forked from kscience/kmath
Some adjustments to the EJML module
This commit is contained in:
Iaroslav Postovalov
parent
248d42c4e0
commit
6375cb5fd8
10
README.md
10
README.md
@ -120,9 +120,9 @@ KMath is a modular library. Different modules provide different features with di
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>
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> **Features:**
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> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
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> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt) : Many-dimensional structures and operations on them.
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> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
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> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
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> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt) : One-dimensional structure
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> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
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> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of
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objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
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performance calculations to code generation.
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@ -206,9 +206,9 @@ One can still use generic algebras though.
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> **Maturity**: EXPERIMENTAL
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>
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> **Features:**
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> - [nd4jarraystructure](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : NDStructure wrapper for INDArray
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> - [nd4jarrayrings](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/structures/NDStructure.kt) : Rings over Nd4jArrayStructure of Int and Long
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> - [nd4jarrayfields](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : Fields over Nd4jArrayStructure of Float and Double
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> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
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> - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
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> - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
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<hr/>
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@ -58,7 +58,7 @@ For example, the following builder:
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DoubleField.mstInField { symbol("x") + 2 }.compile()
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```
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… leads to generation of bytecode, which can be decompiled to the following Java class:
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… leads to generation of bytecode, which can be decompiled to the following Java class:
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```java
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package space.kscience.kmath.asm.generated;
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@ -3,7 +3,7 @@
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The core features of KMath:
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- [algebras](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
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- [nd](src/commonMain/kotlin/space/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures and operations on them.
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- [nd](src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
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- [linear](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
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- [buffers](src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
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- [expressions](src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of
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43
kmath-ejml/README.md
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43
kmath-ejml/README.md
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@ -0,0 +1,43 @@
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# ejml-simple support (`kmath-ejml`)
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This subproject implements the following features:
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- [ejml-vector](src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : The Point implementation using SimpleMatrix.
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- [ejml-matrix](src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : The Matrix implementation using SimpleMatrix.
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- [ejml-linear-space](src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : The LinearSpace implementation using SimpleMatrix.
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> #### Artifact:
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>
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> This module artifact: `space.kscience:kmath-ejml:0.3.0-dev-3`.
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>
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> Bintray release version: [  ](https://bintray.com/mipt-npm/kscience/kmath-ejml/_latestVersion)
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>
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> Bintray development version: [  ](https://bintray.com/mipt-npm/dev/kmath-ejml/_latestVersion)
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>
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> **Gradle:**
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>
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> ```gradle
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> repositories {
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> maven { url 'https://repo.kotlin.link' }
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> maven { url 'https://dl.bintray.com/hotkeytlt/maven' }
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> maven { url "https://dl.bintray.com/kotlin/kotlin-eap" } // include for builds based on kotlin-eap
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> }
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>
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> dependencies {
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> implementation 'space.kscience:kmath-ejml:0.3.0-dev-3'
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> }
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> ```
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> **Gradle Kotlin DSL:**
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>
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> ```kotlin
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> repositories {
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> maven("https://repo.kotlin.link")
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> maven("https://dl.bintray.com/kotlin/kotlin-eap") // include for builds based on kotlin-eap
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> maven("https://dl.bintray.com/hotkeytlt/maven") // required for a
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> }
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>
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> dependencies {
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> implementation("space.kscience:kmath-ejml:0.3.0-dev-3")
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> }
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> ```
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@ -1,12 +1,33 @@
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import ru.mipt.npm.gradle.Maturity
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plugins {
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id("ru.mipt.npm.gradle.jvm")
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}
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dependencies {
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implementation("org.ejml:ejml-simple:0.39")
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implementation(project(":kmath-core"))
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api("org.ejml:ejml-simple:0.40")
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api(project(":kmath-core"))
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}
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readme {
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maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE
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maturity = Maturity.PROTOTYPE
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propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
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feature(
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id = "ejml-vector",
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description = "The Point implementation using SimpleMatrix.",
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ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt"
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)
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feature(
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id = "ejml-matrix",
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description = "The Matrix implementation using SimpleMatrix.",
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ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt"
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)
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feature(
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id = "ejml-linear-space",
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description = "The LinearSpace implementation using SimpleMatrix.",
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ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt"
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)
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}
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7
kmath-ejml/docs/README-TEMPLATE.md
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7
kmath-ejml/docs/README-TEMPLATE.md
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@ -0,0 +1,7 @@
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# ejml-simple support (`kmath-ejml`)
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This subproject implements the following features:
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${features}
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${artifact}
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@ -14,10 +14,13 @@ import kotlin.reflect.cast
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* Represents context of basic operations operating with [EjmlMatrix].
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*
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* @author Iaroslav Postovalov
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* @author Alexander Nozik
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*/
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public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
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override val elementAlgebra: DoubleField get() = DoubleField
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/**
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* The [DoubleField] reference.
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*/
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public override val elementAlgebra: DoubleField get() = DoubleField
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/**
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* Converts this matrix to EJML one.
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@ -38,14 +41,17 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
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})
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}
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override fun buildMatrix(rows: Int, columns: Int, initializer: DoubleField.(i: Int, j: Int) -> Double): EjmlMatrix =
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EjmlMatrix(SimpleMatrix(rows, columns).also {
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(0 until rows).forEach { row ->
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(0 until columns).forEach { col -> it[row, col] = DoubleField.initializer(row, col) }
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}
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})
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public override fun buildMatrix(
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rows: Int,
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columns: Int,
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initializer: DoubleField.(i: Int, j: Int) -> Double,
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): EjmlMatrix = EjmlMatrix(SimpleMatrix(rows, columns).also {
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(0 until rows).forEach { row ->
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(0 until columns).forEach { col -> it[row, col] = DoubleField.initializer(row, col) }
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}
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})
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override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): Point<Double> =
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public override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): Point<Double> =
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EjmlVector(SimpleMatrix(size, 1).also {
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(0 until it.numRows()).forEach { row -> it[row, 0] = DoubleField.initializer(row) }
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})
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@ -53,7 +59,7 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
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private fun SimpleMatrix.wrapMatrix() = EjmlMatrix(this)
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private fun SimpleMatrix.wrapVector() = EjmlVector(this)
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override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * (-1.0)
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public override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * (-1.0)
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public override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
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EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
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@ -67,29 +73,29 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
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public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix =
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toEjml().origin.scale(value).wrapMatrix()
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override fun Point<Double>.unaryMinus(): EjmlVector =
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public override fun Point<Double>.unaryMinus(): EjmlVector =
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toEjml().origin.negative().wrapVector()
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override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlMatrix =
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public override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlMatrix =
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(toEjml().origin + other.toEjml().origin).wrapMatrix()
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override fun Point<Double>.plus(other: Point<Double>): EjmlVector =
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public override fun Point<Double>.plus(other: Point<Double>): EjmlVector =
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(toEjml().origin + other.toEjml().origin).wrapVector()
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override fun Point<Double>.minus(other: Point<Double>): EjmlVector =
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public override fun Point<Double>.minus(other: Point<Double>): EjmlVector =
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(toEjml().origin - other.toEjml().origin).wrapVector()
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override fun Double.times(m: Matrix<Double>): EjmlMatrix =
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public override fun Double.times(m: Matrix<Double>): EjmlMatrix =
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m.toEjml().origin.scale(this).wrapMatrix()
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override fun Point<Double>.times(value: Double): EjmlVector =
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public override fun Point<Double>.times(value: Double): EjmlVector =
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toEjml().origin.scale(value).wrapVector()
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override fun Double.times(v: Point<Double>): EjmlVector =
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public override fun Double.times(v: Point<Double>): EjmlVector =
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v.toEjml().origin.scale(this).wrapVector()
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@UnstableKMathAPI
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override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
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public override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
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//Return the feature if it is intrinsic to the structure
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structure.getFeature(type)?.let { return it }
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@ -160,7 +166,7 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
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}
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/**
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* Solves for X in the following equation: x = a^-1*b, where 'a' is base matrix and 'b' is an n by p matrix.
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* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> · [b]*.
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*
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* @param a the base matrix.
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* @param b n by p matrix.
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@ -171,7 +177,7 @@ public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatr
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EjmlMatrix(a.toEjml().origin.solve(b.toEjml().origin))
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/**
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* Solves for X in the following equation: x = a^(-1)*b, where 'a' is base matrix and 'b' is an n by p matrix.
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* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> · [b]*.
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*
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* @param a the base matrix.
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* @param b n by p vector.
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@ -181,7 +187,17 @@ public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatr
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public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
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EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
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/**
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* Inverts this matrix.
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*
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* @author Alexander Nozik
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*/
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@OptIn(UnstableKMathAPI::class)
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public fun EjmlMatrix.inverted(): EjmlMatrix = getFeature<InverseMatrixFeature<Double>>()!!.inverse as EjmlMatrix
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/**
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* Inverts the given matrix.
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*
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* @author Alexander Nozik
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*/
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public fun EjmlLinearSpace.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()
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@ -4,7 +4,7 @@ import org.ejml.simple.SimpleMatrix
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import space.kscience.kmath.linear.Matrix
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/**
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* Represents featured matrix over EJML [SimpleMatrix].
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* The matrix implementation over EJML [SimpleMatrix].
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*
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* @property origin the underlying [SimpleMatrix].
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* @author Iaroslav Postovalov
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@ -9,7 +9,7 @@ import space.kscience.kmath.linear.Point
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* @property origin the underlying [SimpleMatrix].
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* @author Iaroslav Postovalov
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*/
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public class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
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public inline class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
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public override val size: Int
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get() = origin.numRows()
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