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

@@ -54,6 +54,8 @@ can be used for a wide variety of purposes from high performance calculations to
                            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"
 )