4620 changed files with 587018 additions and 72016 deletions
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@ -1,3 +0,0 @@
-@altavir
-
-/kmath-trajectory @ESchouten
--- a/.github/workflows/build.yml
+++ b/.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
--- a/.github/workflows/pages.yml
+++ b/.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
--- a/.github/workflows/publish.yml
+++ b/.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 }}
--- a/.gitignore
+++ b/.gitignore
@ -1,24 +0,0 @@
-.gradle
-build/
-out/
-
-.idea/
-.vscode/
-.fleet/
-.kotlin/
-
-
-# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)
-!gradle-wrapper.jar
-
-# Cache of project
-.gradletasknamecache
-
-# Generated by javac -h and runtime
-*.class
-*.log
-
-!/.idea/copyright/
-!/.idea/scopes/
-/gradle/yarn.lock
-
--- a/.idea/copyright/kmath.xml
+++ b/.idea/copyright/kmath.xml
@ -1,7 +0,0 @@
-<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>
-</component>
--- a/.idea/copyright/profiles_settings.xml
+++ b/.idea/copyright/profiles_settings.xml
@ -1,21 +0,0 @@
-<component name="CopyrightManager">
-  <settings>
-    <module2copyright>
-      <element module="Apply copyright" copyright="kmath" />
-    </module2copyright>
-    <LanguageOptions name="Groovy">
-      <option name="fileTypeOverride" value="1" />
-    </LanguageOptions>
-    <LanguageOptions name="HTML">
-      <option name="fileTypeOverride" value="1" />
-      <option name="prefixLines" value="false" />
-    </LanguageOptions>
-    <LanguageOptions name="Properties">
-      <option name="fileTypeOverride" value="1" />
-    </LanguageOptions>
-    <LanguageOptions name="XML">
-      <option name="fileTypeOverride" value="1" />
-      <option name="prefixLines" value="false" />
-    </LanguageOptions>
-  </settings>
-</component>
--- a/.idea/scopes/Apply_copyright.xml
+++ b/.idea/scopes/Apply_copyright.xml
@ -1,3 +0,0 @@
-<component name="DependencyValidationManager">
-    <scope name="Apply copyright" pattern="!file[*]:*//testData//*&amp;&amp;!file[*]:testData//*&amp;&amp;!file[*]:*.gradle.kts&amp;&amp;!file[*]:*.gradle&amp;&amp;!file[group:kotlin-ultimate]:*/&amp;&amp;!file[kotlin.libraries]:stdlib/api//*" />
-</component>
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -1,273 +0,0 @@
-# KMath
-
-## Unreleased
-
-### Added
- Metropolis-Hastings sampler
-
-### Changed
-
-### Deprecated
-
-### Removed
-
-### Fixed
- Fix EJML to properly treat vectors as columns
-
-### Security
-
-## 0.4.0 - 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<Float64>` 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
--- a/README.md
+++ b/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:$version") for jvm-specific version
-}
-```
-
-## Contributing
-
-The project requires a lot of additional work. The most important thing we need is feedback about what features are
-required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
-marked
-with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
-label.
--- a/attributes-kt/README.md
+++ b/attributes-kt/README.md
@ -1,21 +0,0 @@
-# Module attributes-kt
-
-
-
-## Usage
-
-## Artifact:
-
-The Maven coordinates of this project are `space.kscience:attributes-kt:0.1.0`.
-
-**Gradle Kotlin DSL:**
-```kotlin
-repositories {
-    maven("https://repo.kotlin.link")
-    mavenCentral()
-}
-
-dependencies {
-    implementation("space.kscience:attributes-kt:0.1.0")
-}
-```
--- a/attributes-kt/api/attributes-kt.api
+++ b/attributes-kt/api/attributes-kt.api
@ -1,104 +0,0 @@
-public abstract interface class space/kscience/attributes/Attribute {
-}
-
-public abstract interface class space/kscience/attributes/AttributeContainer {
-	public abstract fun getAttributes ()Lspace/kscience/attributes/Attributes;
-}
-
-public abstract interface class space/kscience/attributes/AttributeScope {
-}
-
-public abstract interface class space/kscience/attributes/AttributeWithDefault : space/kscience/attributes/Attribute {
-	public abstract fun getDefault ()Ljava/lang/Object;
-}
-
-public abstract interface class space/kscience/attributes/Attributes {
-	public static final field Companion Lspace/kscience/attributes/Attributes$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;
-}
-
--- a/attributes-kt/build.gradle.kts
+++ b/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()
-}
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attribute.kt
+++ b/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>>
-
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributeContainer.kt
+++ b/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>
-
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt
+++ b/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/Attributes.kt
@ -1,157 +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))
-
-/**
- * Create a new [Attributes] that overlays [other] on top of this set of attributes. New attributes are added.
- * Existing attribute keys are replaced.
- */
-public operator fun Attributes.plus(other: Attributes): Attributes = when {
-    isEmpty() -> other
-    other.isEmpty() -> this
-    else -> MapAttributes(content + other.content)
-}
-
-/**
- * Create a new [Attributes] with removed [key] (if it is present).
- */
-public operator fun Attributes.minus(key: Attribute<*>): Attributes =
-    if (content.contains(key)) MapAttributes(content.minus(key)) else this
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/AttributesBuilder.kt
+++ b/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()
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/PolymorphicAttribute.kt
+++ b/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)
-}
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/SafeType.kt
+++ b/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>
-}
--- a/attributes-kt/src/commonMain/kotlin/space/kscience/attributes/annotations.kt
+++ b/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
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@ -1,119 +0,0 @@
-# BenchmarksResult
-
-## Report for benchmark configuration <code>main</code>
-
-* Run on Java HotSpot(TM) 64-Bit Server VM (build 21.0.4+8-LTS-jvmci-23.1-b41) with Java process:
-
-```
-C:\Users\altavir\scoop\apps\graalvm-oracle-21jdk\current\bin\java.exe -XX:ThreadPriorityPolicy=1 -XX:+UnlockExperimentalVMOptions -XX:+EnableJVMCIProduct -XX:-UnlockExperimentalVMOptions -Dfile.encoding=UTF-8 -Duser.country=US -Duser.language=en -Duser.variant
-```
-* JMH 1.21 was used in `thrpt` mode with 5 warmup iterations by 10 s and 5 measurement iterations by 10 s.
-### [ArrayBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`benchmarkArrayRead`|1.9E+07 &plusmn; 2.3E+05 ops/s|
-|`benchmarkBufferRead`|1.4E+07 &plusmn; 8.7E+05 ops/s|
-|`nativeBufferRead`|1.4E+07 &plusmn; 1.3E+06 ops/s|
-### [BigIntBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`jvmAdd`|5.1E+07 &plusmn; 1.3E+06 ops/s|
-|`jvmAddLarge`|5.1E+04 &plusmn; 8.2E+02 ops/s|
-|`jvmMultiply`|8.5E+07 &plusmn; 9.7E+06 ops/s|
-|`jvmMultiplyLarge`|2.5E+02 &plusmn; 15 ops/s|
-|`jvmParsing10`|8.7E+06 &plusmn; 5.1E+05 ops/s|
-|`jvmParsing16`|6.4E+06 &plusmn; 1.8E+05 ops/s|
-|`jvmPower`|28 &plusmn; 0.79 ops/s|
-|`jvmSmallAdd`|7.0E+07 &plusmn; 4.3E+06 ops/s|
-|`kmAdd`|4.8E+07 &plusmn; 2.2E+06 ops/s|
-|`kmAddLarge`|3.5E+04 &plusmn; 3.7E+03 ops/s|
-|`kmMultiply`|6.7E+07 &plusmn; 1.5E+07 ops/s|
-|`kmMultiplyLarge`|54 &plusmn; 4.2 ops/s|
-|`kmParsing10`|4.5E+06 &plusmn; 8.3E+04 ops/s|
-|`kmParsing16`|4.9E+06 &plusmn; 1.1E+05 ops/s|
-|`kmPower`|10 &plusmn; 0.96 ops/s|
-|`kmSmallAdd`|4.1E+07 &plusmn; 5.9E+05 ops/s|
-### [BufferBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`bufferViewReadWrite`|5.8E+06 &plusmn; 1.6E+05 ops/s|
-|`bufferViewReadWriteSpecialized`|5.6E+06 &plusmn; 2.6E+05 ops/s|
-|`complexBufferReadWrite`|6.6E+06 &plusmn; 2.7E+05 ops/s|
-|`doubleArrayReadWrite`|7.5E+06 &plusmn; 1.0E+06 ops/s|
-|`doubleBufferReadWrite`|8.0E+06 &plusmn; 6.7E+05 ops/s|
-### [DotBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`bufferedDot`|1.3 &plusmn; 0.020 ops/s|
-|`cmDot`|0.47 &plusmn; 0.42 ops/s|
-|`cmDotWithConversion`|0.76 &plusmn; 0.13 ops/s|
-|`ejmlDot`|6.7 &plusmn; 0.091 ops/s|
-|`ejmlDotWithConversion`|6.4 &plusmn; 0.82 ops/s|
-|`multikDot`|40 &plusmn; 6.7 ops/s|
-|`parallelDot`|12 &plusmn; 1.8 ops/s|
-|`tensorDot`|1.2 &plusmn; 0.041 ops/s|
-|`tfDot`|5.9 &plusmn; 0.49 ops/s|
-### [ExpressionsInterpretersBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`asmGenericExpression`|29 &plusmn; 1.2 ops/s|
-|`asmPrimitiveExpression`|43 &plusmn; 1.3 ops/s|
-|`asmPrimitiveExpressionArray`|71 &plusmn; 0.38 ops/s|
-|`functionalExpression`|5.6 &plusmn; 0.11 ops/s|
-|`justCalculate`|69 &plusmn; 9.0 ops/s|
-|`mstExpression`|7.1 &plusmn; 0.020 ops/s|
-|`rawExpression`|41 &plusmn; 1.5 ops/s|
-### [IntegrationBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`complexIntegration`|3.6E+03 &plusmn; 1.9E+02 ops/s|
-|`doubleIntegration`|3.7E+03 &plusmn; 12 ops/s|
-### [JafamaBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`core`|38 &plusmn; 0.64 ops/s|
-|`jafama`|52 &plusmn; 0.36 ops/s|
-|`strictJafama`|52 &plusmn; 4.0 ops/s|
-### [MatrixInverseBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`cmLUPInversion`|2.2E+03 &plusmn; 76 ops/s|
-|`ejmlInverse`|1.3E+03 &plusmn; 5.7 ops/s|
-|`kmathLupInversion`|9.5E+02 &plusmn; 1.8E+02 ops/s|
-|`kmathParallelLupInversion`|9.1E+02 &plusmn; 1.4E+02 ops/s|
-### [NDFieldBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`boxingFieldAdd`|7.7 &plusmn; 0.79 ops/s|
-|`multikAdd`|6.5 &plusmn; 0.33 ops/s|
-|`multikInPlaceAdd`|64 &plusmn; 0.79 ops/s|
-|`specializedFieldAdd`|8.0 &plusmn; 0.090 ops/s|
-|`tensorAdd`|9.2 &plusmn; 0.053 ops/s|
-|`tensorInPlaceAdd`|17 &plusmn; 10 ops/s|
-|`viktorAdd`|7.6 &plusmn; 1.2 ops/s|
-### [ViktorBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`doubleFieldAddition`|7.7 &plusmn; 0.34 ops/s|
-|`rawViktor`|5.9 &plusmn; 1.1 ops/s|
-|`viktorFieldAddition`|7.3 &plusmn; 1.1 ops/s|
-### [ViktorLogBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt)
-
-| Benchmark | Score |
-|:---------:|:-----:|
-|`rawViktorLog`|1.4 &plusmn; 0.076 ops/s|
-|`realFieldLog`|1.3 &plusmn; 0.069 ops/s|
-|`viktorFieldLog`|1.3 &plusmn; 0.032 ops/s|
-
-
-
--- a/benchmarks/build.gradle.kts
+++ b/benchmarks/build.gradle.kts
@ -1,280 +0,0 @@
-import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
-import com.fasterxml.jackson.module.kotlin.readValue
-import kotlinx.benchmark.gradle.BenchmarksExtension
-import java.time.LocalDateTime
-import java.time.ZoneId
-import java.util.*
-
-plugins {
-    kotlin("multiplatform")
-    alias(spclibs.plugins.kotlin.plugin.allopen)
-    alias(spclibs.plugins.kotlinx.benchmark)
-}
-
-allOpen.annotation("org.openjdk.jmh.annotations.State")
-sourceSets.register("benchmarks")
-
-repositories {
-    mavenCentral()
-}
-
-kotlin {
-    jvmToolchain(17)
-
-    compilerOptions {
-        optIn.addAll(
-            "space.kscience.kmath.UnstableKMathAPI"
-        )
-    }
-
-    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(libs.multik.default)
-                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")
-    }
-}
-
-
-private data class JmhReport(
-    val jmhVersion: String,
-    val benchmark: String,
-    val mode: String,
-    val threads: Int,
-    val forks: Int,
-    val jvm: String,
-    val jvmArgs: List<String>,
-    val jdkVersion: String,
-    val vmName: String,
-    val vmVersion: String,
-    val warmupIterations: Int,
-    val warmupTime: String,
-    val warmupBatchSize: Int,
-    val measurementIterations: Int,
-    val measurementTime: String,
-    val measurementBatchSize: Int,
-    val params: Map<String, String> = emptyMap(),
-    val primaryMetric: PrimaryMetric,
-    val secondaryMetrics: Map<String, SecondaryMetric>,
-) {
-    interface Metric {
-        val score: Double
-        val scoreError: Double
-        val scoreConfidence: List<Double>
-        val scorePercentiles: Map<Double, Double>
-        val scoreUnit: String
-    }
-
-    data class PrimaryMetric(
-        override val score: Double,
-        override val scoreError: Double,
-        override val scoreConfidence: List<Double>,
-        override val scorePercentiles: Map<Double, Double>,
-        override val scoreUnit: String,
-        val rawDataHistogram: List<List<List<List<Double>>>>? = null,
-        val rawData: List<List<Double>>? = null,
-    ) : Metric
-
-    data class SecondaryMetric(
-        override val score: Double,
-        override val scoreError: Double,
-        override val scoreConfidence: List<Double>,
-        override val scorePercentiles: Map<Double, Double>,
-        override val scoreUnit: String,
-        val rawData: List<List<Double>>,
-    ) : Metric
-}
-
-readme {
-    maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
-
-    val jsonMapper = jacksonObjectMapper()
-
-    fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
-
-    extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
-        val propertyName =
-            "benchmark${cfg.name.replaceFirstChar { if (it.isLowerCase()) it.titlecase(Locale.getDefault()) else it.toString() }}"
-
-        logger.info("Processing benchmark data from benchmark ${cfg.name} into readme property $propertyName")
-
-        val launches = layout.buildDirectory.dir("reports/benchmarks/${cfg.name}").get().asFile
-        if (!launches.exists()) return@forEach
-
-        property(propertyName) {
-            val resDirectory = launches.listFiles()?.maxByOrNull {
-                LocalDateTime.parse(it.name).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"))
-
-                buildString {
-                    appendLine("## Report for benchmark configuration <code>${cfg.name}</code>")
-                    appendLine()
-                    val first = reports.first()
-
-                    appendLine("* Run on ${first.vmName} (build ${first.vmVersion}) with Java process:")
-                    appendLine()
-                    appendLine("```")
-                    appendLine(
-                        "${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 ${first.measurementTime}."
-                    )
-
-                    reports.groupBy { it.benchmark.substringBeforeLast(".") }.forEach { (cl, compare) ->
-                        appendLine("### [${cl.substringAfterLast(".")}](src/jvmMain/kotlin/${cl.replace(".","/")}.kt)")
-                        appendLine()
-                        appendLine("| Benchmark | Score |")
-                        appendLine("|:---------:|:-----:|")
-                        compare.forEach { report ->
-                            val benchmarkName = report.benchmark.substringAfterLast(".")
-                            val score = String.format("%.2G", report.primaryMetric.score)
-                            val error = String.format("%.2G", report.primaryMetric.scoreError)
-
-                            appendLine("|`$benchmarkName`|$score &plusmn; $error ${report.primaryMetric.scoreUnit}|")
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
--- a/benchmarks/docs/README-TEMPLATE.md
+++ b/benchmarks/docs/README-TEMPLATE.md
@ -1,5 +0,0 @@
-# BenchmarksResult
-
-${benchmarkMain}
-
-
--- a/benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
+++ b/benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
@ -1,108 +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 space.kscience.kmath.structures.Float64
-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<Float64>, 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<Float64> { args ->
-            val x = args.getValue(x)
-            x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt
+++ b/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) }
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt
+++ b/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))
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt
+++ b/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() }
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
+++ b/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)
-    }
-
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
+++ b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
@ -1,125 +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 space.kscience.kmath.structures.Float64
-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<Float64>, 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<Float64>)
-
-        private val raw = Expression<Float64> { args ->
-            val x = args[x]!!
-            x * 2.0 + 2.0 / x - 16.0 / sin(x)
-        }
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt
+++ b/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)
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt
+++ b/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())
-    }
-
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
+++ b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
@ -1,97 +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.structures.Float64
-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<Float64> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
-        var res: StructureND<Float64> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
-        var res: StructureND<Float64> = one(shape)
-        repeat(n) { res += 1.0 }
-        blackhole.consume(res)
-    }
-
-    @Benchmark
-    fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
-        var res: StructureND<Float64> = 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<Float64> = one(dim, dim)
-//        repeat(n) { res += 1.0 }
-//        blackhole.consume(res)
-//    }
-
-
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/TensorAlgebraBenchmark.kt
+++ b/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))
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
+++ b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.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.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.structures.Float64
-import space.kscience.kmath.viktor.ViktorFieldND
-
-@State(Scope.Benchmark)
-internal class ViktorBenchmark {
-
-    @Benchmark
-    fun doubleFieldAddition(blackhole: Blackhole) {
-        with(doubleField) {
-            var res: StructureND<Float64> = 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)
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
+++ b/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)
-    }
-}
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/globals.kt
+++ b/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())
--- a/build.gradle.kts
+++ b/build.gradle.kts
@ -1,78 +0,0 @@
-import space.kscience.gradle.useApache2Licence
-import space.kscience.gradle.useSPCTeam
-
-plugins {
-    alias(spclibs.plugins.kscience.project)
-    alias(spclibs.plugins.kotlinx.kover)
-}
-
-val attributesVersion by extra("0.2.0")
-
-allprojects {
-    repositories {
-        maven("https://repo.kotlin.link")
-        maven("https://oss.sonatype.org/content/repositories/snapshots")
-        mavenCentral()
-    }
-
-    group = "space.kscience"
-    version = "0.4.1-dev"
-}
-
-dependencies{
-    subprojects.forEach {
-        dokka(it)
-    }
-}
-
-subprojects {
-    if (name.startsWith("kmath")) apply<MavenPublishPlugin>()
-
-    plugins.withId("org.jetbrains.dokka") {
-        tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
-            dependsOn(tasks["assemble"])
-
-            dokkaSourceSets.all {
-                val readmeFile = this@subprojects.projectDir.resolve("README.md")
-                if (readmeFile.exists()) includes.from(readmeFile)
-                val kotlinDirPath = "src/$name/kotlin"
-                val kotlinDir = file(kotlinDirPath)
-
-                if (kotlinDir.exists()) sourceLink {
-                    localDirectory.set(kotlinDir)
-
-                    remoteUrl.set(
-                        uri("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath").toURL()
-                    )
-                }
-
-                externalDocumentationLink("https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/")
-                externalDocumentationLink("https://deeplearning4j.org/api/latest/")
-                externalDocumentationLink("https://axelclk.bitbucket.io/symja/javadoc/")
-
-                externalDocumentationLink(
-                    "https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/",
-                    "https://kotlin.github.io/kotlinx.coroutines/package-list",
-                )
-
-                externalDocumentationLink(
-                    "https://breandan.net/kotlingrad/kotlingrad/",
-                    "https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list",
-                )
-            }
-        }
-    }
-}
-
-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")
-}
-
-apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")
--- a/docs/algebra.md
+++ b/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`.
--- a/docs/buffers.md
+++ b/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.
--- a/docs/codestyle.md
+++ b/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.
--- a/docs/contexts.md
+++ b/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
-```
--- a/docs/diagrams/core.puml
+++ b/docs/diagrams/core.puml
--- a/docs/expressions.md
+++ b/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`&mdash;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**
--- a/docs/histograms.md
+++ b/docs/histograms.md
@ -1 +0,0 @@
-**TODO**
--- a/docs/images/KM.svg
+++ b/docs/images/KM.svg
--- a/docs/images/KM_mono.svg
+++ b/docs/images/KM_mono.svg
--- a/docs/images/KMath.svg
+++ b/docs/images/KMath.svg
--- a/docs/images/KMath_mono.svg
+++ b/docs/images/KMath_mono.svg
--- a/docs/linear.md
+++ b/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
--- a/docs/nd-structure.md
+++ b/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<Float64> = 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<Float64> = 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).
--- a/docs/polynomials.md
+++ b/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.
--- a/docs/readme.md
+++ b/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
--- a/docs/templates/ARTIFACT-TEMPLATE.md
+++ b/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}")
-}
-```
--- a/docs/templates/README-TEMPLATE.md
+++ b/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.
--- a/examples/README.md
+++ b/examples/README.md
@ -1,4 +0,0 @@
-# Module examples
-
-
-
--- a/examples/build.gradle.kts
+++ b/examples/build.gradle.kts
@ -1,73 +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")
-}
-
-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"))
-    //multik
-    implementation(project(":kmath-multik"))
-    implementation(libs.multik.default)
-
-    //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(17)
-    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
-}
--- a/examples/notebooks/Naive
+++ b/examples/notebooks/Naive
@ -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
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt
+++ b/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)
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt
+++ b/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))
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/ast/symjaSupport.kt
+++ b/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))
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/expressions/autodiff.kt
+++ b/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.
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt
@ -1,115 +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.DifferentiableExpression
-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.kmath.structures.Float64
-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: DifferentiableExpression<Float64> = 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()
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt
+++ b/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()
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt
@ -1,37 +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 space.kscience.kmath.structures.Float64
-import kotlin.math.pow
-
-fun main() {
-    //Define a function
-    val function: Function1D<Float64> = { 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
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt
@ -1,52 +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.kmath.structures.Float64
-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<Float64> = 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()
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/functions/interpolateSquare.kt
@ -1,46 +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.kmath.structures.Float64
-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<Float64> = { 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<Float64> = 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()
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.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.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 space.kscience.kmath.structures.Float64
-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<Float64> = { 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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt
+++ 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)
-
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/linear/eigenValueDecomposition.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/linear/eigenValueDecomposition.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.linear
-
-import space.kscience.kmath.commons.linear.CMLinearSpace
-import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.structures.Float64
-import kotlin.random.Random
-
-fun main() {
-    val dim = 46
-
-    val random = Random(123)
-
-    val u = Float64.algebra.linearSpace.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
-
-    listOf(CMLinearSpace, EjmlLinearSpaceDDRM).forEach { algebra ->
-        with(algebra) {
-            //create a simmetric matrix
-            val matrix = buildMatrix(dim, dim) { row, col ->
-                if (row >= col) u[row, col] else u[col, row]
-            }
-            val eigen = matrix.getOrComputeAttribute(EIG) ?: error("Failed to compute eigenvalue decomposition")
-            check(
-                StructureND.contentEquals(
-                    matrix,
-                    eigen.v dot eigen.d dot eigen.v.transposed(),
-                    1e-4
-                )
-            ) { "$algebra decomposition failed" }
-            println("$algebra eigenvalue decomposition complete and checked" )
-        }
-    }
-
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt
@ -1,34 +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.Float64
-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<Float64>) -> Double = { x ->
-        require(x.size == x0.size)
-        kotlin.math.exp(-((x - x0) / sigma).square().sum())
-    }
-
-    fun ((Point<Float64>) -> Double).grad(x: Point<Float64>): Point<Float64> {
-        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))
-
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/linear/lupPerformance.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt
+++ b/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")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/operations/mixedNDOperations.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.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.structures.Float64
-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<Float64> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
-
-    val res: Float64BufferND = Float64Field.ndAlgebra {
-        exp(viktorStructure) + 2.0 * cmMatrix
-    }
-
-    println(res)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/series/DateTimeSeries.kt
+++ b/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 }
-)
--- a/examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/series/analyzeDif.kt
@ -1,65 +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.Float64
-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<Float64>) {
-        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<Float64> = s1.zip(s2) { l, r -> l + r } //s1 + s2
-    val s4 = s3.map { ln(it) }
-
-    val kmTest: KMComparisonResult<Float64> = 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()
-
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/series/seriesBuilder.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/series/seriesBuilder.kt
@ -1,47 +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.*
-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<Float64> = series1.moveBy(3)
-
-    val res = series2 - series1
-
-    println(res.size)
-
-    println(res)
-
-    fun Plot.series(name: String, buffer: Buffer<Float64>, 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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
@ -1,94 +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 runKMathBlocking(): 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.nextBlocking()
-
-        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.MT.create(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() }
-    val blockingJob = async { runKMathBlocking() }
-    println("KMath Chained: ${chainJob.await()}")
-    println("KMath Blocking: ${blockingJob.await()}")
-    println("Apache Direct: ${directJob.await()}")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
@ -1,40 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.stat
-
-import kotlinx.coroutines.runBlocking
-import space.kscience.kmath.chains.Chain
-import space.kscience.kmath.chains.combineWithState
-import space.kscience.kmath.distributions.NormalDistribution
-import space.kscience.kmath.random.RandomGenerator
-import space.kscience.kmath.structures.Float64
-
-private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
-
-/**
- * Averaging.
- */
-private fun Chain<Float64>.mean(): Chain<Float64> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
-    val next = chain.next()
-    num++
-    value += next
-    return@combineWithState value / num
-}
-
-
-fun main() {
-    val normal = NormalDistribution(0.0, 2.0)
-    val chain = normal.sample(RandomGenerator.default).mean()
-
-    runBlocking {
-        repeat(10001) { counter ->
-            val mean = chain.next()
-            if (counter % 1000 == 0) {
-                println("[$counter] Average value is $mean")
-            }
-        }
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
+++ b/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<Float64> = 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 $complexTime millis")
-}
-
-fun complexExample() {
-    //Create a context for 2-d structure with complex values
-    ComplexField {
-        withNdAlgebra(4, 8) {
-            //a constant real-valued structure
-            val x = one * 2.5
-            operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)
-            //a structure generator specific to this context
-            val matrix = structureND { (k, l) -> k + l * i }
-            //Perform sum
-            val sum = matrix + x + 1.0
-
-            //Represent the sum as 2d-structure and transpose
-            sum.as2D().transposed()
-        }
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
@ -1,92 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.structures
-
-import kotlinx.coroutines.DelicateCoroutinesApi
-import kotlinx.coroutines.GlobalScope
-import org.nd4j.linalg.factory.Nd4j
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.nd4j.nd4j
-import space.kscience.kmath.operations.Float64Field
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.viktor.ViktorFieldND
-import kotlin.contracts.InvocationKind
-import kotlin.contracts.contract
-import kotlin.system.measureTimeMillis
-
-internal inline fun measureAndPrint(title: String, block: () -> Unit) {
-    contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
-    val time = measureTimeMillis(block)
-    println("$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<Float64> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Specialized addition") {
-        doubleField {
-            var res: StructureND<Float64> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Nd4j specialized addition") {
-        nd4jField {
-            var res: StructureND<Float64> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Viktor addition") {
-        viktorField {
-            var res: StructureND<Float64> = one
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Parallel stream addition") {
-        parallelField {
-            var res: StructureND<Float64> = 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 }
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
+++ b/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<Float64>>,
-    ExtendedField<StructureND<Float64>> {
-
-    private val strides = ColumnStrides(shape)
-    override val elementAlgebra: Float64Field get() = Float64Field
-    override val zero: BufferND<Float64> by lazy { structureND(shape) { zero } }
-    override val one: BufferND<Float64> by lazy { structureND(shape) { one } }
-
-    override fun number(value: Number): BufferND<Float64> {
-        val d = value.toDouble() // minimize conversions
-        return structureND(shape) { d }
-    }
-
-    @OptIn(PerformancePitfall::class)
-    private val StructureND<Float64>.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<Float64> {
-        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<Float64> {
-        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<Float64>.map(
-        transform: Float64Field.(Double) -> Double,
-    ): BufferND<Float64> {
-        val array = Arrays.stream(buffer.array).parallel().map { Float64Field.transform(it) }.toArray()
-        return BufferND(strides, array.asBuffer())
-    }
-
-    @OptIn(PerformancePitfall::class)
-    override fun StructureND<Float64>.mapIndexed(
-        transform: Float64Field.(index: IntArray, Double) -> Double,
-    ): BufferND<Float64> {
-        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<Float64>,
-        right: StructureND<Float64>,
-        transform: Float64Field.(Double, Double) -> Double,
-    ): BufferND<Float64> {
-        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<Float64>.unaryMinus(): StructureND<Float64> = map { -it }
-
-    override fun scale(a: StructureND<Float64>, value: Double): StructureND<Float64> = a.map { it * value }
-
-    override fun power(arg: StructureND<Float64>, pow: Number): BufferND<Float64> = arg.map { power(it, pow) }
-
-    override fun exp(arg: StructureND<Float64>): BufferND<Float64> = arg.map { exp(it) }
-
-    override fun ln(arg: StructureND<Float64>): BufferND<Float64> = arg.map { ln(it) }
-
-    override fun sin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sin(it) }
-    override fun cos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cos(it) }
-    override fun tan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tan(it) }
-    override fun asin(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asin(it) }
-    override fun acos(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acos(it) }
-    override fun atan(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atan(it) }
-
-    override fun sinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { sinh(it) }
-    override fun cosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { cosh(it) }
-    override fun tanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { tanh(it) }
-    override fun asinh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { asinh(it) }
-    override fun acosh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { acosh(it) }
-    override fun atanh(arg: StructureND<Float64>): BufferND<Float64> = arg.map { atanh(it) }
-}
-
-fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt
+++ b/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")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
+++ b/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")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt
+++ b/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))
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/mutableNd.kt
+++ b/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<Float64> = 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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt
+++ b/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()
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt
+++ b/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)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt
@ -1,76 +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.structures.Float64
-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<Float64>, MutableStructure2D<Float64>, Int) -> (MutableStructure2D<Float64>),
-    startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int,
-): Flow<MutableStructure2D<Float64>> = 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<Float64>, delta: Double): MutableStructure2D<Float64> {
-    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
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt
+++ b/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()}")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt
@ -1,233 +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
-
-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.structures.Float64
-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.DoubleTensorAlgebra.Companion.max
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
-import space.kscience.kmath.tensors.core.asDoubleTensor
-
-public data class StartDataLm(
-    var lm_matx_y_dat: MutableStructure2D<Float64>,
-    var example_number: Int,
-    var p_init: MutableStructure2D<Float64>,
-    var t: MutableStructure2D<Float64>,
-    var y_dat: MutableStructure2D<Float64>,
-    var weight: Double,
-    var dp: MutableStructure2D<Float64>,
-    var p_min: MutableStructure2D<Float64>,
-    var p_max: MutableStructure2D<Float64>,
-    var consts: MutableStructure2D<Float64>,
-    var opts: DoubleArray,
-)
-
-fun funcEasyForLm(
-    t: MutableStructure2D<Float64>,
-    p: MutableStructure2D<Float64>,
-    exampleNumber: Int,
-): MutableStructure2D<Float64> {
-    val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
-
-    if (exampleNumber == 1) {
-        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
-            DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
-        )
-    } else if (exampleNumber == 2) {
-        val mt = t.max()
-        y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
-                (t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
-                (t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
-                (t.times(1.0 / mt)).pow(4).times(p[3, 0])
-    } else if (exampleNumber == 3) {
-        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
-            .times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
-    }
-
-    return y_hat.as2D()
-}
-
-fun funcMiddleForLm(
-    t: MutableStructure2D<Float64>,
-    p: MutableStructure2D<Float64>,
-    exampleNumber: Int,
-): MutableStructure2D<Float64> {
-    val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
-
-    val mt = t.max()
-    for (i in 0 until p.shape.component1()) {
-        y_hat += (t.times(1.0 / mt)).times(p[i, 0])
-    }
-
-    for (i in 0 until 5) {
-        y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
-    }
-
-    return y_hat.as2D()
-}
-
-fun funcDifficultForLm(
-    t: MutableStructure2D<Float64>,
-    p: MutableStructure2D<Float64>,
-    exampleNumber: Int,
-): MutableStructure2D<Float64> {
-    val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
-
-    val mt = t.max()
-    for (i in 0 until p.shape.component1()) {
-        y_hat = y_hat.plus((t.times(1.0 / mt)).times(p[i, 0]))
-    }
-
-    for (i in 0 until 4) {
-        y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
-    }
-
-    return y_hat.as2D()
-}
-
-
-fun getStartDataForFuncDifficult(): StartDataLm {
-    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 consts = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
-    ).as2D()
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
-
-    return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
-}
-
-fun getStartDataForFuncMiddle(): StartDataLm {
-    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] + 10.0)
-    }
-    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 consts = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
-    ).as2D()
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
-
-    var example_number = 1
-
-    return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
-}
-
-fun getStartDataForFuncEasy(): StartDataLm {
-    val lm_matx_y_dat = doubleArrayOf(
-        19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
-        14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,
-        16.2247, 15.9854, 16.1421, 17.0960, 16.7769, 17.1997, 17.2767, 17.5882, 17.5378, 16.7894,
-        17.7648, 18.2512, 18.1581, 16.7037, 17.8475, 17.9081, 18.3067, 17.9632, 18.2817, 19.1427,
-        18.8130, 18.5658, 18.0056, 18.4607, 18.5918, 18.2544, 18.3731, 18.7511, 19.3181, 17.3066,
-        17.9632, 19.0513, 18.7528, 18.2928, 18.5967, 17.8567, 17.7859, 18.4016, 18.9423, 18.4959,
-        17.8000, 18.4251, 17.7829, 17.4645, 17.5221, 17.3517, 17.4637, 17.7563, 16.8471, 17.4558,
-        17.7447, 17.1487, 17.3183, 16.8312, 17.7551, 17.0942, 15.6093, 16.4163, 15.3755, 16.6725,
-        16.2332, 16.2316, 16.2236, 16.5361, 15.3721, 15.3347, 15.5815, 15.6319, 14.4538, 14.6044,
-        14.7665, 13.3718, 15.0587, 13.8320, 14.7873, 13.6824, 14.2579, 14.2154, 13.5818, 13.8157
-    )
-
-    var example_number = 1
-    val p_init = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(4, 1)), doubleArrayOf(5.0, 2.0, 0.2, 10.0)
-    ).as2D()
-
-    var t = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(100, 1))).as2D()
-    for (i in 0 until 100) {
-        t[i, 0] = t[i, 0] * (i + 1)
-    }
-
-    val y_dat = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
-    ).as2D()
-
-    val weight = 4.0
-
-    val dp = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
-    ).as2D()
-
-    val p_min = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(4, 1)), doubleArrayOf(-50.0, -20.0, -2.0, -100.0)
-    ).as2D()
-
-    val p_max = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(4, 1)), doubleArrayOf(50.0, 20.0, 2.0, 100.0)
-    ).as2D()
-
-    val consts = BroadcastDoubleTensorAlgebra.fromArray(
-        ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
-    ).as2D()
-
-    val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
-
-    return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts)
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.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.tensors
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.randomNormal
-import space.kscience.kmath.tensors.core.randomNormalLike
-import kotlin.math.abs
-
-// OLS estimator using SVD
-
-fun main() {
-    //seed for random
-    val randSeed = 100500L
-
-    // work in context with linear operations
-    DoubleTensorAlgebra {
-        // take coefficient vector from normal distribution
-        val alpha = randomNormal(
-            ShapeND(5),
-            randSeed
-        ) + fromArray(
-            ShapeND(5),
-            doubleArrayOf(1.0, 2.5, 3.4, 5.0, 10.1)
-        )
-
-        println("Real alpha:\n$alpha")
-
-        // also take sample of size 20 from normal distribution for x
-        val x = randomNormal(
-            ShapeND(20, 5),
-            randSeed
-        )
-
-        // calculate y and add gaussian noise (N(0, 0.05))
-        val y = x dot alpha
-        y += randomNormalLike(y, randSeed) * 0.05
-
-        // now restore the coefficient vector with OSL estimator with SVD
-        val (u, singValues, v) = svd(x)
-
-        // we have to make sure the singular values of the matrix are not close to zero
-        println("Singular values:\n$singValues")
-
-
-        // inverse Sigma matrix can be restored from singular values with diagonalEmbedding function
-        val sigma = diagonalEmbedding(singValues.map { if (abs(it) < 1e-3) 0.0 else 1.0 / it })
-
-        val alphaOLS = v dot sigma dot u.transposed() dot y
-        println(
-            "Estimated alpha:\n" +
-                    "$alphaOLS"
-        )
-
-        // figure out MSE of approximation
-        fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
-            require(yTrue.shape.size == 1)
-            require(yTrue.shape == yPred.shape)
-
-            val diff = yTrue - yPred
-            return sqrt(diff.dot(diff)).value()
-        }
-
-        println("MSE: ${mse(alpha, alphaOLS)}")
-    }
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
@ -1,74 +0,0 @@
-/*
- * Copyright 2018-2024 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.tensors
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.tensors.core.*
-
-
-// simple PCA
-
-fun main(): Unit = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
-    val seed = 100500L
-
-    // assume x is range from 0 until 10
-    val x = fromArray(
-        ShapeND(10),
-        DoubleArray(10) { it.toDouble() }
-    )
-
-    // take y dependent on x with noise
-    val y = 2.0 * x + (3.0 + randomNormalLike(x, seed) * 1.5)
-
-    println("x:\n$x")
-    println("y:\n$y")
-
-    // stack them into single dataset
-    val dataset = stack(listOf(x, y)).transposed()
-
-    // normalize both x and y
-    val xMean = mean(x)
-    val yMean = mean(y)
-
-    val xStd = std(x)
-    val yStd = std(y)
-
-    val xScaled: DoubleTensor = (x - xMean) / xStd
-    val yScaled: DoubleTensor = (y - yMean) / yStd
-
-    // save means ans standard deviations for further recovery
-    val mean = fromArray(
-        ShapeND(2),
-        doubleArrayOf(xMean, yMean)
-    )
-    println("Means:\n$mean")
-
-    val std = fromArray(
-        ShapeND(2),
-        doubleArrayOf(xStd, yStd)
-    )
-    println("Standard deviations:\n$std")
-
-    // calculate the covariance matrix of scaled x and y
-    val covMatrix = covariance(listOf(xScaled.asDoubleTensor1D(), yScaled.asDoubleTensor1D()))
-    println("Covariance matrix:\n$covMatrix")
-
-    // and find out eigenvector of it
-    val (_, evecs) = symEig(covMatrix)
-    val v = evecs.getTensor(0)
-    println("Eigenvector:\n$v")
-
-    // reduce dimension of dataset
-    val datasetReduced = v dot stack(listOf(xScaled, yScaled))
-    println("Reduced data:\n$datasetReduced")
-
-    // we can restore original data from reduced data;
-    // for example, find 7th element of dataset.
-    val n = 7
-    val restored = (datasetReduced.getTensor(n) dot v.view(ShapeND(1, 2))) * std + mean
-    println("Original value:\n${dataset.getTensor(n)}")
-    println("Restored value:\n$restored")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt
@ -1,44 +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
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.tensors.core.randomNormal
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.tensors.core.withBroadcast
-
-
-// Dataset normalization
-
-fun main() = Double.tensorAlgebra.withBroadcast {  // work in context with broadcast methods
-    // take dataset of 5-element vectors from normal distribution
-    val dataset = randomNormal(ShapeND(100, 5)) * 1.5 // all elements from N(0, 1.5)
-
-    dataset += fromArray(
-        ShapeND(5),
-        doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means
-    )
-
-
-    // find out mean and standard deviation of each column
-    val mean = mean(dataset, 0, false)
-    val std = std(dataset, 0, false)
-
-    println("Mean:\n$mean")
-    println("Standard deviation:\n$std")
-
-    // also, we can calculate other statistic as minimum and maximum of rows
-    println("Minimum:\n${dataset.min(0, false)}")
-    println("Maximum:\n${dataset.max(0, false)}")
-
-    // now we can scale dataset with mean normalization
-    val datasetScaled = (dataset - mean) / std
-
-    // find out mean and standardDiviation of scaled dataset
-
-    println("Mean of scaled:\n${mean(datasetScaled, 0, false)}")
-    println("Mean of scaled:\n${std(datasetScaled, 0, false)}")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt
@ -1,94 +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
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.tensors.core.DoubleTensor
-import space.kscience.kmath.tensors.core.tensorAlgebra
-import space.kscience.kmath.tensors.core.withBroadcast
-
-// solving linear system with LUP decomposition
-
-fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear operations
-
-    // set true value of x
-    val trueX = fromArray(
-        ShapeND(4),
-        doubleArrayOf(-2.0, 1.5, 6.8, -2.4)
-    )
-
-    // and A matrix
-    val a = fromArray(
-        ShapeND(4, 4),
-        doubleArrayOf(
-            0.5, 10.5, 4.5, 1.0,
-            8.5, 0.9, 12.8, 0.1,
-            5.56, 9.19, 7.62, 5.45,
-            1.0, 2.0, -3.0, -2.5
-        )
-    )
-
-    // calculate y value
-    val b = a dot trueX
-
-    // check out A and b
-    println("A:\n$a")
-    println("b:\n$b")
-
-    // solve `Ax = b` system using LUP decomposition
-
-    // get P, L, U such that PA = LU
-    val (p, l, u) = lu(a)
-
-    // check P is permutation matrix
-    println("P:\n$p")
-    // L is lower triangular matrix and U is upper triangular matrix
-    println("L:\n$l")
-    println("U:\n$u")
-    // and PA = LU
-    println("PA:\n${p dot a}")
-    println("LU:\n${l dot u}")
-
-    /* Ax = b;
-       PAx = Pb;
-       LUx = Pb;
-       let y = Ux, then
-       Ly = Pb -- this system can be easily solved, since the matrix L is lower triangular;
-       Ux = y can be solved the same way, since the matrix L is upper triangular
-        */
-
-
-
-    // this function returns solution x of a system lx = b, l should be lower triangular
-    fun solveLT(l: DoubleTensor, b: DoubleTensor): DoubleTensor {
-        val n = l.shape[0]
-        val x = zeros(ShapeND(n))
-        for (i in 0 until n) {
-            x[intArrayOf(i)] = (b[intArrayOf(i)] - l.getTensor(i).dot(x).value()) / l[intArrayOf(i, i)]
-        }
-        return x
-    }
-
-    val y = solveLT(l, p dot b)
-
-    // solveLT(l, b) function can be easily adapted for upper triangular matrix by the permutation matrix revMat
-    // create it by placing ones on side diagonal
-    val revMat = zeroesLike(u)
-    val n = revMat.shape[0]
-    for (i in 0 until n) {
-        revMat[intArrayOf(i, n - 1 - i)] = 1.0
-    }
-
-    // solution of system ux = b, u should be upper triangular
-    fun solveUT(u: DoubleTensor, b: DoubleTensor): DoubleTensor = revMat dot solveLT(
-        revMat dot u dot revMat, revMat dot b
-    )
-
-    val x = solveUT(u, y)
-
-    println("True x:\n$trueX")
-    println("x founded with LU method:\n$x")
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt
@ -1,22 +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
-
-import org.jetbrains.kotlinx.multik.api.Multik
-import org.jetbrains.kotlinx.multik.api.ndarray
-import org.jetbrains.kotlinx.multik.default.DefaultEngine
-import space.kscience.kmath.multik.MultikDoubleAlgebra
-import space.kscience.kmath.nd.one
-import space.kscience.kmath.structures.Float64
-
-
-val multikAlgebra = MultikDoubleAlgebra(DefaultEngine())
-
-fun main(): Unit = with(multikAlgebra) {
-    val a = Multik.ndarray(intArrayOf(1, 2, 3)).asType<Float64>().wrap()
-    val b = Multik.ndarray(doubleArrayOf(1.0, 2.0, 3.0)).wrap()
-    one(a.shape) - a + b * 3.0
-}
--- a/examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
+++ b/examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
@ -1,236 +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
-
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.operations.asIterable
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.tensors.core.*
-import kotlin.math.sqrt
-
-const val seed = 100500L
-
-// Simple feedforward neural network with backpropagation training
-
-// interface of network layer
-interface Layer {
-    fun forward(input: DoubleTensor): DoubleTensor
-    fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor
-}
-
-// activation layer
-open class Activation(
-    val activation: (DoubleTensor) -> DoubleTensor,
-    val activationDer: (DoubleTensor) -> DoubleTensor,
-) : Layer {
-    override fun forward(input: DoubleTensor): DoubleTensor {
-        return activation(input)
-    }
-
-    override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor {
-        return DoubleTensorAlgebra { outputError * activationDer(input) }
-    }
-}
-
-fun relu(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
-    x.map { if (it > 0) it else 0.0 }
-}
-
-fun reluDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
-    x.map { if (it > 0) 1.0 else 0.0 }
-}
-
-// activation layer with relu activator
-class ReLU : Activation(::relu, ::reluDer)
-
-fun sigmoid(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
-    1.0 / (1.0 + exp((-x)))
-}
-
-fun sigmoidDer(x: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
-    sigmoid(x) * (1.0 - sigmoid(x))
-}
-
-// activation layer with sigmoid activator
-class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
-
-// dense layer
-class Dense(
-    private val inputUnits: Int,
-    private val outputUnits: Int,
-    private val learningRate: Double = 0.1,
-) : Layer {
-
-    private val weights: DoubleTensor = DoubleTensorAlgebra {
-        randomNormal(
-            ShapeND(inputUnits, outputUnits),
-            seed
-        ) * sqrt(2.0 / (inputUnits + outputUnits))
-    }
-
-    private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(ShapeND(outputUnits)) }
-
-    override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
-        (input dot weights) + bias
-    }
-
-    override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
-        val gradInput = outputError dot weights.transposed()
-
-        val gradW = input.transposed() dot outputError
-        val gradBias = mean(structureND = outputError, dim = 0, keepDim = false) * input.shape[0].toDouble()
-
-        weights -= learningRate * gradW
-        bias -= learningRate * gradBias
-
-        gradInput
-    }
-
-}
-
-// simple accuracy equal to the proportion of correct answers
-fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
-    check(yPred.shape == yTrue.shape)
-    val n = yPred.shape[0]
-    var correctCnt = 0
-    for (i in 0 until n) {
-        if (yPred[intArrayOf(i, 0)] == yTrue[intArrayOf(i, 0)]) {
-            correctCnt += 1
-        }
-    }
-    return correctCnt.toDouble() / n.toDouble()
-}
-
-// neural network class
-class NeuralNetwork(private val layers: List<Layer>) {
-    private fun softMaxLoss(yPred: DoubleTensor, yTrue: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
-
-        val onesForAnswers = zeroesLike(yPred)
-        yTrue.source.asIterable().forEachIndexed { index, labelDouble ->
-            val label = labelDouble.toInt()
-            onesForAnswers[intArrayOf(index, label)] = 1.0
-        }
-
-        val softmaxValue = exp(yPred) / exp(yPred).sum(dim = 1, keepDim = true)
-
-        (-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
-    }
-
-
-    private fun forward(x: DoubleTensor): List<DoubleTensor> {
-        var input = x
-
-        return buildList {
-            layers.forEach { layer ->
-                val output = layer.forward(input)
-                add(output)
-                input = output
-            }
-        }
-    }
-
-    private fun train(xTrain: DoubleTensor, yTrain: DoubleTensor) {
-        val layerInputs = buildList {
-            add(xTrain)
-            addAll(forward(xTrain))
-        }
-
-        var lossGrad = softMaxLoss(layerInputs.last(), yTrain)
-
-        layers.zip(layerInputs).reversed().forEach { (layer, input) ->
-            lossGrad = layer.backward(input, lossGrad)
-        }
-    }
-
-    fun fit(xTrain: DoubleTensor, yTrain: DoubleTensor, batchSize: Int, epochs: Int) = DoubleTensorAlgebra {
-        fun iterBatch(x: DoubleTensor, y: DoubleTensor): Sequence<Pair<DoubleTensor, DoubleTensor>> = sequence {
-            val n = x.shape[0]
-            val shuffledIndices = (0 until n).shuffled()
-            for (i in 0 until n step batchSize) {
-                val excerptIndices = shuffledIndices.drop(i).take(batchSize).toIntArray()
-                val batch = x.rowsByIndices(excerptIndices) to y.rowsByIndices(excerptIndices)
-                yield(batch)
-            }
-        }
-
-        for (epoch in 0 until epochs) {
-            println("Epoch ${epoch + 1}/$epochs")
-            for ((xBatch, yBatch) in iterBatch(xTrain, yTrain)) {
-                train(xBatch, yBatch)
-            }
-            println("Accuracy:${accuracy(yTrain, predict(xTrain).argMax(1, true).toDoubleTensor())}")
-        }
-    }
-
-    fun predict(x: DoubleTensor): DoubleTensor {
-        return forward(x).last()
-    }
-
-}
-
-
-fun main() = BroadcastDoubleTensorAlgebra {
-    val features = 5
-    val sampleSize = 250
-    val trainSize = 180
-    //val testSize = sampleSize - trainSize
-
-    // take sample of features from normal distribution
-    val x = randomNormal(ShapeND(sampleSize, features), seed) * 2.5
-
-    x += fromArray(
-        ShapeND(5),
-        doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row means
-    )
-
-
-    // define class like '1' if the sum of features > 0 and '0' otherwise
-    val y = fromArray(
-        ShapeND(sampleSize, 1),
-        DoubleArray(sampleSize) { i ->
-            if (x.getTensor(i).sum() > 0.0) {
-                1.0
-            } else {
-                0.0
-            }
-        }
-    )
-
-    // split train ans test
-    val trainIndices = (0 until trainSize).toList().toIntArray()
-    val testIndices = (trainSize until sampleSize).toList().toIntArray()
-
-    val xTrain = x.rowsByIndices(trainIndices)
-    val yTrain = y.rowsByIndices(trainIndices)
-
-    val xTest = x.rowsByIndices(testIndices)
-    val yTest = y.rowsByIndices(testIndices)
-
-    // build model
-    val layers = buildList {
-        add(Dense(features, 64))
-        add(ReLU())
-        add(Dense(64, 16))
-        add(ReLU())
-        add(Dense(16, 2))
-        add(Sigmoid())
-    }
-    val model = NeuralNetwork(layers)
-
-    // fit it with train data
-    model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
-
-    // make prediction
-    val prediction = model.predict(xTest)
-
-    // process raw prediction via argMax
-    val predictionLabels = prediction.argMax(1, true).toDoubleTensor()
-
-    // find out accuracy
-    val acc = accuracy(yTest, predictionLabels)
-    println("Test accuracy:$acc")
-
-}
--- a/Show More
+++ b/Show More