Even more documentation comments and minor refactorings #144

Merged
CommanderTvis merged 5 commits from even-more-docs into dev 2020-10-01 20:44:30 +03:00
38 changed files with 421 additions and 191 deletions

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@ -2,9 +2,9 @@ plugins {
id("ru.mipt.npm.project") id("ru.mipt.npm.project")
} }
val kmathVersion by extra("0.2.0-dev-2") val kmathVersion: String by extra("0.2.0-dev-2")
val bintrayRepo by extra("kscience") val bintrayRepo: String by extra("kscience")
val githubProject by extra("kmath") val githubProject: String by extra("kmath")
allprojects { allprojects {
repositories { repositories {
@ -22,6 +22,6 @@ subprojects {
if (name.startsWith("kmath")) apply<ru.mipt.npm.gradle.KSciencePublishPlugin>() if (name.startsWith("kmath")) apply<ru.mipt.npm.gradle.KSciencePublishPlugin>()
} }
readme{ readme {
readmeTemplate = file("docs/templates/README-TEMPLATE.md") readmeTemplate = file("docs/templates/README-TEMPLATE.md")
} }

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@ -6,10 +6,10 @@ back-ends. The new operations added as extensions to contexts instead of being m
Two major contexts used for linear algebra and hyper-geometry: Two major contexts used for linear algebra and hyper-geometry:
* `VectorSpace` forms a mathematical space on top of array-like structure (`Buffer` and its typealias `Point` used for geometry). * `VectorSpace` forms a mathematical space on top of array-like structure (`Buffer` and its type alias `Point` used for geometry).
* `MatrixContext` forms a space-like context for 2d-structures. It does not store matrix size and therefore does not implement * `MatrixContext` forms a space-like context for 2d-structures. It does not store matrix size and therefore does not implement
`Space` interface (it is not possible to create zero element without knowing the matrix size). `Space` interface (it is impossible to create zero element without knowing the matrix size).
## Vector spaces ## Vector spaces

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@ -28,7 +28,9 @@ dependencies {
implementation(project(":kmath-dimensions")) implementation(project(":kmath-dimensions"))
implementation("org.jetbrains.kotlinx:kotlinx-io:0.2.0-npm-dev-11") implementation("org.jetbrains.kotlinx:kotlinx-io:0.2.0-npm-dev-11")
implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-20") implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-20")
"benchmarksCompile"(sourceSets.main.get().output + sourceSets.main.get().compileClasspath) //sourceSets.main.output + sourceSets.main.runtimeClasspath implementation("org.slf4j:slf4j-simple:1.7.30")
"benchmarksImplementation"("org.jetbrains.kotlinx:kotlinx.benchmark.runtime-jvm:0.2.0-dev-8")
"benchmarksImplementation"(sourceSets.main.get().output + sourceSets.main.get().runtimeClasspath)
} }
// Configure benchmark // Configure benchmark

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@ -6,34 +6,33 @@ import org.openjdk.jmh.annotations.State
import java.nio.IntBuffer import java.nio.IntBuffer
@State(Scope.Benchmark) @State(Scope.Benchmark)
class ArrayBenchmark { internal class ArrayBenchmark {
@Benchmark @Benchmark
fun benchmarkArrayRead() { fun benchmarkArrayRead() {
var res = 0 var res = 0
for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.array[_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i] for (i in 1..size) res += array[size - i]
} }
@Benchmark @Benchmark
fun benchmarkBufferRead() { fun benchmarkBufferRead() {
var res = 0 var res = 0
for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.arrayBuffer.get( for (i in 1..size) res += arrayBuffer.get(
_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i) size - i
)
} }
@Benchmark @Benchmark
fun nativeBufferRead() { fun nativeBufferRead() {
var res = 0 var res = 0
for (i in 1.._root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) res += _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.nativeBuffer.get( for (i in 1..size) res += nativeBuffer.get(
_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size - i) size - i
)
} }
companion object { companion object {
const val size: Int = 1000 const val size: Int = 1000
val array: IntArray = IntArray(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) { it } val array: IntArray = IntArray(size) { it }
val arrayBuffer: IntBuffer = IntBuffer.wrap(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.array) val arrayBuffer: IntBuffer = IntBuffer.wrap(array)
val nativeBuffer: IntBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) }
val nativeBuffer: IntBuffer = IntBuffer.allocate(_root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size).also {
for (i in 0 until _root_ide_package_.kscience.kmath.structures.ArrayBenchmark.Companion.size) it.put(i, i)
}
} }
} }

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@ -7,11 +7,10 @@ import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State import org.openjdk.jmh.annotations.State
@State(Scope.Benchmark) @State(Scope.Benchmark)
class BufferBenchmark { internal class BufferBenchmark {
@Benchmark @Benchmark
fun genericRealBufferReadWrite() { fun genericRealBufferReadWrite() {
val buffer = RealBuffer(size){it.toDouble()} val buffer = RealBuffer(size) { it.toDouble() }
(0 until size).forEach { (0 until size).forEach {
buffer[it] buffer[it]
@ -20,7 +19,7 @@ class BufferBenchmark {
@Benchmark @Benchmark
fun complexBufferReadWrite() { fun complexBufferReadWrite() {
val buffer = MutableBuffer.complex(size / 2){Complex(it.toDouble(), -it.toDouble())} val buffer = MutableBuffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
(0 until size / 2).forEach { (0 until size / 2).forEach {
buffer[it] buffer[it]
@ -28,6 +27,6 @@ class BufferBenchmark {
} }
companion object { companion object {
const val size = 100 const val size: Int = 100
} }
} }

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@ -7,7 +7,7 @@ import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State import org.openjdk.jmh.annotations.State
@State(Scope.Benchmark) @State(Scope.Benchmark)
class NDFieldBenchmark { internal class NDFieldBenchmark {
@Benchmark @Benchmark
fun autoFieldAdd() { fun autoFieldAdd() {
bufferedField { bufferedField {
@ -40,11 +40,10 @@ class NDFieldBenchmark {
} }
companion object { companion object {
val dim = 1000 const val dim: Int = 1000
val n = 100 const val n: Int = 100
val bufferedField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
val bufferedField = NDField.auto(RealField, dim, dim) val specializedField: RealNDField = NDField.real(dim, dim)
val specializedField = NDField.real(dim, dim) val genericField: BoxingNDField<Double, RealField> = NDField.boxing(RealField, dim, dim)
val genericField = NDField.boxing(RealField, dim, dim)
} }
} }

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@ -9,9 +9,9 @@ import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State import org.openjdk.jmh.annotations.State
@State(Scope.Benchmark) @State(Scope.Benchmark)
class ViktorBenchmark { internal class ViktorBenchmark {
final val dim = 1000 final val dim: Int = 1000
final val n = 100 final val n: Int = 100
// automatically build context most suited for given type. // automatically build context most suited for given type.
final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim) final val autoField: BufferedNDField<Double, RealField> = NDField.auto(RealField, dim, dim)
@ -42,7 +42,7 @@ class ViktorBenchmark {
} }
@Benchmark @Benchmark
fun realdFieldLog() { fun realFieldLog() {
realField { realField {
val fortyTwo = produce { 42.0 } val fortyTwo = produce { 42.0 }
var res = one var res = one

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@ -1,4 +1,4 @@
//package kscience.kmath.ast package kscience.kmath.ast
// //
//import kscience.kmath.asm.compile //import kscience.kmath.asm.compile
//import kscience.kmath.expressions.Expression //import kscience.kmath.expressions.Expression

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@ -10,17 +10,14 @@ import org.apache.commons.rng.simple.RandomSource
import java.time.Duration import java.time.Duration
import java.time.Instant import java.time.Instant
private fun runChain(): Duration {
private suspend fun runChain(): Duration {
val generator = RandomGenerator.fromSource(RandomSource.MT, 123L) val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
val normal = Distribution.normal(NormalSamplerMethod.Ziggurat) val normal = Distribution.normal(NormalSamplerMethod.Ziggurat)
val chain = normal.sample(generator) as BlockingRealChain val chain = normal.sample(generator) as BlockingRealChain
val startTime = Instant.now() val startTime = Instant.now()
var sum = 0.0 var sum = 0.0
repeat(10000001) { counter ->
repeat(10000001) { counter ->
sum += chain.nextDouble() sum += chain.nextDouble()
if (counter % 100000 == 0) { if (counter % 100000 == 0) {
@ -29,6 +26,7 @@ private suspend fun runChain(): Duration {
println("Chain sampler completed $counter elements in $duration: $meanValue") println("Chain sampler completed $counter elements in $duration: $meanValue")
} }
} }
return Duration.between(startTime, Instant.now()) return Duration.between(startTime, Instant.now())
} }
@ -36,10 +34,9 @@ private fun runDirect(): Duration {
val provider = RandomSource.create(RandomSource.MT, 123L) val provider = RandomSource.create(RandomSource.MT, 123L)
val sampler = ZigguratNormalizedGaussianSampler(provider) val sampler = ZigguratNormalizedGaussianSampler(provider)
val startTime = Instant.now() val startTime = Instant.now()
var sum = 0.0 var sum = 0.0
repeat(10000001) { counter ->
repeat(10000001) { counter ->
sum += sampler.sample() sum += sampler.sample()
if (counter % 100000 == 0) { if (counter % 100000 == 0) {
@ -48,6 +45,7 @@ private fun runDirect(): Duration {
println("Direct sampler completed $counter elements in $duration: $meanValue") println("Direct sampler completed $counter elements in $duration: $meanValue")
} }
} }
return Duration.between(startTime, Instant.now()) return Duration.between(startTime, Instant.now())
} }
@ -56,16 +54,9 @@ private fun runDirect(): Duration {
*/ */
fun main() { fun main() {
runBlocking(Dispatchers.Default) { runBlocking(Dispatchers.Default) {
val chainJob = async { val chainJob = async { runChain() }
runChain() val directJob = async { runDirect() }
}
val directJob = async {
runDirect()
}
println("Chain: ${chainJob.await()}") println("Chain: ${chainJob.await()}")
println("Direct: ${directJob.await()}") println("Direct: ${directJob.await()}")
} }
} }

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@ -7,9 +7,9 @@ import kscience.kmath.prob.Distribution
import kscience.kmath.prob.RandomGenerator import kscience.kmath.prob.RandomGenerator
import kscience.kmath.prob.normal import kscience.kmath.prob.normal
data class AveragingChainState(var num: Int = 0, var value: Double = 0.0) private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain -> private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain ->
val next = chain.next() val next = chain.next()
num++ num++
value += next value += next

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@ -1,8 +1,6 @@
package kscience.kmath.operations package kscience.kmath.operations
fun main() { fun main() {
val res = BigIntField { val res = BigIntField { number(1) * 2 }
number(1) * 2
}
println("bigint:$res") println("bigint:$res")
} }

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@ -4,32 +4,30 @@ import kotlin.system.measureTimeMillis
fun main() { fun main() {
val n = 6000 val n = 6000
val array = DoubleArray(n * n) { 1.0 } val array = DoubleArray(n * n) { 1.0 }
val buffer = RealBuffer(array) val buffer = RealBuffer(array)
val strides = DefaultStrides(intArrayOf(n, n)) val strides = DefaultStrides(intArrayOf(n, n))
val structure = BufferNDStructure(strides, buffer) val structure = BufferNDStructure(strides, buffer)
measureTimeMillis { measureTimeMillis {
var res: Double = 0.0 var res = 0.0
strides.indices().forEach { res = structure[it] } strides.indices().forEach { res = structure[it] }
} // warmup } // warmup
val time1 = measureTimeMillis { val time1 = measureTimeMillis {
var res: Double = 0.0 var res = 0.0
strides.indices().forEach { res = structure[it] } strides.indices().forEach { res = structure[it] }
} }
println("Structure reading finished in $time1 millis") println("Structure reading finished in $time1 millis")
val time2 = measureTimeMillis { val time2 = measureTimeMillis {
var res: Double = 0.0 var res = 0.0
strides.indices().forEach { res = buffer[strides.offset(it)] } strides.indices().forEach { res = buffer[strides.offset(it)] }
} }
println("Buffer reading finished in $time2 millis") println("Buffer reading finished in $time2 millis")
val time3 = measureTimeMillis { val time3 = measureTimeMillis {
var res: Double = 0.0 var res = 0.0
strides.indices().forEach { res = array[strides.offset(it)] } strides.indices().forEach { res = array[strides.offset(it)] }
} }
println("Array reading finished in $time3 millis") println("Array reading finished in $time3 millis")

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@ -4,24 +4,17 @@ import kotlin.system.measureTimeMillis
fun main() { fun main() {
val n = 6000 val n = 6000
val structure = NDStructure.build(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 } val structure = NDStructure.build(intArrayOf(n, n), Buffer.Companion::auto) { 1.0 }
structure.mapToBuffer { it + 1 } // warm-up structure.mapToBuffer { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBuffer { it + 1 } }
val time1 = measureTimeMillis {
val res = structure.mapToBuffer { it + 1 }
}
println("Structure mapping finished in $time1 millis") println("Structure mapping finished in $time1 millis")
val array = DoubleArray(n * n) { 1.0 } val array = DoubleArray(n * n) { 1.0 }
val time2 = measureTimeMillis { val time2 = measureTimeMillis {
val target = DoubleArray(n * n) val target = DoubleArray(n * n)
val res = array.forEachIndexed { index, value -> val res = array.forEachIndexed { index, value -> target[index] = value + 1 }
target[index] = value + 1
}
} }
println("Array mapping finished in $time2 millis") println("Array mapping finished in $time2 millis")
val buffer = RealBuffer(DoubleArray(n * n) { 1.0 }) val buffer = RealBuffer(DoubleArray(n * n) { 1.0 })

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@ -6,7 +6,7 @@ import kscience.kmath.dimensions.DMatrixContext
import kscience.kmath.dimensions.Dimension import kscience.kmath.dimensions.Dimension
import kscience.kmath.operations.RealField import kscience.kmath.operations.RealField
fun DMatrixContext<Double, RealField>.simple() { private fun DMatrixContext<Double, RealField>.simple() {
val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() } val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() } val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
@ -14,12 +14,11 @@ fun DMatrixContext<Double, RealField>.simple() {
m1.transpose() + m2 m1.transpose() + m2
} }
private object D5 : Dimension {
object D5 : Dimension {
override val dim: UInt = 5u override val dim: UInt = 5u
} }
fun DMatrixContext<Double, RealField>.custom() { private fun DMatrixContext<Double, RealField>.custom() {
val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() } val m1 = produce<D2, D5> { i, j -> (i + j).toDouble() }
val m2 = produce<D5, D2> { 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() } val m3 = produce<D2, D2> { i, j -> (i - j).toDouble() }

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@ -9,14 +9,17 @@ import org.apache.commons.math3.analysis.differentiation.DerivativeStructure
import kotlin.properties.ReadOnlyProperty import kotlin.properties.ReadOnlyProperty
/** /**
* A field wrapping commons-math derivative structures * A field over commons-math [DerivativeStructure].
*
* @property order The derivation order.
* @property parameters The map of free parameters.
*/ */
public class DerivativeStructureField( public class DerivativeStructureField(
public val order: Int, public val order: Int,
public val parameters: Map<String, Double> public val parameters: Map<String, Double>
) : ExtendedField<DerivativeStructure> { ) : ExtendedField<DerivativeStructure> {
public override val zero: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size) } public override val zero: DerivativeStructure by lazy { DerivativeStructure(parameters.size, order) }
public override val one: DerivativeStructure by lazy { DerivativeStructure(order, parameters.size, 1.0) } public override val one: DerivativeStructure by lazy { DerivativeStructure(parameters.size, order, 1.0) }
private val variables: Map<String, DerivativeStructure> = parameters.mapValues { (key, value) -> private val variables: Map<String, DerivativeStructure> = parameters.mapValues { (key, value) ->
DerivativeStructure(parameters.size, order, parameters.keys.indexOf(key), value) DerivativeStructure(parameters.size, order, parameters.keys.indexOf(key), value)

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@ -4,7 +4,7 @@ import kscience.kmath.operations.*
internal class FunctionalUnaryOperation<T>(val context: Algebra<T>, val name: String, private val expr: Expression<T>) : internal class FunctionalUnaryOperation<T>(val context: Algebra<T>, val name: String, private val expr: Expression<T>) :
Expression<T> { Expression<T> {
public override operator fun invoke(arguments: Map<String, T>): T = override operator fun invoke(arguments: Map<String, T>): T =
context.unaryOperation(name, expr.invoke(arguments)) context.unaryOperation(name, expr.invoke(arguments))
} }
@ -14,17 +14,17 @@ internal class FunctionalBinaryOperation<T>(
val first: Expression<T>, val first: Expression<T>,
val second: Expression<T> val second: Expression<T>
) : Expression<T> { ) : Expression<T> {
public override operator 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)) context.binaryOperation(name, first.invoke(arguments), second.invoke(arguments))
} }
internal class FunctionalVariableExpression<T>(val name: String, val default: T? = null) : Expression<T> { internal class FunctionalVariableExpression<T>(val name: String, val default: T? = null) : Expression<T> {
public override operator fun invoke(arguments: Map<String, T>): T = override operator fun invoke(arguments: Map<String, T>): T =
arguments[name] ?: default ?: error("Parameter not found: $name") arguments[name] ?: default ?: error("Parameter not found: $name")
} }
internal class FunctionalConstantExpression<T>(val value: T) : Expression<T> { internal class FunctionalConstantExpression<T>(val value: T) : Expression<T> {
public override operator fun invoke(arguments: Map<String, T>): T = value override operator fun invoke(arguments: Map<String, T>): T = value
} }
internal class FunctionalConstProductExpression<T>( internal class FunctionalConstProductExpression<T>(
@ -32,7 +32,7 @@ internal class FunctionalConstProductExpression<T>(
private val expr: Expression<T>, private val expr: Expression<T>,
val const: Number val const: Number
) : Expression<T> { ) : Expression<T> {
public override operator 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)
} }
/** /**
@ -139,16 +139,27 @@ public open class FunctionalExpressionField<T, A>(algebra: A) :
public open class FunctionalExpressionExtendedField<T, A>(algebra: A) : public open class FunctionalExpressionExtendedField<T, A>(algebra: A) :
FunctionalExpressionField<T, A>(algebra), FunctionalExpressionField<T, A>(algebra),
ExtendedField<Expression<T>> where A : ExtendedField<T>, A : NumericAlgebra<T> { ExtendedField<Expression<T>> where A : ExtendedField<T>, A : NumericAlgebra<T> {
public override fun sin(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.SIN_OPERATION, arg) public override fun sin(arg: Expression<T>): Expression<T> =
public override fun cos(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.COS_OPERATION, arg) unaryOperation(TrigonometricOperations.SIN_OPERATION, arg)
public override fun asin(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ASIN_OPERATION, arg)
public override fun acos(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ACOS_OPERATION, arg) public override fun cos(arg: Expression<T>): Expression<T> =
public override fun atan(arg: Expression<T>): Expression<T> = unaryOperation(TrigonometricOperations.ATAN_OPERATION, arg) unaryOperation(TrigonometricOperations.COS_OPERATION, arg)
public override fun asin(arg: Expression<T>): Expression<T> =
unaryOperation(TrigonometricOperations.ASIN_OPERATION, arg)
public override fun acos(arg: Expression<T>): Expression<T> =
unaryOperation(TrigonometricOperations.ACOS_OPERATION, arg)
public override fun atan(arg: Expression<T>): Expression<T> =
unaryOperation(TrigonometricOperations.ATAN_OPERATION, arg)
public override fun power(arg: Expression<T>, pow: Number): Expression<T> = public override fun power(arg: Expression<T>, pow: Number): Expression<T> =
binaryOperation(PowerOperations.POW_OPERATION, arg, number(pow)) binaryOperation(PowerOperations.POW_OPERATION, arg, number(pow))
public override fun exp(arg: Expression<T>): Expression<T> = unaryOperation(ExponentialOperations.EXP_OPERATION, arg) public override fun exp(arg: Expression<T>): Expression<T> =
unaryOperation(ExponentialOperations.EXP_OPERATION, arg)
public override fun ln(arg: Expression<T>): Expression<T> = unaryOperation(ExponentialOperations.LN_OPERATION, arg) public override fun ln(arg: Expression<T>): Expression<T> = unaryOperation(ExponentialOperations.LN_OPERATION, arg)
public override fun unaryOperation(operation: String, arg: Expression<T>): Expression<T> = public override fun unaryOperation(operation: String, arg: Expression<T>): Expression<T> =

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@ -24,7 +24,11 @@ public interface FeaturedMatrix<T : Any> : Matrix<T> {
public companion object public companion object
} }
public inline fun Structure2D.Companion.real(rows: Int, columns: Int, initializer: (Int, Int) -> Double): Matrix<Double> = public inline fun Structure2D.Companion.real(
rows: Int,
columns: Int,
initializer: (Int, Int) -> Double
): Matrix<Double> =
MatrixContext.real.produce(rows, columns, initializer) MatrixContext.real.produce(rows, columns, initializer)
/** /**

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@ -1,10 +1,7 @@
package kscience.kmath.misc package kscience.kmath.misc
import kscience.kmath.linear.Point import kscience.kmath.linear.Point
import kscience.kmath.operations.ExtendedField import kscience.kmath.operations.*
import kscience.kmath.operations.Field
import kscience.kmath.operations.invoke
import kscience.kmath.operations.sum
import kscience.kmath.structures.asBuffer import kscience.kmath.structures.asBuffer
import kotlin.contracts.InvocationKind import kotlin.contracts.InvocationKind
import kotlin.contracts.contract import kotlin.contracts.contract
@ -17,23 +14,37 @@ import kotlin.contracts.contract
/** /**
* Differentiable variable with value and derivative of differentiation ([deriv]) result * Differentiable variable with value and derivative of differentiation ([deriv]) result
* with respect to this variable. * with respect to this variable.
*
* @param T the non-nullable type of value.
* @property value The value of this variable.
*/ */
public open class Variable<T : Any>(public val value: T) public open class Variable<T : Any>(public val value: T)
/**
* Represents result of [deriv] call.
*
* @param T the non-nullable type of value.
* @param value the value of result.
* @property deriv The mapping of differentiated variables to their derivatives.
* @property context The field over [T].
*/
public class DerivationResult<T : Any>( public class DerivationResult<T : Any>(
value: T, value: T,
public val deriv: Map<Variable<T>, T>, public val deriv: Map<Variable<T>, T>,
public val context: Field<T> public val context: Field<T>
) : Variable<T>(value) { ) : Variable<T>(value) {
/**
* Returns derivative of [variable] or returns [Ring.zero] in [context].
*/
public fun deriv(variable: Variable<T>): T = deriv[variable] ?: context.zero public fun deriv(variable: Variable<T>): T = deriv[variable] ?: context.zero
/** /**
* compute divergence * Computes the divergence.
*/ */
public fun div(): T = context { sum(deriv.values) } public fun div(): T = context { sum(deriv.values) }
/** /**
* Compute a gradient for variables in given order * Computes the gradient for variables in given order.
*/ */
public fun grad(vararg variables: Variable<T>): Point<T> { public fun grad(vararg variables: Variable<T>): Point<T> {
check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" } check(variables.isNotEmpty()) { "Variable order is not provided for gradient construction" }
@ -53,6 +64,9 @@ public class DerivationResult<T : Any>(
* assertEquals(17.0, y.x) // the value of result (y) * assertEquals(17.0, y.x) // the value of result (y)
* assertEquals(9.0, x.d) // dy/dx * assertEquals(9.0, x.d) // dy/dx
* ``` * ```
*
* @param body the action in [AutoDiffField] context returning [Variable] to differentiate with respect to.
* @return the result of differentiation.
*/ */
public inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> { public inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -> Variable<T>): DerivationResult<T> {
contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) } contract { callsInPlace(body, InvocationKind.EXACTLY_ONCE) }
@ -65,12 +79,15 @@ public inline fun <T : Any, F : Field<T>> F.deriv(body: AutoDiffField<T, F>.() -
} }
} }
/**
* Represents field in context of which functions can be derived.
*/
public abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> { public abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>> {
public abstract val context: F public abstract val context: F
/** /**
* A variable accessing inner state of derivatives. * A variable accessing inner state of derivatives.
* Use this function in inner builders to avoid creating additional derivative bindings * Use this value in inner builders to avoid creating additional derivative bindings.
*/ */
public abstract var Variable<T>.d: T public abstract var Variable<T>.d: T
@ -87,6 +104,9 @@ public abstract class AutoDiffField<T : Any, F : Field<T>> : Field<Variable<T>>
*/ */
public abstract fun <R> derive(value: R, block: F.(R) -> Unit): R public abstract fun <R> derive(value: R, block: F.(R) -> Unit): R
/**
*
*/
public abstract fun variable(value: T): Variable<T> public abstract fun variable(value: T): Variable<T>
public inline fun variable(block: F.() -> T): Variable<T> = variable(context.block()) public inline fun variable(block: F.() -> T): Variable<T> = variable(context.block())

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@ -299,7 +299,7 @@ public class BigInt internal constructor(
for (i in mag.indices) { for (i in mag.indices) {
val cur: ULong = carry + mag[i].toULong() * x.toULong() val cur: ULong = carry + mag[i].toULong() * x.toULong()
result[i] = (cur and BASE.toULong()).toUInt() result[i] = (cur and BASE).toUInt()
carry = cur shr BASE_SIZE carry = cur shr BASE_SIZE
} }
result[resultLength - 1] = (carry and BASE).toUInt() result[resultLength - 1] = (carry and BASE).toUInt()
@ -316,7 +316,7 @@ public class BigInt internal constructor(
for (j in mag2.indices) { for (j in mag2.indices) {
val cur: ULong = result[i + j].toULong() + mag1[i].toULong() * mag2[j].toULong() + carry val cur: ULong = result[i + j].toULong() + mag1[i].toULong() * mag2[j].toULong() + carry
result[i + j] = (cur and BASE.toULong()).toUInt() result[i + j] = (cur and BASE).toUInt()
carry = cur shr BASE_SIZE carry = cur shr BASE_SIZE
} }

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@ -6,6 +6,7 @@ import kscience.kmath.memory.MemoryWriter
import kscience.kmath.structures.Buffer import kscience.kmath.structures.Buffer
import kscience.kmath.structures.MemoryBuffer import kscience.kmath.structures.MemoryBuffer
import kscience.kmath.structures.MutableBuffer import kscience.kmath.structures.MutableBuffer
import kscience.kmath.structures.MutableMemoryBuffer
import kotlin.math.* import kotlin.math.*
/** /**
@ -159,7 +160,7 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex> {
} }
/** /**
* Represents complex number. * Represents `double`-based complex number.
* *
* @property re The real part. * @property re The real part.
* @property im The imaginary part. * @property im The imaginary part.
@ -182,10 +183,10 @@ public data class Complex(val re: Double, val im: Double) : FieldElement<Complex
public companion object : MemorySpec<Complex> { public companion object : MemorySpec<Complex> {
override val objectSize: Int = 16 override val objectSize: Int
get() = 16
override fun MemoryReader.read(offset: Int): Complex = override fun MemoryReader.read(offset: Int): Complex = Complex(readDouble(offset), readDouble(offset + 8))
Complex(readDouble(offset), readDouble(offset + 8))
override fun MemoryWriter.write(offset: Int, value: Complex) { override fun MemoryWriter.write(offset: Int, value: Complex) {
writeDouble(offset, value.re) writeDouble(offset, value.re)
@ -202,8 +203,16 @@ public data class Complex(val re: Double, val im: Double) : FieldElement<Complex
*/ */
public fun Number.toComplex(): Complex = Complex(this, 0.0) public fun Number.toComplex(): Complex = Complex(this, 0.0)
/**
* Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
* specified [init] function.
*/
public inline fun Buffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> = public inline fun Buffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> =
MemoryBuffer.create(Complex, size, init) MemoryBuffer.create(Complex, size, init)
public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> = /**
MemoryBuffer.create(Complex, size, init) * Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
* specified [init] function.
*/
public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): MutableBuffer<Complex> =
MutableMemoryBuffer.create(Complex, size, init)

View File

@ -15,8 +15,9 @@ public class BoxingNDField<T, F : Field<T>>(
public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> =
bufferFactory(size, initializer) bufferFactory(size, initializer)
public override fun check(vararg elements: NDBuffer<T>) { public override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
check(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" } require(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" }
return elements
} }
public override fun produce(initializer: F.(IntArray) -> T): BufferedNDFieldElement<T, F> = public override fun produce(initializer: F.(IntArray) -> T): BufferedNDFieldElement<T, F> =
@ -75,6 +76,6 @@ public inline fun <T : Any, F : Field<T>, R> F.nd(
vararg shape: Int, vararg shape: Int,
action: NDField<T, F, *>.() -> R action: NDField<T, F, *>.() -> R
): R { ): R {
val ndfield: BoxingNDField<T, F> = NDField.boxing(this, *shape, bufferFactory = bufferFactory) val ndfield = NDField.boxing(this, *shape, bufferFactory = bufferFactory)
return ndfield.action() return ndfield.action()
} }

View File

@ -14,8 +14,9 @@ public class BoxingNDRing<T, R : Ring<T>>(
public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = bufferFactory(size, initializer) public fun buildBuffer(size: Int, initializer: (Int) -> T): Buffer<T> = bufferFactory(size, initializer)
override fun check(vararg elements: NDBuffer<T>) { override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
require(elements.all { it.strides == strides }) { "Element strides are not the same as context strides" } if (!elements.all { it.strides == this.strides }) error("Element strides are not the same as context strides")
return elements
} }
override fun produce(initializer: R.(IntArray) -> T): BufferedNDRingElement<T, R> = override fun produce(initializer: R.(IntArray) -> T): BufferedNDRingElement<T, R> =

View File

@ -5,8 +5,10 @@ import kscience.kmath.operations.*
public interface BufferedNDAlgebra<T, C> : NDAlgebra<T, C, NDBuffer<T>> { public interface BufferedNDAlgebra<T, C> : NDAlgebra<T, C, NDBuffer<T>> {
public val strides: Strides public val strides: Strides
public override fun check(vararg elements: NDBuffer<T>): Unit = public override fun check(vararg elements: NDBuffer<T>): Array<out NDBuffer<T>> {
require(elements.all { it.strides == strides }) { ("Strides mismatch") } require(elements.all { it.strides == strides }) { "Strides mismatch" }
return elements
}
/** /**
* Convert any [NDStructure] to buffered structure using strides from this context. * Convert any [NDStructure] to buffered structure using strides from this context.

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@ -46,35 +46,48 @@ public interface Buffer<T> {
asSequence().mapIndexed { index, value -> value == other[index] }.all { it } asSequence().mapIndexed { index, value -> value == other[index] }.all { it }
public companion object { public companion object {
public inline fun real(size: Int, initializer: (Int) -> Double): RealBuffer { /**
val array = DoubleArray(size) { initializer(it) } * Creates a [RealBuffer] with the specified [size], where each element is calculated by calling the specified
return RealBuffer(array) * [initializer] function.
} */
public inline fun real(size: Int, initializer: (Int) -> Double): RealBuffer =
RealBuffer(size) { initializer(it) }
/** /**
* Create a boxing buffer of given type * Creates a [ListBuffer] of given type [T] with given [size]. Each element is calculated by calling the
* specified [initializer] function.
*/ */
public inline fun <T> boxing(size: Int, initializer: (Int) -> T): Buffer<T> = public inline fun <T> boxing(size: Int, initializer: (Int) -> T): Buffer<T> =
ListBuffer(List(size, initializer)) ListBuffer(List(size, initializer))
// TODO add resolution based on Annotation or companion resolution
/**
* Creates a [Buffer] of given [type]. If the type is primitive, specialized buffers are used ([IntBuffer],
* [RealBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST") @Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<T>, size: Int, crossinline initializer: (Int) -> T): Buffer<T> { public inline fun <T : Any> auto(type: KClass<T>, size: Int, initializer: (Int) -> T): Buffer<T> =
//TODO add resolution based on Annotation or companion resolution when (type) {
return when (type) { Double::class -> RealBuffer(size) { initializer(it) as Double } as Buffer<T>
Double::class -> RealBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T> Short::class -> ShortBuffer(size) { initializer(it) as Short } as Buffer<T>
Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as Buffer<T> Int::class -> IntBuffer(size) { initializer(it) as Int } as Buffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T> Long::class -> LongBuffer(size) { initializer(it) as Long } as Buffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as Buffer<T> Float::class -> FloatBuffer(size) { initializer(it) as Float } as Buffer<T>
Complex::class -> complex(size) { initializer(it) as Complex } as Buffer<T> Complex::class -> complex(size) { initializer(it) as Complex } as Buffer<T>
else -> boxing(size, initializer) else -> boxing(size, initializer)
} }
}
/** /**
* Create most appropriate immutable buffer for given type avoiding boxing wherever possible * Creates a [Buffer] of given type [T]. If the type is primitive, specialized buffers are used ([IntBuffer],
* [RealBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/ */
@Suppress("UNCHECKED_CAST") @Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any> auto(size: Int, crossinline initializer: (Int) -> T): Buffer<T> = public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): Buffer<T> =
auto(T::class, size, initializer) auto(T::class, size, initializer)
} }
} }
@ -117,25 +130,40 @@ public interface MutableBuffer<T> : Buffer<T> {
public inline fun <T> boxing(size: Int, initializer: (Int) -> T): MutableBuffer<T> = public inline fun <T> boxing(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
MutableListBuffer(MutableList(size, initializer)) MutableListBuffer(MutableList(size, initializer))
/**
* Creates a [MutableBuffer] of given [type]. If the type is primitive, specialized buffers are used
* ([IntBuffer], [RealBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/
@Suppress("UNCHECKED_CAST") @Suppress("UNCHECKED_CAST")
public inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> = public inline fun <T : Any> auto(type: KClass<out T>, size: Int, initializer: (Int) -> T): MutableBuffer<T> =
when (type) { when (type) {
Double::class -> RealBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T> Double::class -> RealBuffer(size) { initializer(it) as Double } as MutableBuffer<T>
Short::class -> ShortBuffer(ShortArray(size) { initializer(it) as Short }) as MutableBuffer<T> Short::class -> ShortBuffer(size) { initializer(it) as Short } as MutableBuffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T> Int::class -> IntBuffer(size) { initializer(it) as Int } as MutableBuffer<T>
Long::class -> LongBuffer(LongArray(size) { initializer(it) as Long }) as MutableBuffer<T> Float::class -> FloatBuffer(size) { initializer(it) as Float } as MutableBuffer<T>
Long::class -> LongBuffer(size) { initializer(it) as Long } as MutableBuffer<T>
Complex::class -> complex(size) { initializer(it) as Complex } as MutableBuffer<T>
else -> boxing(size, initializer) else -> boxing(size, initializer)
} }
/** /**
* Create most appropriate mutable buffer for given type avoiding boxing wherever possible * Creates a [MutableBuffer] of given type [T]. If the type is primitive, specialized buffers are used
* ([IntBuffer], [RealBuffer], etc.), [ListBuffer] is returned otherwise.
*
* The [size] is specified, and each element is calculated by calling the specified [initializer] function.
*/ */
@Suppress("UNCHECKED_CAST") @Suppress("UNCHECKED_CAST")
public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> = public inline fun <reified T : Any> auto(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
auto(T::class, size, initializer) auto(T::class, size, initializer)
public val real: MutableBufferFactory<Double> = /**
{ size, initializer -> RealBuffer(DoubleArray(size) { initializer(it) }) } * Creates a [RealBuffer] with the specified [size], where each element is calculated by calling the specified
* [initializer] function.
*/
public inline fun real(size: Int, initializer: (Int) -> Double): RealBuffer =
RealBuffer(size) { initializer(it) }
} }
} }

View File

@ -48,7 +48,8 @@ public fun FlaggedBuffer<*>.isMissing(index: Int): Boolean = hasFlag(index, Valu
/** /**
* A real buffer which supports flags for each value like NaN or Missing * A real buffer which supports flags for each value like NaN or Missing
*/ */
public class FlaggedRealBuffer(public val values: DoubleArray, public val flags: ByteArray) : FlaggedBuffer<Double?>, Buffer<Double?> { public class FlaggedRealBuffer(public val values: DoubleArray, public val flags: ByteArray) : FlaggedBuffer<Double?>,
Buffer<Double?> {
init { init {
require(values.size == flags.size) { "Values and flags must have the same dimensions" } require(values.size == flags.size) { "Values and flags must have the same dimensions" }
} }

View File

@ -53,7 +53,7 @@ public class MutableMemoryBuffer<T : Any>(memory: Memory, spec: MemorySpec<T>) :
public inline fun <T : Any> create( public inline fun <T : Any> create(
spec: MemorySpec<T>, spec: MemorySpec<T>,
size: Int, size: Int,
crossinline initializer: (Int) -> T initializer: (Int) -> T
): MutableMemoryBuffer<T> = MutableMemoryBuffer(Memory.allocate(size * spec.objectSize), spec).also { buffer -> ): MutableMemoryBuffer<T> = MutableMemoryBuffer(Memory.allocate(size * spec.objectSize), spec).also { buffer ->
(0 until size).forEach { buffer[it] = initializer(it) } (0 until size).forEach { buffer[it] = initializer(it) }
} }

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@ -7,49 +7,77 @@ import kscience.kmath.operations.Space
import kotlin.native.concurrent.ThreadLocal import kotlin.native.concurrent.ThreadLocal
/** /**
* An exception is thrown when the expected ans actual shape of NDArray differs * An exception is thrown when the expected ans actual shape of NDArray differs.
*
* @property expected the expected shape.
* @property actual the actual shape.
*/ */
public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) : RuntimeException() public class ShapeMismatchException(public val expected: IntArray, public val actual: IntArray) :
RuntimeException("Shape ${actual.contentToString()} doesn't fit in expected shape ${expected.contentToString()}.")
/** /**
* The base interface for all nd-algebra implementations * The base interface for all ND-algebra implementations.
* @param T the type of nd-structure element *
* @param C the type of the element context * @param T the type of ND-structure element.
* @param N the type of the structure * @param C the type of the element context.
* @param N the type of the structure.
*/ */
public interface NDAlgebra<T, C, N : NDStructure<T>> { public interface NDAlgebra<T, C, N : NDStructure<T>> {
/**
* The shape of ND-structures this algebra operates on.
*/
public val shape: IntArray public val shape: IntArray
/**
* The algebra over elements of ND structure.
*/
public val elementContext: C public val elementContext: C
/** /**
* Produce a new [N] structure using given initializer function * Produces a new [N] structure using given initializer function.
*/ */
public fun produce(initializer: C.(IntArray) -> T): N public fun produce(initializer: C.(IntArray) -> T): N
/** /**
* Map elements from one structure to another one * Maps elements from one structure to another one by applying [transform] to them.
*/ */
public fun map(arg: N, transform: C.(T) -> T): N public fun map(arg: N, transform: C.(T) -> T): N
/** /**
* Map indexed elements * Maps elements from one structure to another one by applying [transform] to them alongside with their indices.
*/ */
public fun mapIndexed(arg: N, transform: C.(index: IntArray, T) -> T): N public fun mapIndexed(arg: N, transform: C.(index: IntArray, T) -> T): N
/** /**
* Combine two structures into one * Combines two structures into one.
*/ */
public fun combine(a: N, b: N, transform: C.(T, T) -> T): N public fun combine(a: N, b: N, transform: C.(T, T) -> T): N
/** /**
* Check if given elements are consistent with this context * Checks if given element is consistent with this context.
*
* @param element the structure to check.
* @return the valid structure.
*/ */
public fun check(vararg elements: N): Unit = elements.forEach { public fun check(element: N): N {
if (!shape.contentEquals(it.shape)) throw ShapeMismatchException(shape, it.shape) if (!element.shape.contentEquals(shape)) throw ShapeMismatchException(shape, element.shape)
return element
} }
/** /**
* element-by-element invoke a function working on [T] on a [NDStructure] * Checks if given elements are consistent with this context.
*
* @param elements the structures to check.
* @return the array of valid structures.
*/
public fun check(vararg elements: N): Array<out N> = elements
.map(NDStructure<T>::shape)
.singleOrNull { !shape.contentEquals(it) }
?.let { throw ShapeMismatchException(shape, it) }
?: elements
/**
* Element-wise invocation of function working on [T] on a [NDStructure].
*/ */
public operator fun Function1<T, T>.invoke(structure: N): N = map(structure) { value -> this@invoke(value) } public operator fun Function1<T, T>.invoke(structure: N): N = map(structure) { value -> this@invoke(value) }
@ -57,42 +85,107 @@ public interface NDAlgebra<T, C, N : NDStructure<T>> {
} }
/** /**
* An nd-space over element space * Space of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param S the type of space of structure elements.
*/ */
public interface NDSpace<T, S : Space<T>, N : NDStructure<T>> : Space<N>, NDAlgebra<T, S, N> { public interface NDSpace<T, S : Space<T>, N : NDStructure<T>> : Space<N>, NDAlgebra<T, S, N> {
/** /**
* Element-by-element addition * Element-wise addition.
*
* @param a the addend.
* @param b the augend.
* @return the sum.
*/ */
override fun add(a: N, b: N): N = combine(a, b) { aValue, bValue -> add(aValue, bValue) } public override fun add(a: N, b: N): N = combine(a, b) { aValue, bValue -> add(aValue, bValue) }
/** /**
* Multiply all elements by constant * Element-wise multiplication by scalar.
*
* @param a the multiplicand.
* @param k the multiplier.
* @return the product.
*/ */
override fun multiply(a: N, k: Number): N = map(a) { multiply(it, k) } public override fun multiply(a: N, k: Number): N = map(a) { multiply(it, k) }
//TODO move to extensions after KEEP-176 // TODO move to extensions after KEEP-176
/**
* Adds an ND structure to an element of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun N.plus(arg: T): N = map(this) { value -> add(arg, value) } public operator fun N.plus(arg: T): N = map(this) { value -> add(arg, value) }
/**
* Subtracts an element from ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun N.minus(arg: T): N = map(this) { value -> add(arg, -value) } public operator fun N.minus(arg: T): N = map(this) { value -> add(arg, -value) }
/**
* Adds an element to ND structure of it.
*
* @receiver the addend.
* @param arg the augend.
* @return the sum.
*/
public operator fun T.plus(arg: N): N = map(arg) { value -> add(this@plus, value) } public operator fun T.plus(arg: N): N = map(arg) { value -> add(this@plus, value) }
/**
* Subtracts an ND structure from an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.minus(arg: N): N = map(arg) { value -> add(-this@minus, value) } public operator fun T.minus(arg: N): N = map(arg) { value -> add(-this@minus, value) }
public companion object public companion object
} }
/** /**
* An nd-ring over element ring * Ring of [NDStructure].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param R the type of ring of structure elements.
*/ */
public interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T, R, N> { public interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T, R, N> {
/** /**
* Element-by-element multiplication * Element-wise multiplication.
*
* @param a the multiplicand.
* @param b the multiplier.
* @return the product.
*/ */
override fun multiply(a: N, b: N): N = combine(a, b) { aValue, bValue -> multiply(aValue, bValue) } public override fun multiply(a: N, b: N): N = combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
//TODO move to extensions after KEEP-176 //TODO move to extensions after KEEP-176
/**
* Multiplies an ND structure by an element of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun N.times(arg: T): N = map(this) { value -> multiply(arg, value) } public operator fun N.times(arg: T): N = map(this) { value -> multiply(arg, value) }
/**
* Multiplies an element by a ND structure of it.
*
* @receiver the multiplicand.
* @param arg the multiplier.
* @return the product.
*/
public operator fun T.times(arg: N): N = map(arg) { value -> multiply(this@times, value) } public operator fun T.times(arg: N): N = map(arg) { value -> multiply(this@times, value) }
public companion object public companion object
@ -103,17 +196,35 @@ public interface NDRing<T, R : Ring<T>, N : NDStructure<T>> : Ring<N>, NDSpace<T
* *
* @param T the type of the element contained in ND structure. * @param T the type of the element contained in ND structure.
* @param N the type of ND structure. * @param N the type of ND structure.
* @param F field of structure elements. * @param F the type field of structure elements.
*/ */
public interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F, N> { public interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing<T, F, N> {
/** /**
* Element-by-element division * Element-wise division.
*
* @param a the dividend.
* @param b the divisor.
* @return the quotient.
*/ */
override fun divide(a: N, b: N): N = combine(a, b) { aValue, bValue -> divide(aValue, bValue) } public override fun divide(a: N, b: N): N = combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
//TODO move to extensions after KEEP-176 //TODO move to extensions after KEEP-176
/**
* Divides an ND structure by an element of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun N.div(arg: T): N = map(this) { value -> divide(arg, value) } public operator fun N.div(arg: T): N = map(this) { value -> divide(arg, value) }
/**
* Divides an element by an ND structure of it.
*
* @receiver the dividend.
* @param arg the divisor.
* @return the quotient.
*/
public operator fun T.div(arg: N): N = map(arg) { divide(it, this@div) } public operator fun T.div(arg: N): N = map(arg) { divide(it, this@div) }
@ThreadLocal @ThreadLocal
@ -121,12 +232,12 @@ public interface NDField<T, F : Field<T>, N : NDStructure<T>> : Field<N>, NDRing
private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf() private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf()
/** /**
* Create a nd-field for [Double] values or pull it from cache if it was created previously * Create a nd-field for [Double] values or pull it from cache if it was created previously.
*/ */
public fun real(vararg shape: Int): RealNDField = realNDFieldCache.getOrPut(shape) { RealNDField(shape) } public fun real(vararg shape: Int): RealNDField = realNDFieldCache.getOrPut(shape) { RealNDField(shape) }
/** /**
* Create a nd-field with boxing generic buffer * Create an ND field with boxing generic buffer.
*/ */
public fun <T : Any, F : Field<T>> boxing( public fun <T : Any, F : Field<T>> boxing(
field: F, field: F,

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@ -38,9 +38,8 @@ public interface NDStructure<T> {
*/ */
public fun elements(): Sequence<Pair<IntArray, T>> public fun elements(): Sequence<Pair<IntArray, T>>
override fun equals(other: Any?): Boolean public override fun equals(other: Any?): Boolean
public override fun hashCode(): Int
override fun hashCode(): Int
public companion object { public companion object {
/** /**
@ -50,13 +49,8 @@ public interface NDStructure<T> {
if (st1 === st2) return true if (st1 === st2) return true
// fast comparison of buffers if possible // fast comparison of buffers if possible
if ( if (st1 is NDBuffer && st2 is NDBuffer && st1.strides == st2.strides)
st1 is NDBuffer &&
st2 is NDBuffer &&
st1.strides == st2.strides
) {
return st1.buffer.contentEquals(st2.buffer) return st1.buffer.contentEquals(st2.buffer)
}
//element by element comparison if it could not be avoided //element by element comparison if it could not be avoided
return st1.elements().all { (index, value) -> value == st2[index] } return st1.elements().all { (index, value) -> value == st2[index] }

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@ -17,7 +17,7 @@ public interface Structure1D<T> : NDStructure<T>, Buffer<T> {
/** /**
* A 1D wrapper for nd-structure * A 1D wrapper for nd-structure
*/ */
private inline class Structure1DWrapper<T>(public val structure: NDStructure<T>) : Structure1D<T> { private inline class Structure1DWrapper<T>(val structure: NDStructure<T>) : Structure1D<T> {
override val shape: IntArray get() = structure.shape override val shape: IntArray get() = structure.shape
override val size: Int get() = structure.shape[0] override val size: Int get() = structure.shape[0]

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@ -21,7 +21,8 @@ internal class LazyDeferred<T>(val dispatcher: CoroutineDispatcher, val block: s
} }
public class AsyncFlow<T> internal constructor(internal val deferredFlow: Flow<LazyDeferred<T>>) : Flow<T> { public class AsyncFlow<T> internal constructor(internal val deferredFlow: Flow<LazyDeferred<T>>) : Flow<T> {
override suspend fun collect(collector: FlowCollector<T>): Unit = deferredFlow.collect { collector.emit((it.await())) } override suspend fun collect(collector: FlowCollector<T>): Unit =
deferredFlow.collect { collector.emit((it.await())) }
} }
public fun <T, R> Flow<T>.async( public fun <T, R> Flow<T>.async(

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@ -3,8 +3,9 @@ package kscience.kmath.dimensions
import kotlin.reflect.KClass import kotlin.reflect.KClass
/** /**
* An abstract class which is not used in runtime. Designates a size of some structure. * Represents a quantity of dimensions in certain structure.
* Could be replaced later by fully inline constructs *
* @property dim The number of dimensions.
*/ */
public interface Dimension { public interface Dimension {
public val dim: UInt public val dim: UInt
@ -16,18 +17,33 @@ public fun <D : Dimension> KClass<D>.dim(): UInt = Dimension.resolve(this).dim
public expect fun <D : Dimension> Dimension.Companion.resolve(type: KClass<D>): D public expect fun <D : Dimension> Dimension.Companion.resolve(type: KClass<D>): D
/**
* Finds or creates [Dimension] with [Dimension.dim] equal to [dim].
*/
public expect fun Dimension.Companion.of(dim: UInt): Dimension public expect fun Dimension.Companion.of(dim: UInt): Dimension
/**
* Finds [Dimension.dim] of given type [D].
*/
public inline fun <reified D : Dimension> Dimension.Companion.dim(): UInt = D::class.dim() public inline fun <reified D : Dimension> Dimension.Companion.dim(): UInt = D::class.dim()
/**
* Type representing 1 dimension.
*/
public object D1 : Dimension { public object D1 : Dimension {
override val dim: UInt get() = 1U override val dim: UInt get() = 1U
} }
/**
* Type representing 2 dimensions.
*/
public object D2 : Dimension { public object D2 : Dimension {
override val dim: UInt get() = 2U override val dim: UInt get() = 2U
} }
/**
* Type representing 3 dimensions.
*/
public object D3 : Dimension { public object D3 : Dimension {
override val dim: UInt get() = 3U override val dim: UInt get() = 3U
} }

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@ -4,7 +4,7 @@ import kscience.kmath.operations.Space
public interface Vector public interface Vector
public interface GeometrySpace<V: Vector>: Space<V> { public interface GeometrySpace<V : Vector> : Space<V> {
/** /**
* L2 distance * L2 distance
*/ */

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@ -10,9 +10,10 @@ import kscience.kmath.structures.RealBuffer
*/ */
public interface Bin<T : Any> : Domain<T> { public interface Bin<T : Any> : Domain<T> {
/** /**
* The value of this bin * The value of this bin.
*/ */
public val value: Number public val value: Number
public val center: Point<T> public val center: Point<T>
} }

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@ -5,10 +5,7 @@ import kscience.kmath.histogram.fill
import kscience.kmath.histogram.put import kscience.kmath.histogram.put
import kscience.kmath.real.RealVector import kscience.kmath.real.RealVector
import kotlin.random.Random import kotlin.random.Random
import kotlin.test.Test import kotlin.test.*
import kotlin.test.assertEquals
import kotlin.test.assertFalse
import kotlin.test.assertTrue
internal class MultivariateHistogramTest { internal class MultivariateHistogramTest {
@Test @Test
@ -18,7 +15,7 @@ internal class MultivariateHistogramTest {
(-1.0..1.0) (-1.0..1.0)
) )
histogram.put(0.55, 0.55) histogram.put(0.55, 0.55)
val bin = histogram.find { it.value.toInt() > 0 }!! val bin = histogram.find { it.value.toInt() > 0 } ?: fail()
assertTrue { bin.contains(RealVector(0.55, 0.55)) } assertTrue { bin.contains(RealVector(0.55, 0.55)) }
assertTrue { bin.contains(RealVector(0.6, 0.5)) } assertTrue { bin.contains(RealVector(0.6, 0.5)) }
assertFalse { bin.contains(RealVector(-0.55, 0.55)) } assertFalse { bin.contains(RealVector(-0.55, 0.55)) }

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@ -62,7 +62,7 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
//clear list from previous run //clear list from previous run
tmp.clear() tmp.clear()
//Fill list //Fill list
repeat(size){ repeat(size) {
tmp.add(chain.next()) tmp.add(chain.next())
} }
//return new buffer with elements from tmp //return new buffer with elements from tmp

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@ -3,16 +3,59 @@ package kscience.kmath.prob
import kotlin.random.Random import kotlin.random.Random
/** /**
* A basic generator * An interface that is implemented by random number generator algorithms.
*/ */
public interface RandomGenerator { public interface RandomGenerator {
/**
* Gets the next random [Boolean] value.
*/
public fun nextBoolean(): Boolean public fun nextBoolean(): Boolean
/**
* Gets the next random [Double] value uniformly distributed between 0 (inclusive) and 1 (exclusive).
*/
public fun nextDouble(): Double public fun nextDouble(): Double
/**
* Gets the next random `Int` from the random number generator.
*
* Generates an `Int` random value uniformly distributed between [Int.MIN_VALUE] and [Int.MAX_VALUE] (inclusive).
*/
public fun nextInt(): Int public fun nextInt(): Int
/**
* Gets the next random non-negative `Int` from the random number generator less than the specified [until] bound.
*
* Generates an `Int` random value uniformly distributed between `0` (inclusive) and the specified [until] bound
* (exclusive).
*/
public fun nextInt(until: Int): Int public fun nextInt(until: Int): Int
/**
* Gets the next random `Long` from the random number generator.
*
* Generates a `Long` random value uniformly distributed between [Long.MIN_VALUE] and [Long.MAX_VALUE] (inclusive).
*/
public fun nextLong(): Long public fun nextLong(): Long
/**
* Gets the next random non-negative `Long` from the random number generator less than the specified [until] bound.
*
* Generates a `Long` random value uniformly distributed between `0` (inclusive) and the specified [until] bound (exclusive).
*/
public fun nextLong(until: Long): Long public fun nextLong(until: Long): Long
/**
* Fills a subrange of the specified byte [array] starting from [fromIndex] inclusive and ending [toIndex] exclusive
* with random bytes.
*
* @return [array] with the subrange filled with random bytes.
*/
public fun fillBytes(array: ByteArray, fromIndex: Int = 0, toIndex: Int = array.size) public fun fillBytes(array: ByteArray, fromIndex: Int = 0, toIndex: Int = array.size)
/**
* Creates a byte array of the specified [size], filled with random bytes.
*/
public fun nextBytes(size: Int): ByteArray = ByteArray(size).also { fillBytes(it) } public fun nextBytes(size: Int): ByteArray = ByteArray(size).also { fillBytes(it) }
/** /**
@ -25,12 +68,21 @@ public interface RandomGenerator {
public fun fork(): RandomGenerator public fun fork(): RandomGenerator
public companion object { public companion object {
public val default: DefaultGenerator by lazy { DefaultGenerator() } /**
* The [DefaultGenerator] instance.
*/
public val default: DefaultGenerator by lazy(::DefaultGenerator)
/**
* Returns [DefaultGenerator] of given [seed].
*/
public fun default(seed: Long): DefaultGenerator = DefaultGenerator(Random(seed)) public fun default(seed: Long): DefaultGenerator = DefaultGenerator(Random(seed))
} }
} }
/**
* Implements [RandomGenerator] by delegating all operations to [Random].
*/
public inline class DefaultGenerator(public val random: Random = Random) : RandomGenerator { public inline class DefaultGenerator(public val random: Random = Random) : RandomGenerator {
public override fun nextBoolean(): Boolean = random.nextBoolean() public override fun nextBoolean(): Boolean = random.nextBoolean()
public override fun nextDouble(): Double = random.nextDouble() public override fun nextDouble(): Double = random.nextDouble()

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@ -6,7 +6,7 @@ import kotlin.test.Test
class SamplerTest { class SamplerTest {
@Test @Test
fun bufferSamplerTest(){ fun bufferSamplerTest() {
val sampler: Sampler<Double> = val sampler: Sampler<Double> =
BasicSampler { it.chain { nextDouble() } } BasicSampler { it.chain { nextDouble() } }
val data = sampler.sampleBuffer(RandomGenerator.default, 100) val data = sampler.sampleBuffer(RandomGenerator.default, 100)