add multik transpose direct comparison

Fix transposed in mutlik algebra.

.github/workflows/build.yml

@@ -13,12 +13,12 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-java@v3.5.1
         with:
-          java-version: '11'
+          java-version: '17'
           distribution: 'liberica'
           cache: 'gradle'
       - name: Gradle Wrapper Validation
         uses: gradle/wrapper-validation-action@v1.0.4
       - name: Gradle Build
-        uses: gradle/gradle-build-action@v2.3.2
+        uses: gradle/gradle-build-action@v2.4.2
         with:
           arguments: test jvmTest

.github/workflows/pages.yml

@@ -7,25 +7,25 @@ on:
 
 jobs:
   build:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-24.04
     timeout-minutes: 40
     steps:
-      - uses: actions/checkout@v3.0.0
-      - uses: actions/setup-java@v3.0.0
+      - uses: actions/checkout@v4
+      - uses: actions/setup-java@v4
         with:
-          java-version: 11
+          java-version: 17
           distribution: liberica
       - name: Cache konan
-        uses: actions/cache@v3.0.1
+        uses: actions/cache@v3
         with:
           path: ~/.konan
           key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
           restore-keys: |
             ${{ runner.os }}-gradle-
-      - uses: gradle/gradle-build-action@v2.1.5
+      - uses: gradle/gradle-build-action@v3
         with:
-          arguments: dokkaHtmlMultiModule --no-parallel
-      - uses: JamesIves/github-pages-deploy-action@v4.3.0
+          arguments: dokkaGenerate --no-parallel
+      - uses: JamesIves/github-pages-deploy-action@v4
         with:
           branch: gh-pages
-          folder: build/dokka/htmlMultiModule
+          folder: build/dokka/html

.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: |
-            ${{ runner.os }}-gradle-
-      - name: Publish Windows Artifacts
-        if: matrix.os == 'windows-latest'
-        uses: gradle/gradle-build-action@v2.4.0
-        with:
-          arguments: |
-            publishAllPublicationsToSpaceRepository
-            -Ppublishing.targets=all
-            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
-      - name: Publish Mac Artifacts
-        if: matrix.os == 'macOS-latest'
-        uses: gradle/gradle-build-action@v2.4.0
-        with:
-          arguments: |
-            publishMacosX64PublicationToSpaceRepository
-            publishMacosArm64PublicationToSpaceRepository
-            publishIosX64PublicationToSpaceRepository
-            publishIosArm64PublicationToSpaceRepository
-            publishIosSimulatorArm64PublicationToSpaceRepository
-            -Ppublishing.targets=all
-            -Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-            -Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}

.gitignore

@@ -4,6 +4,8 @@ out/
 
 .idea/
 .vscode/
+.fleet/
+.kotlin/
 
 
 # Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored)

.space.kts

@@ -1,3 +0,0 @@
-job("Build") {
-    gradlew("openjdk:11", "build")
-}

CHANGELOG.md

@@ -3,8 +3,10 @@
 ## Unreleased
 
 ### Added
+- Fit accessors with Attribute
 
 ### Changed
+- Upgrade tensorflow version to 1.0.0
 
 ### Deprecated
 
@@ -14,9 +16,80 @@
 
 ### Security
 
-## 0.3.1-dev-RC - 2023-04-09
+## 0.4.2 - 2025-01-27
 
 ### Added
+
+- Convenient matrix builders for rows, columns, vstacks and hstacks
+- Sparse matrix builder
+
+### Fixed
+
+- Ojalgo conversion bug which made all converted matrices be zero.
+
+## 0.4.1 - 2025-01-12
+
+### Added
+
+- Metropolis-Hastings sampler
+- Ojalgo `LinearSpace` implementation.
+
+### Changed
+
+- attributes-kt moved to a separate project, and the version used is 0.3.0
+- Kotlin 2.1. Now use cross-compilation to deploy macOS targets.
+- Changed `origin` to `cmMatrix` in kmath-commons to avoid property name clash. Expose bidirectional conversion in `CMLinearSpace`
+- (BREAKING CHANGE) Changed implementations in `kmath-ejml` to match CM and ojalgo style. Specifically, provide bidirectional conversion for library types.
+
+### Fixed
+
+- (BREAKING CHANGE) Fix EJML to properly treat vectors as columns
+
+## 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
@@ -26,6 +99,8 @@
 - 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
@@ -33,16 +108,19 @@
 - 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`.
+- 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
@@ -67,6 +145,7 @@
 - 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.
@@ -100,9 +179,11 @@
 - `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.
@@ -113,12 +194,14 @@
 - 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)
@@ -138,6 +221,7 @@
 - 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`)
@@ -147,7 +231,7 @@
 - Full autodiff refactoring based on `Symbol`
 - `kmath-prob` renamed to `kmath-stat`
 - Grid generators moved to `kmath-for-real`
-- Use `Point<Double>` instead of specialized type in `kmath-for-real`
+- 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`
@@ -162,6 +246,7 @@
 - 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.
@@ -170,20 +255,24 @@
 - 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)
+- 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)
+- 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`
@@ -192,10 +281,12 @@
 - 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
+- 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)
@@ -203,6 +294,7 @@
 - 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)

README.md

@@ -2,7 +2,6 @@
 [![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
 
@@ -11,18 +10,22 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
 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 (**WIP**)](https://SciProgCentre.github.io/kmath/)
+[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 and Native)
-  .
+* 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.
 
@@ -54,17 +57,14 @@ module definitions below. The module stability could have the following levels:
 
 
 ### [benchmarks](benchmarks)
-> 
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [examples](examples)
-> 
 >
 > **Maturity**: EXPERIMENTAL
 
 ### [kmath-ast](kmath-ast)
-> 
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -76,7 +76,7 @@ module definitions below. The module stability could have the following levels:
 
 
 ### [kmath-commons](kmath-commons)
-> 
+> Commons math binding for kmath
 >
 > **Maturity**: EXPERIMENTAL
 
@@ -101,24 +101,21 @@ module definitions below. The module stability could have the following levels:
 > - [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
 >
@@ -142,7 +139,7 @@ One can still use generic algebras though.
 
 
 ### [kmath-functions](kmath-functions)
-> 
+> Functions, integration and interpolation
 >
 > **Maturity**: EXPERIMENTAL
 >
@@ -155,31 +152,19 @@ One can still use generic algebras though.
 
 
 ### [kmath-geometry](kmath-geometry)
-> 
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-histograms](kmath-histograms)
-> 
 >
 > **Maturity**: PROTOTYPE
 
-### [kmath-jafama](kmath-jafama)
-> 
->
-> **Maturity**: PROTOTYPE
->
-> **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
 >
@@ -194,14 +179,14 @@ One can still use generic algebras though.
 > **Maturity**: DEVELOPMENT
 
 ### [kmath-multik](kmath-multik)
-> 
+> JetBrains Multik connector
 >
 > **Maturity**: PROTOTYPE
 
 ### [kmath-nd4j](kmath-nd4j)
-> 
+> ND4J NDStructure implementation and according NDAlgebra classes
 >
-> **Maturity**: EXPERIMENTAL
+> **Maturity**: DEPRECATED
 >
 > **Features:**
 > - [nd4jarraystructure](kmath-nd4j/#) : NDStructure wrapper for INDArray
@@ -209,28 +194,30 @@ One can still use generic algebras though.
 > - [nd4jarrayfields](kmath-nd4j/#) : Fields over Nd4jArrayStructure of Float and Double
 
 
+### [kmath-ojalgo](kmath-ojalgo)
+> Ojalgo bindings for kmath
+>
+> **Maturity**: PROTOTYPE
+
 ### [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
 >
@@ -241,12 +228,11 @@ One can still use generic algebras though.
 
 
 ### [kmath-viktor](kmath-viktor)
-> 
+> Binding for https://github.com/JetBrains-Research/viktor
 >
-> **Maturity**: DEVELOPMENT
+> **Maturity**: DEPRECATED
 
 ### [test-utils](test-utils)
-> 
 >
 > **Maturity**: EXPERIMENTAL
 
@@ -256,22 +242,24 @@ One can still use generic algebras though.
 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, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
+reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
 feedback are also welcome.
 
 ## Performance
 
-Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
+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 convenient universal API first and then work on increasing performance for specific
+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.
+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 to use GraalVM-CE 11 for
-execution to get better performance.
+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
 
@@ -291,11 +279,10 @@ dependencies {
 }
 ```
 
-Gradle `6.0+` is required for multiplatform artifacts.
-
 ## Contributing
 
-The project requires a lot of additional work. The most important thing we need is a feedback about what features are
+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
-[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
+marked
+with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
+label.

benchmarks/README.md

@@ -1,4 +1,121 @@
-# Module benchmarks
+# BenchmarksResult
+
+## Report for benchmark configuration <code>main</code>
+
+* Run on OpenJDK 64-Bit Server VM (build 17.0.11+9) with Java process:
+
+```
+C:\Users\altavir\scoop\apps\gradle\current\.gradle\jdks\eclipse_adoptium-17-amd64-windows.2\bin\java.exe -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`|3.9E+06 &plusmn; 3.4E+05 ops/s|
+|`benchmarkBufferRead`|4.0E+06 &plusmn; 3.2E+05 ops/s|
+|`nativeBufferRead`|3.9E+06 &plusmn; 2.0E+05 ops/s|
+### [BigIntBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`jvmAdd`|3.1E+07 &plusmn; 1.8E+07 ops/s|
+|`jvmAddLarge`|4.5E+04 &plusmn; 5.5E+03 ops/s|
+|`jvmMultiply`|3.6E+07 &plusmn; 1.7E+07 ops/s|
+|`jvmMultiplyLarge`|1.9E+02 &plusmn; 95 ops/s|
+|`jvmParsing10`|4.0E+06 &plusmn; 8.8E+05 ops/s|
+|`jvmParsing16`|3.6E+06 &plusmn; 6.5E+05 ops/s|
+|`jvmPower`|25 &plusmn; 1.4 ops/s|
+|`jvmSmallAdd`|5.7E+07 &plusmn; 9.7E+05 ops/s|
+|`kmAdd`|2.6E+07 &plusmn; 8.8E+05 ops/s|
+|`kmAddLarge`|2.3E+04 &plusmn; 1.2E+03 ops/s|
+|`kmMultiply`|3.8E+07 &plusmn; 5.5E+06 ops/s|
+|`kmMultiplyLarge`|36 &plusmn; 3.8 ops/s|
+|`kmParsing10`|2.5E+06 &plusmn; 1.4E+05 ops/s|
+|`kmParsing16`|3.7E+06 &plusmn; 4.7E+05 ops/s|
+|`kmPower`|6.6 &plusmn; 1.0 ops/s|
+|`kmSmallAdd`|2.0E+07 &plusmn; 1.7E+06 ops/s|
+### [BufferBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`bufferViewReadWrite`|6.0E+06 &plusmn; 7.4E+05 ops/s|
+|`bufferViewReadWriteSpecialized`|7.6E+05 &plusmn; 1.1E+04 ops/s|
+|`complexBufferReadWrite`|2.4E+06 &plusmn; 2.7E+05 ops/s|
+|`doubleArrayReadWrite`|7.3E+06 &plusmn; 4.3E+05 ops/s|
+|`doubleBufferReadWrite`|7.3E+06 &plusmn; 3.4E+05 ops/s|
+### [DotBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`bufferedDot`|1.3 &plusmn; 0.032 ops/s|
+|`cmDot`|0.42 &plusmn; 0.20 ops/s|
+|`cmDotWithConversion`|0.83 &plusmn; 0.12 ops/s|
+|`ejmlDot`|2.6 &plusmn; 0.049 ops/s|
+|`ejmlDotWithConversion`|2.5 &plusmn; 0.075 ops/s|
+|`multikDot`|25 &plusmn; 0.52 ops/s|
+|`ojalgoDot`|11 &plusmn; 1.3 ops/s|
+|`parallelDot`|11 &plusmn; 0.17 ops/s|
+|`tensorDot`|1.1 &plusmn; 0.028 ops/s|
+|`tfDot`|4.7 &plusmn; 0.14 ops/s|
+### [ExpressionsInterpretersBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`asmGenericExpression`|12 &plusmn; 0.099 ops/s|
+|`asmPrimitiveExpression`|26 &plusmn; 0.57 ops/s|
+|`asmPrimitiveExpressionArray`|74 &plusmn; 1.7 ops/s|
+|`functionalExpression`|5.3 &plusmn; 0.015 ops/s|
+|`justCalculate`|74 &plusmn; 0.85 ops/s|
+|`mstExpression`|4.2 &plusmn; 0.10 ops/s|
+|`rawExpression`|25 &plusmn; 0.74 ops/s|
+### [IntegrationBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/IntegrationBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`complexIntegration`|2.6E+03 &plusmn; 46 ops/s|
+|`doubleIntegration`|2.8E+03 &plusmn; 1.1E+02 ops/s|
+### [MatrixInverseBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`cmLUPInversion`|2.1E+03 &plusmn; 35 ops/s|
+|`ejmlInverse`|1.2E+03 &plusmn; 27 ops/s|
+|`kmathLupInversion`|4.0E+02 &plusmn; 52 ops/s|
+|`kmathParallelLupInversion`|4.0E+02 &plusmn; 9.6 ops/s|
+|`ojalgoInverse`|2.1E+03 &plusmn; 3.3E+02 ops/s|
+### [MinStatisticBenchmark.kt](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MinStatisticBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`kotlinArrayMin`| 1875.7 &plusmn; 401.5 ops/s |
+|`minBlocking`| 1357.9 &plusmn; 72.0 ops/s  |
+
+### [NDFieldBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`boxingFieldAdd`|1.7 &plusmn; 0.11 ops/s|
+|`multikAdd`|7.0 &plusmn; 0.41 ops/s|
+|`multikInPlaceAdd`|34 &plusmn; 1.7 ops/s|
+|`specializedFieldAdd`|7.2 &plusmn; 1.2 ops/s|
+|`tensorAdd`|7.2 &plusmn; 1.6 ops/s|
+|`tensorInPlaceAdd`|7.4 &plusmn; 4.9 ops/s|
+|`viktorAdd`|5.8 &plusmn; 0.65 ops/s|
+### [ViktorBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`doubleFieldAddition`|7.1 &plusmn; 2.0 ops/s|
+|`rawViktor`|6.2 &plusmn; 1.0 ops/s|
+|`viktorFieldAddition`|6.4 &plusmn; 0.29 ops/s|
+### [ViktorLogBenchmark](src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt)
+
+| Benchmark | Score |
+|:---------:|:-----:|
+|`rawViktorLog`|1.3 &plusmn; 0.016 ops/s|
+|`realFieldLog`|1.3 &plusmn; 0.019 ops/s|
+|`viktorFieldLog`|1.3 &plusmn; 0.020 ops/s|
 
 
 

benchmarks/build.gradle.kts

@@ -1,24 +1,30 @@
-@file:Suppress("UNUSED_VARIABLE")
-
-import org.jetbrains.kotlin.gradle.tasks.KotlinJvmCompile
-import space.kscience.kmath.benchmarks.addBenchmarkProperties
+import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
+import com.fasterxml.jackson.module.kotlin.readValue
+import kotlinx.benchmark.gradle.BenchmarksExtension
+import java.util.*
 
 plugins {
     kotlin("multiplatform")
     alias(spclibs.plugins.kotlin.plugin.allopen)
-    id("org.jetbrains.kotlinx.benchmark")
+    alias(spclibs.plugins.kotlinx.benchmark)
 }
 
 allOpen.annotation("org.openjdk.jmh.annotations.State")
-sourceSets.register("benchmarks")
+//sourceSets.register("benchmarks")
 
 repositories {
     mavenCentral()
 }
 
-val multikVersion: String by rootProject.extra
-
 kotlin {
+    jvmToolchain(17)
+
+    compilerOptions {
+        optIn.addAll(
+            "space.kscience.kmath.UnstableKMathAPI"
+        )
+    }
+
     jvm()
 
     js(IR) {
@@ -29,6 +35,9 @@ kotlin {
         all {
             languageSettings {
                 progressiveMode = true
+                optIn("kotlin.contracts.ExperimentalContracts")
+                optIn("kotlin.ExperimentalUnsignedTypes")
+                optIn("space.kscience.kmath.UnstableKMathAPI")
             }
         }
 
@@ -42,23 +51,24 @@ kotlin {
                 implementation(project(":kmath-dimensions"))
                 implementation(project(":kmath-for-real"))
                 implementation(project(":kmath-tensors"))
-                implementation(project(":kmath-multik"))
-                implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+                implementation(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.kmathCommons)
+                implementation(projects.kmathEjml)
+                implementation(projects.kmathKotlingrad)
+                implementation(projects.kmathViktor)
+                implementation(projects.kmathOjalgo)
                 implementation(projects.kmath.kmathTensorflow)
-                implementation("org.tensorflow:tensorflow-core-platform:0.4.0")
-                implementation("org.nd4j:nd4j-native:1.0.0-M1")
+                implementation(projects.kmathMultik)
+                implementation(libs.tensorflow.core.platform)
+//                implementation(projects.kmathNd4j)
+
+//                implementation(libs.nd4j.native.platform)
                 //    uncomment if your system supports AVX2
                 //    val os = System.getProperty("os.name")
                 //
@@ -93,6 +103,11 @@ benchmark {
         include("BufferBenchmark")
     }
 
+    configurations.register("minStatistic") {
+        commonConfiguration()
+        include("MinStatisticBenchmark")
+    }
+
     configurations.register("nd") {
         commonConfiguration()
         include("NDFieldBenchmark")
@@ -149,23 +164,121 @@ benchmark {
     }
 }
 
-kotlin.sourceSets.all {
-    with(languageSettings) {
-        optIn("kotlin.contracts.ExperimentalContracts")
-        optIn("kotlin.ExperimentalUnsignedTypes")
-        optIn("space.kscience.kmath.misc.UnstableKMathAPI")
-    }
-}
 
-tasks.withType<KotlinJvmCompile> {
-    kotlinOptions {
-        jvmTarget = "11"
-        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xlambdas=indy"
+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
-}
 
-addBenchmarkProperties()
+    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 {
+                it.nameWithoutExtension
+            }
+
+            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}|")
+                        }
+                    }
+                }
+            }
+        }
+    }
+}

benchmarks/docs/README-TEMPLATE.md

@@ -0,0 +1,5 @@
+# BenchmarksResult
+
+${benchmarkMain}
+
+

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -11,9 +11,10 @@ import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
 import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.expressions.*
-import space.kscience.kmath.operations.DoubleField
+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
@@ -67,7 +68,7 @@ class ExpressionsInterpretersBenchmark {
         blackhole.consume(sum)
     }
 
-    private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
+    private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
         val random = Random(0)
         var sum = 0.0
         val m = HashMap<Symbol, Double>()
@@ -84,7 +85,7 @@ class ExpressionsInterpretersBenchmark {
         private val x by symbol
         private const val times = 1_000_000
 
-        private val functional = DoubleField.expression {
+        private val functional = Float64Field.expression {
             val x = bindSymbol(Symbol.x)
             x * number(2.0) + 2.0 / x - 16.0 / sin(x)
         }
@@ -93,13 +94,14 @@ class ExpressionsInterpretersBenchmark {
             x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
         }
 
-        private val mst = node.toExpression(DoubleField)
-        @OptIn(UnstableKMathAPI::class)
-        private val wasm = node.wasmCompileToExpression(DoubleField)
-        private val estree = node.estreeCompileToExpression(DoubleField)
+        private val mst = node.toExpression(Float64Field)
 
-        private val raw = Expression<Double> { args ->
-            val x = args[x]!!
+        @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)
         }
     }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -67,7 +67,7 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
-        blackhole.consume(kmLargeNumber*kmLargeNumber)
+        blackhole.consume(kmLargeNumber * kmLargeNumber)
     }
 
     @Benchmark
@@ -77,7 +77,7 @@ internal class BigIntBenchmark {
 
     @Benchmark
     fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
-        blackhole.consume(jvmLargeNumber*jvmLargeNumber)
+        blackhole.consume(jvmLargeNumber * jvmLargeNumber)
     }
 
     @Benchmark

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -14,7 +14,7 @@ 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.DoubleBuffer
+import space.kscience.kmath.structures.Float64Buffer
 import space.kscience.kmath.structures.getDouble
 import space.kscience.kmath.structures.permute
 
@@ -33,7 +33,7 @@ internal class BufferBenchmark {
 
     @Benchmark
     fun doubleBufferReadWrite(blackhole: Blackhole) {
-        val buffer = DoubleBuffer(size) { it.toDouble() }
+        val buffer = Float64Buffer(size) { it.toDouble() }
         var res = 0.0
         (0 until size).forEach {
             res += buffer[it]
@@ -43,7 +43,7 @@ internal class BufferBenchmark {
 
     @Benchmark
     fun bufferViewReadWrite(blackhole: Blackhole) {
-        val buffer = DoubleBuffer(size) { it.toDouble() }.permute(reversedIndices)
+        val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
         var res = 0.0
         (0 until size).forEach {
             res += buffer[it]
@@ -53,7 +53,7 @@ internal class BufferBenchmark {
 
     @Benchmark
     fun bufferViewReadWriteSpecialized(blackhole: Blackhole) {
-        val buffer = DoubleBuffer(size) { it.toDouble() }.permute(reversedIndices)
+        val buffer = Float64Buffer(size) { it.toDouble() }.permute(reversedIndices)
         var res = 0.0
         (0 until size).forEach {
             res += buffer.getDouble(it)
@@ -75,6 +75,6 @@ internal class BufferBenchmark {
 
     private companion object {
         private const val size = 100
-        private val reversedIndices = IntArray(size){it}.apply { reverse() }
+        private val reversedIndices = IntArray(size) { it }.apply { reverse() }
     }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -11,12 +11,13 @@ 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.DoubleField
-import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.ojalgo.Ojalgo
+import space.kscience.kmath.ojalgo.linearSpace
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.tensorflow.produceWithTF
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.tensorAlgebra
 import kotlin.random.Random
 
@@ -27,10 +28,10 @@ internal class DotBenchmark {
         const val dim = 1000
 
         //creating invertible matrix
-        val matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
+        val matrix1 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
             random.nextDouble()
         }
-        val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
+        val matrix2 = Float64Field.linearSpace.buildMatrix(dim, dim) { _, _ ->
             random.nextDouble()
         }
 
@@ -45,7 +46,7 @@ internal class DotBenchmark {
     @Benchmark
     fun tfDot(blackhole: Blackhole) {
         blackhole.consume(
-            DoubleField.produceWithTF {
+            Float64Field.produceWithTF {
                 matrix1 dot matrix1
             }
         )
@@ -57,13 +58,13 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun cmDot(blackhole: Blackhole) = CMLinearSpace {
-        blackhole.consume(cmMatrix1 dot cmMatrix2)
+    fun cmDot(blackhole: Blackhole): Unit = CMLinearSpace {
+        blackhole.consume(cmMatrix1.asMatrix() dot cmMatrix2.asMatrix())
     }
 
     @Benchmark
-    fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
-        blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
+    fun ejmlDot(blackhole: Blackhole): Unit = EjmlLinearSpaceDDRM {
+        blackhole.consume(ejmlMatrix1.asMatrix() dot ejmlMatrix2.asMatrix())
     }
 
     @Benchmark
@@ -72,7 +73,7 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun tensorDot(blackhole: Blackhole) = with(DoubleField.tensorAlgebra) {
+    fun ojalgoDot(blackhole: Blackhole) = Ojalgo.R064.linearSpace {
         blackhole.consume(matrix1 dot matrix2)
     }
 
@@ -82,17 +83,18 @@ internal class DotBenchmark {
     }
 
     @Benchmark
-    fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
+    fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
         blackhole.consume(matrix1 dot matrix2)
     }
 
     @Benchmark
-    fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
+    fun bufferedDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
 
     @Benchmark
-    fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
+    fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
+
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -12,9 +12,10 @@ 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.DoubleField
+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
 
@@ -83,7 +84,7 @@ internal class ExpressionsInterpretersBenchmark {
         blackhole.consume(sum)
     }
 
-    private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
+    private fun invokeAndSum(expr: Expression<Float64>, blackhole: Blackhole) {
         val random = Random(0)
         var sum = 0.0
         val m = HashMap<Symbol, Double>()
@@ -100,7 +101,7 @@ internal class ExpressionsInterpretersBenchmark {
         private val x by symbol
         private const val times = 1_000_000
 
-        private val functional = DoubleField.expression {
+        private val functional = Float64Field.expression {
             val x = bindSymbol(Symbol.x)
             x * number(2.0) + 2.0 / x - 16.0 / sin(x)
         }
@@ -109,14 +110,14 @@ internal class ExpressionsInterpretersBenchmark {
             x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
         }
 
-        private val mst = node.toExpression(DoubleField)
+        private val mst = node.toExpression(Float64Field)
 
-        private val asmPrimitive = node.compileToExpression(DoubleField)
+        private val asmPrimitive = node.compileToExpression(Float64Field)
         private val xIdx = asmPrimitive.indexer.indexOf(x)
 
-        private val asmGeneric = node.compileToExpression(DoubleField as Algebra<Double>)
+        private val asmGeneric = node.compileToExpression(Float64Field as Algebra<Float64>)
 
-        private val raw = Expression<Double> { args ->
+        private val raw = Expression<Float64> { args ->
             val x = args[x]!!
             x * 2.0 + 2.0 / x - 16.0 / sin(x)
         }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * 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.
  */
 
@@ -24,7 +24,7 @@ internal class IntegrationBenchmark {
     fun doubleIntegration(blackhole: Blackhole) {
         val res = Double.algebra.gaussIntegrator.integrate(0.0..1.0, intervals = 1000) { x: Double ->
             //sin(1 / x)
-            1/x
+            1 / x
         }.value
         blackhole.consume(res)
     }
@@ -33,7 +33,7 @@ internal class IntegrationBenchmark {
     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
+            1 / x - i / x
         }.value
         blackhole.consume(res)
     }

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

@@ -1,42 +0,0 @@
-/*
- * Copyright 2018-2022 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.benchmarks
-
-import kotlinx.benchmark.Blackhole
-import org.openjdk.jmh.annotations.Benchmark
-import org.openjdk.jmh.annotations.Scope
-import org.openjdk.jmh.annotations.State
-import space.kscience.kmath.jafama.JafamaDoubleField
-import space.kscience.kmath.jafama.StrictJafamaDoubleField
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.invoke
-import kotlin.contracts.InvocationKind
-import kotlin.contracts.contract
-import kotlin.random.Random
-
-@State(Scope.Benchmark)
-internal class JafamaBenchmark {
-    @Benchmark
-    fun jafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        JafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-
-    @Benchmark
-    fun core(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        DoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-
-    @Benchmark
-    fun strictJafama(blackhole: Blackhole) = invokeBenchmarks(blackhole) { x ->
-        StrictJafamaDoubleField { x * power(x, 4) * exp(x) / cos(x) + sin(x) }
-    }
-}
-
-private inline fun invokeBenchmarks(blackhole: Blackhole, expr: (Double) -> Double) {
-    contract { callsInPlace(expr, InvocationKind.AT_LEAST_ONCE) }
-    val rng = Random(0)
-    repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
-}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -12,9 +12,9 @@ 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.*
+import space.kscience.kmath.ojalgo.Ojalgo
+import space.kscience.kmath.ojalgo.linearSpace
 import space.kscience.kmath.operations.algebra
 import kotlin.random.Random
 
@@ -38,16 +38,23 @@ internal class MatrixInverseBenchmark {
     }
 
     @Benchmark
-    fun cmLUPInversion(blackhole: Blackhole) {
-        CMLinearSpace {
-            blackhole.consume(lupSolver().inverse(matrix))
-        }
+    fun kmathParallelLupInversion(blackhole: Blackhole) {
+        blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
     }
 
     @Benchmark
-    fun ejmlInverse(blackhole: Blackhole) {
-        EjmlLinearSpaceDDRM {
-            blackhole.consume(matrix.toEjml().inverse())
-        }
+    fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
+        blackhole.consume(lupSolver().inverse(matrix))
     }
+
+    @Benchmark
+    fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
+        blackhole.consume(matrix.inverted())
+    }
+
+    @Benchmark
+    fun ojalgoInverse(blackhole: Blackhole) = Ojalgo.R064.linearSpace {
+        blackhole.consume(matrix.getOrComputeAttribute(Inverted))
+    }
+
 }

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

@@ -0,0 +1,44 @@
+/*
+ * Copyright 2018-2025 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 kotlinx.coroutines.runBlocking
+import space.kscience.kmath.operations.Float64Field
+import space.kscience.kmath.stat.min
+import space.kscience.kmath.structures.*
+
+@State(Scope.Benchmark)
+internal class MinStatisticBenchmark {
+
+    @Benchmark
+    fun kotlinArrayMin(blackhole: Blackhole) {
+        val array = DoubleArray(size) { it.toDouble() }
+        var res = 0.0
+        (0 until size).forEach {
+            res += array.min()
+        }
+        blackhole.consume(res)
+    }
+
+    @Benchmark
+    fun minBlocking(blackhole: Blackhole) {
+        val buffer = Float64Buffer(size) { it.toDouble() }
+        var res = 0.0
+        (0 until size).forEach {
+            res += Float64Field.min.evaluateBlocking(buffer)
+        }
+        blackhole.consume(res)
+    }
+
+
+    private companion object {
+        private const val size = 1000
+    }
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -15,8 +15,8 @@ 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.DoubleField
+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
@@ -25,30 +25,39 @@ 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 viktorField = Float64Field.viktorAlgebra
+    }
+
     @Benchmark
     fun specializedFieldAdd(blackhole: Blackhole) = with(specializedField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun multikAdd(blackhole: Blackhole) = with(multikAlgebra) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
 
     @Benchmark
     fun viktorAdd(blackhole: Blackhole) = with(viktorField) {
-        var res: StructureND<Double> = one(shape)
+        var res: StructureND<Float64> = one(shape)
         repeat(n) { res += 1.0 }
         blackhole.consume(res)
     }
@@ -77,18 +86,10 @@ internal class NDFieldBenchmark {
 
 //    @Benchmark
 //    fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
-//        var res: StructureND<Double> = one(dim, dim)
+//        var res: StructureND<Float64> = one(dim, dim)
 //        repeat(n) { res += 1.0 }
 //        blackhole.consume(res)
 //    }
 
-    private companion object {
-        private const val dim = 1000
-        private const val n = 100
-        private val shape = ShapeND(dim, dim)
-        private val specializedField = DoubleField.ndAlgebra
-        private val genericField = BufferedFieldOpsND(DoubleField)
-        private val nd4jField = DoubleField.nd4j
-        private val viktorField = DoubleField.viktorAlgebra
-    }
+
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -9,10 +9,10 @@ import kotlinx.benchmark.Benchmark
 import kotlinx.benchmark.Blackhole
 import kotlinx.benchmark.Scope
 import kotlinx.benchmark.State
+import space.kscience.kmath.linear.MatrixBuilder
 import space.kscience.kmath.linear.linearSpace
-import space.kscience.kmath.linear.matrix
 import space.kscience.kmath.linear.symmetric
-import space.kscience.kmath.operations.DoubleField
+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
@@ -24,7 +24,7 @@ internal class TensorAlgebraBenchmark {
         private val random = Random(12224)
         private const val dim = 30
 
-        private val matrix = DoubleField.linearSpace.matrix(dim, dim).symmetric { _, _ -> random.nextDouble() }
+        private val matrix = Float64Field.linearSpace.MatrixBuilder(dim, dim).symmetric { _, _ -> random.nextDouble() }
     }
 
     @Benchmark

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -14,7 +14,8 @@ 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.DoubleField
+import space.kscience.kmath.operations.Float64Field
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.viktor.ViktorFieldND
 
 @State(Scope.Benchmark)
@@ -23,7 +24,7 @@ internal class ViktorBenchmark {
     @Benchmark
     fun doubleFieldAddition(blackhole: Blackhole) {
         with(doubleField) {
-            var res: StructureND<Double> = one(shape)
+            var res: StructureND<Float64> = one(shape)
             repeat(n) { res += 1.0 }
             blackhole.consume(res)
         }
@@ -52,7 +53,7 @@ internal class ViktorBenchmark {
         private val shape = ShapeND(dim, dim)
 
         // automatically build context most suited for given type.
-        private val doubleField = DoubleField.ndAlgebra
+        private val doubleField = Float64Field.ndAlgebra
         private val viktorField = ViktorFieldND(dim, dim)
     }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -13,7 +13,7 @@ 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.DoubleField
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.viktor.ViktorFieldND
 
 @State(Scope.Benchmark)
@@ -52,7 +52,7 @@ internal class ViktorLogBenchmark {
         private val shape = ShapeND(dim, dim)
 
         // automatically build context most suited for given type.
-        private val doubleField = DoubleField.ndAlgebra
+        private val doubleField = Float64Field.ndAlgebra
         private val viktorField = ViktorFieldND(dim, dim)
     }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

build.gradle.kts

@@ -1,10 +1,9 @@
-import space.kscience.gradle.isInDevelopment
 import space.kscience.gradle.useApache2Licence
 import space.kscience.gradle.useSPCTeam
 
 plugins {
-    id("space.kscience.gradle.project")
-    id("org.jetbrains.kotlinx.kover") version "0.6.0"
+    alias(spclibs.plugins.kscience.project)
+    alias(spclibs.plugins.kotlinx.kover)
 }
 
 allprojects {
@@ -15,30 +14,49 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.1-dev-RC"
+    version = "0.4.3-dev-1"
+}
+
+dependencies {
+    subprojects.forEach {
+        dokka(it)
+    }
+}
+
+dokka{
+    dokkaSourceSets.configureEach {
+        val readmeFile = projectDir.resolve("README.md")
+        if (readmeFile.exists()) includes.from(readmeFile)
+    }
 }
 
 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")
+        dokka {
+            dokkaSourceSets.configureEach {
+                val readmeFile = 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(
-                        java.net.URL("https://github.com/SciProgCentre/kmath/tree/master/${this@subprojects.name}/$kotlinDirPath")
+                    remoteUrl(
+                        "https://github.com/SciProgCentre/kmath/tree/master/${name}/$kotlinDirPath"
                     )
                 }
 
+                fun externalDocumentationLink(url: String, packageListUrl: String? = null){
+                    externalDocumentationLinks.register(url) {
+                        url(url)
+                        packageListUrl?.let {
+                            packageListUrl(it)
+                        }
+                    }
+                }
+
                 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/")
@@ -49,11 +67,12 @@ subprojects {
                 )
 
                 externalDocumentationLink(
-                    "https://breandan.net/kotlingrad/kotlingrad/",
-                    "https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list",
+                    "https://breandan.net/kotlingrad/kotlingrad",
+                    "https://breandan.net/kotlingrad/package-list",
                 )
             }
         }
+
     }
 }
 
@@ -64,17 +83,8 @@ ksciencePublish {
         useApache2Licence()
         useSPCTeam()
     }
-    github("kmath", "SciProgCentre")
-    space(
-        if (isInDevelopment) {
-            "https://maven.pkg.jetbrains.space/spc/p/sci/dev"
-        } else {
-            "https://maven.pkg.jetbrains.space/spc/p/sci/maven"
-        }
-    )
-    sonatype()
+    repository("spc", "https://maven.sciprog.center/kscience")
+    central()
 }
 
-apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
-
-val multikVersion by extra("0.2.0")
+apiValidation.nonPublicMarkers.add("space.kscience.kmath.UnstableKMathAPI")

buildSrc/build.gradle.kts

@@ -1,31 +0,0 @@
-plugins {
-    `kotlin-dsl`
-    `version-catalog`
-}
-
-java.targetCompatibility = JavaVersion.VERSION_11
-
-repositories {
-    mavenLocal()
-    maven("https://repo.kotlin.link")
-    mavenCentral()
-    gradlePluginPortal()
-}
-
-val toolsVersion = spclibs.versions.tools.get()
-val kotlinVersion = spclibs.versions.kotlin.asProvider().get()
-val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
-
-dependencies {
-    api("space.kscience:gradle-tools:$toolsVersion")
-    //plugins form benchmarks
-    api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.4.7")
-    //api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
-    //to be used inside build-script only
-    //implementation(spclibs.kotlinx.serialization.json)
-    implementation("com.fasterxml.jackson.module:jackson-module-kotlin:2.14.+")
-}
-
-kotlin.sourceSets.all {
-    languageSettings.optIn("kotlin.OptIn")
-}

buildSrc/settings.gradle.kts

@@ -1,33 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
-
-dependencyResolutionManagement {
-    val projectProperties = java.util.Properties()
-    file("../gradle.properties").inputStream().use {
-        projectProperties.load(it)
-    }
-
-    projectProperties.forEach { key, value ->
-        extra.set(key.toString(), value)
-    }
-
-
-    val toolsVersion: String = projectProperties["toolsVersion"].toString()
-
-    repositories {
-        mavenLocal()
-        maven("https://repo.kotlin.link")
-        mavenCentral()
-        gradlePluginPortal()
-    }
-
-    versionCatalogs {
-        create("spclibs") {
-            from("space.kscience:version-catalog:$toolsVersion")
-        }
-    }
-}

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

@@ -1,55 +0,0 @@
-/*
- * Copyright 2018-2022 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
-
-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
-}

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

@@ -1,104 +0,0 @@
-/*
- * Copyright 2018-2022 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.gradle.BenchmarksExtension
-import com.fasterxml.jackson.module.kotlin.jacksonObjectMapper
-import com.fasterxml.jackson.module.kotlin.readValue
-import org.gradle.api.Project
-import space.kscience.gradle.KScienceReadmeExtension
-import java.time.LocalDateTime
-import java.time.ZoneId
-import java.time.format.DateTimeFormatter
-import java.time.format.DateTimeFormatterBuilder
-import java.time.format.SignStyle
-import java.time.temporal.ChronoField.*
-
-private val ISO_DATE_TIME: DateTimeFormatter = DateTimeFormatterBuilder().run {
-    parseCaseInsensitive()
-    appendValue(YEAR, 4, 10, SignStyle.EXCEEDS_PAD)
-    appendLiteral('-')
-    appendValue(MONTH_OF_YEAR, 2)
-    appendLiteral('-')
-    appendValue(DAY_OF_MONTH, 2)
-    appendLiteral('T')
-    appendValue(HOUR_OF_DAY, 2)
-    appendLiteral('.')
-    appendValue(MINUTE_OF_HOUR, 2)
-    optionalStart()
-    appendLiteral('.')
-    appendValue(SECOND_OF_MINUTE, 2)
-    optionalStart()
-    appendFraction(NANO_OF_SECOND, 0, 9, true)
-    optionalStart()
-    appendOffsetId()
-    optionalStart()
-    appendLiteral('[')
-    parseCaseSensitive()
-    appendZoneRegionId()
-    appendLiteral(']')
-    toFormatter()
-}
-
-private fun noun(number: Number, singular: String, plural: String) = if (number.toLong() == 1L) singular else plural
-
-private val jsonMapper = jacksonObjectMapper()
-
-fun Project.addBenchmarkProperties() {
-    val benchmarksProject = this
-    rootProject.subprojects.forEach { p ->
-        p.extensions.findByType(KScienceReadmeExtension::class.java)?.run {
-            benchmarksProject.extensions.findByType(BenchmarksExtension::class.java)?.configurations?.forEach { cfg ->
-                property("benchmark${cfg.name.capitalize()}") {
-                    val launches = benchmarksProject.buildDir.resolve("reports/benchmarks/${cfg.name}")
-
-                    val resDirectory = launches.listFiles()?.maxByOrNull {
-                        LocalDateTime.parse(it.name, ISO_DATE_TIME).atZone(ZoneId.systemDefault()).toInstant()
-                    }
-
-                    if (resDirectory == null || !(resDirectory.resolve("jvm.json")).exists()) {
-                        "> **Can't find appropriate benchmark data. Try generating readme files after running benchmarks**."
-                    } else {
-                        val reports: List<JmhReport> = jsonMapper.readValue<List<JmhReport>>(resDirectory.resolve("jvm.json"))
-
-                        buildString {
-                            appendLine("<details>")
-                            appendLine("<summary>")
-                            appendLine("Report for benchmark configuration <code>${cfg.name}</code>")
-                            appendLine("</summary>")
-                            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 ${first.measurementTime}.")
-
-                            appendLine()
-                            appendLine("| Benchmark | Score |")
-                            appendLine("|:---------:|:-----:|")
-
-                            reports.forEach { report ->
-                                appendLine("|`${report.benchmark}`|${report.primaryMetric.score} &plusmn; ${report.primaryMetric.scoreError} ${report.primaryMetric.scoreUnit}|")
-                            }
-
-                            appendLine("</details>")
-                        }
-                    }
-                }
-            }
-        }
-    }
-}

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

@@ -1,427 +0,0 @@
-/*
- * Copyright 2018-2022 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-@file:Suppress("KDocUnresolvedReference")
-
-package space.kscience.kmath.ejml.codegen
-
-import org.intellij.lang.annotations.Language
-import java.io.File
-
-private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
-    @Language("kotlin") val text = """/**
- * [EjmlVector] specialization for [$type].
- */
-public class Ejml${type}Vector<out M : $ejmlMatrixType>(override val origin: M) : EjmlVector<$type, M>(origin) {
-    init {
-        require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
-    }
-
-    override operator fun get(index: Int): $type = origin[0, index]
-}"""
-    appendLine(text)
-    appendLine()
-}
-
-private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
-    val text = """/**
- * [EjmlMatrix] specialization for [$type].
- */
-public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
-    override operator fun get(i: Int, j: Int): $type = origin[i, j]
-}"""
-    appendLine(text)
-    appendLine()
-}
-
-private fun Appendable.appendEjmlLinearSpace(
-    type: String,
-    kmathAlgebra: String,
-    ejmlMatrixParentTypeMatrix: String,
-    ejmlMatrixType: String,
-    ejmlMatrixDenseType: String,
-    ops: String,
-    denseOps: String,
-    isDense: Boolean,
-) {
-    @Language("kotlin") val text = """/**
- * [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
- * [${ejmlMatrixType}] matrices.
- */
-public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra}, $ejmlMatrixType>() {
-    /**
-     * The [${kmathAlgebra}] reference.
-     */
-    override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
-
-    @Suppress("UNCHECKED_CAST")
-    override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
-        this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
-        else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
-    }
-
-    @Suppress("UNCHECKED_CAST")
-    override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
-        this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
-        else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
-            (0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
-        })
-    }
-
-    override fun buildMatrix(
-        rows: Int,
-        columns: Int,
-        initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
-    ): Ejml${type}Matrix<${ejmlMatrixType}> = ${ejmlMatrixType}(rows, columns).also {
-        (0 until rows).forEach { row ->
-            (0 until columns).forEach { col -> it[row, col] = elementAlgebra.initializer(row, col) }
-        }
-    }.wrapMatrix()
-
-    override fun buildVector(
-        size: Int,
-        initializer: ${kmathAlgebra}.(Int) -> ${type},
-    ): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
-        (0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) }
-    })
-
-    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
-    private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
-
-    override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
-
-    override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
-        return out.wrapMatrix()
-    }
-
-    override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
-        return out.wrapVector()
-    }
-
-    override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-
-        CommonOps_${ops}.add(
-            elementAlgebra.one, 
-            toEjml().origin, 
-            elementAlgebra { -one },
-            other.toEjml().origin, 
-            out,${
-        if (isDense) "" else
-            """
-            null, 
-            null,"""
-    }
-        )
-
-        return out.wrapMatrix()
-    }
-
-    override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
-        val res = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.scale(value, toEjml().origin, res)
-        return res.wrapMatrix()
-    }
-
-    override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
-        val res = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.changeSign(toEjml().origin, res)
-        return res.wrapVector()
-    }
-
-    override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-        
-        CommonOps_${ops}.add(
-            elementAlgebra.one,
-            toEjml().origin,
-            elementAlgebra.one,
-            other.toEjml().origin, 
-            out,${
-        if (isDense) "" else
-            """
-            null, 
-            null,"""
-    }
-        )
-
-        return out.wrapMatrix()
-    }
-
-    override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-
-        CommonOps_${ops}.add(
-            elementAlgebra.one,
-            toEjml().origin,
-            elementAlgebra.one,
-            other.toEjml().origin,
-            out,${
-        if (isDense) "" else
-            """
-            null, 
-            null,"""
-    }
-        )
-
-        return out.wrapVector()
-    }
-
-    override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
-        val out = ${ejmlMatrixType}(1, 1)
-
-        CommonOps_${ops}.add(
-            elementAlgebra.one,
-            toEjml().origin,
-            elementAlgebra { -one },
-            other.toEjml().origin,
-            out,${
-        if (isDense) "" else
-            """
-            null, 
-            null,"""
-    }
-        )
-
-        return out.wrapVector()
-    }
-
-    override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
-
-    override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
-        val res = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.scale(value, toEjml().origin, res)
-        return res.wrapVector()
-    }
-
-    override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
-
-    @UnstableKMathAPI
-    override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
-        structure.getFeature(type)?.let { return it }
-        val origin = structure.toEjml().origin
-
-        return when (type) {
-        ${
-        if (isDense)
-            """    InverseMatrixFeature::class -> object : InverseMatrixFeature<${type}> {
-                override val inverse: Matrix<${type}> by lazy {
-                    val res = origin.copy()
-                    CommonOps_${ops}.invert(res)
-                    res.wrapMatrix()
-                }
-            }
-
-            DeterminantFeature::class -> object : DeterminantFeature<${type}> {
-                override val determinant: $type by lazy { CommonOps_${ops}.det(origin) }
-            }
-
-            SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<${type}> {
-                private val svd by lazy {
-                    DecompositionFactory_${ops}.svd(origin.numRows, origin.numCols, true, true, false)
-                        .apply { decompose(origin.copy()) }
-                }
-
-                override val u: Matrix<${type}> by lazy { svd.getU(null, false).wrapMatrix() }
-                override val s: Matrix<${type}> by lazy { svd.getW(null).wrapMatrix() }
-                override val v: Matrix<${type}> by lazy { svd.getV(null, false).wrapMatrix() }
-                override val singularValues: Point<${type}> by lazy { ${type}Buffer(svd.singularValues) }
-            }
-
-            QRDecompositionFeature::class -> object : QRDecompositionFeature<${type}> {
-                private val qr by lazy {
-                    DecompositionFactory_${ops}.qr().apply { decompose(origin.copy()) }
-                }
-
-                override val q: Matrix<${type}> by lazy {
-                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
-                }
-
-                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
-            }
-
-            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
-                override val l: Matrix<${type}> by lazy {
-                    val cholesky =
-                        DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
-
-                    cholesky.getT(null).wrapMatrix().withFeature(LFeature)
-                }
-            }
-
-            LupDecompositionFeature::class -> object : LupDecompositionFeature<${type}> {
-                private val lup by lazy {
-                    DecompositionFactory_${ops}.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
-                }
-
-                override val l: Matrix<${type}> by lazy {
-                    lup.getLower(null).wrapMatrix().withFeature(LFeature)
-                }
-
-                override val u: Matrix<${type}> by lazy {
-                    lup.getUpper(null).wrapMatrix().withFeature(UFeature)
-                }
-
-                override val p: Matrix<${type}> by lazy { lup.getRowPivot(null).wrapMatrix() }
-            }""" else """    QRDecompositionFeature::class -> object : QRDecompositionFeature<$type> {
-                private val qr by lazy {
-                    DecompositionFactory_${ops}.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
-                }
-
-                override val q: Matrix<${type}> by lazy {
-                    qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
-                }
-
-                override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
-            }
-
-            CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
-                override val l: Matrix<${type}> by lazy {
-                    val cholesky =
-                        DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
-
-                    (cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix().withFeature(LFeature)
-                }
-            }
-
-            LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
-                LUDecompositionFeature<${type}>, DeterminantFeature<${type}>, InverseMatrixFeature<${type}> {
-                private val lu by lazy {
-                    DecompositionFactory_${ops}.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
-                }
-
-                override val l: Matrix<${type}> by lazy {
-                    lu.getLower(null).wrapMatrix().withFeature(LFeature)
-                }
-
-                override val u: Matrix<${type}> by lazy {
-                    lu.getUpper(null).wrapMatrix().withFeature(UFeature)
-                }
-
-                override val inverse: Matrix<${type}> by lazy {
-                    var a = origin
-                    val inverse = ${ejmlMatrixDenseType}(1, 1)
-                    val solver = LinearSolverFactory_${ops}.lu(FillReducing.NONE)
-                    if (solver.modifiesA()) a = a.copy()
-                    val i = CommonOps_${denseOps}.identity(a.numRows)
-                    solver.solve(i, inverse)
-                    inverse.wrapMatrix()
-                }
-
-                override val determinant: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
-            }"""
-    }
-
-            else -> null
-        }?.let{
-            type.cast(it)
-        }
-    }
-
-    /**
-     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
-     *
-     * @param a the base matrix.
-     * @param b n by p matrix.
-     * @return the solution for *x* that is n by p.
-     */
-    public fun solve(a: Matrix<${type}>, b: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
-        val res = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
-        return res.wrapMatrix()
-    }
-
-    /**
-     * Solves for *x* in the following equation: *x = [a] <sup>-1</sup> &middot; [b]*.
-     *
-     * @param a the base matrix.
-     * @param b n by p vector.
-     * @return the solution for *x* that is n by p.
-     */
-    public fun solve(a: Matrix<${type}>, b: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
-        val res = ${ejmlMatrixType}(1, 1)
-        CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
-        return Ejml${type}Vector(res)
-    }
-}"""
-    appendLine(text)
-    appendLine()
-}
-
-
-/**
- * Generates routine EJML classes.
- */
-fun ejmlCodegen(outputFile: String): Unit = File(outputFile).run {
-    parentFile.mkdirs()
-
-    writer().use {
-        it.appendLine("/*")
-        it.appendLine(" * Copyright 2018-2021 KMath contributors.")
-        it.appendLine(" * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.")
-        it.appendLine(" */")
-        it.appendLine()
-        it.appendLine("/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */")
-        it.appendLine()
-        it.appendLine("package space.kscience.kmath.ejml")
-        it.appendLine()
-        it.appendLine("""import org.ejml.data.*
-import org.ejml.dense.row.CommonOps_DDRM
-import org.ejml.dense.row.CommonOps_FDRM
-import org.ejml.dense.row.factory.DecompositionFactory_DDRM
-import org.ejml.dense.row.factory.DecompositionFactory_FDRM
-import org.ejml.sparse.FillReducing
-import org.ejml.sparse.csc.CommonOps_DSCC
-import org.ejml.sparse.csc.CommonOps_FSCC
-import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
-import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
-import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
-import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
-import space.kscience.kmath.linear.*
-import space.kscience.kmath.linear.Matrix
-import space.kscience.kmath.UnstableKMathAPI
-import space.kscience.kmath.nd.StructureFeature
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.operations.FloatField
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.DoubleBuffer
-import space.kscience.kmath.structures.FloatBuffer
-import kotlin.reflect.KClass
-import kotlin.reflect.cast""")
-        it.appendLine()
-        it.appendEjmlVector("Double", "DMatrix")
-        it.appendEjmlVector("Float", "FMatrix")
-        it.appendEjmlMatrix("Double", "DMatrix")
-        it.appendEjmlMatrix("Float", "FMatrix")
-        it.appendEjmlLinearSpace("Double", "DoubleField", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
-        it.appendEjmlLinearSpace("Float", "FloatField", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
-
-        it.appendEjmlLinearSpace(
-            type = "Double",
-            kmathAlgebra = "DoubleField",
-            ejmlMatrixParentTypeMatrix = "DMatrix",
-            ejmlMatrixType = "DMatrixSparseCSC",
-            ejmlMatrixDenseType = "DMatrixRMaj",
-            ops = "DSCC",
-            denseOps = "DDRM",
-            isDense = false,
-        )
-
-        it.appendEjmlLinearSpace(
-            type = "Float",
-            kmathAlgebra = "FloatField",
-            ejmlMatrixParentTypeMatrix = "FMatrix",
-            ejmlMatrixType = "FMatrixSparseCSC",
-            ejmlMatrixDenseType = "FMatrixRMaj",
-            ops = "FSCC",
-            denseOps = "FDRM",
-            isDense = false,
-        )
-    }
-}

docs/buffers.md

@@ -17,4 +17,4 @@ 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  .
+instead.

docs/codestyle.md

@@ -1,27 +1,35 @@
 # 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.
+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.
+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.
+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.
+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 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 
+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.
+There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook
+one-lines seem to better show that the property or function is easily calculated.

docs/expressions.md

@@ -1,21 +1,24 @@
 # Expressions
 
-Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical expressions.
+Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical
+expressions.
 
 The potential use-cases for it (so far) are following:
 
 * lazy evaluation (in general simple lambda is better, but there are some border cases);
 * automatic differentiation in single-dimension and in multiple dimensions;
 * generation of mathematical syntax trees with subsequent code generation for other languages;
-* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with Symja's `IExpr`—integration, simplification, and more);
+* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with
+  Symja's `IExpr`—integration, simplification, and more);
 * visualization with `kmath-jupyter`.
 
-The workhorse of this API is `Expression` interface, which exposes single `operator fun invoke(arguments: Map<Symbol, T>): T`
+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**
+* Auto-differentiation expression in `kmath-commons` module allows using full power of `DerivativeStructure`
+  from commons-math. **TODO: add example**

docs/images/KM.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - 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.
   -->
 

docs/images/KM_mono.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - 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.
   -->
 

docs/images/KMath.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - 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.
   -->
 

docs/images/KMath_mono.svg

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8" standalone="no"?>
 <!--
-  - Copyright 2018-2022 KMath contributors.
+  - 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.
   -->
 

docs/linear.md

@@ -1,8 +1,12 @@
 ## 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.
+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:
+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.*
@@ -28,4 +32,5 @@ LinearSpace.Companion.real {
 ## Backends overview
 
 ### EJML
+
 ### Commons Math

docs/nd-structure.md

@@ -8,6 +8,7 @@ One of the most sought after features of mathematical libraries is the high-perf
 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)
@@ -16,6 +17,7 @@ Let us consider following contexts:
     //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
@@ -24,22 +26,27 @@ To test performance we will take 2d-structures with `dim = 1000` and add a struc
 to it `n = 1000` times.
 
 ## Specialized
+
 The code to run this looks like:
+
 ```kotlin
     specializedField.run {
-        var res: NDBuffer<Double> = one
+        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
@@ -48,13 +55,16 @@ Let's do the same with automatic field inference:
         }
     }
 ```
+
 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
@@ -63,12 +73,14 @@ When one calls
         }
     }
 ```
+
 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 {
@@ -82,30 +94,37 @@ This field still could be used with reasonable performance if call code is chang
         res.elements().forEach { it.second }
     }
 ```
+
 In this case it completes in about `4x-5x` time due to boxing.
 
 ## Boxing
+
 The boxing field produced by
+
 ```kotlin
     genericField.run {
-        var res: NDBuffer<Double> = one
+        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.
@@ -114,6 +133,7 @@ via extension function.
 
 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
 
@@ -121,7 +141,9 @@ 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
+
+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

docs/polynomials.md

@@ -1,27 +1,54 @@
 # 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.
+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.
+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.
+
+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
+For example, polynomial $2 - 3x + x^2 $ (with `Int` coefficients) is represented
+
 ```kotlin
 val polynomial: ListPolynomial<Int> = ListPolynomial(listOf(2, -3, 1))
 // or
@@ -29,6 +56,7 @@ 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)
@@ -41,7 +69,8 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
 
 ### Example: `NumberedPolynomial`
 
-For example, polynomial  $3 + 5 x_1 - 7 x_0^2 x_2 $ (with `Int` coefficients) is represented
+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(
@@ -59,6 +88,7 @@ val polynomial: NumberedPolynomial<Int> = NumberedPolynomial(
 ```
 
 All algebraic operations can be used in corresponding space:
+
 ```kotlin
 val computationResult = Int.algebra.numberedPolynomialSpace {
    NumberedPolynomial(
@@ -83,7 +113,8 @@ For more see [examples](../examples/src/main/kotlin/space/kscience/kmath/functio
 
 ### Example: `LabeledPolynomial`
 
-For example, polynomial  $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
+For example, polynomial $3 + 5 y - 7 x^2 z $ (with `Int` coefficients) is represented
+
 ```kotlin
 val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
    mapOf(
@@ -101,6 +132,7 @@ val polynomial: LabeledPolynomial<Int> = LabeledPolynomial(
 ```
 
 All algebraic operations can be used in corresponding space:
+
 ```kotlin
 val computationResult = Int.algebra.labeledPolynomialSpace {
    LabeledPolynomial(
@@ -150,23 +182,42 @@ classDiagram
    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:
+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.
+- `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.
+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>`).
+
+- `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.
+- `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:
+For all kinds of polynomials there are provided (implementation details depend on kind of polynomials) such common
+utilities as:
+
 1. differentiation and anti-differentiation,
 2. substitution, invocation and functional representation.

docs/readme.md

@@ -11,4 +11,6 @@
 
 * [Expressions](expressions.md)
 
+* [Statistics](statistics.md): statistical functions on data [Buffers](buffers.md)
+
 * Commons math integration

docs/statistics.md

@@ -0,0 +1,34 @@
+# Statistics
+
+Mathematically speaking, a statistic is a measurable numerical function of sample data.
+In KMath, a statistic is a function that operates on a [Buffer](buffers.md) and is implemented as the `evaluate` method
+of the `Statistic` interface.
+There are two subinterfaces of the `Statistic` interface:
+
+* `BlockingStatistic` – A statistic that is computed in a synchronous blocking mode
+* `ComposableStatistic` – A statistic tha could be computed separately on different blocks of data and then composed
+
+
+## Common statistics and Implementation Status
+
+| Category         | Statistic         | Description                        | Implementation Status          |
+|------------------|-------------------|------------------------------------|--------------------------------|
+| **Basic**        | Min               | Minimum value                      | ✅ `ComposableStatistic`        |
+|                  | Max               | Maximum value                      | ✅ `ComposableStatistic`        |
+|                  | Mean              | Arithmetic mean                    | ✅ `ComposableStatistic`        |
+|                  | Sum               | Sum of all values                  | 🚧 Not yet implemented         |
+|                  | Product           | Product of all values              | 🚧 Not yet implemented         |
+| **Distribution** | Median            | Median (50th percentile)           | ✅ `BlockingStatistic`          |
+|                  | Quantile          | Quantile and percentile            | ✅ `BlockingStatistic`          |
+|                  | Variance          | Unbiased sample variance           | ✅ `BlockingStatistic`          |
+|                  | StandardDeviation | Population standard deviation (σ)  | ✅ `BlockingStatistic`          |
+|                  | Skewness          | Measure of distribution asymmetry  | 🚧 *(Requires `ThirdMoment`)*  |
+|                  | Kurtosis          | Measure of distribution tailedness | 🚧 *(Requires `FourthMoment`)* |
+| **Advanced**     | GeometricMean     | Nth root of product of values      | ✅ `ComposableStatistic`        |
+|                  | SumOfLogs         | Sum of natural logarithms          | Does not planned               |
+|                  | SumOfSquares      | Sum of squared values              | 🚧 *(Blocks `Variance`)*       |
+| **Moments**      | FirstMoment       | Mean (same as `Mean`)              | ✅ *(Alias for `Mean`)*         |
+|                  | SecondMoment      | Variance (same as `Variance`)      | ✅ *(Alias for `Variance`)*     |
+|                  | ThirdMoment       | Used in skewness calculation       | 🚧 Not yet implemented         |
+|                  | FourthMoment      | Used in kurtosis calculation       | 🚧 Not yet implemented         |
+| **Risk Metrics** | SemiVariance      | Downside variance                  | 🚧 *(Depends on `Variance`)*   |

docs/templates/ARTIFACT-TEMPLATE.md

@@ -3,25 +3,11 @@
 The Maven coordinates of this project are `${group}:${name}:${version}`.
 
 **Gradle:**
-```groovy
-repositories {
-    maven { url 'https://repo.kotlin.link' }
-    mavenCentral()
-    // development and snapshot versions
-    maven { url 'https://maven.pkg.jetbrains.space/spc/p/sci/dev' }
-}
 
-dependencies {
-    implementation '${group}:${name}:${version}'
-}
-```
-**Gradle Kotlin DSL:**
 ```kotlin
 repositories {
     maven("https://repo.kotlin.link")
     mavenCentral()
-    // development and snapshot versions
-    maven("https://maven.pkg.jetbrains.space/spc/p/sci/dev")
 }
 
 dependencies {

docs/templates/README-TEMPLATE.md

@@ -11,18 +11,22 @@ analog to Python's NumPy library. Later we found that kotlin is much more flexib
 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 (**WIP**)](https://SciProgCentre.github.io/kmath/)
+[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 and Native)
-  .
+* 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.
 
@@ -59,22 +63,24 @@ ${modules}
 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, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
+reasons. Currently, Kotlin/JVM is the primary platform, however, Kotlin/Native and Kotlin/JS contributions and
 feedback are also welcome.
 
 ## Performance
 
-Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
+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 convenient universal API first and then work on increasing performance for specific
+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.
+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 to use GraalVM-CE 11 for
-execution to get better performance.
+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
 
@@ -94,11 +100,10 @@ dependencies {
 }
 ```
 
-Gradle `6.0+` is required for multiplatform artifacts.
-
 ## Contributing
 
-The project requires a lot of additional work. The most important thing we need is a feedback about what features are
+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
-[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
+marked
+with [good first issue](hhttps://github.com/SciProgCentre/kmath/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
+label.

examples/build.gradle.kts

@@ -10,8 +10,6 @@ repositories {
     maven("https://maven.pkg.jetbrains.space/kotlin/p/kotlin/kotlin-js-wrappers")
 }
 
-val multikVersion: String by rootProject.extra
-
 dependencies {
     implementation(project(":kmath-ast"))
     implementation(project(":kmath-kotlingrad"))
@@ -25,20 +23,18 @@ dependencies {
     implementation(project(":kmath-viktor"))
     implementation(project(":kmath-dimensions"))
     implementation(project(":kmath-ejml"))
-    implementation(project(":kmath-nd4j"))
     implementation(project(":kmath-tensors"))
     implementation(project(":kmath-symja"))
     implementation(project(":kmath-for-real"))
-    //jafama
-    implementation(project(":kmath-jafama"))
     //multik
     implementation(project(":kmath-multik"))
-    implementation("org.jetbrains.kotlinx:multik-default:$multikVersion")
+    implementation(libs.multik.default)
 
     //datetime
-    implementation("org.jetbrains.kotlinx:kotlinx-datetime:0.4.0")
+    implementation(spclibs.kotlinx.datetime)
 
-    implementation("org.nd4j:nd4j-native:1.0.0-beta7")
+//    implementation(project(":kmath-nd4j"))
+//    implementation("org.nd4j:nd4j-native:1.0.0-beta7")
 
 //    uncomment if your system supports AVX2
 //    val os = System.getProperty("os.name")
@@ -48,27 +44,27 @@ dependencies {
 //        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.nd4j:nd4j-native-platform:1.0.0-beta7")
 
     implementation("org.slf4j:slf4j-simple:1.7.32")
     // plotting
-    implementation("space.kscience:plotlykt-server:0.5.0")
+    implementation("space.kscience:plotlykt-server:0.7.0")
 }
 
 kotlin {
-    jvmToolchain(11)
+    jvmToolchain(17)
     sourceSets.all {
-        with(languageSettings) {
+        languageSettings {
             optIn("kotlin.contracts.ExperimentalContracts")
             optIn("kotlin.ExperimentalUnsignedTypes")
-            optIn("space.kscience.kmath.misc.UnstableKMathAPI")
+            optIn("space.kscience.kmath.UnstableKMathAPI")
         }
     }
 }
 
 tasks.withType<KotlinJvmCompile> {
-    kotlinOptions {
-        freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn" + "-Xlambdas=indy"
+    compilerOptions {
+        freeCompilerArgs.addAll("-Xjvm-default=all", "-Xopt-in=kotlin.RequiresOptIn", "-Xlambdas=indy")
     }
 }
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -8,13 +8,13 @@ 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.DoubleField
+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(DoubleField)
+    }.compileToExpression(Float64Field)
 
     val m = DoubleArray(expr.indexer.symbols.size)
     val xIdx = expr.indexer.indexOf(x)

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

@@ -1,16 +1,16 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.Symbol.Companion.x
 import space.kscience.kmath.expressions.toExpression
 import space.kscience.kmath.kotlingrad.toKotlingradExpression
-import space.kscience.kmath.operations.DoubleField
+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
@@ -19,9 +19,9 @@ import space.kscience.kmath.operations.DoubleField
 fun main() {
     val actualDerivative = "x^2-4*x-44"
         .parseMath()
-        .toKotlingradExpression(DoubleField)
+        .toKotlingradExpression(Float64Field)
         .derivative(x)
 
-    val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
+    val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
     check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -9,7 +9,7 @@ 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.DoubleField
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.symja.toSymjaExpression
 
 /**
@@ -19,9 +19,9 @@ import space.kscience.kmath.symja.toSymjaExpression
 fun main() {
     val actualDerivative = "x^2-4*x-44"
         .parseMath()
-        .toSymjaExpression(DoubleField)
+        .toSymjaExpression(Float64Field)
         .derivative(x)
 
-    val expectedDerivative = "2*x-4".parseMath().toExpression(DoubleField)
+    val expectedDerivative = "2*x-4".parseMath().toExpression(Float64Field)
     check(actualDerivative(x to 123.0) == expectedDerivative(x to 123.0))
 }

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

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

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -9,19 +9,21 @@ 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.FunctionOptimizationTarget
+import space.kscience.kmath.optimization.minimize
 import space.kscience.kmath.optimization.optimizeWith
-import space.kscience.kmath.optimization.resultPoint
+import space.kscience.kmath.optimization.result
 import space.kscience.kmath.optimization.resultValue
 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
@@ -67,7 +69,7 @@ suspend fun main() {
     val yErr = y.map { sqrt(it) }//RealVector.same(x.size, sigma)
 
     // compute differentiable chi^2 sum for given model ax^2 + bx + c
-    val chi2 = Double.autodiff.chiSquaredExpression(x, y, yErr) { arg ->
+    val chi2: DifferentiableExpression<Float64> = Double.autodiff.chiSquaredExpression(x, y, yErr) { arg ->
         //bind variables to autodiff context
         val a = bindSymbol(a)
         val b = bindSymbol(b)
@@ -80,7 +82,9 @@ suspend fun main() {
     val result = chi2.optimizeWith(
         CMOptimizer,
         mapOf(a to 1.5, b to 0.9, c to 1.0),
-        FunctionOptimizationTarget.MINIMIZE
+        attributesBuilder = {
+            minimize()
+        }
     )
 
     //display a page with plot and numerical results
@@ -98,7 +102,7 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
+                y(x.map { result.result[a]!! * it.pow(2) + result.result[b]!! * it + 1 })
                 name = "fit"
             }
         }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -64,7 +64,9 @@ suspend fun main() {
         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),
-        OptimizationParameters(a, b, c, d)
+        attributesBuilder = {
+            freeParameters(a, b, c, d)
+        },
     ) { arg ->
         //bind variables to autodiff context
         val a by binding
@@ -94,13 +96,13 @@ suspend fun main() {
             scatter {
                 mode = ScatterMode.lines
                 x(x)
-                y(x.map { result.model(result.startPoint + result.resultPoint + (Symbol.x to it)) })
+                y(x.map { result.model(result.startPoint + result.result + (Symbol.x to it)) })
                 name = "fit"
             }
         }
         br()
         h3 {
-            +"Fit result: ${result.resultPoint}"
+            +"Fit result: ${result.result}"
         }
         h3 {
             +"Chi2/dof = ${result.chiSquaredOrNull!! / result.dof}"

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -13,15 +13,16 @@ 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.DoubleField
+import space.kscience.kmath.operations.Float64Field
+import space.kscience.kmath.structures.Float64
 import kotlin.math.pow
 
 fun main() {
     //Define a function
-    val function: Function1D<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
+    val function: Function1D<Float64> = { x -> 3 * x.pow(2) + 2 * x + 1 }
 
     //get the result of the integration
-    val result = DoubleField.gaussIntegrator.integrate(0.0..10.0, function = function)
+    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)

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -7,8 +7,8 @@ package space.kscience.kmath.functions
 
 import space.kscience.kmath.interpolation.SplineInterpolator
 import space.kscience.kmath.interpolation.interpolatePolynomials
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.structures.DoubleBuffer
+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
@@ -24,11 +24,9 @@ fun main() {
         x to sin(x)
     }
 
-    val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
-        DoubleField, ::DoubleBuffer
-    ).interpolatePolynomials(data)
+    val polynomial: PiecewisePolynomial<Float64> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
 
-    val function = polynomial.asFunction(DoubleField, 0.0)
+    val function = polynomial.asFunction(Float64Field, 0.0)
 
     val cmInterpolate = org.apache.commons.math3.analysis.interpolation.SplineInterpolator().interpolate(
         data.map { it.first }.toDoubleArray(),

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -7,9 +7,10 @@ package space.kscience.kmath.functions
 
 import space.kscience.kmath.interpolation.interpolatePolynomials
 import space.kscience.kmath.interpolation.splineInterpolator
-import space.kscience.kmath.operations.DoubleField
+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
@@ -18,7 +19,7 @@ import space.kscience.plotly.scatter
 
 @OptIn(UnstablePlotlyAPI::class)
 fun main() {
-    val function: Function1D<Double> = { x ->
+    val function: Function1D<Float64> = { x ->
         if (x in 30.0..50.0) {
             1.0
         } else {
@@ -28,9 +29,9 @@ fun main() {
     val xs = 0.0..100.0 step 0.5
     val ys = xs.map(function)
 
-    val polynomial: PiecewisePolynomial<Double> = DoubleField.splineInterpolator.interpolatePolynomials(xs, ys)
+    val polynomial: PiecewisePolynomial<Float64> = Float64Field.splineInterpolator.interpolatePolynomials(xs, ys)
 
-    val polyFunction = polynomial.asFunction(DoubleField, 0.0)
+    val polyFunction = polynomial.asFunction(Float64Field, 0.0)
 
     Plotly.plot {
         scatter {

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -12,6 +12,7 @@ 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) {
@@ -22,7 +23,7 @@ fun main(): Unit = Double.algebra.withNdAlgebra(2, 2) {
     }
 
     //Define a function in a nd space
-    val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
+    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)

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

@@ -1,15 +0,0 @@
-/*
- * Copyright 2018-2022 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.jafama
-
-import space.kscience.kmath.operations.invoke
-
-fun main() {
-    val a = 2.0
-    val b = StrictJafamaDoubleField { exp(a) }
-    println(JafamaDoubleField { b + a })
-    println(StrictJafamaDoubleField { ln(b) })
-}

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

@@ -1,33 +1,28 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.operations.algebra
 import kotlin.random.Random
-import kotlin.time.ExperimentalTime
 import kotlin.time.measureTime
 
-@OptIn(ExperimentalTime::class)
-fun main() {
+fun main() = with(Float64ParallelLinearSpace) {
     val random = Random(12224)
     val dim = 1000
 
     //creating invertible matrix
-    val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+    val matrix1 = buildMatrix(dim, dim) { i, j ->
         if (i <= j) random.nextDouble() else 0.0
     }
-    val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
+    val matrix2 = buildMatrix(dim, dim) { i, j ->
         if (i <= j) random.nextDouble() else 0.0
     }
 
     val time = measureTime {
-        with(Double.algebra.linearSpace) {
-            repeat(10) {
-                matrix1 dot matrix2
-            }
+        repeat(30) {
+            matrix1 dot matrix2
         }
     }
 

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

@@ -0,0 +1,42 @@
+/*
+ * 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.PerformancePitfall
+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
+
+@OptIn(PerformancePitfall::class)
+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 symmetric 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" )
+        }
+    }
+
+}

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

@@ -1,27 +1,28 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.DoubleBuffer
+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<Double>) -> Double = { x ->
+    val gaussian: (Point<Float64>) -> Double = { x ->
         require(x.size == x0.size)
         kotlin.math.exp(-((x - x0) / sigma).square().sum())
     }
 
-    fun ((Point<Double>) -> Double).grad(x: Point<Double>): Point<Double> {
+    fun ((Point<Float64>) -> Double).grad(x: Point<Float64>): Point<Float64> {
         require(x.size == x0.size)
-        return DoubleBuffer(x.size) { i ->
+        return Float64Buffer(x.size) { i ->
             val h = sigma[i] / 5
-            val dVector = DoubleBuffer(x.size) { if (it == i) h else 0.0 }
+            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

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

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

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

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

@@ -1,27 +1,28 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.DoubleBufferND
-import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.linear.MatrixBuilder
+import space.kscience.kmath.linear.fill
+import space.kscience.kmath.nd.Float64BufferND
 import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.mutableStructureND
 import space.kscience.kmath.nd.ndAlgebra
-import space.kscience.kmath.viktor.ViktorStructureND
+import space.kscience.kmath.structures.Float64
 import space.kscience.kmath.viktor.viktorAlgebra
 
 fun main() {
-    val viktorStructure: ViktorStructureND = DoubleField.viktorAlgebra.structureND(ShapeND(2, 2)) { (i, j) ->
+    val viktorStructure = Float64Field.viktorAlgebra.mutableStructureND(2, 2) { (i, j) ->
         if (i == j) 2.0 else 0.0
     }
 
-    val cmMatrix: Structure2D<Double> = CMLinearSpace.matrix(2, 2)(0.0, 1.0, 0.0, 3.0)
+    val cmMatrix: Structure2D<Float64> = CMLinearSpace.MatrixBuilder(2, 2).fill(0.0, 1.0, 0.0, 3.0)
 
-    val res: DoubleBufferND = DoubleField.ndAlgebra {
+    val res: Float64BufferND = Float64Field.ndAlgebra {
         exp(viktorStructure) + 2.0 * cmMatrix
     }
 

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2023 KMath contributors.
+ * 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.
  */
 

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

@@ -1,3 +1,8 @@
+/*
+ * 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
 
 
@@ -9,14 +14,18 @@ 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 main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
+fun Double.Companion.seriesAlgebra() = Double.algebra.bufferAlgebra.seriesAlgebra()
 
 
-    fun Plot.plotSeries(name: String, buffer: Buffer<Double>) {
+fun main() = with(Double.seriesAlgebra()) {
+
+
+    fun Plot.plotSeries(name: String, buffer: Buffer<Float64>) {
         scatter {
             this.name = name
             x.numbers = buffer.labels
@@ -29,17 +38,17 @@ fun main() = with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
 
     val s2 = s1.slice(20..50).moveTo(40)
 
-    val s3: Buffer<Double> = s1.zip(s2) { l, r -> l + r } //s1 + s2
+    val s3: Buffer<Float64> = s1.zip(s2) { l, r -> l + r } //s1 + s2
     val s4 = s3.map { ln(it) }
 
-    val kmTest: KMComparisonResult<Double> = ksComparisonStatistic(s1, s2)
+    val kmTest: KMComparisonResult<Float64> = ksComparisonStatistic(s1, s2)
 
     Plotly.page {
         h1 { +"This is my plot" }
-        p{
+        p {
             +"Kolmogorov-smirnov test for s1 and s2: ${kmTest.value}"
         }
-        plot{
+        plot {
             plotSeries("s1", s1)
             plotSeries("s2", s2)
             plotSeries("s3", s3)

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

@@ -0,0 +1,47 @@
+/*
+ * 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)
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -36,8 +36,28 @@ private suspend fun runKMathChained(): Duration {
     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.create(RandomSource.MT, 123L)
+    val rng = RandomSource.MT.create(123L)
 
     val sampler = CMGaussianSampler.of(
         BoxMullerNormalizedGaussianSampler.of(rng),
@@ -67,6 +87,8 @@ private fun runCMDirect(): Duration {
 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()}")
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -10,13 +10,14 @@ 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<Double>.mean(): Chain<Double> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
+private fun Chain<Float64>.mean(): Chain<Float64> = combineWithState(AveragingChainState(), { it.copy() }) { chain ->
     val next = chain.next()
     num++
     value += next

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -8,12 +8,12 @@
 package space.kscience.kmath.structures
 
 import space.kscience.kmath.complex.*
-import space.kscience.kmath.linear.transpose
+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.DoubleField
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.invoke
 import kotlin.system.measureTimeMillis
 
@@ -21,12 +21,12 @@ fun main() {
     val dim = 1000
     val n = 1000
 
-    val realField = DoubleField.ndAlgebra(dim, dim)
+    val realField = Float64Field.ndAlgebra(dim, dim)
     val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
 
     val realTime = measureTimeMillis {
         realField {
-            var res: StructureND<Double> = one
+            var res: StructureND<Float64> = one
             repeat(n) {
                 res += 1.0
             }
@@ -60,7 +60,7 @@ fun complexExample() {
             val sum = matrix + x + 1.0
 
             //Represent the sum as 2d-structure and transpose
-            sum.as2D().transpose()
+            sum.as2D().transposed()
         }
     }
 }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -7,10 +7,8 @@ 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.DoubleField
+import space.kscience.kmath.operations.Float64Field
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.viktor.ViktorFieldND
 import kotlin.contracts.InvocationKind
@@ -26,54 +24,44 @@ internal inline fun measureAndPrint(title: String, block: () -> Unit) {
 @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 = DoubleField.ndAlgebra
+    val doubleField = Float64Field.ndAlgebra
     //A generic field. It should be used for objects, not primitives.
-    val genericField = BufferedFieldOpsND(DoubleField)
-    // Nd4j specialized field.
-    val nd4jField = DoubleField.nd4j
+    val genericField = BufferedFieldOpsND(Float64Field)
     //viktor field
     val viktorField = ViktorFieldND(dim, dim)
     //parallel processing based on Java Streams
-    val parallelField = DoubleField.ndStreaming(dim, dim)
+    val parallelField = Float64Field.ndStreaming(dim, dim)
 
     measureAndPrint("Boxing addition") {
         genericField {
-            var res: StructureND<Double> = one(shape)
+            var res: StructureND<Float64> = one(shape)
             repeat(n) { res += 1.0 }
         }
     }
 
     measureAndPrint("Specialized addition") {
         doubleField {
-            var res: StructureND<Double> = one(shape)
-            repeat(n) { res += 1.0 }
-        }
-    }
-
-    measureAndPrint("Nd4j specialized addition") {
-        nd4jField {
-            var res: StructureND<Double> = one(shape)
+            var res: StructureND<Float64> = one(shape)
             repeat(n) { res += 1.0 }
         }
     }
 
     measureAndPrint("Viktor addition") {
         viktorField {
-            var res: StructureND<Double> = one
+            var res: StructureND<Float64> = one
             repeat(n) { res += 1.0 }
         }
     }
 
     measureAndPrint("Parallel stream addition") {
         parallelField {
-            var res: StructureND<Double> = one
+            var res: StructureND<Float64> = one
             repeat(n) { res += 1.0 }
         }
     }

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -9,6 +9,7 @@ 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
@@ -17,55 +18,67 @@ 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, DoubleField>,
-    NumbersAddOps<StructureND<Double>>,
-    ExtendedField<StructureND<Double>> {
+class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64Field>,
+    NumbersAddOps<StructureND<Float64>>,
+    ExtendedField<StructureND<Float64>> {
 
     private val strides = ColumnStrides(shape)
-    override val elementAlgebra: DoubleField get() = DoubleField
-    override val zero: BufferND<Double> by lazy { structureND(shape) { zero } }
-    override val one: BufferND<Double> by lazy { structureND(shape) { one } }
+    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<Double> {
+    override fun number(value: Number): BufferND<Float64> {
         val d = value.toDouble() // minimize conversions
         return structureND(shape) { d }
     }
 
     @OptIn(PerformancePitfall::class)
-    private val StructureND<Double>.buffer: DoubleBuffer
+    private val StructureND<Float64>.buffer: Float64Buffer
         get() = when {
-            !shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
+            shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
                 this@StreamDoubleFieldND.shape,
                 shape
             )
 
-            this is BufferND && indices == this@StreamDoubleFieldND.strides -> this.buffer as DoubleBuffer
-            else -> DoubleBuffer(strides.linearSize) { offset -> get(strides.index(offset)) }
+            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: DoubleField.(IntArray) -> Double): BufferND<Double> {
+    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)
-            DoubleField.initializer(index)
+            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<Double>.map(
-        transform: DoubleField.(Double) -> Double,
-    ): BufferND<Double> {
-        val array = Arrays.stream(buffer.array).parallel().map { DoubleField.transform(it) }.toArray()
+    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<Double>.mapIndexed(
-        transform: DoubleField.(index: IntArray, Double) -> Double,
-    ): BufferND<Double> {
+    override fun StructureND<Float64>.mapIndexed(
+        transform: Float64Field.(index: IntArray, Double) -> Double,
+    ): BufferND<Float64> {
         val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            DoubleField.transform(
+            Float64Field.transform(
                 strides.index(offset),
                 buffer.array[offset]
             )
@@ -76,39 +89,39 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, DoubleF
 
     @OptIn(PerformancePitfall::class)
     override fun zip(
-        left: StructureND<Double>,
-        right: StructureND<Double>,
-        transform: DoubleField.(Double, Double) -> Double,
-    ): BufferND<Double> {
+        left: StructureND<Float64>,
+        right: StructureND<Float64>,
+        transform: Float64Field.(Double, Double) -> Double,
+    ): BufferND<Float64> {
         val array = IntStream.range(0, strides.linearSize).parallel().mapToDouble { offset ->
-            DoubleField.transform(left.buffer.array[offset], right.buffer.array[offset])
+            Float64Field.transform(left.buffer.array[offset], right.buffer.array[offset])
         }.toArray()
         return BufferND(strides, array.asBuffer())
     }
 
-    override fun StructureND<Double>.unaryMinus(): StructureND<Double> = map { -it }
+    override fun StructureND<Float64>.unaryMinus(): StructureND<Float64> = map { -it }
 
-    override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
+    override fun scale(a: StructureND<Float64>, value: Double): StructureND<Float64> = a.map { it * value }
 
-    override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
+    override fun power(arg: StructureND<Float64>, pow: Number): BufferND<Float64> = arg.map { power(it, pow) }
 
-    override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
+    override fun exp(arg: StructureND<Float64>): BufferND<Float64> = arg.map { exp(it) }
 
-    override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
+    override fun ln(arg: StructureND<Float64>): BufferND<Float64> = arg.map { ln(it) }
 
-    override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
-    override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
-    override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
-    override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
-    override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
-    override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
+    override fun 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<Double>): BufferND<Double> = arg.map { sinh(it) }
-    override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
-    override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
-    override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
-    override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
-    override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
+    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 DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))
+fun Float64Field.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(ShapeND(shape))

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -16,7 +16,7 @@ import kotlin.system.measureTimeMillis
 fun main() {
     val n = 6000
     val array = DoubleArray(n * n) { 1.0 }
-    val buffer = DoubleBuffer(array)
+    val buffer = Float64Buffer(array)
     val strides = ColumnStrides(ShapeND(n, n))
     val structure = BufferND(strides, buffer)
 

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

@@ -1,25 +1,23 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.nd.ShapeND
-import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.operations.mapToBuffer
 import kotlin.system.measureTimeMillis
 
 private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
-    bufferFactory: BufferFactory<R> = BufferFactory.auto(),
+    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 = StructureND.buffered(ShapeND(n, n), Buffer.Companion::auto) { 1.0 }
+    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")
@@ -32,10 +30,10 @@ fun main() {
 
     println("Array mapping finished in $time2 millis")
 
-    val buffer = DoubleBuffer(DoubleArray(n * n) { 1.0 })
+    val buffer = Float64Buffer(DoubleArray(n * n) { 1.0 })
 
     val time3 = measureTimeMillis {
-        val target = DoubleBuffer(DoubleArray(n * n))
+        val target = Float64Buffer(DoubleArray(n * n))
         val res = array.forEachIndexed { index, value ->
             target[index] = value + 1
         }

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

@@ -1,23 +1,23 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.DoubleField
+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> = auto(n) { value }
+    value: R,
+): MutableBuffer<R> = MutableBuffer(n) { value }
 
 
 fun main() {
-    with(DoubleField.bufferAlgebra.withSize(5)) {
+    with(Float64Field.bufferAlgebra.withSize(5)) {
         println(number(2.0) + buffer(1, 2, 3, 4, 5))
     }
 }

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

@@ -0,0 +1,26 @@
+/*
+ * 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)
+}

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

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -15,7 +15,7 @@ private fun DMatrixContext<Double, *>.simple() {
     val m2 = produce<D3, D2> { i, j -> (i + j).toDouble() }
 
     //Dimension-safe addition
-    m1.transpose() + m2
+    m1.transposed() + m2
 }
 
 private object D5 : Dimension {

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

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

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

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

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

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

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

@@ -0,0 +1,76 @@
+/*
+ * 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
+}

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

@@ -0,0 +1,33 @@
+/*
+ * 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()}")
+}

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

@@ -0,0 +1,233 @@
+/*
+ * 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)
+}

examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt

@@ -1,12 +1,11 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.nd.contentEquals
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@@ -62,7 +61,7 @@ fun main() {
         // figure out MSE of approximation
         fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
             require(yTrue.shape.size == 1)
-            require(yTrue.shape contentEquals yPred.shape)
+            require(yTrue.shape == yPred.shape)
 
             val diff = yTrue - yPred
             return sqrt(diff.dot(diff)).value()

examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 

examples/src/main/kotlin/space/kscience/kmath/tensors/multik.kt

@@ -1,5 +1,5 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.
  */
 
@@ -10,12 +10,13 @@ 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<Double>().wrap()
+    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
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt

@@ -1,12 +1,11 @@
 /*
- * Copyright 2018-2022 KMath contributors.
+ * 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.nd.contentEquals
 import space.kscience.kmath.operations.asIterable
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.*
@@ -94,7 +93,7 @@ class Dense(
 
 // simple accuracy equal to the proportion of correct answers
 fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
-    check(yPred.shape contentEquals yTrue.shape)
+    check(yPred.shape == yTrue.shape)
     val n = yPred.shape[0]
     var correctCnt = 0
     for (i in 0 until n) {

gradle.properties

@@ -2,15 +2,15 @@
 # Copyright 2018-2021 KMath contributors.
 # Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
 #
+org.gradle.configureondemand=true
+org.gradle.jvmargs=-Xmx4096m
+org.gradle.parallel=true
+org.gradle.workers.max=4
+
 kotlin.code.style=official
 kotlin.mpp.stability.nowarn=true
 kotlin.native.ignoreDisabledTargets=true
+org.jetbrains.dokka.experimental.gradle.pluginMode=V2Enabled
+kotlin.native.enableKlibsCrossCompilation=true
 
-org.gradle.configureondemand=true
-org.gradle.jvmargs=-Xmx4096m
-
-toolsVersion=0.14.6-kotlin-1.8.20
-
-
-org.gradle.parallel=true
-org.gradle.workers.max=4
+toolsVersion=0.16.1-kotlin-2.1.0

gradle/libs.versions.toml

@@ -0,0 +1,28 @@
+[versions]
+
+commons-rng = "1.6"
+multik = "0.2.3"
+nd4j = "1.0.0-M2.1"
+tensorflow = "1.0.0"
+
+[libraries]
+attributes = "space.kscience:attributes-kt:0.3.0"
+
+commons-math = "org.apache.commons:commons-math3:3.6.1"
+
+commons-rng-simple = { module = "org.apache.commons:commons-rng-simple", version.ref = "commons-rng" }
+commons-rng-sampling = { module = "org.apache.commons:commons-rng-sampling", version.ref = "commons-rng" }
+
+multik-core = { module = "org.jetbrains.kotlinx:multik-core", version.ref = "multik" }
+multik-default = { module = "org.jetbrains.kotlinx:multik-default", version.ref = "multik" }
+
+nd4j-api = { module = "org.nd4j:nd4j-api", version.ref = "nd4j" }
+nd4j-native-platform = { module = "org.nd4j:nd4j-native-platform", version.ref = "nd4j" }
+
+ojalgo = "org.ojalgo:ojalgo:55.1.0"
+
+
+tensorflow-core-api = {module = "org.tensorflow:tensorflow-core-api", version.ref="tensorflow"}
+tensorflow-core-platform = {module = "org.tensorflow:tensorflow-core-platform", version.ref="tensorflow"}
+
+[plugins]

gradle/wrapper/gradle-wrapper.properties

@@ -1,5 +1,5 @@
 distributionBase=GRADLE_USER_HOME
 distributionPath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-7.6-bin.zip
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.11.1-bin.zip
 zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists

kmath-ast/README.md

@@ -10,19 +10,8 @@ Extensions to MST API: transformations, dynamic compilation and visualization.
 
 ## Artifact:
 
-The Maven coordinates of this project are `space.kscience:kmath-ast:0.4.0-dev-1`.
+The Maven coordinates of this project are `space.kscience:kmath-ast:0.4.2`.
 
-**Gradle Groovy:**
-```groovy
-repositories {
-    maven { url 'https://repo.kotlin.link' }
-    mavenCentral()
-}
-
-dependencies {
-    implementation 'space.kscience:kmath-ast:0.4.0-dev-1'
-}
-```
 **Gradle Kotlin DSL:**
 ```kotlin
 repositories {
@@ -31,27 +20,33 @@ repositories {
 }
 
 dependencies {
-    implementation("space.kscience:kmath-ast:0.4.0-dev-1")
+    implementation("space.kscience:kmath-ast:0.4.2")
 }
 ```
 
 ## Parsing expressions
 
-In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
+In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more
+specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
 
 Supported literals:
+
 1. Constants and variables (consist of latin letters, digits and underscores, can't start with digit): `x`, `_Abc2`.
 2. Numbers: `123`, `1.02`, `1e10`, `1e-10`, `1.0e+3`—all parsed either as `kotlin.Long` or `kotlin.Double`.
 
 Supported binary operators (from the highest precedence to the lowest one):
+
 1. `^`
 2. `*`, `/`
 3. `+`, `-`
 
 Supported unary operator:
+
 1. `-`, e. g. `-x`
 
-Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't start with digit. Examples:
+Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't
+start with digit. Examples:
+
 1. `sin(x)`
 2. `add(x, y)`
 
@@ -116,12 +111,15 @@ public final class CompiledExpression_-386104628_0 implements DoubleExpression {
 }
 ```
 
-Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class files to program's working directory, so they can be reviewed manually.
+Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class
+files to program's working directory, so they can be reviewed manually.
 
 #### Limitations
 
-- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class loading overhead.
-- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not support class loaders.
+- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
+  loading overhead.
+- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not
+  support class loaders.
 
 ### On JS
 
@@ -199,7 +197,8 @@ public fun main() {
 
 Result LaTeX:
 
-$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
+$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)
+}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
 
 Result MathML (can be used with MathJax or other renderers):
 

kmath-ast/build.gradle.kts

@@ -2,9 +2,26 @@ plugins {
     id("space.kscience.gradle.mpp")
 }
 
-kscience{
+kscience {
     jvm()
-    js()
+    js{
+        nodejs {
+            testTask {
+                useMocha {
+                    timeout = "0"
+                }
+            }
+        }
+
+        browser {
+            useCommonJs()
+            testTask {
+                useMocha {
+                    timeout = "0"
+                }
+            }
+        }
+    }
     native()
 
     dependencies {
@@ -18,39 +35,26 @@ kscience{
 
     dependencies(jsMain) {
         implementation(npm("astring", "1.7.5"))
-        implementation(npm("binaryen", "101.0.0"))
+        implementation(npm("binaryen", "117.0.0"))
         implementation(npm("js-base64", "3.6.1"))
     }
 
-    dependencies(jvmMain){
-        implementation("org.ow2.asm:asm-commons:9.2")
+    dependencies(jvmMain) {
+        implementation("org.ow2.asm:asm-commons:9.7.1")
     }
 
 }
 
 kotlin {
-    js {
-        nodejs {
-            testTask {
-                useMocha().timeout = "0"
-            }
-        }
-
-        browser {
-            testTask {
-                useMocha().timeout = "0"
-            }
-        }
-    }
 
     sourceSets {
         filter { it.name.contains("test", true) }
             .map(org.jetbrains.kotlin.gradle.plugin.KotlinSourceSet::languageSettings)
-            .forEach { it.optIn("space.kscience.kmath.misc.UnstableKMathAPI") }
+            .forEach { it.optIn("space.kscience.kmath.UnstableKMathAPI") }
     }
 }
 
-if (System.getProperty("space.kscience.kmath.ast.dump.generated.classes") == "1") {
+if (project.properties["space.kscience.kmath.ast.dump.generated.classes"] == "1") {
     tasks.withType<org.jetbrains.kotlin.gradle.targets.jvm.tasks.KotlinJvmTest> {
         jvmArgs("-Dspace.kscience.kmath.ast.dump.generated.classes=1")
     }

kmath-ast/docs/README-TEMPLATE.md

@@ -8,21 +8,27 @@ ${artifact}
 
 ## Parsing expressions
 
-In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
+In this module there is a parser from human-readable strings like `"x^3-x+3"` (in the more
+specific [grammar](reference/ArithmeticsEvaluator.g4)) to MST instances.
 
 Supported literals:
+
 1. Constants and variables (consist of latin letters, digits and underscores, can't start with digit): `x`, `_Abc2`.
 2. Numbers: `123`, `1.02`, `1e10`, `1e-10`, `1.0e+3`—all parsed either as `kotlin.Long` or `kotlin.Double`.
 
 Supported binary operators (from the highest precedence to the lowest one):
+
 1. `^`
 2. `*`, `/`
 3. `+`, `-`
 
 Supported unary operator:
+
 1. `-`, e. g. `-x`
 
-Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't start with digit. Examples:
+Arbitrary unary and binary functions are also supported: names consist of latin letters, digits and underscores, can't
+start with digit. Examples:
+
 1. `sin(x)`
 2. `add(x, y)`
 
@@ -87,12 +93,15 @@ public final class CompiledExpression_-386104628_0 implements DoubleExpression {
 }
 ```
 
-Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class files to program's working directory, so they can be reviewed manually.
+Setting JVM system property `space.kscience.kmath.ast.dump.generated.classes` to `1` makes the translator dump class
+files to program's working directory, so they can be reviewed manually.
 
 #### Limitations
 
-- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class loading overhead.
-- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not support class loaders.
+- The same classes may be generated and loaded twice, so it is recommended to cache compiled expressions to avoid class
+  loading overhead.
+- This API is not supported by non-dynamic JVM implementations like TeaVM or GraalVM Native Image because they may not
+  support class loaders.
 
 ### On JS
 
@@ -170,7 +179,8 @@ public fun main() {
 
 Result LaTeX:
 
-$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
+$$\operatorname{exp}\\,\left(\sqrt{x}\right)-\frac{\frac{\operatorname{arcsin}\\,\left(2\\,x\right)
+}{2\times10^{10}+x^{3}}}{12}+x^{2/3}$$
 
 Result MathML (can be used with MathJax or other renderers):