kscience/kmath
9
0
Fork 1
Code Issues Pull Requests 6 Packages Projects Releases 16 Wiki Activity

Use consistent code style #30

Merged
breandan merged 7 commits from dev into dev 2019-01-04 20:24:57 +03:00
Conversation 1 Commits 7 Files Changed 19 +1263 -544
40 changed files with 1263 additions and 544 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files
Showing only changes of commit 9808b2800c - Show all commits

4
README.md
View File

@ -58,11 +58,11 @@ repositories {
Then use regular dependency like
```groovy
compile(group: 'scientifik', name: 'kmath-core-jvm', version: '0.0.1-SNAPSHOT')
compile(group: 'scientifik', name: 'kmath-core', version: '0.0.1-SNAPSHOT')
```
or in kotlin
```kotlin
compile(group = "scientifik", name = "kmath-core-jvm", version = "0.0.1-SNAPSHOT")
compile(group = "scientifik", name = "kmath-core", version = "0.0.1-SNAPSHOT")
```
Work builds could be obtained with [![](https://jitpack.io/v/altavir/kmath.svg)](https://jitpack.io/#altavir/kmath).

24
build.gradle
View File

@ -1,24 +0,0 @@
buildscript {
ext.kotlin_version = '1.3.0'
repositories {
jcenter()
}
dependencies {
classpath "org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlin_version"
classpath "org.jfrog.buildinfo:build-info-extractor-gradle:4+"
}
}
allprojects {
apply plugin: 'maven-publish'
apply plugin: "com.jfrog.artifactory"
group = 'scientifik'
version = '0.0.1-SNAPSHOT'
}
if(file('artifactory.gradle').exists()){
apply from: 'artifactory.gradle'
}

35
build.gradle.kts Normal file
View File

@ -0,0 +1,35 @@
buildscript {
extra["kotlinVersion"] = "1.3.11"
extra["ioVersion"] = "0.1.2-dev-2"
extra["coroutinesVersion"] = "1.0.1"
val kotlinVersion: String by extra
val ioVersion: String by extra
val coroutinesVersion: String by extra
repositories {
jcenter()
}
dependencies {
classpath("org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlinVersion")
classpath("org.jfrog.buildinfo:build-info-extractor-gradle:4+")
}
}
plugins {
id("com.jfrog.artifactory") version "4.8.1" apply false
// id("org.jetbrains.kotlin.multiplatform") apply false
}
allprojects {
apply(plugin = "maven-publish")
apply(plugin = "com.jfrog.artifactory")
group = "scientifik"
version = "0.0.2-dev-1"
}
if(file("artifactory.gradle").exists()){
apply(from = "artifactory.gradle")
}

13
kmath-core/build.gradle
View File

@ -1,10 +1,5 @@
plugins {
id 'kotlin-multiplatform'
}
repositories {
maven { url = 'http://dl.bintray.com/kotlin/kotlin-eap' }
mavenCentral()
id "org.jetbrains.kotlin.multiplatform"
}
kotlin {
@ -19,7 +14,7 @@ kotlin {
sourceSets {
commonMain {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-common'
api 'org.jetbrains.kotlin:kotlin-stdlib-common'
}
}
commonTest {
@ -30,7 +25,7 @@ kotlin {
}
jvmMain {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-jdk8'
api 'org.jetbrains.kotlin:kotlin-stdlib-jdk8'
}
}
jvmTest {
@ -41,7 +36,7 @@ kotlin {
}
jsMain {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-js'
api 'org.jetbrains.kotlin:kotlin-stdlib-js'
}
}
jsTest {

116
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/FastHistogram.kt
View File

@ -1,63 +1,42 @@
package scientifik.kmath.histogram
import scientifik.kmath.linear.RealVector
import scientifik.kmath.linear.toVector
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.ndStructure
import scientifik.kmath.structures.*
import kotlin.math.floor
class MultivariateBin(override val center: RealVector, val sizes: RealVector, val counter: LongCounter = LongCounter()) : Bin<Double> {
init {
if (center.size != sizes.size) error("Dimension mismatch in bin creation. Expected ${center.size}, but found ${sizes.size}")
}
private operator fun RealPoint.minus(other: RealPoint) = ListBuffer((0 until size).map { get(it) - other[it] })
override fun contains(vector: Buffer<out Double>): Boolean {
if (vector.size != center.size) error("Dimension mismatch for input vector. Expected ${center.size}, but found ${vector.size}")
return vector.asSequence().mapIndexed { i, value -> value in (center[i] - sizes[i] / 2)..(center[i] + sizes[i] / 2) }.all { it }
}
override val value: Number get() = counter.sum()
internal operator fun inc() = this.also { counter.increment() }
override val dimension: Int get() = center.size
}
private inline fun <T> Buffer<out Double>.mapIndexed(crossinline mapper: (Int, Double) -> T): Sequence<T> = (0 until size).asSequence().map { mapper(it, get(it)) }
/**
* Uniform multivariate histogram with fixed borders. Based on NDStructure implementation with complexity of m for bin search, where m is the number of dimensions
* Uniform multivariate histogram with fixed borders. Based on NDStructure implementation with complexity of m for bin search, where m is the number of dimensions.
*/
class FastHistogram(
private val lower: RealVector,
private val upper: RealVector,
private val lower: RealPoint,
private val upper: RealPoint,
private val binNums: IntArray = IntArray(lower.size) { 20 }
) : Histogram<Double, MultivariateBin> {
) : MutableHistogram<Double, PhantomBin<Double>> {
private val strides = DefaultStrides(IntArray(binNums.size) { binNums[it] + 2 })
private val values: NDStructure<LongCounter> = ndStructure(strides) { LongCounter() }
//private val weight: NDStructure<DoubleCounter?> = ndStructure(strides){null}
//TODO optimize binSize performance if needed
private val binSize: RealPoint = ListBuffer((upper - lower).mapIndexed { index, value -> value / binNums[index] }.toList())
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 ((upper - lower).any { it <= 0 }) error("Range for one of axis is not strictly positive")
if ((upper - lower).asSequence().any { it <= 0 }) error("Range for one of axis is not strictly positive")
}
override val dimension: Int get() = lower.size
//TODO optimize binSize performance if needed
private val binSize = (upper - lower).mapIndexed { index, value -> value / binNums[index] }.toVector()
private val bins: NDStructure<MultivariateBin> by lazy {
val actualSizes = IntArray(binNums.size) { binNums[it] + 2 }
ndStructure(actualSizes) { indexArray ->
val center = indexArray.mapIndexed { axis, index ->
when (index) {
0 -> Double.NEGATIVE_INFINITY
actualSizes[axis] -> Double.POSITIVE_INFINITY
else -> lower[axis] + (index - 1) * binSize[axis]
}
}.toVector()
MultivariateBin(center, binSize)
}
}
/**
* Get internal [NDStructure] bin index for given axis
@ -70,17 +49,60 @@ class FastHistogram(
}
}
private fun getIndex(point: Buffer<out Double>): IntArray = IntArray(dimension) { getIndex(it, point[it]) }
override fun get(point: Buffer<out Double>): MultivariateBin? {
val index = IntArray(dimension) { getIndex(it, point[it]) }
return bins[index]
private fun getValue(index: IntArray): Long {
return values[index].sum()
}
override fun put(point: Buffer<out Double>) {
this[point]?.inc() ?: error("Could not find appropriate bin (should not be possible)")
fun getValue(point: Buffer<out Double>): Long {
return getValue(getIndex(point))
}
override fun iterator(): Iterator<MultivariateBin> = bins.asSequence().map { it.second }.iterator()
private fun getTemplate(index: IntArray): BinTemplate<Double> {
val center = index.mapIndexed { axis, i ->
when (i) {
0 -> Double.NEGATIVE_INFINITY
strides.shape[axis] - 1 -> Double.POSITIVE_INFINITY
else -> lower[axis] + (i.toDouble() - 0.5) * binSize[axis]
}
}.toVector()
return BinTemplate(center, binSize)
}
fun getTemplate(point: Buffer<out Double>): BinTemplate<Double> {
return getTemplate(getIndex(point))
}
override fun get(point: Buffer<out Double>): PhantomBin<Double>? {
val index = getIndex(point)
return PhantomBin(getTemplate(index), getValue(index))
}
override fun put(point: Buffer<out Double>, weight: Double) {
if (weight != 1.0) TODO("Implement weighting")
val index = getIndex(point)
values[index].increment()
}
override fun iterator(): Iterator<PhantomBin<Double>> = values.elements().map { (index, value) ->
PhantomBin(getTemplate(index), value.sum())
}.iterator()
/**
* Convert this histogram into NDStructure containing bin values but not bin descriptions
*/
fun asNDStructure(): NDStructure<Number> {
return ndStructure(this.values.shape) { values[it].sum() }
}
/**
* Create a phantom lightweight immutable copy of this histogram
*/
fun asPhantomHistogram(): PhantomHistogram<Double> {
val binTemplates = values.elements().associate { (index, _) -> getTemplate(index) to index }
return PhantomHistogram(binTemplates, asNDStructure())
}
companion object {
@ -108,8 +130,8 @@ class FastHistogram(
*/
fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): FastHistogram {
return FastHistogram(
ranges.map { it.first.start }.toVector(),
ranges.map { it.first.endInclusive }.toVector(),
ListBuffer(ranges.map { it.first.start }),
ListBuffer(ranges.map { it.first.endInclusive }),
ranges.map { it.second }.toIntArray()
)
}

37
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/Histogram.kt
View File

@ -1,15 +1,19 @@
package scientifik.kmath.histogram
import scientifik.kmath.operations.Space
import scientifik.kmath.structures.ArrayBuffer
import scientifik.kmath.structures.Buffer
import scientifik.kmath.structures.DoubleBuffer
typealias Point<T> = Buffer<T>
typealias RealPoint = Buffer<Double>
/**
* A simple geometric domain
* TODO move to geometry module
*/
interface Domain<T: Any> {
operator fun contains(vector: Buffer<out T>): Boolean
operator fun contains(vector: Point<out T>): Boolean
val dimension: Int
}
@ -21,7 +25,7 @@ interface Bin<T: Any> : Domain<T> {
* The value of this bin
*/
val value: Number
val center: Buffer<T>
val center: Point<T>
}
interface Histogram<T: Any, out B : Bin<T>> : Iterable<B> {
@ -29,34 +33,31 @@ interface Histogram<T: Any, out B : Bin<T>> : Iterable<B> {
/**
* Find existing bin, corresponding to given coordinates
*/
operator fun get(point: Buffer<out T>): B?
operator fun get(point: Point<out T>): B?
/**
* Dimension of the histogram
*/
val dimension: Int
}
interface MutableHistogram<T: Any, out B : Bin<T>>: Histogram<T,B>{
/**
* Increment appropriate bin
*/
fun put(point: Buffer<out T>)
fun put(point: Point<out T>, weight: Double = 1.0)
}
fun <T: Any> Histogram<T,*>.put(vararg point: T) = put(ArrayBuffer(point))
fun <T: Any> MutableHistogram<T,*>.put(vararg point: T) = put(ArrayBuffer(point))
fun <T: Any> Histogram<T,*>.fill(sequence: Iterable<Buffer<T>>) = sequence.forEach { put(it) }
fun MutableHistogram<Double,*>.put(vararg point: Number) = put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
fun MutableHistogram<Double,*>.put(vararg point: Double) = put(DoubleBuffer(point))
fun <T: Any> MutableHistogram<T,*>.fill(sequence: Iterable<Point<T>>) = sequence.forEach { put(it) }
/**
* Pass a sequence builder into histogram
*/
fun <T: Any> Histogram<T, *>.fill(buider: suspend SequenceScope<Buffer<T>>.() -> Unit) = fill(sequence(buider).asIterable())
/**
* A space to perform arithmetic operations on histograms
*/
interface HistogramSpace<T: Any, B : Bin<T>, H : Histogram<T,B>> : Space<H> {
/**
* Rules for performing operations on bins
*/
val binSpace: Space<Bin<T>>
}
fun <T: Any> MutableHistogram<T, *>.fill(buider: suspend SequenceScope<Point<T>>.() -> Unit) = fill(sequence(buider).asIterable())

63
kmath-core/src/commonMain/kotlin/scientifik/kmath/histogram/PhantomHistogram.kt Normal file
View File

@ -0,0 +1,63 @@
package scientifik.kmath.histogram
import scientifik.kmath.linear.Vector
import scientifik.kmath.operations.Space
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.asSequence
data class BinTemplate<T : Comparable<T>>(val center: Vector<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 = center.context.run { center + sizes / 2.0}
val lower = center.context.run {center - sizes / 2.0}
return vector.asSequence().mapIndexed { i, value ->
value in lower[i]..upper[i]
}.all { it }
}
}
/**
* A space to perform arithmetic operations on histograms
*/
interface HistogramSpace<T : Any, B : Bin<T>, H : Histogram<T, B>> : Space<H> {
/**
* Rules for performing operations on bins
*/
val binSpace: Space<Bin<T>>
}
class PhantomBin<T : Comparable<T>>(val template: BinTemplate<T>, override val value: Number) : Bin<T> {
override fun contains(vector: Point<out T>): Boolean = template.contains(vector)
override val dimension: Int
get() = template.center.size
override val center: Point<T>
get() = template.center
}
/**
* Immutable histogram with explicit structure for content and additional external bin description.
* Bin search is slow, but full histogram algebra is supported.
* @param bins map a template into structure index
*/
class PhantomHistogram<T : Comparable<T>>(
val bins: Map<BinTemplate<T>, IntArray>,
val data: NDStructure<Number>
) : Histogram<T, PhantomBin<T>> {
override val dimension: Int
get() = data.dimension
override fun iterator(): Iterator<PhantomBin<T>> {
return bins.asSequence().map { entry -> PhantomBin(entry.key, data[entry.value]) }.iterator()
}
override fun get(point: Point<out T>): PhantomBin<T>? {
val template = bins.keys.find { it.contains(point) }
return template?.let { PhantomBin(it, data[bins[it]!!]) }
}
}

18
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LUDecomposition.kt
View File

@ -1,5 +1,7 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.structures.MutableNDStructure
import scientifik.kmath.structures.NDStructure
import scientifik.kmath.structures.genericNdStructure
@ -9,7 +11,7 @@ import kotlin.math.absoluteValue
/**
* Implementation based on Apache common-maths LU-decomposition
*/
abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
abstract class LUDecomposition<T : Comparable<T>, F : Field<T>>(val matrix: Matrix<T, F>) {
private val field get() = matrix.context.field
/** Entries of LU decomposition. */
@ -31,7 +33,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* L is a lower-triangular matrix
* @return the L matrix (or null if decomposed matrix is singular)
*/
val l: Matrix<out T> by lazy {
val l: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
when {
j < i -> lu[i, j]
@ -48,7 +50,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* U is an upper-triangular matrix
* @return the U matrix (or null if decomposed matrix is singular)
*/
val u: Matrix<out T> by lazy {
val u: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
if (j >= i) lu[i, j] else field.zero
}
@ -64,7 +66,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
* @return the P rows permutation matrix (or null if decomposed matrix is singular)
* @see .getPivot
*/
val p: Matrix<out T> by lazy {
val p: Matrix<out T, F> by lazy {
matrix.context.produce { i, j ->
//TODO ineffective. Need sparse matrix for that
if (j == pivot[i]) field.one else field.zero
@ -181,7 +183,7 @@ abstract class LUDecomposition<T : Comparable<T>>(val matrix: Matrix<T>) {
}
class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThreshold: Double = DEFAULT_TOO_SMALL) : LUDecomposition<Double>(matrix) {
class RealLUDecomposition(matrix: RealMatrix, private val singularityThreshold: Double = DEFAULT_TOO_SMALL) : LUDecomposition<Double, DoubleField>(matrix) {
override fun isSingular(value: Double): Boolean {
return value.absoluteValue < singularityThreshold
}
@ -194,12 +196,12 @@ class RealLUDecomposition(matrix: Matrix<Double>, private val singularityThresho
/** Specialized solver. */
object RealLUSolver : LinearSolver<Double> {
object RealLUSolver : LinearSolver<Double, DoubleField> {
fun decompose(mat: Matrix<Double>, threshold: Double = 1e-11): RealLUDecomposition = RealLUDecomposition(mat, threshold)
fun decompose(mat: Matrix<Double, DoubleField>, threshold: Double = 1e-11): RealLUDecomposition = RealLUDecomposition(mat, threshold)
override fun solve(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
override fun solve(a: RealMatrix, b: RealMatrix): RealMatrix {
val decomposition = decompose(a, a.context.field.zero)
if (b.rows != a.rows) {

290
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/LinearAlgrebra.kt
View File

@ -2,282 +2,16 @@ package scientifik.kmath.linear
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.structures.*
import scientifik.kmath.operations.Norm
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
* @param T type of individual element of the vector or matrix
* @param V the type of vector space element
*/
abstract class MatrixSpace<T : Any>(val rows: Int, val columns: Int, val field: Field<T>) : Space<Matrix<T>> {
/**
* Produce the element of this space
*/
abstract fun produce(initializer: (Int, Int) -> T): Matrix<T>
/**
* Produce new matrix space with given dimensions. The space produced could be raised from cache since [MatrixSpace] does not have mutable elements
*/
abstract fun produceSpace(rows: Int, columns: Int): MatrixSpace<T>
override val zero: Matrix<T> by lazy {
produce { _, _ -> field.zero }
}
// val one: Matrix<T> by lazy {
// produce { i, j -> if (i == j) field.one else field.zero }
// }
override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
return produce { i, j -> with(field) { a[i, j] + b[i, j] } }
}
override fun multiply(a: Matrix<T>, k: Double): Matrix<T> {
//TODO it is possible to implement scalable linear elements which normed values and adjustable scale to save memory and processing poser
return produce { i, j -> with(field) { a[i, j] * k } }
}
/**
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
if (a.rows != b.columns) {
//TODO replace by specific exception
error("Dimension mismatch in linear structure dot product: [${a.rows},${a.columns}]*[${b.rows},${b.columns}]")
}
return produceSpace(a.rows, b.columns).produce { i, j ->
(0 until a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
}
}
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other !is MatrixSpace<*>) return false
if (rows != other.rows) return false
if (columns != other.columns) return false
if (field != other.field) return false
return true
}
override fun hashCode(): Int {
var result = rows
result = 31 * result + columns
result = 31 * result + field.hashCode()
return result
}
}
infix fun <T : Any> Matrix<T>.dot(b: Matrix<T>): Matrix<T> = this.context.multiply(this, b)
/**
* A matrix-like structure
*/
interface Matrix<T : Any> : SpaceElement<Matrix<T>, MatrixSpace<T>> {
/**
* Number of rows
*/
val rows: Int
/**
* Number of columns
*/
val columns: Int
/**
* Get element in row [i] and column [j]. Throws error in case of call ounside structure dimensions
*/
operator fun get(i: Int, j: Int): T
override val self: Matrix<T>
get() = this
fun transpose(): Matrix<T> {
return object : Matrix<T> {
override val context: MatrixSpace<T> = this@Matrix.context
override val rows: Int = this@Matrix.columns
override val columns: Int = this@Matrix.rows
override fun get(i: Int, j: Int): T = this@Matrix[j, i]
}
}
companion object {
/**
* Create [ArrayMatrix] with custom field
*/
fun <T : Any> of(rows: Int, columns: Int, field: Field<T>, initializer: (Int, Int) -> T) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, field), initializer)
/**
* Create [ArrayMatrix] of doubles. The implementation in general should be faster than generic one due to boxing.
*/
fun ofReal(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
/**
* Create a diagonal value matrix. By default value equals [Field.one].
*/
fun <T : Any> diagonal(rows: Int, columns: Int, field: Field<T>, values: (Int) -> T = { field.one }): Matrix<T> {
return of(rows, columns, field) { i, j -> if (i == j) values(i) else field.zero }
}
/**
* Equality check on two generic matrices
*/
fun equals(mat1: Matrix<*>, mat2: Matrix<*>): Boolean {
if (mat1 === mat2) return true
if (mat1.context != mat2.context) return false
for (i in 0 until mat1.rows) {
for (j in 0 until mat2.columns) {
if (mat1[i, j] != mat2[i, j]) return false
}
}
return true
}
}
}
/**
* A linear space for vectors
*/
abstract class VectorSpace<T : Any>(val size: Int, val field: Field<T>) : Space<Vector<T>> {
abstract fun produce(initializer: (Int) -> T): Vector<T>
override val zero: Vector<T> by lazy { produce { field.zero } }
override fun add(a: Vector<T>, b: Vector<T>): Vector<T> = produce { with(field) { a[it] + b[it] } }
override fun multiply(a: Vector<T>, k: Double): Vector<T> = produce { with(field) { a[it] * k } }
}
interface Vector<T : Any> : SpaceElement<Vector<T>, VectorSpace<T>>, Buffer<T>, Iterable<T> {
override val size: Int get() = context.size
companion object {
/**
* Create vector with custom field
*/
fun <T : Any> of(size: Int, field: Field<T>, initializer: (Int) -> T) =
ArrayVector(ArrayVectorSpace(size, field), initializer)
/**
* Create vector of [Double]
*/
fun ofReal(size: Int, initializer: (Int) -> Double) =
ArrayVector(ArrayVectorSpace(size, DoubleField, realNDFieldFactory), initializer)
fun ofReal(vararg point: Double) = point.toVector()
fun equals(v1: Vector<*>, v2: Vector<*>): Boolean {
if (v1 === v2) return true
if (v1.context != v2.context) return false
for (i in 0 until v2.size) {
if (v1[i] != v2[i]) return false
}
return true
}
}
}
typealias NDFieldFactory<T> = (IntArray) -> NDField<T>
internal fun <T : Any> genericNDFieldFactory(field: Field<T>): NDFieldFactory<T> = { index -> GenericNDField(index, field) }
internal val realNDFieldFactory: NDFieldFactory<Double> = { index -> GenericNDField(index, DoubleField) }
/**
* NDArray-based implementation of vector space. By default uses slow [SimpleNDField], but could be overridden with custom [NDField] factory.
*/
class ArrayMatrixSpace<T : Any>(
rows: Int,
columns: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = genericNDFieldFactory(field)
) : MatrixSpace<T>(rows, columns, field) {
val ndField by lazy {
ndFactory(intArrayOf(rows, columns))
}
override fun produce(initializer: (Int, Int) -> T): Matrix<T> = ArrayMatrix(this, initializer)
override fun produceSpace(rows: Int, columns: Int): ArrayMatrixSpace<T> {
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}
class ArrayVectorSpace<T : Any>(
size: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = genericNDFieldFactory(field)
) : VectorSpace<T>(size, field) {
val ndField by lazy {
ndFactory(intArrayOf(size))
}
override fun produce(initializer: (Int) -> T): Vector<T> = ArrayVector(this, initializer)
}
/**
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any> internal constructor(override val context: ArrayMatrixSpace<T>, val array: NDArray<T>) : Matrix<T> {
constructor(context: ArrayMatrixSpace<T>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
override val columns: Int get() = context.columns
override fun get(i: Int, j: Int): T {
return array[i, j]
}
override val self: ArrayMatrix<T> get() = this
}
class ArrayVector<T : Any> internal constructor(override val context: ArrayVectorSpace<T>, val array: NDArray<T>) : Vector<T> {
constructor(context: ArrayVectorSpace<T>, initializer: (Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0]) })
init {
if (context.size != array.shape[0]) {
error("Array dimension mismatch")
}
}
override fun get(index: Int): T {
return array[index]
}
override val self: ArrayVector<T> get() = this
override fun iterator(): Iterator<T> = (0 until size).map { array[it] }.iterator()
override fun copy(): ArrayVector<T> = ArrayVector(context, array)
override fun toString(): String = this.joinToString(prefix = "[", postfix = "]", separator = ", ") { it.toString() }
}
typealias RealVector = Vector<Double>
/**
* A group of methods to resolve equation A dot X = B, where A and B are matrices or vectors
*/
interface LinearSolver<T : Any> {
fun solve(a: Matrix<T>, b: Matrix<T>): Matrix<T>
fun solve(a: Matrix<T>, b: Vector<T>): Vector<T> = solve(a, b.toMatrix()).toVector()
fun inverse(a: Matrix<T>): Matrix<T> = solve(a, Matrix.diagonal(a.rows, a.columns, a.context.field))
interface LinearSolver<T : Any, F : Field<T>> {
fun solve(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F>
fun solve(a: Matrix<T, F>, b: Vector<T, F>): Vector<T, F> = solve(a, b.toMatrix()).toVector()
fun inverse(a: Matrix<T, F>): Matrix<T, F> = solve(a, Matrix.diagonal(a.rows, a.columns, a.context.field))
}
/**
@ -291,7 +25,7 @@ fun List<Double>.toVector() = Vector.ofReal(this.size) { this[it] }
/**
* Convert matrix to vector if it is possible
*/
fun <T : Any> Matrix<T>.toVector(): Vector<T> {
fun <T : Any, F : Field<T>> Matrix<T, F>.toVector(): Vector<T, F> {
return when {
this.columns == 1 -> {
// if (this is ArrayMatrix) {
@ -307,7 +41,7 @@ fun <T : Any> Matrix<T>.toVector(): Vector<T> {
}
}
fun <T : Any> Vector<T>.toMatrix(): Matrix<T> {
fun <T : Any, F : Field<T>> Vector<T, F>.toMatrix(): Matrix<T, F> {
// return if (this is ArrayVector) {
// //Reuse existing underlying array
// ArrayMatrix(ArrayMatrixSpace(size, 1, context.field, context.ndFactory), array)
@ -315,6 +49,14 @@ fun <T : Any> Vector<T>.toMatrix(): Matrix<T> {
// //Generic vector
// matrix(size, 1, context.field) { i, j -> get(i) }
// }
return Matrix.of(size, 1, context.field) { i, _ -> get(i) }
return Matrix.of(size, 1, context.space) { i, _ -> get(i) }
}
object VectorL2Norm : Norm<Vector<out Number, *>, Double> {
override fun norm(arg: Vector<out Number, *>): Double {
return kotlin.math.sqrt(arg.sumByDouble { it.toDouble() })
}
}
typealias RealVector = Vector<Double, DoubleField>
typealias RealMatrix = Matrix<Double, DoubleField>

187
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Matrix.kt Normal file
View File

@ -0,0 +1,187 @@
package scientifik.kmath.linear
import scientifik.kmath.operations.*
import scientifik.kmath.structures.*
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
* @param T type of individual element of the vector or matrix
* @param V the type of vector space element
*/
abstract class MatrixSpace<T : Any, F : Ring<T>>(val rows: Int, val columns: Int, val field: F) : Space<Matrix<T, F>> {
/**
* Produce the element of this space
*/
abstract fun produce(initializer: (Int, Int) -> T): Matrix<T, F>
/**
* Produce new matrix space with given dimensions. The space produced could be raised from cache since [MatrixSpace] does not have mutable elements
*/
abstract fun produceSpace(rows: Int, columns: Int): MatrixSpace<T, F>
override val zero: Matrix<T, F> by lazy {
produce { _, _ -> field.zero }
}
// val one: Matrix<T> by lazy {
// produce { i, j -> if (i == j) field.one else field.zero }
// }
override fun add(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F> {
return produce { i, j -> with(field) { a[i, j] + b[i, j] } }
}
override fun multiply(a: Matrix<T, F>, k: Double): Matrix<T, F> {
//TODO it is possible to implement scalable linear elements which normed values and adjustable scale to save memory and processing poser
return produce { i, j -> with(field) { a[i, j] * k } }
}
/**
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: Matrix<T, F>, b: Matrix<T, F>): Matrix<T, F> {
if (a.rows != b.columns) {
//TODO replace by specific exception
error("Dimension mismatch in linear structure dot product: [${a.rows},${a.columns}]*[${b.rows},${b.columns}]")
}
return produceSpace(a.rows, b.columns).produce { i, j ->
(0 until a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
}
}
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other !is MatrixSpace<*,*>) return false
if (rows != other.rows) return false
if (columns != other.columns) return false
if (field != other.field) return false
return true
}
override fun hashCode(): Int {
var result = rows
result = 31 * result + columns
result = 31 * result + field.hashCode()
return result
}
}
infix fun <T : Any, F : Field<T>> Matrix<T, F>.dot(b: Matrix<T, F>): Matrix<T, F> = this.context.multiply(this, b)
/**
* A matrix-like structure
*/
interface Matrix<T : Any, F: Ring<T>> : SpaceElement<Matrix<T, F>, MatrixSpace<T, F>> {
/**
* Number of rows
*/
val rows: Int
/**
* Number of columns
*/
val columns: Int
/**
* Get element in row [i] and column [j]. Throws error in case of call ounside structure dimensions
*/
operator fun get(i: Int, j: Int): T
override val self: Matrix<T, F>
get() = this
fun transpose(): Matrix<T, F> {
return object : Matrix<T, F> {
override val context: MatrixSpace<T, F> = this@Matrix.context
override val rows: Int = this@Matrix.columns
override val columns: Int = this@Matrix.rows
override fun get(i: Int, j: Int): T = this@Matrix[j, i]
}
}
companion object {
/**
* Create [ArrayMatrix] with custom field
*/
fun <T : Any, F: Field<T>> of(rows: Int, columns: Int, field: F, initializer: (Int, Int) -> T) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, field), initializer)
/**
* Create [ArrayMatrix] of doubles. The implementation in general should be faster than generic one due to boxing.
*/
fun ofReal(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArrayMatrixSpace(rows, columns, DoubleField, realNDFieldFactory), initializer)
/**
* Create a diagonal value matrix. By default value equals [Field.one].
*/
fun <T : Any, F: Field<T>> diagonal(rows: Int, columns: Int, field: F, values: (Int) -> T = { field.one }): Matrix<T, F> {
return of(rows, columns, field) { i, j -> if (i == j) values(i) else field.zero }
}
/**
* Equality check on two generic matrices
*/
fun equals(mat1: Matrix<*, *>, mat2: Matrix<*, *>): Boolean {
if (mat1 === mat2) return true
if (mat1.context != mat2.context) return false
for (i in 0 until mat1.rows) {
for (j in 0 until mat2.columns) {
if (mat1[i, j] != mat2[i, j]) return false
}
}
return true
}
}
}
typealias NDFieldFactory<T, F> = (IntArray) -> NDField<T, F>
internal fun <T : Any, F : Field<T>> genericNDFieldFactory(field: F): NDFieldFactory<T, F> = { index -> GenericNDField(index, field) }
internal val realNDFieldFactory: NDFieldFactory<Double, DoubleField> = { index -> ExtendedNDField(index, DoubleField) }
/**
* NDArray-based implementation of vector space. By default uses slow [GenericNDField], but could be overridden with custom [NDField] factory.
*/
class ArrayMatrixSpace<T : Any, F : Field<T>>(
rows: Int,
columns: Int,
field: F,
val ndFactory: NDFieldFactory<T, F> = genericNDFieldFactory(field)
) : MatrixSpace<T, F>(rows, columns, field) {
val ndField by lazy {
ndFactory(intArrayOf(rows, columns))
}
override fun produce(initializer: (Int, Int) -> T): Matrix<T, F> = ArrayMatrix(this, initializer)
override fun produceSpace(rows: Int, columns: Int): ArrayMatrixSpace<T, F> {
return ArrayMatrixSpace(rows, columns, field, ndFactory)
}
}
/**
* Member of [ArrayMatrixSpace] which wraps 2-D array
*/
class ArrayMatrix<T : Any, F : Field<T>> internal constructor(override val context: ArrayMatrixSpace<T, F>, val element: NDElement<T, F>) : Matrix<T, F> {
constructor(context: ArrayMatrixSpace<T, F>, initializer: (Int, Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0], list[1]) })
override val rows: Int get() = context.rows
override val columns: Int get() = context.columns
override fun get(i: Int, j: Int): T {
return element[i, j]
}
override val self: ArrayMatrix<T, F> get() = this
}

103
kmath-core/src/commonMain/kotlin/scientifik/kmath/linear/Vector.kt Normal file
View File

@ -0,0 +1,103 @@
package scientifik.kmath.linear
import scientifik.kmath.histogram.Point
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.structures.NDElement
import scientifik.kmath.structures.get
/**
* A linear space for vectors.
* Could be used on any point-like structure
*/
abstract class VectorSpace<T : Any, S : Space<T>>(val size: Int, val space: S) : Space<Point<T>> {
abstract fun produce(initializer: (Int) -> T): Vector<T, S>
override val zero: Vector<T, S> by lazy { produce { space.zero } }
override fun add(a: Point<T>, b: Point<T>): Vector<T, S> = produce { with(space) { a[it] + b[it] } }
override fun multiply(a: Point<T>, k: Double): Vector<T, S> = produce { with(space) { a[it] * k } }
}
/**
* A point coupled to the linear space
*/
interface Vector<T : Any, S : Space<T>> : SpaceElement<Point<T>, VectorSpace<T, S>>, Point<T>, Iterable<T> {
override val size: Int get() = context.size
override operator fun plus(b: Point<T>): Vector<T, S> = context.add(self, b)
override operator fun minus(b: Point<T>): Vector<T, S> = context.add(self, context.multiply(b, -1.0))
override operator fun times(k: Number): Vector<T, S> = context.multiply(self, k.toDouble())
override operator fun div(k: Number): Vector<T, S> = context.multiply(self, 1.0 / k.toDouble())
companion object {
/**
* Create vector with custom field
*/
fun <T : Any, F : Field<T>> of(size: Int, field: F, initializer: (Int) -> T) =
ArrayVector(ArrayVectorSpace(size, field), initializer)
private val realSpaceCache = HashMap<Int, ArrayVectorSpace<Double, DoubleField>>()
private fun getRealSpace(size: Int): ArrayVectorSpace<Double, DoubleField> {
return realSpaceCache.getOrPut(size){ArrayVectorSpace(size, DoubleField, realNDFieldFactory)}
}
/**
* Create vector of [Double]
*/
fun ofReal(size: Int, initializer: (Int) -> Double) =
ArrayVector(getRealSpace(size), initializer)
fun ofReal(vararg point: Double) = point.toVector()
fun equals(v1: Vector<*, *>, v2: Vector<*, *>): Boolean {
if (v1 === v2) return true
if (v1.context != v2.context) return false
for (i in 0 until v2.size) {
if (v1[i] != v2[i]) return false
}
return true
}
}
}
class ArrayVectorSpace<T : Any, F : Field<T>>(
size: Int,
field: F,
val ndFactory: NDFieldFactory<T, F> = genericNDFieldFactory(field)
) : VectorSpace<T, F>(size, field) {
val ndField by lazy {
ndFactory(intArrayOf(size))
}
override fun produce(initializer: (Int) -> T): Vector<T, F> = ArrayVector(this, initializer)
}
class ArrayVector<T : Any, F : Field<T>> internal constructor(override val context: VectorSpace<T, F>, val element: NDElement<T, F>) : Vector<T, F> {
constructor(context: ArrayVectorSpace<T, F>, initializer: (Int) -> T) : this(context, context.ndField.produce { list -> initializer(list[0]) })
init {
if (context.size != element.shape[0]) {
error("Array dimension mismatch")
}
}
override fun get(index: Int): T {
return element[index]
}
override val self: ArrayVector<T, F> get() = this
override fun iterator(): Iterator<T> = (0 until size).map { element[it] }.iterator()
override fun toString(): String = this.joinToString(prefix = "[", postfix = "]", separator = ", ") { it.toString() }
}

4
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Complex.kt
View File

@ -38,4 +38,8 @@ data class Complex(val re: Double, val im: Double) : FieldElement<Complex, Compl
val abs: Double
get() = kotlin.math.sqrt(square)
companion object {
}
}

50
kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/Fields.kt
View File

@ -2,10 +2,20 @@ package scientifik.kmath.operations
import kotlin.math.pow
/**
* Advanced Number-like field that implements basic operations
*/
interface ExtendedField<N : Any> :
Field<N>,
TrigonometricOperations<N>,
PowerOperations<N>,
ExponentialOperations<N>
/**
* Field for real values
*/
object RealField : Field<Real>, TrigonometricOperations<Real>, PowerOperations<Real>, ExponentialOperations<Real> {
object RealField : ExtendedField<Real>, Norm<Real, Real> {
override val zero: Real = Real(0.0)
override fun add(a: Real, b: Real): Real = Real(a.value + b.value)
override val one: Real = Real(1.0)
@ -21,25 +31,16 @@ object RealField : Field<Real>, TrigonometricOperations<Real>, PowerOperations<R
override fun exp(arg: Real): Real = Real(kotlin.math.exp(arg.value))
override fun ln(arg: Real): Real = Real(kotlin.math.ln(arg.value))
override fun norm(arg: Real): Real = Real(kotlin.math.abs(arg.value))
}
/**
* Real field element wrapping double.
*
* TODO could be replaced by inline class in kotlin 1.3 if it would allow to avoid boxing
* TODO inline does not work due to compiler bug. Waiting for fix for KT-27586
*/
data class Real(val value: Double) : Number(), FieldElement<Real, RealField> {
/*
* The class uses composition instead of inheritance since Double is final
*/
override fun toByte(): Byte = value.toByte()
override fun toChar(): Char = value.toChar()
override fun toDouble(): Double = value
override fun toFloat(): Float = value.toFloat()
override fun toInt(): Int = value.toInt()
override fun toLong(): Long = value.toLong()
override fun toShort(): Short = value.toShort()
inline class Real(val value: Double) : FieldElement<Real, RealField> {
//values are dynamically calculated to save memory
override val self
@ -48,12 +49,15 @@ data class Real(val value: Double) : Number(), FieldElement<Real, RealField> {
override val context
get() = RealField
companion object {
}
}
/**
* A field for double without boxing. Does not produce appropriate field element
*/
object DoubleField : Field<Double>, TrigonometricOperations<Double>, PowerOperations<Double>, ExponentialOperations<Double> {
object DoubleField : ExtendedField<Double>, Norm<Double, Double> {
override val zero: Double = 0.0
override fun add(a: Double, b: Double): Double = a + b
override fun multiply(a: Double, @Suppress("PARAMETER_NAME_CHANGED_ON_OVERRIDE") b: Double): Double = a * b
@ -65,7 +69,21 @@ object DoubleField : Field<Double>, TrigonometricOperations<Double>, PowerOperat
override fun power(arg: Double, pow: Double): Double = arg.pow(pow)
override fun exp(arg: Double): Double =kotlin.math.exp(arg)
override fun exp(arg: Double): Double = kotlin.math.exp(arg)
override fun ln(arg: Double): Double = kotlin.math.ln(arg)
override fun norm(arg: Double): Double = kotlin.math.abs(arg)
}
/**
* A field for double without boxing. Does not produce appropriate field element
*/
object IntField : Field<Int>{
override val zero: Int = 0
override fun add(a: Int, b: Int): Int = a + b
override fun multiply(a: Int, b: Int): Int = a * b
override fun multiply(a: Int, k: Double): Int = (k*a).toInt()
override val one: Int = 1
override fun divide(a: Int, b: Int): Int = a / b
}

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

@ -46,3 +46,9 @@ interface ExponentialOperations<T> {
fun <T : MathElement<T, out ExponentialOperations<T>>> exp(arg: T): T = arg.context.exp(arg)
fun <T : MathElement<T, out ExponentialOperations<T>>> ln(arg: T): T = arg.context.ln(arg)
interface Norm<in T, out R> {
fun norm(arg: T): R
}
fun <T : MathElement<T, out Norm<T, R>>, R> norm(arg: T): R = arg.context.norm(arg)

87
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
View File

@ -2,30 +2,40 @@ package scientifik.kmath.structures
/**
* A generic linear buffer for both primitives and objects
* A generic random access structure for both primitives and objects
*/
interface Buffer<T> : Iterable<T> {
interface Buffer<T> {
val size: Int
operator fun get(index: Int): T
/**
* A shallow copy of the buffer
*/
fun copy(): Buffer<T>
operator fun iterator(): Iterator<T>
}
fun <T> Buffer<T>.asSequence(): Sequence<T> = iterator().asSequence()
interface MutableBuffer<T> : Buffer<T> {
operator fun set(index: Int, value: T)
/**
* A shallow copy of the buffer
*/
override fun copy(): MutableBuffer<T>
fun copy(): MutableBuffer<T>
}
inline class ListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T> {
inline class ListBuffer<T>(private 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()
}
inline class MutableListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T> {
override val size: Int
get() = list.size
@ -38,10 +48,11 @@ inline class ListBuffer<T>(private val list: MutableList<T>) : MutableBuffer<T>
override fun iterator(): Iterator<T> = list.iterator()
override fun copy(): MutableBuffer<T> = ListBuffer(ArrayList(list))
override fun copy(): MutableBuffer<T> = MutableListBuffer(ArrayList(list))
}
class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
//Can't inline because array invariant
override val size: Int
get() = array.size
@ -56,7 +67,7 @@ class ArrayBuffer<T>(private val array: Array<T>) : MutableBuffer<T> {
override fun copy(): MutableBuffer<T> = ArrayBuffer(array.copyOf())
}
class DoubleBuffer(private val array: DoubleArray) : MutableBuffer<Double> {
inline class DoubleBuffer(private val array: DoubleArray) : MutableBuffer<Double> {
override val size: Int
get() = array.size
@ -71,10 +82,58 @@ class DoubleBuffer(private val array: DoubleArray) : MutableBuffer<Double> {
override fun copy(): MutableBuffer<Double> = DoubleBuffer(array.copyOf())
}
inline fun <reified T : Any> buffer(size: Int, noinline initializer: (Int) -> T): Buffer<T> {
return ArrayBuffer(Array(size, initializer))
inline class IntBuffer(private val array: IntArray) : MutableBuffer<Int> {
override val size: Int
get() = array.size
override fun get(index: Int): Int = array[index]
override fun set(index: Int, value: Int) {
array[index] = value
}
override fun iterator(): Iterator<Int> = array.iterator()
override fun copy(): MutableBuffer<Int> = IntBuffer(array.copyOf())
}
inline fun <reified T : Any> mutableBuffer(size: Int, noinline initializer: (Int) -> T): MutableBuffer<T> {
return ArrayBuffer(Array(size, initializer))
inline class ReadOnlyBuffer<T>(private val buffer: MutableBuffer<T>) : Buffer<T> {
override val size: Int get() = buffer.size
override fun get(index: Int): T = buffer.get(index)
override fun iterator(): Iterator<T> = buffer.iterator()
}
/**
* Convert this buffer to read-only buffer
*/
fun <T> Buffer<T>.asReadOnly(): Buffer<T> = if (this is MutableBuffer) {
ReadOnlyBuffer(this)
} else {
this
}
/**
* Create most appropriate immutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> buffer(size: Int, noinline initializer: (Int) -> T): Buffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as Buffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as Buffer<T>
else -> ArrayBuffer(Array(size, initializer))
}
}
/**
* Create most appropriate mutable buffer for given type avoiding boxing wherever possible
*/
@Suppress("UNCHECKED_CAST")
inline fun <reified T : Any> mutableBuffer(size: Int, noinline initializer: (Int) -> T): MutableBuffer<T> {
return when (T::class) {
Double::class -> DoubleBuffer(DoubleArray(size) { initializer(it) as Double }) as MutableBuffer<T>
Int::class -> IntBuffer(IntArray(size) { initializer(it) as Int }) as MutableBuffer<T>
else -> ArrayBuffer(Array(size, initializer))
}
}

42
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/ExtendedNDField.kt Normal file
View File

@ -0,0 +1,42 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.ExponentialOperations
import scientifik.kmath.operations.ExtendedField
import scientifik.kmath.operations.PowerOperations
import scientifik.kmath.operations.TrigonometricOperations
/**
* NDField that supports [ExtendedField] operations on its elements
*/
class ExtendedNDField<N : Any, F : ExtendedField<N>>(shape: IntArray, field: F) : NDField<N, F>(shape, field),
TrigonometricOperations<NDElement<N, F>>,
PowerOperations<NDElement<N, F>>,
ExponentialOperations<NDElement<N, F>> {
override fun produceStructure(initializer: F.(IntArray) -> N): NDStructure<N> {
return genericNdStructure(shape) { field.initializer(it) }
}
override fun power(arg: NDElement<N, F>, pow: Double): NDElement<N, F> {
return arg.transform { d -> with(field) { power(d, pow) } }
}
override fun exp(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { exp(d) } }
}
override fun ln(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { ln(d) } }
}
override fun sin(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { sin(d) } }
}
override fun cos(arg: NDElement<N, F>): NDElement<N, F> {
return arg.transform { d -> with(field) { cos(d) } }
}
}

20
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/LazyStructure.kt Normal file
View File

@ -0,0 +1,20 @@
package scientifik.kmath.structures
//
//class LazyStructureField<T: Any>(val field: Field<T>): Field<LazyStructure<T>>{
//
//}
//
//class LazyStructure<T : Any> : NDStructure<T> {
//
// override val shape: IntArray
// get() = TODO("not implemented") //To change initializer of created properties use File | Settings | File Templates.
//
// override fun get(index: IntArray): T {
// TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
// }
//
// override fun iterator(): Iterator<Pair<IntArray, T>> {
// TODO("not implemented") //To change body of created functions use File | Settings | File Templates.
// }
//}

133
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDField.kt
View File

@ -15,25 +15,25 @@ class ShapeMismatchException(val expected: IntArray, val actual: IntArray) : Run
* @param field - operations field defined on individual array element
* @param T the type of the element contained in NDArray
*/
abstract class NDField<T>(val shape: IntArray, val field: Field<T>) : Field<NDArray<T>> {
abstract class NDField<T, F : Field<T>>(val shape: IntArray, val field: F) : Field<NDElement<T, F>> {
abstract fun produceStructure(initializer: (IntArray) -> T): NDStructure<T>
abstract fun produceStructure(initializer: F.(IntArray) -> T): NDStructure<T>
/**
* Create new instance of NDArray using field shape and given initializer
* The producer takes list of indices as argument and returns contained value
*/
fun produce(initializer: (IntArray) -> T): NDArray<T> = NDArray(this, produceStructure(initializer))
fun produce(initializer: F.(IntArray) -> T): NDElement<T, F> = NDStructureElement(this, produceStructure(initializer))
override val zero: NDArray<T> by lazy {
produce { this.field.zero }
override val zero: NDElement<T, F> by lazy {
produce { zero }
}
/**
* Check the shape of given NDArray and throw exception if it does not coincide with shape of the field
*/
private fun checkShape(vararg arrays: NDArray<T>) {
arrays.forEach {
private fun checkShape(vararg elements: NDElement<T, F>) {
elements.forEach {
if (!shape.contentEquals(it.shape)) {
throw ShapeMismatchException(shape, it.shape)
}
@ -43,7 +43,7 @@ abstract class NDField<T>(val shape: IntArray, val field: Field<T>) : Field<NDAr
/**
* Element-by-element addition
*/
override fun add(a: NDArray<T>, b: NDArray<T>): NDArray<T> {
override fun add(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a, b)
return produce { with(field) { a[it] + b[it] } }
}
@ -51,18 +51,18 @@ abstract class NDField<T>(val shape: IntArray, val field: Field<T>) : Field<NDAr
/**
* Multiply all elements by cinstant
*/
override fun multiply(a: NDArray<T>, k: Double): NDArray<T> {
override fun multiply(a: NDElement<T, F>, k: Double): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] * k } }
}
override val one: NDArray<T>
get() = produce { this.field.one }
override val one: NDElement<T, F>
get() = produce { one }
/**
* Element-by-element multiplication
*/
override fun multiply(a: NDArray<T>, b: NDArray<T>): NDArray<T> {
override fun multiply(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] * b[it] } }
}
@ -70,73 +70,77 @@ abstract class NDField<T>(val shape: IntArray, val field: Field<T>) : Field<NDAr
/**
* Element-by-element division
*/
override fun divide(a: NDArray<T>, b: NDArray<T>): NDArray<T> {
override fun divide(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
checkShape(a)
return produce { with(field) { a[it] / b[it] } }
}
/**
* Reverse sum operation
*/
operator fun <T> T.plus(arg: NDArray<T>): NDArray<T> = arg + this
/**
* Reverse minus operation
*/
operator fun <T> T.minus(arg: NDArray<T>): NDArray<T> = arg.transform { _, value ->
with(arg.context.field) {
this@minus - value
}
}
/**
* Reverse product operation
*/
operator fun <T> T.times(arg: NDArray<T>): NDArray<T> = arg * this
/**
* Reverse division operation
*/
operator fun <T> T.div(arg: NDArray<T>): NDArray<T> = arg.transform { _, value ->
with(arg.context.field) {
this@div / value
}
}
// /**
// * Reverse sum operation
// */
// operator fun T.plus(arg: NDElement<T, F>): NDElement<T, F> = arg + this
//
// /**
// * Reverse minus operation
// */
// operator fun T.minus(arg: NDElement<T, F>): NDElement<T, F> = arg.transformIndexed { _, value ->
// with(arg.context.field) {
// this@minus - value
// }
// }
//
// /**
// * Reverse product operation
// */
// operator fun T.times(arg: NDElement<T, F>): NDElement<T, F> = arg * this
//
// /**
// * Reverse division operation
// */
// operator fun T.div(arg: NDElement<T, F>): NDElement<T, F> = arg.transformIndexed { _, value ->
// with(arg.context.field) {
// this@div / value
// }
// }
}
interface NDElement<T, F : Field<T>>: FieldElement<NDElement<T, F>, NDField<T, F>>, NDStructure<T>
inline fun <T, F : Field<T>> NDElement<T, F>.transformIndexed(crossinline action: F.(IntArray, T) -> T): NDElement<T, F> = context.produce { action(it, get(*it)) }
inline fun <T, F : Field<T>> NDElement<T, F>.transform(crossinline action: F.(T) -> T): NDElement<T, F> = context.produce { action(get(*it)) }
/**
* Immutable [NDStructure] coupled to the context. Emulates Python ndarray
* Read-only [NDStructure] coupled to the context.
*/
data class NDArray<T>(override val context: NDField<T>, private val structure: NDStructure<T>) : FieldElement<NDArray<T>, NDField<T>>, NDStructure<T> by structure {
class NDStructureElement<T, F : Field<T>>(override val context: NDField<T, F>, private val structure: NDStructure<T>) : NDElement<T,F>, NDStructure<T> by structure {
//TODO ensure structure is immutable
override val self: NDArray<T>
get() = this
fun transform(action: (IntArray, T) -> T): NDArray<T> = context.produce { action(it, get(*it)) }
override val self: NDElement<T, F> get() = this
}
/**
* Element by element application of any operation on elements to the whole array. Just like in numpy
*/
operator fun <T> Function1<T, T>.invoke(ndArray: NDArray<T>): NDArray<T> = ndArray.transform { _, value -> this(value) }
operator fun <T, F : Field<T>> Function1<T, T>.invoke(ndElement: NDElement<T, F>): NDElement<T, F> = ndElement.transform {value -> this@invoke(value) }
/* plus and minus */
/**
* Summation operation for [NDArray] and single element
* Summation operation for [NDElement] and single element
*/
operator fun <T> NDArray<T>.plus(arg: T): NDArray<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.plus(arg: T): NDElement<T, F> = transform {value ->
with(context.field) {
arg + value
}
}
/**
* Subtraction operation between [NDArray] and single element
* Subtraction operation between [NDElement] and single element
*/
operator fun <T> NDArray<T>.minus(arg: T): NDArray<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.minus(arg: T): NDElement<T, F> = transform {value ->
with(context.field) {
arg - value
}
@ -145,25 +149,25 @@ operator fun <T> NDArray<T>.minus(arg: T): NDArray<T> = transform { _, value ->
/* prod and div */
/**
* Product operation for [NDArray] and single element
* Product operation for [NDElement] and single element
*/
operator fun <T> NDArray<T>.times(arg: T): NDArray<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.times(arg: T): NDElement<T, F> = transform { value ->
with(context.field) {
arg * value
}
}
/**
* Division operation between [NDArray] and single element
* Division operation between [NDElement] and single element
*/
operator fun <T> NDArray<T>.div(arg: T): NDArray<T> = transform { _, value ->
operator fun <T, F : Field<T>> NDElement<T, F>.div(arg: T): NDElement<T, F> = transform { value ->
with(context.field) {
arg / value
}
}
class GenericNDField<T : Any>(shape: IntArray, field: Field<T>) : NDField<T>(shape, field) {
override fun produceStructure(initializer: (IntArray) -> T): NDStructure<T> = genericNdStructure(shape, initializer)
class GenericNDField<T : Any, F : Field<T>>(shape: IntArray, field: F) : NDField<T, F>(shape, field) {
override fun produceStructure(initializer: F.(IntArray) -> T): NDStructure<T> = genericNdStructure(shape) { field.initializer(it) }
}
//typealias NDFieldFactory<T> = (IntArray)->NDField<T>
@ -172,22 +176,25 @@ object NDArrays {
/**
* Create a platform-optimized NDArray of doubles
*/
fun realNDArray(shape: IntArray, initializer: (IntArray) -> Double = { 0.0 }): NDArray<Double> {
return GenericNDField(shape, DoubleField).produce(initializer)
fun realNDArray(shape: IntArray, initializer: DoubleField.(IntArray) -> Double = { 0.0 }): NDElement<Double, DoubleField> {
return ExtendedNDField(shape, DoubleField).produce(initializer)
}
fun real1DArray(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDArray<Double> {
fun real1DArray(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim)) { initializer(it[0]) }
}
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDArray<Double> {
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim1, dim2)) { initializer(it[0], it[1]) }
}
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDArray<Double> {
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDElement<Double, DoubleField> {
return realNDArray(intArrayOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
}
inline fun produceReal(shape: IntArray, block: ExtendedNDField<Double, DoubleField>.() -> NDElement<Double, DoubleField>) =
ExtendedNDField(shape, DoubleField).run(block)
// /**
// * Simple boxing NDField
// */
@ -196,7 +203,7 @@ object NDArrays {
/**
* Simple boxing NDArray
*/
fun <T : Any> create(field: Field<T>, shape: IntArray, initializer: (IntArray) -> T): NDArray<T> {
fun <T : Any, F : Field<T>> create(field: F, shape: IntArray, initializer: (IntArray) -> T): NDElement<T, F> {
return GenericNDField(shape, field).produce { initializer(it) }
}
}

36
kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/NDStructure.kt
View File

@ -1,7 +1,7 @@
package scientifik.kmath.structures
interface NDStructure<T> : Iterable<Pair<IntArray, T>> {
interface NDStructure<T> {
val shape: IntArray
@ -9,6 +9,8 @@ interface NDStructure<T> : Iterable<Pair<IntArray, T>> {
get() = shape.size
operator fun get(index: IntArray): T
fun elements(): Sequence<Pair<IntArray, T>>
}
operator fun <T> NDStructure<T>.get(vararg index: Int): T = get(index)
@ -18,7 +20,7 @@ interface MutableNDStructure<T> : NDStructure<T> {
}
fun <T> MutableNDStructure<T>.transformInPlace(action: (IntArray, T) -> T) {
for ((index, oldValue) in this) {
elements().forEach { (index, oldValue) ->
this[index] = action(index, oldValue)
}
}
@ -76,22 +78,22 @@ class DefaultStrides(override val shape: IntArray) : Strides {
override fun offset(index: IntArray): Int {
return index.mapIndexed { i, value ->
if (value < 0 || value >= shape[i]) {
throw RuntimeException("Index $value out of shape bounds: (0,${shape[i]})")
throw RuntimeException("Index $value out of shape bounds: (0,${this.shape[i]})")
}
value * strides[i]
}.sum()
}
override fun index(offset: Int): IntArray {
return sequence {
val res = IntArray(shape.size)
var current = offset
var strideIndex = strides.size - 2
while (strideIndex >= 0) {
yield(current / strides[strideIndex])
res[strideIndex] = (current / strides[strideIndex])
current %= strides[strideIndex]
strideIndex--
}
}.toList().reversed().toIntArray()
return res
}
override val linearSize: Int
@ -107,8 +109,8 @@ abstract class GenericNDStructure<T, B : Buffer<T>> : NDStructure<T> {
override val shape: IntArray
get() = strides.shape
override fun iterator(): Iterator<Pair<IntArray, T>> =
strides.indices().map { it to this[it] }.iterator()
override fun elements()=
strides.indices().map { it to this[it] }
}
/**
@ -126,10 +128,10 @@ class BufferNDStructure<T>(
}
}
inline fun <reified T: Any> ndStructure(strides: Strides, noinline initializer: (IntArray) -> T) =
BufferNDStructure<T>(strides, buffer(strides.linearSize){ i-> initializer(strides.index(i))})
inline fun <reified T : Any> ndStructure(strides: Strides, noinline initializer: (IntArray) -> T) =
BufferNDStructure<T>(strides, buffer(strides.linearSize) { i -> initializer(strides.index(i)) })
inline fun <reified T: Any> ndStructure(shape: IntArray, noinline initializer: (IntArray) -> T) =
inline fun <reified T : Any> ndStructure(shape: IntArray, noinline initializer: (IntArray) -> T) =
ndStructure(DefaultStrides(shape), initializer)
@ -153,22 +155,22 @@ class MutableBufferNDStructure<T>(
/**
* Create optimized mutable structure for given type
*/
inline fun <reified T: Any> mutableNdStructure(strides: Strides, noinline initializer: (IntArray) -> T) =
inline fun <reified T : Any> mutableNdStructure(strides: Strides, noinline initializer: (IntArray) -> T) =
MutableBufferNDStructure(strides, mutableBuffer(strides.linearSize) { i -> initializer(strides.index(i)) })
inline fun <reified T: Any> mutableNdStructure(shape: IntArray, noinline initializer: (IntArray) -> T) =
inline fun <reified T : Any> mutableNdStructure(shape: IntArray, noinline initializer: (IntArray) -> T) =
mutableNdStructure(DefaultStrides(shape), initializer)
/**
* Create universal mutable structure
*/
fun <T> genericNdStructure(shape: IntArray, initializer: (IntArray) -> T): MutableBufferNDStructure<T>{
fun <T> genericNdStructure(shape: IntArray, initializer: (IntArray) -> T): MutableBufferNDStructure<T> {
val strides = DefaultStrides(shape)
val sequence = sequence{
strides.indices().forEach{
val sequence = sequence {
strides.indices().forEach {
yield(initializer(it))
}
}
val buffer = ListBuffer<T>(sequence.toMutableList())
val buffer = MutableListBuffer(sequence.toMutableList())
return MutableBufferNDStructure(strides, buffer)
}

11
kmath-core/src/commonTest/kotlin/scientifik/kmath/expressions/FieldExpressionContextTest.kt
View File

@ -39,4 +39,15 @@ class FieldExpressionContextTest {
val expression = FieldExpressionContext(DoubleField).expression()
assertEquals(expression("x" to 1.0), 4.0)
}
@Test
fun valueExpression() {
val expressionBuilder: FieldExpressionContext<Double>.()->Expression<Double> = {
val x = variable("x")
x * x + 2 * x + 1.0
}
val expression = FieldExpressionContext(DoubleField).expressionBuilder()
assertEquals(expression("x" to 1.0), 4.0)
}
}

7
kmath-core/src/commonTest/kotlin/scientifik/kmath/histogram/MultivariateHistogramTest.kt
View File

@ -14,10 +14,11 @@ class MultivariateHistogramTest {
(-1.0..1.0),
(-1.0..1.0)
)
histogram.put(0.6, 0.6)
histogram.put(0.55, 0.55)
val bin = histogram.find { it.value.toInt() > 0 }!!
assertTrue { bin.contains(Vector.ofReal(0.6, 0.6)) }
assertFalse { bin.contains(Vector.ofReal(-0.6, 0.6)) }
assertTrue { bin.contains(Vector.ofReal(0.55, 0.55)) }
assertTrue { bin.contains(Vector.ofReal(0.6, 0.5)) }
assertFalse { bin.contains(Vector.ofReal(-0.55, 0.55)) }
}
@Test

6
kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/RealFieldTest.kt
View File

@ -6,9 +6,11 @@ import kotlin.test.assertEquals
class RealFieldTest {
@Test
fun testSqrt() {
//fails because KT-27586
val sqrt = with(RealField) {
sqrt(25 * one)
sqrt( 25 * one)
}
assertEquals(5.0, sqrt.toDouble())
assertEquals(5.0, sqrt.value)
}
}

31
kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/RealNDFieldTest.kt → kmath-core/src/commonTest/kotlin/scientifik/kmath/structures/NumberNDFieldTest.kt
View File

@ -1,11 +1,14 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Norm
import scientifik.kmath.structures.NDArrays.produceReal
import scientifik.kmath.structures.NDArrays.real2DArray
import kotlin.math.abs
import kotlin.math.pow
import kotlin.test.Test
import kotlin.test.assertEquals
class RealNDFieldTest {
class NumberNDFieldTest {
val array1 = real2DArray(3, 3) { i, j -> (i + j).toDouble() }
val array2 = real2DArray(3, 3) { i, j -> (i - j).toDouble() }
@ -29,7 +32,7 @@ class RealNDFieldTest {
for (i in 0..2) {
for (j in 0..2) {
val expected = (i * 10 + j).toDouble()
assertEquals(expected, array[i, j],"Error at index [$i, $j]")
assertEquals(expected, array[i, j], "Error at index [$i, $j]")
}
}
}
@ -38,6 +41,28 @@ class RealNDFieldTest {
fun testExternalFunction() {
val function: (Double) -> Double = { x -> x.pow(2) + 2 * x + 1 }
val result = function(array1) + 1.0
assertEquals(10.0, result[1,1])
assertEquals(10.0, result[1, 1])
}
@Test
fun testLibraryFunction() {
val abs: (Double) -> Double = ::abs
val result = abs(array2)
assertEquals(2.0, result[0, 2])
}
object L2Norm : Norm<NDElement<out Number, *>, Double> {
override fun norm(arg: NDElement<out Number, *>): Double {
return kotlin.math.sqrt(arg.sumByDouble { it.second.toDouble() })
}
}
@Test
fun testInternalContext() {
produceReal(array1.shape) {
with(L2Norm) {
1 + norm(array1) + exp(array2)
}
}
}
}

7
kmath-core/src/jvmMain/kotlin/scientifik/kmath/histogram/UnivariateHistogram.kt
View File

@ -26,7 +26,7 @@ class UnivariateBin(val position: Double, val size: Double, val counter: LongCou
/**
* Univariate histogram with log(n) bin search speed
*/
class UnivariateHistogram private constructor(private val factory: (Double) -> UnivariateBin) : Histogram<Double,UnivariateBin> {
class UnivariateHistogram private constructor(private val factory: (Double) -> UnivariateBin) : MutableHistogram<Double,UnivariateBin> {
private val bins: TreeMap<Double, UnivariateBin> = TreeMap()
@ -58,7 +58,10 @@ class UnivariateHistogram private constructor(private val factory: (Double) -> U
(get(value) ?: createBin(value)).inc()
}
override fun put(point: Buffer<out Double>) = put(point[0])
override fun put(point: Buffer<out Double>, weight: Double) {
if (weight != 1.0) TODO("Implement weighting")
put(point[0])
}
companion object {
fun uniform(binSize: Double, start: Double = 0.0): UnivariateHistogram {

55
kmath-core/src/jvmMain/kotlin/scientifik/kmath/structures/BufferSpec.kt Normal file
View File

@ -0,0 +1,55 @@
package scientifik.kmath.structures
import java.nio.ByteBuffer
/**
* A specification for serialization and deserialization objects to buffer
*/
interface BufferSpec<T : Any> {
fun fromBuffer(buffer: ByteBuffer): T
fun toBuffer(value: T): ByteBuffer
}
/**
* A [BufferSpec] with fixed unit size. Allows storage of any object without boxing.
*/
interface FixedSizeBufferSpec<T : Any> : BufferSpec<T> {
val unitSize: Int
/**
* Read an object from buffer in current position
*/
fun ByteBuffer.readObject(): T {
val buffer = ByteArray(unitSize)
get(buffer)
return fromBuffer(ByteBuffer.wrap(buffer))
}
/**
* Read an object from buffer in given index (not buffer position
*/
fun ByteBuffer.readObject(index: Int): T {
val dup = duplicate()
dup.position(index*unitSize)
return dup.readObject()
}
/**
* Write object to [ByteBuffer] in current buffer position
*/
fun ByteBuffer.writeObject(obj: T) {
val buffer = toBuffer(obj).apply { rewind() }
assert(buffer.limit() == unitSize)
put(buffer)
}
/**
* Put an object in given index
*/
fun ByteBuffer.writeObject(index: Int, obj: T) {
val dup = duplicate()
dup.position(index*unitSize)
dup.writeObject(obj)
}
}

25
kmath-core/src/jvmMain/kotlin/scientifik/kmath/structures/ComplexBufferSpec.kt Normal file
View File

@ -0,0 +1,25 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Complex
import java.nio.ByteBuffer
object ComplexBufferSpec : FixedSizeBufferSpec<Complex> {
override val unitSize: Int = 16
override fun fromBuffer(buffer: ByteBuffer): Complex {
val re = buffer.getDouble(0)
val im = buffer.getDouble(8)
return Complex(re, im)
}
override fun toBuffer(value: Complex): ByteBuffer = ByteBuffer.allocate(16).apply {
putDouble(value.re)
putDouble(value.im)
}
}
/**
* Create a mutable buffer which ignores boxing
*/
fun Complex.Companion.createBuffer(size: Int) = ObjectBuffer.create(ComplexBufferSpec, size)

28
kmath-core/src/jvmMain/kotlin/scientifik/kmath/structures/ObjectBuffer.kt Normal file
View File

@ -0,0 +1,28 @@
package scientifik.kmath.structures
import java.nio.ByteBuffer
class ObjectBuffer<T : Any>(private val buffer: ByteBuffer, private val spec: FixedSizeBufferSpec<T>) : MutableBuffer<T> {
override val size: Int
get() = buffer.limit() / spec.unitSize
override fun get(index: Int): T = with(spec) { buffer.readObject(index) }
override fun iterator(): Iterator<T> = (0 until size).asSequence().map { get(it) }.iterator()
override fun set(index: Int, value: T) = with(spec) { buffer.writeObject(index, value) }
override fun copy(): MutableBuffer<T> {
val dup = buffer.duplicate()
val copy = ByteBuffer.allocate(dup.capacity())
dup.rewind()
copy.put(dup)
copy.flip()
return ObjectBuffer(copy, spec)
}
companion object {
fun <T : Any> create(spec: FixedSizeBufferSpec<T>, size: Int) =
ObjectBuffer<T>(ByteBuffer.allocate(size * spec.unitSize), spec)
}
}

23
kmath-core/src/jvmMain/kotlin/scientifik/kmath/structures/RealBufferSpec.kt Normal file
View File

@ -0,0 +1,23 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Real
import java.nio.ByteBuffer
object RealBufferSpec : FixedSizeBufferSpec<Real> {
override val unitSize: Int = 8
override fun fromBuffer(buffer: ByteBuffer): Real = Real(buffer.double)
override fun toBuffer(value: Real): ByteBuffer = ByteBuffer.allocate(8).apply { putDouble(value.value) }
}
object DoubleBufferSpec : FixedSizeBufferSpec<Double> {
override val unitSize: Int = 8
override fun fromBuffer(buffer: ByteBuffer): Double = buffer.double
override fun toBuffer(value: Double): ByteBuffer = ByteBuffer.allocate(8).apply { putDouble(value) }
}
fun Double.Companion.createBuffer(size: Int) = ObjectBuffer.create(DoubleBufferSpec, size)
fun Real.Companion.createBuffer(size: Int) = ObjectBuffer.create(RealBufferSpec, size)

17
kmath-core/src/jvmTest/kotlin/scientifik/kmath/structures/ComplexBufferSpecTest.kt Normal file
View File

@ -0,0 +1,17 @@
package scientifik.kmath.structures
import org.junit.Test
import scientifik.kmath.operations.Complex
import kotlin.test.assertEquals
class ComplexBufferSpecTest {
@Test
fun testComplexBuffer() {
val buffer = Complex.createBuffer(20)
(0 until 20).forEach {
buffer[it] = Complex(it.toDouble(), -it.toDouble())
}
assertEquals(Complex(5.0, -5.0), buffer[5])
}
}

42
kmath-coroutines/build.gradle Normal file
View File

@ -0,0 +1,42 @@
plugins {
id "org.jetbrains.kotlin.multiplatform"
}
kotlin {
targets {
fromPreset(presets.jvm, 'jvm')
// For ARM, preset should be changed to presets.iosArm32 or presets.iosArm64
// For Linux, preset should be changed to e.g. presets.linuxX64
// For MacOS, preset should be changed to e.g. presets.macosX64
//fromPreset(presets.mingwX64, 'mingw')
}
sourceSets {
commonMain {
dependencies {
api project(":kmath-core")
api "org.jetbrains.kotlinx:kotlinx-coroutines-core-common:$coroutinesVersion"
}
}
commonTest {
dependencies {
api 'org.jetbrains.kotlin:kotlin-test-common'
api 'org.jetbrains.kotlin:kotlin-test-annotations-common'
}
}
jvmMain {
dependencies {
api "org.jetbrains.kotlinx:kotlinx-coroutines-core:$coroutinesVersion"
}
}
jvmTest {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-test'
implementation 'org.jetbrains.kotlin:kotlin-test-junit'
}
}
// mingwMain {
// }
// mingwTest {
// }
}
}

11
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/structures/CoroutinesExtra.kt Normal file
View File

@ -0,0 +1,11 @@
package scientifik.kmath.structures
import kotlinx.coroutines.CoroutineDispatcher
import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.Dispatchers
import kotlin.coroutines.CoroutineContext
import kotlin.coroutines.EmptyCoroutineContext
expect fun <R> runBlocking(context: CoroutineContext = EmptyCoroutineContext, function: suspend CoroutineScope.()->R): R
val Dispatchers.Math: CoroutineDispatcher get() = Dispatchers.Default

79
kmath-coroutines/src/commonMain/kotlin/scientifik/kmath/structures/LazyNDField.kt Normal file
View File

@ -0,0 +1,79 @@
package scientifik.kmath.structures
import kotlinx.coroutines.*
import scientifik.kmath.operations.Field
class LazyNDField<T, F : Field<T>>(shape: IntArray, field: F, val scope: CoroutineScope = GlobalScope) : NDField<T, F>(shape, field) {
override fun produceStructure(initializer: F.(IntArray) -> T): NDStructure<T> = LazyNDStructure(this) { initializer(field, it) }
override fun add(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
return LazyNDStructure(this) { index ->
val aDeferred = a.deferred(index)
val bDeferred = b.deferred(index)
aDeferred.await() + bDeferred.await()
}
}
override fun multiply(a: NDElement<T, F>, k: Double): NDElement<T, F> {
return LazyNDStructure(this) { index -> a.await(index) * k }
}
override fun multiply(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
return LazyNDStructure(this) { index ->
val aDeferred = a.deferred(index)
val bDeferred = b.deferred(index)
aDeferred.await() * bDeferred.await()
}
}
override fun divide(a: NDElement<T, F>, b: NDElement<T, F>): NDElement<T, F> {
return LazyNDStructure(this) { index ->
val aDeferred = a.deferred(index)
val bDeferred = b.deferred(index)
aDeferred.await() / bDeferred.await()
}
}
}
class LazyNDStructure<T, F : Field<T>>(override val context: LazyNDField<T, F>, val function: suspend F.(IntArray) -> T) : NDElement<T, F>, NDStructure<T> {
override val self: NDElement<T, F> get() = this
override val shape: IntArray get() = context.shape
private val cache = HashMap<IntArray, Deferred<T>>()
fun deferred(index: IntArray) = cache.getOrPut(index) { context.scope.async(context = Dispatchers.Math) { function.invoke(context.field, index) } }
suspend fun await(index: IntArray): T = deferred(index).await()
override fun get(index: IntArray): T = runBlocking {
deferred(index).await()
}
override fun elements(): Sequence<Pair<IntArray, T>> {
val strides = DefaultStrides(shape)
return strides.indices().map { index -> index to runBlocking { await(index) } }
}
}
fun <T> NDElement<T, *>.deferred(index: IntArray) = if (this is LazyNDStructure<T, *>) this.deferred(index) else CompletableDeferred(get(index))
suspend fun <T> NDElement<T, *>.await(index: IntArray) = if (this is LazyNDStructure<T, *>) this.await(index) else get(index)
fun <T, F : Field<T>> NDElement<T, F>.lazy(scope: CoroutineScope = GlobalScope): LazyNDStructure<T, F> {
return if (this is LazyNDStructure<T, F>) {
this
} else {
val context = LazyNDField(context.shape, context.field)
LazyNDStructure(context) { get(it) }
}
}
inline fun <T, F : Field<T>> LazyNDStructure<T, F>.transformIndexed(crossinline action: suspend F.(IntArray, T) -> T) = LazyNDStructure(context) { index ->
action.invoke(this, index, await(index))
}
inline fun <T, F : Field<T>> LazyNDStructure<T, F>.transform(crossinline action: suspend F.(T) -> T) = LazyNDStructure(context) { index ->
action.invoke(this, await(index))
}

20
kmath-coroutines/src/commonTest/kotlin/scientifik/kmath/structures/LazyNDFieldTest.kt Normal file
View File

@ -0,0 +1,20 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.IntField
import kotlin.test.Test
import kotlin.test.assertEquals
class LazyNDFieldTest {
@Test
fun testLazyStructure() {
var counter = 0
val regularStructure = NDArrays.create(IntField, intArrayOf(2, 2, 2)) { it[0] + it[1] - it[2] }
val result = (regularStructure.lazy() + 2).transform {
counter++
it * it
}
assertEquals(4, result[0,0,0])
assertEquals(1, counter)
}
}

6
kmath-coroutines/src/jvmMain/kotlin/scientifik/kmath/structures/_CoroutinesExtra.kt Normal file
View File

@ -0,0 +1,6 @@
package scientifik.kmath.structures
import kotlinx.coroutines.CoroutineScope
import kotlin.coroutines.CoroutineContext
actual fun <R> runBlocking(context: CoroutineContext, function: suspend CoroutineScope.() -> R): R = kotlinx.coroutines.runBlocking(context, function)

55
kmath-io/build.gradle Normal file
View File

@ -0,0 +1,55 @@
plugins {
id "org.jetbrains.kotlin.multiplatform"
}
kotlin {
targets {
fromPreset(presets.jvm, 'jvm')
fromPreset(presets.js, 'js')
// For ARM, preset should be changed to presets.iosArm32 or presets.iosArm64
// For Linux, preset should be changed to e.g. presets.linuxX64
// For MacOS, preset should be changed to e.g. presets.macosX64
//fromPreset(presets.mingwX64, 'mingw')
}
sourceSets {
commonMain {
dependencies {
api project(":kmath-core")
implementation 'org.jetbrains.kotlin:kotlin-stdlib-common'
api "org.jetbrains.kotlinx:kotlinx-io:$ioVersion"
}
}
commonTest {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-test-common'
implementation 'org.jetbrains.kotlin:kotlin-test-annotations-common'
}
}
jvmMain {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-jdk8'
api "org.jetbrains.kotlinx:kotlinx-io-jvm:$ioVersion"
}
}
jvmTest {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-test'
implementation 'org.jetbrains.kotlin:kotlin-test-junit'
}
}
jsMain {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-js'
}
}
jsTest {
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-test-js'
}
}
// mingwMain {
// }
// mingwTest {
// }
}
}

9
kmath-jmh/build.gradle
View File

@ -4,12 +4,7 @@ plugins {
id "me.champeau.gradle.jmh" version "0.4.7"
}
repositories {
maven { url = 'http://dl.bintray.com/kotlin/kotlin-eap' }
mavenCentral()
}
dependencies {
implementation project(':kmath-core')
jmh 'org.jetbrains.kotlin:kotlin-stdlib-jdk8'
compile project(':kmath-core')
//jmh project(':kmath-core')
}

6
kmath-jmh/src/jmh/kotlin/scientifik/kmath/structures/ArrayBenchmark.kt
View File

@ -4,8 +4,7 @@ import org.openjdk.jmh.annotations.*
import java.nio.IntBuffer
@Fork(1)
@Warmup(iterations = 2)
@Warmup(iterations = 1)
@Measurement(iterations = 5)
@State(Scope.Benchmark)
open class ArrayBenchmark {
@ -30,7 +29,6 @@ open class ArrayBenchmark {
for (i in 1..10000) {
res += array[10000 - i]
}
print(res)
}
@Benchmark
@ -39,7 +37,6 @@ open class ArrayBenchmark {
for (i in 1..10000) {
res += arrayBuffer.get(10000 - i)
}
print(res)
}
@Benchmark
@ -48,6 +45,5 @@ open class ArrayBenchmark {
for (i in 1..10000) {
res += nativeBuffer.get(10000 - i)
}
print(res)
}
}

38
kmath-jmh/src/jmh/kotlin/scientifik/kmath/structures/BufferBenchmark.kt Normal file
View File

@ -0,0 +1,38 @@
package scientifik.kmath.structures
import org.openjdk.jmh.annotations.*
import scientifik.kmath.operations.Complex
@Warmup(iterations = 1)
@Measurement(iterations = 5)
@State(Scope.Benchmark)
open class BufferBenchmark {
@Benchmark
fun genericDoubleBufferReadWrite() {
val buffer = Double.createBuffer(size)
(0 until size).forEach {
buffer[it] = it.toDouble()
}
(0 until size).forEach {
buffer[it]
}
}
@Benchmark
fun complexBufferReadWrite() {
val buffer = Complex.createBuffer(size/2)
(0 until size/2).forEach {
buffer[it] = Complex(it.toDouble(), -it.toDouble())
}
(0 until size/2).forEach {
buffer[it]
}
}
companion object {
const val size = 1000
}
}

13
settings.gradle
View File

@ -1,13 +0,0 @@
pluginManagement {
repositories {
mavenCentral()
maven { url = 'https://plugins.gradle.org/m2/' }
}
}
enableFeaturePreview('GRADLE_METADATA')
rootProject.name = 'kmath'
include ':kmath-core'
include ':kmath-jmh'

16
settings.gradle.kts Normal file
View File

@ -0,0 +1,16 @@
pluginManagement {
repositories {
mavenCentral()
maven("https://plugins.gradle.org/m2/")
}
}
//enableFeaturePreview("GRADLE_METADATA")
rootProject.name = "kmath"
include(
":kmath-core",
":kmath-io",
":kmath-coroutines",
":kmath-jmh"
)