* [MutableBuffer] implementation over [MutableList].
@ -176,17 +180,16 @@ inline fun <T> ListBuffer(size: Int, init: (Int) -> T): ListBuffer<T> = List(siz
* @property list The underlying list.
*/
inline class MutableListBuffer<T>(val list: MutableList<T>) : MutableBuffer<T> {
override val size: Int
get() = list.size
override fun get(index: Int): T = list[index]
override operator fun get(index: Int): T = list[index]
override fun set(index: Int, value: T) {
override operator fun set(index: Int, value: T) {
list[index] = value
}
override fun iterator(): Iterator<T> = list.iterator()
override operator fun iterator(): Iterator<T> = list.iterator()
override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
}
@ -201,14 +204,13 @@ class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
override val size: Int
get() = array.size
override fun get(index: Int): T = array[index]
override operator fun get(index: Int): T = array[index]
override fun set(index: Int, value: T) {
override operator fun set(index: Int, value: T) {
array[index] = value
}
override fun iterator(): Iterator<T> = array.iterator()
override operator fun iterator(): Iterator<T> = array.iterator()
override fun copy(): MutableBuffer<T> = ArrayBuffer(array.copyOf())
}
@ -226,9 +228,9 @@ fun <T> Array<T>.asBuffer(): ArrayBuffer<T> = ArrayBuffer(this)
inline class ReadOnlyBuffer<T>(val buffer: MutableBuffer<T>) : Buffer<T> {
override val size: Int get() = buffer.size
override fun get(index: Int): T = buffer[index]
override operator fun get(index: Int): T = buffer[index]
override fun iterator(): Iterator<T> = buffer.iterator()
override operator fun iterator(): Iterator<T> = buffer.iterator()
}
/**
@ -238,12 +240,12 @@ inline class ReadOnlyBuffer<T>(val buffer: MutableBuffer<T>) : Buffer<T> {
* @param T the type of elements provided by the buffer.
*/
class VirtualBuffer<T>(override val size: Int, private val generator: (Int) -> T) : Buffer<T> {
override fun get(index: Int): T {
override operator fun get(index: Int): T {
if (index < 0 || index >= size) throw IndexOutOfBoundsException("Expected index from 0 to ${size - 1}, but found $index")
return generator(index)
}
override fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map(generator).iterator()
override fun contentEquals(other: Buffer<*>): Boolean {
return if (other is VirtualBuffer) {

9
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ComplexNDField.kt
View File

@ -4,6 +4,9 @@ import scientifik.kmath.operations.Complex
import scientifik.kmath.operations.ComplexField
import scientifik.kmath.operations.FieldElement
import scientifik.kmath.operations.complex
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
typealias ComplexNDElement = BufferedNDFieldElement<Complex, ComplexField>
@ -109,7 +112,9 @@ inline fun ComplexNDElement.mapIndexed(crossinline transform: ComplexField.(inde
/**
* Map one [ComplexNDElement] using function without indices.
*/
@OptIn(ExperimentalContracts::class)
inline fun ComplexNDElement.map(crossinline transform: ComplexField.(Complex) -> Complex): ComplexNDElement {
contract { callsInPlace(transform) }
val buffer = Buffer.complex(strides.linearSize) { offset -> ComplexField.transform(buffer[offset]) }
return BufferedNDFieldElement(context, buffer)
}
@ -148,6 +153,8 @@ fun NDElement.Companion.complex(vararg shape: Int, initializer: ComplexField.(In
/**
* Produce a context for n-dimensional operations inside this real field
*/
@OptIn(ExperimentalContracts::class)
inline fun <R> ComplexField.nd(vararg shape: Int, action: ComplexNDField.() -> R): R {
return NDField.complex(*shape).run(action)
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return NDField.complex(*shape).action()
}

18
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/FlaggedBuffer.kt
View File

@ -1,5 +1,7 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.experimental.and
/**
@ -57,17 +59,19 @@ class FlaggedRealBuffer(val values: DoubleArray, val flags: ByteArray) : Flagged
override val size: Int get() = values.size
override fun get(index: Int): Double? = if (isValid(index)) values[index] else null
override operator fun get(index: Int): Double? = if (isValid(index)) values[index] else null
override fun iterator(): Iterator<Double?> = values.indices.asSequence().map {
override operator fun iterator(): Iterator<Double?> = values.indices.asSequence().map {
if (isValid(it)) values[it] else null
}.iterator()
}
@OptIn(ExperimentalContracts::class)
inline fun FlaggedRealBuffer.forEachValid(block: (Double) -> Unit) {
for (i in indices) {
if (isValid(i)) {
block(values[i])
}
}
contract { callsInPlace(block) }
indices
.asSequence()
.filter(::isValid)
.forEach { block(values[it]) }
}

15
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/FloatBuffer.kt
View File

@ -1,5 +1,8 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
/**
* Specialized [MutableBuffer] implementation over [FloatArray].
*
@ -8,13 +11,13 @@ package scientifik.kmath.structures
inline class FloatBuffer(val array: FloatArray) : MutableBuffer<Float> {
override val size: Int get() = array.size
override fun get(index: Int): Float = array[index]
override operator fun get(index: Int): Float = array[index]
override fun set(index: Int, value: Float) {
override operator fun set(index: Int, value: Float) {
array[index] = value
}
override fun iterator(): FloatIterator = array.iterator()
override operator fun iterator(): FloatIterator = array.iterator()
override fun copy(): MutableBuffer<Float> =
FloatBuffer(array.copyOf())
@ -27,7 +30,11 @@ inline class FloatBuffer(val array: FloatArray) : MutableBuffer<Float> {
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an buffer element given its index.
*/
inline fun FloatBuffer(size: Int, init: (Int) -> Float): FloatBuffer = FloatBuffer(FloatArray(size) { init(it) })
@OptIn(ExperimentalContracts::class)
inline fun FloatBuffer(size: Int, init: (Int) -> Float): FloatBuffer {
contract { callsInPlace(init) }
return FloatBuffer(FloatArray(size) { init(it) })
}
/**
* Returns a new [FloatBuffer] of given elements.

17
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/IntBuffer.kt
View File

@ -1,5 +1,9 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* Specialized [MutableBuffer] implementation over [IntArray].
*
@ -8,17 +12,16 @@ package scientifik.kmath.structures
inline class IntBuffer(val array: IntArray) : MutableBuffer<Int> {
override val size: Int get() = array.size
override fun get(index: Int): Int = array[index]
override operator fun get(index: Int): Int = array[index]
override fun set(index: Int, value: Int) {
override operator fun set(index: Int, value: Int) {
array[index] = value
}
override fun iterator(): IntIterator = array.iterator()
override operator fun iterator(): IntIterator = array.iterator()
override fun copy(): MutableBuffer<Int> =
IntBuffer(array.copyOf())
}
/**
@ -28,7 +31,11 @@ inline class IntBuffer(val array: IntArray) : MutableBuffer<Int> {
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an buffer element given its index.
*/
inline fun IntBuffer(size: Int, init: (Int) -> Int): IntBuffer = IntBuffer(IntArray(size) { init(it) })
@OptIn(ExperimentalContracts::class)
inline fun IntBuffer(size: Int, init: (Int) -> Int): IntBuffer {
contract { callsInPlace(init) }
return IntBuffer(IntArray(size) { init(it) })
}
/**
* Returns a new [IntBuffer] of given elements.

15
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/LongBuffer.kt
View File

@ -1,5 +1,8 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
/**
* Specialized [MutableBuffer] implementation over [LongArray].
*
@ -8,13 +11,13 @@ package scientifik.kmath.structures
inline class LongBuffer(val array: LongArray) : MutableBuffer<Long> {
override val size: Int get() = array.size
override fun get(index: Int): Long = array[index]
override operator fun get(index: Int): Long = array[index]
override fun set(index: Int, value: Long) {
override operator fun set(index: Int, value: Long) {
array[index] = value
}
override fun iterator(): LongIterator = array.iterator()
override operator fun iterator(): LongIterator = array.iterator()
override fun copy(): MutableBuffer<Long> =
LongBuffer(array.copyOf())
@ -28,7 +31,11 @@ inline class LongBuffer(val array: LongArray) : MutableBuffer<Long> {
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an buffer element given its index.
*/
inline fun LongBuffer(size: Int, init: (Int) -> Long): LongBuffer = LongBuffer(LongArray(size) { init(it) })
@OptIn(ExperimentalContracts::class)
inline fun LongBuffer(size: Int, init: (Int) -> Long): LongBuffer {
contract { callsInPlace(init) }
return LongBuffer(LongArray(size) { init(it) })
}
/**
* Returns a new [LongBuffer] of given elements.

9
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/MemoryBuffer.kt
View File

@ -14,10 +14,8 @@ open class MemoryBuffer<T : Any>(protected val memory: Memory, protected val spe
private val reader: MemoryReader = memory.reader()
override fun get(index: Int): T = reader.read(spec, spec.objectSize * index)
override fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
override operator fun get(index: Int): T = reader.read(spec, spec.objectSize * index)
override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
companion object {
fun <T : Any> create(spec: MemorySpec<T>, size: Int): MemoryBuffer<T> =
@ -48,8 +46,7 @@ class MutableMemoryBuffer<T : Any>(memory: Memory, spec: MemorySpec<T>) : Memory
private val writer: MemoryWriter = memory.writer()
override fun set(index: Int, value: T): Unit = writer.write(spec, spec.objectSize * index, value)
override operator fun set(index: Int, value: T): Unit = writer.write(spec, spec.objectSize * index, value)
override fun copy(): MutableBuffer<T> = MutableMemoryBuffer(memory.copy(), spec)
companion object {

17
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDElement.kt
View File

@ -26,19 +26,20 @@ interface NDElement<T, C, N : NDStructure<T>> : NDStructure<T> {
fun real(shape: IntArray, initializer: RealField.(IntArray) -> Double = { 0.0 }): RealNDElement =
NDField.real(*shape).produce(initializer)
fun real1D(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): RealNDElement =
inline fun real1D(dim: Int, crossinline initializer: (Int) -> Double = { _ -> 0.0 }): RealNDElement =
real(intArrayOf(dim)) { initializer(it[0]) }
inline fun real2D(
dim1: Int,
dim2: Int,
crossinline initializer: (Int, Int) -> Double = { _, _ -> 0.0 }
): RealNDElement = real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
fun real2D(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): RealNDElement =
real(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
fun real3D(
inline fun real3D(
dim1: Int,
dim2: Int,
dim3: Int,
initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }
crossinline initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }
): RealNDElement = real(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
@ -72,7 +73,6 @@ fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.mapIndexed(transform: C.(index
fun <T, C, N : NDStructure<T>> NDElement<T, C, N>.map(transform: C.(T) -> T): NDElement<T, C, N> =
context.map(unwrap(), transform).wrap()
/**
* Element by element application of any operation on elements to the whole [NDElement]
*/
@ -107,7 +107,6 @@ operator fun <T, R : Ring<T>, N : NDStructure<T>> NDElement<T, R, N>.times(arg:
operator fun <T, F : Field<T>, N : NDStructure<T>> NDElement<T, F, N>.div(arg: T): NDElement<T, F, N> =
map { value -> arg / value }
// /**
// * Reverse sum operation
// */

24
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
View File

@ -1,5 +1,7 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.jvm.JvmName
import kotlin.reflect.KClass
@ -138,10 +140,10 @@ interface MutableNDStructure<T> : NDStructure<T> {
operator fun set(index: IntArray, value: T)
}
@OptIn(ExperimentalContracts::class)
inline fun <T> MutableNDStructure<T>.mapInPlace(action: (IntArray, T) -> T) {
elements().forEach { (index, oldValue) ->
this[index] = action(index, oldValue)
}
contract { callsInPlace(action) }
elements().forEach { (index, oldValue) -> this[index] = action(index, oldValue) }
}
/**
@ -200,14 +202,12 @@ class DefaultStrides private constructor(override val shape: IntArray) : Strides
}.toList()
}
override fun offset(index: IntArray): Int {
return index.mapIndexed { i, value ->
if (value < 0 || value >= this.shape[i]) {
throw RuntimeException("Index $value out of shape bounds: (0,${this.shape[i]})")
}
override fun offset(index: IntArray): Int = index.mapIndexed { i, value ->
if (value < 0 || value >= this.shape[i])
throw IndexOutOfBoundsException("Index $value out of shape bounds: (0,${this.shape[i]})")
value * strides[i]
}.sum()
}
override fun index(offset: Int): IntArray {
val res = IntArray(shape.size)
@ -259,7 +259,7 @@ abstract class NDBuffer<T> : NDStructure<T> {
*/
abstract val strides: Strides
override fun get(index: IntArray): T = buffer[strides.offset(index)]
override operator fun get(index: IntArray): T = buffer[strides.offset(index)]
override val shape: IntArray get() = strides.shape
@ -319,13 +319,13 @@ class MutableBufferNDStructure<T>(
}
}
override fun set(index: IntArray, value: T): Unit = buffer.set(strides.offset(index), value)
override operator fun set(index: IntArray, value: T): Unit = buffer.set(strides.offset(index), value)
}
inline fun <reified T : Any> NDStructure<T>.combine(
struct: NDStructure<T>,
crossinline block: (T, T) -> T
): NDStructure<T> {
if (!this.shape.contentEquals(struct.shape)) error("Shape mismatch in structure combination")
require(shape.contentEquals(struct.shape)) { "Shape mismatch in structure combination" }
return NDStructure.auto(shape) { block(this[it], struct[it]) }
}

15
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/RealBuffer.kt
View File

@ -1,5 +1,8 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
/**
* Specialized [MutableBuffer] implementation over [DoubleArray].
*
@ -8,13 +11,13 @@ package scientifik.kmath.structures
inline class RealBuffer(val array: DoubleArray) : MutableBuffer<Double> {
override val size: Int get() = array.size
override fun get(index: Int): Double = array[index]
override operator fun get(index: Int): Double = array[index]
override fun set(index: Int, value: Double) {
override operator fun set(index: Int, value: Double) {
array[index] = value
}
override fun iterator(): DoubleIterator = array.iterator()
override operator fun iterator(): DoubleIterator = array.iterator()
override fun copy(): MutableBuffer<Double> =
RealBuffer(array.copyOf())
@ -27,7 +30,11 @@ inline class RealBuffer(val array: DoubleArray) : MutableBuffer<Double> {
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an buffer element given its index.
*/
inline fun RealBuffer(size: Int, init: (Int) -> Double): RealBuffer = RealBuffer(DoubleArray(size) { init(it) })
@OptIn(ExperimentalContracts::class)
inline fun RealBuffer(size: Int, init: (Int) -> Double): RealBuffer {
contract { callsInPlace(init) }
return RealBuffer(DoubleArray(size) { init(it) })
}
/**
* Returns a new [RealBuffer] of given elements.

16
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ShortBuffer.kt
View File

@ -1,5 +1,8 @@
package scientifik.kmath.structures
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
/**
* Specialized [MutableBuffer] implementation over [ShortArray].
*
@ -8,17 +11,16 @@ package scientifik.kmath.structures
inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
override val size: Int get() = array.size
override fun get(index: Int): Short = array[index]
override operator fun get(index: Int): Short = array[index]
override fun set(index: Int, value: Short) {
override operator fun set(index: Int, value: Short) {
array[index] = value
}
override fun iterator(): ShortIterator = array.iterator()
override operator fun iterator(): ShortIterator = array.iterator()
override fun copy(): MutableBuffer<Short> =
ShortBuffer(array.copyOf())
}
/**
@ -28,7 +30,11 @@ inline class ShortBuffer(val array: ShortArray) : MutableBuffer<Short> {
* The function [init] is called for each array element sequentially starting from the first one.
* It should return the value for an buffer element given its index.
*/
inline fun ShortBuffer(size: Int, init: (Int) -> Short): ShortBuffer = ShortBuffer(ShortArray(size) { init(it) })
@OptIn(ExperimentalContracts::class)
inline fun ShortBuffer(size: Int, init: (Int) -> Short): ShortBuffer {
contract { callsInPlace(init) }
return ShortBuffer(ShortArray(size) { init(it) })
}
/**
* Returns a new [ShortBuffer] of given elements.

10
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure1D.kt
View File

@ -6,12 +6,12 @@ package scientifik.kmath.structures
interface Structure1D<T> : NDStructure<T>, Buffer<T> {
override val dimension: Int get() = 1
override fun get(index: IntArray): T {
if (index.size != 1) error("Index dimension mismatch. Expected 1 but found ${index.size}")
override operator fun get(index: IntArray): T {
require(index.size == 1) { "Index dimension mismatch. Expected 1 but found ${index.size}" }
return get(index[0])
}
override fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
override operator fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
}
/**
@ -22,7 +22,7 @@ private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Stru
override val shape: IntArray get() = structure.shape
override val size: Int get() = structure.shape[0]
override fun get(index: Int): T = structure[index]
override operator fun get(index: Int): T = structure[index]
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
}
@ -39,7 +39,7 @@ private inline class Buffer1DWrapper<T>(val buffer: Buffer<T>) : Structure1D<T>
override fun elements(): Sequence<Pair<IntArray, T>> =
asSequence().mapIndexed { index, value -> intArrayOf(index) to value }
override fun get(index: Int): T = buffer[index]
override operator fun get(index: Int): T = buffer[index]
}
/**

8
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Structure2D.kt
View File

@ -9,8 +9,8 @@ interface Structure2D<T> : NDStructure<T> {
operator fun get(i: Int, j: Int): T
override fun get(index: IntArray): T {
if (index.size != 2) error("Index dimension mismatch. Expected 2 but found ${index.size}")
override operator fun get(index: IntArray): T {
require(index.size == 2) { "Index dimension mismatch. Expected 2 but found ${index.size}" }
return get(index[0], index[1])
}
@ -39,10 +39,10 @@ interface Structure2D<T> : NDStructure<T> {
* A 2D wrapper for nd-structure
*/
private inline class Structure2DWrapper<T>(val structure: NDStructure<T>) : Structure2D<T> {
override fun get(i: Int, j: Int): T = structure[i, j]
override val shape: IntArray get() = structure.shape
override operator fun get(i: Int, j: Int): T = structure[i, j]
override fun elements(): Sequence<Pair<IntArray, T>> = structure.elements()
}

9
kmath-core/src/commonTest/kotlin/scientifik/kmath/expressions/ExpressionFieldTest.kt
View File

@ -3,6 +3,7 @@ package scientifik.kmath.expressions
import scientifik.kmath.operations.Complex
import scientifik.kmath.operations.ComplexField
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
@ -10,10 +11,12 @@ class ExpressionFieldTest {
@Test
fun testExpression() {
val context = FunctionalExpressionField(RealField)
val expression = with(context) {
val expression = context {
val x = variable("x", 2.0)
x * x + 2 * x + one
}
assertEquals(expression("x" to 1.0), 4.0)
assertEquals(expression(), 9.0)
}
@ -21,10 +24,12 @@ class ExpressionFieldTest {
@Test
fun testComplex() {
val context = FunctionalExpressionField(ComplexField)
val expression = with(context) {
val expression = context {
val x = variable("x", Complex(2.0, 0.0))
x * x + 2 * x + one
}
assertEquals(expression("x" to Complex(1.0, 0.0)), Complex(4.0, 0.0))
assertEquals(expression(), Complex(9.0, 0.0))
}

2
kmath-core/src/commonTest/kotlin/scientifik/kmath/linear/MatrixTest.kt
View File

@ -7,7 +7,6 @@ import kotlin.test.Test
import kotlin.test.assertEquals
class MatrixTest {
@Test
fun testTranspose() {
val matrix = MatrixContext.real.one(3, 3)
@ -51,6 +50,7 @@ class MatrixTest {
fun test2DDot() {
val firstMatrix = NDStructure.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D()
val secondMatrix = NDStructure.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D()
MatrixContext.real.run {
// val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
// val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }

12
kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/NumberNDFieldTest.kt
View File

@ -1,6 +1,7 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Norm
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.NDElement.Companion.real2D
import kotlin.math.abs
import kotlin.math.pow
@ -56,17 +57,12 @@ class NumberNDFieldTest {
}
object L2Norm : Norm<NDStructure<out Number>, Double> {
override fun norm(arg: NDStructure<out Number>): Double {
return kotlin.math.sqrt(arg.elements().sumByDouble { it.second.toDouble() })
}
override fun norm(arg: NDStructure<out Number>): Double =
kotlin.math.sqrt(arg.elements().sumByDouble { it.second.toDouble() })
}
@Test
fun testInternalContext() {
NDField.real(*array1.shape).run {
with(L2Norm) {
1 + norm(array1) + exp(array2)
}
}
(NDField.real(*array1.shape)) { with(L2Norm) { 1 + norm(array1) + exp(array2) } }
}
}

9
kmath-core/src/jvmMain/kotlin/scientifik/kmath/operations/BigNumbers.kt
View File

@ -17,10 +17,10 @@ object JBigIntegerField : Field<BigInteger> {
override fun number(value: Number): BigInteger = BigInteger.valueOf(value.toLong())
override fun divide(a: BigInteger, b: BigInteger): BigInteger = a.div(b)
override fun add(a: BigInteger, b: BigInteger): BigInteger = a.add(b)
override fun BigInteger.minus(b: BigInteger): BigInteger = this.subtract(b)
override operator fun BigInteger.minus(b: BigInteger): BigInteger = subtract(b)
override fun multiply(a: BigInteger, k: Number): BigInteger = a.multiply(k.toInt().toBigInteger())
override fun multiply(a: BigInteger, b: BigInteger): BigInteger = a.multiply(b)
override fun BigInteger.unaryMinus(): BigInteger = negate()
override operator fun BigInteger.unaryMinus(): BigInteger = negate()
}
/**
@ -38,7 +38,7 @@ abstract class JBigDecimalFieldBase internal constructor(val mathContext: MathCo
get() = BigDecimal.ONE
override fun add(a: BigDecimal, b: BigDecimal): BigDecimal = a.add(b)
override fun BigDecimal.minus(b: BigDecimal): BigDecimal = subtract(b)
override operator fun BigDecimal.minus(b: BigDecimal): BigDecimal = subtract(b)
override fun number(value: Number): BigDecimal = BigDecimal.valueOf(value.toDouble())
override fun multiply(a: BigDecimal, k: Number): BigDecimal =
@ -48,8 +48,7 @@ abstract class JBigDecimalFieldBase internal constructor(val mathContext: MathCo
override fun divide(a: BigDecimal, b: BigDecimal): BigDecimal = a.divide(b, mathContext)
override fun power(arg: BigDecimal, pow: Number): BigDecimal = arg.pow(pow.toInt(), mathContext)
override fun sqrt(arg: BigDecimal): BigDecimal = arg.sqrt(mathContext)
override fun BigDecimal.unaryMinus(): BigDecimal = negate(mathContext)
override operator fun BigDecimal.unaryMinus(): BigDecimal = negate(mathContext)
}
/**

9
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/Chain.kt
View File

@ -139,9 +139,10 @@ fun <T, R> Chain<T>.map(func: suspend (T) -> R): Chain<R> = object : Chain<R> {
fun <T> Chain<T>.filter(block: (T) -> Boolean): Chain<T> = object : Chain<T> {
override suspend fun next(): T {
var next: T
do {
next = this@filter.next()
} while (!block(next))
do next = this@filter.next()
while (!block(next))
return next
}
@ -159,6 +160,7 @@ fun <T, R> Chain<T>.collect(mapper: suspend (Chain<T>) -> R): Chain<R> = object
fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: suspend S.(Chain<T>) -> R): Chain<R> =
object : Chain<R> {
override suspend fun next(): R = state.mapper(this@collectWithState)
override fun fork(): Chain<R> =
this@collectWithState.fork().collectWithState(stateFork(state), stateFork, mapper)
}
@ -168,6 +170,5 @@ fun <T, S, R> Chain<T>.collectWithState(state: S, stateFork: (S) -> S, mapper: s
*/
fun <T, U, R> Chain<T>.zip(other: Chain<U>, block: suspend (T, U) -> R): Chain<R> = object : Chain<R> {
override suspend fun next(): R = block(this@zip.next(), other.next())
override fun fork(): Chain<R> = this@zip.fork().zip(other.fork(), block)
}

5
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/chains/flowExtra.kt
View File

@ -7,15 +7,16 @@ import kotlinx.coroutines.flow.scan
import kotlinx.coroutines.flow.scanReduce
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceOperations
import scientifik.kmath.operations.invoke
@ExperimentalCoroutinesApi
fun <T> Flow<T>.cumulativeSum(space: SpaceOperations<T>): Flow<T> = with(space) {
fun <T> Flow<T>.cumulativeSum(space: SpaceOperations<T>): Flow<T> = space {
scanReduce { sum: T, element: T -> sum + element }
}
@ExperimentalCoroutinesApi
fun <T> Flow<T>.mean(space: Space<T>): Flow<T> = with(space) {
fun <T> Flow<T>.mean(space: Space<T>): Flow<T> = space {
class Accumulator(var sum: T, var num: Int)
scan(Accumulator(zero, 0)) { sum, element ->

17
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/coroutines/coroutinesExtra.kt
View File

@ -3,6 +3,8 @@ package scientifik.kmath.coroutines
import kotlinx.coroutines.*
import kotlinx.coroutines.channels.produce
import kotlinx.coroutines.flow.*
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
val Dispatchers.Math: CoroutineDispatcher
get() = Default
@ -81,21 +83,24 @@ suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, collector: FlowCollector<
}
}
@OptIn(ExperimentalContracts::class)
@ExperimentalCoroutinesApi
@FlowPreview
suspend fun <T> AsyncFlow<T>.collect(concurrency: Int, action: suspend (value: T) -> Unit) {
suspend inline fun <T> AsyncFlow<T>.collect(concurrency: Int, crossinline action: suspend (value: T) -> Unit) {
contract { callsInPlace(action) }
collect(concurrency, object : FlowCollector<T> {
override suspend fun emit(value: T): Unit = action(value)
})
}
@OptIn(ExperimentalContracts::class)
@ExperimentalCoroutinesApi
@FlowPreview
fun <T, R> Flow<T>.mapParallel(
inline fun <T, R> Flow<T>.mapParallel(
dispatcher: CoroutineDispatcher = Dispatchers.Default,
transform: suspend (T) -> R
crossinline transform: suspend (T) -> R
): Flow<R> {
return flatMapMerge { value ->
flow { emit(transform(value)) }
}.flowOn(dispatcher)
contract { callsInPlace(transform) }
return flatMapMerge { value -> flow { emit(transform(value)) } }.flowOn(dispatcher)
}

8
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/streaming/RingBuffer.kt
View File

@ -20,7 +20,7 @@ class RingBuffer<T>(
override var size: Int = size
private set
override fun get(index: Int): T {
override operator fun get(index: Int): T {
require(index >= 0) { "Index must be positive" }
require(index < size) { "Index $index is out of circular buffer size $size" }
return buffer[startIndex.forward(index)] as T
@ -31,15 +31,13 @@ class RingBuffer<T>(
/**
* Iterator could provide wrong results if buffer is changed in initialization (iteration is safe)
*/
override fun iterator(): Iterator<T> = object : AbstractIterator<T>() {
override operator fun iterator(): Iterator<T> = object : AbstractIterator<T>() {
private var count = size
private var index = startIndex
val copy = buffer.copy()
override fun computeNext() {
if (count == 0) {
done()
} else {
if (count == 0) done() else {
setNext(copy[index] as T)
index = index.forward(1)
count--

5
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/chains/ChainExt.kt
View File

@ -1,7 +1,6 @@
package scientifik.kmath.chains
import kotlinx.coroutines.runBlocking
import kotlin.sequences.Sequence
/**
* Represent a chain as regular iterator (uses blocking calls)
@ -15,6 +14,4 @@ operator fun <R> Chain<R>.iterator(): Iterator<R> = object : Iterator<R> {
/**
* Represent a chain as a sequence
*/
fun <R> Chain<R>.asSequence(): Sequence<R> = object : Sequence<R> {
override fun iterator(): Iterator<R> = this@asSequence.iterator()
}
fun <R> Chain<R>.asSequence(): Sequence<R> = Sequence { this@asSequence.iterator() }

12
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/LazyNDStructure.kt
View File

@ -18,7 +18,7 @@ class LazyNDStructure<T>(
suspend fun await(index: IntArray): T = deferred(index).await()
override fun get(index: IntArray): T = runBlocking {
override operator fun get(index: IntArray): T = runBlocking {
deferred(index).await()
}
@ -52,10 +52,12 @@ suspend fun <T> NDStructure<T>.await(index: IntArray): T =
/**
* PENDING would benefit from KEEP-176
*/
fun <T, R> NDStructure<T>.mapAsyncIndexed(
inline fun <T, R> NDStructure<T>.mapAsyncIndexed(
scope: CoroutineScope,
function: suspend (T, index: IntArray) -> R
crossinline function: suspend (T, index: IntArray) -> R
): LazyNDStructure<R> = LazyNDStructure(scope, shape) { index -> function(get(index), index) }
fun <T, R> NDStructure<T>.mapAsync(scope: CoroutineScope, function: suspend (T) -> R): LazyNDStructure<R> =
LazyNDStructure(scope, shape) { index -> function(get(index)) }
inline fun <T, R> NDStructure<T>.mapAsync(
scope: CoroutineScope,
crossinline function: suspend (T) -> R
): LazyNDStructure<R> = LazyNDStructure(scope, shape) { index -> function(get(index)) }

62
kmath-dimensions/src/commonMain/kotlin/scientifik/kmath/dimensions/Wrappers.kt
View File

@ -4,7 +4,9 @@ import scientifik.kmath.linear.GenericMatrixContext
import scientifik.kmath.linear.MatrixContext
import scientifik.kmath.linear.Point
import scientifik.kmath.linear.transpose
import scientifik.kmath.operations.RealField
import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.Structure2D
@ -42,7 +44,7 @@ inline class DMatrixWrapper<T, R : Dimension, C : Dimension>(
val structure: Structure2D<T>
) : DMatrix<T, R, C> {
override val shape: IntArray get() = structure.shape
override fun get(i: Int, j: Int): T = structure[i, j]
override operator fun get(i: Int, j: Int): T = structure[i, j]
}
/**
@ -70,9 +72,9 @@ inline class DPointWrapper<T, D : Dimension>(val point: Point<T>) :
DPoint<T, D> {
override val size: Int get() = point.size
override fun get(index: Int): T = point[index]
override operator fun get(index: Int): T = point[index]
override fun iterator(): Iterator<T> = point.iterator()
override operator fun iterator(): Iterator<T> = point.iterator()
}
@ -82,12 +84,14 @@ inline class DPointWrapper<T, D : Dimension>(val point: Point<T>) :
inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixContext<T, Ri>) {
inline fun <reified R : Dimension, reified C : Dimension> Matrix<T>.coerce(): DMatrix<T, R, C> {
if (rowNum != Dimension.dim<R>().toInt()) {
error("Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum")
}
if (colNum != Dimension.dim<C>().toInt()) {
error("Column number mismatch: expected ${Dimension.dim<C>()} but found $colNum")
}
check(
rowNum == Dimension.dim<R>().toInt()
) { "Row number mismatch: expected ${Dimension.dim<R>()} but found $rowNum" }
check(
colNum == Dimension.dim<C>().toInt()
) { "Column number mismatch: expected ${Dimension.dim<C>()} but found $colNum" }
return DMatrix.coerceUnsafe(this)
}
@ -97,11 +101,12 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
inline fun <reified R : Dimension, reified C : Dimension> produce(noinline initializer: (i: Int, j: Int) -> T): DMatrix<T, R, C> {
val rows = Dimension.dim<R>()
val cols = Dimension.dim<C>()
return context.produce(rows.toInt(), cols.toInt(), initializer).coerce<R,C>()
return context.produce(rows.toInt(), cols.toInt(), initializer).coerce<R, C>()
}
inline fun <reified D : Dimension> point(noinline initializer: (Int) -> T): DPoint<T, D> {
val size = Dimension.dim<D>()
return DPoint.coerceUnsafe(
context.point(
size.toInt(),
@ -112,37 +117,28 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
inline infix fun <reified R1 : Dimension, reified C1 : Dimension, reified C2 : Dimension> DMatrix<T, R1, C1>.dot(
other: DMatrix<T, C1, C2>
): DMatrix<T, R1, C2> {
return context.run { this@dot dot other }.coerce()
}
): DMatrix<T, R1, C2> = context { this@dot dot other }.coerce()
inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> {
return DPoint.coerceUnsafe(context.run { this@dot dot vector })
}
inline infix fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.dot(vector: DPoint<T, C>): DPoint<T, R> =
DPoint.coerceUnsafe(context { this@dot dot vector })
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.times(value: T): DMatrix<T, R, C> {
return context.run { this@times.times(value) }.coerce()
}
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, R, C>.times(value: T): DMatrix<T, R, C> =
context { this@times.times(value) }.coerce()
inline operator fun <reified R : Dimension, reified C : Dimension> T.times(m: DMatrix<T, R, C>): DMatrix<T, R, C> =
m * this
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.plus(other: DMatrix<T, C, R>): DMatrix<T, C, R> =
context { this@plus + other }.coerce()
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.plus(other: DMatrix<T, C, R>): DMatrix<T, C, R> {
return context.run { this@plus + other }.coerce()
}
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.minus(other: DMatrix<T, C, R>): DMatrix<T, C, R> =
context { this@minus + other }.coerce()
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.minus(other: DMatrix<T, C, R>): DMatrix<T, C, R> {
return context.run { this@minus + other }.coerce()
}
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.unaryMinus(): DMatrix<T, C, R> =
context { this@unaryMinus.unaryMinus() }.coerce()
inline operator fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.unaryMinus(): DMatrix<T, C, R> {
return context.run { this@unaryMinus.unaryMinus() }.coerce()
}
inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> {
return context.run { (this@transpose as Matrix<T>).transpose() }.coerce()
}
inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transpose(): DMatrix<T, R, C> =
context { (this@transpose as Matrix<T>).transpose() }.coerce()
/**
* A square unit matrix
@ -156,6 +152,6 @@ inline class DMatrixContext<T : Any, Ri : Ring<T>>(val context: GenericMatrixCon
}
companion object {
val real = DMatrixContext(MatrixContext.real)
val real: DMatrixContext<Double, RealField> = DMatrixContext(MatrixContext.real)
}
}

5
kmath-dimensions/src/commonTest/kotlin/scientifik/dimensions/DMatrixContextTest.kt
View File

@ -5,11 +5,10 @@ import scientifik.kmath.dimensions.D3
import scientifik.kmath.dimensions.DMatrixContext
import kotlin.test.Test
class DMatrixContextTest {
@Test
fun testDimensionSafeMatrix() {
val res = DMatrixContext.real.run {
val res = with(DMatrixContext.real) {
val m = produce<D2, D2> { i, j -> (i + j).toDouble() }
//The dimension of `one()` is inferred from type
@ -19,7 +18,7 @@ class DMatrixContextTest {
@Test
fun testTypeCheck() {
val res = DMatrixContext.real.run {
val res = with(DMatrixContext.real) {
val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }

13
kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/RealVector.kt
View File

@ -14,8 +14,8 @@ import kotlin.math.sqrt
typealias RealPoint = Point<Double>
fun DoubleArray.asVector() = RealVector(this.asBuffer())
fun List<Double>.asVector() = RealVector(this.asBuffer())
fun DoubleArray.asVector(): RealVector = RealVector(this.asBuffer())
fun List<Double>.asVector(): RealVector = RealVector(this.asBuffer())
object VectorL2Norm : Norm<Point<out Number>, Double> {
override fun norm(arg: Point<out Number>): Double = sqrt(arg.asIterable().sumByDouble { it.toDouble() })
@ -32,15 +32,14 @@ inline class RealVector(private val point: Point<Double>) :
override val size: Int get() = point.size
override fun get(index: Int): Double = point[index]
override operator fun get(index: Int): Double = point[index]
override fun iterator(): Iterator<Double> = point.iterator()
override operator fun iterator(): Iterator<Double> = point.iterator()
companion object {
private val spaceCache: MutableMap<Int, BufferVectorSpace<Double, RealField>> = hashMapOf()
private val spaceCache = HashMap<Int, BufferVectorSpace<Double, RealField>>()
inline operator fun invoke(dim: Int, initializer: (Int) -> Double) =
inline operator fun invoke(dim: Int, initializer: (Int) -> Double): RealVector =
RealVector(RealBuffer(dim, initializer))
operator fun invoke(vararg values: Double): RealVector = values.asVector()

32
kmath-for-real/src/commonMain/kotlin/scientifik/kmath/real/realMatrix.kt
View File

@ -3,11 +3,14 @@ package scientifik.kmath.real
import scientifik.kmath.linear.MatrixContext
import scientifik.kmath.linear.RealMatrixContext.elementContext
import scientifik.kmath.linear.VirtualMatrix
import scientifik.kmath.operations.invoke
import scientifik.kmath.operations.sum
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.RealBuffer
import scientifik.kmath.structures.asIterable
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.contract
import kotlin.math.pow
/*
@ -27,7 +30,7 @@ typealias RealMatrix = Matrix<Double>
fun realMatrix(rowNum: Int, colNum: Int, initializer: (i: Int, j: Int) -> Double): RealMatrix =
MatrixContext.real.produce(rowNum, colNum, initializer)
fun Array<DoubleArray>.toMatrix(): RealMatrix{
fun Array<DoubleArray>.toMatrix(): RealMatrix {
return MatrixContext.real.produce(size, this[0].size) { row, col -> this[row][col] }
}
@ -117,13 +120,17 @@ operator fun Matrix<Double>.minus(other: Matrix<Double>): RealMatrix =
* Operations on columns
*/
inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double) =
MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
@OptIn(ExperimentalContracts::class)
inline fun Matrix<Double>.appendColumn(crossinline mapper: (Buffer<Double>) -> Double): Matrix<Double> {
contract { callsInPlace(mapper) }
return MatrixContext.real.produce(rowNum, colNum + 1) { row, col ->
if (col < colNum)
this[row, col]
else
mapper(rows[row])
}
}
fun Matrix<Double>.extractColumns(columnRange: IntRange): RealMatrix =
MatrixContext.real.produce(rowNum, columnRange.count()) { row, col ->
@ -135,17 +142,15 @@ fun Matrix<Double>.extractColumn(columnIndex: Int): RealMatrix =
fun Matrix<Double>.sumByColumn(): RealBuffer = RealBuffer(colNum) { j ->
val column = columns[j]
with(elementContext) {
sum(column.asIterable())
}
elementContext { sum(column.asIterable()) }
}
fun Matrix<Double>.minByColumn(): RealBuffer = RealBuffer(colNum) { j ->
columns[j].asIterable().min() ?: throw Exception("Cannot produce min on empty column")
columns[j].asIterable().min() ?: error("Cannot produce min on empty column")
}
fun Matrix<Double>.maxByColumn(): RealBuffer = RealBuffer(colNum) { j ->
columns[j].asIterable().max() ?: throw Exception("Cannot produce min on empty column")
columns[j].asIterable().max() ?: error("Cannot produce min on empty column")
}
fun Matrix<Double>.averageByColumn(): RealBuffer = RealBuffer(colNum) { j ->
@ -156,10 +161,7 @@ fun Matrix<Double>.averageByColumn(): RealBuffer = RealBuffer(colNum) { j ->
* Operations processing all elements
*/
fun Matrix<Double>.sum() = elements().map { (_, value) -> value }.sum()
fun Matrix<Double>.min() = elements().map { (_, value) -> value }.min()
fun Matrix<Double>.max() = elements().map { (_, value) -> value }.max()
fun Matrix<Double>.average() = elements().map { (_, value) -> value }.average()
fun Matrix<Double>.sum(): Double = elements().map { (_, value) -> value }.sum()
fun Matrix<Double>.min(): Double? = elements().map { (_, value) -> value }.min()
fun Matrix<Double>.max(): Double? = elements().map { (_, value) -> value }.max()
fun Matrix<Double>.average(): Double = elements().map { (_, value) -> value }.average()

7
kmath-for-real/src/commonTest/kotlin/scientifik/kmath/linear/VectorTest.kt
View File

@ -1,5 +1,6 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.invoke
import scientifik.kmath.real.RealVector
import kotlin.test.Test
import kotlin.test.assertEquals
@ -24,14 +25,10 @@ class VectorTest {
fun testDot() {
val vector1 = RealVector(5) { it.toDouble() }
val vector2 = RealVector(5) { 5 - it.toDouble() }
val matrix1 = vector1.asMatrix()
val matrix2 = vector2.asMatrix().transpose()
val product = MatrixContext.real.run { matrix1 dot matrix2 }
val product = MatrixContext.real { matrix1 dot matrix2 }
assertEquals(5.0, product[1, 0])
assertEquals(6.0, product[2, 2])
}
}

35
kmath-functions/src/commonMain/kotlin/scientifik/kmath/functions/Polynomial.kt
View File

@ -2,6 +2,10 @@ package scientifik.kmath.functions
import scientifik.kmath.operations.Ring
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.invoke
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.math.max
import kotlin.math.pow
@ -13,20 +17,21 @@ inline class Polynomial<T : Any>(val coefficients: List<T>) {
constructor(vararg coefficients: T) : this(coefficients.toList())
}
fun Polynomial<Double>.value() =
fun Polynomial<Double>.value(): Double =
coefficients.reduceIndexed { index: Int, acc: Double, d: Double -> acc + d.pow(index) }
fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring.run {
if (coefficients.isEmpty()) return@run zero
fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring {
if (coefficients.isEmpty()) return@ring zero
var res = coefficients.first()
var powerArg = arg
for (index in 1 until coefficients.size) {
res += coefficients[index] * powerArg
//recalculating power on each step to avoid power costs on long polynomials
powerArg *= arg
}
return@run res
res
}
/**
@ -34,7 +39,7 @@ fun <T : Any, C : Ring<T>> Polynomial<T>.value(ring: C, arg: T): T = ring.run {
*/
fun <T : Any, C : Ring<T>> Polynomial<T>.asMathFunction(): MathFunction<T, out C, T> = object :
MathFunction<T, C, T> {
override fun C.invoke(arg: T): T = value(this, arg)
override operator fun C.invoke(arg: T): T = value(this, arg)
}
/**
@ -49,18 +54,16 @@ class PolynomialSpace<T : Any, C : Ring<T>>(val ring: C) : Space<Polynomial<T>>
override fun add(a: Polynomial<T>, b: Polynomial<T>): Polynomial<T> {
val dim = max(a.coefficients.size, b.coefficients.size)
ring.run {
return Polynomial(List(dim) { index ->
return ring {
Polynomial(List(dim) { index ->
a.coefficients.getOrElse(index) { zero } + b.coefficients.getOrElse(index) { zero }
})
}
}
override fun multiply(a: Polynomial<T>, k: Number): Polynomial<T> {
ring.run {
return Polynomial(List(a.coefficients.size) { index -> a.coefficients[index] * k })
}
}
override fun multiply(a: Polynomial<T>, k: Number): Polynomial<T> =
ring { Polynomial(List(a.coefficients.size) { index -> a.coefficients[index] * k }) }
override val zero: Polynomial<T> =
Polynomial(emptyList())
@ -68,6 +71,8 @@ class PolynomialSpace<T : Any, C : Ring<T>>(val ring: C) : Space<Polynomial<T>>
operator fun Polynomial<T>.invoke(arg: T): T = value(ring, arg)
}
fun <T : Any, C : Ring<T>, R> C.polynomial(block: PolynomialSpace<T, C>.() -> R): R {
return PolynomialSpace(this).run(block)
@OptIn(ExperimentalContracts::class)
inline fun <T : Any, C : Ring<T>, R> C.polynomial(block: PolynomialSpace<T, C>.() -> R): R {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return PolynomialSpace(this).block()
}

4
kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/LinearInterpolator.kt
View File

@ -4,13 +4,13 @@ import scientifik.kmath.functions.OrderedPiecewisePolynomial
import scientifik.kmath.functions.PiecewisePolynomial
import scientifik.kmath.functions.Polynomial
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.invoke
/**
* Reference JVM implementation: https://github.com/apache/commons-math/blob/master/src/main/java/org/apache/commons/math4/analysis/interpolation/LinearInterpolator.java
*/
class LinearInterpolator<T : Comparable<T>>(override val algebra: Field<T>) : PolynomialInterpolator<T> {
override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra {
require(points.size > 0) { "Point array should not be empty" }
insureSorted(points)

3
kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/SplineInterpolator.kt
View File

@ -4,6 +4,7 @@ import scientifik.kmath.functions.OrderedPiecewisePolynomial
import scientifik.kmath.functions.PiecewisePolynomial
import scientifik.kmath.functions.Polynomial
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.MutableBufferFactory
/**
@ -17,7 +18,7 @@ class SplineInterpolator<T : Comparable<T>>(
//TODO possibly optimize zeroed buffers
override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra.run {
override fun interpolatePolynomials(points: XYPointSet<T, T>): PiecewisePolynomial<T> = algebra {
if (points.size < 3) {
error("Can't use spline interpolator with less than 3 points")
}

8
kmath-functions/src/commonMain/kotlin/scientifik/kmath/interpolation/XYPointSet.kt
View File

@ -14,9 +14,7 @@ interface XYZPointSet<X, Y, Z> : XYPointSet<X, Y> {
}
internal fun <T : Comparable<T>> insureSorted(points: XYPointSet<T, *>) {
for (i in 0 until points.size - 1) {
if (points.x[i + 1] <= points.x[i]) error("Input data is not sorted at index $i")
}
for (i in 0 until points.size - 1) require(points.x[i + 1] > points.x[i]) { "Input data is not sorted at index $i" }
}
class NDStructureColumn<T>(val structure: Structure2D<T>, val column: Int) : Buffer<T> {
@ -26,9 +24,9 @@ class NDStructureColumn<T>(val structure: Structure2D<T>, val column: Int) : Buf
override val size: Int get() = structure.rowNum
override fun get(index: Int): T = structure[index, column]
override operator fun get(index: Int): T = structure[index, column]
override fun iterator(): Iterator<T> = sequence {
override operator fun iterator(): Iterator<T> = sequence {
repeat(size) {
yield(get(it))
}

12
kmath-geometry/src/commonMain/kotlin/scientifik/kmath/geometry/Euclidean2DSpace.kt
View File

@ -9,25 +9,21 @@ import kotlin.math.sqrt
interface Vector2D : Point<Double>, Vector, SpaceElement<Vector2D, Vector2D, Euclidean2DSpace> {
val x: Double
val y: Double
override val context: Euclidean2DSpace get() = Euclidean2DSpace
override val size: Int get() = 2
override fun get(index: Int): Double = when (index) {
override operator fun get(index: Int): Double = when (index) {
1 -> x
2 -> y
else -> error("Accessing outside of point bounds")
}
override fun iterator(): Iterator<Double> = listOf(x, y).iterator()
override val context: Euclidean2DSpace get() = Euclidean2DSpace
override operator fun iterator(): Iterator<Double> = listOf(x, y).iterator()
override fun unwrap(): Vector2D = this
override fun Vector2D.wrap(): Vector2D = this
}
val Vector2D.r: Double get() = Euclidean2DSpace.run { sqrt(norm()) }
val Vector2D.r: Double get() = Euclidean2DSpace { sqrt(norm()) }
@Suppress("FunctionName")
fun Vector2D(x: Double, y: Double): Vector2D = Vector2DImpl(x, y)

11
kmath-geometry/src/commonMain/kotlin/scientifik/kmath/geometry/Euclidean3DSpace.kt
View File

@ -2,6 +2,7 @@ package scientifik.kmath.geometry
import scientifik.kmath.linear.Point
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.operations.invoke
import kotlin.math.sqrt
@ -9,19 +10,17 @@ interface Vector3D : Point<Double>, Vector, SpaceElement<Vector3D, Vector3D, Euc
val x: Double
val y: Double
val z: Double
override val context: Euclidean3DSpace get() = Euclidean3DSpace
override val size: Int get() = 3
override fun get(index: Int): Double = when (index) {
override operator fun get(index: Int): Double = when (index) {
1 -> x
2 -> y
3 -> z
else -> error("Accessing outside of point bounds")
}
override fun iterator(): Iterator<Double> = listOf(x, y, z).iterator()
override val context: Euclidean3DSpace get() = Euclidean3DSpace
override operator fun iterator(): Iterator<Double> = listOf(x, y, z).iterator()
override fun unwrap(): Vector3D = this
@ -31,7 +30,7 @@ interface Vector3D : Point<Double>, Vector, SpaceElement<Vector3D, Vector3D, Euc
@Suppress("FunctionName")
fun Vector3D(x: Double, y: Double, z: Double): Vector3D = Vector3DImpl(x, y, z)
val Vector3D.r: Double get() = Euclidean3DSpace.run { sqrt(norm()) }
val Vector3D.r: Double get() = Euclidean3DSpace { sqrt(norm()) }
private data class Vector3DImpl(
override val x: Double,

17
kmath-histograms/src/commonMain/kotlin/scientifik/kmath/histogram/Histogram.kt
View File

@ -4,6 +4,9 @@ import scientifik.kmath.domains.Domain
import scientifik.kmath.linear.Point
import scientifik.kmath.structures.ArrayBuffer
import scientifik.kmath.structures.RealBuffer
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* The bin in the histogram. The histogram is by definition always done in the real space
@ -37,20 +40,20 @@ interface MutableHistogram<T : Any, out B : Bin<T>> : Histogram<T, B> {
*/
fun putWithWeight(point: Point<out T>, weight: Double)
fun put(point: Point<out T>) = putWithWeight(point, 1.0)
fun put(point: Point<out T>): Unit = putWithWeight(point, 1.0)
}
fun <T : Any> MutableHistogram<T, *>.put(vararg point: T) = put(ArrayBuffer(point))
fun <T : Any> MutableHistogram<T, *>.put(vararg point: T): Unit = put(ArrayBuffer(point))
fun MutableHistogram<Double, *>.put(vararg point: Number) =
fun MutableHistogram<Double, *>.put(vararg point: Number): Unit =
put(RealBuffer(point.map { it.toDouble() }.toDoubleArray()))
fun MutableHistogram<Double, *>.put(vararg point: Double) = put(RealBuffer(point))
fun MutableHistogram<Double, *>.put(vararg point: Double): Unit = put(RealBuffer(point))
fun <T : Any> MutableHistogram<T, *>.fill(sequence: Iterable<Point<T>>) = sequence.forEach { put(it) }
fun <T : Any> MutableHistogram<T, *>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
/**
* Pass a sequence builder into histogram
*/
fun <T : Any> MutableHistogram<T, *>.fill(buider: suspend SequenceScope<Point<T>>.() -> Unit) =
fill(sequence(buider).asIterable())
fun <T : Any> MutableHistogram<T, *>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
fill(sequence(block).asIterable())

66
kmath-histograms/src/commonMain/kotlin/scientifik/kmath/histogram/RealHistogram.kt
View File

@ -2,6 +2,7 @@ package scientifik.kmath.histogram
import scientifik.kmath.linear.Point
import scientifik.kmath.operations.SpaceOperations
import scientifik.kmath.operations.invoke
import scientifik.kmath.real.asVector
import scientifik.kmath.structures.*
import kotlin.math.floor
@ -9,19 +10,16 @@ import kotlin.math.floor
data class BinDef<T : Comparable<T>>(val space: SpaceOperations<Point<T>>, val center: Point<T>, val sizes: Point<T>) {
fun contains(vector: Point<out T>): Boolean {
if (vector.size != center.size) error("Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}")
val upper = space.run { center + sizes / 2.0 }
val lower = space.run { center - sizes / 2.0 }
return vector.asSequence().mapIndexed { i, value ->
value in lower[i]..upper[i]
}.all { it }
require(vector.size == center.size) { "Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}" }
val upper = space { center + sizes / 2.0 }
val lower = space { center - sizes / 2.0 }
return vector.asSequence().mapIndexed { i, value -> value in lower[i]..upper[i] }.all { it }
}
}
class MultivariateBin<T : Comparable<T>>(val def: BinDef<T>, override val value: Number) : Bin<T> {
override fun contains(point: Point<T>): Boolean = def.contains(point)
override operator fun contains(point: Point<T>): Boolean = def.contains(point)
override val dimension: Int
get() = def.center.size
@ -39,47 +37,34 @@ class RealHistogram(
private val upper: Buffer<Double>,
private val binNums: IntArray = IntArray(lower.size) { 20 }
) : MutableHistogram<Double, MultivariateBin<Double>> {
private val strides = DefaultStrides(IntArray(binNums.size) { binNums[it] + 2 })
private val values: NDStructure<LongCounter> = NDStructure.auto(strides) { LongCounter() }
private val weights: NDStructure<DoubleCounter> = NDStructure.auto(strides) { DoubleCounter() }
override val dimension: Int get() = lower.size
private val binSize = RealBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
init {
// argument checks
if (lower.size != upper.size) error("Dimension mismatch in histogram lower and upper limits.")
if (lower.size != binNums.size) error("Dimension mismatch in bin count.")
if ((0 until dimension).any { upper[it] - lower[it] < 0 }) error("Range for one of axis is not strictly positive")
require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
require(!(0 until dimension).any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
}
/**
* Get internal [NDStructure] bin index for given axis
*/
private fun getIndex(axis: Int, value: Double): Int {
return when {
private fun getIndex(axis: Int, value: Double): Int = when {
value >= upper[axis] -> binNums[axis] + 1 // overflow
value < lower[axis] -> 0 // underflow
else -> floor((value - lower[axis]) / binSize[axis]).toInt() + 1
}
}
private fun getIndex(point: Buffer<out Double>): IntArray = IntArray(dimension) { getIndex(it, point[it]) }
private fun getValue(index: IntArray): Long {
return values[index].sum()
}
private fun getValue(index: IntArray): Long = values[index].sum()
fun getValue(point: Buffer<out Double>): Long {
return getValue(getIndex(point))
}
fun getValue(point: Buffer<out Double>): Long = getValue(getIndex(point))
private fun getDef(index: IntArray): BinDef<Double> {
val center = index.mapIndexed { axis, i ->
@ -89,14 +74,13 @@ class RealHistogram(
else -> lower[axis] + (i.toDouble() - 0.5) * binSize[axis]
}
}.asBuffer()
return BinDef(RealBufferFieldOperations, center, binSize)
}
fun getDef(point: Buffer<out Double>): BinDef<Double> {
return getDef(getIndex(point))
}
fun getDef(point: Buffer<out Double>): BinDef<Double> = getDef(getIndex(point))
override fun get(point: Buffer<out Double>): MultivariateBin<Double>? {
override operator fun get(point: Buffer<out Double>): MultivariateBin<Double>? {
val index = getIndex(point)
return MultivariateBin(getDef(index), getValue(index))
}
@ -112,26 +96,21 @@ class RealHistogram(
weights[index].add(weight)
}
override fun iterator(): Iterator<MultivariateBin<Double>> = weights.elements().map { (index, value) ->
override operator fun iterator(): Iterator<MultivariateBin<Double>> = weights.elements().map { (index, value) ->
MultivariateBin(getDef(index), value.sum())
}.iterator()
/**
* Convert this histogram into NDStructure containing bin values but not bin descriptions
*/
fun values(): NDStructure<Number> {
return NDStructure.auto(values.shape) { values[it].sum() }
}
fun values(): NDStructure<Number> = NDStructure.auto(values.shape) { values[it].sum() }
/**
* Sum of weights
*/
fun weights():NDStructure<Double>{
return NDStructure.auto(weights.shape) { weights[it].sum() }
}
fun weights(): NDStructure<Double> = NDStructure.auto(weights.shape) { weights[it].sum() }
companion object {
/**
* Use it like
* ```
@ -141,12 +120,10 @@ class RealHistogram(
*)
*```
*/
fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram {
return RealHistogram(
fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): RealHistogram = RealHistogram(
ranges.map { it.start }.asVector(),
ranges.map { it.endInclusive }.asVector()
)
}
/**
* Use it like
@ -157,13 +134,10 @@ class RealHistogram(
*)
*```
*/
fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogram {
return RealHistogram(
fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): RealHistogram = RealHistogram(
ListBuffer(ranges.map { it.first.start }),
ListBuffer(ranges.map { it.first.endInclusive }),
ranges.map { it.second }.toIntArray()
)
}
}
}

9
kmath-histograms/src/jvmMain/kotlin/scientifik/kmath/histogram/UnivariateHistogram.kt
View File

@ -46,11 +46,11 @@ class UnivariateHistogram private constructor(private val factory: (Double) -> U
synchronized(this) { bins.put(it.position, it) }
}
override fun get(point: Buffer<out Double>): UnivariateBin? = get(point[0])
override operator fun get(point: Buffer<out Double>): UnivariateBin? = get(point[0])
override val dimension: Int get() = 1
override fun iterator(): Iterator<UnivariateBin> = bins.values.iterator()
override operator fun iterator(): Iterator<UnivariateBin> = bins.values.iterator()
/**
* Thread safe put operation
@ -65,15 +65,14 @@ class UnivariateHistogram private constructor(private val factory: (Double) -> U
}
companion object {
fun uniform(binSize: Double, start: Double = 0.0): UnivariateHistogram {
return UnivariateHistogram { value ->
fun uniform(binSize: Double, start: Double = 0.0): UnivariateHistogram = UnivariateHistogram { value ->
val center = start + binSize * floor((value - start) / binSize + 0.5)
UnivariateBin(center, binSize)
}
}
fun custom(borders: DoubleArray): UnivariateHistogram {
val sorted = borders.sortedArray()
return UnivariateHistogram { value ->
when {
value < sorted.first() -> UnivariateBin(

52
kmath-koma/src/commonMain/kotlin/scientifik.kmath.linear/KomaMatrix.kt
View File

@ -3,16 +3,16 @@ package scientifik.kmath.linear
import koma.extensions.fill
import koma.matrix.MatrixFactory
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.invoke
import scientifik.kmath.structures.Matrix
import scientifik.kmath.structures.NDStructure
class KomaMatrixContext<T : Any>(
private val factory: MatrixFactory<koma.matrix.Matrix<T>>,
private val space: Space<T>
) :
MatrixContext<T> {
) : MatrixContext<T> {
override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T) =
override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): KomaMatrix<T> =
KomaMatrix(factory.zeros(rows, columns).fill(initializer))
fun Matrix<T>.toKoma(): KomaMatrix<T> = if (this is KomaMatrix) {
@ -28,31 +28,28 @@ class KomaMatrixContext<T : Any>(
}
override fun Matrix<T>.dot(other: Matrix<T>) =
KomaMatrix(this.toKoma().origin * other.toKoma().origin)
override fun Matrix<T>.dot(other: Matrix<T>): KomaMatrix<T> =
KomaMatrix(toKoma().origin * other.toKoma().origin)
override fun Matrix<T>.dot(vector: Point<T>) =
KomaVector(this.toKoma().origin * vector.toKoma().origin)
override fun Matrix<T>.dot(vector: Point<T>): KomaVector<T> =
KomaVector(toKoma().origin * vector.toKoma().origin)
override fun Matrix<T>.unaryMinus() =
KomaMatrix(this.toKoma().origin.unaryMinus())
override operator fun Matrix<T>.unaryMinus(): KomaMatrix<T> =
KomaMatrix(toKoma().origin.unaryMinus())
override fun add(a: Matrix<T>, b: Matrix<T>) =
override fun add(a: Matrix<T>, b: Matrix<T>): KomaMatrix<T> =
KomaMatrix(a.toKoma().origin + b.toKoma().origin)
override fun Matrix<T>.minus(b: Matrix<T>) =
KomaMatrix(this.toKoma().origin - b.toKoma().origin)
override operator fun Matrix<T>.minus(b: Matrix<T>): KomaMatrix<T> =
KomaMatrix(toKoma().origin - b.toKoma().origin)
override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
produce(a.rowNum, a.colNum) { i, j -> space.run { a[i, j] * k } }
produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
override fun Matrix<T>.times(value: T) =
KomaMatrix(this.toKoma().origin * value)
companion object {
}
override operator fun Matrix<T>.times(value: T): KomaMatrix<T> =
KomaMatrix(toKoma().origin * value)
companion object
}
fun <T : Any> KomaMatrixContext<T>.solve(a: Matrix<T>, b: Matrix<T>) =
@ -70,10 +67,11 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
override val shape: IntArray get() = intArrayOf(origin.numRows(), origin.numCols())
override val features: Set<MatrixFeature> = features ?: setOf(
override val features: Set<MatrixFeature> = features ?: hashSetOf(
object : DeterminantFeature<T> {
override val determinant: T get() = origin.det()
},
object : LUPDecompositionFeature<T> {
private val lup by lazy { origin.LU() }
override val l: FeaturedMatrix<T> get() = KomaMatrix(lup.second)
@ -85,7 +83,7 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
override fun suggestFeature(vararg features: MatrixFeature): FeaturedMatrix<T> =
KomaMatrix(this.origin, this.features + features)
override fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
override operator fun get(i: Int, j: Int): T = origin.getGeneric(i, j)
override fun equals(other: Any?): Boolean {
return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
@ -101,14 +99,12 @@ class KomaMatrix<T : Any>(val origin: koma.matrix.Matrix<T>, features: Set<Matri
}
class KomaVector<T : Any> internal constructor(val origin: koma.matrix.Matrix<T>) : Point<T> {
init {
if (origin.numCols() != 1) error("Only single column matrices are allowed")
}
override val size: Int get() = origin.numRows()
override fun get(index: Int): T = origin.getGeneric(index)
init {
require(origin.numCols() == 1) { error("Only single column matrices are allowed") }
}
override fun iterator(): Iterator<T> = origin.toIterable().iterator()
override operator fun get(index: Int): T = origin.getGeneric(index)
override operator fun iterator(): Iterator<T> = origin.toIterable().iterator()
}

8
kmath-memory/src/commonMain/kotlin/scientifik/memory/Memory.kt
View File

@ -1,5 +1,9 @@
package scientifik.memory
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* Represents a display of certain memory structure.
*/
@ -80,7 +84,9 @@ interface MemoryReader {
/**
* Uses the memory for read then releases the reader.
*/
@OptIn(ExperimentalContracts::class)
inline fun Memory.read(block: MemoryReader.() -> Unit) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
reader().apply(block).release()
}
@ -132,7 +138,9 @@ interface MemoryWriter {
/**
* Uses the memory for write then releases the writer.
*/
@OptIn(ExperimentalContracts::class)
inline fun Memory.write(block: MemoryWriter.() -> Unit) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
writer().apply(block).release()
}

6
kmath-memory/src/commonMain/kotlin/scientifik/memory/MemorySpec.kt
View File

@ -38,11 +38,7 @@ fun <T : Any> MemoryWriter.write(spec: MemorySpec<T>, offset: Int, value: T): Un
* Reads array of [size] objects mapped by [spec] at certain [offset].
*/
inline fun <reified T : Any> MemoryReader.readArray(spec: MemorySpec<T>, offset: Int, size: Int): Array<T> =
Array(size) { i ->
spec.run {
read(offset + i * objectSize)
}
}
Array(size) { i -> with(spec) { read(offset + i * objectSize) } }
/**
* Writes [array] of objects mapped by [spec] at certain [offset].

20
kmath-memory/src/jvmMain/kotlin/scientifik/memory/ByteBufferMemory.kt
View File

@ -1,12 +1,17 @@
package scientifik.memory
import java.io.IOException
import java.nio.ByteBuffer
import java.nio.channels.FileChannel
import java.nio.file.Files
import java.nio.file.Path
import java.nio.file.StandardOpenOption
import kotlin.contracts.ExperimentalContracts
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
private class ByteBufferMemory(
@PublishedApi
internal class ByteBufferMemory(
val buffer: ByteBuffer,
val startOffset: Int = 0,
override val size: Int = buffer.limit()
@ -112,7 +117,12 @@ fun ByteBuffer.asMemory(startOffset: Int = 0, size: Int = limit()): Memory =
/**
* Uses direct memory-mapped buffer from file to read something and close it afterwards.
*/
fun <R> Path.readAsMemory(position: Long = 0, size: Long = Files.size(this), block: Memory.() -> R): R =
FileChannel.open(this, StandardOpenOption.READ).use {
ByteBufferMemory(it.map(FileChannel.MapMode.READ_ONLY, position, size)).block()
}
@OptIn(ExperimentalContracts::class)
@Throws(IOException::class)
inline fun <R> Path.readAsMemory(position: Long = 0, size: Long = Files.size(this), block: Memory.() -> R): R {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FileChannel
.open(this, StandardOpenOption.READ)
.use { ByteBufferMemory(it.map(FileChannel.MapMode.READ_ONLY, position, size)).block() }
}

1
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/RandomChain.kt
View File

@ -1,6 +1,7 @@
package scientifik.kmath.prob
import scientifik.kmath.chains.Chain
import kotlin.contracts.ExperimentalContracts
/**
* A possibly stateful chain producing random values.

5
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/SamplerAlgebra.kt
View File

@ -5,6 +5,7 @@ import scientifik.kmath.chains.ConstantChain
import scientifik.kmath.chains.map
import scientifik.kmath.chains.zip
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.invoke
class BasicSampler<T : Any>(val chainBuilder: (RandomGenerator) -> Chain<T>) : Sampler<T> {
override fun sample(generator: RandomGenerator): Chain<T> = chainBuilder(generator)
@ -22,10 +23,10 @@ class SamplerSpace<T : Any>(val space: Space<T>) : Space<Sampler<T>> {
override val zero: Sampler<T> = ConstantSampler(space.zero)
override fun add(a: Sampler<T>, b: Sampler<T>): Sampler<T> = BasicSampler { generator ->
a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space.run { aValue + bValue } }
a.sample(generator).zip(b.sample(generator)) { aValue, bValue -> space { aValue + bValue } }
}
override fun multiply(a: Sampler<T>, k: Number): Sampler<T> = BasicSampler { generator ->
a.sample(generator).map { space.run { it * k.toDouble() } }
a.sample(generator).map { space { it * k.toDouble() } }
}
}

23
kmath-prob/src/commonMain/kotlin/scientifik/kmath/prob/Statistic.kt
View File

@ -29,8 +29,10 @@ interface Statistic<T, R> {
interface ComposableStatistic<T, I, R> : Statistic<T, R> {
//compute statistic on a single block
suspend fun computeIntermediate(data: Buffer<T>): I
//Compose two blocks
suspend fun composeIntermediate(first: I, second: I): I
//Transform block to result
suspend fun toResult(intermediate: I): R
@ -58,26 +60,26 @@ private fun <T, I, R> ComposableStatistic<T, I, R>.flowIntermediate(
fun <T, I, R> ComposableStatistic<T, I, R>.flow(
flow: Flow<Buffer<T>>,
dispatcher: CoroutineDispatcher = Dispatchers.Default
): Flow<R> = flowIntermediate(flow,dispatcher).map(::toResult)
): Flow<R> = flowIntermediate(flow, dispatcher).map(::toResult)
/**
* Arithmetic mean
*/
class Mean<T>(val space: Space<T>) : ComposableStatistic<T, Pair<T, Int>, T> {
override suspend fun computeIntermediate(data: Buffer<T>): Pair<T, Int> =
space.run { sum(data.asIterable()) } to data.size
space { sum(data.asIterable()) } to data.size
override suspend fun composeIntermediate(first: Pair<T, Int>, second: Pair<T, Int>): Pair<T, Int> =
space.run { first.first + second.first } to (first.second + second.second)
space { first.first + second.first } to (first.second + second.second)
override suspend fun toResult(intermediate: Pair<T, Int>): T =
space.run { intermediate.first / intermediate.second }
space { intermediate.first / intermediate.second }
companion object {
//TODO replace with optimized version which respects overflow
val real = Mean(RealField)
val int = Mean(IntRing)
val long = Mean(LongRing)
val real: Mean<Double> = Mean(RealField)
val int: Mean<Int> = Mean(IntRing)
val long: Mean<Long> = Mean(LongRing)
}
}
@ -85,11 +87,10 @@ class Mean<T>(val space: Space<T>) : ComposableStatistic<T, Pair<T, Int>, T> {
* Non-composable median
*/
class Median<T>(private val comparator: Comparator<T>) : Statistic<T, T> {
override suspend fun invoke(data: Buffer<T>): T {
return data.asSequence().sortedWith(comparator).toList()[data.size / 2] //TODO check if this is correct
}
override suspend fun invoke(data: Buffer<T>): T =
data.asSequence().sortedWith(comparator).toList()[data.size / 2] //TODO check if this is correct
companion object {
val real = Median(Comparator { a: Double, b: Double -> a.compareTo(b) })
val real: Median<Double> = Median(Comparator { a: Double, b: Double -> a.compareTo(b) })
}
}

2
kmath-viktor/src/main/kotlin/scientifik/kmath/viktor/ViktorBuffer.kt
View File

@ -16,5 +16,5 @@ inline class ViktorBuffer(val flatArray: F64FlatArray) : MutableBuffer<Double> {
return ViktorBuffer(flatArray.copy().flatten())
}
override fun iterator(): Iterator<Double> = flatArray.data.iterator()
override operator fun iterator(): Iterator<Double> = flatArray.data.iterator()
}