README.md

@@ -120,10 +120,10 @@ KMath is a modular library. Different modules provide different features with di
 >
 > **Features:**
 > - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
-> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/StructureND.kt) : Many-dimensional structures and operations on them.
+> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
 > - [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.
-> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt) : One-dimensional structure
+> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
-> - [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
+> - [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 
 objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
 performance calculations to code generation.
 > - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
@@ -206,9 +206,9 @@ One can still use generic algebras though.
 > **Maturity**: EXPERIMENTAL
 >
 > **Features:**
-> - [nd4jarraystructure](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : NDStructure wrapper for INDArray
+> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
-> - [nd4jarrayrings](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/structures/NDStructure.kt) : Rings over Nd4jArrayStructure of Int and Long
+> - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
-> - [nd4jarrayfields](kmath-nd4j/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : Fields over Nd4jArrayStructure of Float and Double
+> - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
 
 <hr/>
 

kmath-ast/README.md

@@ -58,7 +58,7 @@ For example, the following builder:
 DoubleField.mstInField { symbol("x") + 2 }.compile()
 ``` 
 
-… leads to generation of bytecode, which can be decompiled to the following Java class:
+… leads to generation of bytecode, which can be decompiled to the following Java class:
 
 ```java
 package space.kscience.kmath.asm.generated;

kmath-core/README.md

@@ -3,10 +3,10 @@
 The core features of KMath:
 
  - [algebras](src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
- - [nd](src/commonMain/kotlin/space/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures and operations on them.
+ - [nd](src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
  - [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.
  - [buffers](src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
- - [expressions](src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of
+ - [expressions](src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of 
 objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
 performance calculations to code generation.
  - [domains](src/commonMain/kotlin/space/kscience/kmath/domains) : Domains

kmath-ejml/README.md

@@ -0,0 +1,43 @@
+# ejml-simple support (`kmath-ejml`)
+
+This subproject implements the following features:
+
+ - [ejml-vector](src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : The Point implementation using SimpleMatrix.
+ - [ejml-matrix](src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : The Matrix implementation using SimpleMatrix.
+ - [ejml-linear-space](src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : The LinearSpace implementation using SimpleMatrix.
+
+
+> #### Artifact:
+>
+> This module artifact: `space.kscience:kmath-ejml:0.3.0-dev-3`.
+>
+> Bintray release version:        [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/kmath-ejml/images/download.svg) ](https://bintray.com/mipt-npm/kscience/kmath-ejml/_latestVersion)
+>
+> Bintray development version:    [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/kmath-ejml/images/download.svg) ](https://bintray.com/mipt-npm/dev/kmath-ejml/_latestVersion)
+>
+> **Gradle:**
+>
+> ```gradle
+> repositories {
+>     maven { url 'https://repo.kotlin.link' }
+>     maven { url 'https://dl.bintray.com/hotkeytlt/maven' }
+>     maven { url "https://dl.bintray.com/kotlin/kotlin-eap" } // include for builds based on kotlin-eap
+> }
+> 
+> dependencies {
+>     implementation 'space.kscience:kmath-ejml:0.3.0-dev-3'
+> }
+> ```
+> **Gradle Kotlin DSL:**
+>
+> ```kotlin
+> repositories {
+>     maven("https://repo.kotlin.link")
+>     maven("https://dl.bintray.com/kotlin/kotlin-eap") // include for builds based on kotlin-eap
+>     maven("https://dl.bintray.com/hotkeytlt/maven") // required for a
+> }
+> 
+> dependencies {
+>     implementation("space.kscience:kmath-ejml:0.3.0-dev-3")
+> }
+> ```

kmath-ejml/build.gradle.kts

@@ -1,12 +1,33 @@
+import ru.mipt.npm.gradle.Maturity
+
 plugins {
     id("ru.mipt.npm.gradle.jvm")
 }
 
 dependencies {
-    implementation("org.ejml:ejml-simple:0.39")
+    api("org.ejml:ejml-simple:0.40")
-    implementation(project(":kmath-core"))
+    api(project(":kmath-core"))
 }
 
 readme {
-    maturity = ru.mipt.npm.gradle.Maturity.PROTOTYPE
+    maturity = Maturity.PROTOTYPE
-}
+    propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
+
+    feature(
+        id = "ejml-vector",
+        description = "The Point implementation using SimpleMatrix.",
+        ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt"
+    )
+
+    feature(
+        id = "ejml-matrix",
+        description = "The Matrix implementation using SimpleMatrix.",
+        ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt"
+    )
+
+    feature(
+        id = "ejml-linear-space",
+        description = "The LinearSpace implementation using SimpleMatrix.",
+        ref = "src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt"
+    )
+}

kmath-ejml/docs/README-TEMPLATE.md

@@ -0,0 +1,7 @@
+# ejml-simple support (`kmath-ejml`)
+
+This subproject implements the following features:
+
+${features}
+
+${artifact}

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt

@@ -14,10 +14,13 @@ import kotlin.reflect.cast
  * Represents context of basic operations operating with [EjmlMatrix].
  *
  * @author Iaroslav Postovalov
+ * @author Alexander Nozik
  */
 public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
-
+    /**
-    override val elementAlgebra: DoubleField get() = DoubleField
+     * The [DoubleField] reference.
+     */
+    public override val elementAlgebra: DoubleField get() = DoubleField
 
     /**
      * Converts this matrix to EJML one.
@@ -38,14 +41,17 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
         })
     }
 
-    override fun buildMatrix(rows: Int, columns: Int, initializer: DoubleField.(i: Int, j: Int) -> Double): EjmlMatrix =
+    public override fun buildMatrix(
-        EjmlMatrix(SimpleMatrix(rows, columns).also {
+        rows: Int,
-            (0 until rows).forEach { row ->
+        columns: Int,
-                (0 until columns).forEach { col -> it[row, col] = DoubleField.initializer(row, col) }
+        initializer: DoubleField.(i: Int, j: Int) -> Double,
-            }
+    ): EjmlMatrix = EjmlMatrix(SimpleMatrix(rows, columns).also {
-        })
+        (0 until rows).forEach { row ->
+            (0 until columns).forEach { col -> it[row, col] = DoubleField.initializer(row, col) }
+        }
+    })
 
-    override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): Point<Double> =
+    public override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): Point<Double> =
         EjmlVector(SimpleMatrix(size, 1).also {
             (0 until it.numRows()).forEach { row -> it[row, 0] = DoubleField.initializer(row) }
         })
@@ -53,7 +59,7 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
     private fun SimpleMatrix.wrapMatrix() = EjmlMatrix(this)
     private fun SimpleMatrix.wrapVector() = EjmlVector(this)
 
-    override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * (-1.0)
+    public override fun Matrix<Double>.unaryMinus(): Matrix<Double> = this * (-1.0)
 
     public override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
         EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
@@ -67,29 +73,29 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
     public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix =
         toEjml().origin.scale(value).wrapMatrix()
 
-    override fun Point<Double>.unaryMinus(): EjmlVector =
+    public override fun Point<Double>.unaryMinus(): EjmlVector =
         toEjml().origin.negative().wrapVector()
 
-    override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlMatrix =
+    public override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlMatrix =
         (toEjml().origin + other.toEjml().origin).wrapMatrix()
 
-    override fun Point<Double>.plus(other: Point<Double>): EjmlVector =
+    public override fun Point<Double>.plus(other: Point<Double>): EjmlVector =
         (toEjml().origin + other.toEjml().origin).wrapVector()
 
-    override fun Point<Double>.minus(other: Point<Double>): EjmlVector =
+    public override fun Point<Double>.minus(other: Point<Double>): EjmlVector =
         (toEjml().origin - other.toEjml().origin).wrapVector()
 
-    override fun Double.times(m: Matrix<Double>): EjmlMatrix =
+    public override fun Double.times(m: Matrix<Double>): EjmlMatrix =
         m.toEjml().origin.scale(this).wrapMatrix()
 
-    override fun Point<Double>.times(value: Double): EjmlVector =
+    public override fun Point<Double>.times(value: Double): EjmlVector =
         toEjml().origin.scale(value).wrapVector()
 
-    override fun Double.times(v: Point<Double>): EjmlVector =
+    public override fun Double.times(v: Point<Double>): EjmlVector =
         v.toEjml().origin.scale(this).wrapVector()
 
     @UnstableKMathAPI
-    override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
+    public override fun <F : Any> getFeature(structure: Matrix<Double>, type: KClass<F>): F? {
         //Return the feature if it is intrinsic to the structure
         structure.getFeature(type)?.let { return it }
 
@@ -160,7 +166,7 @@ public object EjmlLinearSpace : LinearSpace<Double, DoubleField> {
 }
 
 /**
- * Solves for X in the following equation: x = a^-1*b, where 'a' is base matrix and 'b' is an n by p matrix.
+ * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
  *
  * @param a the base matrix.
  * @param b n by p matrix.
@@ -171,7 +177,7 @@ public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatr
     EjmlMatrix(a.toEjml().origin.solve(b.toEjml().origin))
 
 /**
- * Solves for X in the following equation: x = a^(-1)*b, where 'a' is base matrix and 'b' is an n by p matrix.
+ * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
  *
  * @param a the base matrix.
  * @param b n by p vector.
@@ -181,7 +187,17 @@ public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatr
 public fun EjmlLinearSpace.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
     EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
 
+/**
+ * Inverts this matrix.
+ *
+ * @author Alexander Nozik
+ */
 @OptIn(UnstableKMathAPI::class)
 public fun EjmlMatrix.inverted(): EjmlMatrix = getFeature<InverseMatrixFeature<Double>>()!!.inverse as EjmlMatrix
 
+/**
+ * Inverts the given matrix.
+ *
+ * @author Alexander Nozik
+ */
 public fun EjmlLinearSpace.inverse(matrix: Matrix<Double>): Matrix<Double> = matrix.toEjml().inverted()

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt

@@ -4,7 +4,7 @@ import org.ejml.simple.SimpleMatrix
 import space.kscience.kmath.linear.Matrix
 
 /**
- * Represents featured matrix over EJML [SimpleMatrix].
+ * The matrix implementation over EJML [SimpleMatrix].
  *
  * @property origin the underlying [SimpleMatrix].
  * @author Iaroslav Postovalov

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt

@@ -9,7 +9,7 @@ import space.kscience.kmath.linear.Point
  * @property origin the underlying [SimpleMatrix].
  * @author Iaroslav Postovalov
  */
-public class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
+public inline class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
     public override val size: Int
         get() = origin.numRows()