Merge remote-tracking branch 'origin/dev' into mp-samplers

# Conflicts:
#	examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionBenchmark.kt

CHANGELOG.md

@@ -7,6 +7,7 @@
 - Better trigonometric and hyperbolic functions for `AutoDiffField` (https://github.com/mipt-npm/kmath/pull/140).
 - Automatic README generation for features (#139)
 - Native support for `memory`, `core` and `dimensions`
+- `kmath-ejml` to supply EJML SimpleMatrix wrapper.
 
 ### Changed
 - Package changed from `scientifik` to `kscience.kmath`.
@@ -14,6 +15,7 @@
 - Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
 - `Polynomial` secondary constructor made function.
 - Kotlin version: 1.3.72 -> 1.4.20-M1
+- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library.
 
 ### Deprecated
 

README.md

@@ -53,7 +53,9 @@ can be used for a wide variety of purposes from high performance calculations to
 * **Commons-math wrapper** It is planned to gradually wrap most parts of [Apache commons-math](http://commons.apache.org/proper/commons-math/)
                            library in Kotlin code and maybe rewrite some parts to better suit the Kotlin programming paradigm, however there is no fixed roadmap for that. Feel free
                            to submit a feature request if you want something to be done first.
-                           
+
+* **EJML wrapper** Provides EJML `SimpleMatrix` wrapper consistent with the core matrix structures.
+
 ## Planned features
 
 * **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.

build.gradle.kts

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

docs/linear.md

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

examples/build.gradle.kts

@@ -26,9 +26,12 @@ dependencies {
     implementation(project(":kmath-prob"))
     implementation(project(":kmath-viktor"))
     implementation(project(":kmath-dimensions"))
+    implementation(project(":kmath-ejml"))
     implementation("org.jetbrains.kotlinx:kotlinx-io:0.2.0-npm-dev-11")
     implementation("org.jetbrains.kotlinx:kotlinx.benchmark.runtime:0.2.0-dev-20")
-    "benchmarksCompile"(sourceSets.main.get().output + sourceSets.main.get().compileClasspath) //sourceSets.main.output + sourceSets.main.runtimeClasspath
+    implementation("org.slf4j:slf4j-simple:1.7.30")
+    "benchmarksImplementation"("org.jetbrains.kotlinx:kotlinx.benchmark.runtime-jvm:0.2.0-dev-8")
+    "benchmarksImplementation"(sourceSets.main.get().output + sourceSets.main.get().runtimeClasspath)
 }
 
 // Configure benchmark

examples/src/benchmarks/kotlin/kscience/kmath/structures/ArrayBenchmark.kt

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

examples/src/benchmarks/kotlin/kscience/kmath/structures/BufferBenchmark.kt

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

examples/src/benchmarks/kotlin/kscience/kmath/structures/NDFieldBenchmark.kt

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

examples/src/benchmarks/kotlin/kscience/kmath/structures/ViktorBenchmark.kt

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

examples/src/main/kotlin/kscience/kmath/ast/ExpressionsInterpretersBenchmark.kt

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

examples/src/main/kotlin/kscience/kmath/commons/prob/DistributionDemo.kt

@@ -6,9 +6,9 @@ import kscience.kmath.chains.collectWithState
 import kscience.kmath.prob.RandomGenerator
 import kscience.kmath.prob.samplers.ZigguratNormalizedGaussianSampler
 
-data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
+private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
 
-fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain ->
+private fun Chain<Double>.mean(): Chain<Double> = collectWithState(AveragingChainState(), { it.copy() }) { chain ->
     val next = chain.next()
     num++
     value += next

examples/src/main/kotlin/kscience/kmath/linear/LinearAlgebraBenchmark.kt

@@ -0,0 +1,50 @@
+package kscience.kmath.linear
+
+import kscience.kmath.commons.linear.CMMatrixContext
+import kscience.kmath.commons.linear.inverse
+import kscience.kmath.commons.linear.toCM
+import kscience.kmath.ejml.EjmlMatrixContext
+import kscience.kmath.ejml.inverse
+import kscience.kmath.operations.RealField
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+import kotlin.random.Random
+import kotlin.system.measureTimeMillis
+
+fun main() {
+    val random = Random(1224)
+    val dim = 100
+    //creating invertible matrix
+    val u = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+    val l = Matrix.real(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
+    val matrix = l dot u
+
+    val n = 5000 // iterations
+
+    MatrixContext.real {
+        repeat(50) { inverse(matrix) }
+        val inverseTime = measureTimeMillis { repeat(n) { inverse(matrix) } }
+        println("[kmath] Inversion of $n matrices $dim x $dim finished in $inverseTime millis")
+    }
+
+    //commons-math
+
+    val commonsTime = measureTimeMillis {
+        CMMatrixContext {
+            val cm = matrix.toCM()             //avoid overhead on conversion
+            repeat(n) { inverse(cm) }
+        }
+    }
+
+
+    println("[commons-math] Inversion of $n matrices $dim x $dim finished in $commonsTime millis")
+
+    val ejmlTime = measureTimeMillis {
+        (EjmlMatrixContext(RealField)) {
+            val km = matrix.toEjml()      //avoid overhead on conversion
+            repeat(n) { inverse(km) }
+        }
+    }
+
+    println("[ejml] Inversion of $n matrices $dim x $dim finished in $ejmlTime millis")
+}

examples/src/main/kotlin/kscience/kmath/linear/MultiplicationBenchmark.kt

@@ -0,0 +1,38 @@
+package kscience.kmath.linear
+
+import kscience.kmath.commons.linear.CMMatrixContext
+import kscience.kmath.commons.linear.toCM
+import kscience.kmath.ejml.EjmlMatrixContext
+import kscience.kmath.operations.RealField
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+import kotlin.random.Random
+import kotlin.system.measureTimeMillis
+
+fun main() {
+    val random = Random(12224)
+    val dim = 1000
+    //creating invertible matrix
+    val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+    val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
+
+//    //warmup
+//    matrix1 dot matrix2
+
+    CMMatrixContext {
+        val cmMatrix1 = matrix1.toCM()
+        val cmMatrix2 = matrix2.toCM()
+        val cmTime = measureTimeMillis { cmMatrix1 dot cmMatrix2 }
+        println("CM implementation time: $cmTime")
+    }
+
+    (EjmlMatrixContext(RealField)) {
+        val ejmlMatrix1 = matrix1.toEjml()
+        val ejmlMatrix2 = matrix2.toEjml()
+        val ejmlTime = measureTimeMillis { ejmlMatrix1 dot ejmlMatrix2 }
+        println("EJML implementation time: $ejmlTime")
+    }
+
+    val genericTime = measureTimeMillis { val res = matrix1 dot matrix2 }
+    println("Generic implementation time: $genericTime")
+}

examples/src/main/kotlin/kscience/kmath/operations/BigIntDemo.kt

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

examples/src/main/kotlin/kscience/kmath/operations/ComplexDemo.kt

@@ -5,15 +5,19 @@ import kscience.kmath.structures.NDField
 import kscience.kmath.structures.complex
 
 fun main() {
+    // 2d element
     val element = NDElement.complex(2, 2) { index: IntArray ->
         Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble())
     }
+    println(element)
 
-    val compute = (NDField.complex(8)) {
+    // 1d element operation
+    val result = with(NDField.complex(8)) {
         val a = produce { (it) -> i * it - it.toDouble() }
         val b = 3
         val c = Complex(1.0, 1.0)
 
         (a pow b) + c
     }
+    println(result)
 }

examples/src/main/kotlin/kscience/kmath/structures/StructureReadBenchmark.kt

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

examples/src/main/kotlin/kscience/kmath/structures/StructureWriteBenchmark.kt

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

examples/src/main/kotlin/kscience/kmath/structures/typeSafeDimensions.kt

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

kmath-ast/build.gradle.kts

@@ -14,7 +14,6 @@ kotlin.sourceSets {
             implementation("org.ow2.asm:asm:8.0.1")
             implementation("org.ow2.asm:asm-commons:8.0.1")
             implementation("com.github.h0tk3y.betterParse:better-parse:0.4.0")
-            implementation(kotlin("reflect"))
         }
     }
 }

kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/asm.kt

@@ -8,7 +8,6 @@ import kscience.kmath.ast.MST
 import kscience.kmath.ast.MstExpression
 import kscience.kmath.expressions.Expression
 import kscience.kmath.operations.Algebra
-import kotlin.reflect.KClass
 
 /**
  * Compiles given MST to an Expression using AST compiler.
@@ -18,7 +17,8 @@ import kotlin.reflect.KClass
  * @return the compiled expression.
  * @author Alexander Nozik
  */
-public fun <T : Any> MST.compileWith(type: KClass<T>, algebra: Algebra<T>): Expression<T> {
+@PublishedApi
+internal fun <T : Any> MST.compileWith(type: Class<T>, algebra: Algebra<T>): Expression<T> {
     fun AsmBuilder<T>.visit(node: MST): Unit = when (node) {
         is MST.Symbolic -> {
             val symbol = try {
@@ -61,11 +61,12 @@ public fun <T : Any> MST.compileWith(type: KClass<T>, algebra: Algebra<T>): Expr
  *
  * @author Alexander Nozik.
  */
-public inline fun <reified T : Any> Algebra<T>.expression(mst: MST): Expression<T> = mst.compileWith(T::class, this)
+public inline fun <reified T : Any> Algebra<T>.expression(mst: MST): Expression<T> =
+    mst.compileWith(T::class.java, this)
 
 /**
  * Optimizes performance of an [MstExpression] using ASM codegen.
  *
  * @author Alexander Nozik.
  */
-public inline fun <reified T : Any> MstExpression<T>.compile(): Expression<T> = mst.compileWith(T::class, algebra)
+public inline fun <reified T : Any> MstExpression<T>.compile(): Expression<T> = mst.compileWith(T::class.java, algebra)

kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/internal/AsmBuilder.kt

@@ -10,7 +10,6 @@ import org.objectweb.asm.Opcodes.*
 import org.objectweb.asm.commons.InstructionAdapter
 import java.util.*
 import java.util.stream.Collectors
-import kotlin.reflect.KClass
 
 /**
  * ASM Builder is a structure that abstracts building a class designated to unwrap [MST] to plain Java expression.
@@ -23,7 +22,7 @@ import kotlin.reflect.KClass
  * @author Iaroslav Postovalov
  */
 internal class AsmBuilder<T> internal constructor(
-    private val classOfT: KClass<*>,
+    private val classOfT: Class<*>,
     private val algebra: Algebra<T>,
     private val className: String,
     private val invokeLabel0Visitor: AsmBuilder<T>.() -> Unit
@@ -32,7 +31,7 @@ internal class AsmBuilder<T> internal constructor(
      * Internal classloader of [AsmBuilder] with alias to define class from byte array.
      */
     private class ClassLoader(parent: java.lang.ClassLoader) : java.lang.ClassLoader(parent) {
-        internal fun defineClass(name: String?, b: ByteArray): Class<*> = defineClass(name, b, 0, b.size)
+        fun defineClass(name: String?, b: ByteArray): Class<*> = defineClass(name, b, 0, b.size)
     }
 
     /**
@@ -43,7 +42,7 @@ internal class AsmBuilder<T> internal constructor(
     /**
      * ASM Type for [algebra].
      */
-    private val tAlgebraType: Type = algebra::class.asm
+    private val tAlgebraType: Type = algebra.javaClass.asm
 
     /**
      * ASM type for [T].
@@ -55,16 +54,6 @@ internal class AsmBuilder<T> internal constructor(
      */
     private val classType: Type = Type.getObjectType(className.replace(oldChar = '.', newChar = '/'))!!
 
-    /**
-     * Index of `this` variable in invoke method of the built subclass.
-     */
-    private val invokeThisVar: Int = 0
-
-    /**
-     * Index of `arguments` variable in invoke method of the built subclass.
-     */
-    private val invokeArgumentsVar: Int = 1
-
     /**
      * List of constants to provide to the subclass.
      */
@@ -76,22 +65,22 @@ internal class AsmBuilder<T> internal constructor(
     private lateinit var invokeMethodVisitor: InstructionAdapter
 
     /**
-     * State if this [AsmBuilder] needs to generate constants field.
+     * States whether this [AsmBuilder] needs to generate constants field.
      */
     private var hasConstants: Boolean = true
 
     /**
-     * State if [T] a primitive type, so [AsmBuilder] may generate direct primitive calls.
+     * States whether [T] a primitive type, so [AsmBuilder] may generate direct primitive calls.
      */
     internal var primitiveMode: Boolean = false
 
     /**
-     * Primitive type to apple for specific primitive calls. Use [OBJECT_TYPE], if not in [primitiveMode].
+     * Primitive type to apply for specific primitive calls. Use [OBJECT_TYPE], if not in [primitiveMode].
      */
     internal var primitiveMask: Type = OBJECT_TYPE
 
     /**
-     * Boxed primitive type to apple for specific primitive calls. Use [OBJECT_TYPE], if not in [primitiveMode].
+     * Boxed primitive type to apply for specific primitive calls. Use [OBJECT_TYPE], if not in [primitiveMode].
      */
     internal var primitiveMaskBoxed: Type = OBJECT_TYPE
 
@@ -103,7 +92,7 @@ internal class AsmBuilder<T> internal constructor(
     /**
      * Stack of useful objects types on stack expected by algebra calls.
      */
-    internal val expectationStack: ArrayDeque<Type> = ArrayDeque(listOf(tType))
+    internal val expectationStack: ArrayDeque<Type> = ArrayDeque<Type>(1).also { it.push(tType) }
 
     /**
      * The cache for instance built by this builder.
@@ -361,7 +350,7 @@ internal class AsmBuilder<T> internal constructor(
      * from it).
      */
     private fun loadNumberConstant(value: Number, mustBeBoxed: Boolean) {
-        val boxed = value::class.asm
+        val boxed = value.javaClass.asm
         val primitive = BOXED_TO_PRIMITIVES[boxed]
 
         if (primitive != null) {
@@ -475,17 +464,27 @@ internal class AsmBuilder<T> internal constructor(
     internal fun loadStringConstant(string: String): Unit = invokeMethodVisitor.aconst(string)
 
     internal companion object {
+        /**
+         * Index of `this` variable in invoke method of the built subclass.
+         */
+        private const val invokeThisVar: Int = 0
+
+        /**
+         * Index of `arguments` variable in invoke method of the built subclass.
+         */
+        private const val invokeArgumentsVar: Int = 1
+
         /**
          * Maps JVM primitive numbers boxed types to their primitive ASM types.
          */
-        private val SIGNATURE_LETTERS: Map<KClass<out Any>, Type> by lazy {
+        private val SIGNATURE_LETTERS: Map<Class<out Any>, Type> by lazy {
             hashMapOf(
-                java.lang.Byte::class to Type.BYTE_TYPE,
+                java.lang.Byte::class.java to Type.BYTE_TYPE,
-                java.lang.Short::class to Type.SHORT_TYPE,
+                java.lang.Short::class.java to Type.SHORT_TYPE,
-                java.lang.Integer::class to Type.INT_TYPE,
+                java.lang.Integer::class.java to Type.INT_TYPE,
-                java.lang.Long::class to Type.LONG_TYPE,
+                java.lang.Long::class.java to Type.LONG_TYPE,
-                java.lang.Float::class to Type.FLOAT_TYPE,
+                java.lang.Float::class.java to Type.FLOAT_TYPE,
-                java.lang.Double::class to Type.DOUBLE_TYPE
+                java.lang.Double::class.java to Type.DOUBLE_TYPE
             )
         }
 
@@ -523,43 +522,43 @@ internal class AsmBuilder<T> internal constructor(
         /**
          * Provides boxed number types values of which can be stored in JVM bytecode constant pool.
          */
-        private val INLINABLE_NUMBERS: Set<KClass<out Any>> by lazy { SIGNATURE_LETTERS.keys }
+        private val INLINABLE_NUMBERS: Set<Class<out Any>> by lazy { SIGNATURE_LETTERS.keys }
 
         /**
          * ASM type for [Expression].
          */
-        internal val EXPRESSION_TYPE: Type by lazy { Expression::class.asm }
+        internal val EXPRESSION_TYPE: Type by lazy { Type.getObjectType("kscience/kmath/expressions/Expression") }
 
         /**
          * ASM type for [java.lang.Number].
          */
-        internal val NUMBER_TYPE: Type by lazy { java.lang.Number::class.asm }
+        internal val NUMBER_TYPE: Type by lazy { Type.getObjectType("java/lang/Number") }
 
         /**
          * ASM type for [java.util.Map].
          */
-        internal val MAP_TYPE: Type by lazy { java.util.Map::class.asm }
+        internal val MAP_TYPE: Type by lazy { Type.getObjectType("java/util/Map") }
 
         /**
          * ASM type for [java.lang.Object].
          */
-        internal val OBJECT_TYPE: Type by lazy { java.lang.Object::class.asm }
+        internal val OBJECT_TYPE: Type by lazy { Type.getObjectType("java/lang/Object") }
 
         /**
          * ASM type for array of [java.lang.Object].
          */
         @Suppress("PLATFORM_CLASS_MAPPED_TO_KOTLIN", "RemoveRedundantQualifierName")
-        internal val OBJECT_ARRAY_TYPE: Type by lazy { Array<java.lang.Object>::class.asm }
+        internal val OBJECT_ARRAY_TYPE: Type by lazy { Type.getType("[Ljava/lang/Object;") }
 
         /**
          * ASM type for [Algebra].
          */
-        internal val ALGEBRA_TYPE: Type by lazy { Algebra::class.asm }
+        internal val ALGEBRA_TYPE: Type by lazy { Type.getObjectType("kscience/kmath/operations/Algebra") }
 
         /**
          * ASM type for [java.lang.String].
          */
-        internal val STRING_TYPE: Type by lazy { java.lang.String::class.asm }
+        internal val STRING_TYPE: Type by lazy { Type.getObjectType("java/lang/String") }
 
         /**
          * ASM type for MapIntrinsics.

kmath-ast/src/jvmMain/kotlin/kscience/kmath/asm/internal/codegenUtils.kt

@@ -10,9 +10,9 @@ import org.objectweb.asm.*
 import org.objectweb.asm.Opcodes.INVOKEVIRTUAL
 import org.objectweb.asm.commons.InstructionAdapter
 import java.lang.reflect.Method
+import java.util.*
 import kotlin.contracts.InvocationKind
 import kotlin.contracts.contract
-import kotlin.reflect.KClass
 
 private val methodNameAdapters: Map<Pair<String, Int>, String> by lazy {
     hashMapOf(
@@ -26,12 +26,12 @@ private val methodNameAdapters: Map<Pair<String, Int>, String> by lazy {
 }
 
 /**
- * Returns ASM [Type] for given [KClass].
+ * Returns ASM [Type] for given [Class].
  *
  * @author Iaroslav Postovalov
  */
-internal val KClass<*>.asm: Type
+internal inline val Class<*>.asm: Type
-    get() = Type.getType(java)
+    get() = Type.getType(this)
 
 /**
  * Returns singleton array with this value if the [predicate] is true, returns empty array otherwise.
@@ -140,7 +140,7 @@ private fun <T> AsmBuilder<T>.buildExpectationStack(
     if (specific != null)
         mapTypes(specific, parameterTypes).reversed().forEach { expectationStack.push(it) }
     else
-        repeat(arity) { expectationStack.push(tType) }
+        expectationStack.addAll(Collections.nCopies(arity, tType))
 
     return specific != null
 }
@@ -169,7 +169,7 @@ private fun <T> AsmBuilder<T>.tryInvokeSpecific(
     val arity = parameterTypes.size
     val theName = methodNameAdapters[name to arity] ?: name
     val spec = findSpecific(context, theName, parameterTypes) ?: return false
-    val owner = context::class.asm
+    val owner = context.javaClass.asm
 
     invokeAlgebraOperation(
         owner = owner.internalName,

kmath-ast/src/jvmMain/kotlin/kscience/kmath/ast/parser.kt

@@ -7,6 +7,7 @@ import com.github.h0tk3y.betterParse.grammar.parser
 import com.github.h0tk3y.betterParse.grammar.tryParseToEnd
 import com.github.h0tk3y.betterParse.lexer.Token
 import com.github.h0tk3y.betterParse.lexer.TokenMatch
+import com.github.h0tk3y.betterParse.lexer.literalToken
 import com.github.h0tk3y.betterParse.lexer.regexToken
 import com.github.h0tk3y.betterParse.parser.ParseResult
 import com.github.h0tk3y.betterParse.parser.Parser
@@ -23,14 +24,14 @@ public object ArithmeticsEvaluator : Grammar<MST>() {
     // TODO replace with "...".toRegex() when better-parse 0.4.1 is released
     private val num: Token by regexToken("[\\d.]+(?:[eE][-+]?\\d+)?")
     private val id: Token by regexToken("[a-z_A-Z][\\da-z_A-Z]*")
-    private val lpar: Token by regexToken("\\(")
+    private val lpar: Token by literalToken("(")
-    private val rpar: Token by regexToken("\\)")
+    private val rpar: Token by literalToken(")")
-    private val comma: Token by regexToken(",")
+    private val comma: Token by literalToken(",")
-    private val mul: Token by regexToken("\\*")
+    private val mul: Token by literalToken("*")
-    private val pow: Token by regexToken("\\^")
+    private val pow: Token by literalToken("^")
-    private val div: Token by regexToken("/")
+    private val div: Token by literalToken("/")
-    private val minus: Token by regexToken("-")
+    private val minus: Token by literalToken("-")
-    private val plus: Token by regexToken("\\+")
+    private val plus: Token by literalToken("+")
     private val ws: Token by regexToken("\\s+", ignore = true)
 
     private val number: Parser<MST> by num use { MST.Numeric(text.toDouble()) }

kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DiffExpression.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/FeaturedMatrix.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/linear/MatrixContext.kt

@@ -18,20 +18,52 @@ public interface MatrixContext<T : Any> : SpaceOperations<Matrix<T>> {
      */
     public fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> T): Matrix<T>
 
+    public override fun binaryOperation(operation: String, left: Matrix<T>, right: Matrix<T>): Matrix<T> = when (operation) {
+        "dot" -> left dot right
+        else -> super.binaryOperation(operation, left, right)
+    }
+
+    /**
+     * Computes the dot product of this matrix and another one.
+     *
+     * @receiver the multiplicand.
+     * @param other the multiplier.
+     * @return the dot product.
+     */
     public infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T>
 
+    /**
+     * Computes the dot product of this matrix and a vector.
+     *
+     * @receiver the multiplicand.
+     * @param vector the multiplier.
+     * @return the dot product.
+     */
     public infix fun Matrix<T>.dot(vector: Point<T>): Point<T>
 
+    /**
+     * Multiplies a matrix by its element.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
     public operator fun Matrix<T>.times(value: T): Matrix<T>
 
+    /**
+     * Multiplies an element by a matrix of it.
+     *
+     * @receiver the multiplicand.
+     * @param value the multiplier.
+     * @receiver the product.
+     */
     public operator fun T.times(m: Matrix<T>): Matrix<T> = m * this
 
     public companion object {
         /**
          * Non-boxing double matrix
          */
-        public val real: RealMatrixContext
+        public val real: RealMatrixContext = RealMatrixContext
-            get() = RealMatrixContext
 
         /**
          * A structured matrix with custom buffer
@@ -60,7 +92,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
      */
     public fun point(size: Int, initializer: (Int) -> T): Point<T>
 
-    override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
+    public override infix fun Matrix<T>.dot(other: Matrix<T>): Matrix<T> {
         //TODO add typed error
         require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
 
@@ -71,7 +103,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         }
     }
 
-    override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
+    public override infix fun Matrix<T>.dot(vector: Point<T>): Point<T> {
         //TODO add typed error
         require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
 
@@ -81,10 +113,10 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         }
     }
 
-    override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
+    public override operator fun Matrix<T>.unaryMinus(): Matrix<T> =
         produce(rowNum, colNum) { i, j -> elementContext { -get(i, j) } }
 
-    override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
+    public override fun add(a: Matrix<T>, b: Matrix<T>): Matrix<T> {
         require(a.rowNum == b.rowNum && a.colNum == b.colNum) {
             "Matrix operation dimension mismatch. [${a.rowNum},${a.colNum}] + [${b.rowNum},${b.colNum}]"
         }
@@ -92,7 +124,7 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         return produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] + b[i, j] } }
     }
 
-    override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
+    public override operator fun Matrix<T>.minus(b: Matrix<T>): Matrix<T> {
         require(rowNum == b.rowNum && colNum == b.colNum) {
             "Matrix operation dimension mismatch. [$rowNum,$colNum] - [${b.rowNum},${b.colNum}]"
         }
@@ -100,11 +132,11 @@ public interface GenericMatrixContext<T : Any, R : Ring<T>> : MatrixContext<T> {
         return produce(rowNum, colNum) { i, j -> elementContext { get(i, j) + b[i, j] } }
     }
 
-    override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
+    public override fun multiply(a: Matrix<T>, k: Number): Matrix<T> =
         produce(a.rowNum, a.colNum) { i, j -> elementContext { a[i, j] * k } }
 
     public operator fun Number.times(matrix: FeaturedMatrix<T>): Matrix<T> = matrix * this
 
-    override operator fun Matrix<T>.times(value: T): Matrix<T> =
+    public override operator fun Matrix<T>.times(value: T): Matrix<T> =
         produce(rowNum, colNum) { i, j -> elementContext { get(i, j) * value } }
 }

kmath-core/src/commonMain/kotlin/kscience/kmath/misc/AutoDiff.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/operations/BigInt.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Complex.kt

@@ -6,6 +6,7 @@ import kscience.kmath.memory.MemoryWriter
 import kscience.kmath.structures.Buffer
 import kscience.kmath.structures.MemoryBuffer
 import kscience.kmath.structures.MutableBuffer
+import kscience.kmath.structures.MutableMemoryBuffer
 import kotlin.math.*
 
 /**
@@ -159,7 +160,7 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex> {
 }
 
 /**
- * Represents complex number.
+ * Represents `double`-based complex number.
  *
  * @property re The real part.
  * @property im The imaginary part.
@@ -176,11 +177,16 @@ public data class Complex(val re: Double, val im: Double) : FieldElement<Complex
 
     override fun compareTo(other: Complex): Int = r.compareTo(other.r)
 
-    public companion object : MemorySpec<Complex> {
+    override fun toString(): String {
-        override val objectSize: Int = 16
+        return "($re + i*$im)"
+    }
 
-        override fun MemoryReader.read(offset: Int): Complex =
+
-            Complex(readDouble(offset), readDouble(offset + 8))
+    public companion object : MemorySpec<Complex> {
+        override val objectSize: Int
+            get() = 16
+
+        override fun MemoryReader.read(offset: Int): Complex = Complex(readDouble(offset), readDouble(offset + 8))
 
         override fun MemoryWriter.write(offset: Int, value: Complex) {
             writeDouble(offset, value.re)
@@ -197,8 +203,16 @@ public data class Complex(val re: Double, val im: Double) : FieldElement<Complex
  */
 public fun Number.toComplex(): Complex = Complex(this, 0.0)
 
+/**
+ * Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
+ * specified [init] function.
+ */
 public inline fun Buffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> =
     MemoryBuffer.create(Complex, size, init)
 
-public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): Buffer<Complex> =
+/**
-    MemoryBuffer.create(Complex, size, init)
+ * Creates a new buffer of complex numbers with the specified [size], where each element is calculated by calling the
+ * specified [init] function.
+ */
+public inline fun MutableBuffer.Companion.complex(size: Int, init: (Int) -> Complex): MutableBuffer<Complex> =
+    MutableMemoryBuffer.create(Complex, size, init)

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BoxingNDField.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BoxingNDRing.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/BufferedNDAlgebra.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/FlaggedBuffer.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/MemoryBuffer.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDAlgebra.kt

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

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt

@@ -38,9 +38,8 @@ public interface NDStructure<T> {
      */
     public fun elements(): Sequence<Pair<IntArray, T>>
 
-    override fun equals(other: Any?): Boolean
+    public override fun equals(other: Any?): Boolean
-
+    public override fun hashCode(): Int
-    override fun hashCode(): Int
 
     public companion object {
         /**
@@ -50,13 +49,8 @@ public interface NDStructure<T> {
             if (st1 === st2) return true
 
             // fast comparison of buffers if possible
-            if (
+            if (st1 is NDBuffer && st2 is NDBuffer && st1.strides == st2.strides)
-                st1 is NDBuffer &&
-                st2 is NDBuffer &&
-                st1.strides == st2.strides
-            ) {
                 return st1.buffer.contentEquals(st2.buffer)
-            }
 
             //element by element comparison if it could not be avoided
             return st1.elements().all { (index, value) -> value == st2[index] }
@@ -70,7 +64,7 @@ public interface NDStructure<T> {
         public fun <T> build(
             strides: Strides,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
-            initializer: (IntArray) -> T
+            initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             BufferNDStructure(strides, bufferFactory(strides.linearSize) { i -> initializer(strides.index(i)) })
 
@@ -79,40 +73,40 @@ public interface NDStructure<T> {
          */
         public inline fun <reified T : Any> auto(
             strides: Strides,
-            crossinline initializer: (IntArray) -> T
+            crossinline initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             BufferNDStructure(strides, Buffer.auto(strides.linearSize) { i -> initializer(strides.index(i)) })
 
         public inline fun <T : Any> auto(
             type: KClass<T>,
             strides: Strides,
-            crossinline initializer: (IntArray) -> T
+            crossinline initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             BufferNDStructure(strides, Buffer.auto(type, strides.linearSize) { i -> initializer(strides.index(i)) })
 
         public fun <T> build(
             shape: IntArray,
             bufferFactory: BufferFactory<T> = Buffer.Companion::boxing,
-            initializer: (IntArray) -> T
+            initializer: (IntArray) -> T,
         ): BufferNDStructure<T> = build(DefaultStrides(shape), bufferFactory, initializer)
 
         public inline fun <reified T : Any> auto(
             shape: IntArray,
-            crossinline initializer: (IntArray) -> T
+            crossinline initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             auto(DefaultStrides(shape), initializer)
 
         @JvmName("autoVarArg")
         public inline fun <reified T : Any> auto(
             vararg shape: Int,
-            crossinline initializer: (IntArray) -> T
+            crossinline initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             auto(DefaultStrides(shape), initializer)
 
         public inline fun <T : Any> auto(
             type: KClass<T>,
             vararg shape: Int,
-            crossinline initializer: (IntArray) -> T
+            crossinline initializer: (IntArray) -> T,
         ): BufferNDStructure<T> =
             auto(type, DefaultStrides(shape), initializer)
     }
@@ -274,6 +268,22 @@ public abstract class NDBuffer<T> : NDStructure<T> {
         result = 31 * result + buffer.hashCode()
         return result
     }
+
+    override fun toString(): String {
+        val bufferRepr: String = when (shape.size) {
+            1 -> buffer.asSequence().joinToString(prefix = "[", postfix = "]", separator = ", ")
+            2 -> (0 until shape[0]).joinToString(prefix = "[", postfix = "]", separator = ", ") { i ->
+                (0 until shape[1]).joinToString(prefix = "[", postfix = "]", separator = ", ") { j ->
+                    val offset = strides.offset(intArrayOf(i, j))
+                    buffer[offset].toString()
+                }
+            }
+            else -> "..."
+        }
+        return "NDBuffer(shape=${shape.contentToString()}, buffer=$bufferRepr)"
+    }
+
+
 }
 
 /**
@@ -281,7 +291,7 @@ public abstract class NDBuffer<T> : NDStructure<T> {
  */
 public class BufferNDStructure<T>(
     override val strides: Strides,
-    override val buffer: Buffer<T>
+    override val buffer: Buffer<T>,
 ) : NDBuffer<T>() {
     init {
         if (strides.linearSize != buffer.size) {
@@ -295,7 +305,7 @@ public class BufferNDStructure<T>(
  */
 public inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
     factory: BufferFactory<R> = Buffer.Companion::auto,
-    crossinline transform: (T) -> R
+    crossinline transform: (T) -> R,
 ): BufferNDStructure<R> {
     return if (this is BufferNDStructure<T>)
         BufferNDStructure(this.strides, factory.invoke(strides.linearSize) { transform(buffer[it]) })
@@ -310,7 +320,7 @@ public inline fun <T, reified R : Any> NDStructure<T>.mapToBuffer(
  */
 public class MutableBufferNDStructure<T>(
     override val strides: Strides,
-    override val buffer: MutableBuffer<T>
+    override val buffer: MutableBuffer<T>,
 ) : NDBuffer<T>(), MutableNDStructure<T> {
 
     init {
@@ -324,7 +334,7 @@ public class MutableBufferNDStructure<T>(
 
 public inline fun <reified T : Any> NDStructure<T>.combine(
     struct: NDStructure<T>,
-    crossinline block: (T, T) -> T
+    crossinline block: (T, T) -> T,
 ): NDStructure<T> {
     require(shape.contentEquals(struct.shape)) { "Shape mismatch in structure combination" }
     return NDStructure.auto(shape) { block(this[it], struct[it]) }

kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Structure1D.kt

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

kmath-coroutines/src/commonMain/kotlin/kscience/kmath/coroutines/coroutinesExtra.kt

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

kmath-dimensions/src/commonMain/kotlin/kscience/kmath/dimensions/Dimensions.kt

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

kmath-ejml/build.gradle.kts

@@ -0,0 +1,8 @@
+plugins {
+    id("ru.mipt.npm.jvm")
+}
+
+dependencies {
+    implementation("org.ejml:ejml-simple:0.39")
+    implementation(project(":kmath-core"))
+}

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

@@ -0,0 +1,71 @@
+package kscience.kmath.ejml
+
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.DeterminantFeature
+import kscience.kmath.linear.FeaturedMatrix
+import kscience.kmath.linear.LUPDecompositionFeature
+import kscience.kmath.linear.MatrixFeature
+import kscience.kmath.structures.NDStructure
+
+/**
+ * Represents featured matrix over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ * @author Iaroslav Postovalov
+ */
+public class EjmlMatrix(public val origin: SimpleMatrix, features: Set<MatrixFeature>? = null) : FeaturedMatrix<Double> {
+    public override val rowNum: Int
+        get() = origin.numRows()
+
+    public override val colNum: Int
+        get() = origin.numCols()
+
+    public override val shape: IntArray
+        get() = intArrayOf(origin.numRows(), origin.numCols())
+
+    public override val features: Set<MatrixFeature> = setOf(
+        object : LUPDecompositionFeature<Double>, DeterminantFeature<Double> {
+            override val determinant: Double
+                get() = origin.determinant()
+
+            private val lup by lazy {
+                val ludecompositionF64 = DecompositionFactory_DDRM.lu(origin.numRows(), origin.numCols())
+                    .also { it.decompose(origin.ddrm.copy()) }
+
+                Triple(
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))),
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))),
+                    EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))),
+                )
+            }
+
+            override val l: FeaturedMatrix<Double>
+                get() = lup.second
+
+            override val u: FeaturedMatrix<Double>
+                get() = lup.third
+
+            override val p: FeaturedMatrix<Double>
+                get() = lup.first
+        }
+    ) union features.orEmpty()
+
+    public override fun suggestFeature(vararg features: MatrixFeature): EjmlMatrix =
+        EjmlMatrix(origin, this.features + features)
+
+    public override operator fun get(i: Int, j: Int): Double = origin[i, j]
+
+    public override fun equals(other: Any?): Boolean {
+        if (other is EjmlMatrix) return origin.isIdentical(other.origin, 0.0)
+        return NDStructure.equals(this, other as? NDStructure<*> ?: return false)
+    }
+
+    public override fun hashCode(): Int {
+        var result = origin.hashCode()
+        result = 31 * result + features.hashCode()
+        return result
+    }
+
+    public override fun toString(): String = "EjmlMatrix(origin=$origin, features=$features)"
+}

kmath-ejml/src/main/kotlin/kscience/kmath/ejml/EjmlMatrixContext.kt

@@ -0,0 +1,86 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.MatrixContext
+import kscience.kmath.linear.Point
+import kscience.kmath.operations.Space
+import kscience.kmath.operations.invoke
+import kscience.kmath.structures.Matrix
+
+/**
+ * Represents context of basic operations operating with [EjmlMatrix].
+ *
+ * @author Iaroslav Postovalov
+ */
+public class EjmlMatrixContext(private val space: Space<Double>) : MatrixContext<Double> {
+    /**
+     * Converts this matrix to EJML one.
+     */
+    public fun Matrix<Double>.toEjml(): EjmlMatrix =
+        if (this is EjmlMatrix) this else produce(rowNum, colNum) { i, j -> get(i, j) }
+
+    /**
+     * Converts this vector to EJML one.
+     */
+    public fun Point<Double>.toEjml(): EjmlVector =
+        if (this is EjmlVector) this else EjmlVector(SimpleMatrix(size, 1).also {
+            (0 until it.numRows()).forEach { row -> it[row, 0] = get(row) }
+        })
+
+    override fun produce(rows: Int, columns: Int, initializer: (i: Int, j: Int) -> Double): EjmlMatrix =
+        EjmlMatrix(SimpleMatrix(rows, columns).also {
+            (0 until it.numRows()).forEach { row ->
+                (0 until it.numCols()).forEach { col -> it[row, col] = initializer(row, col) }
+            }
+        })
+
+    public override fun Matrix<Double>.dot(other: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin.mult(other.toEjml().origin))
+
+    public override fun Matrix<Double>.dot(vector: Point<Double>): EjmlVector =
+        EjmlVector(toEjml().origin.mult(vector.toEjml().origin))
+
+    public override fun add(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(a.toEjml().origin + b.toEjml().origin)
+
+    public override operator fun Matrix<Double>.minus(b: Matrix<Double>): EjmlMatrix =
+        EjmlMatrix(toEjml().origin - b.toEjml().origin)
+
+    public override fun multiply(a: Matrix<Double>, k: Number): EjmlMatrix =
+        produce(a.rowNum, a.colNum) { i, j -> space { a[i, j] * k } }
+
+    public override operator fun Matrix<Double>.times(value: Double): EjmlMatrix = EjmlMatrix(toEjml().origin.scale(value))
+
+    public companion object
+}
+
+/**
+ * Solves for X in the following equation: x = a^-1*b, where 'a' is base matrix and 'b' is an n by p matrix.
+ *
+ * @param a the base matrix.
+ * @param b n by p matrix.
+ * @return the solution for 'x' that is n by p.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Matrix<Double>): EjmlMatrix =
+    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.
+ *
+ * @param a the base matrix.
+ * @param b n by p vector.
+ * @return the solution for 'x' that is n by p.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.solve(a: Matrix<Double>, b: Point<Double>): EjmlVector =
+    EjmlVector(a.toEjml().origin.solve(b.toEjml().origin))
+
+/**
+ * Returns the inverse of given matrix: b = a^(-1).
+ *
+ * @param a the matrix.
+ * @return the inverse of this matrix.
+ * @author Iaroslav Postovalov
+ */
+public fun EjmlMatrixContext.inverse(a: Matrix<Double>): EjmlMatrix = EjmlMatrix(a.toEjml().origin.invert())

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

@@ -0,0 +1,40 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kscience.kmath.linear.Point
+import kscience.kmath.structures.Buffer
+
+/**
+ * Represents point over EJML [SimpleMatrix].
+ *
+ * @property origin the underlying [SimpleMatrix].
+ * @author Iaroslav Postovalov
+ */
+public class EjmlVector internal constructor(public val origin: SimpleMatrix) : Point<Double> {
+    public override val size: Int
+        get() = origin.numRows()
+
+    init {
+        require(origin.numCols() == 1) { "Only single column matrices are allowed" }
+    }
+
+    public override operator fun get(index: Int): Double = origin[index]
+
+    public override operator fun iterator(): Iterator<Double> = object : Iterator<Double> {
+        private var cursor: Int = 0
+
+        override fun next(): Double {
+            cursor += 1
+            return origin[cursor - 1]
+        }
+
+        override fun hasNext(): Boolean = cursor < origin.numCols() * origin.numRows()
+    }
+
+    public override fun contentEquals(other: Buffer<*>): Boolean {
+        if (other is EjmlVector) return origin.isIdentical(other.origin, 0.0)
+        return super.contentEquals(other)
+    }
+
+    public override fun toString(): String = "EjmlVector(origin=$origin)"
+}

kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlMatrixTest.kt

@@ -0,0 +1,75 @@
+package kscience.kmath.ejml
+
+import kscience.kmath.linear.DeterminantFeature
+import kscience.kmath.linear.LUPDecompositionFeature
+import kscience.kmath.linear.MatrixFeature
+import kscience.kmath.linear.getFeature
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.simple.SimpleMatrix
+import kotlin.random.Random
+import kotlin.random.asJavaRandom
+import kotlin.test.*
+
+internal class EjmlMatrixTest {
+    private val random = Random(0)
+
+    private val randomMatrix: SimpleMatrix
+        get() {
+            val s = random.nextInt(2, 100)
+            return SimpleMatrix.random_DDRM(s, s, 0.0, 10.0, random.asJavaRandom())
+        }
+
+    @Test
+    fun rowNum() {
+        val m = randomMatrix
+        assertEquals(m.numRows(), EjmlMatrix(m).rowNum)
+    }
+
+    @Test
+    fun colNum() {
+        val m = randomMatrix
+        assertEquals(m.numCols(), EjmlMatrix(m).rowNum)
+    }
+
+    @Test
+    fun shape() {
+        val m = randomMatrix
+        val w = EjmlMatrix(m)
+        assertEquals(listOf(m.numRows(), m.numCols()), w.shape.toList())
+    }
+
+    @Test
+    fun features() {
+        val m = randomMatrix
+        val w = EjmlMatrix(m)
+        val det = w.getFeature<DeterminantFeature<Double>>() ?: fail()
+        assertEquals(m.determinant(), det.determinant)
+        val lup = w.getFeature<LUPDecompositionFeature<Double>>() ?: fail()
+
+        val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows(), m.numCols())
+            .also { it.decompose(m.ddrm.copy()) }
+
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getLower(null))), lup.l)
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getUpper(null))), lup.u)
+        assertEquals(EjmlMatrix(SimpleMatrix(ludecompositionF64.getRowPivot(null))), lup.p)
+    }
+
+    private object SomeFeature : MatrixFeature {}
+
+    @Test
+    fun suggestFeature() {
+        assertNotNull(EjmlMatrix(randomMatrix).suggestFeature(SomeFeature).getFeature<SomeFeature>())
+    }
+
+    @Test
+    fun get() {
+        val m = randomMatrix
+        assertEquals(m[0, 0], EjmlMatrix(m)[0, 0])
+    }
+
+    @Test
+    fun origin() {
+        val m = randomMatrix
+        assertSame(m, EjmlMatrix(m).origin)
+    }
+}

kmath-ejml/src/test/kotlin/kscience/kmath/ejml/EjmlVectorTest.kt

@@ -0,0 +1,47 @@
+package kscience.kmath.ejml
+
+import org.ejml.simple.SimpleMatrix
+import kotlin.random.Random
+import kotlin.random.asJavaRandom
+import kotlin.test.Test
+import kotlin.test.assertEquals
+import kotlin.test.assertSame
+
+internal class EjmlVectorTest {
+    private val random = Random(0)
+
+    private val randomMatrix: SimpleMatrix
+        get() = SimpleMatrix.random_DDRM(random.nextInt(2, 100), 1, 0.0, 10.0, random.asJavaRandom())
+
+    @Test
+    fun size() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertEquals(m.numRows(), w.size)
+    }
+
+    @Test
+    fun get() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertEquals(m[0, 0], w[0])
+    }
+
+    @Test
+    fun iterator() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+
+        assertEquals(
+            m.iterator(true, 0, 0, m.numRows() - 1, 0).asSequence().toList(),
+            w.iterator().asSequence().toList()
+        )
+    }
+
+    @Test
+    fun origin() {
+        val m = randomMatrix
+        val w = EjmlVector(m)
+        assertSame(m, w.origin)
+    }
+}

kmath-geometry/src/commonMain/kotlin/kscience/kmath/geometry/GeometrySpace.kt

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

kmath-histograms/src/commonMain/kotlin/kscience/kmath/histogram/Histogram.kt

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

kmath-histograms/src/commonTest/kotlin/scietifik/kmath/histogram/MultivariateHistogramTest.kt

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

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/Distribution.kt

@@ -63,7 +63,7 @@ public fun <T : Any> Sampler<T>.sampleBuffer(
         //clear list from previous run
         tmp.clear()
         //Fill list
-        repeat(size){
+        repeat(size) {
             tmp.add(chain.next())
         }
         //return new buffer with elements from tmp

kmath-prob/src/commonMain/kotlin/kscience/kmath/prob/RandomGenerator.kt

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

kmath-prob/src/jvmTest/kotlin/kscience/kmath/prob/SamplerTest.kt

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

settings.gradle.kts

@@ -40,5 +40,6 @@ include(
     ":kmath-for-real",
     ":kmath-geometry",
     ":kmath-ast",
-    ":examples"
+    ":examples",
+    ":kmath-ejml"
 )