Switch to kotlin 1.3 eap and mpp build

This commit is contained in:
Alexander Nozik 2018-09-30 17:18:04 +03:00
parent 38c7f4382a
commit c4b334976a
26 changed files with 234 additions and 160 deletions

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@ -40,8 +40,33 @@ but worse than optimized native/scipy (mostly due to boxing operations on primit
of optimized parts should be better than scipy.
## Releases
The project is currently in pre-release stage. Work builds could be obtained with
[![](https://jitpack.io/v/altavir/kmath.svg)](https://jitpack.io/#altavir/kmath).
The project is currently in pre-release stage. Nightly builds could be used by adding additional repository to (groovy) gradle config:
```groovy
repositories {
maven { url = "http://npm.mipt.ru:8081/artifactory/gradle-dev" }
mavenCentral()
}
```
or for kotlin gradle dsl:
```kotlin
repositories {
maven { setUrl("http://npm.mipt.ru:8081/artifactory/gradle-dev") }
mavenCentral()
}
```
Then use regular dependency like
```groovy
compile(group: 'scientifik', name: 'kmath-core-jvm', version: '0.0.1-SNAPSHOT')
```
or in kotlin
```kotlin
compile(group = "scientifik", name = "kmath-core-jvm", version = "0.0.1-SNAPSHOT")
```
Work builds could be obtained with [![](https://jitpack.io/v/altavir/kmath.svg)](https://jitpack.io/#altavir/kmath).
## Contributing
The project requires a lot of additional work. Please fill free to contribute in any way and propose new features.

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@ -1,17 +1,19 @@
buildscript {
ext.kotlin_version = '1.2.71'
ext.kotlin_version = '1.3.0-rc-116'
repositories {
jcenter()
maven {
url = "http://dl.bintray.com/kotlin/kotlin-eap"
}
}
dependencies {
classpath "org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlin_version"
classpath "org.jfrog.buildinfo:build-info-extractor-gradle:4+"
}
}
allprojects{
apply plugin: 'maven'
allprojects {
apply plugin: 'maven-publish'
apply plugin: "com.jfrog.artifactory"
@ -27,13 +29,11 @@ artifactory {
repoKey = 'gradle-dev-local'
username = "${artifactory_user}"
password = "${artifactory_password}"
maven = true
}
defaults {
publications 'defaultPublication'
publishBuildInfo = true
publications('jvm', 'kotlinMultiplatform', 'metadata')
publishBuildInfo = false
publishArtifacts = true
publishPom = true
publishIvy = false
@ -44,7 +44,6 @@ artifactory {
repoKey = 'gradle-dev'
username = "${artifactory_user}"
password = "${artifactory_password}"
maven = true
}
}
}

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@ -1,22 +0,0 @@
plugins{
id "kotlin-platform-common"
}
description = "Platform-independent interfaces for kotlin maths"
repositories {
mavenCentral()
}
dependencies {
compile "org.jetbrains.kotlin:kotlin-stdlib-common:$kotlin_version"
testCompile "org.jetbrains.kotlin:kotlin-test-annotations-common:$kotlin_version"
testCompile "org.jetbrains.kotlin:kotlin-test-common:$kotlin_version"
}
kotlin {
experimental {
coroutines "enable"
}
}

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@ -1,38 +0,0 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Field
/**
* Create a platform-optimized NDArray of doubles
*/
expect fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double = { 0.0 }): NDArray<Double>
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDArray<Double> {
return realNDArray(listOf(dim1, dim2)) { initializer(it[0], it[1]) }
}
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDArray<Double> {
return realNDArray(listOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
}
class SimpleNDField<T: Any>(field: Field<T>, shape: List<Int>) : BufferNDField<T>(shape, field) {
override fun createBuffer(capacity: Int, initializer: (Int) -> T): Buffer<T> {
val array = ArrayList<T>(capacity)
(0 until capacity).forEach {
array.add(initializer(it))
}
return object : Buffer<T> {
override fun get(index: Int): T = array[index]
override fun set(index: Int, value: T) {
array[index] = initializer(index)
}
}
}
}
fun <T: Any> simpleNDArray(field: Field<T>, shape: List<Int>, initializer: (List<Int>) -> T): NDArray<T> {
return SimpleNDField(field, shape).produce { initializer(it) }
}

58
kmath-core/build.gradle Normal file
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@ -0,0 +1,58 @@
plugins {
id 'kotlin-multiplatform'// version '1.3.0-rc-116'
id "me.champeau.gradle.jmh" version "0.4.5"
}
repositories {
maven { url = 'http://dl.bintray.com/kotlin/kotlin-eap' }
mavenCentral()
}
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 {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-common'
}
}
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'
}
}
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 {
// }
}
}

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@ -1,8 +1,5 @@
package scientifik.kmath.misc
import kotlin.coroutines.experimental.buildSequence
/**
* Convert double range to sequence.
*
@ -14,14 +11,14 @@ import kotlin.coroutines.experimental.buildSequence
fun ClosedFloatingPointRange<Double>.toSequence(step: Double): Sequence<Double> {
return when {
step == 0.0 -> error("Zero step in double progression")
step > 0 -> buildSequence {
step > 0 -> sequence {
var current = start
while (current <= endInclusive) {
yield(current)
current += step
}
}
else -> buildSequence {
else -> sequence {
var current = endInclusive
while (current >= start) {
yield(current)

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@ -1,7 +1,6 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Field
import kotlin.coroutines.experimental.buildSequence
/**
@ -42,7 +41,7 @@ abstract class BufferNDField<T>(shape: List<Int>, field: Field<T>) : NDField<T>(
//TODO introduce a fast way to calculate index of the next element?
protected fun index(offset: Int): List<Int> {
return buildSequence {
return sequence {
var current = offset
var strideIndex = strides.size - 2
while (strideIndex >= 0) {

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@ -4,6 +4,7 @@ import scientifik.kmath.operations.DoubleField
import scientifik.kmath.operations.Field
import scientifik.kmath.operations.Space
import scientifik.kmath.operations.SpaceElement
import scientifik.kmath.structures.NDArrays.createSimpleNDFieldFactory
/**
* The space for linear elements. Supports scalar product alongside with standard linear operations.
@ -18,7 +19,7 @@ abstract class LinearSpace<T : Any, V : LinearStructure<out T>>(val rows: Int, v
abstract fun produce(initializer: (Int, Int) -> T): V
/**
* Produce new linear space with given dimensions
* Produce new linear space with given dimensions. The space produced could be raised from cache since [LinearSpace] does not have mutable elements
*/
abstract fun produceSpace(rows: Int, columns: Int): LinearSpace<T, V>
@ -36,12 +37,12 @@ abstract class LinearSpace<T : Any, V : LinearStructure<out T>>(val rows: Int, v
}
/**
* Dot product
* Dot product. Throws exception on dimension mismatch
*/
fun multiply(a: V, b: V): V {
if (a.rows != b.columns) {
//TODO replace by specific exception
error("Dimension mismatch in vector dot product")
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..a.columns).asSequence().map { k -> field.multiply(a[i, k], b[k, j]) }.reduce { first, second -> field.add(first, second) }
@ -55,16 +56,25 @@ abstract class LinearSpace<T : Any, V : LinearStructure<out T>>(val rows: Int, v
* A matrix-like structure that is not dependent on specific space implementation
*/
interface LinearStructure<T : Any> {
/**
* 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
fun transpose(): LinearStructure<T> {
return object : LinearStructure<T> {
override val rows: Int = this@LinearStructure.columns
override val columns: Int = this@LinearStructure.rows
override fun get(i: Int, j: Int): T = this@LinearStructure.get(j, i)
override fun get(i: Int, j: Int): T = this@LinearStructure[j, i]
}
}
}
@ -78,15 +88,23 @@ interface Vector<T : Any> : LinearStructure<T> {
/**
* DoubleArray-based implementation of vector space
* NDArray-based implementation of vector space. By default uses slow [SimpleNDField], but could be overridden with custom [NDField] factory.
*/
class ArraySpace<T : Any>(rows: Int, columns: Int, field: Field<T>) : LinearSpace<T, LinearStructure<out T>>(rows, columns, field) {
class ArraySpace<T : Any>(
rows: Int,
columns: Int,
field: Field<T>,
val ndFactory: NDFieldFactory<T> = createSimpleNDFieldFactory(field)
) : LinearSpace<T, LinearStructure<out T>>(rows, columns, field) {
val ndField by lazy {
ndFactory(listOf(rows, columns))
}
override fun produce(initializer: (Int, Int) -> T): LinearStructure<T> = ArrayMatrix<T>(this, initializer)
override fun produceSpace(rows: Int, columns: Int): LinearSpace<T, LinearStructure<out T>> {
return ArraySpace(rows, columns, field)
return ArraySpace(rows, columns, field, ndFactory)
}
}
@ -95,14 +113,16 @@ class ArraySpace<T : Any>(rows: Int, columns: Int, field: Field<T>) : LinearSpac
*/
class ArrayMatrix<T : Any>(override val context: ArraySpace<T>, initializer: (Int, Int) -> T) : LinearStructure<T>, SpaceElement<LinearStructure<out T>, ArraySpace<T>> {
val list: List<List<T>> = (0 until rows).map { i -> (0 until columns).map { j -> initializer(i, j) } }
private val array = context.ndField.produce { list -> initializer(list[0], list[1]) }
//val list: List<List<T>> = (0 until rows).map { i -> (0 until columns).map { j -> initializer(i, j) } }
override val rows: Int get() = context.rows
override val columns: Int get() = context.columns
override fun get(i: Int, j: Int): T {
return list[i][j]
return array[i, j]
}
override val self: ArrayMatrix<T> get() = this
@ -117,7 +137,7 @@ class ArrayVector<T : Any>(override val context: ArraySpace<T>, initializer: (In
}
}
val list: List<T> = (0 until context.rows).map(initializer)
private val array = context.ndField.produce { list -> initializer(list[0]) }
override val rows: Int get() = context.rows
@ -125,20 +145,25 @@ class ArrayVector<T : Any>(override val context: ArraySpace<T>, initializer: (In
override val columns: Int = 1
override fun get(i: Int, j: Int): T {
return list[i]
return array[i]
}
override val self: ArrayVector<T> get() = this
}
fun <T : Any> vector(size: Int, field: Field<T>, initializer: (Int) -> T) = ArrayVector(ArraySpace(size, 1, field), initializer)
//TODO replace by primitive array version
fun realVector(size: Int, initializer: (Int) -> Double) = vector(size, DoubleField, initializer)
fun <T : Any> vector(size: Int, field: Field<T>, initializer: (Int) -> T) =
ArrayVector(ArraySpace(size, 1, field), initializer)
fun realVector(size: Int, initializer: (Int) -> Double) =
ArrayVector(ArraySpace(size, 1, DoubleField, realNDFieldFactory), initializer)
fun <T : Any> Array<T>.asVector(field: Field<T>) = vector(size, field) { this[it] }
//TODO add inferred field from field element
fun DoubleArray.asVector() = realVector(this.size) { this[it] }
fun <T : Any> matrix(rows: Int, columns: Int, field: Field<T>, initializer: (Int, Int) -> T) = ArrayMatrix<T>(ArraySpace(rows, columns, field), initializer)
fun realMatrix(rows: Int, columns: Int, initializer: (Int, Int) -> Double) = matrix(rows, columns, DoubleField, initializer)
fun <T : Any> matrix(rows: Int, columns: Int, field: Field<T>, initializer: (Int, Int) -> T) =
ArrayMatrix(ArraySpace(rows, columns, field), initializer)
fun realMatrix(rows: Int, columns: Int, initializer: (Int, Int) -> Double) =
ArrayMatrix(ArraySpace(rows, columns, DoubleField, realNDFieldFactory), initializer)

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@ -136,7 +136,7 @@ operator fun <T> Function1<T, T>.invoke(ndArray: NDArray<T>): NDArray<T> = ndArr
* Summation operation for [NDArray] and single element
*/
operator fun <T> NDArray<T>.plus(arg: T): NDArray<T> = transform { _, value ->
with(context.field){
with(context.field) {
arg + value
}
}
@ -150,7 +150,7 @@ operator fun <T> T.plus(arg: NDArray<T>): NDArray<T> = arg + this
* Subtraction operation between [NDArray] and single element
*/
operator fun <T> NDArray<T>.minus(arg: T): NDArray<T> = transform { _, value ->
with(context.field){
with(context.field) {
arg - value
}
}
@ -159,7 +159,7 @@ operator fun <T> NDArray<T>.minus(arg: T): NDArray<T> = transform { _, value ->
* Reverse minus operation
*/
operator fun <T> T.minus(arg: NDArray<T>): NDArray<T> = arg.transform { _, value ->
with(arg.context.field){
with(arg.context.field) {
this@minus - value
}
}
@ -170,7 +170,7 @@ operator fun <T> T.minus(arg: NDArray<T>): NDArray<T> = arg.transform { _, value
* Product operation for [NDArray] and single element
*/
operator fun <T> NDArray<T>.times(arg: T): NDArray<T> = transform { _, value ->
with(context.field){
with(context.field) {
arg * value
}
}
@ -184,7 +184,7 @@ operator fun <T> T.times(arg: NDArray<T>): NDArray<T> = arg * this
* Division operation between [NDArray] and single element
*/
operator fun <T> NDArray<T>.div(arg: T): NDArray<T> = transform { _, value ->
with(context.field){
with(context.field) {
arg / value
}
}
@ -193,8 +193,8 @@ operator fun <T> NDArray<T>.div(arg: T): NDArray<T> = transform { _, value ->
* Reverse division operation
*/
operator fun <T> T.div(arg: NDArray<T>): NDArray<T> = arg.transform { _, value ->
with(arg.context.field){
this@div/ value
with(arg.context.field) {
this@div / value
}
}

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@ -0,0 +1,54 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.Field
typealias NDFieldFactory<T> = (shape: List<Int>) -> NDField<T>
/**
* The factory class for fast platform-dependent implementation of NDField of doubles
*/
expect val realNDFieldFactory: NDFieldFactory<Double>
object NDArrays {
/**
* Create a platform-optimized NDArray of doubles
*/
fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double = { 0.0 }): NDArray<Double> {
return realNDFieldFactory(shape).produce(initializer)
}
fun real1DArray(dim: Int, initializer: (Int) -> Double = { _ -> 0.0 }): NDArray<Double> {
return realNDArray(listOf(dim)) { initializer(it[0]) }
}
fun real2DArray(dim1: Int, dim2: Int, initializer: (Int, Int) -> Double = { _, _ -> 0.0 }): NDArray<Double> {
return realNDArray(listOf(dim1, dim2)) { initializer(it[0], it[1]) }
}
fun real3DArray(dim1: Int, dim2: Int, dim3: Int, initializer: (Int, Int, Int) -> Double = { _, _, _ -> 0.0 }): NDArray<Double> {
return realNDArray(listOf(dim1, dim2, dim3)) { initializer(it[0], it[1], it[2]) }
}
class SimpleNDField<T : Any>(field: Field<T>, shape: List<Int>) : BufferNDField<T>(shape, field) {
override fun createBuffer(capacity: Int, initializer: (Int) -> T): Buffer<T> {
val array = ArrayList<T>(capacity)
(0 until capacity).forEach {
array.add(initializer(it))
}
return object : Buffer<T> {
override fun get(index: Int): T = array[index]
override fun set(index: Int, value: T) {
array[index] = initializer(index)
}
}
}
}
fun <T : Any> createSimpleNDFieldFactory(field: Field<T>): NDFieldFactory<T> = { list -> SimpleNDField(field, list) }
fun <T : Any> simpleNDArray(field: Field<T>, shape: List<Int>, initializer: (List<Int>) -> T): NDArray<T> {
return SimpleNDField(field, shape).produce { initializer(it) }
}
}

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@ -1,7 +1,7 @@
package scientifik.kmath.structures
import org.junit.Assert.assertEquals
import org.junit.Test
import kotlin.test.Test
import kotlin.test.assertEquals
class ArrayMatrixTest {
@ -10,7 +10,7 @@ class ArrayMatrixTest {
val vector1 = realVector(5) { it.toDouble() }
val vector2 = realVector(5) { 5 - it.toDouble() }
val sum = vector1 + vector2
assertEquals(5.0, sum[2, 0], 0.1)
assertEquals(5.0, sum[2, 0])
}
@Test
@ -21,6 +21,6 @@ class ArrayMatrixTest {
vector1 dot (vector2.transpose())
}
assertEquals(10.0, product[1, 0], 0.1)
assertEquals(10.0, product[1, 0])
}
}

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@ -1,8 +1,9 @@
package scientifik.kmath.structures
import org.junit.Assert.assertEquals
import scientifik.kmath.structures.NDArrays.real2DArray
import kotlin.math.pow
import kotlin.test.Test
import kotlin.test.assertEquals
class RealNDFieldTest {
val array1 = real2DArray(3, 3) { i, j -> (i + j).toDouble() }
@ -11,13 +12,13 @@ class RealNDFieldTest {
@Test
fun testSum() {
val sum = array1 + array2
assertEquals(4.0, sum[2, 2], 0.1)
assertEquals(4.0, sum[2, 2])
}
@Test
fun testProduct() {
val product = array1 * array2
assertEquals(0.0, product[2, 2], 0.1)
assertEquals(0.0, product[2, 2])
}
@Test
@ -28,7 +29,7 @@ class RealNDFieldTest {
for (i in 0..2) {
for (j in 0..2) {
val expected = (i * 10 + j).toDouble()
assertEquals("Error at index [$i, $j]", expected, array[i, j], 0.1)
assertEquals(expected, array[i, j],"Error at index [$i, $j]")
}
}
}
@ -37,6 +38,6 @@ 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],0.01)
assertEquals(10.0, result[1,1])
}
}

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@ -1,6 +1,7 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.DoubleField
import scientifik.kmath.structures.NDArrays.simpleNDArray
import kotlin.test.Test
import kotlin.test.assertEquals

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@ -0,0 +1,8 @@
package scientifik.kmath.structures
import scientifik.kmath.operations.DoubleField
/**
* Using boxing implementation for js
*/
actual val realNDFieldFactory: NDFieldFactory<Double> = NDArrays.createSimpleNDFieldFactory(DoubleField)

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@ -17,8 +17,4 @@ private class RealNDField(shape: List<Int>) : BufferNDField<Double>(shape, Doubl
}
}
actual fun realNDArray(shape: List<Int>, initializer: (List<Int>) -> Double): NDArray<Double> {
//TODO create a cache for fields to save time generating strides?
return RealNDField(shape).produce { initializer(it) }
}
actual val realNDFieldFactory: NDFieldFactory<Double> = { shape -> RealNDField(shape) }

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@ -1,38 +0,0 @@
plugins{
id "kotlin-platform-jvm"
id "me.champeau.gradle.jmh" version "0.4.5"
}
repositories {
mavenCentral()
}
dependencies {
expectedBy project(":kmath-common")
compile "org.jetbrains.kotlin:kotlin-stdlib-jdk8:$kotlin_version"
testCompile "junit:junit:4.12"
testCompile "org.jetbrains.kotlin:kotlin-test-junit:$kotlin_version"
testCompile "org.jetbrains.kotlin:kotlin-test:$kotlin_version"
}
compileKotlin {
kotlinOptions.jvmTarget = "1.8"
}
compileTestKotlin {
kotlinOptions.jvmTarget = "1.8"
}
sourceCompatibility = "1.8"
kotlin {
experimental {
coroutines "enable"
}
}
publishing {
publications {
defaultPublication(MavenPublication) {
from components.java
}
}
}

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@ -1,4 +1,13 @@
rootProject.name = 'kmath'
include 'kmath-common'
include 'kmath-jvm'
pluginManagement {
repositories {
maven { url = 'http://dl.bintray.com/kotlin/kotlin-eap' }
mavenCentral()
maven { url = 'https://plugins.gradle.org/m2/' }
}
}
enableFeaturePreview('GRADLE_METADATA')
rootProject.name = 'kmath'
include ':kmath-core'