Reduced for context receivers issue

.github/CODEOWNERS

@@ -1,3 +0,0 @@
-@altavir
-
-/kmath-trajectory @ESchouten

.github/workflows/build.yml

@@ -1,24 +0,0 @@
-name: Gradle build
-
-on:
-  push:
-    branches: [ dev, master ]
-  pull_request:
-
-jobs:
-  build:
-    runs-on: windows-latest
-    timeout-minutes: 20
-    steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-java@v3.5.1
-        with:
-          java-version: '11'
-          distribution: 'liberica'
-          cache: 'gradle'
-      - name: Gradle Wrapper Validation
-        uses: gradle/wrapper-validation-action@v1.0.4
-      - name: Gradle Build
-        uses: gradle/gradle-build-action@v2.4.2
-        with:
-          arguments: test jvmTest

.github/workflows/pages.yml

@@ -1,31 +0,0 @@
-name: Dokka publication
-
-on:
-  workflow_dispatch:
-  release:
-    types: [ created ]
-
-jobs:
-  build:
-    runs-on: ubuntu-20.04
-    timeout-minutes: 40
-    steps:
-      - uses: actions/checkout@v3.0.0
-      - uses: actions/setup-java@v3.0.0
-        with:
-          java-version: 11
-          distribution: liberica
-      - name: Cache konan
-        uses: actions/cache@v3.0.1
-        with:
-          path: ~/.konan
-          key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
-          restore-keys: |
-            ${{ runner.os }}-gradle-
-      - uses: gradle/gradle-build-action@v2.4.2
-        with:
-          arguments: dokkaHtmlMultiModule --no-parallel
-      - uses: JamesIves/github-pages-deploy-action@v4.3.0
-        with:
-          branch: gh-pages
-          folder: build/dokka/htmlMultiModule

.github/workflows/publish.yml

@@ -1,50 +0,0 @@
-name: Gradle publish
-
-on:
-  workflow_dispatch:
-  release:
-    types: [ created ]
-
-jobs:
-  publish:
-    environment:
-      name: publish
-    strategy:
-      matrix:
-        os: [ macOS-latest, windows-latest ]
-    runs-on: ${{matrix.os}}
-    steps:
-      - uses: actions/checkout@v3.0.0
-      - uses: actions/setup-java@v3.10.0
-        with:
-          java-version: 11
-          distribution: liberica
-      - name: Cache konan
-        uses: actions/cache@v3.0.1
-        with:
-          path: ~/.konan
-          key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
-          restore-keys: |
-            ${{ runner.os }}-gradle-
-      - name: Publish Windows Artifacts
-        if: matrix.os == 'windows-latest'
-        uses: gradle/gradle-build-action@v2.4.2
-        with:
-          arguments: |
-            publishAllPublicationsToSpaceRepository
-            -Ppublishing.targets=all
-            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
-      - name: Publish Mac Artifacts
-        if: matrix.os == 'macOS-latest'
-        uses: gradle/gradle-build-action@v2.4.2
-        with:
-          arguments: |
-            publishMacosX64PublicationToSpaceRepository
-            publishMacosArm64PublicationToSpaceRepository
-            publishIosX64PublicationToSpaceRepository
-            publishIosArm64PublicationToSpaceRepository
-            publishIosSimulatorArm64PublicationToSpaceRepository
-            -Ppublishing.targets=all
-            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}

.gitignore

@@ -3,11 +3,10 @@ build/
 out/
 
 .idea/
-.vscode/
-.fleet/
-.kotlin/
 
 
+.vscode/
+
 # Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
 !gradle-wrapper.jar
 
@@ -20,5 +19,4 @@ out/
 
 !/.idea/copyright/
 !/.idea/scopes/
-/gradle/yarn.lock
-
+/kotlin-js-store/yarn.lock

.idea/copyright/kmath.xml

@@ -1,7 +1,6 @@
 <component name="CopyrightManager">
-  <copyright>
-    <option name="allowReplaceRegexp" value="Copyright \d{4}-\d{4} KMath" />
-    <option name="notice" value="Copyright 2018-&amp;#36;today.year KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file." />
-    <option name="myName" value="kmath" />
-  </copyright>
+    <copyright>
+        <option name="notice" value="Copyright 2018-2021 KMath contributors.&#10;Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file." />
+        <option name="myName" value="kmath" />
+    </copyright>
 </component>

.idea/copyright/profiles_settings.xml

@@ -1,5 +1,5 @@
 <component name="CopyrightManager">
-  <settings>
+  <settings default="kmath">
     <module2copyright>
       <element module="Apply copyright" copyright="kmath" />
     </module2copyright>

.space.kts

@@ -1,48 +0,0 @@
-import kotlin.io.path.readText
-
-val projectName = "kmath"
-
-job("Build") {
-    //Perform only jvm tests
-    gradlew("spc.registry.jetbrains.space/p/sci/containers/kotlin-ci:1.0.3", "test", "jvmTest")
-}
-
-job("Publish") {
-    startOn {
-        gitPush { enabled = false }
-    }
-    container("spc.registry.jetbrains.space/p/sci/containers/kotlin-ci:1.0.3") {
-        env["SPACE_USER"] = "{{ project:space_user }}"
-        env["SPACE_TOKEN"] = "{{ project:space_token }}"
-        kotlinScript { api ->
-
-            val spaceUser = System.getenv("SPACE_USER")
-            val spaceToken = System.getenv("SPACE_TOKEN")
-
-            // write the version to the build directory
-            api.gradlew("version")
-
-            //read the version from build file
-            val version = java.nio.file.Path.of("build/project-version.txt").readText()
-
-            val revisionSuffix = if (version.endsWith("SNAPSHOT")) {
-                "-" + api.gitRevision().take(7)
-            } else {
-                ""
-            }
-
-            api.space().projects.automation.deployments.start(
-                project = api.projectIdentifier(),
-                targetIdentifier = TargetIdentifier.Key(projectName),
-                version = version + revisionSuffix,
-                // automatically update deployment status based on the status of a job
-                syncWithAutomationJob = true
-            )
-            api.gradlew(
-                "publishAllPublicationsToSpaceRepository",
-                "-Ppublishing.space.user=\"$spaceUser\"",
-                "-Ppublishing.space.token=\"$spaceToken\"",
-            )
-        }
-    }
-}

CHANGELOG.md

@@ -1,271 +0,0 @@
-# KMath
-
-## Unreleased
-
-### Added
-
-### Changed
-
-### Deprecated
-
-### Removed
-
-### Fixed
-
-### Security
-
-## 0.4.0-dev-3 - 2024-02-18
-
-### Added
-
-- Reification. Explicit `SafeType` for algebras.
-- Integer division algebras.
-- Float32 geometries.
-- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
-- Explicit `mutableStructureND` builders for mutable structures.
-- `Buffer.asList()` zero-copy transformation.
-- Wasm support.
-- Parallel implementation of `LinearSpace` for Float64
-- Parallel buffer factories
-
-### Changed
-
-- Buffer copy removed from API (added as an extension).
-- Default naming for algebra and buffers now uses IntXX/FloatXX notation instead of Java types.
-- Remove unnecessary inlines in basic algebras.
-- QuaternionField -> QuaternionAlgebra and does not implement `Field` anymore since it is non-commutative
-- kmath-geometry is split into `euclidean2d` and `euclidean3d`
-- Features replaced with Attributes.
-- Transposed refactored.
-- Kmath-memory is moved on top of core.
-
-### Deprecated
-
-- ND4J engine
-
-### Removed
-
-- `asPolynomial` function due to scope pollution
-- Codegend for ejml (450 lines of codegen for 1000 lines of code is too much)
-
-### Fixed
-
-- Median statistics
-- Complex power of negative real numbers
-- Add proper mutability for MutableBufferND rows and columns
-- Generic Float32 and Float64 vectors are used in geometry algebras.
-
-## 0.3.1 - 2023-04-09
-
-### Added
-
-- Wasm support for `memory`, `core`, `complex` and `functions` modules.
-- Generic builders for `BufferND` and `MutableBufferND`
-- `NamedMatrix` - matrix with symbol-based indexing
-- `Expression` with default arguments
-- Type-aliases for numbers like `Float64`
-- Autodiff for generic algebra elements in core!
-- Algebra now has an obligatory `bufferFactory` (#477).
-
-### Changed
-
-- Removed marker `Vector` type for geometry
-- Geometry uses type-safe angles
-- Tensor operations switched to prefix notation
-- Row-wise and column-wise ND shapes in the core
-- Shape is read-only
-- Major refactor of tensors (only minor API changes)
-- Kotlin 1.8.20
-- `LazyStructure` `deffered` -> `async` to comply with coroutines code style
-- Default `dot` operation in tensor algebra no longer support broadcasting. Instead `matmul` operation is added
-  to `DoubleTensorAlgebra`.
-- Multik went MPP
-
-### Removed
-
-- Trajectory moved to https://github.com/SciProgCentre/maps-kt
-- Polynomials moved to https://github.com/SciProgCentre/kmath-polynomial
-
-## 0.3.0
-
-### Added
-
-- `ScaleOperations` interface
-- `Field` extends `ScaleOperations`
-- Basic integration API
-- Basic MPP distributions and samplers
-- `bindSymbolOrNull`
-- Blocking chains and Statistics
-- Multiplatform integration
-- Integration for any Field element
-- Extended operations for ND4J fields
-- Jupyter Notebook integration module (kmath-jupyter)
-- `@PerformancePitfall` annotation to mark possibly slow API
-- Unified architecture for Integration and Optimization using features.
-- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
-- Integration between `MST` and Symja `IExpr`
-- Complex power
-- Separate methods for UInt, Int and Number powers. NaN safety.
-- Tensorflow prototype
-- `ValueAndErrorField`
-- MST compilation to WASM: #286
-- Jafama integration: #176
-- `contentEquals` with tolerance: #364
-- Compilation to TeX for MST: #254
-
-### Changed
-
-- Annotations moved to `space.kscience.kmath`
-- Exponential operations merged with hyperbolic functions
-- Space is replaced by Group. Space is reserved for vector spaces.
-- VectorSpace is now a vector space
-- Buffer factories for primitives moved to MutableBuffer.Companion
-- Rename `NDStructure` and `NDAlgebra` to `StructureND` and `AlgebraND` respectively
-- `Real` -> `Double`
-- DataSets are moved from functions to core
-- Redesign advanced Chain API
-- Redesign `MST`. Remove `MstExpression`.
-- Move `MST` to core
-- Separated benchmarks and examples
-- Rewrite `kmath-ejml` without `ejml-simple` artifact, support sparse matrices
-- Promote stability of kmath-ast and kmath-kotlingrad to EXPERIMENTAL.
-- ColumnarData returns nullable column
-- `MST` is made sealed interface
-- Replace `MST.Symbolic` by `Symbol`, `Symbol` now implements MST
-- Remove Any restriction on polynomials
-- Add `out` variance to type parameters of `StructureND` and its implementations where possible
-- Rename `DifferentiableMstExpression` to `KotlingradExpression`
-- `FeatureSet` now accepts only `Feature`. It is possible to override keys and use interfaces.
-- Use `Symbol` factory function instead of `StringSymbol`
-- New discoverability pattern: `<Type>.algebra.<nd/etc>`
-- Adjusted commons-math API for linear solvers to match conventions.
-- Buffer algebra does not require size anymore
-- Operations -> Ops
-- Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.
-- Tensor algebra takes read-only structures as input and inherits AlgebraND
-- `UnivariateDistribution` renamed to `Distribution1D`
-- Rework of histograms.
-- `UnivariateFunction` -> `Function1D`, `MultivariateFunction` -> `FunctionND`
-
-### Deprecated
-
-- Specialized `DoubleBufferAlgebra`
-
-### Removed
-
-- Nearest in Domain. To be implemented in geometry package.
-- Number multiplication and division in main Algebra chain
-- `contentEquals` from Buffer. It moved to the companion.
-- MSTExpression
-- Expression algebra builders
-- Complex and Quaternion no longer are elements.
-- Second generic from DifferentiableExpression
-- Algebra elements are completely removed. Use algebra contexts instead.
-
-### Fixed
-
-- Ring inherits RingOperations, not GroupOperations
-- Univariate histogram filling
-
-## 0.2.0
-
-### Added
-
-- `fun` annotation for SAM interfaces in library
-- Explicit `public` visibility for all public APIs
-- 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 (https://github.com/mipt-npm/kmath/pull/136)
-- A separate `Symbol` entity, which is used for global unbound symbol.
-- A `Symbol` indexing scope.
-- Basic optimization API for Commons-math.
-- Chi squared optimization for array-like data in CM
-- `Fitting` utility object in prob/stat
-- ND4J support module submitting `NDStructure` and `NDAlgebra` over `INDArray`
-- Coroutine-deterministic Monte-Carlo scope with a random number generator
-- Some minor utilities to `kmath-for-real`
-- Generic operation result parameter to `MatrixContext`
-- New `MatrixFeature` interfaces for matrix decompositions
-- Basic Quaternion vector support in `kmath-complex`.
-
-### Changed
-
-- Package changed from `scientifik` to `space.kscience`
-- Gradle version: 6.6 -> 6.8.2
-- Minor exceptions refactor (throwing `IllegalArgumentException` by argument checks instead of `IllegalStateException`)
-- `Polynomial` secondary constructor made function
-- Kotlin version: 1.3.72 -> 1.4.30
-- `kmath-ast` doesn't depend on heavy `kotlin-reflect` library
-- Full autodiff refactoring based on `Symbol`
-- `kmath-prob` renamed to `kmath-stat`
-- Grid generators moved to `kmath-for-real`
-- Use `Point<Double>` instead of specialized type in `kmath-for-real`
-- Optimized dot product for buffer matrices moved to `kmath-for-real`
-- EjmlMatrix context is an object
-- Matrix LUP `inverse` renamed to `inverseWithLup`
-- `NumericAlgebra` moved outside of regular algebra chain (`Ring` no longer implements it).
-- Features moved to NDStructure and became transparent.
-- Capitalization of LUP in many names changed to Lup.
-- Refactored `NDStructure` algebra to be more simple, preferring under-the-hood conversion to explicit NDStructure types
-- Refactor histograms. They are marked as prototype
-- `Complex` and related features moved to a separate module `kmath-complex`
-- Refactor AlgebraElement
-- `symbol` method in `Algebra` renamed to `bindSymbol` to avoid ambiguity
-- Add `out` projection to `Buffer` generic
-
-### Removed
-
-- `kmath-koma` module because it doesn't support Kotlin 1.4.
-- Support of `legacy` JS backend (we will support only IR)
-- `toGrid` method.
-- Public visibility of `BufferAccessor2D`
-- `Real` class
-- StructureND identity and equals
-
-### Fixed
-
-- `symbol` method in `MstExtendedField` (https://github.com/mipt-npm/kmath/pull/140)
-
-## 0.1.4
-
-### Added
-
-- Functional Expressions API
-- Mathematical Syntax Tree, its interpreter and API
-- String to MST parser (https://github.com/mipt-npm/kmath/pull/120)
-- MST to JVM bytecode translator (https://github.com/mipt-npm/kmath/pull/94)
-- FloatBuffer (specialized MutableBuffer over FloatArray)
-- FlaggedBuffer to associate primitive numbers buffer with flags (to mark values infinite or missing, etc.)
-- Specialized builder functions for all primitive buffers
-  like `IntBuffer(25) { it + 1 }` (https://github.com/mipt-npm/kmath/pull/125)
-- Interface `NumericAlgebra` where `number` operation is available to convert numbers to algebraic elements
-- Inverse trigonometric functions support in
-  ExtendedField (`asin`, `acos`, `atan`) (https://github.com/mipt-npm/kmath/pull/114)
-- New space extensions: `average` and `averageWith`
-- Local coding conventions
-- Geometric Domains API in `kmath-core`
-- Blocking chains in `kmath-coroutines`
-- Full hyperbolic functions support and default implementations within `ExtendedField`
-- Norm support for `Complex`
-
-### Changed
-
-- `readAsMemory` now has `throws IOException` in JVM signature.
-- Several functions taking functional types were made `inline`.
-- Several functions taking functional types now have `callsInPlace` contracts.
-- BigInteger and BigDecimal algebra: JBigDecimalField has companion object with default math context; minor
-  optimizations
-- `power(T, Int)` extension function has preconditions and supports `Field<T>`
-- Memory objects have more preconditions (overflow checking)
-- `tg` function is renamed to `tan` (https://github.com/mipt-npm/kmath/pull/114)
-- Gradle version: 6.3 -> 6.6
-- Moved probability distributions to commons-rng and to `kmath-prob`
-
-### Fixed
-
-- Missing copy method in Memory implementation on JS (https://github.com/mipt-npm/kmath/pull/106)
-- D3.dim value in `kmath-dimensions`
-- Multiplication in integer rings in `kmath-core` (https://github.com/mipt-npm/kmath/pull/101)
-- Commons RNG compatibility (https://github.com/mipt-npm/kmath/issues/93)
-- Multiplication of BigInt by scalar

README.md

@@ -1,300 +0,0 @@
-[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
-[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
-![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
-[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
-[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
-
-# KMath
-
-Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
-analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
-architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
-experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
-
-[Documentation site](https://SciProgCentre.github.io/kmath/)
-
-## Publications and talks
-
-* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
-* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
-* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
-* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
-* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
-* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
-* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
-
-# Goal
-
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
-  Wasm).
-* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
-* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
-
-## Non-goals
-
-* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
-* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
-* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
-* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
-  for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
-  experience for those, who want to work with specific types.
-
-## Features and stability
-
-KMath is a modular library. Different modules provide different features with different API stability guarantees. All
-core modules are released with the same version, but with different API change policy. The features are described in
-module definitions below. The module stability could have the following levels:
-
-* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
-  break any moment. You can still use it, but be sure to fix the specific version.
-* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
-  with `@UnstableKMathAPI` or other stability warning annotations.
-* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
-  versions, but not in patch versions. API is protected
-  with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
-* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
-
-## Modules
-
-
-### [attributes-kt](attributes-kt)
-> An API and basic implementation for arranging objects in a continuous memory block.
->
-> **Maturity**: DEVELOPMENT
-
-### [benchmarks](benchmarks)
->
-> **Maturity**: EXPERIMENTAL
-
-### [examples](examples)
->
-> **Maturity**: EXPERIMENTAL
-
-### [kmath-ast](kmath-ast)
->
-> **Maturity**: EXPERIMENTAL
->
-> **Features:**
-> - [expression-language](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt) : Expression language and its parser
-> - [mst-jvm-codegen](kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/asm.kt) : Dynamic MST to JVM bytecode compiler
-> - [mst-js-codegen](kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt) : Dynamic MST to JS compiler
-> - [rendering](kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt) : Extendable MST rendering
-
-
-### [kmath-commons](kmath-commons)
-> Commons math binding for kmath
->
-> **Maturity**: EXPERIMENTAL
-
-### [kmath-complex](kmath-complex)
-> Complex numbers and quaternions.
->
-> **Maturity**: PROTOTYPE
->
-> **Features:**
-> - [complex](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt) : Complex numbers operations
-> - [quaternion](kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt) : Quaternions and their composition
-
-
-### [kmath-core](kmath-core)
-> Core classes, algebra definitions, basic linear algebra
->
-> **Maturity**: DEVELOPMENT
->
-> **Features:**
-> - [algebras](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Algebraic structures like rings, spaces and fields.
-> - [nd](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt) : Many-dimensional structures and operations on them.
-> - [linear](kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt) : Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
-> - [buffers](kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
-> - [expressions](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions) : By writing a single mathematical expression once, users will be able to apply different types of 
-objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high 
-performance calculations to code generation.
-> - [domains](kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
-> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
-> - [Parallel linear algebra](kmath-core/#) : Parallel implementation for `LinearAlgebra`
-
-
-### [kmath-coroutines](kmath-coroutines)
->
-> **Maturity**: EXPERIMENTAL
-
-### [kmath-dimensions](kmath-dimensions)
-> A proof of concept module for adding type-safe dimensions to structures
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-ejml](kmath-ejml)
->
-> **Maturity**: PROTOTYPE
->
-> **Features:**
-> - [ejml-vector](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlVector.kt) : Point implementations.
-> - [ejml-matrix](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlMatrix.kt) : Matrix implementation.
-> - [ejml-linear-space](kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/EjmlLinearSpace.kt) : LinearSpace implementations.
-
-
-### [kmath-for-real](kmath-for-real)
-> Extension module that should be used to achieve numpy-like behavior.
-All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
-One can still use generic algebras though.
->
-> **Maturity**: EXPERIMENTAL
->
-> **Features:**
-> - [DoubleVector](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt) : Numpy-like operations for Buffers/Points
-> - [DoubleMatrix](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt) : Numpy-like operations for 2d real structures
-> - [grids](kmath-for-real/src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt) : Uniform grid generators
-
-
-### [kmath-functions](kmath-functions)
-> Functions, integration and interpolation
->
-> **Maturity**: EXPERIMENTAL
->
-> **Features:**
-> - [piecewise](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Piecewise.kt) : Piecewise functions.
-> - [polynomials](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/functions/Polynomial.kt) : Polynomial functions.
-> - [linear interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/LinearInterpolator.kt) : Linear XY interpolator.
-> - [spline interpolation](kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt) : Cubic spline XY interpolator.
-> - [integration](kmath-functions/#) : Univariate and multivariate quadratures
-
-
-### [kmath-geometry](kmath-geometry)
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-histograms](kmath-histograms)
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-jafama](kmath-jafama)
-> Jafama integration module
->
-> **Maturity**: DEPRECATED
->
-> **Features:**
-> - [jafama-double](kmath-jafama/src/main/kotlin/space/kscience/kmath/jafama/) : Double ExtendedField implementations based on Jafama
-
-
-### [kmath-jupyter](kmath-jupyter)
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-kotlingrad](kmath-kotlingrad)
-> Kotlin∇ integration module
->
-> **Maturity**: EXPERIMENTAL
->
-> **Features:**
-> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
-> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.kt) : Conversions between Kotlin∇'s SFun and MST
-
-
-### [kmath-memory](kmath-memory)
-> An API and basic implementation for arranging objects in a continuous memory block.
->
-> **Maturity**: DEVELOPMENT
-
-### [kmath-multik](kmath-multik)
-> JetBrains Multik connector
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-nd4j](kmath-nd4j)
-> ND4J NDStructure implementation and according NDAlgebra classes
->
-> **Maturity**: DEPRECATED
->
-> **Features:**
-> - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
-> - [nd4jarrayrings](kmath-nd4j/#) : Rings over Nd4jArrayStructure of Int and Long
-> - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
-
-
-### [kmath-optimization](kmath-optimization)
->
-> **Maturity**: EXPERIMENTAL
-
-### [kmath-stat](kmath-stat)
->
-> **Maturity**: EXPERIMENTAL
-
-### [kmath-symja](kmath-symja)
-> Symja integration module
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-tensorflow](kmath-tensorflow)
-> Google tensorflow connector
->
-> **Maturity**: PROTOTYPE
-
-### [kmath-tensors](kmath-tensors)
->
-> **Maturity**: PROTOTYPE
->
-> **Features:**
-> - [tensor algebra](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt) : Basic linear algebra operations on tensors (plus, dot, etc.)
-> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt) : Basic linear algebra operations implemented with broadcasting.
-> - [linear algebra operations](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt) : Advanced linear algebra operations like LU decomposition, SVD, etc.
-
-
-### [kmath-viktor](kmath-viktor)
-> Binding for https://github.com/JetBrains-Research/viktor
->
-> **Maturity**: DEPRECATED
-
-### [test-utils](test-utils)
->
-> **Maturity**: EXPERIMENTAL
-
-
-## Multi-platform support
-
-KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
-[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
-are delegated to platform-specific implementations even if they could be provided in the common module for performance
-reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
-feedback are also welcome.
-
-## Performance
-
-Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
-achieve both
-performance and flexibility.
-
-We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
-cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
-better than SciPy.
-
-## Requirements
-
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
-Oracle GraalVM for execution to get better performance.
-
-### Repositories
-
-Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/)
-repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
-[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
-be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
-
-```kotlin
-repositories {
-    maven("https://repo.kotlin.link")
-}
-
-dependencies {
-    api("space.kscience:kmath-core:$version")
-    // api("space.kscience:kmath-core-jvm:$version") for jvm-specific version
-}
-```
-
-## Contributing
-
-The project requires a lot of additional work. The most important thing we need is feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-marked
-with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-label.

attributes-kt/README.md

@@ -1,21 +0,0 @@
-# Module attributes-kt
-
-
-
-## Usage
-
-## Artifact:
-
-The Maven coordinates of this project are `space.kscience:attributes-kt:0.1.0`.
-
-**Gradle Kotlin DSL:**
-```kotlin
-repositories {
-    maven("https://repo.kotlin.link")
-    mavenCentral()
-}
-
-dependencies {
-    implementation("space.kscience:attributes-kt:0.1.0")
-}
-```

attributes-kt/api/attributes-kt.api

@@ -1,104 +0,0 @@
-public abstract interface class space/kscience/attributes/Attribute {
-}
-
-public abstract interface class space/kscience/attributes/AttributeContainer {
-	public abstract fun getAttributes ()Lspace/kscience/attributes/Attributes;
-}
-
-public abstract interface class space/kscience/attributes/AttributeScope {
-}
-
-public abstract interface class space/kscience/attributes/AttributeWithDefault : space/kscience/attributes/Attribute {
-	public abstract fun getDefault ()Ljava/lang/Object;
-}
-
-public abstract interface class space/kscience/attributes/Attributes {
-	public static final field Companion Lspace/kscience/attributes/Attributes$Companion;
-	public abstract fun equals (Ljava/lang/Object;)Z
-	public fun get (Lspace/kscience/attributes/Attribute;)Ljava/lang/Object;
-	public abstract fun getContent ()Ljava/util/Map;
-	public fun getKeys ()Ljava/util/Set;
-	public abstract fun hashCode ()I
-	public abstract fun toString ()Ljava/lang/String;
-}
-
-public final class space/kscience/attributes/Attributes$Companion {
-	public final fun equals (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attributes;)Z
-	public final fun getEMPTY ()Lspace/kscience/attributes/Attributes;
-}
-
-public final class space/kscience/attributes/AttributesBuilder : space/kscience/attributes/Attributes {
-	public final fun add (Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)V
-	public final fun build ()Lspace/kscience/attributes/Attributes;
-	public fun equals (Ljava/lang/Object;)Z
-	public fun getContent ()Ljava/util/Map;
-	public fun hashCode ()I
-	public final fun invoke (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)V
-	public final fun put (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)V
-	public final fun putAll (Lspace/kscience/attributes/Attributes;)V
-	public final fun remove (Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)V
-	public final fun set (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)V
-	public fun toString ()Ljava/lang/String;
-}
-
-public final class space/kscience/attributes/AttributesBuilderKt {
-	public static final fun Attributes (Lkotlin/jvm/functions/Function1;)Lspace/kscience/attributes/Attributes;
-}
-
-public final class space/kscience/attributes/AttributesKt {
-	public static final fun Attributes (Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
-	public static final fun Attributes (Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
-	public static final fun getOrDefault (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/AttributeWithDefault;)Ljava/lang/Object;
-	public static final fun isEmpty (Lspace/kscience/attributes/Attributes;)Z
-	public static final fun modified (Lspace/kscience/attributes/Attributes;Lkotlin/jvm/functions/Function1;)Lspace/kscience/attributes/Attributes;
-	public static final fun plus (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attributes;)Lspace/kscience/attributes/Attributes;
-	public static final fun withAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
-	public static final fun withAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
-	public static final fun withAttributeElement (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
-	public static final fun withoutAttribute (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/Attribute;)Lspace/kscience/attributes/Attributes;
-	public static final fun withoutAttributeElement (Lspace/kscience/attributes/Attributes;Lspace/kscience/attributes/SetAttribute;Ljava/lang/Object;)Lspace/kscience/attributes/Attributes;
-}
-
-public abstract interface class space/kscience/attributes/FlagAttribute : space/kscience/attributes/Attribute {
-}
-
-public abstract class space/kscience/attributes/PolymorphicAttribute : space/kscience/attributes/Attribute {
-	public synthetic fun <init> (Lkotlin/reflect/KType;Lkotlin/jvm/internal/DefaultConstructorMarker;)V
-	public fun equals (Ljava/lang/Object;)Z
-	public final fun getType-V0oMfBY ()Lkotlin/reflect/KType;
-	public fun hashCode ()I
-}
-
-public final class space/kscience/attributes/PolymorphicAttributeKt {
-	public static final fun get (Lspace/kscience/attributes/Attributes;Lkotlin/jvm/functions/Function0;)Ljava/lang/Object;
-	public static final fun set (Lspace/kscience/attributes/AttributesBuilder;Lkotlin/jvm/functions/Function0;Ljava/lang/Object;)V
-}
-
-public final class space/kscience/attributes/SafeType {
-	public static final synthetic fun box-impl (Lkotlin/reflect/KType;)Lspace/kscience/attributes/SafeType;
-	public static fun constructor-impl (Lkotlin/reflect/KType;)Lkotlin/reflect/KType;
-	public fun equals (Ljava/lang/Object;)Z
-	public static fun equals-impl (Lkotlin/reflect/KType;Ljava/lang/Object;)Z
-	public static final fun equals-impl0 (Lkotlin/reflect/KType;Lkotlin/reflect/KType;)Z
-	public final fun getKType ()Lkotlin/reflect/KType;
-	public fun hashCode ()I
-	public static fun hashCode-impl (Lkotlin/reflect/KType;)I
-	public fun toString ()Ljava/lang/String;
-	public static fun toString-impl (Lkotlin/reflect/KType;)Ljava/lang/String;
-	public final synthetic fun unbox-impl ()Lkotlin/reflect/KType;
-}
-
-public final class space/kscience/attributes/SafeTypeKt {
-	public static final fun getKClass-X0YbwmU (Lkotlin/reflect/KType;)Lkotlin/reflect/KClass;
-}
-
-public abstract interface class space/kscience/attributes/SetAttribute : space/kscience/attributes/Attribute {
-}
-
-public abstract interface annotation class space/kscience/attributes/UnstableAttributesAPI : java/lang/annotation/Annotation {
-}
-
-public abstract interface class space/kscience/attributes/WithType {
-	public abstract fun getType-V0oMfBY ()Lkotlin/reflect/KType;
-}
-

attributes-kt/build.gradle.kts

@@ -1,20 +0,0 @@
-plugins {
-    id("space.kscience.gradle.mpp")
-    `maven-publish`
-}
-
-version = rootProject.extra.get("attributesVersion").toString()
-
-kscience {
-    jvm()
-    js()
-    native()
-    wasm()
-}
-
-readme {
-    maturity = space.kscience.gradle.Maturity.DEVELOPMENT
-    description = """
-        An API and basic implementation for arranging objects in a continuous memory block.
-    """.trimIndent()
-}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attribute.kt

@@ -1,29 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * A marker interface for an attribute. Attributes are used as keys to access contents of type [T] in the container.
- */
-public interface Attribute<T>
-
-/**
- * An attribute that could be either present or absent
- */
-public interface FlagAttribute : Attribute<Unit>
-
-/**
- * An attribute with a default value
- */
-public interface AttributeWithDefault<T> : Attribute<T> {
-    public val default: T
-}
-
-/**
- * Attribute containing a set of values
- */
-public interface SetAttribute<V> : Attribute<Set<V>>
-

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributeContainer.kt

@@ -1,20 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * A container for [Attributes]
- */
-public interface AttributeContainer {
-    public val attributes: Attributes
-}
-
-/**
- * A scope, where attribute keys could be resolved.
- * [O] is used only to resolve types in compile-time.
- */
-public interface AttributeScope<O>
-

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt

@@ -1,143 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * A set of attributes. The implementation must guarantee that [content] keys correspond to their value types.
- */
-public interface Attributes {
-    /**
-     * Raw content for this [Attributes]
-     */
-    public val content: Map<out Attribute<*>, Any?>
-
-    /**
-     * Attribute keys contained in this [Attributes]
-     */
-    public val keys: Set<Attribute<*>> get() = content.keys
-
-    /**
-     * Provide an attribute value. Return null if attribute is not present or if its value is null.
-     */
-    @Suppress("UNCHECKED_CAST")
-    public operator fun <T> get(attribute: Attribute<T>): T? = content[attribute] as? T
-
-    override fun toString(): String
-    override fun equals(other: Any?): Boolean
-    override fun hashCode(): Int
-
-    public companion object {
-        public val EMPTY: Attributes = object : Attributes {
-            override val content: Map<out Attribute<*>, Any?> get() = emptyMap()
-
-            override fun toString(): String = "Attributes.EMPTY"
-
-            override fun equals(other: Any?): Boolean = (other as? Attributes)?.isEmpty() ?: false
-
-            override fun hashCode(): Int = Unit.hashCode()
-        }
-
-        public fun equals(a1: Attributes, a2: Attributes): Boolean =
-            a1.keys == a2.keys && a1.keys.all { a1[it] == a2[it] }
-    }
-}
-
-internal class MapAttributes(override val content: Map<out Attribute<*>, Any?>) : Attributes {
-    override fun toString(): String = "Attributes(value=${content.entries})"
-    override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
-    override fun hashCode(): Int = content.hashCode()
-}
-
-public fun Attributes.isEmpty(): Boolean = keys.isEmpty()
-
-/**
- * Get attribute value or default
- */
-public fun <T> Attributes.getOrDefault(attribute: AttributeWithDefault<T>): T = get(attribute) ?: attribute.default
-
-/**
- * Check if there is an attribute that matches given key by type and adheres to [predicate].
- */
-@Suppress("UNCHECKED_CAST")
-public inline fun <T, reified A : Attribute<T>> Attributes.hasAny(predicate: (value: T) -> Boolean): Boolean =
-    content.any { (mapKey, mapValue) -> mapKey is A && predicate(mapValue as T) }
-
-/**
- * Check if there is an attribute of given type (subtypes included)
- */
-public inline fun <reified A : Attribute<*>> Attributes.hasAny(): Boolean =
-    content.any { (mapKey, _) -> mapKey is A }
-
-/**
- * Check if [Attributes] contains a flag. Multiple keys that are instances of a flag could be present
- */
-public inline fun <reified A : FlagAttribute> Attributes.hasFlag(): Boolean =
-    content.keys.any { it is A }
-
-/**
- * Create [Attributes] with an added or replaced attribute key.
- */
-public fun <T, A : Attribute<T>> Attributes.withAttribute(
-    attribute: A,
-    attrValue: T,
-): Attributes = MapAttributes(content + (attribute to attrValue))
-
-public fun <A : Attribute<Unit>> Attributes.withAttribute(attribute: A): Attributes =
-    withAttribute(attribute, Unit)
-
-/**
- * Create a new [Attributes] by modifying the current one
- */
-public fun <O> Attributes.modified(block: AttributesBuilder<O>.() -> Unit): Attributes = Attributes<O> {
-    putAll(this@modified)
-    block()
-}
-
-/**
- * Create new [Attributes] by removing [attribute] key
- */
-public fun Attributes.withoutAttribute(attribute: Attribute<*>): Attributes = MapAttributes(content.minus(attribute))
-
-/**
- * Add an element to a [SetAttribute]
- */
-public fun <T, A : SetAttribute<T>> Attributes.withAttributeElement(
-    attribute: A,
-    attrValue: T,
-): Attributes {
-    val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return MapAttributes(
-        content + (attribute to (currentSet + attrValue))
-    )
-}
-
-/**
- * Remove an element from [SetAttribute]
- */
-public fun <T, A : SetAttribute<T>> Attributes.withoutAttributeElement(
-    attribute: A,
-    attrValue: T,
-): Attributes {
-    val currentSet: Set<T> = get(attribute) ?: emptySet()
-    return MapAttributes(content + (attribute to (currentSet - attrValue)))
-}
-
-/**
- * Create [Attributes] with a single key
- */
-public fun <T, A : Attribute<T>> Attributes(
-    attribute: A,
-    attrValue: T,
-): Attributes = MapAttributes(mapOf(attribute to attrValue))
-
-/**
- * Create Attributes with a single [Unit] valued attribute
- */
-public fun <A : Attribute<Unit>> Attributes(
-    attribute: A,
-): Attributes = MapAttributes(mapOf(attribute to Unit))
-
-public operator fun Attributes.plus(other: Attributes): Attributes = MapAttributes(content + other.content)

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributesBuilder.kt

@@ -1,68 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * A builder for [Attributes].
- * The builder is not thread safe
- *
- * @param O type marker of an owner object, for which these attributes are made
- */
-public class AttributesBuilder<out O> internal constructor() : Attributes {
-
-    private val map = mutableMapOf<Attribute<*>, Any?>()
-
-    override fun toString(): String = "Attributes(value=${map.entries})"
-    override fun equals(other: Any?): Boolean = other is Attributes && Attributes.equals(this, other)
-    override fun hashCode(): Int = map.hashCode()
-
-    override val content: Map<out Attribute<*>, Any?> get() = map
-
-    public operator fun <T> set(attribute: Attribute<T>, value: T?) {
-        if (value == null) {
-            map.remove(attribute)
-        } else {
-            map[attribute] = value
-        }
-    }
-
-    public operator fun <V> Attribute<V>.invoke(value: V?) {
-        set(this, value)
-    }
-
-    public infix fun <V> Attribute<V>.put(value: V?) {
-        set(this, value)
-    }
-
-    /**
-     * Put all attributes for given [attributes]
-     */
-    public fun putAll(attributes: Attributes) {
-        map.putAll(attributes.content)
-    }
-
-    public infix fun <V> SetAttribute<V>.add(attrValue: V) {
-        val currentSet: Set<V> = get(this) ?: emptySet()
-        map[this] = currentSet + attrValue
-    }
-
-    /**
-     * Remove an element from [SetAttribute]
-     */
-    public infix fun <V> SetAttribute<V>.remove(attrValue: V) {
-        val currentSet: Set<V> = get(this) ?: emptySet()
-        map[this] = currentSet - attrValue
-    }
-
-    public fun build(): Attributes = MapAttributes(map)
-}
-
-/**
- * Create [Attributes] with a given [builder]
- * @param O the type for which attributes are built. The type is used only during compilation phase for static extension dispatch
- */
-public fun <O> Attributes(builder: AttributesBuilder<O>.() -> Unit): Attributes =
-    AttributesBuilder<O>().apply(builder).build()

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/PolymorphicAttribute.kt

@@ -1,34 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * An attribute that has a type parameter for value
- * @param type parameter-type
- */
-public abstract class PolymorphicAttribute<T>(public val type: SafeType<T>) : Attribute<T> {
-    override fun equals(other: Any?): Boolean = other != null &&
-            (this::class == other::class) &&
-            (other as? PolymorphicAttribute<*>)?.type == this.type
-
-    override fun hashCode(): Int = this::class.hashCode() + type.hashCode()
-}
-
-
-/**
- * Get a polymorphic attribute using attribute factory
- */
-@UnstableAttributesAPI
-public operator fun <T> Attributes.get(attributeKeyBuilder: () -> PolymorphicAttribute<T>): T? =
-    get(attributeKeyBuilder())
-
-/**
- * Set a polymorphic attribute using its factory
- */
-@UnstableAttributesAPI
-public operator fun <O, T> AttributesBuilder<O>.set(attributeKeyBuilder: () -> PolymorphicAttribute<T>, value: T) {
-    set(attributeKeyBuilder(), value)
-}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/SafeType.kt

@@ -1,35 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-import kotlin.jvm.JvmInline
-import kotlin.reflect.KClass
-import kotlin.reflect.KType
-import kotlin.reflect.typeOf
-
-/**
- * Safe variant ok Kotlin [KType] that ensures that the type parameter is of the same type as [kType]
- *
- * @param kType raw [KType]
- */
-@JvmInline
-public value class SafeType<out T> @PublishedApi internal constructor(public val kType: KType)
-
-public inline fun <reified T> safeTypeOf(): SafeType<T> = SafeType(typeOf<T>())
-
-/**
- * Derive Kotlin [KClass] from this type and fail if the type is not a class (should not happen)
- */
-@Suppress("UNCHECKED_CAST")
-@UnstableAttributesAPI
-public val <T> SafeType<T>.kClass: KClass<T & Any> get() = kType.classifier as KClass<T & Any>
-
-/**
- * An interface containing [type] for dynamic type checking.
- */
-public interface WithType<out T> {
-    public val type: SafeType<T>
-}

attributes-kt/src/commonMain/kotlin/space/kscience/attributes/annotations.kt

@@ -1,17 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.attributes
-
-/**
- * Marks declarations that are still experimental in the Attributes-kt APIs, which means that the design of the corresponding
- * declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is
- * a chance of those declarations will be deprecated in the future or the semantics of their behavior may change
- * in some way that may break some code.
- */
-@MustBeDocumented
-@Retention(value = AnnotationRetention.BINARY)
-@RequiresOptIn("This API is unstable and could change in future", RequiresOptIn.Level.WARNING)
-public annotation class UnstableAttributesAPI

benchmarks/README.md

@@ -1,4 +0,0 @@
-# Module benchmarks
-
-
-

benchmarks/build.gradle.kts

@@ -1,174 +0,0 @@
-@file:Suppress("UNUSED_VARIABLE")
-
-import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
-import space.kscience.kmath.benchmarks.addBenchmarkProperties
-
-plugins {
-    kotlin("multiplatform")
-    alias(spclibs.plugins.kotlin.plugin.allopen)
-    id("org.jetbrains.kotlinx.benchmark")
-}
-
-allOpen.annotation("org.openjdk.jmh.annotations.State")
-sourceSets.register("benchmarks")
-
-repositories {
-    mavenCentral()
-}
-
-val multikVersion: String by rootProject.extra
-
-kotlin {
-    jvm()
-
-    js(IR) {
-        nodejs()
-    }
-
-    sourceSets {
-        all {
-            languageSettings {
-                progressiveMode = true
-                optIn("kotlin.contracts.ExperimentalContracts")
-                optIn("kotlin.ExperimentalUnsignedTypes")
-                optIn("space.kscience.kmath.UnstableKMathAPI")
-            }
-        }
-
-        val commonMain by getting {
-            dependencies {
-                implementation(project(":kmath-ast"))
-                implementation(project(":kmath-core"))
-                implementation(project(":kmath-coroutines"))
-                implementation(project(":kmath-complex"))
-                implementation(project(":kmath-stat"))
-                implementation(project(":kmath-dimensions"))
-                implementation(project(":kmath-for-real"))
-                implementation(project(":kmath-tensors"))
-                implementation(project(":kmath-multik"))
-                implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
-                implementation(spclibs.kotlinx.benchmark.runtime)
-            }
-        }
-
-        val jvmMain by getting {
-            dependencies {
-                implementation(project(":kmath-commons"))
-                implementation(project(":kmath-ejml"))
-                implementation(project(":kmath-nd4j"))
-                implementation(project(":kmath-kotlingrad"))
-                implementation(project(":kmath-viktor"))
-                implementation(project(":kmath-jafama"))
-                implementation(projects.kmath.kmathTensorflow)
-                implementation("org.tensorflow:tensorflow-core-platform:0.4.0")
-                implementation("org.nd4j:nd4j-native:1.0.0-M1")
-                //    uncomment if your system supports AVX2
-                //    val os = System.getProperty("os.name")
-                //
-                //    if (System.getProperty("os.arch") in arrayOf("x86_64", "amd64")) when {
-                //        os.startsWith("Windows") -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:windows-x86_64-avx2")
-                //        os == "Linux" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:linux-x86_64-avx2")
-                //        os == "Mac OS X" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:macosx-x86_64-avx2")
-                //    } else
-                //    implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
-            }
-        }
-    }
-}
-
-// Configure benchmark
-benchmark {
-    // Setup configurations
-    targets {
-        register("jvm")
-        register("js")
-    }
-
-    fun kotlinx.benchmark.gradle.BenchmarkConfiguration.commonConfiguration() {
-        warmups = 2
-        iterations = 5
-        iterationTime = 2000
-        iterationTimeUnit = "ms"
-    }
-
-    configurations.register("buffer") {
-        commonConfiguration()
-        include("BufferBenchmark")
-    }
-
-    configurations.register("nd") {
-        commonConfiguration()
-        include("NDFieldBenchmark")
-    }
-
-    configurations.register("dot") {
-        commonConfiguration()
-        include("DotBenchmark")
-    }
-
-    configurations.register("expressions") {
-        // Some extra precision
-        warmups = 2
-        iterations = 10
-        iterationTime = 10
-        iterationTimeUnit = "s"
-        outputTimeUnit = "s"
-        include("ExpressionsInterpretersBenchmark")
-    }
-
-    configurations.register("matrixInverse") {
-        commonConfiguration()
-        include("MatrixInverseBenchmark")
-    }
-
-    configurations.register("bigInt") {
-        commonConfiguration()
-        include("BigIntBenchmark")
-    }
-
-    configurations.register("jafamaDouble") {
-        commonConfiguration()
-        include("JafamaBenchmark")
-    }
-
-    configurations.register("tensorAlgebra") {
-        commonConfiguration()
-        include("TensorAlgebraBenchmark")
-    }
-
-    configurations.register("viktor") {
-        commonConfiguration()
-        include("ViktorBenchmark")
-    }
-
-    configurations.register("viktorLog") {
-        commonConfiguration()
-        include("ViktorLogBenchmark")
-    }
-
-    configurations.register("integration") {
-        commonConfiguration()
-        include("IntegrationBenchmark")
-    }
-}
-
-kotlin.sourceSets.all {
-    with(languageSettings) {
-        optIn("kotlin.contracts.ExperimentalContracts")
-        optIn("kotlin.ExperimentalUnsignedTypes")
-        optIn("space.kscience.kmath.UnstableKMathAPI")
-    }
-}
-
-tasks.withType<KotlinJvmCompile> {
-    kotlinOptions {
-        jvmTarget = "11"
-        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy"
-    }
-}
-
-readme {
-    maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
-}
-
-addBenchmarkProperties()

benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

@@ -1,107 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.UnstableKMathAPI
-import space.kscience.kmath.expressions.*
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.bindSymbol
-import space.kscience.kmath.operations.invoke
-import kotlin.math.sin
-import kotlin.random.Random
-import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
-import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression
-
-@State(Scope.Benchmark)
-class ExpressionsInterpretersBenchmark {
-    /**
-     * Benchmark case for [Expression] created with [expressionInExtendedField].
-     */
-    @Benchmark
-    fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [toExpression].
-     */
-    @Benchmark
-    fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [compileToExpression].
-     */
-    @Benchmark
-    fun wasmExpression(blackhole: Blackhole) = invokeAndSum(wasm, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [compileToExpression].
-     */
-    @Benchmark
-    fun estreeExpression(blackhole: Blackhole) = invokeAndSum(estree, blackhole)
-
-    /**
-     * Benchmark case for [Expression] implemented manually with `kotlin.math` functions.
-     */
-    @Benchmark
-    fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole)
-
-    /**
-     * Benchmark case for direct computation w/o [Expression].
-     */
-    @Benchmark
-    fun justCalculate(blackhole: Blackhole) {
-        val random = Random(0)
-        var sum = 0.0
-
-        repeat(times) {
-            val x = random.nextDouble()
-            sum += x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-
-        blackhole.consume(sum)
-    }
-
-    private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
-        val random = Random(0)
-        var sum = 0.0
-        val m = HashMap<Symbol, Double>()
-
-        repeat(times) {
-            m[x] = random.nextDouble()
-            sum += expr(m)
-        }
-
-        blackhole.consume(sum)
-    }
-
-    private companion object {
-        private val x by symbol
-        private const val times = 1_000_000
-
-        private val functional = Float64Field.expression {
-            val x = bindSymbol(Symbol.x)
-            x * number(2.0) + 2.0 / x - 16.0 / sin(x)
-        }
-
-        private val node = MstExtendedField {
-            x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
-        }
-
-        private val mst = node.toExpression(Float64Field)
-
-        @OptIn(UnstableKMathAPI::class)
-        private val wasm = node.wasmCompileToExpression(Float64Field)
-        private val estree = node.estreeCompileToExpression(Float64Field)
-
-        private val raw = Expression<Double> { args ->
-            val x = args.getValue(x)
-            x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt

@@ -1,43 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import java.nio.IntBuffer
-
-@State(Scope.Benchmark)
-internal class ArrayBenchmark {
-    @Benchmark
-    fun benchmarkArrayRead(blackhole: Blackhole) {
-        var res = 0
-        for (i in 1..size) res += array[size - i]
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun benchmarkBufferRead(blackhole: Blackhole) {
-        var res = 0
-        for (i in 1..size) res += arrayBuffer[size - i]
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun nativeBufferRead(blackhole: Blackhole) {
-        var res = 0
-        for (i in 1..size) res += nativeBuffer[size - i]
-        blackhole.consume(res)
-    }
-
-    private companion object {
-        private const val size = 1000
-        private val array = IntArray(size) { it }
-        private val arrayBuffer = IntBuffer.wrap(array)
-        private val nativeBuffer = IntBuffer.allocate(size).also { for (i in 0 until size) it.put(i, i) }
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt

@@ -1,112 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-
-import kotlinx.benchmark.Blackhole
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import space.kscience.kmath.UnstableKMathAPI
-import space.kscience.kmath.operations.BigIntField
-import space.kscience.kmath.operations.JBigIntegerField
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.operations.parseBigInteger
-import java.math.BigInteger
-
-
-@UnstableKMathAPI
-@State(Scope.Benchmark)
-internal class BigIntBenchmark {
-
-    val kmSmallNumber = BigIntField.number(100)
-    val jvmSmallNumber = JBigIntegerField.number(100)
-    val kmNumber = BigIntField.number(Int.MAX_VALUE)
-    val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
-    val kmLargeNumber = BigIntField { number(11).pow(100_000U) }
-    val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
-    val bigExponent = 50_000
-
-    @Benchmark
-    fun kmSmallAdd(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber)
-    }
-
-    @Benchmark
-    fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber)
-    }
-
-    @Benchmark
-    fun kmAdd(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmNumber + kmNumber + kmNumber)
-    }
-
-    @Benchmark
-    fun jvmAdd(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmNumber + jvmNumber + jvmNumber)
-    }
-
-    @Benchmark
-    fun kmAddLarge(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber)
-    }
-
-    @Benchmark
-    fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber)
-    }
-
-    @Benchmark
-    fun kmMultiply(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmNumber * kmNumber * kmNumber)
-    }
-
-    @Benchmark
-    fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmLargeNumber * kmLargeNumber)
-    }
-
-    @Benchmark
-    fun jvmMultiply(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmNumber * jvmNumber * jvmNumber)
-    }
-
-    @Benchmark
-    fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmLargeNumber * jvmLargeNumber)
-    }
-
-    @Benchmark
-    fun kmPower(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmNumber.pow(bigExponent.toUInt()))
-    }
-
-    @Benchmark
-    fun jvmPower(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmNumber.pow(bigExponent))
-    }
-
-    @Benchmark
-    fun kmParsing16(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume("0x7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".parseBigInteger())
-    }
-
-    @Benchmark
-    fun kmParsing10(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume("236656783929183747565738292847574838922010".parseBigInteger())
-    }
-
-    @Benchmark
-    fun jvmParsing10(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume("236656783929183747565738292847574838922010".toBigInteger(10))
-    }
-
-    @Benchmark
-    fun jvmParsing16(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume("7f57ed8b89c29a3b9a85c7a5b84ca3929c7b7488593".toBigInteger(16))
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt

@@ -1,80 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.complex.Complex
-import space.kscience.kmath.complex.ComplexField
-import space.kscience.kmath.complex.complex
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.Float64Buffer
-import space.kscience.kmath.structures.getDouble
-import space.kscience.kmath.structures.permute
-
-@State(Scope.Benchmark)
-internal class BufferBenchmark {
-
-    @Benchmark
-    fun doubleArrayReadWrite(blackhole: Blackhole) {
-        val buffer = DoubleArray(size) { it.toDouble() }
-        var res = 0.0
-        (0 until size).forEach {
-            res += buffer[it]
-        }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun doubleBufferReadWrite(blackhole: Blackhole) {
-        val buffer = Float64Buffer(size) { it.toDouble() }
-        var res = 0.0
-        (0 until size).forEach {
-            res += buffer[it]
-        }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun bufferViewReadWrite(blackhole: Blackhole) {
-        val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
-        var res = 0.0
-        (0 until size).forEach {
-            res += buffer[it]
-        }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun bufferViewReadWriteSpecialized(blackhole: Blackhole) {
-        val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
-        var res = 0.0
-        (0 until size).forEach {
-            res += buffer.getDouble(it)
-        }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun complexBufferReadWrite(blackhole: Blackhole) = ComplexField {
-        val buffer = Buffer.complex(size / 2) { Complex(it.toDouble(), -it.toDouble()) }
-
-        var res = zero
-        (0 until size / 2).forEach {
-            res += buffer[it]
-        }
-
-        blackhole.consume(res)
-    }
-
-    private companion object {
-        private const val size = 100
-        private val reversedIndices = IntArray(size) { it }.apply { reverse() }
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt

@@ -1,93 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
-import space.kscience.kmath.linear.Float64ParallelLinearSpace
-import space.kscience.kmath.linear.invoke
-import space.kscience.kmath.linear.linearSpace
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.tensorflow.produceWithTF
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class DotBenchmark {
-    companion object {
-        val random = Random(12224)
-        const val dim = 1000
-
-        //creating invertible matrix
-        val matrix1 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
-            random.nextDouble()
-        }
-        val matrix2 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
-            random.nextDouble()
-        }
-
-        val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
-        val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
-
-        val ejmlMatrix1 = EjmlLinearSpaceDDRM { matrix1.toEjml() }
-        val ejmlMatrix2 = EjmlLinearSpaceDDRM { matrix2.toEjml() }
-    }
-
-
-    @Benchmark
-    fun tfDot(blackhole: Blackhole) {
-        blackhole.consume(
-            Float64Field.produceWithTF {
-                matrix1 dot matrix1
-            }
-        )
-    }
-
-    @Benchmark
-    fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-    @Benchmark
-    fun cmDot(blackhole: Blackhole) = CMLinearSpace {
-        blackhole.consume(cmMatrix1 dot cmMatrix2)
-    }
-
-    @Benchmark
-    fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
-    }
-
-    @Benchmark
-    fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-    @Benchmark
-    fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-    @Benchmark
-    fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-    @Benchmark
-    fun bufferedDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-    @Benchmark
-    fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
-        blackhole.consume(matrix1 dot matrix2)
-    }
-
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt

@@ -1,124 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.asm.compileToExpression
-import space.kscience.kmath.expressions.*
-import space.kscience.kmath.operations.Algebra
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.bindSymbol
-import space.kscience.kmath.operations.invoke
-import kotlin.math.sin
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class ExpressionsInterpretersBenchmark {
-    /**
-     * Benchmark case for [Expression] created with [expressionInExtendedField].
-     */
-    @Benchmark
-    fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [toExpression].
-     */
-    @Benchmark
-    fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [compileToExpression].
-     */
-    @Benchmark
-    fun asmGenericExpression(blackhole: Blackhole) = invokeAndSum(asmGeneric, blackhole)
-
-    /**
-     * Benchmark case for [Expression] created with [compileToExpression].
-     */
-    @Benchmark
-    fun asmPrimitiveExpressionArray(blackhole: Blackhole) {
-        val random = Random(0)
-        var sum = 0.0
-        val m = DoubleArray(1)
-
-        repeat(times) {
-            m[xIdx] = random.nextDouble()
-            sum += asmPrimitive(m)
-        }
-
-        blackhole.consume(sum)
-    }
-
-    /**
-     * Benchmark case for [Expression] created with [compileToExpression].
-     */
-    @Benchmark
-    fun asmPrimitiveExpression(blackhole: Blackhole) = invokeAndSum(asmPrimitive, blackhole)
-
-    /**
-     * Benchmark case for [Expression] implemented manually with `kotlin.math` functions.
-     */
-    @Benchmark
-    fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole)
-
-    /**
-     * Benchmark case for direct computation w/o [Expression].
-     */
-    @Benchmark
-    fun justCalculate(blackhole: Blackhole) {
-        val random = Random(0)
-        var sum = 0.0
-
-        repeat(times) {
-            val x = random.nextDouble()
-            sum += x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-
-        blackhole.consume(sum)
-    }
-
-    private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
-        val random = Random(0)
-        var sum = 0.0
-        val m = HashMap<Symbol, Double>()
-
-        repeat(times) {
-            m[x] = random.nextDouble()
-            sum += expr(m)
-        }
-
-        blackhole.consume(sum)
-    }
-
-    private companion object {
-        private val x by symbol
-        private const val times = 1_000_000
-
-        private val functional = Float64Field.expression {
-            val x = bindSymbol(Symbol.x)
-            x * number(2.0) + 2.0 / x - 16.0 / sin(x)
-        }
-
-        private val node = MstExtendedField {
-            x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
-        }
-
-        private val mst = node.toExpression(Float64Field)
-
-        private val asmPrimitive = node.compileToExpression(Float64Field)
-        private val xIdx = asmPrimitive.indexer.indexOf(x)
-
-        private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Double>)
-
-        private val raw = Expression<Double> { args ->
-            val x = args[x]!!
-            x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt

@@ -1,40 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import org.openjdk.jmh.infra.Blackhole
-import space.kscience.kmath.complex.Complex
-import space.kscience.kmath.complex.algebra
-import space.kscience.kmath.integration.gaussIntegrator
-import space.kscience.kmath.integration.integrate
-import space.kscience.kmath.integration.value
-import space.kscience.kmath.operations.algebra
-
-
-@State(Scope.Benchmark)
-internal class IntegrationBenchmark {
-
-    @Benchmark
-    fun doubleIntegration(blackhole: Blackhole) {
-        val res = Double.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
-            //sin(1 / x)
-            1 / x
-        }.value
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun complexIntegration(blackhole: Blackhole) = with(Complex.algebra) {
-        val res = gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
-//            sin(1 / x) + i * cos(1 / x)
-            1 / x - i / x
-        }.value
-        blackhole.consume(res)
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt

@@ -1,39 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Blackhole
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import space.kscience.kmath.jafama.JafamaDoubleField
-import space.kscience.kmath.jafama.StrictJafamaDoubleField
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class JafamaBenchmark {
-    @Benchmark
-    fun jafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        JafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-
-    @Benchmark
-    fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        Float64Field { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-
-    @Benchmark
-    fun strictJafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        StrictJafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-}
-
-private inline fun invokeBenchmarks(blackhole: Blackhole, expr: (Double) -> Double) {
-    val rng = Random(0)
-    repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt

@@ -1,57 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.commons.linear.lupSolver
-import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
-import space.kscience.kmath.linear.invoke
-import space.kscience.kmath.linear.linearSpace
-import space.kscience.kmath.linear.lupSolver
-import space.kscience.kmath.linear.parallel
-import space.kscience.kmath.operations.algebra
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class MatrixInverseBenchmark {
-    private companion object {
-        private val random = Random(1224)
-        private const val dim = 100
-
-        private val space = Double.algebra.linearSpace
-
-        //creating invertible matrix
-        private val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
-        private val l = space.buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
-        private val matrix = space { l dot u }
-    }
-
-    @Benchmark
-    fun kmathLupInversion(blackhole: Blackhole) {
-        blackhole.consume(Double.algebra.linearSpace.lupSolver().inverse(matrix))
-    }
-
-    @Benchmark
-    fun kmathParallelLupInversion(blackhole: Blackhole) {
-        blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
-    }
-
-    @Benchmark
-    fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
-        blackhole.consume(lupSolver().inverse(matrix))
-    }
-
-
-    @Benchmark
-    fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        blackhole.consume(matrix.toEjml().inverted())
-    }
-
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt

@@ -1,96 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import org.jetbrains.kotlinx.multik.api.Multik
-import org.jetbrains.kotlinx.multik.api.ones
-import org.jetbrains.kotlinx.multik.ndarray.data.DN
-import org.jetbrains.kotlinx.multik.ndarray.data.DataType
-import space.kscience.kmath.UnsafeKMathAPI
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.nd4j.nd4j
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.one
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.viktor.viktorAlgebra
-
-@State(Scope.Benchmark)
-internal class NDFieldBenchmark {
-
-    private companion object {
-        private const val dim = 1000
-        private const val n = 100
-        private val shape = ShapeND(dim, dim)
-        private val specializedField = Float64Field.ndAlgebra
-        private val genericField = BufferedFieldOpsND(Float64Field)
-        private val nd4jField = Float64Field.nd4j
-        private val viktorField = Float64Field.viktorAlgebra
-    }
-
-    @Benchmark
-    fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
-        var res: StructureND<Double> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
-        var res: StructureND<Double> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
-        var res: StructureND<Double> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
-        var res: StructureND<Double> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun tensorAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
-        var res: DoubleTensor = one(shape)
-        repeat(n) { res = res + 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun tensorInPlaceAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
-        val res: DoubleTensor = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @OptIn(UnsafeKMathAPI::class)
-    @Benchmark
-    fun multikInPlaceAdd(blackhole: Blackhole) = with(multikAlgebra) {
-        val res = Multik.ones<Double, DN>(shape.asArray(), DataType.DoubleDataType).wrap()
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-//    @Benchmark
-//    fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
-//        var res: StructureND<Double> = one(dim, dim)
-//        repeat(n) { res += 1.0 }
-//        blackhole.consume(res)
-//    }
-
-
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt

@@ -1,39 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import space.kscience.kmath.linear.linearSpace
-import space.kscience.kmath.linear.matrix
-import space.kscience.kmath.linear.symmetric
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.tensors.core.symEigJacobi
-import space.kscience.kmath.tensors.core.symEigSvd
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class TensorAlgebraBenchmark {
-    companion object {
-        private val random = Random(12224)
-        private const val dim = 30
-
-        private val matrix = Float64Field.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
-    }
-
-    @Benchmark
-    fun tensorSymEigSvd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
-        blackhole.consume(symEigSvd(matrix, 1e-10))
-    }
-
-    @Benchmark
-    fun tensorSymEigJacobi(blackhole: Blackhole) = with(Double.tensorAlgebra) {
-        blackhole.consume(symEigJacobi(matrix, 50, 1e-10))
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt

@@ -1,58 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import org.jetbrains.bio.viktor.F64Array
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.nd.one
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.viktor.ViktorFieldND
-
-@State(Scope.Benchmark)
-internal class ViktorBenchmark {
-
-    @Benchmark
-    fun doubleFieldAddition(blackhole: Blackhole) {
-        with(doubleField) {
-            var res: StructureND<Double> = one(shape)
-            repeat(n) { res += 1.0 }
-            blackhole.consume(res)
-        }
-    }
-
-    @Benchmark
-    fun viktorFieldAddition(blackhole: Blackhole) {
-        with(viktorField) {
-            var res = one(shape)
-            repeat(n) { res += 1.0 }
-            blackhole.consume(res)
-        }
-    }
-
-    @Benchmark
-    fun rawViktor(blackhole: Blackhole) {
-        val one = F64Array.full(init = 1.0, shape = intArrayOf(dim, dim))
-        var res = one
-        repeat(n) { res = res + one }
-        blackhole.consume(res)
-    }
-
-    private companion object {
-        private const val dim = 1000
-        private const val n = 100
-        private val shape = ShapeND(dim, dim)
-
-        // automatically build context most suited for given type.
-        private val doubleField = Float64Field.ndAlgebra
-        private val viktorField = ViktorFieldND(dim, dim)
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt

@@ -1,58 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Benchmark
-import kotlinx.benchmark.Blackhole
-import kotlinx.benchmark.Scope
-import kotlinx.benchmark.State
-import org.jetbrains.bio.viktor.F64Array
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.nd.one
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.viktor.ViktorFieldND
-
-@State(Scope.Benchmark)
-internal class ViktorLogBenchmark {
-    @Benchmark
-    fun realFieldLog(blackhole: Blackhole) {
-        with(doubleField) {
-            val fortyTwo = structureND(shape) { 42.0 }
-            var res = one(shape)
-            repeat(n) { res = ln(fortyTwo) }
-            blackhole.consume(res)
-        }
-    }
-
-    @Benchmark
-    fun viktorFieldLog(blackhole: Blackhole) {
-        with(viktorField) {
-            val fortyTwo = structureND(shape) { 42.0 }
-            var res = one
-            repeat(n) { res = ln(fortyTwo) }
-            blackhole.consume(res)
-        }
-    }
-
-    @Benchmark
-    fun rawViktorLog(blackhole: Blackhole) {
-        val fortyTwo = F64Array.full(dim, dim, init = 42.0)
-        lateinit var res: F64Array
-        repeat(n) { res = fortyTwo.log() }
-        blackhole.consume(res)
-    }
-
-    private companion object {
-        private const val dim = 1000
-        private const val n = 100
-        private val shape = ShapeND(dim, dim)
-
-        // automatically build context most suited for given type.
-        private val doubleField = Float64Field.ndAlgebra
-        private val viktorField = ViktorFieldND(dim, dim)
-    }
-}

benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/globals.kt

@@ -1,11 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import org.jetbrains.kotlinx.multik.default.DefaultEngine
-import space.kscience.kmath.multik.MultikDoubleAlgebra
-
-val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())

build.gradle.kts

@@ -1,13 +1,8 @@
-import space.kscience.gradle.useApache2Licence
-import space.kscience.gradle.useSPCTeam
-
 plugins {
-    id("space.kscience.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.7.6"
+    id("ru.mipt.npm.gradle.project")
+    id("org.jetbrains.kotlinx.kover") version "0.5.0"
 }
 
-val attributesVersion by extra("0.2.0")
-
 allprojects {
     repositories {
         maven("https://repo.kotlin.link")
@@ -16,13 +11,13 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.4.0"
+    version = "0.3.0-dev-20"
 }
 
 subprojects {
     if (name.startsWith("kmath")) apply<MavenPublishPlugin>()
 
-    plugins.withId("org.jetbrains.dokka") {
+    plugins.withId("org.jetbrains.dokka"){
         tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
             dependsOn(tasks["assemble"])
 
@@ -36,7 +31,7 @@ subprojects {
                     localDirectory.set(kotlinDir)
 
                     remoteUrl.set(
-                        uri("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath").toURL()
+                        java.net.URL("https://github.com/mipt-npm/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
                     )
                 }
 
@@ -61,14 +56,9 @@ subprojects {
 readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
 
 ksciencePublish {
-    pom("https://github.com/SciProgCentre/kmath") {
-        useApache2Licence()
-        useSPCTeam()
-    }
-    repository("spc", "https://maven.sciprog.center/kscience")
-    sonatype("https://oss.sonatype.org")
+    github("kmath", addToRelease = false)
+    space()
+    sonatype()
 }
 
-apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
-
-val multikVersion by extra("0.2.3")
+apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")

buildSrc/build.gradle.kts

@@ -1,8 +1,11 @@
 plugins {
     `kotlin-dsl`
     `version-catalog`
+    alias(miptNpmLibs.plugins.kotlin.plugin.serialization)
 }
 
+java.targetCompatibility = JavaVersion.VERSION_11
+
 repositories {
     mavenLocal()
     maven("https://repo.kotlin.link")
@@ -10,25 +13,19 @@ repositories {
     gradlePluginPortal()
 }
 
-val toolsVersion = spclibs.versions.tools.get()
-val kotlinVersion = spclibs.versions.kotlin.asProvider().get()
-val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
+val toolsVersion: String by extra
+val kotlinVersion = miptNpmLibs.versions.kotlin.asProvider().get()
+val benchmarksVersion = miptNpmLibs.versions.kotlinx.benchmark.get()
 
 dependencies {
-    api("space.kscience:gradle-tools:$toolsVersion")
+    api("ru.mipt.npm:gradle-tools:$toolsVersion")
     //plugins form benchmarks
     api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
-    //api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
+    api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
     //to be used inside build-script only
-    //implementation(spclibs.kotlinx.serialization.json)
-    implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
+    implementation(miptNpmLibs.kotlinx.serialization.json)
 }
 
-kotlin {
-    jvmToolchain {
-        languageVersion.set(JavaLanguageVersion.of(11))
-    }
-    sourceSets.all {
-        languageSettings.optIn("kotlin.OptIn")
-    }
+kotlin.sourceSets.all {
+    languageSettings.optIn("kotlin.OptIn")
 }

buildSrc/gradle.properties

@@ -0,0 +1,7 @@
+#
+# Copyright 2018-2021 KMath contributors.
+# Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+#
+
+kotlin.code.style=official
+toolsVersion=0.11.2-kotlin-1.6.20

buildSrc/settings.gradle.kts

@@ -5,24 +5,9 @@
 
 enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
 
-plugins {
-    id("org.gradle.toolchains.foojay-resolver-convention") version "0.8.0"
-}
-
 dependencyResolutionManagement {
-    val projectProperties = java.util.Properties()
-    file("../gradle.properties").inputStream().use {
-        projectProperties.load(it)
-    }
+    val toolsVersion: String by extra
 
-    projectProperties.forEach { key, value ->
-        extra.set(key.toString(), value)
-    }
-
-
-    val toolsVersion: String = projectProperties["toolsVersion"].toString()
-
-    @Suppress("UnstableApiUsage")
     repositories {
         mavenLocal()
         maven("https://repo.kotlin.link")
@@ -31,8 +16,8 @@ dependencyResolutionManagement {
     }
 
     versionCatalogs {
-        create("spclibs") {
-            from("space.kscience:version-catalog:$toolsVersion")
+        create("miptNpmLibs") {
+            from("ru.mipt.npm:version-catalog:$toolsVersion")
         }
     }
 }

buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/JmhReport.kt

@@ -1,10 +1,13 @@
 /*
- * Copyright 2018-2024 KMath contributors.
+ * Copyright 2018-2021 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
 package space.kscience.kmath.benchmarks
 
+import kotlinx.serialization.Serializable
+
+@Serializable
 data class JmhReport(
     val jmhVersion: String,
     val benchmark: String,
@@ -34,6 +37,7 @@ data class JmhReport(
         val scoreUnit: String
     }
 
+    @Serializable
     data class PrimaryMetric(
         override val score: Double,
         override val scoreError: Double,
@@ -44,6 +48,7 @@ data class JmhReport(
         val rawData: List<List<Double>>? = null,
     ) : Metric
 
+    @Serializable
     data class SecondaryMetric(
         override val score: Double,
         override val scoreError: Double,

buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/addBenchmarkProperties.kt

@@ -1,22 +1,21 @@
 /*
- * Copyright 2018-2024 KMath contributors.
+ * Copyright 2018-2021 KMath contributors.
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
 package space.kscience.kmath.benchmarks
 
-import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
-import com.fasterxml.jackson.module.kotlin.readValue
 import kotlinx.benchmark.gradle.BenchmarksExtension
+import kotlinx.serialization.decodeFromString
+import kotlinx.serialization.json.Json
 import org.gradle.api.Project
-import space.kscience.gradle.KScienceReadmeExtension
+import ru.mipt.npm.gradle.KScienceReadmeExtension
 import java.time.LocalDateTime
 import java.time.ZoneId
 import java.time.format.DateTimeFormatter
 import java.time.format.DateTimeFormatterBuilder
 import java.time.format.SignStyle
 import java.time.temporal.ChronoField.*
-import java.util.*
 
 private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
     parseCaseInsensitive()
@@ -46,25 +45,23 @@ private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
 
 private fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
 
-private val jsonMapper = jacksonObjectMapper()
-
 fun Project.addBenchmarkProperties() {
     val benchmarksProject = this
     rootProject.subprojects.forEach { p ->
         p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
             benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
-                property("benchmark${cfg.name.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.getDefault()) else it.toString() }}") {
-                    val launches = benchmarksProject.layout.buildDirectory.dir("reports/benchmarks/${cfg.name}").get()
+                property("benchmark${cfg.name.capitalize()}") {
+                    val launches = benchmarksProject.buildDir.resolve("reports/benchmarks/${cfg.name}")
 
-                    val resDirectory = launches.files().maxByOrNull {
+                    val resDirectory = launches.listFiles()?.maxByOrNull {
                         LocalDateTime.parse(it.name, ISO_DATE_TIME).atZone(ZoneId.systemDefault()).toInstant()
                     }
 
                     if (resDirectory == null || !(resDirectory.resolve("jvm.json")).exists()) {
                         "> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
                     } else {
-                        val reports: List<JmhReport> =
-                            jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
+                        val reports =
+                            Json.decodeFromString<List<JmhReport>>(resDirectory.resolve("jvm.json").readText())
 
                         buildString {
                             appendLine("<details>")
@@ -77,20 +74,16 @@ fun Project.addBenchmarkProperties() {
                             appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
                             appendLine()
                             appendLine("```")
-                            appendLine(
-                                "${first.jvm} ${
-                                    first.jvmArgs.joinToString(" ")
-                                }"
-                            )
+                            appendLine("${first.jvm} ${
+                                first.jvmArgs.joinToString(" ")
+                            }")
                             appendLine("```")
 
-                            appendLine(
-                                "* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
-                                    noun(first.warmupIterations, "iteration", "iterations")
-                                } by ${first.warmupTime} and ${first.measurementIterations} measurement ${
-                                    noun(first.measurementIterations, "iteration", "iterations")
-                                } by ${first.measurementTime}."
-                            )
+                            appendLine("* JMH ${first.jmhVersion} was used in `${first.mode}` mode with ${first.warmupIterations} warmup ${
+                                noun(first.warmupIterations, "iteration", "iterations")
+                            } by ${first.warmupTime} and ${first.measurementIterations} measurement ${
+                                noun(first.measurementIterations, "iteration", "iterations")
+                            } by ${first.measurementTime}.")
 
                             appendLine()
                             appendLine("| Benchmark | Score |")

buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt

@@ -0,0 +1,425 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:Suppress("KDocUnresolvedReference")
+
+package space.kscience.kmath.ejml.codegen
+
+import org.intellij.lang.annotations.Language
+import java.io.File
+
+private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
+    @Language("kotlin") val text = """/**
+ * [EjmlVector] specialization for [$type].
+ */
+public class Ejml${type}Vector<out M : $ejmlMatrixType>(override val origin: M) : EjmlVector<$type, M>(origin) {
+    init {
+        require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
+    }
+
+    override operator fun get(index: Int): $type = origin[0, index]
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
+    val text = """/**
+ * [EjmlMatrix] specialization for [$type].
+ */
+public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
+    override operator fun get(i: Int, j: Int): $type = origin[i, j]
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+private fun Appendable.appendEjmlLinearSpace(
+    type: String,
+    kmathAlgebra: String,
+    ejmlMatrixParentTypeMatrix: String,
+    ejmlMatrixType: String,
+    ejmlMatrixDenseType: String,
+    ops: String,
+    denseOps: String,
+    isDense: Boolean,
+) {
+    @Language("kotlin") val text = """/**
+ * [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
+ * [${ejmlMatrixType}] matrices.
+ */
+public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra}, $ejmlMatrixType>() {
+    /**
+     * The [${kmathAlgebra}] reference.
+     */
+    override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
+
+    @Suppress("UNCHECKED_CAST")
+    override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
+        this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
+        else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
+    }
+
+    @Suppress("UNCHECKED_CAST")
+    override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
+        this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
+        else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
+            (0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
+        })
+    }
+
+    override fun buildMatrix(
+        rows: Int,
+        columns: Int,
+        initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
+    ): Ejml${type}Matrix<${ejmlMatrixType}> = ${ejmlMatrixType}(rows, columns).also {
+        (0 until rows).forEach { row ->
+            (0 until columns).forEach { col -> it[row, col] = elementAlgebra.initializer(row, col) }
+        }
+    }.wrapMatrix()
+
+    override fun buildVector(
+        size: Int,
+        initializer: ${kmathAlgebra}.(Int) -> ${type},
+    ): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
+        (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) }
+    })
+
+    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
+    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
+
+    override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
+
+    override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
+        return out.wrapMatrix()
+    }
+
+    override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
+        return out.wrapVector()
+    }
+
+    override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one, 
+            toEjml().origin, 
+            elementAlgebra { -one },
+            other.toEjml().origin, 
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapMatrix()
+    }
+
+    override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.scale(value, toEjml().origin, res)
+        return res.wrapMatrix()
+    }
+
+    override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.changeSign(toEjml().origin, res)
+        return res.wrapVector()
+    }
+
+    override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+        
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra.one,
+            other.toEjml().origin, 
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapMatrix()
+    }
+
+    override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra.one,
+            other.toEjml().origin,
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapVector()
+    }
+
+    override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val out = ${ejmlMatrixType}(1, 1)
+
+        CommonOps_${ops}.add(
+            elementAlgebra.one,
+            toEjml().origin,
+            elementAlgebra { -one },
+            other.toEjml().origin,
+            out,${
+        if (isDense) "" else
+            """
+            null, 
+            null,"""
+    }
+        )
+
+        return out.wrapVector()
+    }
+
+    override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
+
+    override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.scale(value, toEjml().origin, res)
+        return res.wrapVector()
+    }
+
+    override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
+
+    @UnstableKMathAPI
+    override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
+        structure.getFeature(type)?.let { return it }
+        val origin = structure.toEjml().origin
+
+        return when (type) {
+        ${
+        if (isDense)
+            """    InverseMatrixFeature::class -> object : InverseMatrixFeature<${type}> {
+                override val inverse: Matrix<${type}> by lazy {
+                    val res = origin.copy()
+                    CommonOps_${ops}.invert(res)
+                    res.wrapMatrix()
+                }
+            }
+
+            DeterminantFeature::class -> object : DeterminantFeature<${type}> {
+                override val determinant: $type by lazy { CommonOps_${ops}.det(origin) }
+            }
+
+            SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<${type}> {
+                private val svd by lazy {
+                    DecompositionFactory_${ops}.svd(origin.numRows, origin.numCols, true, true, false)
+                        .apply { decompose(origin.copy()) }
+                }
+
+                override val u: Matrix<${type}> by lazy { svd.getU(null, false).wrapMatrix() }
+                override val s: Matrix<${type}> by lazy { svd.getW(null).wrapMatrix() }
+                override val v: Matrix<${type}> by lazy { svd.getV(null, false).wrapMatrix() }
+                override val singularValues: Point<${type}> by lazy { ${type}Buffer(svd.singularValues) }
+            }
+
+            QRDecompositionFeature::class -> object : QRDecompositionFeature<${type}> {
+                private val qr by lazy {
+                    DecompositionFactory_${ops}.qr().apply { decompose(origin.copy()) }
+                }
+
+                override val q: Matrix<${type}> by lazy {
+                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
+                }
+
+                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
+                override val l: Matrix<${type}> by lazy {
+                    val cholesky =
+                        DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
+
+                    cholesky.getT(null).wrapMatrix().withFeature(LFeature)
+                }
+            }
+
+            LupDecompositionFeature::class -> object : LupDecompositionFeature<${type}> {
+                private val lup by lazy {
+                    DecompositionFactory_${ops}.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
+                }
+
+                override val l: Matrix<${type}> by lazy {
+                    lup.getLower(null).wrapMatrix().withFeature(LFeature)
+                }
+
+                override val u: Matrix<${type}> by lazy {
+                    lup.getUpper(null).wrapMatrix().withFeature(UFeature)
+                }
+
+                override val p: Matrix<${type}> by lazy { lup.getRowPivot(null).wrapMatrix() }
+            }""" else """    QRDecompositionFeature::class -> object : QRDecompositionFeature<$type> {
+                private val qr by lazy {
+                    DecompositionFactory_${ops}.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
+                }
+
+                override val q: Matrix<${type}> by lazy {
+                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
+                }
+
+                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
+            }
+
+            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
+                override val l: Matrix<${type}> by lazy {
+                    val cholesky =
+                        DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
+
+                    (cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix().withFeature(LFeature)
+                }
+            }
+
+            LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
+                LUDecompositionFeature<${type}>, DeterminantFeature<${type}>, InverseMatrixFeature<${type}> {
+                private val lu by lazy {
+                    DecompositionFactory_${ops}.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
+                }
+
+                override val l: Matrix<${type}> by lazy {
+                    lu.getLower(null).wrapMatrix().withFeature(LFeature)
+                }
+
+                override val u: Matrix<${type}> by lazy {
+                    lu.getUpper(null).wrapMatrix().withFeature(UFeature)
+                }
+
+                override val inverse: Matrix<${type}> by lazy {
+                    var a = origin
+                    val inverse = ${ejmlMatrixDenseType}(1, 1)
+                    val solver = LinearSolverFactory_${ops}.lu(FillReducing.NONE)
+                    if (solver.modifiesA()) a = a.copy()
+                    val i = CommonOps_${denseOps}.identity(a.numRows)
+                    solver.solve(i, inverse)
+                    inverse.wrapMatrix()
+                }
+
+                override val determinant: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
+            }"""
+    }
+
+            else -> null
+        }?.let(type::cast)
+    }
+
+    /**
+     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
+     *
+     * @param a the base matrix.
+     * @param b n by p matrix.
+     * @return the solution for *x* that is n by p.
+     */
+    public fun solve(a: Matrix<${type}>, b: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
+        return res.wrapMatrix()
+    }
+
+    /**
+     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
+     *
+     * @param a the base matrix.
+     * @param b n by p vector.
+     * @return the solution for *x* that is n by p.
+     */
+    public fun solve(a: Matrix<${type}>, b: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
+        val res = ${ejmlMatrixType}(1, 1)
+        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
+        return Ejml${type}Vector(res)
+    }
+}"""
+    appendLine(text)
+    appendLine()
+}
+
+
+/**
+ * Generates routine EJML classes.
+ */
+fun ejmlCodegen(outputFile: String): Unit = File(outputFile).run {
+    parentFile.mkdirs()
+
+    writer().use {
+        it.appendLine("/*")
+        it.appendLine(" * Copyright 2018-2021 KMath contributors.")
+        it.appendLine(" * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.")
+        it.appendLine(" */")
+        it.appendLine()
+        it.appendLine("/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */")
+        it.appendLine()
+        it.appendLine("package space.kscience.kmath.ejml")
+        it.appendLine()
+        it.appendLine("""import org.ejml.data.*
+import org.ejml.dense.row.CommonOps_DDRM
+import org.ejml.dense.row.CommonOps_FDRM
+import org.ejml.dense.row.factory.DecompositionFactory_DDRM
+import org.ejml.dense.row.factory.DecompositionFactory_FDRM
+import org.ejml.sparse.FillReducing
+import org.ejml.sparse.csc.CommonOps_DSCC
+import org.ejml.sparse.csc.CommonOps_FSCC
+import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
+import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
+import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
+import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
+import space.kscience.kmath.linear.*
+import space.kscience.kmath.linear.Matrix
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.StructureFeature
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.FloatField
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.DoubleBuffer
+import space.kscience.kmath.structures.FloatBuffer
+import kotlin.reflect.KClass
+import kotlin.reflect.cast""")
+        it.appendLine()
+        it.appendEjmlVector("Double", "DMatrix")
+        it.appendEjmlVector("Float", "FMatrix")
+        it.appendEjmlMatrix("Double", "DMatrix")
+        it.appendEjmlMatrix("Float", "FMatrix")
+        it.appendEjmlLinearSpace("Double", "DoubleField", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
+        it.appendEjmlLinearSpace("Float", "FloatField", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
+
+        it.appendEjmlLinearSpace(
+            type = "Double",
+            kmathAlgebra = "DoubleField",
+            ejmlMatrixParentTypeMatrix = "DMatrix",
+            ejmlMatrixType = "DMatrixSparseCSC",
+            ejmlMatrixDenseType = "DMatrixRMaj",
+            ops = "DSCC",
+            denseOps = "DDRM",
+            isDense = false,
+        )
+
+        it.appendEjmlLinearSpace(
+            type = "Float",
+            kmathAlgebra = "FloatField",
+            ejmlMatrixParentTypeMatrix = "FMatrix",
+            ejmlMatrixType = "FMatrixSparseCSC",
+            ejmlMatrixDenseType = "FMatrixRMaj",
+            ops = "FSCC",
+            denseOps = "FDRM",
+            isDense = false,
+        )
+    }
+}

docs/algebra.md

@@ -1,86 +0,0 @@
-# Algebraic Structures and Algebraic Elements
-
-The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an
-operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up,
-say `Group<T>`. Next one needs to run the actual operation in the context:
-
-```kotlin
-import space.kscience.kmath.operations.*
-
-val a: T = ...
-val b: T = ...
-val group: Group<T> = ...
-
-val c = group { a + b }
-```
-
-At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in
-mathematics, one could draw up different operations on same objects. For example, one could use different types of
-geometry for vectors.
-
-## Algebraic Structures
-
-Primary mathematical contexts have the following hierarchy:
-
-`Field <: Ring <: Group <: Algebra`
-
-These interfaces follow real algebraic structures:
-
-- [Group](https://mathworld.wolfram.com/Group.html) defines addition, its identity element (i.e., 0) and additive
-  inverse (-x);
-- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its identity element (i.e., 1);
-- [Field](http://mathworld.wolfram.com/Field.html) adds division operation.
-
-A typical implementation of `Field<T>` is the `DoubleField` which works on doubles, and `VectorSpace` for `Space<T>`.
-
-In some cases algebra context can hold additional operations like `exp` or `sin`, and then it inherits appropriate
-interface. Also, contexts may have operations, which produce elements outside the context. For example, `Matrix.dot`
-operation produces a matrix with new dimensions, which can be incompatible with initial matrix in linear operations.
-
-## Spaces and Fields
-
-KMath introduces contexts for builtin algebraic structures:
-
-```kotlin
-import space.kscience.kmath.operations.*
-
-val c1 = Complex(1.0, 2.0)
-val c2 = ComplexField.i
-
-val c3 = c1 + c2
-// or
-val c3 = ComplexField { c1 + c2 }
-```
-
-Also, `ComplexField` features special operations to mix complex and real numbers, for example:
-
-```kotlin
-import space.kscience.kmath.operations.*
-
-val c1 = Complex(1.0, 2.0)
-val c2 = ComplexField { c1 - 1.0 } // Returns: Complex(re=0.0, im=2.0)
-val c3 = ComplexField { c1 - i * 2.0 }
-```
-
-**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support
-that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and
-[KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates.
-
-## Nested fields
-
-Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex
-elements like so:
-
-```kotlin
-val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray ->
-    Complex(index[0].toDouble() - index[1].toDouble(), index[0].toDouble() + index[1].toDouble())
-}
-```
-
-The `element` in this example is a member of the `Field` of 2D structures, each element of which is a member of its own
-`ComplexField`. It is important one does not need to create a special n-d class to hold complex numbers and implement
-operations on it, one just needs to provide a field for its elements.
-
-**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts
-like
-`MemorySpec`.

docs/buffers.md

@@ -1,20 +0,0 @@
-# Buffers
-
-Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (
-with `MutableBuffer`). There are different types of buffers:
-
-* Primitive buffers wrapping like `DoubleBuffer` which are wrapping primitive arrays.
-* Boxing `ListBuffer` wrapping a list
-* Functionally defined `VirtualBuffer` which does not hold a state itself, but provides a function to calculate value
-* `MemoryBuffer` allows direct allocation of objects in continuous memory block.
-
-Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions
-defined in
-`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the
-most suitable buffer for given reified type (for types with custom memory buffer it still better to use their
-own `MemoryBuffer.create()` factory).
-
-## Buffer performance
-
-One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers
-instead.

docs/codestyle.md

@@ -1,35 +0,0 @@
-# Coding Conventions
-
-Generally, KMath code follows
-general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of
-small changes and clarifications.
-
-## Utility Class Naming
-
-Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe
-its contents.
-
-The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that
-file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and
-aggregators with a small letter seems to be a good way to visually separate those files.
-
-This convention could be changed in future in a non-breaking way.
-
-## Private Variable Naming
-
-Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public
-read-only value with the same meaning.
-
-This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and
-private versions draw up the same entity. It is allowed only for private variables.
-
-This convention could be changed in future in a non-breaking way.
-
-## Functions and Properties One-liners
-
-Use one-liners when they occupy single code window line both for functions and properties with getters like
-`val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be
-cleanly separated.
-
-There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook
-one-lines seem to better show that the property or function is easily calculated.

docs/contexts.md

@@ -1,73 +0,0 @@
-# Context-oriented mathematics
-
-## The problem
-
-A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different sets
-of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to treat
-some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to
-define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem
-arises when one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are
-usually solved by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks.
-
-## Context-oriented approach
-
-One possible solution to these problems is to divorce numerical representations from behaviors. For example in Kotlin
-one can define a separate class representing some entity without any operations, ex. a complex number:
-
-```kotlin
-data class Complex(val re: Double, val im: Double)
-```
-
-And then to define a separate class or singleton, representing an operation on those complex numbers:
-
-```kotlin
-object ComplexOperations {
-    operator fun Complex.plus(other: Complex) = Complex(re + other.re, im + other.im)
-    operator fun Complex.minus(other: Complex) = Complex(re - other.re, im - other.im)
-}
-```
-
-In Java, applying such external operations could be cumbersome, but Kotlin has a unique feature that allows us
-implement this
-naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions). In
-Kotlin, an operation on complex number could be implemented as:
-
-```kotlin
-with(ComplexOperations) { c1 + c2 - c3 }
-```
-
-Kotlin also allows the creation of functions with receivers:
-
-```kotlin
-fun ComplexOperations.doSomethingWithComplex(c1: Complex, c2: Complex, c3: Complex) = c1 + c2 - c3
-
-ComplexOperations.doComethingWithComplex(c1, c2, c3)
-```
-
-In fact, whole parts of a program may be run within a mathematical context or even multiple nested contexts.
-
-In KMath, contexts are not only responsible for operations, but also for raw object creation and advanced features.
-
-## Other possibilities
-
-### Type classes
-
-An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior
-to a specific type without modifying the type itself. On the plus side, type classes do not require explicit context
-declaration, so the code looks cleaner. On the minus side, if there are different sets of behaviors for the same types,
-it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even state.
-For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize performance in
-case of a large amount of structures.
-
-### Wildcard imports and importing-on-demand
-
-Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members from
-a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file with a
-single context. However, when using multiple contexts, this technique can introduce operator ambiguity, due to namespace
-pollution. If there are multiple scoped contexts that define the same operation, it is still possible to import
-specific operations as needed, without using an explicit context with extension functions, for example:
-
-```
-import context.complex.op1
-import context.quaternion.op2
-```

docs/expressions.md

@@ -1,24 +0,0 @@
-# Expressions
-
-Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical
-expressions.
-
-The potential use-cases for it (so far) are following:
-
-* lazy evaluation (in general simple lambda is better, but there are some border cases);
-* automatic differentiation in single-dimension and in multiple dimensions;
-* generation of mathematical syntax trees with subsequent code generation for other languages;
-* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with
-  Symja's `IExpr`—integration, simplification, and more);
-* visualization with `kmath-jupyter`.
-
-The workhorse of this API is `Expression` interface, which exposes
-single `operator fun invoke(arguments: Map<Symbol, T>): T`
-method. `ExpressionAlgebra` is used to generate expressions and introduce variables.
-
-Currently there are two implementations:
-
-* Generic `ExpressionField` in `kmath-core` which allows construction of custom lazy expressions
-
-* Auto-differentiation expression in `kmath-commons` module allows using full power of `DerivativeStructure`
-  from commons-math. **TODO: add example**

docs/histograms.md

@@ -1 +0,0 @@
-**TODO**

docs/linear.md

@@ -1,36 +0,0 @@
-## Basic linear algebra layout
-
-KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases
-declared in context classes, and are not the members of classes that store data. This allows more flexible approach to
-maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of
-data structures.
-
-The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products
-of matrices and vectors:
-
-```kotlin
-import space.kscience.kmath.linear.*
-
-LinearSpace.Companion.real {
-    val vec = buildVector(10) { i -> i.toDouble() }
-    val mat = buildMatrix(10, 10) { i, j -> i.toDouble() + j }
-
-    // Addition
-    vec + vec
-    mat + mat
-
-    // Multiplication by scalar
-    vec * 2.0
-    mat * 2.0
-
-    // Dot product
-    mat dot vec
-    mat dot mat
-}
-```
-
-## Backends overview
-
-### EJML
-
-### Commons Math

docs/nd-structure.md

@@ -1,150 +0,0 @@
-# ND-structure generation and operations
-
-**TODO**
-
-# Performance for n-dimensional structures operations
-
-One of the most sought after features of mathematical libraries is the high-performance operations on n-dimensional
-structures. In `kmath` performance depends on which particular context was used for operation.
-
-Let us consider following contexts:
-
-```kotlin
-    // automatically build context most suited for given type.
-    val autoField = NDField.auto(DoubleField, dim, dim)
-    // specialized nd-field for Double. It works as generic Double field as well.
-    val specializedField = NDField.real(dim, dim)
-    //A generic boxing field. It should be used for objects, not primitives.
-    val genericField = NDField.buffered(DoubleField, dim, dim)
-```
-
-Now let us perform several tests and see, which implementation is best suited for each case:
-
-## Test case
-
-To test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
-to it `n = 1000` times.
-
-## Specialized
-
-The code to run this looks like:
-
-```kotlin
-    specializedField.run {
-        var res: NDBuffer<Double> = one
-        repeat(n) {
-            res += 1.0
-        }
-    }
-```
-
-The performance of this code is the best of all tests since it inlines all operations and is specialized for operation
-with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time
-on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type
-from the beginning. Everyone does so anyway, so it is the recommended approach.
-
-## Automatic
-
-Let's do the same with automatic field inference:
-
-```kotlin
-    autoField.run {
-        var res = one
-        repeat(n) {
-            res += 1.0
-        }
-    }
-```
-
-Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
-returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
-
-## Lazy
-
-Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
-using coroutines to parallelize computations.
-When one calls
-
-```kotlin
-    lazyField.run {
-        var res = one
-        repeat(n) {
-            res += 1.0
-        }
-    }
-```
-
-The result will be calculated almost immediately but the result will be empty. To get the full result
-structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
-should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
-save a lot of time.
-
-This field still could be used with reasonable performance if call code is changed:
-
-```kotlin
-    lazyField.run {
-        val res = one.map {
-            var c = 0.0
-            repeat(n) {
-                c += 1.0
-            }
-            c
-        }
-
-        res.elements().forEach { it.second }
-    }
-```
-
-In this case it completes in about `4x-5x` time due to boxing.
-
-## Boxing
-
-The boxing field produced by
-
-```kotlin
-    genericField.run {
-        var res: NDBuffer<Double> = one
-        repeat(n) {
-            res += 1.0
-        }
-    }
-```
-
-is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about
-`15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should
-never be used for primitives.
-
-## Element operation
-
-Let us also check the speed for direct operations on elements:
-
-```kotlin
-    var res = genericField.one
-    repeat(n) {
-        res += 1.0
-    }
-```
-
-One would expect to be at least as slow as field operation, but in fact, this one takes only `2x` time to complete.
-It happens, because in this particular case it does not use actual `NDField` but instead calculated directly
-via extension function.
-
-## What about python?
-
-Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
-work completely without frame of reference. In this case, simple numpy code:
-
-```python
-import numpy as np
-
-res = np.ones((1000,1000))
-for i in range(1000):
-    res = res + 1.0
-```
-
-gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think
-it is
-because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
-but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
-available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping
-functions).

docs/polynomials.md

@@ -1,223 +0,0 @@
-# Polynomials and Rational Functions
-
-KMath provides a way to work with uni- and multivariate polynomials and rational functions. It includes full support of
-arithmetic operations of integers, **constants** (elements of ring polynomials are build over), variables (for certain
-multivariate implementations), polynomials and rational functions encapsulated in so-called **polynomial space** and *
-*rational function space** and some other utilities such as algebraic differentiation and substitution.
-
-## Concrete realizations
-
-There are 3 approaches to represent polynomials:
-
-1. For univariate polynomials one can represent and store polynomial as a list of coefficients for each power of the
-   variable. I.e. polynomial $a_0 + \dots + a_n x^n $ can be represented as a finite sequence $(a_0; \dots; a_n)$. (
-   Compare to sequential definition of polynomials.)
-2. For multivariate polynomials one can represent and store polynomial as a matching (in programming it is called "map"
-   or "dictionary", in math it is
-   called [functional relation](https://en.wikipedia.org/wiki/Binary_relation#Special_types_of_binary_relations)) of
-   each "**term signature**" (that describes what variables and in what powers appear in the term) with corresponding
-   coefficient of the term. But there are 2 possible approaches of term signature representation:
-    1. One can number all the variables, so term signature can be represented as a sequence describing powers of the
-       variables. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for natural or zero $d_i $) can be
-       represented as a finite sequence $(d_0; \dots; d_n)$.
-    2. One can represent variables as objects ("**labels**"), so term signature can be also represented as a matching of
-       each appeared variable with its power in the term. I.e. signature of term $c \\; x_0^{d_0} \dots x_n^{d_n} $ (for
-       natural non-zero $d_i $) can be represented as a finite matching $(x_0 \to d_1; \dots; x_n \to d_n)$.
-
-All that three approaches are implemented by "list", "numbered", and "labeled" versions of polynomials and polynomial
-spaces respectively. Whereas all rational functions are represented as fractions with corresponding polynomial numerator
-and denominator, and rational functions' spaces are implemented in the same way as usual field of rational numbers (or
-more precisely, as any field of fractions over integral domain) should be implemented.
-
-So here are a bit of details. Let `C` by type of constants. Then:
-
-1. `ListPolynomial`, `ListPolynomialSpace`, `ListRationalFunction` and `ListRationalFunctionSpace` implement the first
-   scenario. `ListPolynomial` stores polynomial $a_0 + \dots + a_n x^n $ as a coefficients
-   list `listOf(a_0, ..., a_n)` (of type `List<C>`).
-
-   They also have variation `ScalableListPolynomialSpace` that replaces former polynomials and
-   implements `ScaleOperations`.
-2. `NumberedPolynomial`, `NumberedPolynomialSpace`, `NumberedRationalFunction` and `NumberedRationalFunctionSpace`
-   implement second scenario. `NumberedPolynomial` stores polynomials as structures of type `Map<List<UInt>, C>`.
-   Signatures are stored as `List<UInt>`. To prevent ambiguity signatures should not end with zeros.
-3. `LabeledPolynomial`, `LabeledPolynomialSpace`, `LabeledRationalFunction` and `LabeledRationalFunctionSpace` implement
-   third scenario using common `Symbol` as variable type. `LabeledPolynomial` stores polynomials as structures of
-   type `Map<Map<Symbol, UInt>, C>`. Signatures are stored as `Map<Symbol, UInt>`. To prevent ambiguity each signature
-   should not map any variable to zero.
-
-### Example: `ListPolynomial`
-
-For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
-
-```kotlin
-val polynomial: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
-// or
-val polynomial: ListPolynomial<Int> = ListPolynomial(2, -3, 1)
-```
-
-All algebraic operations can be used in corresponding space:
-
-```kotlin
-val computationResult = Int.algebra.listPolynomialSpace {
-   ListPolynomial(2, -3, 1) + ListPolynomial(0, 6) == ListPolynomial(2, 3, 1)
-}
-
-println(computationResult) // true
-```
-
-For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
-
-### Example: `NumberedPolynomial`
-
-For example, polynomial $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
-
-```kotlin
-val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
-   mapOf(
-      listOf<UInt>() to 3,
-      listOf(0u, 1u) to 5,
-      listOf(2u, 0u, 1u) to -7,
-   )
-)
-// or
-val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
-   listOf<UInt>() to 3,
-   listOf(0u, 1u) to 5,
-   listOf(2u, 0u, 1u) to -7,
-)
-```
-
-All algebraic operations can be used in corresponding space:
-
-```kotlin
-val computationResult = Int.algebra.numberedPolynomialSpace {
-   NumberedPolynomial(
-      listOf<UInt>() to 3,
-      listOf(0u, 1u) to 5,
-      listOf(2u, 0u, 1u) to -7,
-   ) + NumberedPolynomial(
-      listOf(0u, 1u) to -5,
-      listOf(0u, 0u, 0u, 4u) to 4,
-   ) == NumberedPolynomial(
-      listOf<UInt>() to 3,
-      listOf(0u, 1u) to 0,
-      listOf(2u, 0u, 1u) to -7,
-      listOf(0u, 0u, 0u, 4u) to 4,
-   )
-}
-
-println(computationResult) // true
-```
-
-For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
-
-### Example: `LabeledPolynomial`
-
-For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
-
-```kotlin
-val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
-   mapOf(
-      mapOf<Symbol, UInt>() to 3,
-      mapOf(y to 1u) to 5,
-      mapOf(x to 2u, z to 1u) to -7,
-   )
-)
-// or
-val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
-   mapOf<Symbol, UInt>() to 3,
-   mapOf(y to 1u) to 5,
-   mapOf(x to 2u, z to 1u) to -7,
-)
-```
-
-All algebraic operations can be used in corresponding space:
-
-```kotlin
-val computationResult = Int.algebra.labeledPolynomialSpace {
-   LabeledPolynomial(
-      listOf<UInt>() to 3,
-      listOf(0u, 1u) to 5,
-      listOf(2u, 0u, 1u) to -7,
-   ) + LabeledPolynomial(
-      listOf(0u, 1u) to -5,
-      listOf(0u, 0u, 0u, 4u) to 4,
-   ) == LabeledPolynomial(
-      listOf<UInt>() to 3,
-      listOf(0u, 1u) to 0,
-      listOf(2u, 0u, 1u) to -7,
-      listOf(0u, 0u, 0u, 4u) to 4,
-   )
-}
-
-println(computationResult) // true
-```
-
-For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functions/polynomials.kt).
-
-## Abstract entities (interfaces and abstract classes)
-
-```mermaid
-classDiagram
-   Polynomial <|-- ListPolynomial
-   Polynomial <|-- NumberedPolynomial
-   Polynomial <|-- LabeledPolynomial
-   
-   RationalFunction <|-- ListRationalFunction
-   RationalFunction <|-- NumberedRationalFunction
-   RationalFunction <|-- LabeledRationalFunction
-   
-   Ring <|-- PolynomialSpace
-   PolynomialSpace <|-- MultivariatePolynomialSpace
-   PolynomialSpace <|-- PolynomialSpaceOverRing
-   
-   Ring <|-- RationalFunctionSpace
-   RationalFunctionSpace <|-- MultivariateRationalFunctionSpace
-   RationalFunctionSpace <|-- RationalFunctionSpaceOverRing
-   RationalFunctionSpace <|-- RationalFunctionSpaceOverPolynomialSpace
-   RationalFunctionSpace <|-- PolynomialSpaceOfFractions
-   RationalFunctionSpaceOverPolynomialSpace <|-- MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace
-   MultivariateRationalFunctionSpace <|-- MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace
-   MultivariateRationalFunctionSpace <|-- MultivariatePolynomialSpaceOfFractions
-   PolynomialSpaceOfFractions <|-- MultivariatePolynomialSpaceOfFractions
-```
-
-There are implemented `Polynomial` and `RationalFunction` interfaces as abstractions of polynomials and rational
-functions respectively (although, there is not a lot of logic in them) and `PolynomialSpace`
-and `RationalFunctionSpace` (that implement `Ring` interface) as abstractions of polynomials' and rational functions'
-spaces respectively. More precisely, that means they allow to declare common logic of interaction with such objects and
-spaces:
-
-- `Polynomial` does not provide any logic. It is marker interface.
-- `RationalFunction` provides numerator and denominator of rational function and destructuring declaration for them.
-- `PolynomialSpace` provides all possible arithmetic interactions of integers, constants (of type `C`), and
-  polynomials (of type `P`) like addition, subtraction, multiplication, and some others and common properties like
-  degree of polynomial.
-- `RationalFunctionSpace` provides the same as `PolynomialSpace` but also for rational functions: all possible
-  arithmetic interactions of integers, constants (of type `C`), polynomials (of type `P`), and rational functions (of
-  type `R`) like addition, subtraction, multiplication, division (in some cases), and some others and common properties
-  like degree of polynomial.
-
-Then to add abstraction of similar behaviour with variables (in multivariate case) there are
-implemented `MultivariatePolynomialSpace` and `MultivariateRationalFunctionSpace`. They just include variables (of
-type `V`) in the interactions of the entities.
-
-Also, to remove boilerplates there were provided helping subinterfaces and abstract subclasses:
-
-- `PolynomialSpaceOverRing` allows to replace implementation of interactions of integers and constants with
-  implementations from provided ring over constants (of type `A: Ring<C>`).
-- `RationalFunctionSpaceOverRing` &mdash; the same but for `RationalFunctionSpace`.
-- `RationalFunctionSpaceOverPolynomialSpace` &mdash; the same but "the inheritance" includes interactions with
-  polynomials from provided `PolynomialSpace`.
-- `PolynomialSpaceOfFractions` is actually abstract subclass of `RationalFunctionSpace` that implements all fractions
-  boilerplates with provided (`protected`) constructor of rational functions by polynomial numerator and denominator.
-- `MultivariateRationalFunctionSpaceOverMultivariatePolynomialSpace` and `MultivariatePolynomialSpaceOfFractions`
-  &mdash; the same stories of operators inheritance and fractions boilerplates respectively but in multivariate case.
-
-## Utilities
-
-For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common
-utilities as:
-
-1. differentiation and anti-differentiation,
-2. substitution, invocation and functional representation.

docs/readme.md

@@ -1,14 +0,0 @@
-# Documentation
-
-* [Algebra](algebra.md): [context-based](contexts.md) operations on different primitives and structures.
-
-* [NDStructures](nd-structure.md)
-
-* [Linear algebra](linear.md): matrices, operations and linear equations solving. To be moved to separate module.
-  Currently, supports basic API and multiple library back-ends.
-
-* [Histograms](histograms.md): multidimensional histogram calculation and operations.
-
-* [Expressions](expressions.md)
-
-* Commons math integration

docs/templates/ARTIFACT-TEMPLATE.md

@@ -1,16 +0,0 @@
-## Artifact:
-
-The Maven coordinates of this project are `${group}:${name}:${version}`.
-
-**Gradle:**
-
-```kotlin
-repositories {
-    maven("https://repo.kotlin.link")
-    mavenCentral()
-}
-
-dependencies {
-    implementation("${group}:${name}:${version}")
-}
-```

docs/templates/README-TEMPLATE.md

@@ -1,109 +0,0 @@
-[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
-[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
-![Gradle build](https://github.com/SciProgCentre/kmath/workflows/Gradle%20build/badge.svg)
-[![Maven Central](https://img.shields.io/maven-central/v/space.kscience/kmath-core.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22space.kscience%22)
-[![Space](https://img.shields.io/badge/dynamic/xml?color=orange&label=Space&query=//metadata/versioning/latest&url=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fspace%2Fkscience%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
-
-# KMath
-
-Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
-analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
-architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
-experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
-
-[Documentation site](https://SciProgCentre.github.io/kmath/)
-
-## Publications and talks
-
-* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
-* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
-* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
-* [A talk at KotlinConf 2019 about using kotlin for science](https://youtu.be/LI_5TZ7tnOE?si=4LknX41gl_YeUbIe)
-* [A talk on architecture at Joker-2021 (in Russian)](https://youtu.be/1bZ2doHiRRM?si=9w953ro9yu98X_KJ)
-* [The same talk in English](https://youtu.be/yP5DIc2fVwQ?si=louZzQ1dcXV6gP10)
-* [A seminar on tensor API](https://youtu.be/0H99wUs0xTM?si=6c__04jrByFQtVpo)
-
-# Goal
-
-* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS, Native and
-  Wasm).
-* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
-* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
-
-## Non-goals
-
-* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
-* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
-* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
-* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
-  for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
-  experience for those, who want to work with specific types.
-
-## Features and stability
-
-KMath is a modular library. Different modules provide different features with different API stability guarantees. All
-core modules are released with the same version, but with different API change policy. The features are described in
-module definitions below. The module stability could have the following levels:
-
-* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
-  break any moment. You can still use it, but be sure to fix the specific version.
-* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
-  with `@UnstableKMathAPI` or other stability warning annotations.
-* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
-  versions, but not in patch versions. API is protected
-  with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
-* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
-
-## Modules
-
-${modules}
-
-## Multi-platform support
-
-KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
-[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
-are delegated to platform-specific implementations even if they could be provided in the common module for performance
-reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
-feedback are also welcome.
-
-## Performance
-
-Calculation of performance is one of the major goals of KMath in the future, but in some cases it is impossible to
-achieve both
-performance and flexibility.
-
-We expect to focus on creating a convenient universal API first and then work on increasing performance for specific
-cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
-native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
-better than SciPy.
-
-## Requirements
-
-KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend using GraalVM-CE or
-Oracle GraalVM for execution to get better performance.
-
-### Repositories
-
-Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/)
-repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
-[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
-be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
-
-```kotlin
-repositories {
-    maven("https://repo.kotlin.link")
-}
-
-dependencies {
-    api("${group}:kmath-core:$version")
-    // api("${group}:kmath-core-jvm:$version") for jvm-specific version
-}
-```
-
-## Contributing
-
-The project requires a lot of additional work. The most important thing we need is feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-marked
-with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-label.

examples/README.md

@@ -1,4 +0,0 @@
-# Module examples
-
-
-

examples/build.gradle.kts

@@ -1,77 +0,0 @@
-import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
-
-plugins {
-    kotlin("jvm")
-}
-
-repositories {
-    mavenCentral()
-    maven("https://repo.kotlin.link")
-    maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
-}
-
-val multikVersion: String by rootProject.extra
-
-dependencies {
-    implementation(project(":kmath-ast"))
-    implementation(project(":kmath-kotlingrad"))
-    implementation(project(":kmath-core"))
-    implementation(project(":kmath-coroutines"))
-    implementation(project(":kmath-commons"))
-    implementation(project(":kmath-complex"))
-    implementation(project(":kmath-functions"))
-    implementation(project(":kmath-optimization"))
-    implementation(project(":kmath-stat"))
-    implementation(project(":kmath-viktor"))
-    implementation(project(":kmath-dimensions"))
-    implementation(project(":kmath-ejml"))
-    implementation(project(":kmath-nd4j"))
-    implementation(project(":kmath-tensors"))
-    implementation(project(":kmath-symja"))
-    implementation(project(":kmath-for-real"))
-    //jafama
-    implementation(project(":kmath-jafama"))
-    //multik
-    implementation(project(":kmath-multik"))
-    implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
-
-    //datetime
-    implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")
-
-    implementation("org.nd4j:nd4j-native:1.0.0-beta7")
-
-//    uncomment if your system supports AVX2
-//    val os = System.getProperty("os.name")
-//
-//    if (System.getProperty("os.arch") in arrayOf("x86_64", "amd64")) when {
-//        os.startsWith("Windows") -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:windows-x86_64-avx2")
-//        os == "Linux" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:linux-x86_64-avx2")
-//        os == "Mac OS X" -> implementation("org.nd4j:nd4j-native:1.0.0-beta7:macosx-x86_64-avx2")
-//    } else
-    implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
-
-    implementation("org.slf4j:slf4j-simple:1.7.32")
-    // plotting
-    implementation("space.kscience:plotlykt-server:0.7.0")
-}
-
-kotlin {
-    jvmToolchain(11)
-    sourceSets.all {
-        languageSettings {
-            optIn("kotlin.contracts.ExperimentalContracts")
-            optIn("kotlin.ExperimentalUnsignedTypes")
-            optIn("space.kscience.kmath.UnstableKMathAPI")
-        }
-    }
-}
-
-tasks.withType<KotlinJvmCompile> {
-    compilerOptions {
-        freeCompilerArgs.addAll("-Xjvm-default=all", "-Xopt-in=kotlin.RequiresOptIn", "-Xlambdas=indy")
-    }
-}
-
-readme {
-    maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
-}

examples/notebooks/Naive classifier.ipynb

@@ -1,418 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "source": [
-    "%use kmath(0.3.1-dev-5)\n",
-    "%use plotly(0.5.0)\n",
-    "@file:DependsOn(\"space.kscience:kmath-commons:0.3.1-dev-5\")"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "lQbSB87rNAn9lV6poArVWW",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "//Uncomment to work in Jupyter classic or DataLore\n",
-    "//Plotly.jupyter.notebook()"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "0UP158hfccGgjQtHz0wAi6",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# The model\n",
-    "\n",
-    "Defining the input data format, the statistic abstraction and the statistic implementation based on a weighted sum of elements."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "class XYValues(val xValues: DoubleArray, val yValues: DoubleArray) {\n",
-    "    init {\n",
-    "        require(xValues.size == yValues.size)\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "fun interface XYStatistic {\n",
-    "    operator fun invoke(values: XYValues): Double\n",
-    "}\n",
-    "\n",
-    "class ConvolutionalXYStatistic(val weights: DoubleArray) : XYStatistic {\n",
-    "    override fun invoke(values: XYValues): Double {\n",
-    "        require(weights.size == values.yValues.size)\n",
-    "        val norm = values.yValues.sum()\n",
-    "        return values.yValues.zip(weights) { value, weight -> value * weight }.sum()/norm\n",
-    "    }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "Zhgz1Ui91PWz0meJiQpHol",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Generator\n",
-    "Generate sample data for parabolas and hyperbolas"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "fun generateParabolas(xValues: DoubleArray, a: Double, b: Double, c: Double): XYValues {\n",
-    "    val yValues = xValues.map { x -> a * x * x + b * x + c }.toDoubleArray()\n",
-    "    return XYValues(xValues, yValues)\n",
-    "}\n",
-    "\n",
-    "fun generateHyperbols(xValues: DoubleArray, gamma: Double, x0: Double, y0: Double): XYValues {\n",
-    "    val yValues = xValues.map { x -> y0 + gamma / (x - x0) }.toDoubleArray()\n",
-    "    return XYValues(xValues, yValues)\n",
-    "}"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "val xValues = (1.0..10.0).step(1.0).toDoubleArray()\n",
-    "\n",
-    "val xy = generateHyperbols(xValues, 1.0, 0.0, 0.0)\n",
-    "\n",
-    "Plotly.plot {\n",
-    "    scatter {\n",
-    "        this.x.doubles = xValues\n",
-    "        this.y.doubles = xy.yValues\n",
-    "    }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "ZE2atNvFzQsCvpAF8KK4ch",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Create a default statistic with uniform weights"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "val statistic = ConvolutionalXYStatistic(DoubleArray(xValues.size){1.0})\n",
-    "statistic(xy)"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "EA5HaydTddRKYrtAUwd29h",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "import kotlin.random.Random\n",
-    "\n",
-    "val random = Random(1288)\n",
-    "\n",
-    "val parabolas = buildList{\n",
-    "    repeat(500){\n",
-    "        add(\n",
-    "            generateParabolas(\n",
-    "                xValues, \n",
-    "                random.nextDouble(), \n",
-    "                random.nextDouble(), \n",
-    "                random.nextDouble()\n",
-    "            )\n",
-    "        )\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "val hyperbolas: List<XYValues> =  buildList{\n",
-    "    repeat(500){\n",
-    "        add(\n",
-    "            generateHyperbols(\n",
-    "                xValues, \n",
-    "                random.nextDouble()*10, \n",
-    "                random.nextDouble(), \n",
-    "                random.nextDouble()\n",
-    "            )\n",
-    "        )\n",
-    "    }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "t5t6IYmD7Q1ykeo9uijFfQ",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "Plotly.plot { \n",
-    "    scatter { \n",
-    "        x.doubles = xValues\n",
-    "        y.doubles = parabolas[257].yValues\n",
-    "    }\n",
-    "    scatter { \n",
-    "        x.doubles = xValues\n",
-    "        y.doubles = hyperbolas[252].yValues\n",
-    "    }\n",
-    " }"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "oXB8lmju7YVYjMRXITKnhO",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "Plotly.plot { \n",
-    "    histogram { \n",
-    "        name = \"parabolae\"\n",
-    "        x.numbers = parabolas.map { statistic(it) }\n",
-    "    }\n",
-    "    histogram { \n",
-    "        name = \"hyperbolae\"\n",
-    "        x.numbers = hyperbolas.map { statistic(it) }\n",
-    "    }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "8EIIecUZrt2NNrOkhxG5P0",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "val lossFunction: (XYStatistic) -> Double = { statistic ->\n",
-    "    - abs(parabolas.sumOf { statistic(it) } - hyperbolas.sumOf { statistic(it) })\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "h7UmglJW5zXkAfKHK40oIL",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Using commons-math optimizer to optimize weights"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "import org.apache.commons.math3.optim.*\n",
-    "import org.apache.commons.math3.optim.nonlinear.scalar.*\n",
-    "import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.*\n",
-    "\n",
-    "val optimizer = SimplexOptimizer(1e-1, Double.MAX_VALUE)\n",
-    "\n",
-    "val result = optimizer.optimize(\n",
-    "    ObjectiveFunction { point ->\n",
-    "        lossFunction(ConvolutionalXYStatistic(point))\n",
-    "    },\n",
-    "    NelderMeadSimplex(xValues.size),\n",
-    "    InitialGuess(DoubleArray(xValues.size){ 1.0 }),\n",
-    "    GoalType.MINIMIZE,\n",
-    "    MaxEval(100000)\n",
-    ")"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "0EG3K4aCUciMlgGQKPvJ57",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Print resulting weights of optimization"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "result.point"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "LelUlY0ZSlJEO9yC6SLk5B",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "Plotly.plot { \n",
-    "    scatter { \n",
-    "        y.doubles = result.point\n",
-    "     }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "AuFOq5t9KpOIkGrOLsVXNf",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# The resulting statistic distribution"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "val resultStatistic = ConvolutionalXYStatistic(result.point)\n",
-    "Plotly.plot { \n",
-    "    histogram { \n",
-    "        name = \"parabolae\"\n",
-    "        x.numbers = parabolas.map { resultStatistic(it) }\n",
-    "    }\n",
-    "    histogram { \n",
-    "        name = \"hyperbolae\"\n",
-    "        x.numbers = hyperbolas.map { resultStatistic(it) }\n",
-    "    }\n",
-    "}"
-   ],
-   "execution_count": null,
-   "outputs": [],
-   "metadata": {
-    "datalore": {
-     "node_id": "zvmq42DRdM5mZ3SpzviHwI",
-     "type": "CODE",
-     "hide_input_from_viewers": false,
-     "hide_output_from_viewers": false
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Kotlin",
-   "language": "kotlin",
-   "name": "kotlin"
-  },
-  "datalore": {
-   "version": 1,
-   "computation_mode": "JUPYTER",
-   "package_manager": "pip",
-   "base_environment": "default",
-   "packages": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt

@@ -1,26 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.ast
-
-import space.kscience.kmath.ast.rendering.FeaturedMathRendererWithPostProcess
-import space.kscience.kmath.ast.rendering.LatexSyntaxRenderer
-import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer
-import space.kscience.kmath.ast.rendering.renderWithStringBuilder
-
-fun main() {
-    val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(-12)".parseMath()
-    val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
-    println("MathSyntax:")
-    println(syntax)
-    println()
-    val latex = LatexSyntaxRenderer.renderWithStringBuilder(syntax)
-    println("LaTeX:")
-    println(latex)
-    println()
-    val mathML = MathMLSyntaxRenderer.renderWithStringBuilder(syntax)
-    println("MathML:")
-    println(mathML)
-}

examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt

@@ -1,26 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.ast
-
-import space.kscience.kmath.asm.compileToExpression
-import space.kscience.kmath.expressions.MstExtendedField
-import space.kscience.kmath.expressions.Symbol.Companion.x
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
-
-fun main() {
-    val expr = MstExtendedField {
-        x * 2.0 + number(2.0) / x - number(16.0) + asinh(x) / sin(x)
-    }.compileToExpression(Float64Field)
-
-    val m = DoubleArray(expr.indexer.symbols.size)
-    val xIdx = expr.indexer.indexOf(x)
-
-    repeat(10000000) {
-        m[xIdx] = 1.0
-        expr(m)
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt

@@ -1,27 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.ast
-
-import space.kscience.kmath.expressions.Symbol.Companion.x
-import space.kscience.kmath.expressions.derivative
-import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.expressions.toExpression
-import space.kscience.kmath.kotlingrad.toKotlingradExpression
-import space.kscience.kmath.operations.Float64Field
-
-/**
- * In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Kotlin∇, and the
- * derivation result is compared with valid derivative in a certain point.
- */
-fun main() {
-    val actualDerivative = "x^2-4*x-44"
-        .parseMath()
-        .toKotlingradExpression(Float64Field)
-        .derivative(x)
-
-    val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
-    check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
-}

examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt

@@ -1,27 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.ast
-
-import space.kscience.kmath.expressions.Symbol.Companion.x
-import space.kscience.kmath.expressions.derivative
-import space.kscience.kmath.expressions.invoke
-import space.kscience.kmath.expressions.toExpression
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.symja.toSymjaExpression
-
-/**
- * In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Symja, and the
- * derivation result is compared with valid derivative in a certain point.
- */
-fun main() {
-    val actualDerivative = "x^2-4*x-44"
-        .parseMath()
-        .toSymjaExpression(Float64Field)
-        .derivative(x)
-
-    val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
-    check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
-}

examples/src/main/kotlin/space/kscience/kmath/expressions/autodiff.kt

@@ -1,92 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.expressions
-
-import space.kscience.kmath.UnstableKMathAPI
-
-// Only kmath-core is needed.
-
-// Let's declare some variables
-val x by symbol
-val y by symbol
-val z by symbol
-
-@OptIn(UnstableKMathAPI::class)
-fun main() {
-    // Let's define some random expression.
-    val someExpression = Double.autodiff.differentiate {
-        // We bind variables `x` and `y` to the builder scope,
-        val x = bindSymbol(x)
-        val y = bindSymbol(y)
-
-        // Then we use the bindings to define expression `xy + x + y - 1`
-        x * y + x + y - 1
-    }
-
-    // Then we can evaluate it at any point ((-1, -1) in the case):
-    println(someExpression(x to -1.0, y to -1.0))
-    // >>> -2.0
-
-    // We can also construct its partial derivatives:
-    val dxExpression = someExpression.derivative(x) // ∂/∂x. Must be `y+1`
-    val dyExpression = someExpression.derivative(y) // ∂/∂y. Must be `x+1`
-    val dxdxExpression = someExpression.derivative(x, x) // ∂^2/∂x^2. Must be `0`
-
-    // We can evaluate them as well
-    println(dxExpression(x to 57.0, y to 6.0))
-    // >>> 7.0
-    println(dyExpression(x to -1.0, y to 179.0))
-    // >>> 0.0
-    println(dxdxExpression(x to 239.0, y to 30.0))
-    // >>> 0.0
-
-    // You can also provide extra arguments that obviously won't affect the result:
-    println(dxExpression(x to 57.0, y to 6.0, z to 42.0))
-    // >>> 7.0
-    println(dyExpression(x to -1.0, y to 179.0, z to 0.0))
-    // >>> 0.0
-    println(dxdxExpression(x to 239.0, y to 30.0, z to 100_000.0))
-    // >>> 0.0
-
-    // But in case you forgot to specify bound symbol's value, exception is thrown:
-    println(runCatching { someExpression(z to 4.0) })
-    // >>> Failure(java.lang.IllegalStateException: Symbol 'x' is not supported in ...)
-
-    // The reason is that the expression is evaluated lazily,
-    // and each `bindSymbol` operation actually substitutes the provided symbol with the corresponding value.
-
-    // For example, let there be an expression
-    val simpleExpression = Double.autodiff.differentiate {
-        val x = bindSymbol(x)
-        x pow 2
-    }
-    // When you evaluate it via
-    simpleExpression(x to 1.0, y to 57.0, z to 179.0)
-    // lambda above has the context of map `{x: 1.0, y: 57.0, z: 179.0}`.
-    // When x is bound, you can think of it as substitution `x -> 1.0`.
-    // Other values are unused which does not make any problem to us.
-    // But in the case the corresponding value is not provided,
-    // we cannot bind the variable. Thus, exception is thrown.
-
-    // There is also a function `bindSymbolOrNull` that fixes the problem:
-    val fixedExpression = Double.autodiff.differentiate {
-        val x = bindSymbolOrNull(x) ?: const(8.0)
-        x pow -2
-    }
-    println(fixedExpression())
-    // >>> 0.015625
-    // It works!
-
-    // The expression provides a bunch of operations:
-    // 1. Constant bindings (via `const` and `number`).
-    // 2. Variable bindings (via `bindVariable`, `bindVariableOrNull`).
-    // 3. Arithmetic operations (via `+`, `-`, `*`, and `-`).
-    // 4. Exponentiation (via `pow` or `power`).
-    // 5. `exp` and `ln`.
-    // 6. Trigonometrical functions (`sin`, `cos`, `tan`, `cot`).
-    // 7. Inverse trigonometrical functions (`asin`, `acos`, `atan`, `acot`).
-    // 8. Hyperbolic functions and inverse hyperbolic functions.
-}

examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt

@@ -1,113 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.fit
-
-import kotlinx.html.br
-import kotlinx.html.h3
-import space.kscience.kmath.commons.optimization.CMOptimizer
-import space.kscience.kmath.distributions.NormalDistribution
-import space.kscience.kmath.expressions.autodiff
-import space.kscience.kmath.expressions.symbol
-import space.kscience.kmath.operations.asIterable
-import space.kscience.kmath.operations.toList
-import space.kscience.kmath.optimization.*
-import space.kscience.kmath.random.RandomGenerator
-import space.kscience.kmath.real.DoubleVector
-import space.kscience.kmath.real.map
-import space.kscience.kmath.real.step
-import space.kscience.kmath.stat.chiSquaredExpression
-import space.kscience.plotly.*
-import space.kscience.plotly.models.ScatterMode
-import space.kscience.plotly.models.TraceValues
-import kotlin.math.pow
-import kotlin.math.sqrt
-
-// Forward declaration of symbols that will be used in expressions.
-private val a by symbol
-private val b by symbol
-private val c by symbol
-
-/**
- * Shortcut to use buffers in plotly
- */
-operator fun TraceValues.invoke(vector: DoubleVector) {
-    numbers = vector.asIterable()
-}
-
-/**
- * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
- */
-suspend fun main() {
-    //A generator for a normally distributed values
-    val generator = NormalDistribution(0.0, 1.0)
-
-    //A chain/flow of random values with the given seed
-    val chain = generator.sample(RandomGenerator.default(112667))
-
-
-    //Create a uniformly distributed x values like numpy.arrange
-    val x = 1.0..100.0 step 1.0
-
-
-    //Perform an operation on each x value (much more effective, than numpy)
-    val y = x.map { it ->
-        val value = it.pow(2) + it + 1
-        value + chain.next() * sqrt(value)
-    }
-    // this will also work, but less effective:
-    // val y = x.pow(2)+ x + 1 + chain.nextDouble()
-
-    // create same errors for all xs
-    val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
-
-    // compute differentiable chi^2 sum for given model ax^2 + bx + c
-    val chi2 = Double.autodiff.chiSquaredExpression(x, y, yErr) { arg ->
-        //bind variables to autodiff context
-        val a = bindSymbol(a)
-        val b = bindSymbol(b)
-        //Include default value for c if it is not provided as a parameter
-        val c = bindSymbolOrNull(c) ?: one
-        a * arg.pow(2) + b * arg + c
-    }
-
-    //minimize the chi^2 in given starting point. Derivatives are not required, they are already included.
-    val result = chi2.optimizeWith(
-        CMOptimizer,
-        mapOf(a to 1.5, b to 0.9, c to 1.0),
-    ) {
-        FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
-    }
-
-    //display a page with plot and numerical results
-    val page = Plotly.page {
-        plot {
-            scatter {
-                mode = ScatterMode.markers
-                x(x)
-                y(y)
-                error_y {
-                    array = yErr.toList()
-                }
-                name = "data"
-            }
-            scatter {
-                mode = ScatterMode.lines
-                x(x)
-                y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
-                name = "fit"
-            }
-        }
-        br()
-        h3 {
-            +"Fit result: $result"
-        }
-        h3 {
-            +"Chi2/dof = ${result.resultValue / (x.size - 3)}"
-        }
-    }
-
-    page.makeFile()
-}

examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt

@@ -1,112 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.fit
-
-import kotlinx.html.br
-import kotlinx.html.h3
-import space.kscience.attributes.Attributes
-import space.kscience.kmath.data.XYErrorColumnarData
-import space.kscience.kmath.distributions.NormalDistribution
-import space.kscience.kmath.expressions.Symbol
-import space.kscience.kmath.expressions.autodiff
-import space.kscience.kmath.expressions.binding
-import space.kscience.kmath.expressions.symbol
-import space.kscience.kmath.operations.asIterable
-import space.kscience.kmath.operations.toList
-import space.kscience.kmath.optimization.*
-import space.kscience.kmath.random.RandomGenerator
-import space.kscience.kmath.real.map
-import space.kscience.kmath.real.step
-import space.kscience.plotly.*
-import space.kscience.plotly.models.ScatterMode
-import kotlin.math.abs
-import kotlin.math.pow
-import kotlin.math.sqrt
-
-// Forward declaration of symbols that will be used in expressions.
-private val a by symbol
-private val b by symbol
-private val c by symbol
-private val d by symbol
-private val e by symbol
-
-
-/**
- * Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
- */
-suspend fun main() {
-    //A generator for a normally distributed values
-    val generator = NormalDistribution(0.0, 1.0)
-
-    //A chain/flow of random values with the given seed
-    val chain = generator.sample(RandomGenerator.default(112667))
-
-
-    //Create a uniformly distributed x values like numpy.arrange
-    val x = 1.0..100.0 step 1.0
-
-
-    //Perform an operation on each x value (much more effective, than numpy)
-    val y = x.map { it ->
-        val value = it.pow(2) + it + 1
-        value + chain.next() * sqrt(value)
-    }
-    // this will also work, but less effective:
-    // val y = x.pow(2)+ x + 1 + chain.nextDouble()
-
-    // create same errors for all xs
-    val yErr = y.map { sqrt(abs(it)) }
-    require(yErr.asIterable().all { it > 0 }) { "All errors must be strictly positive" }
-
-    val result = XYErrorColumnarData.of(x, y, yErr).fitWith(
-        QowOptimizer,
-        Double.autodiff,
-        mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
-        attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
-    ) { arg ->
-        //bind variables to autodiff context
-        val a by binding
-        val b by binding
-        //Include default value for c if it is not provided as a parameter
-        val c = bindSymbolOrNull(c) ?: one
-        val d by binding
-        val e by binding
-
-        a * arg.pow(2) + b * arg + c + d * arg.pow(3) + e / arg
-    }
-
-    println("Resulting chi2/dof: ${result.chiSquaredOrNull}/${result.dof}")
-
-    //display a page with plot and numerical results
-    val page = Plotly.page {
-        plot {
-            scatter {
-                mode = ScatterMode.markers
-                x(x)
-                y(y)
-                error_y {
-                    array = yErr.toList()
-                }
-                name = "data"
-            }
-            scatter {
-                mode = ScatterMode.lines
-                x(x)
-                y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
-                name = "fit"
-            }
-        }
-        br()
-        h3 {
-            +"Fit result: ${result.result}"
-        }
-        h3 {
-            +"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"
-        }
-    }
-
-    page.makeFile()
-}

examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt

@@ -1,36 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.functions
-
-import space.kscience.kmath.complex.Complex
-import space.kscience.kmath.complex.ComplexField
-import space.kscience.kmath.complex.ComplexField.div
-import space.kscience.kmath.complex.ComplexField.minus
-import space.kscience.kmath.complex.algebra
-import space.kscience.kmath.integration.gaussIntegrator
-import space.kscience.kmath.integration.integrate
-import space.kscience.kmath.integration.value
-import space.kscience.kmath.operations.Float64Field
-import kotlin.math.pow
-
-fun main() {
-    //Define a function
-    val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
-
-    //get the result of the integration
-    val result = Float64Field.gaussIntegrator.integrate(0.0..10.0, function = function)
-
-    //the value is nullable because in some cases the integration could not succeed
-    println(result.value)
-
-
-    repeat(100000) {
-        Complex.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
-//            sin(1 / x) + i * cos(1 / x)
-            1 / x - ComplexField.i / x
-        }.value
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt

@@ -1,51 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.functions
-
-import space.kscience.kmath.interpolation.SplineInterpolator
-import space.kscience.kmath.interpolation.interpolatePolynomials
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.plotly.Plotly
-import space.kscience.plotly.UnstablePlotlyAPI
-import space.kscience.plotly.makeFile
-import space.kscience.plotly.models.functionXY
-import space.kscience.plotly.scatter
-import kotlin.math.PI
-import kotlin.math.sin
-
-@OptIn(UnstablePlotlyAPI::class)
-fun main() {
-    val data = (0..10).map {
-        val x = it.toDouble() / 5 * PI
-        x to sin(x)
-    }
-
-    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
-
-    val function = polynomial.asFunction(Float64Field, 0.0)
-
-    val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
-        data.map { it.first }.toDoubleArray(),
-        data.map { it.second }.toDoubleArray()
-    )
-
-    Plotly.plot {
-        scatter {
-            name = "interpolated"
-            x.numbers = data.map { it.first }
-            y.numbers = x.doubles.map { function(it) }
-        }
-        scatter {
-            name = "original"
-            functionXY(0.0..(2 * PI), 0.1) { sin(it) }
-        }
-        scatter {
-            name = "cm"
-            x.numbers = data.map { it.first }
-            y.numbers = x.doubles.map { cmInterpolate.value(it) }
-        }
-    }.makeFile()
-}

examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt

@@ -1,45 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.functions
-
-import space.kscience.kmath.interpolation.interpolatePolynomials
-import space.kscience.kmath.interpolation.splineInterpolator
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.real.map
-import space.kscience.kmath.real.step
-import space.kscience.plotly.Plotly
-import space.kscience.plotly.UnstablePlotlyAPI
-import space.kscience.plotly.makeFile
-import space.kscience.plotly.models.functionXY
-import space.kscience.plotly.scatter
-
-@OptIn(UnstablePlotlyAPI::class)
-fun main() {
-    val function: Function1D<Double> = { x ->
-        if (x in 30.0..50.0) {
-            1.0
-        } else {
-            0.0
-        }
-    }
-    val xs = 0.0..100.0 step 0.5
-    val ys = xs.map(function)
-
-    val polynomial: PiecewisePolynomial<Double> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
-
-    val polyFunction = polynomial.asFunction(Float64Field, 0.0)
-
-    Plotly.plot {
-        scatter {
-            name = "interpolated"
-            functionXY(25.0..55.0, 0.1) { polyFunction(it) }
-        }
-        scatter {
-            name = "original"
-            functionXY(25.0..55.0, 0.1) { function(it) }
-        }
-    }.makeFile()
-}

examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt

@@ -1,32 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.functions
-
-import space.kscience.kmath.integration.gaussIntegrator
-import space.kscience.kmath.integration.integrate
-import space.kscience.kmath.integration.value
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.structureND
-import space.kscience.kmath.nd.withNdAlgebra
-import space.kscience.kmath.operations.algebra
-import kotlin.math.pow
-
-fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
-
-    //Produce a diagonal StructureND
-    fun diagonal(v: Double) = structureND { (i, j) ->
-        if (i == j) v else 0.0
-    }
-
-    //Define a function in a nd space
-    val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
-
-    //get the result of the integration
-    val result = gaussIntegrator.integrate(0.0..10.0, function = function)
-
-    //the value is nullable because in some cases the integration could not succeed
-    println(result.value)
-}

examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt

@@ -1,15 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.jafama
-
-import space.kscience.kmath.operations.invoke
-
-fun main() {
-    val a = 2.0
-    val b = StrictJafamaDoubleField { exp(a) }
-    println(JafamaDoubleField { b + a })
-    println(StrictJafamaDoubleField { ln(b) })
-}

examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt

@@ -1,31 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.linear
-
-import kotlin.random.Random
-import kotlin.time.measureTime
-
-fun main() = with(Float64ParallelLinearSpace) {
-    val random = Random(12224)
-    val dim = 1000
-
-    //creating invertible matrix
-    val matrix1 = buildMatrix(dim, dim) { i, j ->
-        if (i <= j) random.nextDouble() else 0.0
-    }
-    val matrix2 = buildMatrix(dim, dim) { i, j ->
-        if (i <= j) random.nextDouble() else 0.0
-    }
-
-    val time = measureTime {
-        repeat(30) {
-            matrix1 dot matrix2
-        }
-    }
-
-    println(time)
-
-}

examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt

@@ -1,33 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.linear
-
-import space.kscience.kmath.real.*
-import space.kscience.kmath.structures.Float64Buffer
-
-fun main() {
-    val x0 = DoubleVector(0.0, 0.0, 0.0)
-    val sigma = DoubleVector(1.0, 1.0, 1.0)
-
-    val gaussian: (Point<Double>) -> Double = { x ->
-        require(x.size == x0.size)
-        kotlin.math.exp(-((x - x0) / sigma).square().sum())
-    }
-
-    fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
-        require(x.size == x0.size)
-        return Float64Buffer(x.size) { i ->
-            val h = sigma[i] / 5
-            val dVector = Float64Buffer(x.size) { if (it == i) h else 0.0 }
-            val f1 = this(x + dVector / 2)
-            val f0 = this(x - dVector / 2)
-            (f1 - f0) / h
-        }
-    }
-
-    println(gaussian.grad(x0))
-
-}

examples/src/main/kotlin/space/kscience/kmath/linear/lupPerformance.kt

@@ -1,27 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.linear
-
-import kotlin.random.Random
-import kotlin.time.measureTime
-
-fun main(): Unit = with(Float64LinearSpace) {
-    val random = Random(1224)
-    val dim = 500
-
-    //creating invertible matrix
-    val u = buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
-    val l = buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
-    val matrix = l dot u
-
-    val time = measureTime {
-        repeat(20) {
-            lupSolver().inverse(matrix)
-        }
-    }
-
-    println(time)
-}

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

@@ -1,11 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.operations
-
-fun main() {
-    val res = BigIntField { number(1) * 2 }
-    println("bigint:$res")
-}

examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt

@@ -1,40 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.operations
-
-import space.kscience.kmath.complex.Complex
-import space.kscience.kmath.complex.algebra
-import space.kscience.kmath.complex.ndAlgebra
-import space.kscience.kmath.nd.BufferND
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.structureND
-
-fun main() = Complex.algebra {
-    val complex = 2 + 2 * i
-    println(complex * 8 - 5 * i)
-
-    //flat buffer
-    val buffer = with(bufferAlgebra) {
-        buffer(8) { Complex(it, -it) }.map { Complex(it.im, it.re) }
-    }
-    println(buffer)
-
-    // 2d element
-    val element: BufferND<Complex> = ndAlgebra.structureND(2, 2) { (i, j) ->
-        Complex(i - j, i + j)
-    }
-    println(element)
-
-    // 1d element operation
-    val result: StructureND<Complex> = ndAlgebra {
-        val a = structureND(8) { (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/space/kscience/kmath/operations/mixedNDOperations.kt

@@ -1,30 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.operations
-
-import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.linear.matrix
-import space.kscience.kmath.nd.Float64BufferND
-import space.kscience.kmath.nd.Structure2D
-import space.kscience.kmath.nd.mutableStructureND
-import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.viktor.viktorAlgebra
-import kotlin.collections.component1
-import kotlin.collections.component2
-
-fun main() {
-    val viktorStructure = Float64Field.viktorAlgebra.mutableStructureND(2, 2) { (i, j) ->
-        if (i == j) 2.0 else 0.0
-    }
-
-    val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
-
-    val res: Float64BufferND = Float64Field.ndAlgebra {
-        exp(viktorStructure) + 2.0 * cmMatrix
-    }
-
-    println(res)
-}

examples/src/main/kotlin/space/kscience/kmath/series/DateTimeSeries.kt

@@ -1,17 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.series
-
-import kotlinx.datetime.Instant
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.operations.bufferAlgebra
-import kotlin.time.Duration
-
-fun SeriesAlgebra.Companion.time(zero: Instant, step: Duration) = MonotonicSeriesAlgebra(
-    bufferAlgebra = Double.algebra.bufferAlgebra,
-    offsetToLabel = { zero + step * it },
-    labelToOffset = { (it - zero) / step }
-)

examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt

@@ -1,64 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.series
-
-
-import kotlinx.html.h1
-import kotlinx.html.p
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.operations.bufferAlgebra
-import space.kscience.kmath.operations.toList
-import space.kscience.kmath.stat.KMComparisonResult
-import space.kscience.kmath.stat.ksComparisonStatistic
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.slice
-import space.kscience.plotly.*
-import kotlin.math.PI
-
-fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebra()
-
-
-fun main() = with(Double.seriesAlgebra()) {
-
-
-    fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {
-        scatter {
-            this.name = name
-            x.numbers = buffer.labels
-            y.numbers = buffer.toList()
-        }
-    }
-
-
-    val s1 = series(100) { sin(2 * PI * it / 100) + 1.0 }
-
-    val s2 = s1.slice(20..50).moveTo(40)
-
-    val s3: Buffer<Double> = s1.zip(s2) { l, r -> l + r } //s1 + s2
-    val s4 = s3.map { ln(it) }
-
-    val kmTest: KMComparisonResult<Double> = ksComparisonStatistic(s1, s2)
-
-    Plotly.page {
-        h1 { +"This is my plot" }
-        p {
-            +"Kolmogorov-smirnov test for s1 and s2: ${kmTest.value}"
-        }
-        plot {
-            plotSeries("s1", s1)
-            plotSeries("s2", s2)
-            plotSeries("s3", s3)
-            plotSeries("s4", s4)
-            layout {
-                xaxis {
-                    range(0.0..100.0)
-                }
-            }
-        }
-
-    }.makeFile()
-
-}

examples/src/main/kotlin/space/kscience/kmath/series/seriesBuilder.kt

@@ -1,50 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.series
-
-
-import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.Float64Buffer
-import space.kscience.kmath.structures.asBuffer
-import space.kscience.kmath.structures.toDoubleArray
-import space.kscience.plotly.*
-import space.kscience.plotly.models.Scatter
-import space.kscience.plotly.models.ScatterMode
-import kotlin.random.Random
-
-fun main(): Unit = with(Double.seriesAlgebra()) {
-
-    val random = Random(1234)
-
-    val arrayOfRandoms = DoubleArray(20) { random.nextDouble() }
-
-    val series1: Float64Buffer = arrayOfRandoms.asBuffer()
-    val series2: Series<Double> = series1.moveBy(3)
-
-    val res = series2 - series1
-
-    println(res.size)
-
-    println(res)
-
-    fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
-        scatter {
-            this.name = name
-            x.numbers = buffer.offsetIndices
-            y.doubles = buffer.toDoubleArray()
-            block()
-        }
-    }
-
-    Plotly.plot {
-        series("series1", series1)
-        series("series2", series2)
-        series("dif", res) {
-            mode = ScatterMode.lines
-            line.color("magenta")
-        }
-    }.makeFile(resourceLocation = ResourceLocation.REMOTE)
-}

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt

@@ -1,72 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.stat
-
-import kotlinx.coroutines.Dispatchers
-import kotlinx.coroutines.async
-import kotlinx.coroutines.runBlocking
-import org.apache.commons.rng.sampling.distribution.BoxMullerNormalizedGaussianSampler
-import org.apache.commons.rng.simple.RandomSource
-import space.kscience.kmath.random.RandomGenerator
-import space.kscience.kmath.samplers.GaussianSampler
-import java.time.Duration
-import java.time.Instant
-import org.apache.commons.rng.sampling.distribution.GaussianSampler as CMGaussianSampler
-
-private suspend fun runKMathChained(): Duration {
-    val generator = RandomGenerator.fromSource(RandomSource.MT, 123L)
-    val normal = GaussianSampler(7.0, 2.0)
-    val chain = normal.sample(generator)
-    val startTime = Instant.now()
-    var sum = 0.0
-
-    repeat(10000001) { counter ->
-        sum += chain.next()
-
-        if (counter % 100000 == 0) {
-            val duration = Duration.between(startTime, Instant.now())
-            val meanValue = sum / counter
-            println("Chain sampler completed $counter elements in $duration: $meanValue")
-        }
-    }
-
-    return Duration.between(startTime, Instant.now())
-}
-
-private fun runCMDirect(): Duration {
-    val rng = RandomSource.create(RandomSource.MT, 123L)
-
-    val sampler = CMGaussianSampler.of(
-        BoxMullerNormalizedGaussianSampler.of(rng),
-        7.0,
-        2.0
-    )
-
-    val startTime = Instant.now()
-    var sum = 0.0
-
-    repeat(10000001) { counter ->
-        sum += sampler.sample()
-
-        if (counter % 100000 == 0) {
-            val duration = Duration.between(startTime, Instant.now())
-            val meanValue = sum / counter
-            println("Direct sampler completed $counter elements in $duration: $meanValue")
-        }
-    }
-
-    return Duration.between(startTime, Instant.now())
-}
-
-/**
- * Comparing chain sampling performance with direct sampling performance
- */
-fun main(): Unit = runBlocking(Dispatchers.Default) {
-    val directJob = async { runCMDirect() }
-    val chainJob = async { runKMathChained() }
-    println("KMath Chained: ${chainJob.await()}")
-    println("Apache Direct: ${directJob.await()}")
-}

examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt

@@ -1,39 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.stat
-
-import kotlinx.coroutines.runBlocking
-import space.kscience.kmath.chains.Chain
-import space.kscience.kmath.chains.combineWithState
-import space.kscience.kmath.distributions.NormalDistribution
-import space.kscience.kmath.random.RandomGenerator
-
-private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
-
-/**
- * Averaging.
- */
-private fun Chain<Double>.mean(): Chain<Double> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
-    val next = chain.next()
-    num++
-    value += next
-    return@combineWithState value / num
-}
-
-
-fun main() {
-    val normal = NormalDistribution(0.0, 2.0)
-    val chain = normal.sample(RandomGenerator.default).mean()
-
-    runBlocking {
-        repeat(10001) { counter ->
-            val mean = chain.next()
-            if (counter % 1000 == 0) {
-                println("[$counter] Average value is $mean")
-            }
-        }
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt

@@ -1,66 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-@file:Suppress("unused")
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.complex.*
-import space.kscience.kmath.linear.transposed
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.nd.structureND
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
-import kotlin.system.measureTimeMillis
-
-fun main() {
-    val dim = 1000
-    val n = 1000
-
-    val realField = Float64Field.ndAlgebra(dim, dim)
-    val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
-
-    val realTime = measureTimeMillis {
-        realField {
-            var res: StructureND<Double> = one
-            repeat(n) {
-                res += 1.0
-            }
-        }
-    }
-
-    println("Real addition completed in $realTime millis")
-
-    val complexTime = measureTimeMillis {
-        complexField {
-            var res: StructureND<Complex> = one
-            repeat(n) {
-                res += 1.0
-            }
-        }
-    }
-
-    println("Complex addition completed in $complexTime millis")
-}
-
-fun complexExample() {
-    //Create a context for 2-d structure with complex values
-    ComplexField {
-        withNdAlgebra(4, 8) {
-            //a constant real-valued structure
-            val x = one * 2.5
-            operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)
-            //a structure generator specific to this context
-            val matrix = structureND { (k, l) -> k + l * i }
-            //Perform sum
-            val sum = matrix + x + 1.0
-
-            //Represent the sum as 2d-structure and transpose
-            sum.as2D().transposed()
-        }
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt

@@ -1,92 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import kotlinx.coroutines.DelicateCoroutinesApi
-import kotlinx.coroutines.GlobalScope
-import org.nd4j.linalg.factory.Nd4j
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.nd4j.nd4j
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.viktor.ViktorFieldND
-import kotlin.contracts.InvocationKind
-import kotlin.contracts.contract
-import kotlin.system.measureTimeMillis
-
-internal inline fun measureAndPrint(title: String, block: () -> Unit) {
-    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
-    val time = measureTimeMillis(block)
-    println("$title completed in $time millis")
-}
-
-@OptIn(DelicateCoroutinesApi::class)
-fun main() {
-    // initializing Nd4j
-    Nd4j.zeros(0)
-    val dim = 1000
-    val n = 1000
-    val shape = ShapeND(dim, dim)
-
-
-    // specialized nd-field for Double. It works as generic Double field as well.
-    val doubleField = Float64Field.ndAlgebra
-    //A generic field. It should be used for objects, not primitives.
-    val genericField = BufferedFieldOpsND(Float64Field)
-    // Nd4j specialized field.
-    val nd4jField = Float64Field.nd4j
-    //viktor field
-    val viktorField = ViktorFieldND(dim, dim)
-    //parallel processing based on Java Streams
-    val parallelField = Float64Field.ndStreaming(dim, dim)
-
-    measureAndPrint("Boxing addition") {
-        genericField {
-            var res: StructureND<Double> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Specialized addition") {
-        doubleField {
-            var res: StructureND<Double> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Nd4j specialized addition") {
-        nd4jField {
-            var res: StructureND<Double> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Viktor addition") {
-        viktorField {
-            var res: StructureND<Double> = one
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Parallel stream addition") {
-        parallelField {
-            var res: StructureND<Double> = one
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Lazy addition") {
-        val res = doubleField.one(shape).mapAsync(GlobalScope) {
-            var c = 0.0
-            repeat(n) {
-                c += 1.0
-            }
-            c
-        }
-
-        res.elements().forEach { it.second }
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt

@@ -1,127 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.PerformancePitfall
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.ExtendedField
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.NumbersAddOps
-import java.util.*
-import java.util.stream.IntStream
-
-/**
- * A demonstration implementation of NDField over Real using Java [java.util.stream.DoubleStream] for parallel
- * execution.
- */
-class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
-    NumbersAddOps<StructureND<Double>>,
-    ExtendedField<StructureND<Double>> {
-
-    private val strides = ColumnStrides(shape)
-    override val elementAlgebra: Float64Field get() = Float64Field
-    override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
-    override val one: BufferND<Double> by lazy { structureND(shape) { one } }
-
-    override fun number(value: Number): BufferND<Double> {
-        val d = value.toDouble() // minimize conversions
-        return structureND(shape) { d }
-    }
-
-    @OptIn(PerformancePitfall::class)
-    private val StructureND<Double>.buffer: Float64Buffer
-        get() = when {
-            shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
-                this@StreamDoubleFieldND.shape,
-                shape
-            )
-
-            this is BufferND && indices == this@StreamDoubleFieldND.strides -> this.buffer as Float64Buffer
-            else -> Float64Buffer(strides.linearSize) { offset -> get(strides.index(offset)) }
-        }
-
-    override fun structureND(shape: ShapeND, initializer: Float64Field.(IntArray) -> Double): BufferND<Double> {
-        val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            val index = strides.index(offset)
-            Float64Field.initializer(index)
-        }.toArray()
-
-        return BufferND(strides, array.asBuffer())
-    }
-
-    override fun mutableStructureND(
-        shape: ShapeND,
-        initializer: DoubleField.(IntArray) -> Double,
-    ): MutableBufferND<Double> {
-        val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            val index = strides.index(offset)
-            DoubleField.initializer(index)
-        }.toArray()
-
-        return MutableBufferND(strides, array.asBuffer())
-    }
-
-    @OptIn(PerformancePitfall::class)
-    override fun StructureND<Double>.map(
-        transform: Float64Field.(Double) -> Double,
-    ): BufferND<Double> {
-        val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
-        return BufferND(strides, array.asBuffer())
-    }
-
-    @OptIn(PerformancePitfall::class)
-    override fun StructureND<Double>.mapIndexed(
-        transform: Float64Field.(index: IntArray, Double) -> Double,
-    ): BufferND<Double> {
-        val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            Float64Field.transform(
-                strides.index(offset),
-                buffer.array[offset]
-            )
-        }.toArray()
-
-        return BufferND(strides, array.asBuffer())
-    }
-
-    @OptIn(PerformancePitfall::class)
-    override fun zip(
-        left: StructureND<Double>,
-        right: StructureND<Double>,
-        transform: Float64Field.(Double, Double) -> Double,
-    ): BufferND<Double> {
-        val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
-        }.toArray()
-        return BufferND(strides, array.asBuffer())
-    }
-
-    override fun StructureND<Double>.unaryMinus(): StructureND<Double> = map { -it }
-
-    override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
-
-    override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
-
-    override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
-
-    override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
-
-    override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
-    override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
-    override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
-    override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
-    override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
-    override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
-
-    override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
-    override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
-    override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
-    override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
-    override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
-    override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
-}
-
-fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

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

@@ -1,45 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.PerformancePitfall
-import space.kscience.kmath.nd.BufferND
-import space.kscience.kmath.nd.ColumnStrides
-import space.kscience.kmath.nd.ShapeND
-import kotlin.system.measureTimeMillis
-
-@Suppress("ASSIGNED_BUT_NEVER_ACCESSED_VARIABLE")
-@OptIn(PerformancePitfall::class)
-fun main() {
-    val n = 6000
-    val array = DoubleArray(n * n) { 1.0 }
-    val buffer = Float64Buffer(array)
-    val strides = ColumnStrides(ShapeND(n, n))
-    val structure = BufferND(strides, buffer)
-
-    measureTimeMillis {
-        var res = 0.0
-        strides.asSequence().forEach { res = structure[it] }
-    } // warmup
-
-    val time1 = measureTimeMillis {
-        var res = 0.0
-        strides.asSequence().forEach { res = structure[it] }
-    }
-    println("Structure reading finished in $time1 millis")
-
-    val time2 = measureTimeMillis {
-        var res = 0.0
-        strides.asSequence().forEach { res = buffer[strides.offset(it)] }
-    }
-    println("Buffer reading finished in $time2 millis")
-
-    val time3 = measureTimeMillis {
-        var res = 0.0
-        strides.asSequence().forEach { res = array[strides.offset(it)] }
-    }
-    println("Array reading finished in $time3 millis")
-}

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

@@ -1,42 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.nd.BufferND
-import space.kscience.kmath.operations.mapToBuffer
-import kotlin.system.measureTimeMillis
-
-private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
-    bufferFactory: BufferFactory<R> = BufferFactory(),
-    crossinline block: (T) -> R,
-): BufferND<R> = BufferND(indices, buffer.mapToBuffer(bufferFactory, block))
-
-@Suppress("UNUSED_VARIABLE")
-fun main() {
-    val n = 6000
-    val structure = BufferND(n, n) { 1.0 }
-    structure.mapToBufferND { it + 1 } // warm-up
-    val time1 = measureTimeMillis { val res = structure.mapToBufferND { 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 -> target[index] = value + 1 }
-    }
-
-    println("Array mapping finished in $time2 millis")
-
-    val buffer = Float64Buffer(DoubleArray(n * n) { 1.0 })
-
-    val time3 = measureTimeMillis {
-        val target = Float64Buffer(DoubleArray(n * n))
-        val res = array.forEachIndexed { index, value ->
-            target[index] = value + 1
-        }
-    }
-    println("Buffer mapping finished in $time3 millis")
-}

examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt

@@ -1,23 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.buffer
-import space.kscience.kmath.operations.bufferAlgebra
-import space.kscience.kmath.operations.withSize
-
-inline fun <reified R : Any> MutableBuffer.Companion.same(
-    n: Int,
-    value: R,
-): MutableBuffer<R> = MutableBuffer(n) { value }
-
-
-fun main() {
-    with(Float64Field.bufferAlgebra.withSize(5)) {
-        println(number(2.0) + buffer(1, 2, 3, 4, 5))
-    }
-}

examples/src/main/kotlin/space/kscience/kmath/structures/mutableNd.kt

@@ -1,26 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.PerformancePitfall
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.operations.algebra
-
-@OptIn(PerformancePitfall::class)
-fun main(): Unit = with(Double.algebra.ndAlgebra) {
-    val structure: MutableStructure2D<Double> = mutableStructureND(ShapeND(2, 2)) { (i, j) ->
-        i.toDouble() + j.toDouble()
-    }.as2D()
-
-    structure[0, 1] = -2.0
-
-    val structure2 = mutableStructureND(2, 2) { (i, j) -> i.toDouble() + j.toDouble() }.as2D()
-
-    structure2[0, 1] = 2.0
-
-
-    println(structure + structure2)
-}

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

@@ -1,35 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import space.kscience.kmath.dimensions.D2
-import space.kscience.kmath.dimensions.D3
-import space.kscience.kmath.dimensions.DMatrixContext
-import space.kscience.kmath.dimensions.Dimension
-
-private fun DMatrixContext<Double, *>.simple() {
-    val m1 = produce<D2, D3> { i, j -> (i + j).toDouble() }
-    val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
-
-    //Dimension-safe addition
-    m1.transposed() + m2
-}
-
-private object D5 : Dimension {
-    override val dim: Int = 5
-}
-
-private fun DMatrixContext<Double, *>.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() }
-    (m1 dot m2) + m3
-}
-
-fun main(): Unit = with(DMatrixContext.real) {
-    simple()
-    custom()
-}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt

@@ -1,92 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMInput
-import space.kscience.kmath.tensors.core.levenbergMarquardt
-import kotlin.math.roundToInt
-
-fun main() {
-    val NData = 200
-    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
-    for (i in 0 until NData) {
-        t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
-    }
-
-    val Nparams = 15
-    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
-    for (i in 0 until Nparams) {
-        p_example[i, 0] = p_example[i, 0] + i - 25
-    }
-
-    val exampleNumber = 1
-
-    var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
-
-    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
-    for (i in 0 until Nparams) {
-        p_init[i, 0] = (p_example[i, 0] + 0.9)
-    }
-
-    var t = t_example
-    val y_dat = y_hat
-    val weight = 1.0 / Nparams * 1.0 - 0.085
-    val dp = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
-    ).as2D()
-    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
-    p_min = p_min.div(1.0 / -50.0)
-    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
-    p_min = p_min.div(1.0 / 50.0)
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
-//    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
-
-    val inputData = LMInput(
-        ::funcDifficultForLm,
-        p_init.as2D(),
-        t,
-        y_dat,
-        weight,
-        dp,
-        p_min.as2D(),
-        p_max.as2D(),
-        opts[1].toInt(),
-        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
-        doubleArrayOf(opts[6], opts[7], opts[8]),
-        opts[9].toInt(),
-        10,
-        1
-    )
-
-    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
-
-    println("Parameters:")
-    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
-        print("$x ")
-    }
-    println()
-
-    println("Y true and y received:")
-    var y_hat_after = funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
-    for (i in 0 until y_hat.shape.component1()) {
-        val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
-        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
-        println("$x $y")
-    }
-
-    println("Сhi_sq:")
-    println(result.resultChiSq)
-    println("Number of iterations:")
-    println(result.iterations)
-}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt

@@ -1,59 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
-import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMInput
-import space.kscience.kmath.tensors.core.levenbergMarquardt
-import kotlin.math.roundToInt
-
-fun main() {
-    val startedData = getStartDataForFuncEasy()
-    val inputData = LMInput(
-        ::funcEasyForLm,
-        DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
-        startedData.t,
-        startedData.y_dat,
-        startedData.weight,
-        startedData.dp,
-        startedData.p_min,
-        startedData.p_max,
-        startedData.opts[1].toInt(),
-        doubleArrayOf(startedData.opts[2], startedData.opts[3], startedData.opts[4], startedData.opts[5]),
-        doubleArrayOf(startedData.opts[6], startedData.opts[7], startedData.opts[8]),
-        startedData.opts[9].toInt(),
-        10,
-        startedData.example_number
-    )
-
-    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
-
-    println("Parameters:")
-    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
-        print("$x ")
-    }
-    println()
-
-    println("Y true and y received:")
-    var y_hat_after = funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
-    for (i in 0 until startedData.y_dat.shape.component1()) {
-        val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
-        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
-        println("$x $y")
-    }
-
-    println("Сhi_sq:")
-    println(result.resultChiSq)
-    println("Number of iterations:")
-    println(result.iterations)
-}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt

@@ -1,91 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcMiddleForLm
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMInput
-import space.kscience.kmath.tensors.core.levenbergMarquardt
-import kotlin.math.roundToInt
-
-fun main() {
-    val NData = 100
-    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
-    for (i in 0 until NData) {
-        t_example[i, 0] = t_example[i, 0] * (i + 1)
-    }
-
-    val Nparams = 20
-    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
-    for (i in 0 until Nparams) {
-        p_example[i, 0] = p_example[i, 0] + i - 25
-    }
-
-    val exampleNumber = 1
-
-    var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
-
-    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
-    for (i in 0 until Nparams) {
-        p_init[i, 0] = (p_example[i, 0] + 0.9)
-    }
-
-    var t = t_example
-    val y_dat = y_hat
-    val weight = 1.0
-    val dp = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
-    ).as2D()
-    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
-    p_min = p_min.div(1.0 / -50.0)
-    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
-    p_min = p_min.div(1.0 / 50.0)
-    val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
-
-    val inputData = LMInput(
-        ::funcMiddleForLm,
-        p_init.as2D(),
-        t,
-        y_dat,
-        weight,
-        dp,
-        p_min.as2D(),
-        p_max.as2D(),
-        opts[1].toInt(),
-        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
-        doubleArrayOf(opts[6], opts[7], opts[8]),
-        opts[9].toInt(),
-        10,
-        1
-    )
-
-    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
-
-    println("Parameters:")
-    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
-        print("$x ")
-    }
-    println()
-
-
-    var y_hat_after = funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
-    for (i in 0 until y_hat.shape.component1()) {
-        val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
-        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
-        println("$x $y")
-    }
-
-    println("Сhi_sq:")
-    println(result.resultChiSq)
-    println("Number of iterations:")
-    println(result.iterations)
-}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt

@@ -1,75 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
-
-import kotlinx.coroutines.delay
-import kotlinx.coroutines.flow.Flow
-import kotlinx.coroutines.flow.flow
-import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
-import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMInput
-import space.kscience.kmath.tensors.core.levenbergMarquardt
-import kotlin.random.Random
-
-fun streamLm(
-    lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
-    startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int,
-): Flow<MutableStructure2D<Double>> = flow {
-
-    var example_number = startData.example_number
-    var p_init = startData.p_init
-    var t = startData.t
-    var y_dat = startData.y_dat
-    val weight = startData.weight
-    val dp = startData.dp
-    val p_min = startData.p_min
-    val p_max = startData.p_max
-    val opts = startData.opts
-
-    var steps = numberOfLaunches
-    val isEndless = (steps <= 0)
-
-    val inputData = LMInput(
-        lm_func,
-        p_init,
-        t,
-        y_dat,
-        weight,
-        dp,
-        p_min,
-        p_max,
-        opts[1].toInt(),
-        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
-        doubleArrayOf(opts[6], opts[7], opts[8]),
-        opts[9].toInt(),
-        10,
-        example_number
-    )
-
-    while (isEndless || steps > 0) {
-        val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
-        emit(result.resultParameters)
-        delay(launchFrequencyInMs)
-        inputData.realValues = generateNewYDat(y_dat, 0.1)
-        inputData.startParameters = result.resultParameters
-        if (!isEndless) steps -= 1
-    }
-}
-
-fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double> {
-    val n = y_dat.shape.component1()
-    val y_dat_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
-    for (i in 0 until n) {
-        val randomEps = Random.nextDouble(delta + delta) - delta
-        y_dat_new[i, 0] = y_dat[i, 0] + randomEps
-    }
-    return y_dat_new
-}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt

@@ -1,33 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
-
-import space.kscience.kmath.nd.component1
-import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
-import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncDifficult
-import kotlin.math.roundToInt
-
-suspend fun main() {
-    val startData = getStartDataForFuncDifficult()
-    // Создание потока:
-    val lmFlow = streamLm(::funcDifficultForLm, startData, 0, 100)
-    var initialTime = System.currentTimeMillis()
-    var lastTime: Long
-    val launches = mutableListOf<Long>()
-    // Запуск потока
-    lmFlow.collect { parameters ->
-        lastTime = System.currentTimeMillis()
-        launches.add(lastTime - initialTime)
-        initialTime = lastTime
-        for (i in 0 until parameters.shape.component1()) {
-            val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
-            print("$x ")
-            if (i == parameters.shape.component1() - 1) println()
-        }
-    }
-
-    println("Average without first is: ${launches.subList(1, launches.size - 1).average()}")
-}